# Search result: Catalogue data in Spring Semester 2021

Electrical Engineering and Information Technology Master | ||||||

Master Studies (Programme Regulations 2008) | ||||||

Major Courses A total of 42 CP must be achieved form courses during the Master Program. 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 | |
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227-0111-00L | Communication ElectronicsDoes not take place this semester. | W | 6 credits | 2V + 2U | to be announced | |

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. | |||||

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. | |||||

227-0112-00L | High-Speed Signal Propagation | W | 6 credits | 2V + 2U | C. Bolognesi | |

Abstract | Understanding of high-speed signal propagation in microwave cables and integrated circuits and printed circuit boards. As clock frequencies rise in the GHz domain, there is a need grasp signal propagation to maintain good signal integrity in the face of symbol interference and cross-talk. The course is of high value to all interested in high-speed analog (RF, microwave) or digital systems. | |||||

Objective | Understanding of high-speed signal propagation in interconnects, microwave cables and integrated transmission lines such as microwave integrated circuits and/or printed circuit boards. As system clock frequencies continuously rise in the GHz domain, a need urgently develops to understand high-speed signal propagation in order to maintain good signal integrity in the face of phenomena such as inter-symbol interference (ISI) and cross-talk. Concepts such as Scattering parameters (or S-parameters) are key to the characterization of networks over wide bandwidths. At high frequencies, all structures effectively become "transmission lines." Unless care is taken, it is highly probable that one ends-up with a bad transmission line that causes the designed system to malfunction. Filters will also be considered because it turns out that some of the problems associated by lossy transmission channels (lines, cables, etc) can be corrected by adequate filtering in a process called "equalization." | |||||

Content | Transmission line equations of the lossless and lossy TEM-transmission line. Introduction of current and voltage waves. Representation of reflections in the time and frequency domain. Application of the Smith chart. Behavior of low-loss transmission lines. Attenuation and impulse distortion due to skin effect. Transmission line equivalent circuits. Group delay and signal dispersion. Coupled transmission lines. Scattering parameters. Butterworth-, Chebychev- and Bessel filter approximations: filter synthesis from low-pass filter prototypes. | |||||

Lecture notes | Script: Leitungen und Filter (In German). | |||||

Prerequisites / Notice | Exercises will be held in English. | |||||

227-0120-00L | 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. | |||||

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) will be useful. | |||||

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

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

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 | |||||

227-0434-10L | 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, greedy algorithms, uniform laws of large numbers, Rademacher complexity, Vapnik-Chervonenkis dimension | |||||

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, super-resolution, spectrum-blind sampling, subspace algorithms (ESPRIT), 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, best M-term approximation, greedy algorithms, fundamental limits on compressibility of signal classes, Kolmogorov-Tikhomirov epsilon-entropy of signal classes, optimal compression of signal classes 5. Uniform laws of large numbers: Rademacher complexity, Vapnik-Chervonenkis dimension, classes with polynomial discrimination | |||||

Lecture notes | Detailed lecture notes will be provided at the beginning of the semester. | |||||

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 | |||||

227-0455-00L | 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. | |||||

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, R. Pieren | |

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

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 | 8 credits | 3V + 2U + 2A | 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. | |||||

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. |

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