# Suchergebnis: Katalogdaten im Herbstsemester 2020

Elektrotechnik und Informationstechnologie Master | ||||||

Master-Studium (Studienreglement 2018) | ||||||

Communication The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Communication", see Link. The individual study plan is subject to the tutor's approval. | ||||||

Kernfächer These core courses are particularly recommended for the field of "Communication". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT. | ||||||

Foundation Core Courses Fundamentals at bachelor level, for master students who need to strengthen or refresh their background in the area. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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227-0121-00L | Kommunikationssysteme | W | 6 KP | 2V + 2U | A. Wittneben | |

Kurzbeschreibung | Informationstheorie, Signalraumanalyse, Basisbandübertragung, Passbandübertragung, Systembeispiel und Kanal, Sicherungsschicht, MAC, Beispiele Layer 2, Layer 3, Internet | |||||

Lernziel | Ziel der Vorlesung ist die Einführung der wichtigsten Konzepte und Verfahren, die in modernen digitalen Kommunikationssystemen Anwendung finden, sowie eine Übersicht über bestehende und zukünftige Systeme. | |||||

Inhalt | Es werden die untersten drei Schichten des OSI-Referenzmodells behandelt: die Bitübertragungsschicht, die Sicherungsschicht mit dem Zugriff auf das Übertragungsmedium und die Vermittlung. Die wichtigsten Begriffe der Informationstheorie werden eingeführt. Anschliessend konzentrieren sich die Betrachtungen auf die Verfahren der Punkt-zu-Punkt-Übertragung, welche sich mittels der Signalraumdarstellung elegant und kohärent behandeln lassen. Den Methoden der Fehlererkennung und –korrektur, sowie Protokollen für die erneute Übermittlung gestörter Daten wird Rechnung getragen. Auch der Vielfachzugriff bei geteiltem Übertragungsmedium wird diskutiert. Den Abschluss bilden Algorithmen für das Routing in Kommunikationsnetzen und der Flusssteuerung. Die Anwendung der grundlegenden Verfahren wird ausführlich anhand von bestehenden und zukünftigen drahtlosen und drahtgebundenen Systemen erläutert. | |||||

Skript | Vorlesungsfolien | |||||

Literatur | [1] Simon Haykin, Communication Systems, 4. Auflage, John Wiley & Sons, 2001 [2] Andrew S. Tanenbaum, Computernetzwerke, 3. Auflage, Pearson Studium, 2003 [3] M. Bossert und M. Breitbach, Digitale Netze, 1. Auflage, Teubner, 1999 | |||||

227-0101-00L | Discrete-Time and Statistical Signal Processing | W | 6 KP | 4G | H.‑A. Loeliger | |

Kurzbeschreibung | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm. | |||||

Lernziel | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter. | |||||

Inhalt | 1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm. | |||||

Skript | Lecture Notes | |||||

Advanced Core Courses Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0301-00L | Optical Communication Fundamentals | W | 6 KP | 2V + 1U + 1P | J. Leuthold | |

Kurzbeschreibung | The path of an analog signal in the transmitter to the digital world in a communication link and back to the analog world at the receiver is discussed. The lecture covers the fundamentals of all important optical and optoelectronic components in a fiber communication system. This includes the transmitter, the fiber channel and the receiver with the electronic digital signal processing elements. | |||||

Lernziel | An in-depth understanding on how information is transmitted from source to destination. Also the mathematical framework to describe the important elements will be passed on. Students attending the lecture will further get engaged in critical discussion on societal, economical and environmental aspects related to the on-going exponential growth in the field of communications. | |||||

Inhalt | * Chapter 1: Introduction: Analog/Digital conversion, The communication channel, Shannon channel capacity, Capacity requirements. * Chapter 2: The Transmitter: Components of a transmitter, Lasers, The spectrum of a signal, Optical modulators, Modulation formats. * Chapter 3: The Optical Fiber Channel: Geometrical optics, The wave equations in a fiber, Fiber modes, Fiber propagation, Fiber losses, Nonlinear effects in a fiber. * Chapter 4: The Receiver: Photodiodes, Receiver noise, Detector schemes (direct detection, coherent detection), Bit-error ratios and error estimations. * Chapter 5: Digital Signal Processing Techniques: Digital signal processing in a coherent receiver, Error detection teqchniques, Error correction coding. * Chapter 6: Pulse Shaping and Multiplexing Techniques: WDM/FDM, TDM, OFDM, Nyquist Multiplexing, OCDMA. * Chapter 7: Optical Amplifiers : Semiconductor Optical Amplifiers, Erbium Doped Fiber Amplifiers, Raman Amplifiers. | |||||

Skript | Lecture notes are handed out. | |||||

Literatur | Govind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010 | |||||

Voraussetzungen / Besonderes | Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics. | |||||

227-0417-00L | Information Theory I | W | 6 KP | 4G | A. Lapidoth | |

Kurzbeschreibung | This course covers the basic concepts of information theory and of communication theory. Topics covered include the entropy rate of a source, mutual information, typical sequences, the asymptotic equi-partition property, Huffman coding, channel capacity, the channel coding theorem, the source-channel separation theorem, and feedback capacity. | |||||

Lernziel | The fundamentals of Information Theory including Shannon's source coding and channel coding theorems | |||||

Inhalt | The entropy rate of a source, Typical sequences, the asymptotic equi-partition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the source-channel separation theorem, feedback capacity | |||||

Literatur | T.M. Cover and J. Thomas, Elements of Information Theory (second edition) | |||||

227-0427-00L | Signal Analysis, Models, and Machine LearningFindet dieses Semester nicht statt. This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester). | W | 6 KP | 4G | H.‑A. Loeliger | |

Kurzbeschreibung | Mathematical methods in signal processing and machine learning. I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity. II. Learning linear and nonlinear functions and filters: neural networks, kernel methods. III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events. | |||||

Lernziel | The course is an introduction to some basic topics in signal processing and machine learning. | |||||

Inhalt | Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis. Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods. Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events. | |||||

Skript | Lecture notes. | |||||

Voraussetzungen / Besonderes | Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory | |||||

227-0439-00L | Wireless Access Systems | W | 6 KP | 2V + 2U | A. Wittneben | |

Kurzbeschreibung | The lecture course covers current and upcoming wireless systems for data communication and localization in diverse applications. Important topics are broadband data networks, indoor localization, internet-of-things, biomedical sensor networks and smart grid communications. The course consists of two tracks, the lecture part “Technology & Systems” and the group exercise part “Simulate & Practice”. | |||||

Lernziel | General learning goals of the course: By the end of this course, students will be able to - understand and illustrate the physical layer and MAC layer limits and challenges of wireless systems with emphasis on data communication and localization - understand and explain the functioning of the most widely used wireless systems - model and simulate the physical layer of state-of-the-art wireless systems - explain challenges and solutions of indoor localization - understand research challenges of future wireless networks Specific learning goals include: - Understanding the principles of OFDM and analyzing its performance on the physical layer - Understanding and evaluating the challenges regarding current applications of wireless networks, e.g. for the internet-of-things, smart grid communication, biomedical sensor communication - Illustrating the characteristics of the wireless channel - Simulation of localization and user tracking based on wireless systems - Explaining the basics of smart grid communications approaches (including narrowband PLC, G3-PLC) | |||||

Inhalt | - Introduction - Wireless communication: fundamental Physical layer and MAC layer limits and challenges - Basics of OFDM - Wireless systems: WiFi / WLAN - Wireless systems: Bluetooth, RFID (Radio Frequency Identification) and NFC (Near Field Communication) - Indoor localization based on wireless systems - Internet-of-things: Challenges and solutions regarding wireless data communication and localization - Smart grid communications - Biomedical sensor communication - Next generation designs (glimpse on current research topics) The goal of the course is to explain and analyze modern and future wireless systems for data communication and localization. The course covers designs for generic applications (e.g. WiFi, Bluetooth) as well as systems optimized for specific applications (e.g. biomedical sensor networks, smart grid communications). The course consists of two parallel tracks. The track "Technology&Systems" is structured as regular lecture. In the introduction, we discuss the challenges and potential of wireless access and study some fundamental limits of wireless communications and localization approaches. The second part of this track is devoted to the most widely used wireless systems, WiFi/WLAN, Bluetooth, RFID, NFC. Furthermore, we study the potential of using existing wireless communication systems for indoor localization. The third part follows with an introduction to the internet-of-things, where we focus on data communication and localization challenges and solutions in wireless networks with a massive number of nodes. Next, we study communication technologies for the smart grid, which combine wireless as well as power line communication approaches to optimize availability and efficiency. The track is completed by a comprehensive survey of short-range magneto-inductive micro sensor networks for communication and localization - as a promising technology for biomedical sensor communication (in-body, out-of-body). In the track "Simulate&Practice" we form student teams to simulate and analyze functional blocks of the physical layer of advanced wireless systems (based on MATLAB simulations). The track includes combination tasks in which different teams combine their functional blocks (e.g. transmitter, receiver) in order to simulate the complete physical layer of a wireless system. The focus is on data communication and localization. The tasks include modeling and simulating of single-carrier systems (as, e.g., used in Bluetooth), multi-carrier OFDM systems (e.g. used in WiFi or power line communication), and indoor localization approaches (e.g. relevant for IoT and sensor networks). | |||||

Skript | Lecture slides are available. | |||||

Literatur | Will be announced in the lecture. | |||||

Voraussetzungen / Besonderes | English | |||||

Vertiefungsfächer These specialisation courses are particularly recommended for the area of "Communication", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0102-00L | Diskrete Ereignissysteme | W | 6 KP | 4G | L. Thiele, L. Vanbever, R. Wattenhofer | |

Kurzbeschreibung | Einführung in Diskrete Ereignissysteme (DES). Zuerst studieren wir populäre Modelle für DES. Im zweiten Teil analysieren wir DES, aus einer Average-Case und einer Worst-Case Sicht. Stichworte: Automaten und Sprachen, Spezifikationsmodelle, Stochastische DES, Worst-Case Ereignissysteme, Verifikation, Netzwerkalgebra. | |||||

Lernziel | Over the past few decades the rapid evolution of computing, communication, and information technologies has brought about the proliferation of new dynamic systems. A significant part of activity in these systems is governed by operational rules designed by humans. The dynamics of these systems are characterized by asynchronous occurrences of discrete events, some controlled (e.g. hitting a keyboard key, sending a message), some not (e.g. spontaneous failure, packet loss). The mathematical arsenal centered around differential equations that has been employed in systems engineering to model and study processes governed by the laws of nature is often inadequate or inappropriate for discrete event systems. The challenge is to develop new modeling frameworks, analysis techniques, design tools, testing methods, and optimization processes for this new generation of systems. In this lecture we give an introduction to discrete event systems. We start out the course by studying popular models of discrete event systems, such as automata and Petri nets. In the second part of the course we analyze discrete event systems. We first examine discrete event systems from an average-case perspective: we model discrete events as stochastic processes, and then apply Markov chains and queuing theory for an understanding of the typical behavior of a system. In the last part of the course we analyze discrete event systems from a worst-case perspective using the theory of online algorithms and adversarial queuing. | |||||

Inhalt | 1. Introduction 2. Automata and Languages 3. Smarter Automata 4. Specification Models 5. Stochastic Discrete Event Systems 6. Worst-Case Event Systems 7. Network Calculus | |||||

Skript | Available | |||||

Literatur | [bertsekas] Data Networks Dimitri Bersekas, Robert Gallager Prentice Hall, 1991, ISBN: 0132009161 [borodin] Online Computation and Competitive Analysis Allan Borodin, Ran El-Yaniv. Cambridge University Press, 1998 [boudec] Network Calculus J.-Y. Le Boudec, P. Thiran Springer, 2001 [cassandras] Introduction to Discrete Event Systems Christos Cassandras, Stéphane Lafortune. Kluwer Academic Publishers, 1999, ISBN 0-7923-8609-4 [fiat] Online Algorithms: The State of the Art A. Fiat and G. Woeginger [hochbaum] Approximation Algorithms for NP-hard Problems (Chapter 13 by S. Irani, A. Karlin) D. Hochbaum [schickinger] Diskrete Strukturen (Band 2: Wahrscheinlichkeitstheorie und Statistik) T. Schickinger, A. Steger Springer, Berlin, 2001 [sipser] Introduction to the Theory of Computation Michael Sipser. PWS Publishing Company, 1996, ISBN 053494728X | |||||

227-0103-00L | Regelsysteme | W | 6 KP | 2V + 2U | F. Dörfler | |

Kurzbeschreibung | Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems. | |||||

Lernziel | Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems. | |||||

Inhalt | Process automation, concept of control. Modelling of dynamical systems - examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems - effect of additional poles and zeros. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multi-loop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement - choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation. | |||||

Literatur | K. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010. R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007. G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010. J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014. J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014. | |||||

Voraussetzungen / Besonderes | Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation. | |||||

227-0112-00L | High-Speed Signal Propagation Findet dieses Semester nicht statt. | W | 6 KP | 2V + 2U | C. Bolognesi | |

Kurzbeschreibung | Verständnis der Hochgeschwindigkeits-Signalausbreitung in Mikrowellenkabel, integr. Mikrowellenschaltungen und Leiterplatten. Da Sytemtaktfrequenzen stets in höhere GHz Bereiche vordringen, ist es notwendig die Hochgeschwindigkeits-Signalausbreitung zu verstehen, um Signalintegrität zu gewährleisten. Der Kurs richtet sich an Interessierte an analogen/digitalen Hochgeschwindigkeitssystemen. | |||||

Lernziel | Verständnis der Hochgeschwindigkeits-Signalausbreitung in Verbindungsleitern, Mikrowellenkabel und integrierten Übertragungsleitungen wie zum Beispiel in integrierten Mikrowellenschaltungen und/oder Leiterplatten. Da Systemtaktfrequenzen kontinuierlich in höhere GHz Bereiche vordringen, entwickelt sich das dringende Bedürfnis die Hochgeschwindigkeits-Signalausbreitung zu verstehen um nach wie vor eine hohe Signalintegrität zu gewährleisten, insbesondere angesichts Phänomenen wie der Intersymbol-Interferenz (ISI) und des Übersprechens. Konzepte wie Streuparameter (oder S-Parameter) übernehmen eine Schlüsselrolle in der Charakterisierung von Netzwerken über grosse Bandbreiten. Bei hohen Frequenzen werden alle Strukturen effektiv zu "Übertragungsleitungen". Ohne besondere Vorsicht ist es sehr wahrscheinlich, dass eine schlecht entworfene Übertragungsleitung zum Versagen des gesamten entworfenen Systems führt. Filter werden ebenfalls behandelt, da sich herausstellt, dass einige der Probleme von verlustbehafteten Übertragungskanälen (Leitungen, Kabel, etc.) durch adäquates filtern korrigiert werden können. Ein Prozess der "Entzerrung" genannt wird. | |||||

Inhalt | Leitungsgleichungen der TEM-Leitung (Telegraphengleichungen). Beschreibung elektrischer Grössen auf der TEM Leitung; Reflexion im Zeit- und Frequenzbereich, Smith-Diagramm. Verhalten schwach bedämpfter Leitungen. Einfluss des Skineffekts auf Dämpfung und Impulsverzerrung. Leitungsersatzschaltungen. Gruppenlaufzeit und Dispersion. Eigenschaften gekoppelter Leitungen. Streuparameter. Butterworth-, Tschebyscheff- und Besselfilter: Einführung zum Filterentwurf mit Filterprototypen (Tiefpass, Hochpass, Bandpass, Bandsperre). Einfache aktive Filter. | |||||

Skript | Skript: Leitungen und Filter (In deutscher Sprache). | |||||

Voraussetzungen / Besonderes | Die Uebungen werden auf Englisch gehalten. | |||||

227-0116-00L | VLSI I: From Architectures to VLSI Circuits and FPGAs | W | 6 KP | 5G | F. K. Gürkaynak, L. Benini | |

Kurzbeschreibung | This first course in a series that extends over three consecutive terms is concerned with tailoring algorithms and with devising high performance hardware architectures for their implementation as ASIC or with FPGAs. The focus is on front end design using HDLs and automatic synthesis for producing industrial-quality circuits. | |||||

Lernziel | Understand Very-Large-Scale Integrated Circuits (VLSI chips), Application-Specific Integrated Circuits (ASIC), and Field-Programmable Gate-Arrays (FPGA). Know their organization and be able to identify suitable application areas. Become fluent in front-end design from architectural conception to gate-level netlists. How to model digital circuits with SystemVerilog. How to ensure they behave as expected with the aid of simulation, testbenches, and assertions. How to take advantage of automatic synthesis tools to produce industrial-quality VLSI and FPGA circuits. Gain practical experience with the hardware description language SystemVerilog and with industrial Electronic Design Automation (EDA) tools. | |||||

Inhalt | This course is concerned with system-level issues of VLSI design and FPGA implementations. Topics include: - Overview on design methodologies and fabrication depths. - Levels of abstraction for circuit modeling. - Organization and configuration of commercial field-programmable components. - FPGA design flows. - Dedicated and general purpose architectures compared. - How to obtain an architecture for a given processing algorithm. - Meeting throughput, area, and power goals by way of architectural transformations. - Hardware Description Languages (HDL) and the underlying concepts. - SystemVerilog - Register Transfer Level (RTL) synthesis and its limitations. - Building blocks of digital VLSI circuits. - Functional verification techniques and their limitations. - Modular and largely reusable testbenches. - Assertion-based verification. - Synchronous versus asynchronous circuits. - The case for synchronous circuits. - Periodic events and the Anceau diagram. - Case studies, ASICs compared to microprocessors, DSPs, and FPGAs. During the exercises, students learn how to model FPGAs with SystemVerilog. They write testbenches for simulation purposes and synthesize gate-level netlists for FPGAs. Commercial EDA software by leading vendors is being used throughout. | |||||

Skript | Textbook and all further documents in English. | |||||

Literatur | H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303. | |||||

Voraussetzungen / Besonderes | Prerequisites: Basics of digital circuits. Examination: In written form following the course semester (spring term). Problems are given in English, answers will be accepted in either English oder German. Further details: Link | |||||

227-0148-00L | VLSI III: Test and Fabrication of VLSI Circuits | W | 6 KP | 4G | F. K. Gürkaynak, L. Benini | |

Kurzbeschreibung | In this course, we will cover how modern microchips are fabricated, and we will focus on methods and tools to uncover fabrication defects, if any, in these microchips. As part of the exercises, students will get to work on an industrial 1 million dollar automated test equipment. | |||||

Lernziel | Learn about modern IC manufacturing methodologies, understand the problem of IC testing. Cover the basic methods, algorithms and techniques to test circuits in an efficient way. Learn about practical aspects of IC testing and apply what you learn in class using a state-of-the art tester. | |||||

Inhalt | In this course we will deal with modern integrated circuit (IC) manufacturing technology and cover topics such as: - Today's nanometer CMOS fabrication processes (HKMG). - Optical and post optical Photolithography. - Potential alternatives to CMOS technology and MOSFET devices. - Evolution paths for design methodology. - Industrial roadmaps for the future evolution of semiconductor technology (ITRS). If you want to earn money by selling ICs, you will have to deliver a product that will function properly with a very large probability. The main emphasis of the lecture will be discussing how this can be achieved. We will discuss fault models and practical techniques to improve testability of VLSI circuits. At the IIS we have a state-of-the-art automated test equipment (Advantest SoC V93000) that we will make available for in class exercises and projects. At the end of the lecture you will be able to design state-of-the art digital integrated circuits such as to make them testable and to use automatic test equipment (ATE) to carry out the actual testing. During the first weeks of the course there will be weekly practical exercises where you will work in groups of two. For the last 5 weeks of the class students will be able to choose a class project that can be: - The test of their own chip developed during a previous semester thesis - Developing new setups and measurement methods in C++ on the tester - Helping to debug problems encountered in previous microchips by IIS. Half of the oral exam will consist of a short presentation on this class project. | |||||

Skript | Main course book: "Essentials of Electronic Testing for Digital, Memory and Mixed-Signal VLSI Circuits" by Michael L. Bushnell and Vishwani D. Agrawal, Springer, 2004. This book is available online within ETH through Link | |||||

Voraussetzungen / Besonderes | Although this is the third part in a series of lectures on VLSI design, you can follow this course even if you have not visited VLSI I and VLSI II lectures. An interest in integrated circuit design, and basic digital circuit knowledge is required though. Course website: Link | |||||

227-0166-00L | Analog Integrated Circuits | W | 6 KP | 2V + 2U | T. Jang | |

Kurzbeschreibung | This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies. | |||||

Lernziel | Integrated circuits are responsible for much of the progress in electronics in the last 50 years, particularly the revolutions in the Information and Communications Technologies we witnessed in recent years. Analog integrated circuits play a crucial part in the highly integrated systems that power the popular electronic devices we use daily. Understanding their design is beneficial to both future designers and users of such systems. The basic elements, design issues and techniques for analog integrated circuits will be taught in this course. | |||||

Inhalt | Review of bipolar and MOS devices and their small-signal equivalent circuit models; Building blocks in analog circuits such as current sources, active load, current mirrors, supply independent biasing etc; Amplifiers: differential amplifiers, cascode amplifier, high gain structures, output stages, gain bandwidth product of op-amps; stability; comparators; second-order effects in analog circuits such as mismatch, noise and offset; data converters; frequency synthesizers; switched capacitors. The exercise sessions aim to reinforce the lecture material by well guided step-by-step design tasks. The circuit simulator SPECTRE is used to facilitate the tasks. There is also an experimental session on op-amp measurements. | |||||

Skript | Handouts of presented slides. No script but an accompanying textbook is recommended. | |||||

Literatur | Behzad Razavi, Design of Analog CMOS Integrated Circuits (Irwin Electronics & Computer Engineering) 1st or 2nd edition, McGraw-Hill Education | |||||

227-0301-00L | Optical Communication Fundamentals | W | 6 KP | 2V + 1U + 1P | J. Leuthold | |

Kurzbeschreibung | The path of an analog signal in the transmitter to the digital world in a communication link and back to the analog world at the receiver is discussed. The lecture covers the fundamentals of all important optical and optoelectronic components in a fiber communication system. This includes the transmitter, the fiber channel and the receiver with the electronic digital signal processing elements. | |||||

Lernziel | An in-depth understanding on how information is transmitted from source to destination. Also the mathematical framework to describe the important elements will be passed on. Students attending the lecture will further get engaged in critical discussion on societal, economical and environmental aspects related to the on-going exponential growth in the field of communications. | |||||

Inhalt | * Chapter 1: Introduction: Analog/Digital conversion, The communication channel, Shannon channel capacity, Capacity requirements. * Chapter 2: The Transmitter: Components of a transmitter, Lasers, The spectrum of a signal, Optical modulators, Modulation formats. * Chapter 3: The Optical Fiber Channel: Geometrical optics, The wave equations in a fiber, Fiber modes, Fiber propagation, Fiber losses, Nonlinear effects in a fiber. * Chapter 4: The Receiver: Photodiodes, Receiver noise, Detector schemes (direct detection, coherent detection), Bit-error ratios and error estimations. * Chapter 5: Digital Signal Processing Techniques: Digital signal processing in a coherent receiver, Error detection teqchniques, Error correction coding. * Chapter 6: Pulse Shaping and Multiplexing Techniques: WDM/FDM, TDM, OFDM, Nyquist Multiplexing, OCDMA. * Chapter 7: Optical Amplifiers : Semiconductor Optical Amplifiers, Erbium Doped Fiber Amplifiers, Raman Amplifiers. | |||||

Skript | Lecture notes are handed out. | |||||

Literatur | Govind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010 | |||||

Voraussetzungen / Besonderes | Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics. | |||||

227-0377-10L | Physics of Failure and Reliability of Electronic Devices and Systems | W | 3 KP | 2V | I. Shorubalko, M. Held | |

Kurzbeschreibung | Understanding the physics of failures and failure mechanisms enables reliability analysis and serves as a practical guide for electronic devices design, integration, systems development and manufacturing. The field gains additional importance in the context of managing safety, sustainability and environmental impact for continuously increasing complexity and scaling-down trends in electronics. | |||||

Lernziel | Provide an understanding of the physics of failure and reliability. Introduce the degradation and failure mechanisms, basics of failure analysis, methods and tools of reliability testing. | |||||

Inhalt | Summary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis; basics and properties of instruments; quality assurance of technical systems (introduction); introduction to stochastic processes; reliability analysis; component selection and qualification; maintainability analysis (introduction); design rules for reliability, maintainability, reliability tests (introduction). | |||||

Skript | Comprehensive copy of transparencies | |||||

Literatur | Reliability Engineering: Theory and Practice, 8th Edition, Springer 2017, DOI 10.1007/978-3-662-54209-5 Reliability Engineering: Theory and Practice, 8th Edition (2017), DOI 10.1007/978-3-662-54209-5 | |||||

227-0423-00L | Neural Network Theory | W | 4 KP | 2V + 1U | H. Bölcskei | |

Kurzbeschreibung | The class focuses on fundamental mathematical aspects of neural networks with an emphasis on deep networks: Universal approximation theorems, basics of approximation theory, fundamental limits of deep neural network learning, geometry of decision surfaces, capacity of separating surfaces, dimension measures relevant for generalization, VC dimension of neural networks. | |||||

Lernziel | After attending this lecture, participating in the exercise sessions, and working on the homework problem sets, students will have acquired a working knowledge of the mathematical foundations of (deep) neural networks. | |||||

Inhalt | 1. Universal approximation with single- and multi-layer networks 2. Introduction to approximation theory: Fundamental limits on compressibility of signal classes, Kolmogorov epsilon-entropy of signal classes, non-linear approximation theory 3. Fundamental limits of deep neural network learning 4. Geometry of decision surfaces 5. Separating capacity of nonlinear decision surfaces 6. Dimension measures: Pseudo-dimension, fat-shattering dimension, Vapnik-Chervonenkis (VC) dimension 7. Dimensions of neural networks 8. Generalization error in neural network learning | |||||

Skript | Detailed lecture notes will be provided. | |||||

Voraussetzungen / Besonderes | This course is aimed at students with a strong mathematical background in general, and in linear algebra, analysis, and probability theory in particular. | |||||

227-0447-00L | Image Analysis and Computer Vision | W | 6 KP | 3V + 1U | L. Van Gool, E. Konukoglu, F. Yu | |

Kurzbeschreibung | Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks. | |||||

Lernziel | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||

Inhalt | This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given. | |||||

Skript | Course material Script, computer demonstrations, exercises and problem solutions | |||||

Voraussetzungen / Besonderes | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | |||||

227-0468-00L | Analog Signal Processing and Filtering Suitable for Master Students as well as Doctoral Students. | W | 6 KP | 2V + 2U | H. Schmid | |

Kurzbeschreibung | This lecture provides a wide overview over analog filters (continuous-time and discrete-time), signal-processing systems, and sigma-delta conversion, and gives examples with sensor interfaces and class-D audio drivers. All systems and circuits are treated using a signal-flow view. The lecture is suitable for both analog and digital designers. | |||||

Lernziel | This lecture provides a wide overview over analog filters (continuous-time and discrete-time), signal-processing systems, and sigma-delta conversion, and gives examples with sensor interfaces and class-D audio drivers. All systems and circuits are treated using a signal-flow view. The lecture is suitable for both analog and digital designers. The way the exam is done allows for the different interests of the two groups. The learning goal is that the students can apply signal-flow graphs and can understand the signal flow in such circuits and systems (including non-ideal effects) well enough to gain an understanding of further circuits and systems by themselves. | |||||

Inhalt | At the beginning, signal-flow graphs in general and driving-point signal-flow graphs in particular are introduced. We will use them during the whole term to analyze circuits on a system level (analog continuous-time, analog discrete-time, mixed-signal and digital) and understand how signals propagate through them. The theory and CMOS implementation of active Filters is then discussed in detail using the example of Gm-C filters and active-RC filters. The ideal and nonideal behaviour of opamps, current conveyors, and inductor simulators follows. The link to the practical design of circuits and systems is done with an overview over different quality measures and figures of merit used in scientific literature and datasheets. Finally, an introduction to discrete-time and mixed-domain filters and circuits is given, including sensor read-out amplifiers, correlated double sampling, and chopping, and an introduction to sigma-delta A/D and D/A conversion on a system level. This lecture does not go down to the details of transistor implementations. The lecture "227-0166-00L Analog Integrated Circuits" complements This lecture very well in that respect. | |||||

Skript | The base for these lectures are lecture notes and two or three published scientific papers. From these papers we will together develop the technical content. Details: Link The graph methods are also supported with teaching videos: Link Some material is protected by password; students from ETHZ who are interested can write to Link to ask for the password even if they do not attend the lecture. | |||||

Voraussetzungen / Besonderes | Live stream: due to Covids rules, the lecture will be streamed live. Join here: Link Prerequisites: Recommended (but not required): Stochastic models and signal processing, Communication Electronics, Analog Integrated Circuits, Transmission Lines and Filters. Knowledge of the Laplace transform and z transform and their interpretation (transfer functions, poles and zeros, bode diagrams, stability criteria ...) and of the main properties of linear systems is necessary. | |||||

227-0477-00L | Acoustics I | W | 6 KP | 4G | K. Heutschi | |

Kurzbeschreibung | Introduction to the fundamentals of acoustics in the area of sound field calculations, measurement of acoustical events, outdoor sound propagation and room acoustics of large and small enclosures. | |||||

Lernziel | Introduction to acoustics. Understanding of basic acoustical mechanisms. Survey of the technical literature. Illustration of measurement techniques in the laboratory. | |||||

Inhalt | Fundamentals of acoustics, measuring and analyzing of acoustical events, anatomy and properties of the ear. Outdoor sound propagation, absorption and transmission of sound, room acoustics of large and small enclosures, architectural acoustics, noise and noise control, calculation of sound fields. | |||||

Skript | yes | |||||

252-0535-00L | Advanced Machine Learning | W | 10 KP | 3V + 2U + 4A | J. M. Buhmann, C. Cotrini Jimenez | |

Kurzbeschreibung | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects. | |||||

Lernziel | Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data. | |||||

Inhalt | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems | |||||

Skript | No lecture notes, but slides will be made available on the course webpage. | |||||

Literatur | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. | |||||

Voraussetzungen / Besonderes | The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points. | |||||

263-4640-00L | Network Security | W | 8 KP | 2V + 2U + 3A | A. Perrig, S. Frei, M. Legner | |

Kurzbeschreibung | Some of today's most damaging attacks on computer systems involve exploitation of network infrastructure, either as the target of attack or as a vehicle to attack end systems. This course provides an in-depth study of network attack techniques and methods to defend against them. | |||||

Lernziel | - Students are familiar with fundamental network security concepts. - Students can assess current threats that Internet services and networked devices face, and can evaluate appropriate countermeasures. - Students can identify and assess known vulnerabilities in a software system that is connected to the Internet (through analysis and penetration testing tools). - Students have an in-depth understanding of a range of important security technologies. - Students learn how formal analysis techniques can help in the design of secure networked systems. | |||||

Inhalt | The course will cover topics spanning five broad themes: (1) network defense mechanisms such as secure routing protocols, TLS, anonymous communication systems, network intrusion detection systems, and public-key infrastructures; (2) network attacks such as denial of service (DoS) and distributed denial-of-service (DDoS) attacks; (3) analysis and inference topics such as network forensics and attack economics; (4) formal analysis techniques for verifying the security properties of network architectures; and (5) new technologies related to next-generation networks. | |||||

Voraussetzungen / Besonderes | This lecture is intended for students with an interest in securing Internet communication services and network devices. Students are assumed to have knowledge in networking as taught in a Communication Networks lecture. The course will involve a course project and some smaller programming projects as part of the homework. Students are expected to have basic knowledge in network programming in a programming language such as C/C++, Go, or Python. | |||||

Computers and Networks The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Computers and Networks", see Link. The individual study plan is subject to the tutor's approval. | ||||||

Kernfächer These core courses are particularly recommended for the field of "Computers and Networks". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT. | ||||||

Foundation Core Courses Fundamentals at bachelor level, for master students who need to strengthen or refresh their background in the area. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0102-00L | Diskrete Ereignissysteme | W | 6 KP | 4G | L. Thiele, L. Vanbever, R. Wattenhofer | |

Kurzbeschreibung | Einführung in Diskrete Ereignissysteme (DES). Zuerst studieren wir populäre Modelle für DES. Im zweiten Teil analysieren wir DES, aus einer Average-Case und einer Worst-Case Sicht. Stichworte: Automaten und Sprachen, Spezifikationsmodelle, Stochastische DES, Worst-Case Ereignissysteme, Verifikation, Netzwerkalgebra. | |||||

Lernziel | Over the past few decades the rapid evolution of computing, communication, and information technologies has brought about the proliferation of new dynamic systems. A significant part of activity in these systems is governed by operational rules designed by humans. The dynamics of these systems are characterized by asynchronous occurrences of discrete events, some controlled (e.g. hitting a keyboard key, sending a message), some not (e.g. spontaneous failure, packet loss). The mathematical arsenal centered around differential equations that has been employed in systems engineering to model and study processes governed by the laws of nature is often inadequate or inappropriate for discrete event systems. The challenge is to develop new modeling frameworks, analysis techniques, design tools, testing methods, and optimization processes for this new generation of systems. In this lecture we give an introduction to discrete event systems. We start out the course by studying popular models of discrete event systems, such as automata and Petri nets. In the second part of the course we analyze discrete event systems. We first examine discrete event systems from an average-case perspective: we model discrete events as stochastic processes, and then apply Markov chains and queuing theory for an understanding of the typical behavior of a system. In the last part of the course we analyze discrete event systems from a worst-case perspective using the theory of online algorithms and adversarial queuing. | |||||

Inhalt | 1. Introduction 2. Automata and Languages 3. Smarter Automata 4. Specification Models 5. Stochastic Discrete Event Systems 6. Worst-Case Event Systems 7. Network Calculus | |||||

Skript | Available | |||||

Literatur | [bertsekas] Data Networks Dimitri Bersekas, Robert Gallager Prentice Hall, 1991, ISBN: 0132009161 [borodin] Online Computation and Competitive Analysis Allan Borodin, Ran El-Yaniv. Cambridge University Press, 1998 [boudec] Network Calculus J.-Y. Le Boudec, P. Thiran Springer, 2001 [cassandras] Introduction to Discrete Event Systems Christos Cassandras, Stéphane Lafortune. Kluwer Academic Publishers, 1999, ISBN 0-7923-8609-4 [fiat] Online Algorithms: The State of the Art A. Fiat and G. Woeginger [hochbaum] Approximation Algorithms for NP-hard Problems (Chapter 13 by S. Irani, A. Karlin) D. Hochbaum [schickinger] Diskrete Strukturen (Band 2: Wahrscheinlichkeitstheorie und Statistik) T. Schickinger, A. Steger Springer, Berlin, 2001 [sipser] Introduction to the Theory of Computation Michael Sipser. PWS Publishing Company, 1996, ISBN 053494728X | |||||

227-0121-00L | Kommunikationssysteme | W | 6 KP | 2V + 2U | A. Wittneben | |

Kurzbeschreibung | Informationstheorie, Signalraumanalyse, Basisbandübertragung, Passbandübertragung, Systembeispiel und Kanal, Sicherungsschicht, MAC, Beispiele Layer 2, Layer 3, Internet | |||||

Lernziel | Ziel der Vorlesung ist die Einführung der wichtigsten Konzepte und Verfahren, die in modernen digitalen Kommunikationssystemen Anwendung finden, sowie eine Übersicht über bestehende und zukünftige Systeme. | |||||

Inhalt | Es werden die untersten drei Schichten des OSI-Referenzmodells behandelt: die Bitübertragungsschicht, die Sicherungsschicht mit dem Zugriff auf das Übertragungsmedium und die Vermittlung. Die wichtigsten Begriffe der Informationstheorie werden eingeführt. Anschliessend konzentrieren sich die Betrachtungen auf die Verfahren der Punkt-zu-Punkt-Übertragung, welche sich mittels der Signalraumdarstellung elegant und kohärent behandeln lassen. Den Methoden der Fehlererkennung und –korrektur, sowie Protokollen für die erneute Übermittlung gestörter Daten wird Rechnung getragen. Auch der Vielfachzugriff bei geteiltem Übertragungsmedium wird diskutiert. Den Abschluss bilden Algorithmen für das Routing in Kommunikationsnetzen und der Flusssteuerung. Die Anwendung der grundlegenden Verfahren wird ausführlich anhand von bestehenden und zukünftigen drahtlosen und drahtgebundenen Systemen erläutert. | |||||

Skript | Vorlesungsfolien | |||||

Literatur | [1] Simon Haykin, Communication Systems, 4. Auflage, John Wiley & Sons, 2001 [2] Andrew S. Tanenbaum, Computernetzwerke, 3. Auflage, Pearson Studium, 2003 [3] M. Bossert und M. Breitbach, Digitale Netze, 1. Auflage, Teubner, 1999 | |||||

227-0124-00L | Embedded Systems | W | 6 KP | 4G | L. Thiele | |

Kurzbeschreibung | An embedded system is some combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. The course covers theoretical and practical aspects of embedded system design and includes a series of lab sessions. | |||||

Lernziel | Understanding specific requirements and problems arising in embedded system applications. Understanding architectures and components, their hardware-software interfaces, the memory architecture, communication between components, embedded operating systems, real-time scheduling theory, shared resources, low-power and low-energy design as well as hardware architecture synthesis. Using the formal models and methods in embedded system design in practical applications using the programming language C, the operating system FreeRTOS, a commercial embedded system platform and the associated design environment. | |||||

Inhalt | An embedded system is some combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. For example, they are part of industrial machines, agricultural and process industry devices, automobiles, medical equipment, cameras, household appliances, airplanes, sensor networks, internet-of-things, as well as mobile devices. The focus of this lecture is on the design of embedded systems using formal models and methods as well as computer-based synthesis methods. Besides, the lecture is complemented by laboratory sessions where students learn to program in C, to base their design on the embedded operating systems FreeRTOS, to use a commercial embedded system platform including sensors, and to edit/debug via an integrated development environment. Specifically the following topics will be covered in the course: Embedded system architectures and components, hardware-software interfaces and memory architecture, software design methodology, communication, embedded operating systems, real-time scheduling, shared resources, low-power and low-energy design, hardware architecture synthesis. More information is available at Link . | |||||

Skript | The following information will be available: Lecture material, publications, exercise sheets and laboratory documentation at Link . | |||||

Literatur | P. Marwedel: Embedded System Design, Springer, ISBN 978-3-319-56045-8, 2018. G.C. Buttazzo: Hard Real-Time Computing Systems. Springer Verlag, ISBN 978-1-4614-0676-1, 2011. Edward A. Lee and Sanjit A. Seshia: Introduction to Embedded Systems, A Cyber-Physical Systems Approach, Second Edition, MIT Press, ISBN 978-0-262-53381-2, 2017. M. Wolf: Computers as Components – Principles of Embedded System Design. Morgan Kaufman Publishers, ISBN 978-0-128-05387-4, 2016. | |||||

Voraussetzungen / Besonderes | Prerequisites: Basic knowledge in computer architectures and programming. | |||||

Advanced Core Courses Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0575-00L | Advanced Topics in Communication Networks (Autumn 2020) | W | 6 KP | 2V + 2U | L. Vanbever | |

Kurzbeschreibung | This course covers advanced topics and technologies in computer networks, both theoretically and practically. It is offered each Fall semester, with rotating topics. Repetition for credit is possible with consent of the instructor. In the Fall 2020, the course will cover advanced topics in Internet routing and forwarding. | |||||

Lernziel | The goals of this course is to provide students with a deeper understanding of the existing and upcoming Internet routing and forwarding technologies used in large-scale computer networks such as Internet Service Providers (e.g., Swisscom or Deutsche Telekom), Content Delivery Networks (e.g., Netflix) and Data Centers (e.g., Google). Besides covering the fundamentals, the course will be “hands-on” and will enable students to play with the technologies in realistic network environments, and even implement some of them on their own during labs and a final group project. | |||||

Inhalt | The course will cover advanced topics in Internet routing and forwarding such as: - Tunneling - Hierarchical routing - Traffic Engineering and Load Balancing - Virtual Private Networks - Quality of Service/Queuing/Scheduling - IP Multicast - Fast Convergence - Network virtualization - Network programmability (OpenFlow, P4) - Network measurements The course will be divided in two main blocks. The first block (~10 weeks) will interleave classical lectures with practical exercises and labs. The second block (~4 weeks) will consist of a practical project which will be performed in small groups (~3 students). During the second block, lecture slots will be replaced by feedback sessions where students will be able to ask questions and get feedback about their project. The last week of the semester will be dedicated to student presentations and demonstrations. | |||||

Skript | Lecture notes and material will be made available before each course on the course website. | |||||

Literatur | Relevant references will be made available through the course website. | |||||

Voraussetzungen / Besonderes | Prerequisites: Communication Networks (227-0120-00L) or equivalents / good programming skills (in any language) are expected as both the exercices and the final project will involve coding. | |||||

227-0579-00L | Hardware Security | W | 6 KP | 4G | K. Razavi | |

Kurzbeschreibung | This course covers the security of commodity computer hardware (e.g., CPU, DRAM, etc.) with a special focus on cutting-edge hands-on research. The aim of the course is familiarizing the students with hardware security and more specifically microarchitectural and circuit-level attacks and defenses through lectures, reviewing and discussing papers, and executing some of these advanced attacks. | |||||

Lernziel | By the end of the course, the students will be familiar with the state of the art in commodity computer hardware attacks and defenses. More specifically, the students will learn about: - security problems of commodity hardware that we use everyday and how you can defend against them. - relevant computer architecture and operating system aspects of these issues. - hands-on techniques for performing hardware attacks. - writing critical reviews and constructive discussions with peers on this topic. This is the course where you get credit points by building some of the most advanced exploits on the planet! The luckiest team will collect a Best Demo Award at the end of the course. | |||||

Literatur | Slides, relevant literature and manuals will be made available during the course. | |||||

Voraussetzungen / Besonderes | Knowledge of systems programming and computer architecture is a plus. | |||||

227-0781-00L | Low-Power System DesignFindet dieses Semester nicht statt. | W | 6 KP | 2V + 2U | ||

Kurzbeschreibung | Introduction to low-power and low-energy design techniques from a systems perspective including aspects both from hard- and software. The focus of this lecture is on cutting across a number of related fields discussing architectural concepts, modeling and measurement techniques as well as software design mainly using the example of networked embedded systems. | |||||

Lernziel | Knowledge of the state-of-the-art in low power system design, understanding recent research results and their implication on industrial products. | |||||

Inhalt | Designing systems with a low energy footprint is an increasingly important. There are many applications for low-power systems ranging from mobile devices powered from batteries such as today's smart phones to energy efficient household appliances and datacenters. Key drivers are to be found mainly in the tremendous increase of mobile devices and the growing integration density requiring to carefully reason about power, both from a provision and consumption viewpoint. Traditional circuit design classes introduce low-power solely from a hardware perspective with a focus on the power performance of a single or at most a hand full of circuit elements. Similarly, low-power aspects are touched in a multitude of other classes, mostly as a side topic. However in successfully designing systems with a low energy footprint it is not sufficient to only look at low-power as an aspect of second class. In modern low-power system design advanced CMOS circuits are of course a key ingredient but successful low-power integration involves many more disciplines such as system architecture, different sources of energy as well as storage and most importantly software and algorithms. In this lecture we will discuss aspects of low-power design as a first class citizen introducing key concepts as well as modeling and measurement techniques focusing mainly on the design of networked embedded systems but of course equally applicable to many other classes of systems. The lecture is further accompanied by a reading seminar as well as exercises and lab sessions. | |||||

Skript | Exercise and lab materials, copies of lecture slides. | |||||

Literatur | A detailed reading list will be made available in the lecture. | |||||

Voraussetzungen / Besonderes | Knowledge in embedded systems, system software, (wireless) networking, possibly integrated circuits, and hardware software codesign. | |||||

227-2210-00L | Computer Architecture | W | 8 KP | 6G + 1A | O. Mutlu | |

Kurzbeschreibung | Computer architecture is the science & art of designing and optimizing hardware components and the hardware/software interface to create a computer that meets design goals. This course covers basic components of a modern computing system (processors, memory, interconnects, accelerators). The course takes a hardware/software cooperative approach to understanding and designing computing systems. | |||||

Lernziel | We will learn the fundamental concepts of the different parts of modern computing systems, as well as the latest trends by exploring the recent research in Industry and Academia. We will extensively cover memory technologies (including DRAM and new Non-Volatile Memory technologies), memory scheduling, parallel computing systems (including multicore processors and GPUs), heterogeneous computing, processing-in-memory, interconnection networks, specialized systems for major data-intensive workloads (e.g. graph processing, bioinformatics, machine learning), etc. | |||||

Inhalt | The principles presented in the lecture are reinforced in the laboratory through 1) the design and implementation of a cycle-accurate simulator, where we will explore different components of a modern computing system (e.g., pipeline, memory hierarchy, branch prediction, prefetching, caches, multithreading), and 2) the extension of state-of-the-art research simulators (e.g., Ramulator) for more in-depth understanding of specific system components (e.g., memory scheduling, prefetching). | |||||

Skript | All the materials (including lecture slides) will be provided on the course website: Link The video recordings of the lectures are expected to be made available after lectures. | |||||

Literatur | We will provide required and recommended readings in every lecture. They will mainly consist of research papers presented in major Computer Architecture and related conferences and journals. | |||||

Voraussetzungen / Besonderes | Digital Design and Computer Architecture. | |||||

252-1414-00L | System Security | W | 7 KP | 2V + 2U + 2A | S. Capkun, A. Perrig | |

Kurzbeschreibung | The first part of the lecture covers individual system aspects starting with tamperproof or tamper-resistant hardware in general over operating system related security mechanisms to application software systems, such as host based intrusion detection systems. In the second part, the focus is on system design and methodologies for building secure systems. | |||||

Lernziel | In this lecture, students learn about the security requirements and capabilities that are expected from modern hardware, operating systems, and other software environments. An overview of available technologies, algorithms and standards is given, with which these requirements can be met. | |||||

Inhalt | The first part of the lecture covers individual system's aspects starting with tamperproof or tamperresistant hardware in general over operating system related security mechanisms to application software systems such as host based intrusion detetction systems. The main topics covered are: tamper resistant hardware, CPU support for security, protection mechanisms in the kernel, file system security (permissions / ACLs / network filesystem issues), IPC Security, mechanisms in more modern OS, such as Capabilities and Zones, Libraries and Software tools for security assurance, etc. In the second part, the focus is on system design and methodologies for building secure systems. Topics include: patch management, common software faults (buffer overflows, etc.), writing secure software (design, architecture, QA, testing), compiler-supported security, language-supported security, logging and auditing (BSM audit, dtrace, ...), cryptographic support, and trustworthy computing (TCG, SGX). Along the lectures, model cases will be elaborated and evaluated in the exercises. | |||||

263-4640-00L | Network Security | W | 8 KP | 2V + 2U + 3A | A. Perrig, S. Frei, M. Legner | |

Kurzbeschreibung | Some of today's most damaging attacks on computer systems involve exploitation of network infrastructure, either as the target of attack or as a vehicle to attack end systems. This course provides an in-depth study of network attack techniques and methods to defend against them. | |||||

Lernziel | - Students are familiar with fundamental network security concepts. - Students can assess current threats that Internet services and networked devices face, and can evaluate appropriate countermeasures. - Students can identify and assess known vulnerabilities in a software system that is connected to the Internet (through analysis and penetration testing tools). - Students have an in-depth understanding of a range of important security technologies. - Students learn how formal analysis techniques can help in the design of secure networked systems. | |||||

Inhalt | The course will cover topics spanning five broad themes: (1) network defense mechanisms such as secure routing protocols, TLS, anonymous communication systems, network intrusion detection systems, and public-key infrastructures; (2) network attacks such as denial of service (DoS) and distributed denial-of-service (DDoS) attacks; (3) analysis and inference topics such as network forensics and attack economics; (4) formal analysis techniques for verifying the security properties of network architectures; and (5) new technologies related to next-generation networks. | |||||

Voraussetzungen / Besonderes | This lecture is intended for students with an interest in securing Internet communication services and network devices. Students are assumed to have knowledge in networking as taught in a Communication Networks lecture. The course will involve a course project and some smaller programming projects as part of the homework. Students are expected to have basic knowledge in network programming in a programming language such as C/C++, Go, or Python. | |||||

Vertiefungsfächer These specialisation courses are particularly recommended for the area of "Computers and Networks", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0101-00L | Discrete-Time and Statistical Signal Processing | W | 6 KP | 4G | H.‑A. Loeliger | |

Kurzbeschreibung | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm. | |||||

Lernziel | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter. | |||||

Inhalt | 1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm. | |||||

Skript | Lecture Notes | |||||

227-0103-00L | Regelsysteme | W | 6 KP | 2V + 2U | F. Dörfler | |

Kurzbeschreibung | Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems. | |||||

Lernziel | ||||||

Inhalt | Process automation, concept of control. Modelling of dynamical systems - examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems - effect of additional poles and zeros. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multi-loop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement - choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation. | |||||

Literatur | K. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010. R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007. G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010. J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014. J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014. | |||||

Voraussetzungen / Besonderes | Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation. | |||||

227-0116-00L | VLSI I: From Architectures to VLSI Circuits and FPGAs | W | 6 KP | 5G | F. K. Gürkaynak, L. Benini | |

Kurzbeschreibung | This first course in a series that extends over three consecutive terms is concerned with tailoring algorithms and with devising high performance hardware architectures for their implementation as ASIC or with FPGAs. The focus is on front end design using HDLs and automatic synthesis for producing industrial-quality circuits. | |||||

Lernziel | Understand Very-Large-Scale Integrated Circuits (VLSI chips), Application-Specific Integrated Circuits (ASIC), and Field-Programmable Gate-Arrays (FPGA). Know their organization and be able to identify suitable application areas. Become fluent in front-end design from architectural conception to gate-level netlists. How to model digital circuits with SystemVerilog. How to ensure they behave as expected with the aid of simulation, testbenches, and assertions. How to take advantage of automatic synthesis tools to produce industrial-quality VLSI and FPGA circuits. Gain practical experience with the hardware description language SystemVerilog and with industrial Electronic Design Automation (EDA) tools. | |||||

Inhalt | This course is concerned with system-level issues of VLSI design and FPGA implementations. Topics include: - Overview on design methodologies and fabrication depths. - Levels of abstraction for circuit modeling. - Organization and configuration of commercial field-programmable components. - FPGA design flows. - Dedicated and general purpose architectures compared. - How to obtain an architecture for a given processing algorithm. - Meeting throughput, area, and power goals by way of architectural transformations. - Hardware Description Languages (HDL) and the underlying concepts. - SystemVerilog - Register Transfer Level (RTL) synthesis and its limitations. - Building blocks of digital VLSI circuits. - Functional verification techniques and their limitations. - Modular and largely reusable testbenches. - Assertion-based verification. - Synchronous versus asynchronous circuits. - The case for synchronous circuits. - Periodic events and the Anceau diagram. - Case studies, ASICs compared to microprocessors, DSPs, and FPGAs. During the exercises, students learn how to model FPGAs with SystemVerilog. They write testbenches for simulation purposes and synthesize gate-level netlists for FPGAs. Commercial EDA software by leading vendors is being used throughout. | |||||

Skript | Textbook and all further documents in English. | |||||

Literatur | H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303. | |||||

Voraussetzungen / Besonderes | Prerequisites: Basics of digital circuits. Examination: In written form following the course semester (spring term). Problems are given in English, answers will be accepted in either English oder German. Further details: Link | |||||

227-2211-00L | Seminar in Computer Architecture Number of participants limited to 22. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 KP | 2S | O. Mutlu, M. H. K. Alser, J. Gómez Luna | |

Kurzbeschreibung | This seminar course covers fundamental and cutting-edge research papers in computer architecture. It consists of multiple components that are aimed at improving students' (1) technical skills in computer architecture, (2) critical thinking and analysis abilities on computer architecture concepts, as well as (3) technical presentation of concepts and papers in both spoken and written forms. | |||||

Lernziel | The main objective is to learn how to rigorously analyze and present papers and ideas on computer architecture. We will have rigorous presentation and discussion of selected papers during lectures and a written report delivered by each student at the end of the semester. This course is for those interested in computer architecture. Registered students are expected to attend every meeting, participate in the discussion, and create a synthesis report at the end of the course. | |||||

Inhalt | Topics will center around computer architecture. We will, for example, discuss papers on hardware security; accelerators for key applications like machine learning, graph processing and bioinformatics; memory systems; interconnects; processing in memory; various fundamental and emerging paradigms in computer architecture; hardware/software co-design and cooperation; fault tolerance; energy efficiency; heterogeneous and parallel systems; new execution models; predictable computing, etc. | |||||

Skript | All materials will be posted on the course website: Link Past course materials, including the synthesis report assignment, can be found in the Spring 2020 website for the course: Link | |||||

Literatur | Key papers and articles, on both fundamentals and cutting-edge topics in computer architecture will be provided and discussed. These will be posted on the course website. | |||||

Voraussetzungen / Besonderes | Digital Design and Computer Architecture. Students should (1) have done very well in Digital Design and Computer Architecture and (2) show a genuine interest in Computer Architecture. | |||||

227-0377-10L | Physics of Failure and Reliability of Electronic Devices and Systems | W | 3 KP | 2V | I. Shorubalko, M. Held | |

Kurzbeschreibung | Understanding the physics of failures and failure mechanisms enables reliability analysis and serves as a practical guide for electronic devices design, integration, systems development and manufacturing. The field gains additional importance in the context of managing safety, sustainability and environmental impact for continuously increasing complexity and scaling-down trends in electronics. | |||||

Lernziel | Provide an understanding of the physics of failure and reliability. Introduce the degradation and failure mechanisms, basics of failure analysis, methods and tools of reliability testing. | |||||

Inhalt | Summary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis; basics and properties of instruments; quality assurance of technical systems (introduction); introduction to stochastic processes; reliability analysis; component selection and qualification; maintainability analysis (introduction); design rules for reliability, maintainability, reliability tests (introduction). | |||||

Skript | Comprehensive copy of transparencies | |||||

Literatur | Reliability Engineering: Theory and Practice, 8th Edition, Springer 2017, DOI 10.1007/978-3-662-54209-5 Reliability Engineering: Theory and Practice, 8th Edition (2017), DOI 10.1007/978-3-662-54209-5 | |||||

227-0447-00L | Image Analysis and Computer Vision | W | 6 KP | 3V + 1U | L. Van Gool, E. Konukoglu, F. Yu | |

Kurzbeschreibung | Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks. | |||||

Lernziel | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||

Inhalt | This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given. | |||||

Skript | Course material Script, computer demonstrations, exercises and problem solutions | |||||

Voraussetzungen / Besonderes | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | |||||

227-0555-00L | Distributed Systems Enrolled students will be notified by e-mail about the lecture start. | W | 4 KP | 3G + 1A | R. Wattenhofer | |

Kurzbeschreibung | This course introduces the fundamentals of distributed systems. We study different protocols and algorithms that allow for fault-tolerant operation, and discuss practical systems that implement these techniques. | |||||

Lernziel | The objective of the course is for students to understand the theoretical principles and practical considerations of distributed systems. This includes the main models of fault-tolerant distributed systems (crash failures, byzantine failures, and selfishness), and the most important algorithms, protocols and impossibility results. By the end of the course, students should be able to reason about various concepts such as consistency, durability, availability, fault tolerance, and replication. | |||||

Inhalt | We discuss the following concepts related to fault-tolerant distributed systems: client-server, serialization, two-phase protocols, three-phase protocols, paxos, two generals problem, crash failures, impossibility of consensus, byzantine failures, agreement, termination, validity, byzantine agreement, king algorithm, asynchronous byzantine agreement, authentication, signatures, reliable and atomic broadcast, eventual consistency, blockchain, cryptocurrencies such as bitcoin and ethereum, proof-of-work, proof-of-*, smart contracts, quorum systems, fault-tolerant protocols such as piChain or pbft, distributed storage, distributed hash tables, physical and logical clocks, causality, selfishness, game theoretic models, mechanism design. | |||||

Skript | A script is available on the web page. | |||||

Literatur | The script is self-contained, but links to additional material are available on the web page. | |||||

Voraussetzungen / Besonderes | This lecture takes place in roughly the second half of the semester, as the lecture is the second part of the lecture "Computer Systems" (252-0217-00). Students may attend at most one of the two lectures, NOT both. | |||||

227-0559-10L | Seminar in Communication Networks: Learning, Reasoning and Control Findet dieses Semester nicht statt. Number of participants limited to 24. | W | 2 KP | 2S | L. Vanbever | |

Kurzbeschreibung | In this seminar participating students review, present, and discuss (mostly recent) research papers in the area of computer networks. During the fall semester of 2019, the seminar will focus on topics blending networks with machine learning and control theory. | |||||

Lernziel | The two main goals of this seminar are: 1) learning how to read and review scientific papers; and 2) learning how to present and discuss technical topics with an audience of peers. Students are required to attend the entire seminar, choose a paper to present from a given list, prepare and give a presentation on that topic, and lead the follow-up discussion. To ensure the talks' quality, each student will be mentored by a teaching assistant. In addition to presenting one paper, every student is also required to submit one (short) review for one of the two papers presented every week in-class (12 reviews in total). The students will be evaluated based on their submitted reviews, their presentation, their leadership in animating the discussion for their own paper, and their participation in the discussions of other papers. | |||||

Inhalt | The seminar will start with two introductory lectures in week 1 and week 2. Starting from week 3, participating students will start reviewing, presenting, and discussing research papers. Each week will see two presentations, for a total of 24 papers. The course content will vary from semester to semester. During the fall semester of 2019, the seminar will focus on topics blending networks with machine learning and control theory. For details, please see: Link | |||||

Skript | The slides of each presentation will be made available on the website. | |||||

Literatur | The paper selection will be made available on the course website: Link | |||||

Voraussetzungen / Besonderes | Communication Networks (227-0120-00L) or equivalents. For fall 2019, it is expected that students have prior knowledge in machine learning and control theory, for instance by having attended appropriate courses. | |||||

227-0627-00L | Angewandte Computer Architektur | W | 6 KP | 4G | A. Gunzinger | |

Kurzbeschreibung | Diese Vorlesung gibt einen Überblick über die Anforderungen und die Architektur von parallelen Computersystemen unter Berücksichtigung von Rechenleistung, Zuverlässigkeit und Kosten. | |||||

Lernziel | Arbeitsweise von parallelen Computersystemen verstehen, solche Systeme entwerfen und modellieren. | |||||

Inhalt | Die Vorlesung Angewandte Computer Architektur gibt technische und unternehmerische Einblicke in innovative Computersysteme/Architekturen (CPU, GPU, FPGA, Spezialprozessoren) und deren praxisnahe Umsetzung. Dabei werden oft die Grenzen der technologischen Möglichkeiten ausgereizt. Wie ist das Computersystem aufgebaut, das die über 1000 Magneten an der Swiss Light Source (SLS) steuert? Wie ist das hochverfügbare Alarmzentrum der SBB aufgebaut? Welche Computer Architekturen werden in Fahrerassistenzsystemen verwendet? Welche Computerarchitektur versteckt sich hinter einem professionellen digitalen Audio Mischpult? Wie können Datenmengen von 30 TB/s, wie sie bei einem Protonen-Beschleuniger entstehen, in Echtzeit verarbeitet werden? Kann die aufwändige Berechnung der Wettervorhersage auch mit GPUs erfolgen? Nach welcher Systematik können optimale Computerarchitekturen gefunden werden? Welche Faktoren sind entscheidend, um solche Projekte erfolgreich umzusetzen? | |||||

Skript | Skript und Übungsblätter. | |||||

Voraussetzungen / Besonderes | Voraussetzungen: Grundlagen der Computerarchitektur. | |||||

151-0593-00L | Embedded Control SystemsFindet dieses Semester nicht statt. | W | 4 KP | 6G | ||

Kurzbeschreibung | This course provides a comprehensive overview of embedded control systems. The concepts introduced are implemented and verified on a microprocessor-controlled haptic device. | |||||

Lernziel | Familiarize students with main architectural principles and concepts of embedded control systems. | |||||

Inhalt | An embedded system is a microprocessor used as a component in another piece of technology, such as cell phones or automobiles. In this intensive two-week block course the students are presented the principles of embedded digital control systems using a haptic device as an example for a mechatronic system. A haptic interface allows for a human to interact with a computer through the sense of touch. Subjects covered in lectures and practical lab exercises include: - The application of C-programming on a microprocessor - Digital I/O and serial communication - Quadrature decoding for wheel position sensing - Queued analog-to-digital conversion to interface with the analog world - Pulse width modulation - Timer interrupts to create sampling time intervals - System dynamics and virtual worlds with haptic feedback - Introduction to rapid prototyping | |||||

Skript | Lecture notes, lab instructions, supplemental material | |||||

Voraussetzungen / Besonderes | Prerequisite courses are Control Systems I and Informatics I. This course is restricted to 33 students due to limited lab infrastructure. Interested students please contact Marianne Schmid (E-Mail: Link) After your reservation has been confirmed please register online at Link. Detailed information can be found on the course website Link | |||||

252-1411-00L | Security of Wireless Networks | W | 6 KP | 2V + 1U + 2A | S. Capkun, K. Kostiainen | |

Kurzbeschreibung | Core Elements: Wireless communication channel, Wireless network architectures and protocols, Attacks on wireless networks, Protection techniques. | |||||

Lernziel | After this course, the students should be able to: describe and classify security goals and attacks in wireless networks; describe security architectures of the following wireless systems and networks: 802.11, GSM/UMTS, RFID, ad hoc/sensor networks; reason about security protocols for wireless network; implement mechanisms to secure 802.11 networks. | |||||

Inhalt | Wireless channel basics. Wireless electronic warfare: jamming and target tracking. Basic security protocols in cellular, WLAN and multi-hop networks. Recent advances in security of multi-hop networks; RFID privacy challenges and solutions. | |||||

263-3900-01L | Communication Networks Seminar Number of participants limited to 20. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 KP | 2S | A. Singla, L. Vanbever | |

Kurzbeschreibung | We explore recent advances in networking by reading high quality research papers, and discussing open research opportunities, most of which are suitable for students to later take up as thesis or semester projects. | |||||

Lernziel | The objectives are (a) to understand the state-of-the-art in the field; (b) to learn to read, present and critique papers; (c) to engage in discussion and debate about research questions; and (d) to identify opportunities for new research. Students are expected to attend the entire seminar, choose a topic for presentation from a given list, make a presentation on that topic, and lead the discussion. Further, for each reading, every student needs to submit a review before the in-class discussion. Students are evaluated on their submitted reviews, their presentation and discussion leadership, and participation in seminar discussions. | |||||

Literatur | A program will be posted here: Link, comprising of a list of papers the seminar group will cover. | |||||

Voraussetzungen / Besonderes | An undergraduate-level understanding of networking, such that the student is familiar with concepts like reliable transport protocols (like TCP) and basics of Internet routing. ETH courses that fulfill this requirement: Computer Networks (252-0064-00L) and Communication Networks (227-0120-00L). Similar courses at other universities are also sufficient. | |||||

Electronics and Photonics The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Electronics and Photonics", see Link. The individual study plan is subject to the tutor's approval. | ||||||

Kernfächer These core courses are particularly recommended for the field of "Electronics and Photonics". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT. | ||||||

Foundation Core Courses Fundamentals at bachelor level, for master students who need to strengthen or refresh their background in the area. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0110-00L | Elektromagnetische Wellen für Fortgeschrittene | W | 6 KP | 2V + 2U | P. Leuchtmann, U. Koch | |

Kurzbeschreibung | Die Vorlesung gibt einen vertieften Einblick in das Verhalten elektromagnetischer Wellen in linearen Materialien, inklusive negativem Brechungsindex oder Metamaterialien. | |||||

Lernziel | Sie verstehen das Verhalten elektromagnetischer Wellen sowohl im homogenen Raum als auch in ausgewählten Strukturen (Oberflächen, geschichtete Medien, zylindrische Strukturen, Wellenleiter) und wissen auch über zeitharmonische Materialmodelle in Plasmonik Bescheid. | |||||

Inhalt | Beschreibung von zeitharmonischen Feldern; die Rolle des Materials in den Maxwell'schen Gleichungen; Energietransport- und -absorbierungsmechanismen; Elektromagnetische Wellen im homogenen Raum: gewöhnliche und evaneszente Ebene Wellen, Zylinderwellen, Kugelwellen, "Complex origin"-Wellen und -Strahlen; Reflexion an beschichteten Grenzflächen; Oberflächen-Wellen; Wellen in geschichteten Strukturen; Mechanismus der Führung elektromagnetischer Wellen; TEM-Wellen; Hohlleiter und dielektrische Wellenleiter. | |||||

Skript | Ein englischsprachiges Skript mit animierten Darstellungen kann heruntergeladen werden, ebenso die in der Vorlesung gezeigten Folien. | |||||

Literatur | Das Skript enthält eine Literaturliste. | |||||

Voraussetzungen / Besonderes | Die Vorlesung wird auf Deutsch gehalten, das Skript und die Präsentationen sind auf Englisch. | |||||

227-0112-00L | High-Speed Signal Propagation Findet dieses Semester nicht statt. | W | 6 KP | 2V + 2U | C. Bolognesi | |

Kurzbeschreibung | Verständnis der Hochgeschwindigkeits-Signalausbreitung in Mikrowellenkabel, integr. Mikrowellenschaltungen und Leiterplatten. Da Sytemtaktfrequenzen stets in höhere GHz Bereiche vordringen, ist es notwendig die Hochgeschwindigkeits-Signalausbreitung zu verstehen, um Signalintegrität zu gewährleisten. Der Kurs richtet sich an Interessierte an analogen/digitalen Hochgeschwindigkeitssystemen. | |||||

Lernziel | Verständnis der Hochgeschwindigkeits-Signalausbreitung in Verbindungsleitern, Mikrowellenkabel und integrierten Übertragungsleitungen wie zum Beispiel in integrierten Mikrowellenschaltungen und/oder Leiterplatten. Da Systemtaktfrequenzen kontinuierlich in höhere GHz Bereiche vordringen, entwickelt sich das dringende Bedürfnis die Hochgeschwindigkeits-Signalausbreitung zu verstehen um nach wie vor eine hohe Signalintegrität zu gewährleisten, insbesondere angesichts Phänomenen wie der Intersymbol-Interferenz (ISI) und des Übersprechens. Konzepte wie Streuparameter (oder S-Parameter) übernehmen eine Schlüsselrolle in der Charakterisierung von Netzwerken über grosse Bandbreiten. Bei hohen Frequenzen werden alle Strukturen effektiv zu "Übertragungsleitungen". Ohne besondere Vorsicht ist es sehr wahrscheinlich, dass eine schlecht entworfene Übertragungsleitung zum Versagen des gesamten entworfenen Systems führt. Filter werden ebenfalls behandelt, da sich herausstellt, dass einige der Probleme von verlustbehafteten Übertragungskanälen (Leitungen, Kabel, etc.) durch adäquates filtern korrigiert werden können. Ein Prozess der "Entzerrung" genannt wird. | |||||

Inhalt | Leitungsgleichungen der TEM-Leitung (Telegraphengleichungen). Beschreibung elektrischer Grössen auf der TEM Leitung; Reflexion im Zeit- und Frequenzbereich, Smith-Diagramm. Verhalten schwach bedämpfter Leitungen. Einfluss des Skineffekts auf Dämpfung und Impulsverzerrung. Leitungsersatzschaltungen. Gruppenlaufzeit und Dispersion. Eigenschaften gekoppelter Leitungen. Streuparameter. Butterworth-, Tschebyscheff- und Besselfilter: Einführung zum Filterentwurf mit Filterprototypen (Tiefpass, Hochpass, Bandpass, Bandsperre). Einfache aktive Filter. | |||||

Skript | Skript: Leitungen und Filter (In deutscher Sprache). | |||||

Voraussetzungen / Besonderes | Die Uebungen werden auf Englisch gehalten. | |||||

227-0116-00L | VLSI I: From Architectures to VLSI Circuits and FPGAs | W | 6 KP | 5G | F. K. Gürkaynak, L. Benini | |

Kurzbeschreibung | This first course in a series that extends over three consecutive terms is concerned with tailoring algorithms and with devising high performance hardware architectures for their implementation as ASIC or with FPGAs. The focus is on front end design using HDLs and automatic synthesis for producing industrial-quality circuits. | |||||

Lernziel | Understand Very-Large-Scale Integrated Circuits (VLSI chips), Application-Specific Integrated Circuits (ASIC), and Field-Programmable Gate-Arrays (FPGA). Know their organization and be able to identify suitable application areas. Become fluent in front-end design from architectural conception to gate-level netlists. How to model digital circuits with SystemVerilog. How to ensure they behave as expected with the aid of simulation, testbenches, and assertions. How to take advantage of automatic synthesis tools to produce industrial-quality VLSI and FPGA circuits. Gain practical experience with the hardware description language SystemVerilog and with industrial Electronic Design Automation (EDA) tools. | |||||

Inhalt | This course is concerned with system-level issues of VLSI design and FPGA implementations. Topics include: - Overview on design methodologies and fabrication depths. - Levels of abstraction for circuit modeling. - Organization and configuration of commercial field-programmable components. - FPGA design flows. - Dedicated and general purpose architectures compared. - How to obtain an architecture for a given processing algorithm. - Meeting throughput, area, and power goals by way of architectural transformations. - Hardware Description Languages (HDL) and the underlying concepts. - SystemVerilog - Register Transfer Level (RTL) synthesis and its limitations. - Building blocks of digital VLSI circuits. - Functional verification techniques and their limitations. - Modular and largely reusable testbenches. - Assertion-based verification. - Synchronous versus asynchronous circuits. - The case for synchronous circuits. - Periodic events and the Anceau diagram. - Case studies, ASICs compared to microprocessors, DSPs, and FPGAs. During the exercises, students learn how to model FPGAs with SystemVerilog. They write testbenches for simulation purposes and synthesize gate-level netlists for FPGAs. Commercial EDA software by leading vendors is being used throughout. | |||||

Skript | Textbook and all further documents in English. | |||||

Literatur | H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303. | |||||

Voraussetzungen / Besonderes | Prerequisites: Basics of digital circuits. Examination: In written form following the course semester (spring term). Problems are given in English, answers will be accepted in either English oder German. Further details: Link | |||||

227-0145-00L | Solid State Electronics and Optics | W | 6 KP | 4G | N. Yazdani, V. Wood | |

Kurzbeschreibung | "Solid State Electronics" is an introductory condensed matter physics course covering crystal structure, electron models, classification of metals, semiconductors, and insulators, band structure engineering, thermal and electronic transport in solids, magnetoresistance, and optical properties of solids. | |||||

Lernziel | Understand the fundamental physics behind the mechanical, thermal, electric, magnetic, and optical properties of materials. | |||||

Voraussetzungen / Besonderes | Recommended background: Undergraduate physics, mathematics, semiconductor devices | |||||

227-0166-00L | Analog Integrated Circuits | W | 6 KP | 2V + 2U | T. Jang | |

Kurzbeschreibung | This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies. | |||||

Lernziel | Integrated circuits are responsible for much of the progress in electronics in the last 50 years, particularly the revolutions in the Information and Communications Technologies we witnessed in recent years. Analog integrated circuits play a crucial part in the highly integrated systems that power the popular electronic devices we use daily. Understanding their design is beneficial to both future designers and users of such systems. The basic elements, design issues and techniques for analog integrated circuits will be taught in this course. | |||||

Inhalt | Review of bipolar and MOS devices and their small-signal equivalent circuit models; Building blocks in analog circuits such as current sources, active load, current mirrors, supply independent biasing etc; Amplifiers: differential amplifiers, cascode amplifier, high gain structures, output stages, gain bandwidth product of op-amps; stability; comparators; second-order effects in analog circuits such as mismatch, noise and offset; data converters; frequency synthesizers; switched capacitors. The exercise sessions aim to reinforce the lecture material by well guided step-by-step design tasks. The circuit simulator SPECTRE is used to facilitate the tasks. There is also an experimental session on op-amp measurements. | |||||

Skript | Handouts of presented slides. No script but an accompanying textbook is recommended. | |||||

Literatur | Behzad Razavi, Design of Analog CMOS Integrated Circuits (Irwin Electronics & Computer Engineering) 1st or 2nd edition, McGraw-Hill Education | |||||

Advanced Core Courses Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0146-00L | Analog-to-Digital Converters Findet dieses Semester nicht statt. Course will be moved to the autumn semester 2021. | W | 6 KP | 2V + 2U | ||

Kurzbeschreibung | This course provides a thorough treatment of integrated data conversion systems from system level specifications and trade-offs, over architecture choice down to circuit implementation. | |||||

Lernziel | Data conversion systems are substantial sub-parts of many electronic systems, e.g. the audio conversion system of a home-cinema systems or the base-band front-end of a wireless modem. Data conversion systems usually determine the performance of the overall system in terms of dynamic range and linearity. The student will learn to understand the basic principles behind data conversion and be introduced to the different methods and circuit architectures to implement such a conversion. The conversion methods such as successive approximation or algorithmic conversion are explained with their principle of operation accompanied with the appropriate mathematical calculations, including the effects of non-idealties in some cases. After successful completion of the course the student should understand the concept of an ideal ADC, know all major converter architectures, their principle of operation and what governs their performance. | |||||

Inhalt | - Introduction: information representation and communication; abstraction, categorization and symbolic representation; basic conversion algorithms; data converter application; tradeoffs among key parameters; ADC taxonomy. - Dual-slope & successive approximation register (SAR) converters: dual slope principle & converter; SAR ADC operating principle; SAR implementation with a capacitive array; range extension with segmented array. - Algorithmic & pipelined A/D converters: algorithmic conversion principle; sample & hold stage; pipe-lined converter; multiplying DAC; flash sub-ADC and n-bit MDAC; redundancy for correction of non-idealties, error correction. - Performance metrics and non-linearity: ideal ADC; offset, gain error, differential and integral non-linearities; capacitor mismatch; impact of capacitor mismatch on SAR ADC's performance. - Flash, folding an interpolating analog-to-digital converters: flash ADC principle, thermometer to binary coding, sparkle correction; limitations of flash converters; the folding principle, residue extraction; folding amplifiers; cascaded folding; interpolation for folding converters; cascaded folding and interpolation. - Noise in analog-to-digital converters: types of noise; noise calculation in electronic circuit, kT/C-noise, sampled noise; noise analysis in switched-capacitor circuits; aperture time uncertainty and sampling jitter. - Delta-sigma A/D-converters: linearity and resolution; from delta-modulation to delta-sigma modulation; first-oder delta-sigma modulation, circuit level implementation; clock-jitter & SNR in delta-sigma modulators; second-order delta-sigma modulation, higher-order modulation, design procedure for a single-loop modulator. - Digital-to-analog converters: introduction; current scaling D/A converter, current steering DAC, calibration for improved performance. | |||||

Skript | Slides are available online under Link | |||||

Literatur | - B. Razavi, Principles of Data Conversion System Design, IEEE Press, 1994 - M. Gustavsson et. al., CMOS Data Converters for Communications, Springer, 2010 - R.J. van de Plassche, CMOS Integrated Analog-to-Digital and Digital-to-Analog Converters, Springer, 2010 | |||||

Voraussetzungen / Besonderes | It is highly recommended to attend the course "Analog Integrated Circuits" of Prof. Huang as a preparation for this course. | |||||

227-0148-00L | VLSI III: Test and Fabrication of VLSI Circuits | W | 6 KP | 4G | F. K. Gürkaynak, L. Benini | |

Kurzbeschreibung | In this course, we will cover how modern microchips are fabricated, and we will focus on methods and tools to uncover fabrication defects, if any, in these microchips. As part of the exercises, students will get to work on an industrial 1 million dollar automated test equipment. | |||||

Lernziel | Learn about modern IC manufacturing methodologies, understand the problem of IC testing. Cover the basic methods, algorithms and techniques to test circuits in an efficient way. Learn about practical aspects of IC testing and apply what you learn in class using a state-of-the art tester. | |||||

Inhalt | In this course we will deal with modern integrated circuit (IC) manufacturing technology and cover topics such as: - Today's nanometer CMOS fabrication processes (HKMG). - Optical and post optical Photolithography. - Potential alternatives to CMOS technology and MOSFET devices. - Evolution paths for design methodology. - Industrial roadmaps for the future evolution of semiconductor technology (ITRS). If you want to earn money by selling ICs, you will have to deliver a product that will function properly with a very large probability. The main emphasis of the lecture will be discussing how this can be achieved. We will discuss fault models and practical techniques to improve testability of VLSI circuits. At the IIS we have a state-of-the-art automated test equipment (Advantest SoC V93000) that we will make available for in class exercises and projects. At the end of the lecture you will be able to design state-of-the art digital integrated circuits such as to make them testable and to use automatic test equipment (ATE) to carry out the actual testing. During the first weeks of the course there will be weekly practical exercises where you will work in groups of two. For the last 5 weeks of the class students will be able to choose a class project that can be: - The test of their own chip developed during a previous semester thesis - Developing new setups and measurement methods in C++ on the tester - Helping to debug problems encountered in previous microchips by IIS. Half of the oral exam will consist of a short presentation on this class project. | |||||

Skript | Main course book: "Essentials of Electronic Testing for Digital, Memory and Mixed-Signal VLSI Circuits" by Michael L. Bushnell and Vishwani D. Agrawal, Springer, 2004. This book is available online within ETH through Link | |||||

Voraussetzungen / Besonderes | Although this is the third part in a series of lectures on VLSI design, you can follow this course even if you have not visited VLSI I and VLSI II lectures. An interest in integrated circuit design, and basic digital circuit knowledge is required though. Course website: Link | |||||

227-0301-00L | Optical Communication Fundamentals | W | 6 KP | 2V + 1U + 1P | J. Leuthold | |

Kurzbeschreibung | The path of an analog signal in the transmitter to the digital world in a communication link and back to the analog world at the receiver is discussed. The lecture covers the fundamentals of all important optical and optoelectronic components in a fiber communication system. This includes the transmitter, the fiber channel and the receiver with the electronic digital signal processing elements. | |||||

Lernziel | An in-depth understanding on how information is transmitted from source to destination. Also the mathematical framework to describe the important elements will be passed on. Students attending the lecture will further get engaged in critical discussion on societal, economical and environmental aspects related to the on-going exponential growth in the field of communications. | |||||

Inhalt | * Chapter 1: Introduction: Analog/Digital conversion, The communication channel, Shannon channel capacity, Capacity requirements. * Chapter 2: The Transmitter: Components of a transmitter, Lasers, The spectrum of a signal, Optical modulators, Modulation formats. * Chapter 3: The Optical Fiber Channel: Geometrical optics, The wave equations in a fiber, Fiber modes, Fiber propagation, Fiber losses, Nonlinear effects in a fiber. * Chapter 4: The Receiver: Photodiodes, Receiver noise, Detector schemes (direct detection, coherent detection), Bit-error ratios and error estimations. * Chapter 5: Digital Signal Processing Techniques: Digital signal processing in a coherent receiver, Error detection teqchniques, Error correction coding. * Chapter 6: Pulse Shaping and Multiplexing Techniques: WDM/FDM, TDM, OFDM, Nyquist Multiplexing, OCDMA. * Chapter 7: Optical Amplifiers : Semiconductor Optical Amplifiers, Erbium Doped Fiber Amplifiers, Raman Amplifiers. | |||||

Skript | Lecture notes are handed out. | |||||

Literatur | Govind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010 | |||||

Voraussetzungen / Besonderes | Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics. | |||||

227-0655-00L | Nonlinear Optics | W | 6 KP | 2V + 2U | J. Leuthold | |

Kurzbeschreibung | Nonlinear Optics deals with the interaction of light with material, such as the response of material to light. We will introduce the framework to describe the phenomena based on a classical and quantum description. As an example we will cover fundamental phenomena such as the linear and nonlinear refractive index, the electro-optic effect, second harmonic generation, spontaneous four-wave mixing. | |||||

Lernziel | The important nonlinear optical phenomena are understood and can be classified. The effects can be described mathematical by means of the susceptibility. | |||||

Inhalt | Chapter 1: The Wave Equations in Nonlinear Optics Chapter 2: Nonlinear Effects - An Overview Chapter 3: The Nonlinear Optical Susceptibility (Classical & Quantum) Chapter 4: Second Harmonic Generation Chapter 5: The Electro-Optic Effect and the Electro-Optic Modulator Chapter 6: Third Order Nonlinearities in Waveguides (Classical & Quantum) Chapter 7: Acousto-Optic Effect Chapter 8: Nonlinear Effects in Media with Gain The exercise focuses on phrasing the content of the lecture content from the perspective of an PhD (tutorial form). Furthermore, a journal club is offered to connect students with the current research, successful participation provides a bonus for the exam. Problem sets are also offered for independent learning of the students. | |||||

Literatur | Lecture notes are distributed. For students enrolled in the course, additional information, lecture notes and exercises can be found on moodle (Link). | |||||

Voraussetzungen / Besonderes | Fundamentals of Electromagnetic Fields (Maxwell Equations) & Bachelor Lectures on Physics | |||||

227-0663-00L | Nano-Optics | W | 6 KP | 2V + 2U | M. Frimmer | |

Kurzbeschreibung | Nano-Optics is the study of light-matter interaction at the sub-wavelength scale. It is an flourishing field of fundamental and applied research enabled by the rapid advance of nanotechnology. Nano-optics embraces topics such as plasmonics, optical antennas, optical trapping and manipulation, and high/super-resolution imaging and spectroscopy. | |||||

Lernziel | Understanding concepts of light localization and light-matter interactions on the sub-wavelength scale. | |||||

Inhalt | We start with the angular spectrum representation of fields to understand the classical resolution limit. We continue with the theory of strongly focused light, the point spread function, and resolution criteria of conventional microscopy, before turning to super-resolution techniques, based on near- and far-fields. We introduce the local density of states and approaches to control spontaneous emission rates in inhomogeneous environments, including optical antennas. Finally, we touch upon optical forces and their applications in optical tweezers. | |||||

Voraussetzungen / Besonderes | - Electromagnetic fields and waves (or equivalent) - Physics I+II | |||||

Vertiefungsfächer These specialisation courses are particularly recommended for the area of "Electronics and Photonics", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0121-00L | Kommunikationssysteme | W | 6 KP | 2V + 2U | A. Wittneben | |

Kurzbeschreibung | Informationstheorie, Signalraumanalyse, Basisbandübertragung, Passbandübertragung, Systembeispiel und Kanal, Sicherungsschicht, MAC, Beispiele Layer 2, Layer 3, Internet | |||||

Lernziel | Ziel der Vorlesung ist die Einführung der wichtigsten Konzepte und Verfahren, die in modernen digitalen Kommunikationssystemen Anwendung finden, sowie eine Übersicht über bestehende und zukünftige Systeme. | |||||

Inhalt | Es werden die untersten drei Schichten des OSI-Referenzmodells behandelt: die Bitübertragungsschicht, die Sicherungsschicht mit dem Zugriff auf das Übertragungsmedium und die Vermittlung. Die wichtigsten Begriffe der Informationstheorie werden eingeführt. Anschliessend konzentrieren sich die Betrachtungen auf die Verfahren der Punkt-zu-Punkt-Übertragung, welche sich mittels der Signalraumdarstellung elegant und kohärent behandeln lassen. Den Methoden der Fehlererkennung und –korrektur, sowie Protokollen für die erneute Übermittlung gestörter Daten wird Rechnung getragen. Auch der Vielfachzugriff bei geteiltem Übertragungsmedium wird diskutiert. Den Abschluss bilden Algorithmen für das Routing in Kommunikationsnetzen und der Flusssteuerung. Die Anwendung der grundlegenden Verfahren wird ausführlich anhand von bestehenden und zukünftigen drahtlosen und drahtgebundenen Systemen erläutert. | |||||

Skript | Vorlesungsfolien | |||||

Literatur | [1] Simon Haykin, Communication Systems, 4. Auflage, John Wiley & Sons, 2001 [2] Andrew S. Tanenbaum, Computernetzwerke, 3. Auflage, Pearson Studium, 2003 [3] M. Bossert und M. Breitbach, Digitale Netze, 1. Auflage, Teubner, 1999 | |||||

227-0155-00L | Machine Learning on Microcontrollers Registration in this class requires the permission of the instructors. Class size will be limited to 16. Preference is given to students in the MSc EEIT. | W | 6 KP | 3G | M. Magno, L. Benini | |

Kurzbeschreibung | Machine Learning (ML) and artificial intelligence are pervading the digital society. Today, even low power embedded systems are incorporating ML, becoming increasingly “smart”. This lecture gives an overview of ML methods and algorithms to process and extract useful near-sensor information in end-nodes of the “internet-of-things”, using low-power microcontrollers/ processors (ARM-Cortex-M; RISC-V) | |||||

Lernziel | Learn how to Process data from sensors and how to extract useful information with low power microprocessors using ML techniques. We will analyze data coming from real low-power sensors (accelerometers, microphones, ExG bio-signals, cameras…). The main objective is to study in details how Machine Learning algorithms can be adapted to the performance constraints and limited resources of low-power microcontrollers. | |||||

Inhalt | The final goal of the course is a deep understanding of machine learning and its practical implementation on single- and multi-core microcontrollers, coupled with performance and energy efficiency analysis and optimization. The main topics of the course include: - Sensors and sensor data acquisition with low power embedded systems - Machine Learning: Overview of supervised and unsupervised learning and in particular supervised learning (Bayes Decision Theory, Decision Trees, Random Forests, kNN-Methods, Support Vector Machines, Convolutional Networks and Deep Learning) - Low-power embedded systems and their architecture. Low Power microcontrollers (ARM-Cortex M) and RISC-V-based Parallel Ultra Low Power (PULP) systems-on-chip. - Low power smart sensor system design: hardware-software tradeoffs, analysis, and optimization. Implementation and performance evaluation of ML in battery-operated embedded systems. The laboratory exercised will show how to address concrete design problems, like motion, gesture recognition, emotion detection, image and sound classification, using real sensors data and real MCU boards. Presentations from Ph.D. students and the visit to the Digital Circuits and Systems Group will introduce current research topics and international research projects. | |||||

Skript | Script and exercise sheets. Books will be suggested during the course. | |||||

Voraussetzungen / Besonderes | Prerequisites: C language programming. Basics of Digital Signal Processing. Basics of processor and computer architecture. Some exposure to machine learning concepts is also desirable | |||||

227-0157-00L | Semiconductor Devices: Physical Bases and Simulation | W | 4 KP | 3G | A. Schenk | |

Kurzbeschreibung | The course addresses the physical principles of modern semiconductor devices and the foundations of their modeling and numerical simulation. Necessary basic knowledge on quantum-mechanics, semiconductor physics and device physics is provided. Computer simulations of the most important devices and of interesting physical effects supplement the lectures. | |||||

Lernziel | The course aims at the understanding of the principle physics of modern semiconductor devices, of the foundations in the physical modeling of transport and its numerical simulation. During the course also basic knowledge on quantum-mechanics, semiconductor physics and device physics is provided. | |||||

Inhalt | The main topics are: transport models for semiconductor devices (quantum transport, Boltzmann equation, drift-diffusion model, hydrodynamic model), physical characterization of silicon (intrinsic properties, scattering processes), mobility of cold and hot carriers, recombination (Shockley-Read-Hall statistics, Auger recombination), impact ionization, metal-semiconductor contact, metal-insulator-semiconductor structure, and heterojunctions. The exercises are focussed on the theory and the basic understanding of the operation of special devices, as single-electron transistor, resonant tunneling diode, pn-diode, bipolar transistor, MOSFET, and laser. Numerical simulations of such devices are performed with an advanced simulation package (Sentaurus-Synopsys). This enables to understand the physical effects by means of computer experiments. | |||||

Skript | The script (in book style) can be downloaded from: Link | |||||

Literatur | The script (in book style) is sufficient. Further reading will be recommended in the lecture. | |||||

Voraussetzungen / Besonderes | Qualifications: Physics I+II, Semiconductor devices (4. semester). | |||||

227-0158-00L | Semiconductor Devices: Transport Theory and Monte Carlo Simulation Findet dieses Semester nicht statt. The course was offered for the last time in HS19. | W | 4 KP | 2G | ||

Kurzbeschreibung | The lecture combines quasi-ballistic transport theory with application to realistic devices of current and future CMOS technology. All aspects such as quantum mechanics, phonon scattering or Monte Carlo techniques to solve the Boltzmann equation are introduced. In the exercises advanced devices such as FinFETs and nanosheets are simulated. | |||||

Lernziel | The aim of the course is a fundamental understanding of the derivation of the Boltzmann equation and its solution by Monte Carlo methods. The practical aspect is to become familiar with technology computer-aided design (TCAD) and perform simulations of advanced CMOS devices. | |||||

Inhalt | The covered topics include: - quantum mechanics and second quantization, - band structure calculation including the pseudopotential method - phonons - derivation of the Boltzmann equation including scattering in the Markov limit - stochastic Monte Carlo techniques to solve the Boltzmann equation - TCAD environment and geometry generation - Stationary bulk Monte Carlo simulation of velocity-field curves - Transient Monte Carlo simulation for quasi-ballistic velocity overshoot - Monte Carlo device simulation of FinFETs and nanosheets | |||||

Skript | Lecture notes (in German) | |||||

Literatur | Further reading will be recommended in the lecture. | |||||

Voraussetzungen / Besonderes | Knowledge of quantum mechanics is not required. Basic knowledge of semiconductor physics is useful, but not necessary. | |||||

227-0166-00L | Analog Integrated Circuits | W | 6 KP | 2V + 2U | T. Jang | |

Kurzbeschreibung | This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies. | |||||

Lernziel | Integrated circuits are responsible for much of the progress in electronics in the last 50 years, particularly the revolutions in the Information and Communications Technologies we witnessed in recent years. Analog integrated circuits play a crucial part in the highly integrated systems that power the popular electronic devices we use daily. Understanding their design is beneficial to both future designers and users of such systems. The basic elements, design issues and techniques for analog integrated circuits will be taught in this course. | |||||

Inhalt | Review of bipolar and MOS devices and their small-signal equivalent circuit models; Building blocks in analog circuits such as current sources, active load, current mirrors, supply independent biasing etc; Amplifiers: differential amplifiers, cascode amplifier, high gain structures, output stages, gain bandwidth product of op-amps; stability; comparators; second-order effects in analog circuits such as mismatch, noise and offset; data converters; frequency synthesizers; switched capacitors. The exercise sessions aim to reinforce the lecture material by well guided step-by-step design tasks. The circuit simulator SPECTRE is used to facilitate the tasks. There is also an experimental session on op-amp measurements. | |||||

Skript | Handouts of presented slides. No script but an accompanying textbook is recommended. | |||||

Literatur | Behzad Razavi, Design of Analog CMOS Integrated Circuits (Irwin Electronics & Computer Engineering) 1st or 2nd edition, McGraw-Hill Education | |||||

227-0377-10L | Physics of Failure and Reliability of Electronic Devices and Systems | W | 3 KP | 2V | I. Shorubalko, M. Held | |

Kurzbeschreibung | Understanding the physics of failures and failure mechanisms enables reliability analysis and serves as a practical guide for electronic devices design, integration, systems development and manufacturing. The field gains additional importance in the context of managing safety, sustainability and environmental impact for continuously increasing complexity and scaling-down trends in electronics. | |||||

Lernziel | Provide an understanding of the physics of failure and reliability. Introduce the degradation and failure mechanisms, basics of failure analysis, methods and tools of reliability testing. | |||||

Inhalt | Summary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis; basics and properties of instruments; quality assurance of technical systems (introduction); introduction to stochastic processes; reliability analysis; component selection and qualification; maintainability analysis (introduction); design rules for reliability, maintainability, reliability tests (introduction). | |||||

Skript | Comprehensive copy of transparencies | |||||

Literatur | Reliability Engineering: Theory and Practice, 8th Edition, Springer 2017, DOI 10.1007/978-3-662-54209-5 Reliability Engineering: Theory and Practice, 8th Edition (2017), DOI 10.1007/978-3-662-54209-5 | |||||

227-0468-00L | Analog Signal Processing and Filtering Suitable for Master Students as well as Doctoral Students. | W | 6 KP | 2V + 2U | H. Schmid | |

Kurzbeschreibung | This lecture provides a wide overview over analog filters (continuous-time and discrete-time), signal-processing systems, and sigma-delta conversion, and gives examples with sensor interfaces and class-D audio drivers. All systems and circuits are treated using a signal-flow view. The lecture is suitable for both analog and digital designers. | |||||

Lernziel | This lecture provides a wide overview over analog filters (continuous-time and discrete-time), signal-processing systems, and sigma-delta conversion, and gives examples with sensor interfaces and class-D audio drivers. All systems and circuits are treated using a signal-flow view. The lecture is suitable for both analog and digital designers. The way the exam is done allows for the different interests of the two groups. The learning goal is that the students can apply signal-flow graphs and can understand the signal flow in such circuits and systems (including non-ideal effects) well enough to gain an understanding of further circuits and systems by themselves. | |||||

Inhalt | At the beginning, signal-flow graphs in general and driving-point signal-flow graphs in particular are introduced. We will use them during the whole term to analyze circuits on a system level (analog continuous-time, analog discrete-time, mixed-signal and digital) and understand how signals propagate through them. The theory and CMOS implementation of active Filters is then discussed in detail using the example of Gm-C filters and active-RC filters. The ideal and nonideal behaviour of opamps, current conveyors, and inductor simulators follows. The link to the practical design of circuits and systems is done with an overview over different quality measures and figures of merit used in scientific literature and datasheets. Finally, an introduction to discrete-time and mixed-domain filters and circuits is given, including sensor read-out amplifiers, correlated double sampling, and chopping, and an introduction to sigma-delta A/D and D/A conversion on a system level. This lecture does not go down to the details of transistor implementations. The lecture "227-0166-00L Analog Integrated Circuits" complements This lecture very well in that respect. | |||||

Skript | The base for these lectures are lecture notes and two or three published scientific papers. From these papers we will together develop the technical content. Details: Link The graph methods are also supported with teaching videos: Link Some material is protected by password; students from ETHZ who are interested can write to Link to ask for the password even if they do not attend the lecture. | |||||

Voraussetzungen / Besonderes | Live stream: due to Covids rules, the lecture will be streamed live. Join here: Link Prerequisites: Recommended (but not required): Stochastic models and signal processing, Communication Electronics, Analog Integrated Circuits, Transmission Lines and Filters. Knowledge of the Laplace transform and z transform and their interpretation (transfer functions, poles and zeros, bode diagrams, stability criteria ...) and of the main properties of linear systems is necessary. | |||||

227-0615-00L | Simulation of Photovoltaic Devices - From Materials to Modules | W | 3 KP | 2G | U. Aeberhard | |

Kurzbeschreibung | The lecture provides an introduction to the theoretical foundations and numerical approaches for the simulation of photovoltaic energy conversion, from the microscopic description of component materials, nanostructures and interfaces to macroscopic continuum modelling of solar cells and network simulation or effective models for entire solar modules and large scale photovoltaic systems. | |||||

Lernziel | Know how to obtain and assess by simulation the key material properties and device parameters relevant for photovoltaic energy conversion. | |||||

Inhalt | The lecture provides an introduction to the theoretical foundations and numerical approaches for the simulation of photovoltaic energy conversion, from the microscopic description of component materials, nanostructures and interfaces to macroscopic continuum modelling of solar cells and network simulation or effective models for entire solar modules and large scale photovoltaic systems. | |||||

Voraussetzungen / Besonderes | Undergraduate physics, mathematics, semiconductor devices | |||||

227-0617-00L | Solar Cells | W | 4 KP | 3G | A. N. Tiwari, R. Carron, Y. Romanyuk | |

Kurzbeschreibung | Physics, technology, characteristics and applications of photovoltaic solar cells. | |||||

Lernziel | Introduction to solar radiation, physics, technology, characteristics and applications of photovoltaic solar cells and systems. | |||||

Inhalt | Solar radiation characteristics, physical mechanisms for the light to electrical power conversion, properties of semiconductors for solar cells, processing and properties of conventional Si and GaAs based solar cells, technology and physics of thin film solar cells based on compound semiconductors, other solar cells including organic and dye sensitized cells, problems and new developments for power generation in space, interconnection of cells and solar module design, measurement techniques, system design of photovoltaic plants, system components such as inverters and controllers, engineering procedures with software domonstration, integration in buildings and other specific examples. | |||||

Skript | Lecture reprints (in english). | |||||

Voraussetzungen / Besonderes | Prerequisites: Basic knowledge of semiconductor properties. | |||||

227-0618-00L | Modeling, Characterization and Reliability of Power Semiconductors Findet dieses Semester nicht statt. | W | 6 KP | 4G | ||

Kurzbeschreibung | This lecture provides theoretical and experimental knowledge on the techniques for the characterization and numerical modeling of power semiconductors, as well on the related built-in reliability strategies. | |||||

Lernziel | The students shall get acquainted with the most important concepts and techniques for characterization, numerical modeling and built-in reliability of modern power semiconductor devices. This knowledge is intended to provide the future engineer with the theoretical background and tools for the design of dependable power devices and systems. | |||||

Inhalt | This lecture consists of a theoretical part (50%) and of laboratory exercises and demonstrations (50%). The theoretical part covers the basic techniques and procedures for characterization, modeling and built-in reliability of modern power semiconductor devices with special attention to MOS and IGBT. The starting part on technology provides an overview on the main device families and includes a review of the most relevant application-oriented aspects of the device physics, thermal management, and packaging. The second section deals with the basic experimental characterization techniques for the definition of the semiconductor material properties, electrical characteristics, safe operating area, and junction temperature of the devices. The following section introduces the basic principles for electrical, thermal, and electro-thermal simulation of power semiconductors by Technology Computed Aided Design (TCAD) and compact modeling. Finally, procedures are methods are presented to implement efficient built-in reliability programs targeted on power semiconductors. They include failure physics, dedicated failure analysis techniques, accelerated testing, defect screening, and lifetime modeling. During the laboratory activities, selections of the experimental techniques presented in the lecture are demonstrated on the base of realistic examples. Furthermore, schematic power devices will be simulated by the students with advanced TCAD tools and circuit simulators. | |||||

Skript | Handouts to the lecture (approx. 250 pp.) | |||||

Literatur | Eiichi Ohno: "Introduction to Power Electronics" B. Murari et al.: "Smart Power ICs" B. J. Baliga: "Physics Modern Power Devices" S. K. Ghandi: "Semiconductor Power Devices" | |||||

227-0619-00L | Charge Transport in Energy Conversion and Storage Devices | W | 6 KP | 2V + 2U | C. Battaglia | |

Kurzbeschreibung | The students will be introduced to the fundamental concepts of charge transport in solar cells, batteries, and electrolysers. Emphasizing analogies between semiconductor physics and electrochemistry, this course is designed to provide a unified modern perspective of energy conversion and storage concepts for students in electrical engineering, materials science, physics, and chemistry. | |||||

Lernziel | By the end of this course, the student is expected to be able (1) to list the equations governing charge transport in solar cells and battery cells, (2) to explain their operational principles and fundamental performance limits and how to overcome them, (3) to interpret current-voltage and charge-voltage characteristics of solar cells and battery cells along with other device characteristics under different operating conditions. During the exercises, the students will learn to simulate realistic solar cell and battery architectures from materials properties. | |||||

Literatur | P. Würfel, Physics of Solar Cells: From Principles to New Concepts, DOI:10.1002/9783527618545 R. Huggins, Advanced Batteries, DOI:10.1007/9780387764245 R. Huggins, Energy Storage, DOI:10.1007/9781441910240 | |||||

Voraussetzungen / Besonderes | Be passionate to change the world to renewable energies! Elements of calculus will be reviewed where necessary, but we leave the task of solving coupled differential charge transport equations to the computer and focus on developing a strong intuition. Prior knowledge in semiconductor physics or electrochemistry is an advantage, but not a prerequisite. Students are required to bring a windows-compatible computer with a common data analysis software to the exercises. Apps for simulating devices under different operating conditions will be made available to the students. A visit to a solar cell or battery fab will be organized during the semester. | |||||

227-0627-00L | Angewandte Computer Architektur | W | 6 KP | 4G | A. Gunzinger | |

Kurzbeschreibung | Diese Vorlesung gibt einen Überblick über die Anforderungen und die Architektur von parallelen Computersystemen unter Berücksichtigung von Rechenleistung, Zuverlässigkeit und Kosten. | |||||

Lernziel | Arbeitsweise von parallelen Computersystemen verstehen, solche Systeme entwerfen und modellieren. | |||||

Inhalt | Die Vorlesung Angewandte Computer Architektur gibt technische und unternehmerische Einblicke in innovative Computersysteme/Architekturen (CPU, GPU, FPGA, Spezialprozessoren) und deren praxisnahe Umsetzung. Dabei werden oft die Grenzen der technologischen Möglichkeiten ausgereizt. Wie ist das Computersystem aufgebaut, das die über 1000 Magneten an der Swiss Light Source (SLS) steuert? Wie ist das hochverfügbare Alarmzentrum der SBB aufgebaut? Welche Computer Architekturen werden in Fahrerassistenzsystemen verwendet? Welche Computerarchitektur versteckt sich hinter einem professionellen digitalen Audio Mischpult? Wie können Datenmengen von 30 TB/s, wie sie bei einem Protonen-Beschleuniger entstehen, in Echtzeit verarbeitet werden? Kann die aufwändige Berechnung der Wettervorhersage auch mit GPUs erfolgen? Nach welcher Systematik können optimale Computerarchitekturen gefunden werden? Welche Faktoren sind entscheidend, um solche Projekte erfolgreich umzusetzen? | |||||

Skript | Skript und Übungsblätter. | |||||

Voraussetzungen / Besonderes | Voraussetzungen: Grundlagen der Computerarchitektur. | |||||

227-0653-00L | Electromagnetic Precision Measurements and Opto-MechanicsFindet dieses Semester nicht statt. | W | 4 KP | 2V + 1U | M. Frimmer | |

Kurzbeschreibung | The measurement process is at the heart of both science and engineering. Electromagnetic fields have proven to be particularly powerful probes. This course provides the basic knowledge necessary to understand current state-of-the-art optomechanical measurement systems operating at the precision limits set by the laws of quantum mechanics. | |||||

Lernziel | The goal of this coarse is to understand the fundamental limitations of measurement systems relying on electromagnetic fields. | |||||

Inhalt | The lecture starts with summarizing the relevant fundamentals of the treatment of noisy signals. Starting with the resolution limit of optical imaging systems, we familiarize ourselves with the concept of measurement imprecision in light-based measurement systems. We consider the process of photodetection and discuss the statistical fluctuations arising from the quantization of the electromagnetic fields into photons. We exemplify our insights at hand of concrete examples, such as homodyne and heterodyne photodetection. Furthermore, we focus on the process of measurement backaction, the inevitable result of the interaction of the probe with the system under investigation. The course emphasizes the connection between the taught concepts and current state-of-the-art research carried out in the field of optomechanics. | |||||

Voraussetzungen / Besonderes | 1. Electrodynamics 2. Physics 1,2 3. Introduction to quantum mechanics | |||||

227-0659-00L | Integrated Systems Seminar | W | 1 KP | 1S | A. Schenk | |

Kurzbeschreibung | In the "Fachseminar IIS" the students learn to communicate topics, ideas or problems of scientific research by listening to more experienced authors and by presenting scientific work in a conference-like situation for a specific audience. | |||||

Lernziel | The seminar aims at instructing graduate and PhD students in the basics of presentation techniques, i.e. "how to give a professional talk". Attendees have the possibility to become acquainted with a current topic by a literature study, and to present the results thereof in a 20 minutes talk in English. The participation at the seminar gives also an overview on current problems in modern nano- and opto-electronics. | |||||

Inhalt | The seminar topics' are simulation of nanoelectronic processes and devices, and the optical as well as electronical simulation of optoelectronic devices as lasers, photodiodes, etc. The studens learn how to find the right literature for a certain topic quickly, as well as how to prepare a talk for a scientific conference, i.e. presentation techniques. | |||||

Skript | Presentation material | |||||

227-0665-00L | Battery Integration EngineeringFindet dieses Semester nicht statt. Priority given to Electrical and Mechanical Engineering students Students are required to have attended one of the following courses: 227-0664-00L Technology and Policy of Electrical Energy Storage 529-0440-00L Physical Electrochemistry and Electrocatalysis 529-0191-01L Renewable Energy Technologies II, Energy Storage and Conversion 529-0659-00L Electrochemistry (Exception for PhD students). | W | 3 KP | 2V + 1U | ||

Kurzbeschreibung | Batteries enable sustainable mobility, renewable power integration, various power grid services, and residential energy storage. Linked with low cost PV, Li-ion batteries are positioned to shift the 19th-century centralized power grid into a 21st-century distributed one. As with battery integration, this course combines understanding of electrochemistry, heat & mass transfer, device engineering. | |||||

Lernziel | The learning objectives are: - Apply critical thinking on advancements in battery integration engineering. Assessment reflects this objective and is based on review of a scientific paper, with mark weighting of 10 / 25 / 65 for a proposal / oral presentation / final report, respectively. - Design battery system concepts for various applications in the modern power system and sustainable mobility, with a deep focus on replacing diesel buses with electric buses combined with charging infrastructure. - Critically assess progresses in battery integration engineering: from material science of novel battery technologies to battery system design. - Apply "lessons learned" from the history of batteries to assess progress in battery technology. - Apply experimental and physical concepts to develop battery models in order to predict lifetime. | |||||

Inhalt | - Battery systems for the modern power grid and sustainable mobility. - Battery lifetime modeling by aging, thermal, and electric sub-models. - Electrical architecture of battery energy storage systems. - History and review of electrochemistry & batteries, and metrics to assess future developments in electrochemical energy stroage. - Sustainability and life cycle analysis of battery system innovations. | |||||

Voraussetzungen / Besonderes | Limited to 30 Students. Priority given to Electrical and Mechanical Engineering students. Mandatory - background knowledge in batteries & electrochemistry acquired in one of the following courses: 227-0664-00L Technology and Policy of Electrical Energy Storage 529-0440-00L Physical Electrochemistry and Electrocatalysis 529-0191-01L Renewable Energy Technologies II, Energy Storage and Conversion 529-0659-00L Electrochemistry Exception given for PhD students | |||||

227-1033-00L | Neuromorphic Engineering I Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module INI404 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link | W | 6 KP | 2V + 3U | T. Delbrück, G. Indiveri, S.‑C. Liu | |

Kurzbeschreibung | This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions. | |||||

Lernziel | Understanding of the characteristics of neuromorphic circuit elements. | |||||

Inhalt | Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems. | |||||

Literatur | S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications. | |||||

Voraussetzungen / Besonderes | Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. Prerequisites: Background in basics of semiconductor physics helpful, but not required. | |||||

227-2037-00L | Physical Modelling and Simulation | W | 6 KP | 4G | J. Smajic | |

Kurzbeschreibung | This module consists of (a) an introduction to fundamental equations of electromagnetics, mechanics and heat transfer, (b) a detailed overview of numerical methods for field simulations, and (c) practical examples solved in form of small projects. | |||||

Lernziel | Basic knowledge of the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. Knowledge of the main concepts of numerical methods for physical modelling and simulation. Ability (a) to develop own simple field simulation programs, (b) to select an appropriate field solver for a given problem, (c) to perform field simulations, (d) to evaluate the obtained results, and (e) to interactively improve the models until sufficiently accurate results are obtained. | |||||

Inhalt | The module begins with an introduction to the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. After the introduction follows a detailed overview of the available numerical methods for solving electromagnetic, thermal and mechanical boundary value problems. This part of the course contains a general introduction into numerical methods, differential and integral forms, linear equation systems, Finite Difference Method (FDM), Boundary Element Method (BEM), Method of Moments (MoM), Multiple Multipole Program (MMP) and Finite Element Method (FEM). The theoretical part of the course finishes with a presentation of multiphysics simulations through several practical examples of HF-engineering such as coupled electromagnetic-mechanical and electromagnetic-thermal analysis of MEMS. In the second part of the course the students will work in small groups on practical simulation problems. For solving practical problems the students can develop and use own simulation programs or chose an appropriate commercial field solver for their specific problem. This practical simulation work of the students is supervised by the lecturers. | |||||

151-0601-00L | Theory of Robotics and Mechatronics | W | 4 KP | 3G | P. Korba, S. Stoeter | |

Kurzbeschreibung | This course provides an introduction and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. | |||||

Lernziel | Robotics is often viewed from three perspectives: perception (sensing), manipulation (affecting changes in the world), and cognition (intelligence). Robotic systems integrate aspects of all three of these areas. This course provides an introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. | |||||

Inhalt | An introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. | |||||

Skript | available. | |||||

151-0605-00L | Nanosystems | W | 4 KP | 4G | A. Stemmer | |

Kurzbeschreibung | From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles. Intermolecular forces, their macroscopic manifestations, and ways to control such interactions. Self-assembly and directed assembly of 2D and 3D structures. Special emphasis on the emerging field of molecular electronic devices. | |||||

Lernziel | Familiarize students with basic science and engineering principles governing the nano domain. | |||||

Inhalt | The course addresses basic science and engineering principles ruling the nano domain. We particularly work out the links between topics that are traditionally taught separately. Familiarity with basic concepts of quantum mechanics is expected. Special emphasis is placed on the emerging field of molecular electronic devices, their working principles, applications, and how they may be assembled. Topics are treated in 2 blocks: (I) From Quantum to Continuum From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles. (II) Interaction Forces on the Micro and Nano Scale Intermolecular forces, their macroscopic manifestations, and ways to control such interactions. Self-assembly and directed assembly of 2D and 3D structures. | |||||

Literatur | - Kuhn, Hans; Försterling, H.D.: Principles of Physical Chemistry. Understanding Molecules, Molecular Assemblies, Supramolecular Machines. 1999, Wiley, ISBN: 0-471-95902-2 - Chen, Gang: Nanoscale Energy Transport and Conversion. 2005, Oxford University Press, ISBN: 978-0-19-515942-4 - Ouisse, Thierry: Electron Transport in Nanostructures and Mesoscopic Devices. 2008, Wiley, ISBN: 978-1-84821-050-9 - Wolf, Edward L.: Nanophysics and Nanotechnology. 2004, Wiley-VCH, ISBN: 3-527-40407-4 - Israelachvili, Jacob N.: Intermolecular and Surface Forces. 2nd ed., 1992, Academic Press,ISBN: 0-12-375181-0 - Evans, D.F.; Wennerstrom, H.: The Colloidal Domain. Where Physics, Chemistry, Biology, and Technology Meet. Advances in Interfacial Engineering Series. 2nd ed., 1999, Wiley, ISBN: 0-471-24247-0 - Hunter, Robert J.: Foundations of Colloid Science. 2nd ed., 2001, Oxford, ISBN: 0-19-850502-7 | |||||

Voraussetzungen / Besonderes | Course format: Lectures and Mini-Review presentations: Thursday 10-13, ML F 36 Homework: Mini-Review (compulsory continuous performance assessment) Each student selects a paper (list distributed in class) and expands the topic into a Mini-Review that illuminates the particular field beyond the immediate results reported in the paper. Each Mini-Review will be presented both orally and as a written paper. | |||||

151-0620-00L | Embedded MEMS Lab | W | 5 KP | 3P | C. Hierold, S. Blunier, M. Haluska | |

Kurzbeschreibung | Praktischer Kurs: Die Teilnehmer lernen die Einzelprozessschritte zur Herstellung eines MEMS (Micro Electro Mechanical System) kennen und führen diese in Reinräumen selbständig durch. Sie erlernen ausserdem die Anforderungen für die Arbeit in Reinräumen. Die Prozessierung und Charakterisierung wird in einem Abschlussbericht dokumentiert und ausgewertet. Beschränkte Platzzahl | |||||

Lernziel | Die Teilnehmer lernen die Einzelprozessschritte zur Herstellung eines MEMS (Micro Electro Mechanical System) kennen. Sie führen diese in Laboren und Reinräumen selbständig durch. Die Teilnehmer erlernen ausserdem die speziellen Anforderungen (Sauberkeit, Sicherheit, Umgang mit Geräten und gefährlichen Chemikalien) für die Arbeit in Reinräumen und Laboren. Die gesamte Herstellung, Prozessierung und Charakterisierung wird in einem Abschlussbericht dokumentiert und ausgewertet. | |||||

Inhalt | Unter Anleitung werden die Einzelprozessschritte der Mikrosystem- und Siliziumprozesstechnik zur Herstellung eines Beschleunigungssensors durchgeführt: -Photolithographie, Trockenätzen, Nassätzen, Opferschichtätzung, diverse Reinigungsprozesse - Aufbau- und Verbindungstechnik am Beispiel der elektrischen Verbindung von MEMS und elektronischer Schaltung in einem Gehäuse - Funktionstest und Charakterisierung des MEMS - Schriftliche Dokumentation und Auswertung der gesamten Herstellung, Prozessierung und Charakterisierung | |||||

Skript | Ein Skript wird an der ersten Veranstaltung verteilt. | |||||

Literatur | Das Skript ist ausreichend für die erfolgreiche Teilnahme des Praktikums. | |||||

Voraussetzungen / Besonderes | Die Teilnahme an allen hier aufgeführten Veranstaltungen ist Pflicht. Beschränkte Platzzahl, sehen Sie den englischen Text: Participating students are required to provide proof that they have personal accident insurance prior to the start of the laboratory classes of the course. For safety and efficiency reasons the number of participating students is limited. We regret to restrict access to this course by the following rules: Priority 1: master students of the master's program in "Micro and Nanosystems" Priority 2: master students of the master's program in "Mechanical Engineering" with a specialization in Microsystems and Nanoscale Engineering (MAVT-tutors Profs Daraio, Dual, Hierold, Koumoutsakos, Nelson, Norris, Poulikakos, Pratsinis, Stemmer), who attended the bachelor course "151-0621-00L Microsystems Technology" successfully. Priority 3: master students, who attended the bachelor course "151-0621-00L Microsystems Technology" successfully. Priority 4: all other students (PhD, bachelor, master) with a background in silicon or microsystems process technology. If there are more students in one of these priority groups than places available, we will decide by (in following order) best achieved grade from 151-0621-00L Microsystems Technology, registration to this practicum at previous semester, and by drawing lots. Students will be notified at the first lecture of the course (introductory lecture) as to whether they are able to participate. The course is offered in autumn and spring semester. | |||||

151-0911-00L | Introduction to Plasmonics | W | 4 KP | 2V + 1U | D. J. Norris | |

Kurzbeschreibung | This course provides fundamental knowledge of surface plasmon polaritons and discusses their applications in plasmonics. | |||||

Lernziel | Electromagnetic oscillations known as surface plasmon polaritons have many unique properties that are useful across a broad set of applications in biology, chemistry, physics, and optics. The field of plasmonics has arisen to understand the behavior of surface plasmon polaritons and to develop applications in areas such as catalysis, imaging, photovoltaics, and sensing. In particular, metallic nanoparticles and patterned metallic interfaces have been developed to utilize plasmonic resonances. The aim of this course is to provide the basic knowledge to understand and apply the principles of plasmonics. The course will strive to be approachable to students from a diverse set of science and engineering backgrounds. | |||||

Inhalt | Fundamentals of Plasmonics - Basic electromagnetic theory - Optical properties of metals - Surface plasmon polaritons on surfaces - Surface plasmon polariton propagation - Localized surface plasmons Applications of Plasmonics - Waveguides - Extraordinary optical transmission - Enhanced spectroscopy - Sensing - Metamaterials | |||||

Skript | Class notes and handouts | |||||

Literatur | S. A. Maier, Plasmonics: Fundamentals and Applications, 2007, Springer | |||||

Voraussetzungen / Besonderes | Physics I, Physics II | |||||

327-2132-00L | Multifunctional Ferroic Materials: Growth, Characterisation, Simulation | W | 2 KP | 2G | M. Trassin | |

Kurzbeschreibung | The course will explore the growth of (multi-) ferroic oxide thin films. The structural characterization and ferroic state investigation by force microscopy and by laser-optical techniques will be addressed. Oxide electronics device concepts will be discussed. | |||||

Lernziel | Oxide films with a thickness of just a few atoms can now be grown with a precision matching that of semiconductors. This opens up a whole world of functional device concepts and fascinating phenomena that would not occur in the expanded bulk crystal. Particularly interesting phenomena occur in films showing magnetic or electric order or, even better, both of these ("multiferroics"). In this course students will obtain an overarching view on oxide thin epitaxial films and heterostructures design, reaching from their growth by pulsed laser deposition to an understanding of their magnetoelectric functionality from advanced characterization techniques. Students will therefore understand how to fabricate and characterize highly oriented films with magnetic and electric properties not found in nature. | |||||

Inhalt | Types of ferroic order, multiferroics, oxide materials, thin-film growth by pulsed laser deposition, molecular beam epitaxy, RF sputtering, structural characterization (reciprocal space - basics-, XRD for thin films, RHEED) epitaxial strain related effects, scanning probe microscopy techniques, laser-optical characterization, oxide thin film based devices and examples. | |||||

363-0389-00L | Technology and Innovation Management | W | 3 KP | 2G | S. Brusoni, A. Zeijen | |

Kurzbeschreibung | This course focuses on the analysis of innovation as a pervasive process that cut across organizational and functional boundaries. It looks at the sources of innovation, at the tools and techniques that organizations deploy to routinely innovate, and the strategic implications of technical change. | |||||

Lernziel | This course intends to enable all students to: - understand the core concepts necessary to analyze how innovation happens - master the most common methods and tools organizations deploy to innovate - develop the ability to critically evaluate the innovation process, and act upon the main obstacles to innovation | |||||

Inhalt | This course looks at technology and innovation management as a process. Continuously, organizations are faced with a fundamental decision: they have to allocate resources between well-known tasks that reliably generate positive results; or explore new ways of doing things, new technologies, products and services. The latter is a high risk choice. Its rewards can be high, but the chances of success are small. How do firms organize to take these decisions? What kind of management skills are necessary to take them? What kind of tools and methods are deployed to sustain managerial decision-making in highly volatile environments? These are the central questions on which this course focuses, relying on a combination of lectures, case-based discussion, guest speakers, simulations and group work. | |||||

Skript | Slides will be available on the Moodle page | |||||

Literatur | Readings will be available on the Moodle page | |||||

Voraussetzungen / Besonderes | The course content and methods are designed for students with some background in management and/or economics | |||||

Energy and Power Electronics The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Energy and Power Electronics", see Link. The individual study plan is subject to the tutor's approval. | ||||||

Kernfächer These core courses are particularly recommended for the field of "Energy and Power Electronics". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT. | ||||||

Foundation Core Courses | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0113-00L | Leistungselektronik | W | 6 KP | 4G | J. W. Kolar | |

Kurzbeschreibung | Verständnis der Grundfunktion leistungselektronischer Energieumformer, Einsatzbereiche. Methoden der Analyse des Betriebsverhaltens und des regelungstechnischen Verhaltens, Dimensionierung. Beurteilung der Beeinflussung umgebender Systeme, Elektromagnetische Verträglichkeit. | |||||

Lernziel | Verständnis der Grundfunktion leistungselektronischer Energieumformer, Einsatzbereiche. Methoden der Analyse des Betriebsverhaltens und des regelungstechnischen Verhaltens, Dimensionierung. Beurteilung der Beeinflussung umgebender Systeme, Elektromagnetische Verträglichkeit. | |||||

Inhalt | Grundstruktur leistungselektronischer Systeme, Beispiele. DC/DC-Konverter, Potentialtrennung. Regelungstechnische Modellierung von DC/DC-Konvertern, State-Space-Averaging, PWM-Switch-Model. Leistungshalbleiter, Nichtidealitäten, Kühlung. Magnetische Bauelemente, Skin- und Proximity- Effekt, Dimensionierung. EMV. Einphasen- Diodenbrücke mit kapazitiver Glättung, Netzrückwirkungen, Leistungsfaktorkorrektur. Selbstgeführte Einphasen- u. Dreiphasen-Brückenschaltung mit eingeprägter Ausgangsspannung, Modulation, Raumzeigerbegriff. Netzgeführte Einphasen-Brückenschaltung, Kommutierung, Wechselrichterbetrieb, WR-Kippen. Netzgeführte Dreiphasen-Brückenschaltung, ungesteuert und gesteuert/kapazitive und induktive Glättung. Parallelschaltung netzgeführter Stromrichter, Saugdrosselschaltung. Gegenparallelschaltung netzgeführter Dreiphasen-Brückenschaltungen, Vierquadranten-Gleichstrommaschinenantrieb. Resonanz-Thyristorstromrichter, u-Zi-Diagramm. | |||||

Skript | Skript und Simulationsprogramm für interaktives Lernen und Visualisierung, Uebungen mit Musterlösungen | |||||

Voraussetzungen / Besonderes | Voraussetzungen: Grundkenntnisse der Elektrotechnik und Signaltheorie. | |||||

227-0122-00L | Introduction to Electric Power Transmission: System & TechnologyStudents that complete the course from HS 2020 onwards obtain 4 credits. | W | 4 KP | 2V + 2U | C. Franck, G. Hug | |

Kurzbeschreibung | Introduction to theory and technology of electric power transmission systems. | |||||

Lernziel | At the end of this course, the student will be able to: describe the structure of electric power systems, name the most important components and describe what they are needed for, apply models for transformers and overhead power lines, explain the technology of transformers and lines, calculate stationary power flows and other basic parameters in simple power systems. | |||||

Inhalt | Structure of electric power systems, transformer and power line models, analysis of and power flow calculation in basic systems, technology and principle of electric power systems. | |||||

Skript | Lecture script in English, exercises and sample solutions. | |||||

Advanced Core Courses | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0117-00L | High Voltage Engineering | W | 6 KP | 4G | C. Franck, U. Straumann | |

Kurzbeschreibung | High electric fields are used in numerous technological and industrial applications such as electric power transmission and distribution, X-ray devices, DNA sequencers, flue gas cleaning, power electronics, lasers, particle accelerators, copying machines, .... High Voltage Engineering is the art of gaining technological control of high electrical field strengths and high voltages. | |||||

Lernziel | The students know the fundamental phenomena and principles associated with the occurrence of high electric field strengths. They understand the different mechanisms leading to the failure of insulation systems and are able to apply failure criteria on the dimensioning of high voltage components. They have the ability to identify of weak spots in insulation systems and to propose options for improvement. Further, they know the different insulation systems and their dimensioning in practice. | |||||

Inhalt | - discussion of the field equations relevant for high voltage engineering. - analytical and numerical solutions/solving of this equations, as well as the derivation of the important equivalent circuits for the description of the fields and losses in insulations - introduction to kinetic gas theory - mechanisms of the breakdown in gaseous, liquid and solid insulations, as well as insulation systems - methods for the mathematical determination of the electric withstand of gaseous, liquid and solid insulations - application of the expertise on high voltage components - excursions to manufacturers of high voltage components | |||||

Skript | Handouts | |||||

Literatur | A. Küchler, High Voltage Engineering: Fundamentals – Technology – Applications, Springer Berlin, 2018 (ISBN 978-3-642-11992-7) | |||||

227-0247-00L | Power Electronic Systems I | W | 6 KP | 4G | J. W. Kolar | |

Kurzbeschreibung | Basics of the switching behavior, gate drive and snubber circuits of power semiconductors are discussed. Soft-switching and resonant DC/DC converters are analyzed in detail and high frequency loss mechanisms of magnetic components are explained. Space vector modulation of three-phase inverters is introduced and the main power components are designed for typical industry applications. | |||||

Lernziel | Detailed understanding of the principle of operation and modulation of advanced power electronics converter systems, especially of zero voltage switching and zero current switching non-isolated and isolated DC/DC converter systems and three-phase voltage DC link inverter systems. Furthermore, the course should convey knowledge on the switching frequency related losses of power semiconductors and inductive power components and introduce the concept of space vector calculus which provides a basis for the comprehensive discussion of three-phase PWM converters systems in the lecture Power Electronic Systems II. | |||||

Inhalt | Basics of the switching behavior and gate drive circuits of power semiconductor devices and auxiliary circuits for minimizing the switching losses are explained. Furthermore, zero voltage switching, zero current switching, and resonant DC/DC converters are discussed in detail; the operating behavior of isolated full-bridge DC/DC converters is detailed for different secondary side rectifier topologies; high frequency loss mechanisms of magnetic components of converter circuits are explained and approximate calculation methods are presented; the concept of space vector calculus for analyzing three-phase systems is introduced; finally, phase-oriented and space vector modulation of three-phase inverter systems are discussed related to voltage DC link inverter systems and the design of the main power components based on analytical calculations is explained. | |||||

Skript | Lecture notes and associated exercises including correct answers, simulation program for interactive self-learning including visualization/animation features. | |||||

Voraussetzungen / Besonderes | Prerequisites: Introductory course on power electronics. | |||||

227-0526-00L | Power System Analysis | W | 6 KP | 4G | G. Hug | |

Kurzbeschreibung | Ziel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge. Die Herleitung der stationären Modelle der Komponenten des elektrischen Netzes, die Aufstellung der mathematischen Gleichungssysteme, deren spezielle Charakteristiken und Lösungsmethoden stehen im Vordergrund. | |||||

Lernziel | Ziel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge und die Anwendung von Analysemethoden in stationären und dynamischen Zuständen des elektrischen Netzes. | |||||

Inhalt | Der Kurs beinhaltet die Herleitung von stationären und dynamischen Modellen des elektrischen Netzwerks, deren mathematische Darstellungen und spezielle Charakteristiken sowie Lösungsmethoden für die Behandlung von grossen linearen und nichtlinearen Gleichungssystemen im Zusammenhang mit dem elektrischen Netz. Ansätze wie der Netwon-Raphson Algorithmus angewendet auf die Lastflussgleichungen, Superpositions Prinzip für Kurzschlussberechnung, Methoden für Stabilitätsanalysen und Lastflussberechnungsmethoden für das Verteilnetz werden präsentiert. | |||||

Skript | Vorlesungsskript. | |||||

Vertiefungsfächer These specialisation courses are particularly recommended for the area of "Energy and Power Electronics", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0101-00L | Discrete-Time and Statistical Signal Processing | W | 6 KP | 4G | H.‑A. Loeliger | |

Kurzbeschreibung | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm. | |||||

Lernziel | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter. | |||||

Inhalt | 1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm. | |||||

Skript | Lecture Notes | |||||

227-0103-00L | Regelsysteme | W | 6 KP | 2V + 2U | F. Dörfler | |

Kurzbeschreibung | ||||||

Lernziel | ||||||

Inhalt | Process automation, concept of control. Modelling of dynamical systems - examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems - effect of additional poles and zeros. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multi-loop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement - choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation. | |||||

Literatur | K. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010. R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007. G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010. J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014. J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014. | |||||

Voraussetzungen / Besonderes | Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation. | |||||

227-0121-00L | Kommunikationssysteme | W | 6 KP | 2V + 2U | A. Wittneben | |

Kurzbeschreibung | ||||||

Lernziel | ||||||

Inhalt | Es werden die untersten drei Schichten des OSI-Referenzmodells behandelt: die Bitübertragungsschicht, die Sicherungsschicht mit dem Zugriff auf das Übertragungsmedium und die Vermittlung. Die wichtigsten Begriffe der Informationstheorie werden eingeführt. Anschliessend konzentrieren sich die Betrachtungen auf die Verfahren der Punkt-zu-Punkt-Übertragung, welche sich mittels der Signalraumdarstellung elegant und kohärent behandeln lassen. Den Methoden der Fehlererkennung und –korrektur, sowie Protokollen für die erneute Übermittlung gestörter Daten wird Rechnung getragen. Auch der Vielfachzugriff bei geteiltem Übertragungsmedium wird diskutiert. Den Abschluss bilden Algorithmen für das Routing in Kommunikationsnetzen und der Flusssteuerung. Die Anwendung der grundlegenden Verfahren wird ausführlich anhand von bestehenden und zukünftigen drahtlosen und drahtgebundenen Systemen erläutert. | |||||

Skript | Vorlesungsfolien | |||||

Literatur | [1] Simon Haykin, Communication Systems, 4. Auflage, John Wiley & Sons, 2001 [2] Andrew S. Tanenbaum, Computernetzwerke, 3. Auflage, Pearson Studium, 2003 [3] M. Bossert und M. Breitbach, Digitale Netze, 1. Auflage, Teubner, 1999 | |||||

227-0225-00L | Linear System Theory | W | 6 KP | 5G | M. Colombino | |

Kurzbeschreibung | The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems. | |||||

Lernziel | Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems. | |||||

Inhalt | - Proof techniques and practices. - Linear spaces, normed linear spaces and Hilbert spaces. - Ordinary differential equations, existence and uniqueness of solutions. - Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case. - Controllability and observability, duality. Time invariant systems treated as a special case. - Stability and stabilization, observers, state and output feedback, separation principle. | |||||

Skript | Available on the course Moodle platform. | |||||

Voraussetzungen / Besonderes | Sufficient mathematical maturity, in particular in linear algebra, analysis. | |||||

227-0517-10L | Fundamentals of Electric Machines | W | 6 KP | 4G | D. Bortis | |

Kurzbeschreibung | This course introduces to different electric machine concepts and provides a deeper understanding of their detailed operating principles. Different aspects arising in the design of electric machines, like dimensioning of magnetic and electric circuits as well as consideration of mechanical and thermal constraints, are investigated. The exercises are used to consolidate the concepts discussed. | |||||

Lernziel | The objective of this course is to convey knowledge on the operating principles of different types of electric machines. Further objectives are to evaluate machine types for given specification and to acquire the ability to perform a rough design of an electrical machine while considering the versatile aspects with respect to magnetic, electrical, mechanical and thermal limitations. Exercises are used to consolidate the presented theoretical concepts. | |||||

Inhalt | ‐ Fundamentals in magnetic circuits and electromechanical energy conversion. ‐ Force and torque calculation. ‐ Operating principles, magnetic and electric modelling and design of different electric machine concepts: DC machine, AC machines (permanent magnet synchronous machine, reluctance machine and induction machine). ‐ Complex space vector notation, rotating coordinate system (dqtransformation). ‐ Loss components in electric machines, scaling laws of electromechanical actuators. ‐ Mechanical and thermal modelling. | |||||

Skript | Lecture notes and associated exercises including correct answers | |||||

Voraussetzungen / Besonderes | Prerequisites: Introductory course on power electronics. | |||||

227-0523-00L | Eisenbahn-Systemtechnik I | W | 6 KP | 4G | M. Meyer | |

Kurzbeschreibung | Grundlagen der Eisenbahnfahrzeuge und ihr Zusammenspiel mit der Bahninfrastruktur: - Zugförderungsaufgaben und Fahrzeugarten - Fahrdynamik - Mechanischer Aufbau der Eisenbahnfahrzeuge - Bremssysteme - Antriebsstrang und Hilfsbetriebeversorgung - Bahnstromversorgung - Sicherungsanlagen - Normen - Verfügbarkeit und Sicherheit - Betriebsleitung und Instandhaltung | |||||

Lernziel | - Überblick über die technischen Eigenschaften von Eisenbahnsystemen - Kenntnisse über den Aufbau der Eisenbahnfahrzeuge - Verständnis für die Abhängigkeiten verschiedenster Ingenieur-Disziplinen in einem vielfältigen System (Mechanik, Elektro- und Informationstechnik, Verkehrstechnik) - Verständnis für die Aufgaben und Möglichkeiten eines Ingenieurs in einem stark von wirtschaftlichen und politischen Randbedingungen geprägten Umfeld - Einblick in die Aktivitäten der Schienenfahrzeug-Industrie und der Bahnen in der Schweiz - Begeisterung des Ingenieurnachwuchses für die berufliche Tätigkeit im Bereich Schienenverker und Schienenfahrzeuge | |||||

Inhalt | EST I (Herbstsemester) - Begriffen, Grundlagen, Merkmale 1 Einführung: 1.1 Geschichte und Struktur des Bahnsystems 1.2 Fahrdynamik 2 Vollbahnfahrzeuge: 2.3 Mechanik: Kasten, Drehgestelle, Lauftechnik, Adhäsion 2.2 Bremsen 2.3 Traktionsantriebssysteme 2.4 Hilfsbetriebe und Komfortanlagen 2.5 Steuerung und Regelung 3 Infrastruktur: 3.1 Fahrweg 3.2 Bahnstromversorgung 3.3 Sicherungsanlagen 4 Betrieb: 4.1 Interoperabilität, Normen und Zulassung 4.2 RAMS, LCC 4.3 Anwendungsbeispiele Voraussichtlich ein oder zwei Gastreferate Geplante Exkursionen: Betriebszentrale SBB, Zürich Flughafen Reparatur und Unterhalt, SBB Zürich Altstetten Fahrzeugfertigung, Stadler Bussnang | |||||

Skript | Abgabe der Unterlagen (gegen eine Schutzgebühr) zu Beginn des Semesters. Rechtzeitig eingschriebene Teilnehmer können die Unterlagen auf Wunsch und gegen eine Zusatzgebühr auch in Farbe beziehen. | |||||

Voraussetzungen / Besonderes | Dozent: Dr. Markus Meyer, Emkamatik GmbH Voraussichtlich ein oder zwei Gastvorträge von anderen Referenten. EST I (Herbstsemester) kann als in sich geschlossene einsemestrige Vorlesung besucht werden. EST II (Frühjahrssemester) dient der weiteren Vertiefung der Fahrzeugtechnik und der Integration in die Bahninfrastruktur. | |||||

227-0567-00L | Design of Power Electronic Systems | W | 6 KP | 4G | F. Krismer | |

Kurzbeschreibung | Complete design process: from given specifications to a complete power electronic system; selection / design of suitable passive power components; static and dynamic properties of power semiconductors; optimized EMI filter design; heat sink optimization; additional circuitry, e.g. gate driver; system optimization. | |||||

Lernziel | Basic knowledge of design and optimization of a power electronic system; furthermore, lecture and exercises thoroughly discuss key subjects of power electronics that are important with respect to a practical realization, e.g. how to select suitable power components, to understand switching operations, calculation of high frequency losses, EMI filter design and realization, thermal considerations. | |||||

Inhalt | Complete design process: from given specifications to a complete power electronic system. Selection and / or design of suitable passive power components: specific properties, parasitic components, tolerances, high frequency losses, thermal considerations, reliability. Static and dynamic characteristics of power semiconductors. Optimized design of the EMI filter. Thermal characterization of the converter, optimized heat sink design. Additional circuitry: gate driver, measurement, control. Converter start up: typical sequence of events, circuitry required. Overall system optimization: identifying couplings between different components of the considered power electronic system, optimization targets and issues. | |||||

Skript | Lecture notes and complementary exercises including correct answers. | |||||

Voraussetzungen / Besonderes | Prerequisites: Introductory course on power electronics. | |||||

227-0618-00L | Modeling, Characterization and Reliability of Power Semiconductors Findet dieses Semester nicht statt. | W | 6 KP | 4G | ||

Kurzbeschreibung | This lecture provides theoretical and experimental knowledge on the techniques for the characterization and numerical modeling of power semiconductors, as well on the related built-in reliability strategies. | |||||

Lernziel | The students shall get acquainted with the most important concepts and techniques for characterization, numerical modeling and built-in reliability of modern power semiconductor devices. This knowledge is intended to provide the future engineer with the theoretical background and tools for the design of dependable power devices and systems. | |||||

Inhalt | This lecture consists of a theoretical part (50%) and of laboratory exercises and demonstrations (50%). The theoretical part covers the basic techniques and procedures for characterization, modeling and built-in reliability of modern power semiconductor devices with special attention to MOS and IGBT. The starting part on technology provides an overview on the main device families and includes a review of the most relevant application-oriented aspects of the device physics, thermal management, and packaging. The second section deals with the basic experimental characterization techniques for the definition of the semiconductor material properties, electrical characteristics, safe operating area, and junction temperature of the devices. The following section introduces the basic principles for electrical, thermal, and electro-thermal simulation of power semiconductors by Technology Computed Aided Design (TCAD) and compact modeling. Finally, procedures are methods are presented to implement efficient built-in reliability programs targeted on power semiconductors. They include failure physics, dedicated failure analysis techniques, accelerated testing, defect screening, and lifetime modeling. During the laboratory activities, selections of the experimental techniques presented in the lecture are demonstrated on the base of realistic examples. Furthermore, schematic power devices will be simulated by the students with advanced TCAD tools and circuit simulators. | |||||

Skript | Handouts to the lecture (approx. 250 pp.) | |||||

Literatur | Eiichi Ohno: "Introduction to Power Electronics" B. Murari et al.: "Smart Power ICs" B. J. Baliga: "Physics Modern Power Devices" S. K. Ghandi: "Semiconductor Power Devices" | |||||

227-0697-00L | Industrial Process Control | W | 4 KP | 3G | A. Horch, M. Mercangöz | |

Kurzbeschreibung | Introduction to industrial automation systems with application to the process industry, power generation as well as discrete manufacturing. | |||||

Lernziel | General understanding of industrial automation systems in different industries. Purpose, architecture, technologies, application examples, current and future trends. | |||||

Inhalt | Introduction to process automation: system architecture, data handling, communication (fieldbuses), process visualization, and engineering. Differences and characteristics of discrete manufacturing and process industries. Analysis and design of open loop control problems: discrete automata, finite state machines, decision tables, and petri-nets. Practical analysis and design of closed-loop control for the process industry. Automation Engineering: Application programming in IEC 61131-3 (ladder diagrams, function blocks, sequence control, structured text); PLC programming and simulation, process visualization and operation; engineering integration from sensors, cabling, topology design, function, visualization, diagnosis, to documentation; Industry standards (e.g. OPC, Profibus); Ergonomic design, safety (IEC61508) and availability, supervision and diagnosis. Process Automation: Communication standards, Architecture, dependable systems, process safety, automation security. Extensive practical examples from different process industries, power generation, gas compressor control, and automotive manufacturing. | |||||

Skript | Slides will be available as .PDF documents, see "Learning materials" (for registered students only). Each online lecture will be recorded. Recordings will be published together with the course material (PDF documents). | |||||

Literatur | References will be given at the end of individual lectures. | |||||

Voraussetzungen / Besonderes | The lecture will be conducted as an online course via Zoom only. Exercises: Tuesday 15-16 Practical exercises will illustrate some topics, e.g. some control software coding using industry standard programming tools based on IEC61131-3. All lectures will be online only. The same Zoom Link works each Tuesday. Please download and import the following iCalendar (.ics) files to your calendar system. Weekly: Link Join Zoom Meeting Link Meeting ID: 917 3375 5951 | |||||

227-0731-00L | Power Market I - Portfolio and Risk Management | W | 6 KP | 4G | D. Reichelt, G. A. Koeppel | |

Kurzbeschreibung | Portfolio und Risiko Management für Energieversorgungsunternehmen, Europäischer Strommarkt und -handel, Terminkontrakte, Preisabsicherung, Optionen und Derivate, Kennzahlen für das Risikomanagement, finanztechnische Modellierung von Kraftwerken, grenzüberschreitender Stromhandel, Systemdienstleistungen, Regelenergiemarkt, Bilanzgruppenmodell. | |||||

Lernziel | Erwerb von umfassenden Kenntnissen über die weltweite Liberalisierung der Strommärkte, den internationalen Stromhandel sowie die Funktion von Strombörsen. Verstehen der Finanzprodukte (Derivate) basierend auf dem Strompreis. Abbilden des Portfolios aus physischer Produktion, Verträgen und Finanzprodukten. Beurteilen von Strategien zur Absicherung des Marktpreisrisikos. Beherrschen der Methoden und Werkzeuge des Risiko Managements. | |||||

Inhalt | 1. Europäischer Strommarkt und –handel 1.1. Einführung Stromhandel 1.2. Entwicklung des Marktes 1.3. Energiewirtschaft 1.4. Spothandel und OTC-Handel 1.5. Strombörse EEX 2. Marktmodell 2.1. Marktplatz und Organisation 2.2. Bilanzgruppenmodell / Ausgleichsenergie 2.3. Systemdienstleistungen 2.4. Regelenergiemarkt 2.5. Grenzüberschreitender Handel 2.6. Kapazitätsauktionen 3. Portfolio und Risiko Management 3.1. Portfoliomanagement 1 (Einführung) 3.2. Terminkontrakte (EEX Futures) 3.3. Risk Management 1 (m2m, VaR, hpfc, Volatilität, cVaR) 3.4. Risk Management 2 (PaR) 3.5. Vertragsbewertung (HPFC) 3.6. Portfoliomanagement 2 3.7. Risk Management 3 (Energiegeschäft) 4. Energie & Finance I 4.1. Optionen 1 – Grundlagen 4.2. Optionen 2 – Absicherungsstrategien 4.3. Einführung Derivate (Swaps, Cap, Floor, Collar) 4.4. Finanztechnische Modellierung von Kraftwerken 4.5. Wasserkraft und Handel 4.6. Anreizregulierung | |||||

Skript | Handouts mit den Folien der Vorlesung | |||||

Voraussetzungen / Besonderes | 1 Exkursion pro Semester, 2 Case Studies, externe Referaten für ausgewählte Themen. Kurs Moodle: Link | |||||

227-0759-00L | International Business Management for Engineers | W | 3 KP | 2V | W. Hofbauer | |

Kurzbeschreibung | Globalization of markets increases global competition and requires enterprises to continuously improve their performance to sustainably survive. Engineers substantially contribute to the success of an enterprise provided they understand and follow fundamental international market forces, economic basics and operational business management. | |||||

Lernziel | The goal of the lecture is to get a basic understanding of international market mechanisms and their consequences for a successful enterprise. Students will learn by practical examples how to analyze international markets, competition as well as customer needs and how they convert into a successful portfolio an enterprise offers to the global market. They will understand the basics of international business management, why efficient organizations and effective business processes are crucial for the successful survival of an enterprise and how all this can be implemented. | |||||

Inhalt | The first part of the course provides an overview about the development of international markets, the expected challenges and the players in the market. The second part is focusing on the economic aspects of an enterprise, their importance for the long term success and how to effectively manage an international business. Based on these fundamentals the third part of the course explains how an innovative product portfolio of a company can be derived from considering the most important external factors and which consequences in respect of product innovation, competitive product pricing, organization and business processes emerge. Each part of the course includes practical examples to demonstrate the procedure. | |||||

Skript | A script is provided for this lecture. | |||||

Voraussetzungen / Besonderes | The lecture will be held in three blocks each of them on a Saturday (starts on September 19, 2020). Each block will focus on one of the three main topics of the course. Between the blocks the students will work on specific case studies to deepen the subject matter. About two weeks after the third block a written examination will be conducted. | |||||

Systems and Control The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Systems and Control", see Link. The individual study plan is subject to the tutor's approval. | ||||||

Kernfächer These core courses are particularly recommended for the field of "Systems and Control". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT. | ||||||

Foundation Core Courses | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0103-00L | Regelsysteme | W | 6 KP | 2V + 2U | F. Dörfler | |

Kurzbeschreibung | ||||||

Lernziel | ||||||

Inhalt | ||||||

Literatur | K. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010. R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007. G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010. J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014. J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014. | |||||

Voraussetzungen / Besonderes | Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation. | |||||

Advanced Core Courses | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0225-00L | Linear System Theory | W | 6 KP | 5G | M. Colombino | |

Kurzbeschreibung | The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems. | |||||

Lernziel | Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems. | |||||

Inhalt | - Proof techniques and practices. - Linear spaces, normed linear spaces and Hilbert spaces. - Ordinary differential equations, existence and uniqueness of solutions. - Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case. - Controllability and observability, duality. Time invariant systems treated as a special case. - Stability and stabilization, observers, state and output feedback, separation principle. | |||||

Skript | Available on the course Moodle platform. | |||||

Voraussetzungen / Besonderes | Sufficient mathematical maturity, in particular in linear algebra, analysis. | |||||

227-0697-00L | Industrial Process Control | W | 4 KP | 3G | A. Horch, M. Mercangöz | |

Kurzbeschreibung | Introduction to industrial automation systems with application to the process industry, power generation as well as discrete manufacturing. | |||||

Lernziel | General understanding of industrial automation systems in different industries. Purpose, architecture, technologies, application examples, current and future trends. | |||||

Inhalt | Introduction to process automation: system architecture, data handling, communication (fieldbuses), process visualization, and engineering. Differences and characteristics of discrete manufacturing and process industries. Analysis and design of open loop control problems: discrete automata, finite state machines, decision tables, and petri-nets. Practical analysis and design of closed-loop control for the process industry. Automation Engineering: Application programming in IEC 61131-3 (ladder diagrams, function blocks, sequence control, structured text); PLC programming and simulation, process visualization and operation; engineering integration from sensors, cabling, topology design, function, visualization, diagnosis, to documentation; Industry standards (e.g. OPC, Profibus); Ergonomic design, safety (IEC61508) and availability, supervision and diagnosis. Process Automation: Communication standards, Architecture, dependable systems, process safety, automation security. Extensive practical examples from different process industries, power generation, gas compressor control, and automotive manufacturing. | |||||

Skript | Slides will be available as .PDF documents, see "Learning materials" (for registered students only). Each online lecture will be recorded. Recordings will be published together with the course material (PDF documents). | |||||

Literatur | References will be given at the end of individual lectures. | |||||

Voraussetzungen / Besonderes | The lecture will be conducted as an online course via Zoom only. Exercises: Tuesday 15-16 Practical exercises will illustrate some topics, e.g. some control software coding using industry standard programming tools based on IEC61131-3. All lectures will be online only. The same Zoom Link works each Tuesday. Please download and import the following iCalendar (.ics) files to your calendar system. Weekly: Link Join Zoom Meeting Link Meeting ID: 917 3375 5951 | |||||

151-0563-01L | Dynamic Programming and Optimal Control | W | 4 KP | 2V + 1U | R. D'Andrea | |

Kurzbeschreibung | Introduction to Dynamic Programming and Optimal Control. | |||||

Lernziel | Covers the fundamental concepts of Dynamic Programming & Optimal Control. | |||||

Inhalt | Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control. | |||||

Literatur | Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover. | |||||

Voraussetzungen / Besonderes | Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra. | |||||

Vertiefungsfächer These specialisation courses are particularly recommended for the area of "Systems and Control", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0102-00L | Diskrete Ereignissysteme | W | 6 KP | 4G | L. Thiele, L. Vanbever, R. Wattenhofer | |

Kurzbeschreibung | Einführung in Diskrete Ereignissysteme (DES). Zuerst studieren wir populäre Modelle für DES. Im zweiten Teil analysieren wir DES, aus einer Average-Case und einer Worst-Case Sicht. Stichworte: Automaten und Sprachen, Spezifikationsmodelle, Stochastische DES, Worst-Case Ereignissysteme, Verifikation, Netzwerkalgebra. | |||||

Lernziel | Over the past few decades the rapid evolution of computing, communication, and information technologies has brought about the proliferation of new dynamic systems. A significant part of activity in these systems is governed by operational rules designed by humans. The dynamics of these systems are characterized by asynchronous occurrences of discrete events, some controlled (e.g. hitting a keyboard key, sending a message), some not (e.g. spontaneous failure, packet loss). The mathematical arsenal centered around differential equations that has been employed in systems engineering to model and study processes governed by the laws of nature is often inadequate or inappropriate for discrete event systems. The challenge is to develop new modeling frameworks, analysis techniques, design tools, testing methods, and optimization processes for this new generation of systems. In this lecture we give an introduction to discrete event systems. We start out the course by studying popular models of discrete event systems, such as automata and Petri nets. In the second part of the course we analyze discrete event systems. We first examine discrete event systems from an average-case perspective: we model discrete events as stochastic processes, and then apply Markov chains and queuing theory for an understanding of the typical behavior of a system. In the last part of the course we analyze discrete event systems from a worst-case perspective using the theory of online algorithms and adversarial queuing. | |||||

Inhalt | 1. Introduction 2. Automata and Languages 3. Smarter Automata 4. Specification Models 5. Stochastic Discrete Event Systems 6. Worst-Case Event Systems 7. Network Calculus | |||||

Skript | Available | |||||

Literatur | [bertsekas] Data Networks Dimitri Bersekas, Robert Gallager Prentice Hall, 1991, ISBN: 0132009161 [borodin] Online Computation and Competitive Analysis Allan Borodin, Ran El-Yaniv. Cambridge University Press, 1998 [boudec] Network Calculus J.-Y. Le Boudec, P. Thiran Springer, 2001 [cassandras] Introduction to Discrete Event Systems Christos Cassandras, Stéphane Lafortune. Kluwer Academic Publishers, 1999, ISBN 0-7923-8609-4 [fiat] Online Algorithms: The State of the Art A. Fiat and G. Woeginger [hochbaum] Approximation Algorithms for NP-hard Problems (Chapter 13 by S. Irani, A. Karlin) D. Hochbaum [schickinger] Diskrete Strukturen (Band 2: Wahrscheinlichkeitstheorie und Statistik) T. Schickinger, A. Steger Springer, Berlin, 2001 [sipser] Introduction to the Theory of Computation Michael Sipser. PWS Publishing Company, 1996, ISBN 053494728X | |||||

227-0447-00L | Image Analysis and Computer Vision | W | 6 KP | 3V + 1U | L. Van Gool, E. Konukoglu, F. Yu | |

Kurzbeschreibung | Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks. | |||||

Lernziel | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||

Inhalt | This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given. | |||||

Skript | Course material Script, computer demonstrations, exercises and problem solutions | |||||

Voraussetzungen / Besonderes | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | |||||

227-0526-00L | Power System Analysis | W | 6 KP | 4G | G. Hug | |

Kurzbeschreibung | Ziel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge. Die Herleitung der stationären Modelle der Komponenten des elektrischen Netzes, die Aufstellung der mathematischen Gleichungssysteme, deren spezielle Charakteristiken und Lösungsmethoden stehen im Vordergrund. | |||||

Lernziel | Ziel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge und die Anwendung von Analysemethoden in stationären und dynamischen Zuständen des elektrischen Netzes. | |||||

Inhalt | Der Kurs beinhaltet die Herleitung von stationären und dynamischen Modellen des elektrischen Netzwerks, deren mathematische Darstellungen und spezielle Charakteristiken sowie Lösungsmethoden für die Behandlung von grossen linearen und nichtlinearen Gleichungssystemen im Zusammenhang mit dem elektrischen Netz. Ansätze wie der Netwon-Raphson Algorithmus angewendet auf die Lastflussgleichungen, Superpositions Prinzip für Kurzschlussberechnung, Methoden für Stabilitätsanalysen und Lastflussberechnungsmethoden für das Verteilnetz werden präsentiert. | |||||

Skript | Vorlesungsskript. | |||||

227-0689-00L | System Identification | W | 4 KP | 2V + 1U | R. Smith | |

Kurzbeschreibung | Theory and techniques for the identification of dynamic models from experimentally obtained system input-output data. | |||||

Lernziel | To provide a series of practical techniques for the development of dynamical models from experimental data, with the emphasis being on the development of models suitable for feedback control design purposes. To provide sufficient theory to enable the practitioner to understand the trade-offs between model accuracy, data quality and data quantity. | |||||

Inhalt | Introduction to modeling: Black-box and grey-box models; Parametric and non-parametric models; ARX, ARMAX (etc.) models. Predictive, open-loop, black-box identification methods. Time and frequency domain methods. Subspace identification methods. Optimal experimental design, Cramer-Rao bounds, input signal design. Parametric identification methods. On-line and batch approaches. Closed-loop identification strategies. Trade-off between controller performance and information available for identification. | |||||

Literatur | "System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999. "Dynamic system identification: Experimental design and data analysis", GC Goodwin and RL Payne, Academic Press, 1977. | |||||

Voraussetzungen / Besonderes | Control systems (227-0216-00L) or equivalent. | |||||

227-0945-00L | Cell and Molecular Biology for Engineers IThis course is part I of a two-semester course. | W | 3 KP | 2G | C. Frei | |

Kurzbeschreibung | The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology. | |||||

Lernziel | After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested. | |||||

Inhalt | Lectures will include the following topics (part I and II): DNA, chromosomes, genome engineering, RNA, proteins, genetics, synthetic biology, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer and stem cells. In addition, 4 journal clubs will be held, where recent publications will be discussed (2 journal clubs in part I and 2 journal clubs in part II). For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded and count as 40% for the final grade. | |||||

Skript | Scripts of all lectures will be available. | |||||

Literatur | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter. | |||||

151-0532-00L | Nonlinear Dynamics and Chaos I | W | 4 KP | 2V + 2U | G. Haller | |

Kurzbeschreibung | Basic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics. | |||||

Lernziel | This course is intended for Masters and Ph.D. students in engineering sciences, physics and applied mathematics who are interested in the behavior of nonlinear dynamical systems. It offers an introduction to the qualitative study of nonlinear physical phenomena modeled by differential equations or discrete maps. We discuss applications in classical mechanics, electrical engineering, fluid mechanics, and biology. A more advanced Part II of this class is offered every other year. | |||||

Inhalt | (1) Basic facts about nonlinear systems: Existence, uniqueness, and dependence on initial data. (2) Near equilibrium dynamics: Linear and Lyapunov stability (3) Bifurcations of equilibria: Center manifolds, normal forms, and elementary bifurcations (4) Nonlinear dynamical systems on the plane: Phase plane techniques, limit sets, and limit cycles. (5) Time-dependent dynamical systems: Floquet theory, Poincare maps, averaging methods, resonance | |||||

Skript | The class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture. | |||||

Voraussetzungen / Besonderes | - Prerequisites: Analysis, linear algebra and a basic course in differential equations. - Exam: two-hour written exam in English. - Homework: A homework assignment will be due roughly every other week. Hints to solutions will be posted after the homework due dates. | |||||

151-0573-00L | System Modeling | W | 4 KP | 2V + 1U | L. Guzzella | |

Kurzbeschreibung | Einführung in die Systemmodellierung für die Steuerung. Generische Modellierungsansätze auf der Grundlage erster Prinzipien, Lagrangealer Formalismus, Energieansätze und experimentelle Daten. Modellparametrierung und Parametrierung. Grundlegende Analyse von linearen und nichtlinearen Systemen. | |||||

Lernziel | Erfahren Sie, wie man mathematisch ein physisches System oder einen Prozess in Form eines Modells beschreibt, das für Analyse- und Kontrollzwecke verwendbar ist. | |||||

Inhalt | Diese Klasse führt generische Systemmodellierungsansätze für steuerungsorientierte Modelle ein, die auf ersten Prinzipien und experimentellen Daten basieren. Die Klasse umfasst zahlreiche Beispiele für mechatronische, thermodynamische, chemische, flüssigkeitsdynamische, energie- und verfahrenstechnische Systeme. Modellskalierung, Linearisierung, Auftragsreduktion und Ausgleich. Parameterschätzung mit Methoden der kleinsten Quadrate. Verschiedene Fallstudien: Lautsprecher, Turbinen, Wasser angetriebene Rakete, geostationäre Satelliten usw. Die Übungen behandeln praktische Beispiele. | |||||

Skript | Das Skript in englischer Sprache wird in der ersten Lektion verkauft. | |||||

Literatur | Eine Literaturliste ist im Skript enthalten. | |||||

151-0601-00L | Theory of Robotics and Mechatronics | W | 4 KP | 3G | P. Korba, S. Stoeter | |

Kurzbeschreibung | This course provides an introduction and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. | |||||

Lernziel | Robotics is often viewed from three perspectives: perception (sensing), manipulation (affecting changes in the world), and cognition (intelligence). Robotic systems integrate aspects of all three of these areas. This course provides an introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. | |||||

Inhalt | An introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. | |||||

Skript | available. | |||||

376-1219-00L | Rehabilitation Engineering II: Rehabilitation of Sensory and Vegetative Functions | W | 3 KP | 2V | R. Gassert, O. Lambercy | |

Kurzbeschreibung | Rehabilitation Engng is the application of science and technology to ameliorate the handicaps of individuals with disabilities to reintegrate them into society.The goal is to present classical and new rehabilitation engineering principles applied to compensate or enhance motor, sensory, and cognitive deficits. Focus is on the restoration and treatment of the human sensory and vegetative system. | |||||

Lernziel | Provide knowledge on the anatomy and physiology of the human sensory system, related dysfunctions and pathologies, and how rehabilitation engineering can provide sensory restoration and substitution. This lecture is independent from Rehabilitation Engineering I. Thus, both lectures can be visited in arbitrary order. | |||||

Inhalt | Introduction, problem definition, overview Rehabilitation of visual function - Anatomy and physiology of the visual sense - Technical aids (glasses, sensor substitution) - Retina and cortex implants Rehabilitation of hearing function - Anatomy and physiology of the auditory sense - Hearing aids - Cochlea Implants Rehabilitation and use of kinesthetic and tactile function - Anatomy and physiology of the kinesthetic and tactile sense - Tactile/haptic displays for motion therapy (incl. electrical stimulation) - Role of displays in motor learning Rehabilitation of vestibular function - Anatomy and physiology of the vestibular sense - Rehabilitation strategies and devices (e.g. BrainPort) Rehabilitation of vegetative Functions - Cardiac Pacemaker - Phrenic stimulation, artificial breathing aids - Bladder stimulation, artificial sphincter Brain stimulation and recording - Deep brain stimulation for patients with Parkinson, epilepsy, depression - Brain-Computer Interfaces | |||||

Literatur | Introductory Books: An Introduction to Rehabilitation Engineering. R. A. Cooper, H. Ohnabe, D. A. Hobson (Eds.). Taylor & Francis, 2007. Principles of Neural Science. E. R. Kandel, J. H. Schwartz, T. M Jessell (Eds.). Mc Graw Hill, New York, 2000. Force and Touch Feedback for Virtual Reality. G. C. Burdea (Ed.). Wiley, New York, 1996 (available on NEBIS). Human Haptic Perception, Basics and Applications. M. Grunwald (Ed.). Birkhäuser, Basel, 2008. The Sense of Touch and Its Rendering, Springer Tracts in Advanced Robotics 45, A. Bicchi et al.(Eds). Springer-Verlag Berlin, 2008. Interaktive und autonome Systeme der Medizintechnik - Funktionswiederherstellung und Organersatz. Herausgeber: J. Werner, Oldenbourg Wissenschaftsverlag 2005. Neural prostheses - replacing motor function after desease or disability. Eds.: R. Stein, H. Peckham, D. Popovic. New York and Oxford: Oxford University Press. Advances in Rehabilitation Robotics - Human-Friendly Technologies on Movement Assistance and Restoration for People with Disabilities. Eds: Z.Z. Bien, D. Stefanov (Lecture Notes in Control and Information Science, No. 306). Springer Verlag Berlin 2004. Intelligent Systems and Technologies in Rehabilitation Engineering. Eds: H.N.L. Teodorescu, L.C. Jain (International Series on Computational Intelligence). CRC Press Boca Raton, 2001. Selected Journal Articles and Web Links: Abbas, J., Riener, R. (2001) Using mathematical models and advanced control systems techniques to enhance neuroprosthesis function. Neuromodulation 4, pp. 187-195. Bach-y-Rita P., Tyler M., and Kaczmarek K (2003). Seeing with the brain. International journal of human-computer-interaction, 15(2):285-295. Burdea, G., Popescu, V., Hentz, V., and Colbert, K. (2000): Virtual reality-based orthopedic telerehabilitation, IEEE Trans. Rehab. Eng., 8, pp. 430-432 Colombo, G., Jörg, M., Schreier, R., Dietz, V. (2000) Treadmill training of paraplegic patients using a robotic orthosis. Journal of Rehabilitation Research and Development, vol. 37, pp. 693-700. Hayward, V. (2008): A Brief Taxonomy of Tactile Illusions and Demonstrations That Can Be Done In a Hardware Store. Brain Research Bulletin, Vol 75, No 6, pp 742-752 Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T. (1998): Robot-aided neurorehabilitation, IEEE Trans. Rehab. Eng., 6, pp. 75-87 Levesque. V. (2005). Blindness, technology and haptics. Technical report, McGill University. Available at: Link Quintern, J. (1998) Application of functional electrical stimulation in paraplegic patients. NeuroRehabilitation 10, pp. 205-250. Riener, R., Nef, T., Colombo, G. (2005) Robot-aided neurorehabilitation for the upper extremities. Medical & Biological Engineering & Computing 43(1), pp. 2-10. Riener, R. (1999) Model-based development of neuroprostheses for paraplegic patients. Royal Philosophical Transactions: Biological Sciences 354, pp. 877-894. The vOICe. Link. VideoTact, ForeThought Development, LLC. Link | |||||

Voraussetzungen / Besonderes | Target Group: Students of higher semesters and PhD students of - D-MAVT, D-ITET, D-INFK, D-HEST - Biomedical Engineering, Robotics, Systems and Control - Medical Faculty, University of Zurich Students of other departments, faculties, courses are also welcome This lecture is independent from Rehabilitation Engineering I. Thus, both lectures can be visited in arbitrary order. | |||||

401-0647-00L | Introduction to Mathematical Optimization | W | 5 KP | 2V + 1U | D. Adjiashvili | |

Kurzbeschreibung | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | |||||

Lernziel | The goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering. | |||||

Inhalt | Topics covered in this course include: - Linear programming (simplex method, duality theory, shadow prices, ...). - Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...). - Modelling with mathematical optimization: applications of mathematical programming in engineering. | |||||

Literatur | Information about relevant literature will be given in the lecture. | |||||

Voraussetzungen / Besonderes | This course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications. | |||||

401-3901-00L | Mathematical Optimization | W | 11 KP | 4V + 2U | R. Zenklusen | |

Kurzbeschreibung | Mathematical treatment of diverse optimization techniques. | |||||

Lernziel | The goal of this course is to get a thorough understanding of various classical mathematical optimization techniques with an emphasis on polyhedral approaches. In particular, we want students to develop a good understanding of some important problem classes in the field, of structural mathematical results linked to these problems, and of solution approaches based on this structural understanding. | |||||

Inhalt | Key topics include: - Linear programming and polyhedra; - Flows and cuts; - Combinatorial optimization problems and techniques; - Equivalence between optimization and separation; - Brief introduction to Integer Programming. | |||||

Literatur | - Bernhard Korte, Jens Vygen: Combinatorial Optimization. 6th edition, Springer, 2018. - Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003. This work has 3 volumes. - Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993. - Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986. | |||||

Voraussetzungen / Besonderes | Solid background in linear algebra. | |||||

636-0007-00L | Computational Systems Biology | W | 6 KP | 3V + 2U | J. Stelling | |

Kurzbeschreibung | Study of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification). | |||||

Lernziel | The aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks. | |||||

Inhalt | Biology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods. | |||||

Skript | Link | |||||

Literatur | U. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006. Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2010. B. Ingalls, Mathematical modeling in systems biology: an introduction. MIT Press, 2013 | |||||

Signal Processing and Machine Learning The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Signal Processing and Machine Learning ", see Link. The individual study plan is subject to the tutor's approval. | ||||||

Kernfächer These core courses are particularly recommended for the field of "Signal Processing and Machine Learning". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT. | ||||||

Foundation Core Courses | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0101-00L | Discrete-Time and Statistical Signal Processing | W | 6 KP | 4G | H.‑A. Loeliger | |

Kurzbeschreibung | ||||||

Lernziel | ||||||

Inhalt | 1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm. | |||||

Skript | Lecture Notes | |||||

227-0105-00L | Introduction to Estimation and Machine Learning | W | 6 KP | 4G | H.‑A. Loeliger | |

Kurzbeschreibung | Mathematical basics of estimation and machine learning, with a view towards applications in signal processing. | |||||

Lernziel | Students master the basic mathematical concepts and algorithms of estimation and machine learning. | |||||

Inhalt | Review of probability theory; basics of statistical estimation; least squares and linear learning; Hilbert spaces; Gaussian random variables; singular-value decomposition; kernel methods, neural networks, and more | |||||

Skript | Lecture notes will be handed out as the course progresses. | |||||

Voraussetzungen / Besonderes | solid basics in linear algebra and probability theory | |||||

Advanced Core Courses | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0423-00L | Neural Network Theory | W | 4 KP | 2V + 1U | H. Bölcskei | |

Kurzbeschreibung | The class focuses on fundamental mathematical aspects of neural networks with an emphasis on deep networks: Universal approximation theorems, basics of approximation theory, fundamental limits of deep neural network learning, geometry of decision surfaces, capacity of separating surfaces, dimension measures relevant for generalization, VC dimension of neural networks. | |||||

Lernziel | After attending this lecture, participating in the exercise sessions, and working on the homework problem sets, students will have acquired a working knowledge of the mathematical foundations of (deep) neural networks. | |||||

Inhalt | 1. Universal approximation with single- and multi-layer networks 2. Introduction to approximation theory: Fundamental limits on compressibility of signal classes, Kolmogorov epsilon-entropy of signal classes, non-linear approximation theory 3. Fundamental limits of deep neural network learning 4. Geometry of decision surfaces 5. Separating capacity of nonlinear decision surfaces 6. Dimension measures: Pseudo-dimension, fat-shattering dimension, Vapnik-Chervonenkis (VC) dimension 7. Dimensions of neural networks 8. Generalization error in neural network learning | |||||

Skript | Detailed lecture notes will be provided. | |||||

Voraussetzungen / Besonderes | This course is aimed at students with a strong mathematical background in general, and in linear algebra, analysis, and probability theory in particular. | |||||

227-0427-00L | Signal Analysis, Models, and Machine LearningFindet dieses Semester nicht statt. This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester). | W | 6 KP | 4G | H.‑A. Loeliger | |

Kurzbeschreibung | Mathematical methods in signal processing and machine learning. I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity. II. Learning linear and nonlinear functions and filters: neural networks, kernel methods. III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events. | |||||

Lernziel | The course is an introduction to some basic topics in signal processing and machine learning. | |||||

Inhalt | Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis. Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods. Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events. | |||||

Skript | Lecture notes. | |||||

Voraussetzungen / Besonderes | Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory | |||||

227-0447-00L | Image Analysis and Computer Vision | W | 6 KP | 3V + 1U | L. Van Gool, E. Konukoglu, F. Yu | |

Kurzbeschreibung | ||||||

Lernziel | ||||||

Inhalt | This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given. | |||||

Skript | Course material Script, computer demonstrations, exercises and problem solutions | |||||

Voraussetzungen / Besonderes | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | |||||

252-0535-00L | Advanced Machine Learning | W | 10 KP | 3V + 2U + 4A | J. M. Buhmann, C. Cotrini Jimenez | |

Kurzbeschreibung | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects. | |||||

Lernziel | Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data. | |||||

Inhalt | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems | |||||

Skript | No lecture notes, but slides will be made available on the course webpage. | |||||

Literatur | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. | |||||

Voraussetzungen / Besonderes | The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points. | |||||

263-3210-00L | Deep Learning | W | 8 KP | 3V + 2U + 2A | T. Hofmann | |

Kurzbeschreibung | Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations. | |||||

Lernziel | In recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology. | |||||

Voraussetzungen / Besonderes | This is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit. The participation in the course is subject to the following condition: - Students must have taken the exam in Advanced Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below: Advanced Machine Learning Link Computational Intelligence Lab Link Introduction to Machine Learning Link Statistical Learning Theory Link Computational Statistics Link Probabilistic Artificial Intelligence Link | |||||

Vertiefungsfächer These specialisation courses are particularly recommended for the area of "Signal Processing and Machine Learning", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the MSc EEIT. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0116-00L | VLSI I: From Architectures to VLSI Circuits and FPGAs | W | 6 KP | 5G | F. K. Gürkaynak, L. Benini | |

Kurzbeschreibung | ||||||

Lernziel | ||||||

Inhalt | This course is concerned with system-level issues of VLSI design and FPGA implementations. Topics include: - Overview on design methodologies and fabrication depths. - Levels of abstraction for circuit modeling. - Organization and configuration of commercial field-programmable components. - FPGA design flows. - Dedicated and general purpose architectures compared. - How to obtain an architecture for a given processing algorithm. - Meeting throughput, area, and power goals by way of architectural transformations. - Hardware Description Languages (HDL) and the underlying concepts. - SystemVerilog - Register Transfer Level (RTL) synthesis and its limitations. - Building blocks of digital VLSI circuits. - Functional verification techniques and their limitations. - Modular and largely reusable testbenches. - Assertion-based verification. - Synchronous versus asynchronous circuits. - The case for synchronous circuits. - Periodic events and the Anceau diagram. - Case studies, ASICs compared to microprocessors, DSPs, and FPGAs. During the exercises, students learn how to model FPGAs with SystemVerilog. They write testbenches for simulation purposes and synthesize gate-level netlists for FPGAs. Commercial EDA software by leading vendors is being used throughout. | |||||

Skript | Textbook and all further documents in English. | |||||

Literatur | ||||||

Voraussetzungen / Besonderes | Prerequisites: Basics of digital circuits. Examination: In written form following the course semester (spring term). Problems are given in English, answers will be accepted in either English oder German. Further details: Link | |||||

227-0155-00L | Machine Learning on Microcontrollers Registration in this class requires the permission of the instructors. Class size will be limited to 16. Preference is given to students in the MSc EEIT. | W | 6 KP | 3G | M. Magno, L. Benini | |

Kurzbeschreibung | Machine Learning (ML) and artificial intelligence are pervading the digital society. Today, even low power embedded systems are incorporating ML, becoming increasingly “smart”. This lecture gives an overview of ML methods and algorithms to process and extract useful near-sensor information in end-nodes of the “internet-of-things”, using low-power microcontrollers/ processors (ARM-Cortex-M; RISC-V) | |||||

Lernziel | Learn how to Process data from sensors and how to extract useful information with low power microprocessors using ML techniques. We will analyze data coming from real low-power sensors (accelerometers, microphones, ExG bio-signals, cameras…). The main objective is to study in details how Machine Learning algorithms can be adapted to the performance constraints and limited resources of low-power microcontrollers. | |||||

Inhalt | The final goal of the course is a deep understanding of machine learning and its practical implementation on single- and multi-core microcontrollers, coupled with performance and energy efficiency analysis and optimization. The main topics of the course include: - Sensors and sensor data acquisition with low power embedded systems - Machine Learning: Overview of supervised and unsupervised learning and in particular supervised learning (Bayes Decision Theory, Decision Trees, Random Forests, kNN-Methods, Support Vector Machines, Convolutional Networks and Deep Learning) - Low-power embedded systems and their architecture. Low Power microcontrollers (ARM-Cortex M) and RISC-V-based Parallel Ultra Low Power (PULP) systems-on-chip. - Low power smart sensor system design: hardware-software tradeoffs, analysis, and optimization. Implementation and performance evaluation of ML in battery-operated embedded systems. The laboratory exercised will show how to address concrete design problems, like motion, gesture recognition, emotion detection, image and sound classification, using real sensors data and real MCU boards. Presentations from Ph.D. students and the visit to the Digital Circuits and Systems Group will introduce current research topics and international research projects. | |||||

Skript | Script and exercise sheets. Books will be suggested during the course. | |||||

Voraussetzungen / Besonderes | Prerequisites: C language programming. Basics of Digital Signal Processing. Basics of processor and computer architecture. Some exposure to machine learning concepts is also desirable | |||||

227-0121-00L | Kommunikationssysteme | W | 6 KP | 2V + 2U | A. Wittneben | |

Kurzbeschreibung | ||||||

Lernziel | ||||||

Inhalt | Es werden die untersten drei Schichten des OSI-Referenzmodells behandelt: die Bitübertragungsschicht, die Sicherungsschicht mit dem Zugriff auf das Übertragungsmedium und die Vermittlung. Die wichtigsten Begriffe der Informationstheorie werden eingeführt. Anschliessend konzentrieren sich die Betrachtungen auf die Verfahren der Punkt-zu-Punkt-Übertragung, welche sich mittels der Signalraumdarstellung elegant und kohärent behandeln lassen. Den Methoden der Fehlererkennung und –korrektur, sowie Protokollen für die erneute Übermittlung gestörter Daten wird Rechnung getragen. Auch der Vielfachzugriff bei geteiltem Übertragungsmedium wird diskutiert. Den Abschluss bilden Algorithmen für das Routing in Kommunikationsnetzen und der Flusssteuerung. Die Anwendung der grundlegenden Verfahren wird ausführlich anhand von bestehenden und zukünftigen drahtlosen und drahtgebundenen Systemen erläutert. | |||||

Skript | Vorlesungsfolien | |||||

Literatur | [1] Simon Haykin, Communication Systems, 4. Auflage, John Wiley & Sons, 2001 [2] Andrew S. Tanenbaum, Computernetzwerke, 3. Auflage, Pearson Studium, 2003 [3] M. Bossert und M. Breitbach, Digitale Netze, 1. Auflage, Teubner, 1999 | |||||

227-0225-00L | Linear System Theory | W | 6 KP | 5G | M. Colombino | |

Kurzbeschreibung | The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems. | |||||

Lernziel | Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems. | |||||

Inhalt | - Proof techniques and practices. - Linear spaces, normed linear spaces and Hilbert spaces. - Ordinary differential equations, existence and uniqueness of solutions. - Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case. - Controllability and observability, duality. Time invariant systems treated as a special case. - Stability and stabilization, observers, state and output feedback, separation principle. | |||||

Skript | Available on the course Moodle platform. | |||||

Voraussetzungen / Besonderes | Sufficient mathematical maturity, in particular in linear algebra, analysis. | |||||

227-0417-00L | Information Theory I | W | 6 KP | 4G | A. Lapidoth | |

Kurzbeschreibung | This course covers the basic concepts of information theory and of communication theory. Topics covered include the entropy rate of a source, mutual information, typical sequences, the asymptotic equi-partition property, Huffman coding, channel capacity, the channel coding theorem, the source-channel separation theorem, and feedback capacity. | |||||

Lernziel | The fundamentals of Information Theory including Shannon's source coding and channel coding theorems | |||||

Inhalt | The entropy rate of a source, Typical sequences, the asymptotic equi-partition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the source-channel separation theorem, feedback capacity | |||||

Literatur | T.M. Cover and J. Thomas, Elements of Information Theory (second edition) | |||||

227-0421-00L | Learning in Deep Artificial and Biological Neuronal Networks | W | 4 KP | 3G | B. Grewe | |

Kurzbeschreibung | Deep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods. | |||||

Lernziel | The main goal of this lecture is to provide a comprehensive overview into the learning principles neuronal networks as well as to introduce a diverse skill set (e.g. simulating a spiking neuronal network) that is required to understand learning in large, hierarchical neuronal networks. To achieve this the lectures and exercises will merge ideas, concepts and methods from machine learning and neuroscience. These will include training basic ANNs, simulating spiking neuronal networks as well as being able to read and understand the main ideas presented in today’s neuroscience papers. After this course students will be able to: - read and understand the main ideas and methods that are presented in today’s neuroscience papers - explain the basic ideas and concepts of plasticity in the mammalian brain - implement alternative ANN learning algorithms to ‘error backpropagation’ in order to train deep neuronal networks. - use a diverse set of ANN regularization methods to improve learning - simulate spiking neuronal networks that learn simple (e.g. digit classification) tasks in a supervised manner. | |||||

Inhalt | Deep-learning a brain-inspired weak form of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. The origins of deep hierarchical learning can be traced back to early neuroscience research by Hubel and Wiesel in the 1960s, who first described the neuronal processing of visual inputs in the mammalian neocortex. Similar to their neocortical counterparts ANNs seem to learn by interpreting and structuring the data provided by the external world. However, while on specific tasks such as playing (video) games deep ANNs outperform humans (Minh et al, 2015, Silver et al., 2018), ANNs are still not performing on par when it comes to recognizing actions in movie data and their ability to act as generalizable problem solvers is still far behind of what the human brain seems to achieve effortlessly. Moreover, biological neuronal networks can learn far more effectively with fewer training examples, they achieve a much higher performance in recognizing complex patterns in time series data (e.g. recognizing actions in movies), they dynamically adapt to new tasks without losing performance and they achieve unmatched performance to detect and integrate out-of-domain data examples (data they have not been trained with). In other words, many of the big challenges and unknowns that have emerged in the field of deep learning over the last years are already mastered exceptionally well by biological neuronal networks in our brain. On the other hand, many facets of typical ANN design and training algorithms seem biologically implausible, such as the non-local weight updates, discrete processing of time, and scalar communication between neurons. Recent evidence suggests that learning in biological systems is the result of the complex interplay of diverse error feedback signaling processes acting at multiple scales, ranging from single synapses to entire networks. | |||||

Skript | The lecture slides will be provided as a PDF after each lecture. | |||||

Voraussetzungen / Besonderes | This advanced level lecture requires some basic background in machine/deep learning. Thus, students are expected to have a basic mathematical foundation, including linear algebra, multivariate calculus, and probability. The course is not to be meant as an extended tutorial of how to train deep networks in PyTorch or Tensorflow, although these tools used. The participation in the course is subject to the following conditions: 1) The number of participants is limited to 120 students (MSc and PhDs). 2) Students must have taken the exam in Deep Learning (263-3210-00L) or have acquired equivalent knowledge. | |||||

227-0445-10L | Mathematical Methods of Signal Processing | W | 6 KP | 4G | H. G. Feichtinger | |

Kurzbeschreibung | This course offers a mathematical correct but still non-technical description of key objects relevant for signal processing, such as Dirac measures, Dirac combs, various function spaces (like L^2), impulse response, transfer function, Gabor expansion, and so on. The approach is based on properties of "Feichtinger's algebra". MATLAB routines will serve as illustration. | |||||

Lernziel | The aim of the class to familiarize the participants with the idea of generalized functions (usual called distributions), and to provide a (novel approach) to a theory of mild distributions, which cannot be found in books so far (the course will contribute to the development of such a book). From the physical point of view, such an object is something, which can be measured or captured by (linear) measurements, such as an audio signal. The Harmonic Analysis perspective is, that the Fourier transform and time-frequency transforms are possible over any locally compact group. Engineers talk about discrete or continuous, periodic and non-periodic signals. Hence, a unified approach to these settings and a discussion of their interconnection (e.g. approximately computing the Fourier transform of a function using the DFT) is at the heart of this course. | |||||

Inhalt | Mathematical Foundations of Signal Processing: 0. Recalling (on and off) concepts from linear algebra (e.g. linear mappings, etc.) and introducing concepts from basic linear functional analysis (Hilbert spaces, Banach spaces) 1. Translation invariant systems and convolution, elementary functional analytic approach; 2. Pure frequencies and the Fourier transform, convolution theorem 3. The subalgebra L1(Rd) of integrable functions (without Lebesgue integration), Riemann Lebesgue Lemma 4. Plancherels Theorem, L2(Rd) and basic Hilbert space theory, unitary mappings 5. Short-time Fourier transform, the Feichtinger algebra S0(Rd) as algebra of test functions 6. The dual space of mild distributions, relationship to tempered distributions (for this familiar); various characterization 7. Gabor expansions of signals, characterization of smoothness and decay, Gabor frames and Riesz bases; 8. Transition from continuous to discrete variables, from periodic to the non-periodic case; 9. The kernel theorem, as the continuous analogue of matrix representations; 10. Sobolev spaces (describing smoothness) and weighted spaces; 11. Spreading representation and Kohn-Nirenberg representation of operators; 12. Gabor multipliers and approximation of slowly varying systems; 13. As time permits: the idea of generalized stochastic processes 14. Further subjects as demanded by the audience can be covered on demand. Detailed lecture notes will be provided. This material will become part of an on-going book-project, which has many facets. | |||||

Skript | This material will be regularly updated and posted at the lecturer's homepage, at Link There will be also a dedicated WEB page at Link (to be installed in the near future). | |||||

Voraussetzungen / Besonderes | We encourage students who are interested in mathematics, but also students of physics or mathematics who want to learn about application of modern methods from functional analysis to their sciences, especially those who are interested to understand what the connections between the continuous and the discrete world are (from continuous functions or images to samples or pixels, and back). Hans G. Feichtinger (Link) For any kind of questions concerning this course please contact the lecturer. He will be in Zurich most of the time, even if the course has to be held offline. It will start by October 1st 2020 only. | |||||

227-0477-00L | Acoustics I | W | 6 KP | 4G | K. Heutschi | |

Kurzbeschreibung | Introduction to the fundamentals of acoustics in the area of sound field calculations, measurement of acoustical events, outdoor sound propagation and room acoustics of large and small enclosures. | |||||

Lernziel | Introduction to acoustics. Understanding of basic acoustical mechanisms. Survey of the technical literature. Illustration of measurement techniques in the laboratory. | |||||

Inhalt | Fundamentals of acoustics, measuring and analyzing of acoustical events, anatomy and properties of the ear. Outdoor sound propagation, absorption and transmission of sound, room acoustics of large and small enclosures, architectural acoustics, noise and noise control, calculation of sound fields. | |||||

Skript | yes | |||||

263-5210-00L | Probabilistic Artificial Intelligence | W | 8 KP | 3V + 2U + 2A | A. Krause | |

Kurzbeschreibung | This course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics and the Internet. | |||||

Lernziel | How can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for graduate students. | |||||

Inhalt | Topics covered: - Probability - Probabilistic inference (variational inference, MCMC) - Bayesian learning (Gaussian processes, Bayesian deep learning) - Probabilistic planning (MDPs, POMPDPs) - Multi-armed bandits and Bayesian optimization - Reinforcement learning | |||||

Voraussetzungen / Besonderes | Solid basic knowledge in statistics, algorithms and programming. The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite. | |||||

401-0647-00L | Introduction to Mathematical Optimization | W | 5 KP | 2V + 1U | D. Adjiashvili | |

Kurzbeschreibung | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | |||||

Lernziel | The goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering. | |||||

Inhalt | Topics covered in this course include: - Linear programming (simplex method, duality theory, shadow prices, ...). - Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...). - Modelling with mathematical optimization: applications of mathematical programming in engineering. | |||||

Literatur | Information about relevant literature will be given in the lecture. | |||||

Voraussetzungen / Besonderes | This course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications. | |||||

401-3054-14L | Probabilistic Methods in Combinatorics | W | 6 KP | 2V + 1U | B. Sudakov | |

Kurzbeschreibung | This course provides a gentle introduction to the Probabilistic Method, with an emphasis on methodology. We will try to illustrate the main ideas by showing the application of probabilistic reasoning to various combinatorial problems. | |||||

Lernziel | ||||||

Inhalt | The topics covered in the class will include (but are not limited to): linearity of expectation, the second moment method, the local lemma, correlation inequalities, martingales, large deviation inequalities, Janson and Talagrand inequalities and pseudo-randomness. | |||||

Literatur | - The Probabilistic Method, by N. Alon and J. H. Spencer, 3rd Edition, Wiley, 2008. - Random Graphs, by B. Bollobás, 2nd Edition, Cambridge University Press, 2001. - Random Graphs, by S. Janson, T. Luczak and A. Rucinski, Wiley, 2000. - Graph Coloring and the Probabilistic Method, by M. Molloy and B. Reed, Springer, 2002. | |||||

401-3621-00L | Fundamentals of Mathematical Statistics | W | 10 KP | 4V + 1U | S. van de Geer | |

Kurzbeschreibung | The course covers the basics of inferential statistics. | |||||

Lernziel | ||||||

401-3901-00L | Mathematical Optimization | W | 11 KP | 4V + 2U | R. Zenklusen | |

Kurzbeschreibung | Mathematical treatment of diverse optimization techniques. | |||||

Lernziel | The goal of this course is to get a thorough understanding of various classical mathematical optimization techniques with an emphasis on polyhedral approaches. In particular, we want students to develop a good understanding of some important problem classes in the field, of structural mathematical results linked to these problems, and of solution approaches based on this structural understanding. | |||||

Inhalt | Key topics include: - Linear programming and polyhedra; - Flows and cuts; - Combinatorial optimization problems and techniques; - Equivalence between optimization and separation; - Brief introduction to Integer Programming. | |||||

Literatur | - Bernhard Korte, Jens Vygen: Combinatorial Optimization. 6th edition, Springer, 2018. - Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003. This work has 3 volumes. - Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993. - Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986. | |||||

Voraussetzungen / Besonderes | Solid background in linear algebra. | |||||

401-4619-67L | Advanced Topics in Computational StatisticsFindet dieses Semester nicht statt. | W | 4 KP | 2V | keine Angaben | |

Kurzbeschreibung | This lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling. | |||||

Lernziel | Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes. | |||||

Inhalt | The main focus will be on graphical models in various forms: Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models | |||||

Voraussetzungen / Besonderes | We assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics. | |||||

Wahlfächer ***more courses coming soon*** This is only a short selection. Other courses from the ETH course catalogue may be chosen in agreement with your tutor. As an alternative to the elective courses, students may do a second semester project or an internship in industry. Please consult your tutor. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

363-0511-00L | Managerial EconomicsNot for MSc students belonging to D-MTEC! | W | 4 KP | 3V | P. Egger, M. Köthenbürger, N. Loumeau | |

Kurzbeschreibung | "Managerial Economics" wendet Theorien und Methoden aus dem Bereich der Wirtschaftwissenschaften (Volks- und Betriebswirtschaftslehre) an, um das Entscheidungsverhalten von Unternehmen und Konsumenten im Kontext von Märkten zu analysieren. Der Kurs richtet sich an Studenten ohne wirtschaftswissenschaftliches Vorwissen. | |||||

Lernziel | Ziel des Kurses ist es, in die Grundlagen des mikroökonomischen Denkens einzuführen. Aufbauend auf Prinzipien von Optimierung und Gleichgewicht stehen hierbei zentrale ökonomische Konzepte des Individual- und Firmenverhaltens und deren Interaktion in Entscheidungskontexten von Märkten im Mittelpunkt. Aus einer Analyse des Verhaltens einzelner Konsumenten und Produzenten werden wir die Nachfrage, das Angebot und Gleichgewichte von Märkten unter verschiedenen Annahmen zur vorherrschenden Marktstruktur (vollständiger Wettbewerb, Monopol, oligopolistische Marktformen) entwickeln und ökonomisch diskutieren. Die in diesem Kurs vermittelten Inhalte bilden eine wesentliche Grundlage für eine volks- und betriebswirtschaftliche Kompetenz mit Hinblick auf Entscheidungskontexte des privatwirtschaftlichen und öffentlichen Sektors. | |||||

Literatur | "Mikroökonomie" von Robert Pindyck & Daniel Rubinfeld, aktualisierte 8. Auflage, 8/2013, (Pearson Studium - Economic VWL). | |||||

Voraussetzungen / Besonderes | Der Kurs richtet sich sowohl an Bachelor als auch an Master Studenten. Es ist kein spezielles Vorwissen in den Bereichen Ökonomik und Management erforderlich. | |||||

351-0778-00L | Discovering ManagementEntry level course in management for BSc, MSc and PHD students at all levels not belonging to D-MTEC. This course can be complemented with Discovering Management (Excercises) 351-0778-01. | W | 3 KP | 3G | B. Clarysse, S. Brusoni, S. Feuerriegel, G. Grote, V. Hoffmann, T. Netland, G. von Krogh | |

Kurzbeschreibung | Discovering Management offers an introduction to the field of business management and entrepreneurship for engineers and natural scientists. The module provides an overview of the principles of management, teaches knowledge about management that is highly complementary to the students' technical knowledge, and provides a basis for advancing the knowledge of the various subjects offered at D-MTEC. | |||||

Lernziel | Discovering Management combines in an innovate format a set of theory lectures and a series of case studies. The learning model for Discovering Management involves 'learning by doing'. The objective is to introduce the students to the relevant topics of the management literature and give them a good introduction in entrepreneurship topics too. The course is a series of lectures on the topics of strategy, innovation, leadership, productions and operations management and corporate social responsibility. While the different theory lectures provide the theoretical and conceptual foundations, the experiential learning outcomes result from the case studies. | |||||

Inhalt | Discovering Management aims to broaden the students' understanding of the principles of business management, emphasizing the interdependence of various topics in the development and management of a firm. The lectures introduce students not only to topics relevant for managing large corporations, but also touch upon the different aspects of starting up your own venture. The lectures will be presented by the respective area specialists at D-MTEC. The course broadens the view and understanding of technology by linking it with its commercial applications and with society. The lectures are designed to introduce students to topics related to strategy, corporate innovation, leadership, value chain analysis, corporate social responsibility, and information management. Practical examples from case studies will stimulate the students to critically assess these issues. | |||||

Voraussetzungen / Besonderes | Discovering Management is designed to suit the needs and expectations of Bachelor students at all levels as well as Master and PhD students not belonging to D-MTEC. By providing an overview of Business Management, this course is an ideal enrichment of the standard curriculum at ETH Zurich. No prior knowledge of business or economics is required to successfully complete this course. | |||||

351-0778-01L | Discovering Management (Exercises)Complementary exercises for the module Discovering Managment. Prerequisite: Participation and successful completion of the module Discovering Management (351-0778-00L) is mandatory. | W | 1 KP | 1U | B. Clarysse, L. De Cuyper | |

Kurzbeschreibung | This course is offered complementary to the basis course 351-0778-00L, "Discovering Management". The course offers additional exercises and case studies. | |||||

Lernziel | This course is offered to complement the course 351-0778-00L. The course offers additional exercises and case studies. | |||||

Inhalt | The course offers additional exercises and case studies concering: Strategic Management; Technology and Innovation Management; Operations and Supply Chain Management; Finance and Accounting; Marketing and Sales. Please refer to the course website for further information on the content, credit conditions and schedule of the module: Link | |||||

363-0790-00L | Technology Entrepreneurship | W | 2 KP | 2V | F. Hacklin | |

Kurzbeschreibung | This course aims to equip future leaders with strategies, frameworks and tools for understanding, analyzing and building technology ventures. In so doing, this course lays particular emphasis on providing an overview of various technology-related dimensions of the entrepreneurial journey, including founding, financing and growing a venture. | |||||

Lernziel | - Understand both the tension and link between entrepreneurship and technology - Evaluate cases of success and failure in technology ventures - Discuss a variety of approaches and frameworks for building and growing technology ventures - Interact with entrepreneurial leaders and gain insight into their entrepreneurial journey - Experiment with building blocks and tools for analyzing, structuring and prototyping technology ventures | |||||

Inhalt | Many industries are approaching, or find themselves in the midst of, dramatic structural changes. In many cases, such transformations are rooted in underlying technological shifts, such as digitization, nanoscale engineering, or 3D printing. Well known cases in point of affected sectors are in consumer electronics, media or manufacturing industries who are currently undergoing significant technology-driven disruptions. But also emerging shifts in the automotive sector or financial services give rise to severe questions of where and how the future value will be created and captured. In a world characterized by disruption and change, technology ventures have taken a paramount role in significantly altering the global economic picture. As a consequence, there is a rising demand for complementing technological skills by entrepreneurial understanding. Against this background, this course aims to equip future leaders with strategies, frameworks and tools for understanding, analyzing and building technology ventures. In so doing, this course lays particular emphasis on providing an overview of various technology-related dimensions of the entrepreneurial journey, including founding, financing and growing a venture. See course website: Link | |||||

Skript | - Lecture slides, cases and additional learning material provided during the course | |||||

363-1049-00L | Principles of Conflict Resolution | W | 3 KP | 2V | P. Grech | |

Kurzbeschreibung | This course provides a transdisciplinary introduction to conflict resolution in international relations (primary focus), business and interpersonal relations. Some time is devoted to analytic methods (non-cooperative game theory), making this course specifically suited for ETH students who are curious to apply their engineering/natural science background to a new domain. | |||||

Lernziel | Recognizing and understanding commonalities as well as differences between different conflict types, both structurally and topically. Assessing different approaches to conflict analysis and resolution regarding their strengths and weaknesses. Equilibrium computation in simple games. Illustrating specific aspects of conflicts with real-life/historical examples. Applying the presented theoretical approaches to real-life and stylized conflict situations in international relations, business and interpersonal relations. | |||||

Inhalt | Topics discussed: 1. Approaches to conflict analysis: international relations theory/political philosophy, (social) psychology, non-cooperative game theory, behavioral economics 2. Emphasis on strategic analysis: non-cooperative game theory (models for trust, commitment, brinkmanship, threats, promises etc.) 3. Conflictual negotiations: basic concepts, relationship building, dealing with non-cooperative counterparties, collaborative solution finding 4. Resolution methods with third-party intervention: mediation/conciliation, arbitration, adjudication, questions of implementation and enforcement (domestic measures, interstate measures: peacekeeping, peace enforcement, humanitarian interventions, sanctions etc.), conflict transformation: long-term measures for conflict resolution, peacebuilding. Theoretical input will be amply illustrated by a variety of real-world examples in -international relations (primary focus; e.g. wars, establishment of the international system, arms races, etc.), -business (energy, music, sports, etc.) -interpersonal relations (divorce cases, neighborhood disputes, etc.). | |||||

Skript | A slide deck will be made available. | |||||

Literatur | Relevant references will be indicated in the slide deck. | |||||

363-1065-00L | Design Thinking: Human-Centred Solutions to Real World Challenges Findet dieses Semester nicht statt. | W | 5 KP | 5G | S. Brusoni | |

Kurzbeschreibung | The goal of this course is to engage students in a multidisciplinary collaboration to tackle real world problems. Following a design thinking approach, students will work in teams to solve a set of design challenges that are organized as a one-week, a three-week, and a final six-week project in collaboration with an external project partner. Information and application: Link | |||||

Lernziel | During the course, students will learn about different design thinking methods and tools. This will enable them to: - Generate deep insights through the systematic observation and interaction of key stakeholders (empathy). - Engage in collaborative ideation with a multidisciplinary team. - Rapidly prototype and iteratively test ideas and concepts by using various materials and techniques. | |||||

Inhalt | The purpose of this course is to equip the students with methods and tools to tackle a broad range of problems. Following a Design Thinking approach, the students will learn how to observe and interact with key stakeholders in order to develop an in-depth understanding of what is truly important and emotionally meaningful to the people at the center of a problem. Based on these insights, the students ideate on possible solutions and immediately validated them through quick iterations of prototyping and testing using different tools and materials. The students will work in multidisciplinary teams on a set of challenges that are organized as a one-week, a three-week, and a final six-week project with an external project partner. In this course, the students will learn about the different Design Thinking methods and tools that are needed to generate deep insights, to engage in collaborative ideation, rapid prototyping and iterative testing. Design Thinking is a deeply human process that taps into the creative abilities we all have, but that get often overlooked by more conventional problem solving practices. It relies on our ability to be intuitive, to recognize patterns, to construct ideas that are emotionally meaningful as well as functional, and to express ourselves through means beyond words or symbols. Design Thinking provides an integrated way by incorporating tools, processes and techniques from design, engineering, the humanities and social sciences to identify, define and address diverse challenges. This integration leads to a highly productive collaboration between different disciplines. For more information and the application visit: Link | |||||

Voraussetzungen / Besonderes | Open mind, ability to manage uncertainty and to work with students from various background. Class attendance and active participation is crucial as much of the learning occurs through the work in teams during class. Therefore, attendance is obligatory for every session. Please also note that the group work outside class is an essential element of this course, so that students must expect an above-average workload. Please note that the class is designed for full-time MSc students. Interested MAS students need to send an email to Linda Armbruster to learn about the requirements of the class. | |||||

363-1082-00L | Enabling Entrepreneurship: From Science to Startup Students should provide a brief overview (unto 1 page) of their business ideas that they would like to commercialise through the course. If they do not have an idea, they are required to provide a motivation letter stating why they would like to do this elective. If you are unsure about the readiness of your idea or technology to be converted into a startup, please drop me a line to schedule a call or meeting to discuss. The total number of students will be limited to 40. It is preferable that the students already form teams of at least two persons, where both the team-members would like to do the course. The names of the team-members should be provided together with the business idea or the motivation letter submitted by the students. The students should submit the necessary information and apply to Link until 23 August 2020. | W | 3 KP | 2V | A. Sethi | |

Kurzbeschreibung | This elective is relevant for students who have developed a technology and are keen to evaluate the steps in starting a startup. This is also relevant for students who would like to start a startup but do not have a technology, but are clear on a specific market and the impact they would like to create. | |||||

Lernziel | Students have technology competence or an idea that they would like to convert into a startup. They are now in the process of evaluating the steps necessary to do so. In summary: 1. Students want to become entrepreneurs 2. The students can be from business or science & technology 3. The course will enable the students to identify the relevance of their technology or idea from the market relevance perspective and thereby create a business case to take it to market. 4. The students will have exposure to investors and entrepreneurs (with a focus on ETH spin-offs) through the course, to gain insight to commercialise their idea | |||||

Inhalt | The students would cover the following topics, as the build their idea into a business case: 1. Technology excellence: this assumes that the student has achieved a certain degree of competence in the area of technology that he or she expects to bring to the market 2. Market need and market relevance: The student would then be expected to identify the possible markets that may find the technology of relevance. Market relevance implies the process of identification of how relevant the market perceives the technology, and whether this can sustain over a longer period of time 3. IP and IP strategy: Intellectual property, whether in the form of a patent or a trade secret, implies the secret ingredient that enables the student to achieve certain results that competitors are unable to copy. This enables the student (and subsequently the startup) to hold on to the market that they create with customers 4. Team including future capabilities required: a startup requires multiple people with complementary capabilities. They also need to be motivated while at the same time protecting the interests of the startup 5. Financials: There is a need of funding to achieve milestones. This includes funding for salaries and running of the company 6. Investors and funding options: There are multiple funding options for a startup. They all come with different advantages and limitations. It's important for a startup to recognise its needs and find the investors that fit these needs and are best aligned with the vision of the founders 7. Preparation of business case: The students will finally prepare the business case that can help them to articulate the link of the technology with the market need and its willingness to pay 8. Legal overview, company forms and shareholders’ agreements (including pitfalls) The seminar includes talks from invited investors, entrepreneurs and legal experts regarding the importance of the various elements being covered in content, workshops and teamwork. There is a particular emphasis on market validation on each step of the journey, to ensure relevance. | |||||

Skript | Since the course will revolve around the ideas of the students, the notes will be for the sole purpose of providing guidance to the students to help convert their technologies or ideas into business cases for the purpose of forming startups. Theoretical subject matter will be kept to a minimum and is not the focus of the course. | |||||

Literatur | Book Sethi, A. "From Science to Startup" ISBN 978-3-319-30422-9 | |||||

Voraussetzungen / Besonderes | This course is only relevant for those students who aspire to become entrepreneurs. Students applying for this course are requested to submit a 1 page business idea or, in case they don't have a business idea, a brief motivation letter stating why they would like to do this course. If you are unsure about the readiness of your idea or technology to be converted into a startup, please drop me a line to schedule a call or meeting to discuss. | |||||

851-0703-00L | Grundzüge des RechtsStudierende, die die Vorlesung "Grundzüge des Rechts für Bauwissenschaften" (851-0703-03L) oder "Grundzüge des Rechts" (851-0708-00L) belegt haben oder belegen werden, sollen sich in dieser Lerneinheit nicht einschreiben. Besonders geeignet für Studierende D-ARCH, D-MAVT, D- MATL | W | 2 KP | 2V | O. Streiff Gnöpff | |

Kurzbeschreibung | Die Vorlesung führt in die Grundzüge der Rechtsordnung ein. Es werden Grundfragen des Verfassungs- und Verwaltungsrechts, des Privatrechts sowie des Europarechts behandelt. | |||||

Lernziel | Studierende erkennen grundlegende Strukturen der Rechtsordnung, verstehen ausgewählte Probleme des öffentlichen Rechts und des Privatrechts und können die erworbenen Grundlagen in weitergehenden rechtswissenschaftlichen Lehrveranstaltungen anwenden. | |||||

Inhalt | Grundlegende Konzepte des Rechts, Rechtsquellen. Privatrecht: Vertragsrecht (inkl. Werk- und Ingenieurverträge), Deliktsrecht und Sachenrecht. Öffentliches Recht: Grundrechte, Verwaltungsrecht (inkl. Bezüge zu Umwelt und Raum), Staat als Nachfrager (öffentliche Beschaffung), prozessuales Denken. Grundzüge des Europarechts und des Strafrechts. | |||||

Skript | Jaap Hage, Bram Akkermans (Hg.), Introduction to Law, Cham 2017 (Online-Ressource ETH Bibliothek) | |||||

Literatur | Weiterführende Unterlagen werden auf der Moodle-Lernumgebung bereitgestellt (vgl. Link). | |||||

851-0735-10L | Wirtschaftsrecht Maximale Teilnehmerzahl: 100 Besonders geeignet für Studierende D-ITET, D-MAVT | W | 2 KP | 2V | P. Peyrot | |

Kurzbeschreibung | Die Vorlesung führt die Studierenden in praxisnaher Weise in die rechtlichen Aspekte der Gründung und Führung eines Unternehmens ein. | |||||

Lernziel | Die Studierenden verfügen über grundlegende Kenntnisse des Wirtschaftsrechts. Sie sind in der Lage, selbständig wirtschaftsrechtliche Problemstellungen zu erkennen und interessengerecht zu lösen. Sie verfügen über folgende Kompetenzen: - Sie verfügen über das Grundlagenwissen zur Gründung und Führung eines Unternehmens. - Sie sind vertraut mit den Themen contracting, negotiation, claims management und dispute resolution - Sie kennen die Bedeutung eines Systems zur Einhaltung der rechtlichen Rahmenordnung einzurichten (compliance). - Sie können zum legal management des Unternehmens beitragen und rechtliche Fragestellungen mit Juristen besprechen. - Sie verstehen das Recht als Teil der Unternehmensstrategie und als wertvolle Ressource für die Unternehmung. | |||||

Skript | Ein umfassendes Skript wird auf der Plattform Moodle online zur Verfügung gestellt. | |||||

851-0738-00L | Geistiges Eigentum: Eine EinführungBesonders geeignet für Studierende D-CHAB, D-INFK, D-ITET, D-MAVT, D- MATL, D-MTEC | W | 2 KP | 2V | M. Schweizer | |

Kurzbeschreibung | Die Vorlesung bietet eine Einführung in das schweizerische und europäische Immaterialgüterrecht (Marken-, Urheber-, Patent- und Designrecht). Auch werden die Aspekte des Wettbewerbsrechts behandelt, die für den Schutz geistiger Schöpfungen und unternehmens- oder produktbezogener Zeichen relevant sind. Die rechtlichen Grundlagen werden anhand aktueller Fälle erarbeitet. | |||||

Lernziel | Ziel der Vorlesung ist es, ETH-Studierende in die Lage zu versetzen, zu erkennen, welche Schutzrechte die von ihnen geschaffenen Leistungen möglicherweise schützen oder verletzen können. Dadurch lernen die Studierenden, die immaterialgüterrechtlichen Chancen und Risiken bei der Entwicklung und Vermarktung von Produkten abzuschätzen. Dazu müssen sie die Schutzvoraussetzungen und den Schutzumfang der verschiedenen immaterialgüterrechtlichen Schutzrechte ebenso kennen wie die Probleme, die typischerweise bei der Durchsetzung von Schutzrechten auftreten. Diese Kenntnisse sollen praxisnah aufgrund von aktuellen Urteilen und Fällen vermittelt werden. Ein weiteres Ziel ist es, den Studierenden zu ermöglichen, informiert an der aktuellen Diskussion über die Ziele und Wünschbarkeit des Schutzes geistiger Leistungen teilzunehmen, wie sie insbesondere auf den Gebieten des Urheberrechts (Stichworte fair use, Creative Commons, Copyleft) und Patentrechts (Software-Patente, patent trolls, patent thickets), geführt wird. | |||||

851-0738-01L | Die Rolle des Geistigen Eigentums im Ingenieurwesen und den technischen Wissenschaften Maximale Teilnehmerzahl: 40 Besonders geeignet für Studierende D-BAUG, D-BIOL, D-BSSE, D-CHAB, D-ITET, D-MAVT | W | 2 KP | 2V | K. Houshang Pour Islam | |

Kurzbeschreibung | Patente und andere Formen des Geistigen Eigentums haben in den letzten Jahrzehnten einen starken Bedeutungszuwachs im Alltag von Ingenieuren und Wissenschaftern erfahren. Ziel der Vorlesung ist es, einen Überblick über grundlegende Aspekte des Geistigen Eigentums zu vermitteln und die Vorlesungsteilnehmer in die Lage zu versetzen, das Wissen später im Berufsalltag einzusetzen. | |||||

Lernziel | Das Wissen über Geistiges Eigentum ist für Ingenieure und Wissenschafter in den letzten Jahrzehnten zunehmend wichtiger geworden und bildet mittlerweile eine Schlüsselqualifikation. Sowohl in Produktion und Vertrieb als auch in Forschung und Entwicklung sind sie dabei insbesondere mit Fragen zum Schutz von technischen Erfindungen und mit der Nutzung von Patentinformationen konfrontiert. Im Rahmen der Vorlesung werden die Vorlesungsteilnehmer mit den praxisrelevanten Aspekten des Geistigen Eigentums vertraut gemacht und in die Lage versetzt, das erworbene Wissen später im Berufsalltag einzusetzen. Unter anderem werden in der Vorlesung die folgenden Themen behandelt: - Die Bedeutung von Innovationen in industrialisierten Ländern - Überblick über die Formen des Geistigen Eigentums - Der Schutz von technischen Erfindungen und die Absicherung der kommerziellen Umsetzung - Patente als Quelle für technische und andere wichtige Informationen - Praktische Aspekte des Geistigen Eigentum im Forschungsalltag, bei der Arbeit im Unternehmen und bei der Gründung von Startups. Das in der Vorlesung vermittelte Wissen wird anhand von Beispielen aus verschiedenen technischen Bereichen veranschaulicht und vertieft. Die Vorlesung umfasst praktische Übungen zur Nutzung und Recherche von Patentinformationen. Es wird dabei das Grundwissen vermittelt, wie Patentdokumente gelesen und ausgewertet werden und öffentlich zugängliche Patentdatenbanken genutzt werden können, um die benötigten Patentinformationen zu beschaffen und im Alltag einzusetzen. | |||||

Voraussetzungen / Besonderes | Die Vorlesung ist für Studierende ingenieurwissenschaftlicher, naturwissenschaftlicher und anderer technischer Studienfächer geeignet. | |||||

Industriepraktikum | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-1550-10L | Internship in Industry Nur für Elektrotechnik und Informationstechnologie MSc (Studienreglement 2018). | W | 12 KP | externe Veranstalter | ||

Kurzbeschreibung | Es ist das Ziel der 12-wöchigen Praxis, Master-Studierenden die industriellen Arbeitsumgebungen näher zu bringen. Während dieser Zeit bietet sich ihnen die Gelegenheit, in aktuelle Projekte der Gastinstitution involviert zu werden. | |||||

Lernziel | siehe oben |