Suchergebnis: Katalogdaten im Herbstsemester 2022
Elektrotechnik und Informationstechnologie Master  
MasterStudium (Studienreglement 2018)  
Communication The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Communication", see https://www.ee.ethz.ch/studies/mainmaster/areasofspecialisation.html. The individual study plan is subject to the tutor's approval.  
Kernfächer These core courses are particularly recommended for the field of "Communication". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT.  
Foundation Core Courses Fundamentals at bachelor level, for master students who need to strengthen or refresh their background in the area.  
Nummer  Titel  Typ  ECTS  Umfang  Dozierende  

227012100L  Kommunikationssysteme Findet dieses Semester nicht statt.  W  6 KP  4G  Noch nicht bekannt  
Kurzbeschreibung  Informationstheorie, Signalraumanalyse, Basisbandübertragung, Passbandübertragung, Systembeispiel und Kanal, Sicherungsschicht, MAC, Beispiele Layer 2, Layer 3, Internet  
Lernziel  Ziel der Vorlesung ist die Einführung der wichtigsten Konzepte und Verfahren, die in modernen digitalen Kommunikationssystemen Anwendung finden, sowie eine Übersicht über bestehende und zukünftige Systeme.  
Inhalt  Es werden die untersten drei Schichten des OSIReferenzmodells behandelt: die Bitübertragungsschicht, die Sicherungsschicht mit dem Zugriff auf das Übertragungsmedium und die Vermittlung. Die wichtigsten Begriffe der Informationstheorie werden eingeführt. Anschliessend konzentrieren sich die Betrachtungen auf die Verfahren der PunktzuPunktÜbertragung, welche sich mittels der Signalraumdarstellung elegant und kohärent behandeln lassen. Den Methoden der Fehlererkennung und –korrektur, sowie Protokollen für die erneute Übermittlung gestörter Daten wird Rechnung getragen. Auch der Vielfachzugriff bei geteiltem Übertragungsmedium wird diskutiert. Den Abschluss bilden Algorithmen für das Routing in Kommunikationsnetzen und der Flusssteuerung. Die Anwendung der grundlegenden Verfahren wird ausführlich anhand von bestehenden und zukünftigen drahtlosen und drahtgebundenen Systemen erläutert.  
Skript  Vorlesungsfolien  
Literatur  [1] Simon Haykin, Communication Systems, 4. Auflage, John Wiley & Sons, 2001 [2] Andrew S. Tanenbaum, Computernetzwerke, 3. Auflage, Pearson Studium, 2003 [3] M. Bossert und M. Breitbach, Digitale Netze, 1. Auflage, Teubner, 1999  
227010100L  DiscreteTime and Statistical Signal Processing  W  6 KP  4G  H.‑A. Loeliger  
Kurzbeschreibung  The course is about some fundamental topics of digital signal processing with a bias towards applications in communications: discretetime linear filters, inverse filters and equalization, DFT, discretetime stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.  
Lernziel  The course is about 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 discretetime linear filters. In the second part of the course, we review the basics of probability theory and discretetime stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter.  
Inhalt  1. Discretetime linear systems and filters: statespace realizations, ztransform 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, discretetime stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm.  
Skript  Lecture Notes  
Advanced Core Courses Advanced core courses bring students to gain indepth knowledge of the chosen specialization. They are MSc level only.  
Nummer  Titel  Typ  ECTS  Umfang  Dozierende  
227030100L  Optical Communication Fundamentals  W  6 KP  2V + 1U + 1P  J. Leuthold  
Kurzbeschreibung  The path of an analog signal in the transmitter to the digital world in a communication link and back to the analog world at the receiver is discussed. The lecture covers the fundamentals of all important optical and optoelectronic components in a fiber communication system. This includes the transmitter, the fiber channel and the receiver with the electronic digital signal processing elements.  
Lernziel  An indepth 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 ongoing exponential growth in the field of communications.  
Inhalt  * Chapter 1: Introduction: Analog/Digital conversion, The communication channel, Shannon channel capacity, Capacity requirements. * Chapter 2: The Transmitter: Components of a transmitter, Lasers, The spectrum of a signal, Optical modulators, Modulation formats. * Chapter 3: The Optical Fiber Channel: Geometrical optics, The wave equations in a fiber, Fiber modes, Fiber propagation, Fiber losses, Nonlinear effects in a fiber. * Chapter 4: The Receiver: Photodiodes, Receiver noise, Detector schemes (direct detection, coherent detection), Biterror ratios and error estimations. * Chapter 5: Digital Signal Processing Techniques: Digital signal processing in a coherent receiver, Error detection teqchniques, Error correction coding. * Chapter 6: Pulse Shaping and Multiplexing Techniques: WDM/FDM, TDM, OFDM, Nyquist Multiplexing, OCDMA. * Chapter 7: Optical Amplifiers : Semiconductor Optical Amplifiers, Erbium Doped Fiber Amplifiers, Raman Amplifiers.  
Skript  Lecture notes are handed out.  
Literatur  Govind P. Agrawal; "FiberOptic Communication Systems"; Wiley, 2010  
Voraussetzungen / Besonderes  Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics.  
227041700L  Information Theory I  W  6 KP  4G  A. Lapidoth  
Kurzbeschreibung  This course covers the basic concepts of information theory and of communication theory. Topics covered include the entropy rate of a source, mutual information, typical sequences, the asymptotic equipartition property, Huffman coding, channel capacity, the channel coding theorem, the sourcechannel separation theorem, and feedback capacity.  
Lernziel  The fundamentals of Information Theory including Shannon's source coding and channel coding theorems  
Inhalt  The entropy rate of a source, Typical sequences, the asymptotic equipartition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the sourcechannel separation theorem, feedback capacity  
Literatur  T.M. Cover and J. Thomas, Elements of Information Theory (second edition)  
Vertiefungsfächer These specialisation courses are particularly recommended for the area of "Communication", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme.  
Nummer  Titel  Typ  ECTS  Umfang  Dozierende  
227010200L  Diskrete Ereignissysteme  W  6 KP  4G  L. Josipovic, L. Vanbever, R. Wattenhofer  
Kurzbeschreibung  Einführung in Diskrete Ereignissysteme (DES). Zuerst studieren wir populäre Modelle für DES. Im zweiten Teil analysieren wir DES, aus einer AverageCase und einer WorstCase Sicht. Stichworte: Automaten und Sprachen, Spezifikationsmodelle, Stochastische DES, WorstCase Ereignissysteme, Verifikation, Netzwerkalgebra.  
Lernziel  Over the past few decades the rapid evolution of computing, communication, and information technologies has brought about the proliferation of new dynamic systems. A significant part of activity in these systems is governed by operational rules designed by humans. The dynamics of these systems are characterized by asynchronous occurrences of discrete events, some controlled (e.g. hitting a keyboard key, sending a message), some not (e.g. spontaneous failure, packet loss). The mathematical arsenal centered around differential equations that has been employed in systems engineering to model and study processes governed by the laws of nature is often inadequate or inappropriate for discrete event systems. The challenge is to develop new modeling frameworks, analysis techniques, design tools, testing methods, and optimization processes for this new generation of systems. In this lecture we give an introduction to discrete event systems. We start out the course by studying popular models of discrete event systems, such as automata and Petri nets. In the second part of the course we analyze discrete event systems. We first examine discrete event systems from an averagecase 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 worstcase perspective using the theory of online algorithms and adversarial queuing.  
Inhalt  1. Introduction 2. Automata and Languages 3. Smarter Automata 4. Specification Models 5. Stochastic Discrete Event Systems 6. WorstCase Event Systems 7. Network Calculus  
Skript  Available  
Literatur  [bertsekas] Data Networks Dimitri Bersekas, Robert Gallager Prentice Hall, 1991, ISBN: 0132009161 [borodin] Online Computation and Competitive Analysis Allan Borodin, Ran ElYaniv. 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 0792386094 [fiat] Online Algorithms: The State of the Art A. Fiat and G. Woeginger [hochbaum] Approximation Algorithms for NPhard 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  
227010300L  Control Systems  W  6 KP  2V + 2U  F. Dörfler  
Kurzbeschreibung  Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input  single output and multivariable systems.  
Lernziel  Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input  single output and multivariable systems.  
Inhalt  Process automation, concept of control. Modelling of dynamical systems  examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems  effect of additional poles and zeros. Closedloop control  idea of feedback. PID control, Ziegler  Nichols tuning. Stability, RouthHurwitz 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), multiloop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement  choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation.  
Literatur  K. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010. R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007. G. F. Franklin, J. D. Powell, and A. EmamiNaeini. Feedback Control of Dynamic Systems. AddisonWesley, 2010. J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014. J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014.  
Voraussetzungen / Besonderes  Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation.  
227011600L  VLSI 1: HDL Based Design for FPGAs  W  6 KP  5G  F. K. Gürkaynak, L. Benini  
Kurzbeschreibung  This first course in a series that extends over three consecutive terms is concerned with tailoring algorithms and with devising high performance hardware architectures for their implementation as ASIC or with FPGAs. The focus is on front end design using HDLs and automatic synthesis for producing industrialquality circuits.  
Lernziel  Understand VeryLargeScale Integrated Circuits (VLSI chips), ApplicationSpecific Integrated Circuits (ASIC), and FieldProgrammable GateArrays (FPGA). Know their organization and be able to identify suitable application areas. Become fluent in frontend design from architectural conception to gatelevel netlists. How to model digital circuits with SystemVerilog. How to ensure they behave as expected with the aid of simulation, testbenches, and assertions. How to take advantage of automatic synthesis tools to produce industrialquality VLSI and FPGA circuits. Gain practical experience with the hardware description language SystemVerilog and with industrial Electronic Design Automation (EDA) tools.  
Inhalt  This course is concerned with systemlevel 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 fieldprogrammable components.  FPGA design flows.  Dedicated and general purpose architectures compared.  How to obtain an architecture for a given processing algorithm.  Meeting throughput, area, and power goals by way of architectural transformations.  Hardware Description Languages (HDL) and the underlying concepts.  SystemVerilog  Register Transfer Level (RTL) synthesis and its limitations.  Building blocks of digital VLSI circuits.  Functional verification techniques and their limitations.  Modular and largely reusable testbenches.  Assertionbased verification.  Synchronous versus asynchronous circuits.  The case for synchronous circuits.  Periodic events and the Anceau diagram.  Case studies, ASICs compared to microprocessors, DSPs, and FPGAs. During the exercises, students learn how to model FPGAs with SystemVerilog. They write testbenches for simulation purposes and synthesize gatelevel netlists for FPGAs. Commercial EDA software by leading vendors is being used throughout.  
Skript  Textbook and all further documents in English.  
Literatur  H. Kaeslin: "TopDown Digital VLSI Design, from Architectures to GateLevel Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303.  
Voraussetzungen / Besonderes  Prerequisites: Basics of digital circuits. Examination: In written form following the course semester (spring term). Problems are given in English, answers will be accepted in either English oder German. Further details: https://iisstudents.ee.ethz.ch/lectures/vlsii/  
227016600L  Analog Integrated Circuits  W  6 KP  2V + 2U  T. Jang  
Kurzbeschreibung  This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies.  
Lernziel  Integrated circuits are responsible for much of the progress in electronics in the last 50 years, particularly the revolutions in the Information and Communications Technologies we witnessed in recent years. Analog integrated circuits play a crucial part in the highly integrated systems that power the popular electronic devices we use daily. Understanding their design is beneficial to both future designers and users of such systems. The basic elements, design issues and techniques for analog integrated circuits will be taught in this course.  
Inhalt  Review of bipolar and MOS devices and their smallsignal 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 opamps; stability; comparators; secondorder effects in analog circuits such as mismatch, noise and offset; data converters; frequency synthesizers; switched capacitors. The exercise sessions aim to reinforce the lecture material by well guided stepbystep design tasks. The circuit simulator SPECTRE is used to facilitate the tasks. There is also an experimental session on opamp measurements.  
Skript  Handouts of presented slides. No script but an accompanying textbook is recommended.  
Literatur  Behzad Razavi, Design of Analog CMOS Integrated Circuits (Irwin Electronics & Computer Engineering) 1st or 2nd edition, McGrawHill Education  
227030100L  Optical Communication Fundamentals  W  6 KP  2V + 1U + 1P  J. Leuthold  
Kurzbeschreibung  The path of an analog signal in the transmitter to the digital world in a communication link and back to the analog world at the receiver is discussed. The lecture covers the fundamentals of all important optical and optoelectronic components in a fiber communication system. This includes the transmitter, the fiber channel and the receiver with the electronic digital signal processing elements.  
Lernziel  An indepth 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 ongoing exponential growth in the field of communications.  
Inhalt  * Chapter 1: Introduction: Analog/Digital conversion, The communication channel, Shannon channel capacity, Capacity requirements. * Chapter 2: The Transmitter: Components of a transmitter, Lasers, The spectrum of a signal, Optical modulators, Modulation formats. * Chapter 3: The Optical Fiber Channel: Geometrical optics, The wave equations in a fiber, Fiber modes, Fiber propagation, Fiber losses, Nonlinear effects in a fiber. * Chapter 4: The Receiver: Photodiodes, Receiver noise, Detector schemes (direct detection, coherent detection), Biterror ratios and error estimations. * Chapter 5: Digital Signal Processing Techniques: Digital signal processing in a coherent receiver, Error detection teqchniques, Error correction coding. * Chapter 6: Pulse Shaping and Multiplexing Techniques: WDM/FDM, TDM, OFDM, Nyquist Multiplexing, OCDMA. * Chapter 7: Optical Amplifiers : Semiconductor Optical Amplifiers, Erbium Doped Fiber Amplifiers, Raman Amplifiers.  
Skript  Lecture notes are handed out.  
Literatur  Govind P. Agrawal; "FiberOptic Communication Systems"; Wiley, 2010  
Voraussetzungen / Besonderes  Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics.  
227037710L  Physics of Failure and Reliability of Electronic Devices and Systems  W  3 KP  2V  I. Shorubalko, M. Held  
Kurzbeschreibung  Understanding the physics of failures and failure mechanisms enables reliability analysis and serves as a practical guide for electronic devices design, integration, systems development and manufacturing. The field gains additional importance in the context of managing safety, sustainability and environmental impact for continuously increasing complexity and scalingdown trends in electronics.  
Lernziel  Provide an understanding of the physics of failure and reliability. Introduce the degradation and failure mechanisms, basics of failure analysis, methods and tools of reliability testing.  
Inhalt  Summary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis; basics and properties of instruments; quality assurance of technical systems (introduction); introduction to stochastic processes; reliability analysis; component selection and qualification; maintainability analysis (introduction); design rules for reliability, maintainability, reliability tests (introduction).  
Skript  Comprehensive copy of transparencies  
Literatur  Reliability Engineering: Theory and Practice, 8th Edition, Springer 2017, DOI 10.1007/9783662542095 Reliability Engineering: Theory and Practice, 8th Edition (2017), DOI 10.1007/9783662542095  
227042300L  Neural Network Theory Findet dieses Semester nicht statt.  W  4 KP  2V + 1U  H. Bölcskei  
Kurzbeschreibung  The class focuses on fundamental mathematical aspects of neural networks with an emphasis on deep networks: Universal approximation theorems, capacity of separating surfaces, generalization, fundamental limits of deep neural network learning, VC dimension.  
Lernziel  After attending this lecture, participating in the exercise sessions, and working on the homework problem sets, students will have acquired a working knowledge of the mathematical foundations of neural networks.  
Inhalt  1. Universal approximation with single and multilayer networks 2. Introduction to approximation theory: Fundamental limits on compressibility of signal classes, Kolmogorov epsilonentropy of signal classes, nonlinear approximation theory 3. Fundamental limits of deep neural network learning 4. Geometry of decision surfaces 5. Separating capacity of nonlinear decision surfaces 6. VapnikChervonenkis (VC) dimension 7. VC dimension of neural networks 8. Generalization error in neural network learning  
Skript  Detailed lecture notes are available on the course web page https://www.mins.ee.ethz.ch/teaching/nnt/  
Voraussetzungen / Besonderes  This course is aimed at students with a strong mathematical background in general, and in linear algebra, analysis, and probability theory in particular.  
227044700L  Image Analysis and Computer Vision  W  6 KP  3V + 1U  E. Konukoglu, F. Yu  
Kurzbeschreibung  Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks.  
Lernziel  Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.  
Inhalt  This course aims at offering a selfcontained 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 preprocessing steps, that enhance image quality and/or detect specific features. Linear and nonlinear 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 AIbased approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given.  
Skript  Course material Script, computer demonstrations, exercises and problem solutions  
Voraussetzungen / Besonderes  Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English.  
227046800L  Analog Signal Processing and Filtering Suitable for Master Students as well as Doctoral Students.  W  6 KP  2V + 2U  H. Schmid  
Kurzbeschreibung  This lecture provides a wide overview over analog filters (continuoustime and discretetime), signalprocessing systems, and sigmadelta conversion, and gives examples with sensor interfaces and classD audio drivers. All systems and circuits are treated using a signalflow view. The lecture is suitable for both analog and digital designers.  
Lernziel  This lecture provides a wide overview over analog filters (continuoustime and discretetime), signalprocessing systems, and sigmadelta conversion, and gives examples with sensor interfaces and classD audio drivers. All systems and circuits are treated using a signalflow 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 signalflow graphs and can understand the signal flow in such circuits and systems (including nonideal effects) well enough to gain an understanding of further circuits and systems by themselves.  
Inhalt  At the beginning, signalflow graphs in general and drivingpoint signalflow graphs in particular are introduced. We will use them during the whole term to analyze circuits on a system level (analog continuoustime, analog discretetime, mixedsignal 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 GmC filters and activeRC 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 discretetime and mixeddomain filters and circuits is given, including sensor readout amplifiers, correlated double sampling, and chopping, and an introduction to sigmadelta A/D and D/A conversion on a system level. This lecture does not go down to the details of transistor implementations. The lecture "227016600L Analog Integrated Circuits" complements This lecture very well in that respect.  
Skript  The base for these lectures are lecture notes and two or three published scientific papers. From these papers we will together develop the technical content. Details: https://people.ee.ethz.ch/~haschmid/asfwiki/ The graph methods are also supported with teaching videos: https://tube.switch.ch/channels/d206c96c?order=episodes , and a Pythonbased opensource tool to manipulate graphs is available on https://github.com/hanspi42/signalflowgrapher 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.  
Voraussetzungen / Besonderes  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.  
Kompetenzen 
 
227047700L  Acoustics I  W  3 KP  2G  K. Heutschi  
Kurzbeschreibung  Introduction to the fundamentals of acoustics in the field of sound field calculations, measurement of acoustical events, outdoor sound propagation and room acoustics of large and small enclosures.  
Lernziel  Understanding of the basic acoustical concepts and methods. Ability to understand the technical and scientific literature. Confidence in the use of measuring instruments.  
Inhalt  Fundamentals of acoustics, measurement and analysis of acoustical events, anatomy and properties of the ear, outdoor sound propagation, absorption and transmission of sound, room acoustics of large and small enclosures, architectural acoustics, noise and noise control, calculation of sound fields.  
Skript  yes  
Kompetenzen 
 
227065200L  Maxwell, Einstein, and the GPS  W  6 KP  2V + 2U  T. Zambelli  
Kurzbeschreibung  Maxwell’s equations are reinterpreted in the framework of Einstein's special relativity theory using the Lagrangian formalism in order to discover the deep interconnection between the electric and magnetic field. Its daily relevance is emphasized by pinpointing how GPS atomic clocks in satellites and on the earth are affected by frequency shifts which can be explained only in terms of relativity.  
Lernziel  DITET is the depository of the Maxwell’s equations, which are dissected from all perspectives in the courses Physics I, Electromagnetic Fields and Waves, and Advanced Electromagnetic Waves. Only one aspect is left over: the fact that they are not invariant with respect to the classical Galilean transformation… On the contrary, Maxwell’s equations predict that the light speed is the same for every inertial frame of reference. In this course, we will deepen how Einstein solved this clash elaborating the theory of “special relativity”. Maxwell's equations are thus naturally derived in a breathtaking fashion from the principle of stationary action within the Lagrangian formalism. Not only its elegance, but also the daily importance of the relativity theory will be finally highlighted explaining how the GPS can work only if the relativistic view of synchronous clocks is taken into account.  
Inhalt  • GalileoNewton, the Ether, MichelsonMorley's Experiment • Lorentz Transformations • 4Vectors in Minkowski’s Spacetime: Tensor Calculus • The Lagrangian, the Principle of Stationary Action for Particles and Fields, Noether's Theorem • Maxwell’s Equations and the EnergyMomentum Tensor • Waves • Radiation from Accelerated Charged Particles • Very First Notions of General Relativity: Einstein's Equivalence Principle and Time Dilation • Sagnac's Effect • GPS  
Skript  No lecture notes because the proposed textbooks together with the provided supplementary material are more than exhaustive! !!!!! I am using OneNote. All lectures and exercises will be broadcast via ZOOM and correspondingly recorded (link in Moodle) !!!!!  
Literatur  • (Special Relativity) L. Susskind and A. Friedman, "Special Relativity and Classical Field Theory: The Theoretical Minimum", 2019, Hachette Book Group USA • (Lagrangian Formalism) L. Susskind and G. Hrabovsky, "Theoretical Minimum: What You Need to Know to Start Doing Physics", 2014, Hachette Book Group USA Supplementary material will be uploaded in Moodle. _ _ _ _ _ _ _ + (the classical and probably unsurpassed treatise) L.D. Landau, E.M. Lifshitz, "The Classical Theory of Fields", 1980, ButterworthHeinemann + (on the GPS) E.D. Kaplan, C. Hegarty, "Understanding GPS/GNSS", 2017, ARTECH HOUSE USA + (as account of that annus mirabilis) J.S. Rigden, "Einstein 1905: The Standard of Greatness", 2006, Harvard University Press  
Voraussetzungen / Besonderes  Notions of a course on Electromagnetism like DITET "Electromagnetic Fields and Waves" are indispensable. Furthermore, a solid base of Analysis I & II as well as of Linear Algebra is really helpful. IMPORTANT: a few Wednesdays are lectures (NOT exercises!), details in Moodle!  
Kompetenzen 
 
252053500L  Advanced Machine Learning  W  10 KP  3V + 2U + 4A  J. M. Buhmann, C. Cotrini Jimenez  
Kurzbeschreibung  Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.  
Lernziel  Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.  
Inhalt  The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both penandpaper and programming exercises, where they implement and apply famous algorithms to realworld 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 Nonparametric density estimation Learning Dynamical Systems  
Skript  No lecture notes, but slides will be made available on the course webpage.  
Literatur  C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004.  
Voraussetzungen / Besonderes  The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.  
263464000L  Network Security  W  8 KP  2V + 2U + 3A  A. Perrig, S. Frei, M. Legner, K. Paterson  
Kurzbeschreibung  Some of today's most damaging attacks on computer systems involve exploitation of network infrastructure, either as the target of attack or as a vehicle to attack end systems. This course provides an indepth study of network attack techniques and methods to defend against them.  
Lernziel   Students are familiar with fundamental networksecurity concepts.  Students can assess current threats that Internet services and networked devices face, and can evaluate appropriate countermeasures.  Students can identify and assess vulnerabilities in software systems and network protocols.  Students have an indepth understanding of a range of important stateoftheart security technologies.  Students can implement networksecurity protocols based on cryptographic libraries.  
Inhalt  The course will cover topics spanning four broad themes with a focus on the first two themes: (1) network defense mechanisms such as publickey infrastructures, TLS, VPNs, anonymouscommunication systems, secure routing protocols, secure DNS systems, and network intrusiondetection systems; (2) network attacks such as hijacking, spoofing, denialofservice (DoS), and distributed denialofservice (DDoS) attacks; (3) analysis and inference topics such as traffic monitoring and network forensics; and (4) new technologies related to nextgeneration networks. In addition, several guest lectures will provide indepth insights into specific current realworld networksecurity topics.  
Voraussetzungen / Besonderes  This lecture is intended for students with an interest in securing Internet communication services and network devices. Students are assumed to have knowledge in networking as taught in a communication networks lecture like 252006400L or 227012000L. Basic knowledge of information security or applied cryptography as taught in 252021100L or 263466000L is beneficial, but an overview of the most important cryptographic primitives will be provided at the beginning of the course. The course will involve several graded course projects. Students are expected to be familiar with a generalpurpose or network programming language such as C/C++, Go, Python, or Rust.  
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401305564L  Algebraic Methods in Combinatorics Findet dieses Semester nicht statt.  W  6 KP  2V + 1U  B. Sudakov  
Kurzbeschreibung  Combinatorics is a fundamental mathematical discipline as well as an essential component of many mathematical areas, and its study has experienced an impressive growth in recent years. This course provides a gentle introduction to Algebraic methods, illustrated by examples and focusing on basic ideas and connections to other areas.  
Lernziel  The students will get an overview of various algebraic methods for solving combinatorial problems. We expect them to understand the proof techniques and to use them autonomously on related problems.  
Inhalt  Combinatorics is a fundamental mathematical discipline as well as an essential component of many mathematical areas, and its study has experienced an impressive growth in recent years. While in the past many of the basic combinatorial results were obtained mainly by ingenuity and detailed reasoning, the modern theory has grown out of this early stage and often relies on deep, welldeveloped tools. One of the main general techniques that played a crucial role in the development of Combinatorics was the application of algebraic methods. The most fruitful such tool is the dimension argument. Roughly speaking, the method can be described as follows. In order to bound the cardinality of of a discrete structure A one maps its elements to vectors in a linear space, and shows that the set A is mapped to linearly independent vectors. It then follows that the cardinality of A is bounded by the dimension of the corresponding linear space. This simple idea is surprisingly powerful and has many famous applications. This course provides a gentle introduction to Algebraic methods, illustrated by examples and focusing on basic ideas and connections to other areas. The topics covered in the class will include (but are not limited to): Basic dimension arguments, Spaces of polynomials and tensor product methods, Eigenvalues of graphs and their application, the Combinatorial Nullstellensatz and the ChevalleyWarning theorem. Applications such as: Solution of Kakeya problem in finite fields, counterexample to Borsuk's conjecture, chromatic number of the unit distance graph of Euclidean space, explicit constructions of Ramsey graphs and many others. The course website can be found at https://moodleapp2.let.ethz.ch/course/view.php?id=15757  
Skript  Lectures will be on the blackboard only, but there will be a set of typeset lecture notes which follow the class closely.  
Voraussetzungen / Besonderes  Students are expected to have a mathematical background and should be able to write rigorous proofs.  
227014710L  VLSI 3: FullCustom Digital Circuit Design  W  6 KP  2V + 3U  C. Studer, O. Castañeda Fernández  
Kurzbeschreibung  This third course in our VLSI series is concerned with fullcustom digital integrated circuits. The goals include learning the design of digital circuits on the schematic, layout, gate, and registertransfer levels. The use of stateoftheart CAD software (Cadence Virtuoso) in order to simulate, optimize, and characterize digital circuits is another important topic of this course.  
Lernziel  At the end of this course, you will • understand the design of the main building blocks of stateoftheart digital integrated circuits • be able to design and optimize digital integrated circuits on the schematic, layout, and gate levels • be able to use standard industry software (Cadence Virtuoso) for drawing, simulating, and characterizing digital circuits • understand the performance tradeoffs between delay, area, and power consumption  
Inhalt  The third VLSI course begins with the basics of metaloxidesemiconductor (MOS) fieldeffect transistors (FETs) and moves up the stack towards logic gates and increasingly complex digital circuit structures. The topics of this course include: • Nanometer MOSFETs • Static and dynamic behavior of complementary MOS (CMOS) inverters • CMOS gate design, sizing, and timing • Fullcustom standardcell design • Wire models and parasitics • Latch and flipflop circuits • Gatelevel timing analysis and optimization • Static and dynamic power consumption; lowpower techniques • Alternative logic styles (dynamic logic, passtransistor logic, etc.) • Arithmetic and logic circuits • Fixedpoint and floatingpoint arithmetic • Synchronous and asynchronous design principles • Memory circuits (ROM, SRAM, and DRAM) • In and nearmemory processing architectures • Fullcustom accelerator circuits for machine learning The exercises are concerned with schematic entry, layout, and simulation of digital integrated circuits using a disciplined standardcellbased approach with Cadence Virtuoso.  
Literatur  N. H. E. Weste and D. M Harris, CMOS VLSI Design: A Circuits and Systems Perspective (4th Ed.), AddisonWesley  
Voraussetzungen / Besonderes  VLSI 3 can be taken in parallel with “VLSI 1: HDLbased design for FPGAs” and is designed to complement the topics of this course. Basic analog circuit knowledge is required.  
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