Search result: Catalogue data in Autumn Semester 2022
Electrical Engineering and Information Technology Master  
Master Studies (Programme Regulations 2008)  
Major Courses A total of 42 CP must be achieved during the Master Programme. The individual study plan is subject to the tutor's approval.  
Communication  
Recommended Subjects These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor.  
Number  Title  Type  ECTS  Hours  Lecturers  

227010200L  Discrete Event Systems  W  6 credits  4G  L. Josipovic, 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 averagecase and from a worstcase perspective. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems, WorstCase Event Systems, Verification, Network Calculus.  
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 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.  
Content  1. Introduction 2. Automata and Languages 3. Smarter Automata 4. Specification Models 5. Stochastic Discrete Event Systems 6. WorstCase 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 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 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.  
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. 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.  
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. EmamiNaeini. Feedback Control of Dynamic Systems. AddisonWesley, 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.  
227011600L  VLSI 1: HDL Based Design for 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 industrialquality circuits.  
Objective  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.  
Content  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.  
Lecture notes  Textbook and all further documents in English.  
Literature  H. Kaeslin: "TopDown Digital VLSI Design, from Architectures to GateLevel 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: Link  
227016600L  Analog Integrated Circuits  W  6 credits  2V + 2U  T. Jang  
Abstract  This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies.  
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 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.  
Lecture notes  Handouts of presented slides. No script but an accompanying textbook is recommended.  
Literature  Behzad Razavi, Design of Analog CMOS Integrated Circuits (Irwin Electronics & Computer Engineering) 1st or 2nd edition, McGrawHill Education  
227030100L  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.  
Objective  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.  
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), 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.  
Lecture notes  Lecture notes are handed out.  
Literature  Govind P. Agrawal; "FiberOptic Communication Systems"; Wiley, 2010  
Prerequisites / Notice  Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics.  
227042300L  Neural Network Theory Does not take place this semester.  W  4 credits  2V + 1U  H. Bölcskei  
Abstract  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.  
Objective  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.  
Content  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  
Lecture notes  Detailed lecture notes are available on the course web page Link  
Prerequisites / Notice  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 credits  3V + 1U  E. Konukoglu, F. Yu  
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.  
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 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.  
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.  
227046800L  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 (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.  
Objective  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.  
Content  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.  
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: Link The graph methods are also supported with teaching videos: Link , and a Pythonbased opensource tool to manipulate graphs is available on Link Some material is protected by password; students from ETHZ who are interested can write to Link to ask for the password even if they do not attend the lecture.  
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.  
Competencies 
 
227047700L  Acoustics I  W  3 credits  2G  K. Heutschi  
Abstract  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.  
Objective  Understanding of the basic acoustical concepts and methods. Ability to understand the technical and scientific literature. Confidence in the use of measuring instruments.  
Content  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.  
Lecture notes  yes  
Competencies 
 
252053500L  Advanced Machine Learning  W  10 credits  3V + 2U + 4A  J. M. Buhmann, C. Cotrini Jimenez  
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.  
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 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  
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. 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 credits  2V + 2U + 3A  A. Perrig, S. Frei, M. Legner, K. Paterson  
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 indepth study of network attack techniques and methods to defend against them.  
Objective   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.  
Content  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.  
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 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.  
Competencies 
 
401305564L  Algebraic Methods in Combinatorics Does not take place this semester.  W  6 credits  2V + 1U  B. Sudakov  
Abstract  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.  
Objective  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.  
Content  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 Link  
Lecture notes  Lectures will be on the blackboard only, but there will be a set of typeset lecture notes which follow the class closely.  
Prerequisites / Notice  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 credits  2V + 3U  C. Studer, O. Castañeda Fernández  
Abstract  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.  
Objective  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  
Content  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.  
Literature  N. H. E. Weste and D. M Harris, CMOS VLSI Design: A Circuits and Systems Perspective (4th Ed.), AddisonWesley  
Prerequisites / Notice  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.  
Competencies 

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