Search result: Catalogue data in Autumn Semester 2018

Number	Title	Type	ECTS	Hours	Lecturers
Electrical Engineering and Information Technology Master
Master Studies (Programme Regulations 2018)
Communication The core courses and specialization courses below are a selection for students who wish to specialize in the area of "Communication", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html. The individual study plan is subject to the tutor's approval.
Core Courses 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
227-0121-00L	Communication Systems	W	6 credits	4G	A. Wittneben
Abstract	Information Theory, Signal Space Analysis, Baseband Transmission, Passband Transmission, Example und Channel, Data Link Layer, MAC, Example Layer 2, Layer 3, Internet
Learning objective	Introduction into the fundamentals of digital communication systems. Selected examples on the application of the fundamental principles in existing and upcoming communication systems
Content	Covered are the lower three layer of the OSI reference model: the physical, the data link, and the network layer. The basic terms of information theory are introduced. After this, we focus on the methods for the point to point communication, which may be addressed elegantly and coherently in the signal space. Methods for error detection and correction as well as protocols for the retransmission of perturbed data will be covered. Also the medium access for systems with shared medium will be discussed. Finally, algorithms for routing and flow control will be treated. The application of the basic methods will be extensively explained using existing and future wireless and wired systems.
Lecture notes	Lecture Slides
Literature	[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 credits	4G	H.‑A. Loeliger
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	Lecture Notes

Advanced Core Courses
Number	Title	Type	ECTS	Hours	Lecturers
227-0301-00L	Optical Communication Fundamentals	W	6 credits	2V + 1U + 1P	J. Leuthold
Abstract	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.
Learning objective	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.
Content	* 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.
Lecture notes	Lecture notes are handed out.
Literature	Govind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010
Prerequisites / Notice	Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics.
227-0417-00L	Information Theory I	W	6 credits	4G	A. Lapidoth
Abstract	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.
Learning objective	The fundamentals of Information Theory including Shannon's source coding and channel coding theorems
Content	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
Literature	T.M. Cover and J. Thomas, Elements of Information Theory (second edition)
227-0427-00L	Signal Analysis, Models, and Machine Learning	W	6 credits	4G	H.‑A. Loeliger
Abstract	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.
Learning objective	The course is an introduction to some basic topics in signal processing and machine learning.
Content	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.
Lecture notes	Lecture notes.
Prerequisites / Notice	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 Does not take place this semester.	E-	6 credits	2V + 2U	A. Wittneben
Abstract	Wireless access systems support locally constrained wireless connectivity and mobile access to a backbone network (typically the Internet). In this course the student develops a comprehensive understanding of existing and upcoming wireless access technologies (including WiFi, Bluetooth, RFID, NFC, VANET) and related Physical Layer and Medium Access Control Layer problems and opportunities.
Learning objective	The course consists of two tracks. The track "Technology&Systems" is structured as regular lecture. In the introduction we will discuss the challenges and potential of pervasive wireless access and study some fundamentals of short/medium range wireless communications. The main body of this track is devoted to existing and upcoming systems. A comprehensive survey of Ultrawide band (UWB) as the promising transmission technology for pervasive wireless access completes this track. In the track "Simulate&Practice" we form student teams that implement and analyze functional blocks of the physical layer of various advanced wireless access systems based on MATLAB simulations. The track includes combination tasks where different teams combine their functional blocks (e.g. transmitter, receiver) in order to simulate the complete physical layer.
Content	1. Short range wireless communication : fundamental Physical Layer challenges and solutions 2. Wireless Local Area Network (WLAN) 3. Vehicular Networks (VANET) 4. Ultra-Wideband (UWB) technology: fundamental principles, promises and solutions 5. Wireless Body Area Networks (WBAN) 6. Wireless Personal Area Networks (Bluetooth, Zigbee) 7. Radio Frequency Identification (RFID) and Near Field Communication (NFC)
Lecture notes	Lecture Slides and handouts.
Literature	Selected Books
Prerequisites / Notice	Requirements: Knowledge of fundamental principles of digital communication systems (e.g. 227-0121-00 G Kommunikationssysteme) is helpful but not mandatory. Lecture is given in English.

Specialization Courses These specialization 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 specialization courses during the Master's Programme.
Number	Title	Type	ECTS	Hours	Lecturers
227-0102-00L	Discrete Event Systems	W	6 credits	4G	L. Thiele, L. Vanbever, R. Wattenhofer
Abstract	Introduction to discrete event systems. We start out by studying popular models of discrete event systems. In the second part of the course we analyze discrete event systems from an average-case and from a worst-case perspective. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems, Worst-Case Event Systems, Verification, Network Calculus.
Learning objective	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.
Content	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
Lecture notes	Available
Literature	[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	Control Systems	W	6 credits	2V + 2U	F. Dörfler
Abstract	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.
Learning objective	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.
Content	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.
Literature	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.
Prerequisites / Notice	Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation.
227-0112-00L	High-Speed Signal Propagation	W	6 credits	2V + 2U	C. Bolognesi
Abstract	Understanding of high-speed signal propagation in microwave cables and integrated circuits and printed circuit boards. As clock frequencies rise in the GHz domain, there is a need grasp signal propagation to maintain good signal integrity in the face of symbol interference and cross-talk. The course is of high value to all interested in high-speed analog (RF, microwave) or digital systems.
Learning objective	Understanding of high-speed signal propagation in interconnects, microwave cables and integrated transmission lines such as microwave integrated circuits and/or printed circuit boards. As system clock frequencies continuously rise in the GHz domain, a need urgently develops to understand high-speed signal propagation in order to maintain good signal integrity in the face of phenomena such as inter-symbol interference (ISI) and cross-talk. Concepts such as Scattering parameters (or S-parameters) are key to the characterization of networks over wide bandwidths. At high frequencies, all structures effectively become "transmission lines." Unless care is taken, it is highly probable that one ends-up with a bad transmission line that causes the designed system to malfunction. Filters will also be considered because it turns out that some of the problems associated by lossy transmission channels (lines, cables, etc) can be corrected by adequate filtering in a process called "equalization."
Content	Transmission line equations of the lossless and lossy TEM-transmission line. Introduction of current and voltage waves. Representation of reflections in the time and frequency domain. Application of the Smith chart. Behavior of low-loss transmission lines. Attenuation and impulse distortion due to skin effect. Transmission line equivalent circuits. Group delay and signal dispersion. Coupled transmission lines. Scattering parameters. Butterworth-, Chebychev- and Bessel filter approximations: filter synthesis from low-pass filter prototypes.
Lecture notes	Script: Leitungen und Filter (In German).
Prerequisites / Notice	Exercises will be held in English.
227-0116-00L	VLSI I: From Architectures to VLSI Circuits and FPGAs	W	6 credits	5G	F. K. Gürkaynak, L. Benini
Abstract	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.
Learning objective	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 VHDL or 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 VHDL and with industrial Electronic Design Automation (EDA) tools.
Content	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. - VLSI and 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. - VHDL and SystemVerilog compared. - VHDL (IEEE standard 1076) for simulation and synthesis. - A suitable nine-valued logic system (IEEE standard 1164). - 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 digital ICs with VHDL. They write testbenches for simulation purposes and synthesize gate-level netlists for VLSI chips and FPGAs. Commercial EDA software by leading vendors is being used throughout.
Lecture notes	Textbook and all further documents in English.
Literature	H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303.
Prerequisites / Notice	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: https://iis-students.ee.ethz.ch/lectures/vlsi-i/
227-0148-00L	VLSI III: Test and Fabrication of VLSI Circuits	W	6 credits	4G	F. K. Gürkaynak, L. Benini
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	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 http://link.springer.com/book/10.1007%2Fb117406
Prerequisites / Notice	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: https://iis-students.ee.ethz.ch/lectures/vlsi-iii/
227-0166-00L	Analog Integrated Circuits	W	6 credits	2V + 2U	Q. Huang
Abstract	This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies.
Learning objective	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.
Content	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; A/D and D/A converters; Introduction to switched capacitor circuits. 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 measurments.
Lecture notes	Handouts of presented slides. No script but an accompanying textbook is recommended.
Literature	Gray, Hurst, Lewis, Meyer, "Analysis and Design of Analog Integrated Circuits", 5th Ed. Wiley, 2010.
227-0301-00L	Optical Communication Fundamentals	W	6 credits	2V + 1U + 1P	J. Leuthold
Abstract	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.
Learning objective	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.
Content	* 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.
Lecture notes	Lecture notes are handed out.
Literature	Govind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010
Prerequisites / Notice	Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics.
227-0377-00L	Physics of Failure and Failure Analysis of Electronic Devices and Equipment	W	3 credits	2V	U. Sennhauser
Abstract	Failures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.
Learning objective	Introduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.
Content	Summary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis
Lecture notes	Comprehensive copy of transparencies
227-0447-00L	Image Analysis and Computer Vision	W	6 credits	3V + 1U	L. Van Gool, O. Göksel, E. Konukoglu
Abstract	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.
Learning objective	Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
Content	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.
Lecture notes	Course material Script, computer demonstrations, exercises and problem solutions
Prerequisites / Notice	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 credits	2V + 2U	H. Schmid
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	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: https://people.ee.ethz.ch/~haschmid/asfwiki/ Some material is protected by password; students from ETHZ who are interested can write to haschmid@ethz.ch to ask for the password even if they do not attend the lecture.
Prerequisites / Notice	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 credits	4G	K. Heutschi
Abstract	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.
Learning objective	Introduction to acoustics. Understanding of basic acoustical mechanisms. Survey of the technical literature. Illustration of measurement techniques in the laboratory.
Content	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.
Lecture notes	yes
227-0778-00L	Hardware/Software Codesign	W	6 credits	2V + 2U	L. Thiele
Abstract	The course provides advanced knowledge in the design of complex computer systems, in particular embedded systems. Models and methods are discussed that are fundamental for systems that consist of software and hardware components.
Learning objective	The course provides advanced knowledge in the design of complex computer systems, in particular embedded systems. Models and methods are discussed that are fundamental for systems that consist of software and hardware components.
Content	The course covers the following subjects: (a) Models for describing hardware and software components (specification), (b) Hardware-Software Interfaces (instruction set, hardware and software components, reconfigurable computing, heterogeneous computer architectures, System-on-Chip), (c) Application specific instruction sets, code generation and retargetable compilation, (d) Performance analysis and estimation techniques, (e) System design (hardware-software partitioning and design space exploration).
Lecture notes	Material for exercises, copies of transparencies.
Literature	Peter Marwedel, Embedded System Design, Springer, ISBN-13 978-94-007-0256-1, 2011. Wayne Wolf. Computers as Components. Morgan Kaufmann, ISBN-13: 978-0123884367, 2012.
Prerequisites / Notice	Prerequisites for the course is a basic knowledge in the following areas: computer architecture, digital design, software design, embedded systems
252-0535-00L	Advanced Machine Learning	W	8 credits	3V + 2U + 2A	J. M. Buhmann
Abstract	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.
Learning objective	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.
Content	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
Lecture notes	No lecture notes, but slides will be made available on the course webpage.
Literature	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.
Prerequisites / Notice	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.
263-4640-00L	Network Security	W	6 credits	2V + 1U + 2A	A. Perrig, S. Frei
Abstract	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.
Learning objective	- 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.
Content	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.
Prerequisites / Notice	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 specialization courses below are a selection for students who wish to specialize in the area of "Computers and Networks", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html. The individual study plan is subject to the tutor's approval.
Core Courses 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
Number	Title	Type	ECTS	Hours	Lecturers
227-0102-00L	Discrete Event Systems	W	6 credits	4G	L. Thiele, L. Vanbever, R. Wattenhofer
Abstract	Introduction to discrete event systems. We start out by studying popular models of discrete event systems. In the second part of the course we analyze discrete event systems from an average-case and from a worst-case perspective. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems, Worst-Case Event Systems, Verification, Network Calculus.
Learning objective	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.
Content	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
Lecture notes	Available
Literature	[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	Communication Systems	W	6 credits	4G	A. Wittneben
Abstract	Information Theory, Signal Space Analysis, Baseband Transmission, Passband Transmission, Example und Channel, Data Link Layer, MAC, Example Layer 2, Layer 3, Internet
Learning objective	Introduction into the fundamentals of digital communication systems. Selected examples on the application of the fundamental principles in existing and upcoming communication systems
Content	Covered are the lower three layer of the OSI reference model: the physical, the data link, and the network layer. The basic terms of information theory are introduced. After this, we focus on the methods for the point to point communication, which may be addressed elegantly and coherently in the signal space. Methods for error detection and correction as well as protocols for the retransmission of perturbed data will be covered. Also the medium access for systems with shared medium will be discussed. Finally, algorithms for routing and flow control will be treated. The application of the basic methods will be extensively explained using existing and future wireless and wired systems.
Lecture notes	Lecture Slides
Literature	[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

Advanced Core Courses
Number	Title	Type	ECTS	Hours	Lecturers
227-0575-00L	Advanced Topics in Communication Networks (Autumn 2018)	W	6 credits	2V + 2U	L. Vanbever
Abstract	This class will introduce students to advanced, research-level topics in the area of communication networks, both theoretically and practically. Coverage will vary from semester to semester. Repetition for credit is possible, upon consent of the instructor. During the Fall Semester of 2018, the class will concentrate on network programmability and network data plane programming.
Learning objective	The goal of this lecture is to introduce students to the latest advances in the area of computer networks, both theoretically and practically. The course will be divided in two main blocks. The first block (~7 weeks) will interleave classical lectures with practical exercises and paper readings. The second block (~6 weeks) will consist of a practical project involving real network hardware and 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. During the Fall Semester 2018, the class will focus on programmable network data planes and will involve developing network applications on top of the the latest generation of programmable network hardware: Barefoot Network’s Tofino switch ASICs. By leveraging data-plane programmability, these applications can build deep traffic insights to, for instance, detect traffic anomalies (e.g. using Machine Learning), flexibly adapt forwarding behaviors (to improve performance), speed-up distributed applications (e.g. Map Reduce), or track network-wide health. More importantly, all this can now be done at line-rate, at forwarding speeds that can reach Terabits per second.
Content	Traditionally, computer networks have been composed of "closed" network devices (routers, switches, middleboxes) whose features, forwarding behaviors and configuration interfaces are exclusively defined on a per-vendor basis. Innovating in such networks is a slow-paced process (if at all possible): it often takes years for new features to make it to mainstream network equipments. Worse yet, managing the network is hard and prone to failures as operators have to painstakingly coordinate the behavior of heterogeneous network devices so that they, collectively, compute a compatible forwarding state. Actually, it has been shown that the majority of the network downtimes are caused by humans, not equipment failures. Network programmability and Software-Defined Networking (SDN) have recently emerged as a way to fundamentally change the way we build, innovate, and operate computer networks, both at the software and at the hardware level. Specifically, programmable networks now allow: (i) to adapt how traffic flows in the entire network through standardized software interfaces; and (ii) to reprogram the hardware pipeline of the network devices, i.e. the ASICs used to forward data packets. This year, the course will focus on reprogrammable network hardware/ASICs. It will involve hands-on experience on the world's fastest programmable switch to date (i.e. Barefoot Tofino switch ASIC). Among others, we'll cover the following topics: - The fundamentals and motivation behind network programmability; - The design and optimization of network control loops; - The use of advanced network data structures adapted for in-network execution; - The P4 programming language and associated runtime environment; - Hands-on examples of in-network applications solving hard problems in the area of data-centers, wide-area networks, and ISP networks. The course will be divided in two blocks of 7 weeks. The first block will consist in traditional lectures introducing the concepts along with practical exercises to get acquainted with programmable data planes. The second block will consist of a (mandatory) project to be done in groups of few students (~3 students). The project will involve developing a fully working network application and run it on top of real programmable network hardware. Students will be free to propose their own application or pick one from a list. At the end of the course, each group will present its application in front of the class.
Lecture notes	Lecture notes and material will be made available before each course on the course website.
Literature	Relevant references will be made available through the course website.
Prerequisites / Notice	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.
227-0778-00L	Hardware/Software Codesign	W	6 credits	2V + 2U	L. Thiele
Abstract	The course provides advanced knowledge in the design of complex computer systems, in particular embedded systems. Models and methods are discussed that are fundamental for systems that consist of software and hardware components.
Learning objective	The course provides advanced knowledge in the design of complex computer systems, in particular embedded systems. Models and methods are discussed that are fundamental for systems that consist of software and hardware components.
Content	The course covers the following subjects: (a) Models for describing hardware and software components (specification), (b) Hardware-Software Interfaces (instruction set, hardware and software components, reconfigurable computing, heterogeneous computer architectures, System-on-Chip), (c) Application specific instruction sets, code generation and retargetable compilation, (d) Performance analysis and estimation techniques, (e) System design (hardware-software partitioning and design space exploration).
Lecture notes	Material for exercises, copies of transparencies.
Literature	Peter Marwedel, Embedded System Design, Springer, ISBN-13 978-94-007-0256-1, 2011. Wayne Wolf. Computers as Components. Morgan Kaufmann, ISBN-13: 978-0123884367, 2012.
Prerequisites / Notice	Prerequisites for the course is a basic knowledge in the following areas: computer architecture, digital design, software design, embedded systems
227-0781-00L	Low-Power System Design	W	6 credits	2V + 2U	J. Beutel
Abstract	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.
Learning objective	Knowledge of the state-of-the-art in low power system design, understanding recent research results and their implication on industrial products.
Content	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.
Lecture notes	Exercise and lab materials, copies of lecture slides.
Literature	A detailed reading list will be made available in the lecture.
Prerequisites / Notice	Knowledge in embedded systems, system software, (wireless) networking, possibly integrated circuits, and hardware software codesign.
252-1414-00L	System Security	W	5 credits	2V + 2U	S. Capkun, A. Perrig
Abstract	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.
Learning objective	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.
Content	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	6 credits	2V + 1U + 2A	A. Perrig, S. Frei
Abstract	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.
Learning objective	- 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.
Content	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.
Prerequisites / Notice	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.

Specialization Courses These specialization 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 specialization courses during the Master's Programme.
Number	Title	Type	ECTS	Hours	Lecturers
227-0101-00L	Discrete-Time and Statistical Signal Processing	W	6 credits	4G	H.‑A. Loeliger
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	Lecture Notes
227-0103-00L	Control Systems	W	6 credits	2V + 2U	F. Dörfler
Abstract	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.
Learning objective	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.
Content	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.
Literature	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.
Prerequisites / Notice	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 credits	5G	F. K. Gürkaynak, L. Benini
Abstract	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.
Learning objective	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 VHDL or 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 VHDL and with industrial Electronic Design Automation (EDA) tools.
Content	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. - VLSI and 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. - VHDL and SystemVerilog compared. - VHDL (IEEE standard 1076) for simulation and synthesis. - A suitable nine-valued logic system (IEEE standard 1164). - 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 digital ICs with VHDL. They write testbenches for simulation purposes and synthesize gate-level netlists for VLSI chips and FPGAs. Commercial EDA software by leading vendors is being used throughout.
Lecture notes	Textbook and all further documents in English.
Literature	H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303.
Prerequisites / Notice	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: https://iis-students.ee.ethz.ch/lectures/vlsi-i/
227-0377-00L	Physics of Failure and Failure Analysis of Electronic Devices and Equipment	W	3 credits	2V	U. Sennhauser
Abstract	Failures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.
Learning objective	Introduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.
Content	Summary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis
Lecture notes	Comprehensive copy of transparencies
252-0437-00L	Distributed Algorithms	W	4 credits	3V	F. Mattern
Abstract	Models of distributed computations, time space diagrams, virtual time, logical clocks and causality, wave algorithms, parallel and distributed graph traversal, consistent snapshots, mutual exclusion, election and symmetry breaking, distributed termination detection, garbage collection in distributed systems, monitoring distributed systems, global predicates.
Learning objective	Become acquainted with models and algorithms for distributed systems.
Content	Verteilte Algorithmen sind Verfahren, die dadurch charakterisiert sind, dass mehrere autonome Prozesse gleichzeitig Teile eines gemeinsamen Problems in kooperativer Weise bearbeiten und der dabei erforderliche Informationsaustausch ausschliesslich über Nachrichten erfolgt. Derartige Algorithmen kommen im Rahmen verteilter Systeme zum Einsatz, bei denen kein gemeinsamer Speicher existiert und die Übertragungszeit von Nachrichten i.a. nicht vernachlässigt werden kann. Da dabei kein Prozess eine aktuelle konsistente Sicht des globalen Zustands besitzt, führt dies zu interessanten Problemen. Im einzelnen werden u.a. folgende Themen behandelt: Modelle verteilter Berechnungen; Raum-Zeit Diagramme; Virtuelle Zeit; Logische Uhren und Kausalität; Wellenalgorithmen; Verteilte und parallele Graphtraversierung; Berechnung konsistenter Schnappschüsse; Wechselseitiger Ausschluss; Election und Symmetriebrechung; Verteilte Terminierung; Garbage-Collection in verteilten Systemen; Beobachten verteilter Systeme; Berechnung globaler Prädikate.
Literature	- F. Mattern: Verteilte Basisalgorithmen, Springer-Verlag - G. Tel: Topics in Distributed Algorithms, Cambridge University Press - G. Tel: Introduction to Distributed Algorithms, Cambridge University Press, 2nd edition - A.D. Kshemkalyani, M. Singhal: Distributed Computing, Cambridge University Press - N. Lynch: Distributed Algorithms, Morgan Kaufmann Publ
227-0447-00L	Image Analysis and Computer Vision	W	6 credits	3V + 1U	L. Van Gool, O. Göksel, E. Konukoglu
Abstract	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.
Learning objective	Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
Content	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.
Lecture notes	Course material Script, computer demonstrations, exercises and problem solutions
Prerequisites / Notice	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	W	4 credits	3G	R. Wattenhofer
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	A script is available on the web page.
Literature	The script is self-contained, but links to additional material are available on the web page.
Prerequisites / Notice	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.
227-0627-00L	Applied Computer Architecture	W	6 credits	4G	A. Gunzinger
Abstract	This lecture gives an overview of the requirements and the architecture of parallel computer systems, performance, reliability and costs.
Learning objective	Understand the function, the design and the performance modeling of parallel computer systems.
Content	The lecture "Applied Computer Architecture" gives technical and corporate insights in the innovative Computer Systems/Architectures (CPU, GPU, FPGA, special processors) and their real implementations and applications. Often the designs have to deal with technical limits. Which computer architecture allows the control of the over 1000 magnets at the Swiss Light Source (SLS)? Which architecture is behind the alarm center of the Swiss Railway (SBB)? Which computer architectures are applied for driver assistance systems? Which computer architecture is hidden behind a professional digital audio mixing desk? How can data streams of about 30 TB/s, produced by a protone accelerator, be processed in real time? Can the weather forecast also be processed with GPUs? How can a good computer architecture be found? Which are the driving factors in succesful computer architecture design?
Lecture notes	Script and exercices sheets.
Prerequisites / Notice	Prerequisites: Basics of computer architecture.
151-0593-00L	Embedded Control Systems	W	4 credits	6G	J. S. Freudenberg, M. Schmid Daners
Abstract	This course provides a comprehensive overview of embedded control systems. The concepts introduced are implemented and verified on a microprocessor-controlled haptic device.
Learning objective	Familiarize students with main architectural principles and concepts of embedded control systems.
Content	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
Lecture notes	Lecture notes, lab instructions, supplemental material
Prerequisites / Notice	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: marischm@ethz.ch) After your reservation has been confirmed please register online at www.mystudies.ethz.ch. Detailed information can be found on the course website http://www.idsc.ethz.ch/education/lectures/embedded-control-systems.html
252-1411-00L	Security of Wireless Networks	W	5 credits	2V + 1U + 1A	S. Capkun
Abstract	Core Elements: Wireless communication channel, Wireless network architectures and protocols, Attacks on wireless networks, Protection techniques.
Learning objective	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.
Content	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.

Electronics and Photonics The core courses and specialization courses below are a selection for students who wish to specialize in the area of "Electronics and Photonics", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html. The individual study plan is subject to the tutor's approval.
Core Courses 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
Number	Title	Type	ECTS	Hours	Lecturers
227-0110-00L	Advanced Electromagnetic Waves	W	6 credits	2V + 2U	P. Leuchtmann
Abstract	This course provides advanced knowledge of electromagnetic waves in linear materials including negative index and other non classical materials.
Learning objective	The behavior of electromagnetic waves both in free space and in selected environments including stratified media, material interfaces and waveguides is understood. Material models in the time harmonic regime including negative index and plasmonic materials are clarified.
Content	Description of generic time harmonic electromagnetic fields; the role of the material in Maxwell's equations; energy transport and power loss mechanism; EM-waves in homogeneous space: ordinary and evanescent plane waves, cylindrical and spherical waves, "complex origin"-waves and beams; EM-waves in stratified media; generic guiding mechanism for EM waves; classical wave guides, dielectric wave guides.
Lecture notes	A script including animated wave representations as well as the view graphs are provided in electronic form.
Literature	See literature list in the script.
Prerequisites / Notice	The lecture is taught in German while both the script and the view graphs are in English.
227-0112-00L	High-Speed Signal Propagation	W	6 credits	2V + 2U	C. Bolognesi
Abstract	Understanding of high-speed signal propagation in microwave cables and integrated circuits and printed circuit boards. As clock frequencies rise in the GHz domain, there is a need grasp signal propagation to maintain good signal integrity in the face of symbol interference and cross-talk. The course is of high value to all interested in high-speed analog (RF, microwave) or digital systems.
Learning objective	Understanding of high-speed signal propagation in interconnects, microwave cables and integrated transmission lines such as microwave integrated circuits and/or printed circuit boards. As system clock frequencies continuously rise in the GHz domain, a need urgently develops to understand high-speed signal propagation in order to maintain good signal integrity in the face of phenomena such as inter-symbol interference (ISI) and cross-talk. Concepts such as Scattering parameters (or S-parameters) are key to the characterization of networks over wide bandwidths. At high frequencies, all structures effectively become "transmission lines." Unless care is taken, it is highly probable that one ends-up with a bad transmission line that causes the designed system to malfunction. Filters will also be considered because it turns out that some of the problems associated by lossy transmission channels (lines, cables, etc) can be corrected by adequate filtering in a process called "equalization."
Content	Transmission line equations of the lossless and lossy TEM-transmission line. Introduction of current and voltage waves. Representation of reflections in the time and frequency domain. Application of the Smith chart. Behavior of low-loss transmission lines. Attenuation and impulse distortion due to skin effect. Transmission line equivalent circuits. Group delay and signal dispersion. Coupled transmission lines. Scattering parameters. Butterworth-, Chebychev- and Bessel filter approximations: filter synthesis from low-pass filter prototypes.
Lecture notes	Script: Leitungen und Filter (In German).
Prerequisites / Notice	Exercises will be held in English.
227-0116-00L	VLSI I: From Architectures to VLSI Circuits and FPGAs	W	6 credits	5G	F. K. Gürkaynak, L. Benini
Abstract	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.
Learning objective	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 VHDL or 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 VHDL and with industrial Electronic Design Automation (EDA) tools.
Content	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. - VLSI and 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. - VHDL and SystemVerilog compared. - VHDL (IEEE standard 1076) for simulation and synthesis. - A suitable nine-valued logic system (IEEE standard 1164). - 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 digital ICs with VHDL. They write testbenches for simulation purposes and synthesize gate-level netlists for VLSI chips and FPGAs. Commercial EDA software by leading vendors is being used throughout.
Lecture notes	Textbook and all further documents in English.
Literature	H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303.
Prerequisites / Notice	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: https://iis-students.ee.ethz.ch/lectures/vlsi-i/
227-0145-00L	Solid State Electronics and Optics	W	6 credits	4G	V. Wood, R. Zahn
Abstract	"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.
Learning objective	Understand the fundamental physics behind the mechanical, thermal, electric, magnetic, and optical properties of materials.
Prerequisites / Notice	Recommended background: Undergraduate physics, mathematics, semiconductor devices
227-0166-00L	Analog Integrated Circuits	W	6 credits	2V + 2U	Q. Huang
Abstract	This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies.
Learning objective	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.
Content	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; A/D and D/A converters; Introduction to switched capacitor circuits. 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 measurments.
Lecture notes	Handouts of presented slides. No script but an accompanying textbook is recommended.
Literature	Gray, Hurst, Lewis, Meyer, "Analysis and Design of Analog Integrated Circuits", 5th Ed. Wiley, 2010.

Advanced Core Courses
Number	Title	Type	ECTS	Hours	Lecturers
227-0148-00L	VLSI III: Test and Fabrication of VLSI Circuits	W	6 credits	4G	F. K. Gürkaynak, L. Benini
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	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 http://link.springer.com/book/10.1007%2Fb117406
Prerequisites / Notice	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: https://iis-students.ee.ethz.ch/lectures/vlsi-iii/
227-0301-00L	Optical Communication Fundamentals	W	6 credits	2V + 1U + 1P	J. Leuthold
Abstract	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.
Learning objective	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.
Content	* 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.
Lecture notes	Lecture notes are handed out.
Literature	Govind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010
Prerequisites / Notice	Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics.
227-0663-00L	Nano-Optics	W	6 credits	2V + 2U	M. Frimmer
Abstract	Nano-Optics is the study of optical phenomena and techniques on the nanometer scale. It is an emerging field of study motivated by the rapid advance of nanoscience and technology. It embraces topics such as plasmonics, optical antennas, optical trapping and manipulation, and high-resolution imaging and spectroscopy.
Learning objective	Understanding concepts of light localization and light-matter interactions on the nanoscale.
Content	Starting with an angular spectrum representation of optical fields the role of inhomogeneous evanescent fields is discussed. Among the topics are: theory of strongly focused light, point spread functions, resolution criteria, confocal microscopy, and near-field optical microscopy. Further topics are: optical interactions between nanoparticles, atomic decay rates in inhomogeneous environments, single molecule spectroscopy, light forces and optical trapping, photonic bandgap materials, and theoretical methods in nano-optics.
Prerequisites / Notice	- Electrodynamics (or equivalent) - Physics I+II

Specialization Courses These specialization 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 specialization courses during the Master's Programme.
Number	Title	Type	ECTS	Hours	Lecturers
227-0121-00L	Communication Systems	W	6 credits	4G	A. Wittneben
Abstract	Information Theory, Signal Space Analysis, Baseband Transmission, Passband Transmission, Example und Channel, Data Link Layer, MAC, Example Layer 2, Layer 3, Internet
Learning objective	Introduction into the fundamentals of digital communication systems. Selected examples on the application of the fundamental principles in existing and upcoming communication systems
Content	Covered are the lower three layer of the OSI reference model: the physical, the data link, and the network layer. The basic terms of information theory are introduced. After this, we focus on the methods for the point to point communication, which may be addressed elegantly and coherently in the signal space. Methods for error detection and correction as well as protocols for the retransmission of perturbed data will be covered. Also the medium access for systems with shared medium will be discussed. Finally, algorithms for routing and flow control will be treated. The application of the basic methods will be extensively explained using existing and future wireless and wired systems.
Lecture notes	Lecture Slides
Literature	[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
227-0157-00L	Semiconductor Devices: Physical Bases and Simulation	W	4 credits	3G	A. Schenk
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	The script (in book style) can be downloaded from: https://iis-students.ee.ethz.ch/lectures/
Literature	The script (in book style) is sufficient. Further reading will be recommended in the lecture.
Prerequisites / Notice	Qualifications: Physics I+II, Semiconductor devices (4. semester).
227-0166-00L	Analog Integrated Circuits	W	6 credits	2V + 2U	Q. Huang
Abstract	This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies.
Learning objective	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.
Content	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; A/D and D/A converters; Introduction to switched capacitor circuits. 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 measurments.
Lecture notes	Handouts of presented slides. No script but an accompanying textbook is recommended.
Literature	Gray, Hurst, Lewis, Meyer, "Analysis and Design of Analog Integrated Circuits", 5th Ed. Wiley, 2010.
227-0377-00L	Physics of Failure and Failure Analysis of Electronic Devices and Equipment	W	3 credits	2V	U. Sennhauser
Abstract	Failures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.
Learning objective	Introduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.
Content	Summary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis
Lecture notes	Comprehensive copy of transparencies
227-0468-00L	Analog Signal Processing and Filtering Suitable for Master Students as well as Doctoral Students.	W	6 credits	2V + 2U	H. Schmid
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	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: https://people.ee.ethz.ch/~haschmid/asfwiki/ Some material is protected by password; students from ETHZ who are interested can write to haschmid@ethz.ch to ask for the password even if they do not attend the lecture.
Prerequisites / Notice	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.
227-0617-00L	Solar Cells	W	4 credits	3G	A. N. Tiwari, S. Bücheler, Y. Romanyuk
Abstract	Physics, technology, characteristics and applications of photovoltaic solar cells.
Learning objective	Introduction to solar radiation, physics, technology, characteristics and applications of photovoltaic solar cells and systems.
Content	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.
Lecture notes	Lecture reprints (in english).
Prerequisites / Notice	Prerequisites: Basic knowledge of semiconductor properties.
227-0618-00L	Modeling, Characterization and Reliability of Power Semiconductors	W	6 credits	4G	M. P. M. Ciappa
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	Handouts to the lecture (approx. 250 pp.)
Literature	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"
227-0620-00L	Characterization of the Electronic Properties of Materials for Semiconductor Devices Does not take place this semester.	W	4 credits	3G
Abstract	This lecture provides theoretical and experimental knowledge on the techniques for the characterization of the main electronic properties of semiconductors and thin film materials used in microelectronics, with special focus on silicon.
Learning objective	The characterization of the electronic properties of semiconductor and related materials is fundamental to manufacture integrated devices, which fulfill the required specifications. By this lecture, the students shall get acquainted with the main electrical characterization techniques of the electronic properties of semiconductors and thin film materials used in microelectronics, as well as with their physical principles. This knowledge is intended to provide the future engineer with the theoretical background and experimental tools for process control in semiconductor manufacturing, parameter extraction in device simulation, and design of dependable devices.
Content	This lecture consists of a theoretical part (80%) and of laboratory exercises and demonstrations (20%). In the first section of the lecture, methods and procedures are presented for the experimental characterization of relevant electronic parameters in the bare semiconductor (mainly silicon), like resistivity, carrier and doping density, contact resistance, and Schottky barriers, defect density, carrier lifetime, mobility. The second section deals with techniques involving basic structures and devices (contact chains, MIS capacitors, diodes, gated diodes, BJT, MOSFET) for the characterization of atomic transport, mechanical stress, dielectric thickness, impact ionization, channel mobility, instabilities, defect formation at interfaces and in thin film dielectrics, carrier transport and trapping in thin film dielectrics, quasi-static and dynamic device characteristics. The list of the covered methods includes among others probing, Kelvin measurements, VanderPauw technique, Hall spectroscopy, SIMS, Raman spectroscopy, spreading resistance, scanning probe techniques, static/high-speed I-V, static/high-frequency C-V, open circuit voltage decay, carrier recombination techniques, Zerbst techniques, deep level transient spectroscopy, split C-V, charge pumping, and inverse modeling techniques using TCAD. All methods are presented in conjunction with the proper test structures. During the laboratory activities, a selection of the experimental techniques discussed in the lecture are demonstrated on the base of realistic examples.
Lecture notes	Handouts to the lecture (approx. 200 pp.)
Literature	Schroeder D.K, Semiconductor Material and Device Characterization, Wiley Ed. F. Balestra Ed., Nanoscale CMOS : innovative materials, modeling and characterization, ISTE
227-0627-00L	Applied Computer Architecture	W	6 credits	4G	A. Gunzinger
Abstract	This lecture gives an overview of the requirements and the architecture of parallel computer systems, performance, reliability and costs.
Learning objective	Understand the function, the design and the performance modeling of parallel computer systems.
Content	The lecture "Applied Computer Architecture" gives technical and corporate insights in the innovative Computer Systems/Architectures (CPU, GPU, FPGA, special processors) and their real implementations and applications. Often the designs have to deal with technical limits. Which computer architecture allows the control of the over 1000 magnets at the Swiss Light Source (SLS)? Which architecture is behind the alarm center of the Swiss Railway (SBB)? Which computer architectures are applied for driver assistance systems? Which computer architecture is hidden behind a professional digital audio mixing desk? How can data streams of about 30 TB/s, produced by a protone accelerator, be processed in real time? Can the weather forecast also be processed with GPUs? How can a good computer architecture be found? Which are the driving factors in succesful computer architecture design?
Lecture notes	Script and exercices sheets.
Prerequisites / Notice	Prerequisites: Basics of computer architecture.
227-0659-00L	Integrated Systems Seminar	W	1 credit	1S	A. Schenk
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	Presentation material
227-0665-00L	Battery Integration Engineering Number of participants limited to 30. Enrolment possible until September 28, 2018. 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) Priority given to Electrical and Mechanical Engineering students	W	3 credits	2V + 1U	T. J. Patey
Abstract	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.
Learning objective	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 material science for novel battery technologies reported in literature, and understand the opportunities and challenges these materials could have. - 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.
Content	- 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 introduction to electrochemistry & batteries. - Li-ion batteries & next generation batteries. - Sustainability and recycling of batteries.
Prerequisites / Notice	Limited to 30 Students Priority given to Electrical and Mechanical Engineering students Recommended to attended 227-0664-00L
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.	W	6 credits	2V + 3U	T. Delbrück, G. Indiveri, S.‑C. Liu
Abstract	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.
Learning objective	Understanding of the characteristics of neuromorphic circuit elements.
Content	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.
Literature	S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications.
Prerequisites / Notice	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 credits	4G	J. Smajic
Abstract	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.
Learning objective	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.
Content	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 credits	3G	P. Korba, S. Stoeter
Abstract	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. It’s a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems.
Learning objective	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. This course is a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems.
Content	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.
Lecture notes	available.
151-0605-00L	Nanosystems	W	4 credits	4G	A. Stemmer
Abstract	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.
Learning objective	Familiarize students with basic science and engineering principles governing the nano domain.
Content	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.
Literature	- 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
Prerequisites / Notice	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 credits	3P	C. Hierold, S. Blunier, M. Haluska
Abstract	Practical course: Students are introduced to the process steps required for the fabrication of MEMS (Micro Electro Mechanical System) and carry out the fabrication and testing steps in the clean rooms by themselves. Additionally, they learn the requirements for working in clean rooms. Processing and characterization will be documented and analyzed in a final report. Limited access
Learning objective	Students learn the individual process steps that are required to make a MEMS (Micro Electro Mechanical System). Students carry out the process steps themselves in laboratories and clean rooms. Furthermore, participants become familiar with the special requirements (cleanliness, safety, operation of equipment and handling hazardous chemicals) of working in the clean rooms and laboratories. The entire production, processing, and characterization of the MEMS is documented and evaluated in a final report.
Content	With guidance from a tutor, the individual silicon microsystem process steps that are required for the fabrication of an accelerometer are carried out: - Photolithography, dry etching, wet etching, sacrificial layer etching, various cleaning procedures - Packaging and electrical connection of a MEMS device - Testing and characterization of the MEMS device - Written documentation and evaluation of the entire production, processing and characterization
Lecture notes	A document containing theory, background and practical course content is distributed at the first meeting of the course.
Literature	The document provides sufficient information for the participants to successfully participate in the course.
Prerequisites / Notice	Participating students are required to attend all scheduled lectures and meetings of the course. Participating students are required to provide proof that they have personal accident insurance prior to the start of the laboratory portion of the course. This master's level course is limited to 15 students per semester for safety and efficiency reasons. If there are more than 15 students registered, 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, Park, 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 credits	2V + 1U	D. J. Norris
Abstract	This course provides fundamental knowledge of surface plasmon polaritons and discusses their applications in plasmonics.
Learning objective	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.
Content	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
Lecture notes	Class notes and handouts
Literature	S. A. Maier, Plasmonics: Fundamentals and Applications, 2007, Springer
Prerequisites / Notice	Physics I, Physics II
327-2132-00L	Multifunctional Ferroic Materials: Growth, Characterisation, Simulation	W	2 credits	2G	M. Trassin, M. Fiebig
Abstract	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.
Learning objective	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.
Content	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 credits	2G	S. Brusoni
Abstract	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.
Learning objective	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
Content	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.
Lecture notes	Slides will be available on the Moodle page
Literature	Readings will be available on the Moodle page
Prerequisites / Notice	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 specialization courses below are a selection for students who wish to specialize in the area of "Energy and Power Electronics", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html. The individual study plan is subject to the tutor's approval.
Core Courses 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
Number	Title	Type	ECTS	Hours	Lecturers
227-0113-00L	Power Electronics	W	6 credits	4G	J. W. Kolar
Abstract	Fields of application of power electronic systems. Principle of operation of basic pulse-width modulated and line-commutated power electronic converters, analysis of the operating behavior and of the control oriented behavior, converter design. Reduction of effects of line-commutated rectifiers on the mains, electromagnetic compatibility.
Learning objective	Fields of application of power electronic systems. Principle of operation of basic pulse-width modulated and line-commutated power electronic converters, analysis of the operating behavior and of the controloriented behavior, converter design. Reduction of effects of line-commutated rectifiers on the mains, electromagnetic compatibility.
Content	Basic structure of power electronic systems, applications. DC/DC converters, high frequency isolation, control oriented modeling / state-space averaging and PWM switch model. Power semiconductors, non-idealities, cooling. Magnetic components, skin and proximity effect, design. Electromagnetic compatibility. Single-phase diode bridge with capacitive smoothing, effects on the mains, power factor correction / PWM rectifier. Pulse-width modulated single-phase and three-phase full bridge converter with impressed DC voltage, modulation schemes, space vector calculus. Line-commutated single-phase full bridge with impressed output current, commutation, phase-control, inverter operation, commutation failure. Line-commutated three-phase full bridge converter, impressed output voltage, impressed output current / phase-control. Parallel connection of three-phase line-commutated thyristor circuits, inter-phase transformer. Anti-parallel connection of three-phase line-commutated thyristor bridge circuits, four-quadrant DC motor drive. Load-resonant converters, state plane analysis.
Lecture notes	Lecture notes and associated exercises including correct answers, simulation program for interactive self-learning including visualization/animation features.
Prerequisites / Notice	Prerequisites: Basic knowledge of electric circuit analysis and signal theory.
227-0122-00L	Introduction to Electric Power Transmission: System & Technology	W	6 credits	4G	C. Franck, G. Hug
Abstract	Introduction to theory and technology of electric power transmission systems.
Learning objective	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 lines, explain the technology of overhead power lines, calculate stationary power flows, current and voltage transients and other basic parameters in simple power systems.
Content	Structure of electric power systems, transformer and power line models, analysis of and power flow calculation in basic systems, symmetrical and unsymmetrical three-phase systems, transient current and voltage processes, technology and principle of electric power systems.
Lecture notes	Lecture script in English, exercises and sample solutions, translation of important vocabulary: english-german.

Advanced Core Courses
Number	Title	Type	ECTS	Hours	Lecturers
227-0117-00L	High Voltage Engineering II: Insulation Technology The lectures High Voltage Engineering I: Experimental Techniques (227-0117-10L) and High Voltage Engineering II: Insulation Technology (227-0117-00L) can be taken independently from one another.	W	6 credits	4G	C. Franck, U. Straumann
Abstract	Understanding of the fundamental phenomena and principles connected with the occurrence of extensive electric field strengths. This knowledge is applied to the dimensioning of equipment of electric power systems. Methods of computer-modeling in use today are presented and applied within the framework of the exercises.
Learning objective	The students know the fundamental phenomena and principles connected with the occurrence of extensive electric field strengths. They comprehend 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 name possibilities for improvement. Further they know the different insulation systems and their dimensioning in practice.
Content	- 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 theory of gases - 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
Lecture notes	Handouts
Literature	A. Küchler, Hochspannungstechnik, Springer Berlin, 4. Auflage, 2017 (ISBN: 978-3-662-54699-4)
227-0247-00L	Power Electronic Systems I	W	6 credits	4G	J. W. Kolar
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	Lecture notes and associated exercises including correct answers, simulation program for interactive self-learning including visualization/animation features.
Prerequisites / Notice	Prerequisites: Introductory course on power electronics.
227-0526-00L	Power System Analysis	W	6 credits	4G	G. Hug
Abstract	The goal of this course is understanding the stationary and dynamic problems in electrical power systems. The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power networks.
Learning objective	The goal of this course is understanding the stationary and dynamic problems in electrical power systems and the application of analysis tools in steady and dynamic states.
Content	The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power grids. Approaches such as the Newton-Raphson algorithm applied to power flow equations, superposition technique for short-circuit analysis, equal area criterion and nose curve analysis are discussed as well as power flow computation techniques for distribution grids.
Lecture notes	Lecture notes.

Specialization Courses These specialization 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 specialization courses during the Master's Programme.
Number	Title	Type	ECTS	Hours	Lecturers
227-0101-00L	Discrete-Time and Statistical Signal Processing	W	6 credits	4G	H.‑A. Loeliger
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	Lecture Notes
227-0103-00L	Control Systems	W	6 credits	2V + 2U	F. Dörfler
Abstract	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.
Learning objective	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.
Content	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.
Literature	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.
Prerequisites / Notice	Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation.
227-0121-00L	Communication Systems	W	6 credits	4G	A. Wittneben
Abstract	Information Theory, Signal Space Analysis, Baseband Transmission, Passband Transmission, Example und Channel, Data Link Layer, MAC, Example Layer 2, Layer 3, Internet
Learning objective	Introduction into the fundamentals of digital communication systems. Selected examples on the application of the fundamental principles in existing and upcoming communication systems
Content	Covered are the lower three layer of the OSI reference model: the physical, the data link, and the network layer. The basic terms of information theory are introduced. After this, we focus on the methods for the point to point communication, which may be addressed elegantly and coherently in the signal space. Methods for error detection and correction as well as protocols for the retransmission of perturbed data will be covered. Also the medium access for systems with shared medium will be discussed. Finally, algorithms for routing and flow control will be treated. The application of the basic methods will be extensively explained using existing and future wireless and wired systems.
Lecture notes	Lecture Slides
Literature	[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 credits	5G	M. Kamgarpour
Abstract	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.
Learning objective	Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems.
Content	- 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.
Lecture notes	Available on the course Moodle platform.
Prerequisites / Notice	Sufficient mathematical maturity with special focus on logic, linear algebra, analysis.
227-0517-00L	Electrical Drive Systems II	W	6 credits	4G	P. Steimer, G. Scheuer, C. A. Stulz
Abstract	In the course "Drive System II" the power semiconductors are repeated. The creation of converters based on the combination of switches/cells and based topologies is explained. Another main focus is on the 3-level inverter with its switching and transfer functions. Further topics are the control of the synchronous machine, of line-side converters and issues with converter-fed machines
Learning objective	The students establish a deeper understanding in regards of the design of the main components of an electrical drive system. They establish knowledge on the most important interaction with the grid and the machine and their related high dynamic control.
Content	Converter topologies (switch or cell based), multi-pulse diode rectifiers, system aspects of transfomer and electrical machines, 3-level inverter with its switching and transfer functions, grid side harmonics, modeling and control of synchronous machines (including permanent magnet machines), control of line-side converters, reflection effects with power cables, winding isolation and bearing stress. Field trip to ABB Semionductors.
Lecture notes	Skript is sold at the beginning of the lectures or can be downloaded from Ilias
Literature	Skript of lecture; References in skript to related technical publications and books
Prerequisites / Notice	Prerequisites: Electrical Drive Systems I (recommended), Basics in electrical engineering, power electronics, automation and mechatronics
227-0523-00L	Railway Systems I	W	6 credits	4G	M. Meyer
Abstract	Basic characteristis of railway vehicles and their interfaces with the railway infrastructure: - Transportation tasks and vehicle types - Running dynamics - Mechanical part of rail vehicles - Brakes - Traction chain and auxiliary supply - Railway power supply - Signalling systems - Traffic control and maintenance
Learning objective	- Overview of the technical characteristics of railway systems - Know-how about the design and construction principles of rail vehicles - Interrelationship between different fields of engineering sciences (mechanics, electro and information technology, transport systems) - Understanding tasks and opportunities of engineers working in an environment which has strong economical and political boundaries - Insight into the activities of the railway vehicle industry and railway operators in Switzerland - Motivation of young engineers to start a career in the railway industry or with railway operators
Content	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
Lecture notes	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.
Prerequisites / Notice	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 credits	4G	F. Krismer
Abstract	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.
Learning objective	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, how to determine switching losses, calculation of high frequency losses, EMI filter design and realization, thermal considerations.
Content	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.
Lecture notes	Lecture notes and complementary exercises including correct answers.
Prerequisites / Notice	Prerequisites: Introductory course on power electronics.
227-0618-00L	Modeling, Characterization and Reliability of Power Semiconductors	W	6 credits	4G	M. P. M. Ciappa
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	Handouts to the lecture (approx. 250 pp.)
Literature	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 credits	3G	M. Mercangöz, A. Horch
Abstract	Introduction to process automation and its application in process industry and power generation
Learning objective	Knowledge of process automation and its application in industry and power generation
Content	Introduction to process automation: system architecture, data handling, communication (fieldbusses), process visualization, engineering, etc. Analysis and design of open loop control problems: discrete automata, decision tables, petri-nets, drive control and object oriented function group automation philosophy, RT-UML. Engineering: Application programming in IEC61131-3 (function blocks, sequence control, structured text); process visualization and operation; engineering integration from sensor, cabling, topology design, function, visualization, diagnosis, to documentation; Industry standards (e.g. OPC, Profibus); Ergonomic design, safety (IEC61508) and availability, supervision and diagnosis. Practical examples from process industry, power generation and newspaper production.
Lecture notes	Slides will be available as .PDF documents, see "Learning materials" (for registered students only)
Prerequisites / Notice	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.
227-0731-00L	Power Market I - Portfolio and Risk Management	W	6 credits	4G	D. Reichelt, G. A. Koeppel
Abstract	Portfolio and risk management in the electrical power business, Pan-European power market and trading, futures and forward contracts, hedging, options and derivatives, performance indicators for the risk management, modelling of physical assets, cross-border trading, ancillary services, balancing power market, Swiss market model
Learning objective	Knowlege on the worldwide liberalisation of electricity markets, pan-european power trading and the role of power exchanges. Understand financial products (derivatives) based on power. Management of a portfolio containing physical production, contracts and derivatives. Evaluate trading and hedging strategies. Apply methods and tools of risk management.
Content	1. Pan-European power market and trading 1.1. Power trading 1.2. Development of the European power markets 1.3. Energy economics 1.4. Spot and OTC trading 1.5. European energy exchange EEX 2. Market model 2.1. Market place and organisation 2.2. Balance groups / balancing energy 2.3. Ancillary services 2.4. Market for ancillary services 2.5. Cross-border trading 2.6. Capacity auctions 3. Portfolio and Risk management 3.1. Portfolio management 1 (introduction) 3.2. Forward and futures contracts 3.3. Risk management 1 (m2m, VaR, hpfc, volatility, cVaR) 3.4. Risk management 2 (PaR) 3.5. Contract valuation (HPFC) 3.6. Portfolio management 2 2.8. Risk Management 3 (enterprise wide) 4. Energy & Finance I 4.1. Options 1 – basics 4.2. Options 2 – hedging with options 4.3. Introduction to derivatives (swaps, cap, floor, collar) 4.4. Financial modelling of physical assets 4.5. Trading and hydro power 4.6. Incentive regulation
Lecture notes	Handouts of the lecture
Prerequisites / Notice	1 excursion per semester, 2 case studies, guest speakers for specific topics. Course Moodle: https://moodle-app2.let.ethz.ch/course/view.php?id=4398
227-0759-00L	International Business Management for Engineers	W	3 credits	2V	W. Hofbauer
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	A script is provided for this lecture.
Prerequisites / Notice	The lecture will be held in three blocks each of them on a Saturday. 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 specialization courses below are a selection for students who wish to specialize in the area of "Systems and Control", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html. The individual study plan is subject to the tutor's approval.
Core Courses 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
Number	Title	Type	ECTS	Hours	Lecturers
227-0103-00L	Control Systems	W	6 credits	2V + 2U	F. Dörfler
Abstract	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.
Learning objective	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.
Content	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.
Literature	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.
Prerequisites / Notice	Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation.

Advanced Core Courses
Number	Title	Type	ECTS	Hours	Lecturers
227-0225-00L	Linear System Theory	W	6 credits	5G	M. Kamgarpour
Abstract	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.
Learning objective	Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems.
Content	- 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.
Lecture notes	Available on the course Moodle platform.
Prerequisites / Notice	Sufficient mathematical maturity with special focus on logic, linear algebra, analysis.
227-0697-00L	Industrial Process Control	W	4 credits	3G	M. Mercangöz, A. Horch
Abstract	Introduction to process automation and its application in process industry and power generation
Learning objective	Knowledge of process automation and its application in industry and power generation
Content	Introduction to process automation: system architecture, data handling, communication (fieldbusses), process visualization, engineering, etc. Analysis and design of open loop control problems: discrete automata, decision tables, petri-nets, drive control and object oriented function group automation philosophy, RT-UML. Engineering: Application programming in IEC61131-3 (function blocks, sequence control, structured text); process visualization and operation; engineering integration from sensor, cabling, topology design, function, visualization, diagnosis, to documentation; Industry standards (e.g. OPC, Profibus); Ergonomic design, safety (IEC61508) and availability, supervision and diagnosis. Practical examples from process industry, power generation and newspaper production.
Lecture notes	Slides will be available as .PDF documents, see "Learning materials" (for registered students only)
Prerequisites / Notice	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.
151-0563-01L	Dynamic Programming and Optimal Control	W	4 credits	2V + 1U	R. D'Andrea
Abstract	Introduction to Dynamic Programming and Optimal Control.
Learning objective	Covers the fundamental concepts of Dynamic Programming & Optimal Control.
Content	Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.
Literature	Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.
Prerequisites / Notice	Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.

Specialization Courses These specialization 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 specialization courses during the Master's Programme.
Number	Title	Type	ECTS	Hours	Lecturers
227-0102-00L	Discrete Event Systems	W	6 credits	4G	L. Thiele, L. Vanbever, R. Wattenhofer
Abstract	Introduction to discrete event systems. We start out by studying popular models of discrete event systems. In the second part of the course we analyze discrete event systems from an average-case and from a worst-case perspective. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems, Worst-Case Event Systems, Verification, Network Calculus.
Learning objective	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.
Content	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
Lecture notes	Available
Literature	[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 credits	3V + 1U	L. Van Gool, O. Göksel, E. Konukoglu
Abstract	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.
Learning objective	Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
Content	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.
Lecture notes	Course material Script, computer demonstrations, exercises and problem solutions
Prerequisites / Notice	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 credits	4G	G. Hug
Abstract	The goal of this course is understanding the stationary and dynamic problems in electrical power systems. The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power networks.
Learning objective	The goal of this course is understanding the stationary and dynamic problems in electrical power systems and the application of analysis tools in steady and dynamic states.
Content	The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power grids. Approaches such as the Newton-Raphson algorithm applied to power flow equations, superposition technique for short-circuit analysis, equal area criterion and nose curve analysis are discussed as well as power flow computation techniques for distribution grids.
Lecture notes	Lecture notes.
227-0689-00L	System Identification	W	4 credits	2V + 1U	R. Smith
Abstract	Theory and techniques for the identification of dynamic models from experimentally obtained system input-output data.
Learning objective	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.
Content	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.
Literature	"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.
Prerequisites / Notice	Control systems (227-0216-00L) or equivalent.
227-0945-00L	Cell and Molecular Biology for Engineers I This course is part I of a two-semester course.	W	3 credits	2G	C. Frei
Abstract	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.
Learning objective	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.
Content	Lectures will include the following topics (part I and II): DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development 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.
Lecture notes	Scripts of all lectures will be available.
Literature	"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 credits	2V + 2U	F. Kogelbauer
Abstract	Basic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.
Learning objective	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.
Content	(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
Lecture notes	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.
Prerequisites / Notice	- 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 credits	2V + 2U	G. Ducard
Abstract	Introduction to system modeling for control. Generic modeling approaches based on first principles, Lagrangian formalism, energy approaches and experimental data. Model parametrization and parameter estimation. Basic analysis of linear and nonlinear systems.
Learning objective	Learn how to mathematically describe a physical system or a process in the form of a model usable for analysis and control purposes.
Content	This class introduces generic system-modeling approaches for control-oriented models based on first principles and experimental data. The class will span numerous examples related to mechatronic, thermodynamic, chemistry, fluid dynamic, energy, and process engineering systems. Model scaling, linearization, order reduction, and balancing. Parameter estimation with least-squares methods. Various case studies: loud-speaker, turbines, water-propelled rocket, geostationary satellites, etc. The exercises address practical examples.
Lecture notes	The handouts in English will be sold in the first lecture.
Literature	A list of references is included in the handouts.
151-0601-00L	Theory of Robotics and Mechatronics	W	4 credits	3G	P. Korba, S. Stoeter
Abstract	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. It’s a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems.
Learning objective	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. This course is a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems.
Content	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.
Lecture notes	available.
151-0563-01L	Dynamic Programming and Optimal Control	W	4 credits	2V + 1U	R. D'Andrea
Abstract	Introduction to Dynamic Programming and Optimal Control.
Learning objective	Covers the fundamental concepts of Dynamic Programming & Optimal Control.
Content	Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.
Literature	Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.
Prerequisites / Notice	Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.
376-1219-00L	Rehabilitation Engineering II: Rehabilitation of Sensory and Vegetative Functions	W	3 credits	2V	R. Riener, R. Gassert, O. Lambercy
Abstract	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.
Learning objective	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.
Content	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
Literature	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: http://www.cim.mcgill.ca/~vleves/docs/VL-CIM-TR-05.08.pdf 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. http://www.seeingwithsound.com. VideoTact, ForeThought Development, LLC. http://my.execpc.com/?dwysocki/videotac.html
Prerequisites / Notice	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 credits	2V + 1U	D. Adjiashvili
Abstract	Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering.
Learning objective	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.
Content	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.
Literature	Information about relevant literature will be given in the lecture.
Prerequisites / Notice	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 credits	4V + 2U	R. Weismantel
Abstract	Mathematical treatment of diverse optimization techniques.
Learning objective	Advanced optimization theory and algorithms.
Content	1) Linear optimization: The geometry of linear programming, the simplex method for solving linear programming problems, Farkas' Lemma and infeasibility certificates, duality theory of linear programming. 2) Nonlinear optimization: Lagrange relaxation techniques, Newton method and gradient schemes for convex optimization. 3) Integer optimization: Ties between linear and integer optimization, total unimodularity, complexity theory, cutting plane theory. 4) Combinatorial optimization: Network flow problems, structural results and algorithms for matroids, matchings, and, more generally, independence systems.
Literature	1) D. Bertsimas & R. Weismantel, "Optimization over Integers". Dynamic Ideas, 2005. 2) A. Schrijver, "Theory of Linear and Integer Programming". John Wiley, 1986. 3) D. Bertsimas & J.N. Tsitsiklis, "Introduction to Linear Optimization". Athena Scientific, 1997. 4) Y. Nesterov, "Introductory Lectures on Convex Optimization: a Basic Course". Kluwer Academic Publishers, 2003. 5) C.H. Papadimitriou, "Combinatorial Optimization". Prentice-Hall Inc., 1982.
Prerequisites / Notice	Linear algebra.
636-0007-00L	Computational Systems Biology	W	6 credits	3V + 2U	J. Stelling
Abstract	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).
Learning objective	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.
Content	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.
Lecture notes	http://www.csb.ethz.ch/education/lectures.html
Literature	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 specialization courses below are a selection for students who wish to specialize in the area of "Signal Processing and Machine Learning ", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html. The individual study plan is subject to the tutor's approval.
Core Courses 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
Number	Title	Type	ECTS	Hours	Lecturers
227-0101-00L	Discrete-Time and Statistical Signal Processing	W	6 credits	4G	H.‑A. Loeliger
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	Lecture Notes

Advanced Core Courses
Number	Title	Type	ECTS	Hours	Lecturers
227-0427-00L	Signal Analysis, Models, and Machine Learning	W	6 credits	4G	H.‑A. Loeliger
Abstract	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.
Learning objective	The course is an introduction to some basic topics in signal processing and machine learning.
Content	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.
Lecture notes	Lecture notes.
Prerequisites / Notice	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 credits	3V + 1U	L. Van Gool, O. Göksel, E. Konukoglu
Abstract	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.
Learning objective	Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
Content	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.
Lecture notes	Course material Script, computer demonstrations, exercises and problem solutions
Prerequisites / Notice	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	8 credits	3V + 2U + 2A	J. M. Buhmann
Abstract	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.
Learning objective	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.
Content	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
Lecture notes	No lecture notes, but slides will be made available on the course webpage.
Literature	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.
Prerequisites / Notice	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.
263-3210-00L	Deep Learning Number of participants limited to 300.	W	4 credits	2V + 1U	F. Perez Cruz
Abstract	Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
Learning objective	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.
Prerequisites / Notice	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 conditions: 1) The number of participants is limited to 300 students (MSc and PhDs). 2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below: Machine Learning https://ml2.inf.ethz.ch/courses/ml/ Computational Intelligence Lab http://da.inf.ethz.ch/teaching/2018/CIL/ Learning and Intelligent Systems/Introduction to Machine Learning https://las.inf.ethz.ch/teaching/introml-S18 Statistical Learning Theory http://ml2.inf.ethz.ch/courses/slt/ Computational Statistics https://stat.ethz.ch/lectures/ss18/comp-stats.php Probabilistic Artificial Intelligence https://las.inf.ethz.ch/teaching/pai-f17 Data Mining: Learning from Large Data Sets https://las.inf.ethz.ch/teaching/dm-f17

Specialization Courses These specialization 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 specialization courses during the MSc EEIT.
Number	Title	Type	ECTS	Hours	Lecturers
227-0116-00L	VLSI I: From Architectures to VLSI Circuits and FPGAs	W	6 credits	5G	F. K. Gürkaynak, L. Benini
Abstract	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.
Learning objective	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 VHDL or 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 VHDL and with industrial Electronic Design Automation (EDA) tools.
Content	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. - VLSI and 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. - VHDL and SystemVerilog compared. - VHDL (IEEE standard 1076) for simulation and synthesis. - A suitable nine-valued logic system (IEEE standard 1164). - 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 digital ICs with VHDL. They write testbenches for simulation purposes and synthesize gate-level netlists for VLSI chips and FPGAs. Commercial EDA software by leading vendors is being used throughout.
Lecture notes	Textbook and all further documents in English.
Literature	H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303.
Prerequisites / Notice	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: https://iis-students.ee.ethz.ch/lectures/vlsi-i/
227-0121-00L	Communication Systems	W	6 credits	4G	A. Wittneben
Abstract	Information Theory, Signal Space Analysis, Baseband Transmission, Passband Transmission, Example und Channel, Data Link Layer, MAC, Example Layer 2, Layer 3, Internet
Learning objective	Introduction into the fundamentals of digital communication systems. Selected examples on the application of the fundamental principles in existing and upcoming communication systems
Content	Covered are the lower three layer of the OSI reference model: the physical, the data link, and the network layer. The basic terms of information theory are introduced. After this, we focus on the methods for the point to point communication, which may be addressed elegantly and coherently in the signal space. Methods for error detection and correction as well as protocols for the retransmission of perturbed data will be covered. Also the medium access for systems with shared medium will be discussed. Finally, algorithms for routing and flow control will be treated. The application of the basic methods will be extensively explained using existing and future wireless and wired systems.
Lecture notes	Lecture Slides
Literature	[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 credits	5G	M. Kamgarpour
Abstract	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.
Learning objective	Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems.
Content	- 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.
Lecture notes	Available on the course Moodle platform.
Prerequisites / Notice	Sufficient mathematical maturity with special focus on logic, linear algebra, analysis.
227-0417-00L	Information Theory I	W	6 credits	4G	A. Lapidoth
Abstract	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.
Learning objective	The fundamentals of Information Theory including Shannon's source coding and channel coding theorems
Content	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
Literature	T.M. Cover and J. Thomas, Elements of Information Theory (second edition)
227-0477-00L	Acoustics I	W	6 credits	4G	K. Heutschi
Abstract	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.
Learning objective	Introduction to acoustics. Understanding of basic acoustical mechanisms. Survey of the technical literature. Illustration of measurement techniques in the laboratory.
Content	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.
Lecture notes	yes
263-5210-00L	Probabilistic Artificial Intelligence	W	4 credits	2V + 1U	A. Krause
Abstract	This course introduces core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet.
Learning objective	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 upper-level undergraduate and graduate students.
Content	Topics covered: - Search (BFS, DFS, A*), constraint satisfaction and optimization - Tutorial in logic (propositional, first-order) - Probability - Bayesian Networks (models, exact and approximative inference, learning) - Temporal models (Hidden Markov Models, Dynamic Bayesian Networks) - Probabilistic palnning (MDPs, POMPDPs) - Reinforcement learning - Combining logic and probability
Prerequisites / Notice	Solid basic knowledge in statistics, algorithms and programming
401-4619-67L	Advanced Topics in Computational Statistics Does not take place this semester.	W	4 credits	2V	N. Meinshausen
Abstract	This lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling.
Learning objective	Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes.
Content	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
Prerequisites / Notice	We assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics.
401-3901-00L	Mathematical Optimization	W	11 credits	4V + 2U	R. Weismantel
Abstract	Mathematical treatment of diverse optimization techniques.
Learning objective	Advanced optimization theory and algorithms.
Content	1) Linear optimization: The geometry of linear programming, the simplex method for solving linear programming problems, Farkas' Lemma and infeasibility certificates, duality theory of linear programming. 2) Nonlinear optimization: Lagrange relaxation techniques, Newton method and gradient schemes for convex optimization. 3) Integer optimization: Ties between linear and integer optimization, total unimodularity, complexity theory, cutting plane theory. 4) Combinatorial optimization: Network flow problems, structural results and algorithms for matroids, matchings, and, more generally, independence systems.
Literature	1) D. Bertsimas & R. Weismantel, "Optimization over Integers". Dynamic Ideas, 2005. 2) A. Schrijver, "Theory of Linear and Integer Programming". John Wiley, 1986. 3) D. Bertsimas & J.N. Tsitsiklis, "Introduction to Linear Optimization". Athena Scientific, 1997. 4) Y. Nesterov, "Introductory Lectures on Convex Optimization: a Basic Course". Kluwer Academic Publishers, 2003. 5) C.H. Papadimitriou, "Combinatorial Optimization". Prentice-Hall Inc., 1982.
Prerequisites / Notice	Linear algebra.
401-3621-00L	Fundamentals of Mathematical Statistics	W	10 credits	4V + 1U	S. van de Geer
Abstract	The course covers the basics of inferential statistics.
Learning objective

Electives *more courses coming soon* This is but 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.
Number	Title	Type	ECTS	Hours	Lecturers
363-0511-00L	Managerial Economics Not for MSc students belonging to D-MTEC!	W	4 credits	3V	S. Rausch
Abstract	"Managerial Economics" provides an introduction to the theories and methods from Economics and Management Science to analyze economic decision-making in the context of markets. The course targets students with no prior knowledge in Economics and Management.
Learning objective	The objective of this course is to provide an introduction to microeconomic thinking. Based on the fundamental principles of economic analysis (optimization and equilibrium), the focus lies on understanding key economic concepts relevant for understanding and analyzing economic behavior of firms and consumers in the context of markets. Market demand and supply are derived from the individual decision-making of economic agents and market outcomes under different assumptions about the market structure and market power (perfect competition, monopoly, oligopoly, game theory) are studied. This introductory course aims at providing essential knowledge from the fields of Economics and Management relevant for economic decision-making in the context of both the private and public sector.
Literature	"Mikroökonomie" von Robert Pindyck & Daniel Rubinfeld, aktualisierte 8. Auflage, 8/2013, (Pearson Studium - Economic VWL).
Prerequisites / Notice	The course targets both Bachelor and Master students. No prior knowledge in the areas of Economics and Management is required.
351-0778-00L	Discovering Management Entry 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 credits	3G	B. Clarysse, M. Ambühl, S. Brusoni, E. Fleisch, G. Grote, V. Hoffmann, T. Netland, G. von Krogh, F. von Wangenheim
Abstract	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.
Learning objective	Discovering Management combines in an innovate format a set of lectures and an advanced business game. 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, corporate finance, leadership, design thinking and corporate social responsibility. While the 14 different lectures provide the theoretical and conceptual foundations, the experiential learning outcomes result from the interactive business game. The purpose of the business game is to analyse the innovative needs of a large multinational company and develop a business case for the company to grow. This business case is as relevant to someone exploring innovation within an organisation as it is if you are planning to start your own business. By discovering the key aspects of entrepreneurial management, the purpose of the course is to advance students' understanding of factors driving innovation, entrepreneurship, and company success.
Content	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, corporate and entrepreneurial finance, value chain analysis, corporate social responsibility, and business model innovation. Practical examples from industry experts will stimulate the students to critically assess these issues. Creative skills will be trained by the business game exercise, a participant-centered learning activity, which provides students with the opportunity to place themselves in the role of Chief Innovation Officer of a large multinational company. As they learn more about the specific case and identify the challenge they are faced with, the students will have to develop an innovative business case for this multinational corporation. Doing so, this exercise will provide an insight into the context of managerial problem-solving and corporate innovation, and enhance the students' appreciation for the complex tasks companies and managers deal with. The business game presents a realistic model of a company and provides a valuable learning platform to integrate the increasingly important development of the skills and competences required to identify entrepreneurial opportunities, analyse the future business environment and successfully respond to it by taking systematic decisions, e.g. critical assessment of technological possibilities.
Prerequisites / Notice	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 credit	1U	B. Clarysse, L. De Cuyper
Abstract	This course is offered complementary to the basis course 351-0778-00L, "Discovering Management". The course offers additional exercises and case studies.
Learning objective	This course is offered to complement the course 351-0778-00L. The course offers additional exercises and case studies.
Content	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 credits	2V	U. Claesson, B. Clarysse
Abstract	Technology ventures are significantly changing the global economic picture. Technological skills increasingly need to be complemented by entrepreneurial understanding. This course offers the fundamentals in theory and practice of entrepreneurship in new technology ventures. Main topics covered are success factors in the creation of new firms, including founding, financing and growing a venture.
Learning objective	This course provides theory-grounded knowledge and practice-driven skills for founding, financing, and growing new technology ventures. A critical understanding of dos and don'ts is provided through highlighting and discussing real life examples and cases.
Content	See course website: Link
Lecture notes	Lecture slides and case material
363-1049-00L	Contemporary Conflict Management 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 exam, will officially fail the course.	W	3 credits	2V	V. Butenko
Abstract	The course provides students with theoretical and practical insights of the modern approaches to conflict management. The course covers conflicts in 3 areas: International, business and interpersonal relations. Students are introduced into tools and methods used to analyze conflicts illustrated by the real world current cases, old and new international/regional conflicts, business and mediation.
Learning objective	Students will gain - knowledge of history of conflict management; - comprehension of major ideas in the theory and practice of conflict management, mediation, transformation and resolution; - application of theoretical concepts to current conflict situations; - evaluation of conflict situations in international relations and business.
Content	The following topics will be covered: - history of international and regional conflicts; - theoretical concepts of conflict management; - models of arms races, conflict escalations, strategic behaviour; - case studies in international conflicts, as well as in business. Distinguished guest speakers will be invited.
Literature	- Jacob Bercovitch, Victor Kremenyuk, and I. William Zartman (editors) (2013): The SAGE Handbook of Conflict Resolution. SAGE, Los Angeles, LA - Oliver Ramsbotham, Tom Woodhouse, and Hugh Miall (2012): Contemporary Conflict Resolution. Polity Press, Cambridge, UK -Jacob Bercovitch and Richard Jackson (2012): Conflict Resolution in the Twenty-first Century: Principles, Methods, and Approaches. University of Michigan Press, Ann Arbor, MI - Peter Wallensteen (2012): Understanding Conflict Resolution. SAGE, London, UK - Tricia Jones and Ross Brinkert (2007): Conflict Coaching: Conflict Management Strategies and Skills for the Individual. SAGE Publications, London, UK - Susan S. Raines (2013): Conflict Management for Managers: Resolving Workplace, Client, and Policy Disputes (The Jossey-Bass Business & Management Series). Jossey-Bass, San-Francisco, CA - William Ury (2015): Getting past no: Negotiating with difficult people. Random House, UK - Philip D. Straffin (1993): Game theory and strategy. Mathematical Association of America, Washington, DC.
363-1065-00L	Design Thinking: Human-Centred Solutions to Real World Challenges Due to didactic reasons, the number of participants is limited to 30. All interested students are invited to apply for this course by sending a short motivation letter to Linda Armbruster (larmbruster@ethz.ch). Additionally please enroll via mystudies. Please note that all students are put on the waiting list and that your current position on the waiting list is irrelevant, as places will be assigned after the first lecture on the basis of your motivation letter and commitment for the class.	W	5 credits	5G	A. Cabello Llamas, S. Brusoni, L. Cabello
Abstract	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: http://sparklabs.ch/
Learning objective	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.
Content	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: http://sparklabs.ch/
Prerequisites / Notice	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 anilsethi@ethz.ch.	W	3 credits	2V	A. Sethi
Abstract	Participants form teams and identify an idea, which is then taken through the steps necessary to form a startup. The primary focus of the course is geared to technology startups that want to reach scale.
Learning objective	Participants want to become entrepreneurs. Participants can be from business or science & technology The course will enable the students to identify an idea and take all necessary steps to convert it into a company, through the duration of the two semesters. The participants will have constant exposure to investors and entrepreneurs (with a focus on ETH spin-offs) through the course, to gain an understanding of their vision and different perspectives.
Content	Participants start from idea identification, forming team, technology and market size validation, assessing time-to-market, customer focus, IP strategy & financials, to become capable of starting the company and finally making the pitch to investors. The seminar comprises lectures, talks from invited investors 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 the relevance of the idea, relevance to customers, time to market and customer value.
Literature	Book Sethi, A. "From Science to Startup" ISBN 978-3-319-30422-9
Prerequisites / Notice	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. The course will be in two modules (autumn and spring), which will run in two consecutive semesters. Priority for the second semester will be given to those students who have attended the first semester. 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	Introduction to Law Students who have attended or will attend the lecture "Introduction to Law for Civil Engineering and Architecture " (851-0703-03L) or " Introduction to Law" (851-0708-00L), cannot register for this course unit. Particularly suitable for students of D-ARCH, D-MAVT, D-MATL	W	2 credits	2V	O. Streiff Gnöpff
Abstract	This class introduces students into basic features of the legal system. Fundamental issues of constitutional law, administrative law, private law and the law of the EU are covered.
Learning objective	Students are able to identify basic structures of the legal system. They unterstand selected topics of public and private law and are able to apply the fundamentals in more advanced law classes.
Content	Basic concepts of law, sources of law. Private law: Contract law (particularly contract for work and services), tort law, property law. Public law: Human rights, administrative law, procurement law, procedural law. Insights into the law of the EU and into criminal law.
Lecture notes	Jaap Hage, Bram Akkermans (Eds.), Introduction to Law, Cham 2017 (Online Resource ETH Library)
Literature	Further documents will be available online (see https://moodle-app2.let.ethz.ch/course/view.php?id=4516).
851-0735-10L	Business Law Number of participants limited to 100 Particularly suitable for students of D-ITET, D-MAVT	W	2 credits	2V	P. Peyrot
Abstract	The students shall obtain a basic knowledge about business law. They shall be able to recognize and evaluate issues in the area of business law and suggest possible solutions.
Learning objective	The students shall obtain the following competence: - They shall obtain a working knowledge on the legal aspects involved in setting up and managing an enterprize. - They shall be acquainted with corporate functions as contracting, negotiation, claims management and dispute resolution - They shall be familiar with the issues of corporate compliance, i.e. the system to ascertain that all legal and ethical rules are observed. - They shall be able to contribute to the legal management of the company and to discuss legal issues. - They shall have an understanding of the law as a part of the corporate strategy and as a valuable ressource of the company.
Lecture notes	A comprehensive script will be made available online on the moodle platform.
851-0738-00L	Intellectual Property: Introduction Particularly suitable for students of D-CHAB, D-INFK, D-ITET, D-MAVT, D- MATL, D-MTEC	W	2 credits	2V	M. Schweizer
Abstract	The course provides an introduction to Swiss and European intellectual property law (trademarks, copyright, patent and design rights). Aspects of competition law are treated insofar as they are relevant for the protection of intellectual creations and source designations. The legal principles are developed based on current cases.
Learning objective	The aim of this course is to enable students at ETH Zurich to recognize which rights may protect their creations, and which rights may be infringed as a result of their activities. Students should learn to assess the risks and opportunities of intellectual property rights in the development and marketing of new products. To put them in this position, they need to know the prerequisites and scope of protection afforded by the various intellectual property rights as well as the practical difficulties involved in the enforcement of intellectual property rights. This knowledge is imparted based on current rulings and cases. Another goal is to enable the students to participate in the current debate over the goals and desirability of protecting intellectual creations, particularly in the areas of copyright (keywords: fair use, Creative Commons, Copyleft) and patent law (software patents, patent trolls, patent thickets).
851-0738-01L	The Role of Intellectual Property in the Engineering and Technical Sector Particularly suitable for students of D-BAUG, D-BIOL, D-BSSE, D-CHAB, D-ITET, D-MAVT	W	2 credits	2V	C. Soltmann
Abstract	The lecture gives an overview of the fundamental aspects of intellectual property, which plays an important role in the daily routine of engineers and scientists. The lecture aims to make participants aware of the various methods of protection and to put them in a position to use this knowledge in the workplace.
Learning objective	In recent years, knowledge about intellectual property has become increasingly important for engineers and scientists. Both in production and distribution and in research and development, they are increasingly being confronted with questions concerning the patenting of technical inventions and the use of patent information. The lecture will acquaint participants with practical aspects of intellectual property and enable them to use the acquired knowledge in their future professional life. Topics covered during the lecture will include: - The importance of innovation in industrialised countries - An overview of the different forms of intellectual property - The protection of technical inventions and how to safeguard their commercialisation - Patents as a source of technical and business information - Practical aspects of intellectual property in day-to-day research, at the workplace and for the formation of start-ups. Case studies will illustrate and deepen the topics addressed during the lecture. The seminar will include practical exercises on how to use and search patent information. Basic knowledge of how to read and evaluate patent documents as well as how to use publicly available patent databases to obtain the required patent information will also be provided.
Prerequisites / Notice	The lecture addresses students in the fields of engineering, science and other related technical fields.

Industrial Internship
Number	Title	Type	ECTS	Hours	Lecturers
227-1550-10L	Internship in Industry Only for Electrical Engineering and Information Technology MSc (Programme Regulations 2018).	W	12 credits		external organisers
Abstract	The main objective of the 12-week internship is to expose master's students to the industrial work environment. During this period, students have the opportunity to be involved in on-going projects at the host institution.
Learning objective	see above

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