# Search result: Catalogue data in Spring Semester 2020

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 | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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227-0104-00L | Communication and Detection Theory | W | 6 credits | 4G | A. Lapidoth | |

Abstract | This course teaches the foundations of modern digital communications and detection theory. Topics include the geometry of the space of energy-limited signals; the baseband representation of passband signals, spectral efficiency and the Nyquist Criterion; the power and power spectral density of PAM and QAM; hypothesis testing; Gaussian stochastic processes; and detection in white Gaussian noise. | |||||

Learning objective | This is an introductory class to the field of wired and wireless communication. It offers a glimpse at classical analog modulation (AM, FM), but mainly focuses on aspects of modern digital communication, including modulation schemes, spectral efficiency, power budget analysis, block and convolu- tional codes, receiver design, and multi- accessing schemes such as TDMA, FDMA and Spread Spectrum. | |||||

Content | - Baseband representation of passband signals. - Bandwidth and inner products in baseband and passband. - The geometry of the space of energy-limited signals. - The Sampling Theorem as an orthonormal expansion. - Sampling passband signals. - Pulse Amplitude Modulation (PAM): energy, power, and power spectral density. - Nyquist Pulses. - Quadrature Amplitude Modulation (QAM). - Hypothesis testing. - The Bhattacharyya Bound. - The multivariate Gaussian distribution - Gaussian stochastic processes. - Detection in white Gaussian noise. | |||||

Lecture notes | n/a | |||||

Literature | A. Lapidoth, A Foundation in Digital Communication, Cambridge University Press, 2nd edition (2017) | |||||

227-0120-00L | Communication Networks | W | 6 credits | 4G | L. Vanbever | |

Abstract | At the end of this course, you will understand the fundamental concepts behind communication networks and the Internet. Specifically, you will be able to: - understand how the Internet works; - build and operate Internet-like infrastructures; - identify the right set of metrics to evaluate the performance of a network and propose ways to improve it. | |||||

Learning objective | At the end of the course, the students will understand the fundamental concepts of communication networks and Internet-based communications. Specifically, students will be able to: - understand how the Internet works; - build and operate Internet-like network infrastructures; - identify the right set of metrics to evaluate the performance or the adequacy of a network and propose ways to improve it (if any). The course will introduce the relevant mechanisms used in today's networks both from an abstract perspective but also from a practical one by presenting many real-world examples and through multiple hands-on projects. For more information about the lecture, please visit: https://comm-net.ethz.ch | |||||

Lecture notes | Lecture notes and material for the course will be available before each course on: https://comm-net.ethz.ch | |||||

Literature | Most of course follows the textbook "Computer Networking: A Top-Down Approach (6th Edition)" by Kurose and Ross. | |||||

Prerequisites / Notice | No prior networking background is needed. The course will include some programming assignments (in Python) for which the material covered in Technische Informatik 1 (227-0013-00L) and Technische Informatik 2 (227-0014-00L) will be useful. | |||||

227-0125-00L | Optics and Photonics | W | 6 credits | 2V + 2U | J. Leuthold | |

Abstract | This lecture covers both - the fundamentals of "Optics" such as e.g. "ray optics", "coherence", the "Planck law" or the "Einstein relations" but also the fundamentals of "Photonics" on the generation, processing, transmission and detection of photons. | |||||

Learning objective | A sound base for work in the field of optics and photonics will be given. | |||||

Content | Chapter 1: Ray Optics Chapter 2: Electromagnetic Optics Chapter 3: Polarization Chapter 4: Coherence and Interference Chapter 5: Fourier Optics and Diffraction Chapter 6: Guided Wave Optics Chapter 7: Optical Fibers Chapter 8: The Laser | |||||

Lecture notes | Lecture notes will be handed out. | |||||

Prerequisites / Notice | Fundamentals of Electromagnetic Fields (Maxwell Equations) & Bachelor Lectures on Physics. | |||||

Advanced Core Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

227-0147-00L | VLSI II: Design of Very Large Scale Integration Circuits | W | 6 credits | 5G | F. K. Gürkaynak, L. Benini | |

Abstract | This second course in our VLSI series is concerned with how to turn digital circuit netlists into safe, testable and manufacturable mask layout, taking into account various parasitic effects. Low-power circuit design is another important topic. Economic aspects and management issues of VLSI projects round off the course. | |||||

Learning objective | Know how to design digital VLSI circuits that are safe, testable, durable, and make economic sense. | |||||

Content | The second course begins with a thorough discussion of various technical aspects at the circuit and layout level before moving on to economic issues of VLSI. Topics include: - The difficulties of finding fabrication defects in large VLSI chips. - How to make integrated circuit testable (design for test). - Synchronous clocking disciplines compared, clock skew, clock distribution, input/output timing. - Synchronization and metastability. - CMOS transistor-level circuits of gates, flip-flops and random access memories. - Sinks of energy in CMOS circuits. - Power estimation and low-power design. - Current research in low-energy computing. - Layout parasitics, interconnect delay, static timing analysis. - Switching currents, ground bounce, IR-drop, power distribution. - Floorplanning, chip assembly, packaging. - Layout design at the mask level, physical design verification. - Electromigration, electrostatic discharge, and latch-up. - Models of industrial cooperation in microelectronics. - The caveats of virtual components. - The cost structures of ASIC development and manufacturing. - Market requirements, decision criteria, and case studies. - Yield models. - Avenues to low-volume fabrication. - Marketing considerations and case studies. - Management of VLSI projects. Exercises are concerned with back-end design (floorplanning, placement, routing, clock and power distribution, layout verification). Industrial CAD tools are being used. | |||||

Lecture notes | H. Kaeslin: "Top-Down Digital VLSI Design, from Gate-Level Circuits to CMOS Fabrication", Lecture Notes Vol.2 , 2015. All written 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 | Highlight: Students are offered the opportunity to design a circuit of their own which then gets actually fabricated as a microchip! Students who elect to participate in this program register for a term project at the Integrated Systems Laboratory in parallel to attending the VLSI II course. Prerequisites: "VLSI I: from Architectures to Very Large Scale Integration Circuits and FPGAs" or equivalent knowledge. Further details: https://vlsi2.ethz.ch | |||||

227-0418-00L | Algebra and Error Correcting Codes | W | 6 credits | 4G | H.‑A. Loeliger | |

Abstract | The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra. | |||||

Learning objective | The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra. | |||||

Content | Error correcting codes: coding and modulation, linear codes, Hamming space codes, Euclidean space codes, trellises and Viterbi decoding, convolutional codes, factor graphs and message passing algorithms, low-density parity check codes, turbo codes, polar codes, Reed-Solomon codes. Algebra: groups, rings, homomorphisms, quotient groups, ideals, finite fields, vector spaces, polynomials. | |||||

Lecture notes | Lecture Notes (english) | |||||

227-0420-00L | Information Theory II Does not take place this semester. | W | 6 credits | 2V + 2U | A. Lapidoth | |

Abstract | This course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory. | |||||

Learning objective | The course has two objectives: to introduce the students to the key information theoretic results that underlay the design of communication systems and to equip the students with the tools that are needed to conduct research in Information Theory. | |||||

Content | Differential entropy, maximum entropy, the Gaussian channel and water filling, the entropy-power inequality, Sanov's Theorem, Fisher information, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, and the Gelfand-Pinsker problem. | |||||

Lecture notes | n/a | |||||

Literature | T.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006 | |||||

227-0436-00L | Digital Communication and Signal Processing | W | 6 credits | 2V + 2U | A. Wittneben | |

Abstract | A comprehensive presentation of modern digital modulation, detection and synchronization schemes and relevant aspects of signal processing enables the student to analyze, simulate, implement and research the physical layer of advanced digital communication schemes. The course both covers the underlying theory and provides problem solving and hands-on experience. | |||||

Learning objective | Digital communication systems are characterized by ever increasing requirements on data rate, spectral efficiency and reliability. Due to the huge advances in very large scale integration (VLSI) we are now able to implement extremely complex digital signal processing algorithms to meet these challenges. As a result the physical layer (PHY) of digital communication systems has become the dominant function in most state-of-the-art system designs. In this course we discuss the major elements of PHY implementations in a rigorous theoretical fashion and present important practical examples to illustrate the application of the theory. In Part I we treat discrete time linear adaptive filters, which are a core component to handle multiuser and intersymbol interference in time-variant channels. Part II is a seminar block, in which the students develop their analytical and experimental (simulation) problem solving skills. After a review of major aspects of wireless communication we discuss, simulate and present the performance of novel cooperative and adaptive multiuser wireless communication systems. As part of this seminar each students has to give a 15 minute presentation and actively attends the presentations of the classmates. In Part III we cover parameter estimation and synchronization. Based on the classical discrete detection and estimation theory we develop maximum likelihood inspired digital algorithms for symbol timing and frequency synchronization. | |||||

Content | Part I: Linear adaptive filters for digital communication • Finite impulse response (FIR) filter for temporal and spectral shaping • Wiener filters • Method of steepest descent • Least mean square adaptive filters Part II: Seminar block on cooperative wireless communication • review of the basic concepts of wireless communication • multiuser amplify&forward relaying • performance evaluation of adaptive A&F relaying schemes and student presentations Part III: Parameter estimation and synchronization • Discrete detection theory • Discrete estimation theory • Synthesis of synchronization algorithms • Frequency estimation • Timing adjustment by interpolation | |||||

Lecture notes | Lecture notes. | |||||

Literature | [1] Oppenheim, A. V., Schafer, R. W., "Discrete-time signal processing", Prentice-Hall, ISBN 0-13-754920-2. [2] Haykin, S., "Adaptive filter theory", Prentice-Hall, ISBN 0-13-090126-1. [3] Van Trees, H. L., "Detection , estimation and modulation theory", John Wiley&Sons, ISBN 0-471-09517-6. [4] Meyr, H., Moeneclaey, M., Fechtel, S. A., "Digital communication receivers: synchronization, channel estimation and signal processing", John Wiley&Sons, ISBN 0-471-50275-8. | |||||

Prerequisites / Notice | Formal prerequisites: none Recommended: Communication Systems or equivalent | |||||

227-0558-00L | Principles of Distributed Computing | W | 7 credits | 2V + 2U + 2A | R. Wattenhofer, M. Ghaffari | |

Abstract | We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques. | |||||

Learning objective | Distributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week. | |||||

Content | Distributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems. Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds | |||||

Lecture notes | Available. Our course script is used at dozens of other universities around the world. | |||||

Literature | Lecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world. Distributed Computing: Fundamentals, Simulations and Advanced Topics Hagit Attiya, Jennifer Welch. McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6 Introduction to Algorithms Thomas Cormen, Charles Leiserson, Ronald Rivest. The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8 Disseminatin of Information in Communication Networks Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger. Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2 Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Frank Thomson Leighton. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1 Distributed Computing: A Locality-Sensitive Approach David Peleg. Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8 | |||||

Prerequisites / Notice | Course pre-requisites: Interest in algorithmic problems. (No particular course needed.) | |||||

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 | |

227-0216-00L | Control Systems II | W | 6 credits | 4G | R. Smith | |

Abstract | Introduction to basic and advanced concepts of modern feedback control. | |||||

Learning objective | Introduction to basic and advanced concepts of modern feedback control. | |||||

Content | This course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues. | |||||

Lecture notes | The slides of the lecture are available to download. | |||||

Literature | Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005. | |||||

Prerequisites / Notice | Prerequisites: Control Systems or equivalent | |||||

227-0384-00L | Ultrasound Fundamentals, Imaging, and Medical ApplicationsCourse is offered for the last time in Spring Semester 2020. | W | 4 credits | 3G | O. Göksel | |

Abstract | Ultrasound is the only imaging modality that is nonionizing (safe), real-time, cost-effective, and portable, with many medical uses in diagnosis, intervention guidance, surgical navigation, and as a therapeutic option. In this course, we introduce conventional and prospective applications of ultrasound, starting with the fundamentals of ultrasound physics and imaging. | |||||

Learning objective | Students can use the fundamentals of ultrasound, to analyze and evaluate ultrasound imaging techniques and applications, in particular in the field of medicine, as well as to design and implement basic applications. | |||||

Content | Ultrasound is used in wide range of products, from car parking sensors, to assessing fault lines in tram wheels. Medical imaging is the eye of the doctor into body; and ultrasound is the only imaging modality that is nonionizing (safe), real-time, cheap, and portable. Some of its medical uses include diagnosing breast and prostate cancer, guiding needle insertions/biopsies, screening for fetal anomalies, and monitoring cardiac arrhythmias. Ultrasound physically interacts with the tissue, and thus can also be used therapeutically, e.g., to deliver heat to treat tumors, break kidney stones, and targeted drug delivery. Recent years have seen several novel ultrasound techniques and applications – with many more waiting in the horizon to be discovered. This course covers ultrasonic equipment, physics of wave propagation, numerical methods for its simulation, image generation, beamforming (basic delay-and-sum and advanced methods), transducers (phased-, linear-, convex-arrays), near- and far-field effect, imaging modes (e.g., A-, M-, B-mode), Doppler and harmonic imaging, ultrasound signal processing techniques (e.g., filtering, time-gain-compensation, displacement tracking), image analysis techniques (deconvolution, real-time processing, tracking, segmentation, computer-assisted interventions), acoustic-radiation force, plane-wave imaging, contrast agents, micro-bubbles, elastography, biomechanical characterization, high-intensity focused ultrasound and therapy, lithotripsy, histotripsy, photo-acoustics phenomenon and opto-acoustic imaging, as well as sample non-medical applications such as the basics of non-destructive testing (NDT). Hands-on exercises: These will help to apply the concepts learned in the course, using simulation environments (such as Matlab k-Wave and FieldII toolboxes). The exercises will involve a mix of design, implementation, and evaluation examples commonly encountered in practical applications. Project: Current and relevant applications in the field of ultrasound are offered as project topics. Projects will be carried out throughout the course, where the project reporting and presentations will be due towards the end of the semester. These will be part of the assessment in grading. | |||||

Prerequisites / Notice | Prerequisites: Familiarity with basic numerical methods. Basic programming skills in Matlab. | |||||

227-0434-10L | Mathematics of Information | W | 8 credits | 3V + 2U + 2A | H. Bölcskei | |

Abstract | The class focuses on mathematical aspects of 1. Information science: Sampling theorems, frame theory, compressed sensing, sparsity, super-resolution, spectrum-blind sampling, subspace algorithms, dimensionality reduction 2. Learning theory: Approximation theory, uniform laws of large numbers, Rademacher complexity, Vapnik-Chervonenkis dimension | |||||

Learning objective | The aim of the class is to familiarize the students with the most commonly used mathematical theories in data science, high-dimensional data analysis, and learning theory. The class consists of the lecture, exercise sessions with homework problems, and of a research project, which can be carried out either individually or in groups. The research project consists of either 1. software development for the solution of a practical signal processing or machine learning problem or 2. the analysis of a research paper or 3. a theoretical research problem of suitable complexity. Students are welcome to propose their own project at the beginning of the semester. The outcomes of all projects have to be presented to the entire class at the end of the semester. | |||||

Content | Mathematics of Information 1. Signal representations: Frame theory, wavelets, Gabor expansions, sampling theorems, density theorems 2. Sparsity and compressed sensing: Sparse linear models, uncertainty relations in sparse signal recovery, matching pursuits, super-resolution, spectrum-blind sampling, subspace algorithms (MUSIC, ESPRIT, matrix pencil), estimation in the high-dimensional noisy case, Lasso 3. Dimensionality reduction: Random projections, the Johnson-Lindenstrauss Lemma Mathematics of Learning 4. Approximation theory: Nonlinear approximation theory, fundamental limits on compressibility of signal classes, Kolmogorov-Tikhomirov epsilon-entropy of signal classes, optimal compression of signal classes, recovery from incomplete data, information-based complexity, curse of dimensionality 5. Uniform laws of large numbers: Rademacher complexity, Vapnik-Chervonenkis dimension, classes with polynomial discrimination, blessings of dimensionality | |||||

Lecture notes | Detailed lecture notes will be provided at the beginning of the semester and as we go along. | |||||

Prerequisites / Notice | This course is aimed at students with a background in basic linear algebra, analysis, statistics, and probability. We encourage students who are interested in mathematical data science to take both this course and "401-4944-20L Mathematics of Data Science" by Prof. A. Bandeira. The two courses are designed to be complementary. H. Bölcskei and A. Bandeira | |||||

227-0441-00L | Mobile Communications: 5G and Internet of Things Offered for the last time in spring 2020. | W | 6 credits | 4G | M. Kuhn | |

Abstract | Present and future (5G and cellular IoT) of mobile communication systems. The lecture course covers design, analysis and applications of current and upcoming mobile communication systems. Important topics include: mobile wireless channels, PHY technologies, cellular networks, Internet-of-Things, autonomous driving, QoS measurements and evaluation, benchmarking. | |||||

Learning objective | By the end of this course, students will be able to - understand and explain the characteristics of mobile wireless channels - illustrate physical layer limits and challenges of mobile communication systems - model and simulate PHY technologies of WiFi 6 (802.11ax) and 3G, 4G, and 5G mobile communications standards - explain the use of wireless technologies in the context of IoT and autonomous driving - understand and illustrate the use of QoS measurements in mobile networks for benchmarking | |||||

Content | The 4G and 5G mobile communication networks are key enablers for digitalization in general and, particularly, for applications in the areas of IoT and autonomous systems (e.g. autonomous driving). We start with an analysis of the physical (PHY) layer of current and future wireless communication systems, and compare different PHY implementations in 4G, 5G and WiFi 6. Based on these considerations we study applications of wireless technologies in the area of the Internet of Things (IoT) and autonomous systems and identify the potential as well as the challenges. - Introduction - The mobile wireless channel, propagation of electromagnetic waves - Mobile communication, modulation techniques, OFDM, MIMO - Wireless networks and wireless standards (WiFi 6 and 3G, 4G, 5G) - Applications: IoT, autonomous driving etc. - Quality of service (QoS) in wireless networks (definitions, Key Performance Indicators) - QoS measurements (e.g. network coverage, delay, throughput) and their statistical evaluation - Benchmarking (methodology, statistical methods and models) Weekly exercises are included in the lecture. | |||||

Lecture notes | Lecture slides are available. | |||||

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

Prerequisites / Notice | English | |||||

227-0455-00L | Terahertz: Technology and Applications | W | 5 credits | 3G + 3A | K. Sankaran | |

Abstract | This block course will provide a solid foundation for understanding physical principles of THz applications. We will discuss various building blocks of THz technology - components dealing with generation, manipulation, and detection of THz electromagnetic radiation. We will introduce THz applications in the domain of imaging, sensing, communications, non-destructive testing and evaluations. | |||||

Learning objective | This is an introductory course on Terahertz (THz) technology and applications. Devices operating in THz frequency range (0.1 to 10 THz) have been increasingly studied in the recent years. Progress in nonlinear optical materials, ultrafast optical and electronic techniques has strengthened research in THz application developments. Due to unique interaction of THz waves with materials, applications with new capabilities can be developed. In theory, they can penetrate somewhat like X-rays, but are not considered harmful radiation, because THz energy level is low. They should be able to provide resolution as good as or better than magnetic resonance imaging (MRI), possibly with simpler equipment. Imaging, very-high bandwidth communication, and energy harvesting are the most widely explored THz application areas. We will study the basics of THz generation, manipulation, and detection. Our emphasis will be on the physical principles and applications of THz in the domain of imaging, sensing, communications, non-destructive testing and evaluations. The second part of the block course will be a short project work related to the topics covered in the lecture. The learnings from the project work should be presented in the end. | |||||

Content | PART I: - INTRODUCTION - Chapter 1: Introduction to THz Physics Chapter 2: Components of THz Technology - THz TECHNOLOGY MODULES - Chapter 3: THz Generation Chapter 4: THz Detection Chapter 5: THz Manipulation - APPLICATIONS - Chapter 6: THz Imaging / Sensing / Communication Chapter 7: THz Non-destructive Testing Chapter 8: THz Applications in Plastic & Recycling Industries PART 2: - PROJECT WORK - Short project work related to the topics covered in the lecture. Short presentation of the learnings from the project work. Full guidance and supervision will be given for successful completion of the short project work. | |||||

Lecture notes | Soft-copy of lectures notes will be provided. | |||||

Literature | - Yun-Shik Lee, Principles of Terahertz Science and Technology, Springer 2009 - Ali Rostami, Hassan Rasooli, and Hamed Baghban, Terahertz Technology: Fundamentals and Applications, Springer 2010 | |||||

Prerequisites / Notice | Basic foundation in physics, particularly, electromagnetics is required. Students who want to refresh their electromagnetics fundamentals can get additional material required for the course. | |||||

227-0478-00L | Acoustics II | W | 6 credits | 4G | K. Heutschi | |

Abstract | Advanced knowledge of the functioning and application of electro-acoustic transducers. | |||||

Learning objective | Advanced knowledge of the functioning and application of electro-acoustic transducers. | |||||

Content | Electrical, mechanical and acoustical analogies. Transducers, microphones and loudspeakers, acoustics of musical instruments, sound recording, sound reproduction, digital audio. | |||||

Lecture notes | available | |||||

252-0526-00L | Statistical Learning Theory | W | 7 credits | 3V + 2U + 1A | J. M. Buhmann, C. Cotrini Jimenez | |

Abstract | The course covers advanced methods of statistical learning: - Variational methods and optimization. - Deterministic annealing. - Clustering for diverse types of data. - Model validation by information theory. | |||||

Learning objective | The course surveys recent methods of statistical learning. The fundamentals of machine learning, as presented in the courses "Introduction to Machine Learning" and "Advanced Machine Learning", are expanded from the perspective of statistical learning. | |||||

Content | - Variational methods and optimization. We consider optimization approaches for problems where the optimizer is a probability distribution. We will discuss concepts like maximum entropy, information bottleneck, and deterministic annealing. - Clustering. This is the problem of sorting data into groups without using training samples. We discuss alternative notions of "similarity" between data points and adequate optimization procedures. - Model selection and validation. This refers to the question of how complex the chosen model should be. In particular, we present an information theoretic approach for model validation. - Statistical physics models. We discuss approaches for approximately optimizing large systems, which originate in statistical physics (free energy minimization applied to spin glasses and other models). We also study sampling methods based on these models. | |||||

Lecture notes | A draft of a script will be provided. Lecture slides will be made available. | |||||

Literature | Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001. L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996 | |||||

Prerequisites / Notice | Knowledge of machine learning (introduction to machine learning and/or advanced machine learning) Basic knowledge of statistics. | |||||

227-0111-00L | Communication Electronics | W | 6 credits | 2V + 2U | Q. Huang | |

Abstract | Electronics for communications systems, with emphasis on realization. Low noise amplifiers, modulators and demodulators, transmit amplifiers and oscillators are discussed in the context of wireless communications. Wireless receiver, transmitter and frequency synthesizer will be described. Importance of and trade offs among sensitivity, linearity and selectivity are discussed extensively. | |||||

Learning objective | Foundation course for understanding modern electronic circuits for communication applications. We learn how theoretical communications principles are reduced to practice using transistors, switches, inductors, capacitors and resistors. The harsh environment such communication electronics will be exposed to and the resulting requirements on the sensitivity, linearity and selectivity help explain the design trade offs encountered in every circuit block found in a modern transceiver. | |||||

Content | Accounting for more than two trillion dollars per year, communications is one of the most important drivers for advanced economies of our time. Wired networks have been a key enabler to the internet age and the proliferation of search engines, social networks and electronic commerce, whereas wireless communications, cellular networks in particular, have liberated people and increased productivity in developed and developing nations alike. Integrated circuits that make such communications devices light weight and affordable have played a key role in the proliferation of communications. This course introduces our students to the key components that realize the tangible products in electronic form. We begin with an introduction to wireless communications, and describe the harsh environment in which a transceiver has to work reliably. In this context we highlight the importance of sensitivity or low noise, linearity, selectivity, power consumption and cost, that are all vital to a competitive device in such applications. We shall review bipolar and MOS devices from a designer's prospectives, before discussing basic amplifier structures - common emitter/source, common base/gate configurations, their noise performance and linearity, impedance matching, and many other things one needs to know about a low noise amplifier. We will discuss modulation, and the mixer that enables its implementation. Noise and linearity form an inseparable part of the discussion of its design, but we also introduce the concept of quadrature demodulator, image rejection, and the effects of mismatch on performance. When mixers are used as a modulator the signals they receive are usually large and the natural linearity of transistors becomes insufficient. The concept of feedback will be introduced and its function as an improver of linearity studied in detail. Amplifiers in the transmit path are necessary to boost the power level before the signal leaves an integrated circuit to drive an even more powerful amplifier (PA) off chip. Linearized pre-amplifiers will be studied as part of the transmitter. A crucial part of a mobile transceiver terminal is the generation of local oscillator signals at the desired frequencies that are required for modulation and demodulation. Oscillators will be studied, starting from stability criteria of an electronic system, then leading to criteria for controlled instability or oscillation. Oscillator design will be discussed in detail, including that of crystal controlled oscillators which provide accurate time base. An introduction to phase-locked loops will be made, illustrating how it links a variable frequency oscillator to a very stable fixed frequency crystal oscillator, and how phase detector, charge pump and programmable dividers all serve to realize an agile frequency synthesizer that is very stable in each frequency synthesized. | |||||

Lecture notes | Script is available online under https://iis-students.ee.ethz.ch/lectures/communication-electronics/ | |||||

Prerequisites / Notice | The course Analog Integrated Circuits is recommended as preparation for this course. | |||||

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 | |

227-0104-00L | Communication and Detection Theory | W | 6 credits | 4G | A. Lapidoth | |

Abstract | This course teaches the foundations of modern digital communications and detection theory. Topics include the geometry of the space of energy-limited signals; the baseband representation of passband signals, spectral efficiency and the Nyquist Criterion; the power and power spectral density of PAM and QAM; hypothesis testing; Gaussian stochastic processes; and detection in white Gaussian noise. | |||||

Learning objective | This is an introductory class to the field of wired and wireless communication. It offers a glimpse at classical analog modulation (AM, FM), but mainly focuses on aspects of modern digital communication, including modulation schemes, spectral efficiency, power budget analysis, block and convolu- tional codes, receiver design, and multi- accessing schemes such as TDMA, FDMA and Spread Spectrum. | |||||

Content | - Baseband representation of passband signals. - Bandwidth and inner products in baseband and passband. - The geometry of the space of energy-limited signals. - The Sampling Theorem as an orthonormal expansion. - Sampling passband signals. - Pulse Amplitude Modulation (PAM): energy, power, and power spectral density. - Nyquist Pulses. - Quadrature Amplitude Modulation (QAM). - Hypothesis testing. - The Bhattacharyya Bound. - The multivariate Gaussian distribution - Gaussian stochastic processes. - Detection in white Gaussian noise. | |||||

Lecture notes | n/a | |||||

Literature | A. Lapidoth, A Foundation in Digital Communication, Cambridge University Press, 2nd edition (2017) | |||||

227-0120-00L | Communication Networks | W | 6 credits | 4G | L. Vanbever | |

Abstract | At the end of this course, you will understand the fundamental concepts behind communication networks and the Internet. Specifically, you will be able to: - understand how the Internet works; - build and operate Internet-like infrastructures; - identify the right set of metrics to evaluate the performance of a network and propose ways to improve it. | |||||

Learning objective | At the end of the course, the students will understand the fundamental concepts of communication networks and Internet-based communications. Specifically, students will be able to: - understand how the Internet works; - build and operate Internet-like network infrastructures; - identify the right set of metrics to evaluate the performance or the adequacy of a network and propose ways to improve it (if any). The course will introduce the relevant mechanisms used in today's networks both from an abstract perspective but also from a practical one by presenting many real-world examples and through multiple hands-on projects. For more information about the lecture, please visit: https://comm-net.ethz.ch | |||||

Lecture notes | Lecture notes and material for the course will be available before each course on: https://comm-net.ethz.ch | |||||

Literature | Most of course follows the textbook "Computer Networking: A Top-Down Approach (6th Edition)" by Kurose and Ross. | |||||

Prerequisites / Notice | No prior networking background is needed. The course will include some programming assignments (in Python) for which the material covered in Technische Informatik 1 (227-0013-00L) and Technische Informatik 2 (227-0014-00L) will be useful. | |||||

Advanced Core Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

227-0558-00L | Principles of Distributed Computing | W | 7 credits | 2V + 2U + 2A | R. Wattenhofer, M. Ghaffari | |

Abstract | We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques. | |||||

Learning objective | Distributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week. | |||||

Content | Distributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems. Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds | |||||

Lecture notes | Available. Our course script is used at dozens of other universities around the world. | |||||

Literature | Lecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world. Distributed Computing: Fundamentals, Simulations and Advanced Topics Hagit Attiya, Jennifer Welch. McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6 Introduction to Algorithms Thomas Cormen, Charles Leiserson, Ronald Rivest. The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8 Disseminatin of Information in Communication Networks Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger. Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2 Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Frank Thomson Leighton. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1 Distributed Computing: A Locality-Sensitive Approach David Peleg. Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8 | |||||

Prerequisites / Notice | Course pre-requisites: Interest in algorithmic problems. (No particular course needed.) | |||||

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 | |

101-0178-01L | Uncertainty Quantification in Engineering | W | 3 credits | 2G | S. Marelli | |

Abstract | Uncertainty quantification aims at studying the impact of aleatory and epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data (copula theory), uncertainty propagation techniques (Monte Carlo simulation, polynomial chaos expansions), and sensitivity analysis. | |||||

Learning objective | After this course students will be able to properly pose an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. The course is suitable for any master/Ph.D. student in engineering or natural sciences, physics, mathematics, computer science with a basic knowledge in probability theory. | |||||

Content | The course introduces uncertainty quantification through a set of practical case studies that come from civil, mechanical, nuclear and electrical engineering, from which a general framework is introduced. The course in then divided into three blocks: probabilistic modelling (introduction to copula theory), uncertainty propagation (Monte Carlo simulation and polynomial chaos expansions) and sensitivity analysis (correlation measures, Sobol' indices). Each block contains lectures and tutorials using Matlab and the in-house software UQLab (www.uqlab.com). | |||||

Lecture notes | Detailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester. | |||||

Prerequisites / Notice | A basic background in probability theory and statistics (bachelor level) is required. A summary of useful notions will be handed out at the beginning of the course. A good knowledge of Matlab is required to participate in the tutorials and for the mini-project. |

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