# Search result: Catalogue data in Spring Semester 2015

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

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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227-0116-00L | VLSI I: From Architectures to VLSI Circuits and FPGAs | W | 7 credits | 5G | H. Kaeslin, N. Felber | |

Abstract | This first course in a series that extends over three consecutive terms is concerned with tailoring algorithms and with devising high performance hardware architectures for their implementation as ASIC or with FPGAs. The focus is on front end design using HDLs and automatic synthesis for producing industrial-quality circuits. | |||||

Objective | Understand Very-Large-Scale Integrated Circuits (VLSI chips), Application-Specific Integrated Circuits (ASIC), and Field-Programmable Gate-Arrays (FPGA). Know their organization and be able to identify suitable application areas. Become fluent in front-end design from architectural conception to gate-level netlists. How to model digital circuits with VHDL or SystemVerilog. How to ensure they behave as expected with the aid of simulation, testbenches, and assertions. How to take advantage of automatic synthesis tools to produce industrial-quality VLSI and FPGA circuits. Gain practical experience with the hardware description language VHDL and with industrial Electronic Design Automation (EDA) tools. | |||||

Content | This course is concerned with system-level issues of VLSI design and FPGA implementations. Topics include: - Overview on design methodologies and fabrication depths. - Levels of abstraction for circuit modeling. - Organization and configuration of commercial field-programmable components. - VLSI and FPGA design flows. - Dedicated and general purpose architectures compared. - How to obtain an architecture for a given processing algorithm. - Meeting throughput, area, and power goals by way of architectural transformations. - Hardware Description Languages (HDL) and the underlying concepts. - VHDL and SystemVerilog compared. - VHDL (IEEE standard 1076) for simulation and synthesis. - A suitable nine-valued logic system (IEEE standard 1164). - Register Transfer Level (RTL) synthesis and its limitations. - Building blocks of digital VLSI circuits. - Functional verification techniques and their limitations. - Modular and largely reusable testbenches. - Assertion-based verification. - Synchronous versus asynchronous circuits. - The case for synchronous circuits. - Periodic events and the Anceau diagram. - Case studies, ASICs compared to microprocessors, DSPs, and FPGAs. During the exercises, students learn how to model digital ICs with VHDL. They write testbenches for simulation purposes and synthesize gate-level netlists for VLSI chips and FPGAs. Only commercial EDA software by leading vendors is being used. | |||||

Lecture notes | Textbook and all further documents in English. | |||||

Literature | H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303. | |||||

Prerequisites / Notice | Prerequisites: Basics of digital circuits. Examination: In written form following the course semester (spring term). Problems are given in English, answers will be accepted in either English oder German. Further details: http://www.iis.ee.ethz.ch/stud_area/vorlesungen/vlsi1.en.html | |||||

227-0148-00L | VLSI III: Test and Fabrication of VLSI Circuits | W | 6 credits | 4G | N. Felber, H. Kaeslin | |

Abstract | This last course in our VLSI series is concerned with the manufacturing of integrated circuits (IC) in CMOS technology, with defects that may occur during the process, and ---above all--- with the methods and tools for detecting design flaws and fabrication defects. | |||||

Objective | Know how to apply methods, software tools and equipment for designing testable VLSI circuits, for testing fabricated ICs, and for physical analysis in the occurrence of defective parts. A basic understanding of modern semiconductor technologies. | |||||

Content | This final course in a series of three focusses on manufacturing, testing, physical analysis, and packaging of VLSI circuits. Future prospects of micro- and nanoelectronics are also being discussed. Topics include: - Effects of fabrication defects. - Abstraction from physical to transistor- and gate-level fault models. - Fault grading in the occurrence of large ASICs. - Generation of efficient test vector sets. - Enhancement of testability with built-in self test. - Organisation and application of automated test equipment. - Physical analysis of devices. - Packaging problems and solutions. - Today's nanometer CMOS fabrication processes (HKMG). - Optical and post optical Photolithography. - Potential alternatives to CMOS technology and MOSFET devices. - Evolution paths for design methodology. - Industrial roadmaps for the future evolution of semiconductor technology (ITRS). Exercises teach students how to use CAE/CAD software and automated equipment for testing ASICs after fabrication. Students that have submitted a design for manufacturing at the end of the 7th term do so on their own circuits. Physical analysis methods with professional equipment (AFM, DLTS) complement this training. | |||||

Lecture notes | English lecture notes. All written documents in English. | |||||

Prerequisites / Notice | Prerequisites: Basics of digital design. Further details: http://www.iis.ee.ethz.ch/stud_area/vorlesungen/vlsi3.en.html | |||||

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 | |||||

227-0366-00L | Introduction to Computational Electromagnetics | W | 6 credits | 4G | C. Hafner, J. Leuthold, J. Smajic | |

Abstract | An overview over the most prominent methods for the simulation of electromagnetic fields is given This includes domain methods such as finite differences and finite elements, method of moments, and boundary methods. Both time domain and frequency domain techniques are considered. | |||||

Objective | Overview of numerical methods for the simulation of electromagnetic fields and hands-on experiments with selected methods. | |||||

Content | Overview of concepts of the main numerical methods for the simulation of electromagnetic fields: Finite Difference Method, Finite Element Method, Transmission Line Matrix Method, Matrix Methods, Multipole Methods, Image Methods, Method of Moments, Integral Equation Methods, Beam Propagation Method, Mode Matching Technique, Spectral Domain Analysis, Method of Lines. Applications: Problems in electrostatic and magnetostatic, guided waves and free-space propagation problems, antennas, resonators, inhomogeneous transmissionlLines, nanotechnic, optics etc. | |||||

Lecture notes | Download from: http://alphard.ethz.ch/hafner/Vorles/lect.htm | |||||

Prerequisites / Notice | First half of the semester: lectures; second half of the semester: exercises in form of small projects | |||||

227-0434-00L | Harmonic Analysis: Theory and Applications in Advanced Signal Processing | W | 6 credits | 2V + 2U | H. Bölcskei | |

Abstract | This course is an introduction to the field of applied harmonic analysis with emphasis on applications in signal processing such as transform coding, inverse problems, imaging, signal recovery, and inpainting. We will consider theoretical, applied, and algorithmic aspects. | |||||

Objective | This course is an introduction to the field of applied harmonic analysis with emphasis on applications in signal processing such as transform coding, inverse problems, imaging, signal recovery, and inpainting. We will consider theoretical, applied, and algorithmic aspects. | |||||

Content | Frame theory: Frames in finite-dimensional spaces, frames for Hilbert spaces, sampling theorems as frame expansions Spectrum-blind sampling: Sampling of multi-band signals with known support set, density results by Beurling and Landau, unknown support sets, multi-coset sampling, the modulated wideband converter, reconstruction algorithms Sparse signals and compressed sensing: Uncertainty principles, recovery of sparse signals with unknown support set, recovery of sparsely corrupted signals, orthogonal matching pursuit, basis pursuit, the multiple measurement vector problem High-dimensional data and dimension reduction: Random projections, the Johnson-Lindenstrauss Lemma, the Restricted Isometry Property, concentration inequalities, covering numbers, Kashin widths | |||||

Lecture notes | Lecture notes, problem sets with documented solutions. | |||||

Literature | S. Mallat, ''A wavelet tour of signal processing: The sparse way'', 3rd ed., Elsevier, 2009 I. Daubechies, ''Ten lectures on wavelets'', SIAM, 1992 O. Christensen, ''An introduction to frames and Riesz bases'', Birkhäuser, 2003 K. Gröchenig, ''Foundations of time-frequency analysis'', Springer, 2001 M. Elad, ''Sparse and redundant representations -- From theory to applications in signal and image processing'', Springer, 2010 | |||||

Prerequisites / Notice | The course is heavy on linear algebra, operator theory, and functional analysis. A solid background in these areas is beneficial. We will, however, try to bring everybody on the same page in terms of the mathematical background required, mostly through reviews of the mathematical basics in the discussion sessions. Moreover, the lecture notes contain detailed material on the advanced mathematical concepts used in the course. If you are unsure about the prerequisites, please contact C. Aubel or H. Bölcskei. | |||||

227-0441-00L | Mobile Communications: Technology and Quality of Service | W | 6 credits | 4G | M. Kuhn | |

Abstract | Based on an introduction to wireless communications, the lecture course covers: WLAN and cellular networks, PHY technologies, MAC schemes, mechanisms supporting QoS in wireless networks, QoS measurements and evaluation, benchmarking. | |||||

Objective | Introduction to mobile wireless communications, including characteristics of the wireless channel, PHY layer technologies (for example MIMO, OFDM etc.) and MAC layer schemes; comparison of different cellular standards; definition of QoS and support of QoS in wireless networks; understanding QoS measurements, their evaluation and benchmarking in cellular networks. | |||||

Content | - Introduction - Wireless channel, propagation of electromagnetic waves, antenna structures - Mobile communication, modulation techniques, OFDM, MIMO - Wireless networks (cellular networks, access networks) - Wireless standards (e.g. UMTS, LTE, IEEE 802.11) - Services in wireless networks - Quality of service (QoS) in wireless networks (definitions, Key Performance Indicators, mechanisms used to support QoS) - QoS measurements (e.g. voice quality, coverage, delay) and their statistical evaluation - Benchmarking (methodology, statistical methods and models) Weekly exercises included in the lecture | |||||

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

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

Prerequisites / Notice | English | |||||

227-0456-00L | High Frequency and Microwave Electronics I Does not take place this semester. | W | 6 credits | 4G | C. Bolognesi | |

Abstract | Understanding of basic building blocks of microwave electronics technology, with a focus on active semiconductor devices. | |||||

Objective | Understanding the fundamentals of microwave electronics technology, with emphasis on active components. | |||||

Content | Introduction, microstrip transmission lines, matching, semiconductors, pn-junction, noise, PIN-diode and applications, Schottky diodes and detectors, bipolar transistors and heterojunction bipolar transistors, MESFET physics and properties, high-electron mobility transistors, microwave amplifiers. | |||||

Lecture notes | Script: Mikrowellentechnik and Mikrowellenelektronik, by Werner Bächtold (In German). | |||||

Prerequisites / Notice | The lectures will be held in English. | |||||

227-0468-00L | Analog Signal Processing and Filtering Suitable for Master Students as well as Doctoral Students. This course will be offered in Autumn Semester from HS 2015 on. It won't be offered in Spring 2016 anymore. | W | 6 credits | 2V + 2U | H. Schmid | |

Abstract | This lecture provides a wide overview over analogue (mostly integrated) filters (continuous-time and discrete-time), amplifiers, and sigma-delta converters, and gives examples with sensor interfaces and class-D audio drivers. All circuits are treated using a signal-flow view. The lecture is suitable for both analog and digital designers. | |||||

Objective | This lecture provides a wide overview over analogue (mostly integrated) filters (continuous-time and discrete-time), amplifiers, and sigma-delta converters, and gives examples with sensor interfaces and class-D audio drivers. All these circuits are treated using a signal-flow view. The lecture is suitable for both analog and digital designers. The way the exam is done allows for the different interests of the two groups. The learning goal is that the students can apply signal-flow graphs and can understand the signal flow in such circuits and systems (including non-ideal effects) well enough to enable them to gain an understanding of further circuits and systems by themselves. | |||||

Content | At the beginning, signal-flow graphs in general and driving-point signal-flow graphs in particular are introduced. We will use them during the whole term to analyze circuits and understand how signals propagate through them. The theory and CMOS implementation of active Filters is then discussed in detail using the example of Gm-C filters. Theory and implementation of opamps, current conveyors, and inductor simulators follow. 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 switched-capacitor filters and circuits is given, including sensor read-out amplifiers, correlated double sampling, and chopping. These topics form the basis for the longest part of the lecture: the discussion of sigma-delta A/D and D/A converters, which are portrayed as mixed analog-digital (MAD) filters in this lecture. | |||||

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: http://people.ee.ethz.ch/~hps/asfwiki/ Some material is protected by password; students from ETHZ who are interested can write to haschmid@ethz.ch to ask for the password even if they do not attend the lecture. | |||||

Prerequisites / Notice | Prerequisites: Recommended (but not required): Stochastic models and signal processing, Communication Electronics, Analog Integrated Circuits, Transmission Lines and Filters. Knowledge of the Laplace Transform (transfer functions, poles and zeros, bode diagrams, stability criteria ...) and of the main properties of linear systems is necessary. | |||||

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

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 | |||||

227-0678-00L | Speech Processing II "Speech Processing II" takes place for the last time in spring 2015. | W | 6 credits | 2V + 2U | B. Pfister | |

Abstract | Interdisciplinary approaches to text-to-speech synthesis and speech recognition (continuation of course speech processing I). | |||||

Objective | In this course selected concepts and interdisciplinary approaches to text-to-speech synthesis and speech recognition are presented. | |||||

Content | Fundamentals of representation and application of linguistic knowledge: Introduction of the theory of formal languages, the Chomsky hierarchy, word analysis, finite state machines, parsing. Speech synthesis: Natural language analysis (for words and sentences), lexicon, grammar for natural language; generation of the abstract representation of pronunciation (phone sequence, accents, phrases). Additionally, the ETH text-to-speech system SVOX is discussed. Speech recognition: The statistical approach to speech recognition with hidden Markov models is detailed: Basic algorithms (forward, Viterbi and Baum-Welch algorithm), problems of implementation, HMM training, whole vs. subword modeling, isolated word recognition, continuous speech recognition, statistical and rule-based language models. | |||||

Lecture notes | The following textbook will be used: "Sprachverarbeitung - Grundlagen und Methoden der Sprachsynthese und Spracherkennung", B. Pfister und T. Kaufmann, Springer Verlag, ISBN: 978-3-540-75909-6 | |||||

Prerequisites / Notice | Prerequisites: Speech Processing I. | |||||

227-1032-00L | Neuromorphic Engineering II | W | 6 credits | 5G | T. Delbrück, G. Indiveri, S.‑C. Liu | |

Abstract | This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I". | |||||

Objective | Design of a neuromorphic circuit for implementation with CMOS technology. | |||||

Content | This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I". The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester. | |||||

Literature | S.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation. | |||||

Prerequisites / Notice | Prerequisites: Neuromorphic Engineering I strongly recommended | |||||

252-0526-00L | Statistical Learning Theory | W | 4 credits | 2V + 1U | J. M. Buhmann | |

Abstract | The course covers advanced methods of statistical learning : PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models. | |||||

Objective | The course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed. | |||||

Content | # Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification. # Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come. # Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error? # Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include: * Maximum Entropy * Information Bottleneck * Deterministic Annealing # Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures. # Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike. # Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future. | |||||

Lecture notes | no script; transparencies of the lectures will be made available. | |||||

Literature | Duda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000. 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 | Requirements: basic knowledge of statistics, interest in statistical methods. It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Statistical Learning Theory can be followed without the introductory course. | |||||

227-0120-00L | Communication Networks | W | 6 credits | 4G | B. Plattner, B. L. H. Ager, P. Georgopoulos, K. A. Hummel, L. Vanbever | |

Abstract | The students will understand the fundamental concepts of communication networks, with a focus on computer networking. They will learn to identify relevant mechanisms that are used in networks, and will see a reasonable set of examples implementing such mechanisms, both as seen from an abstract perspective and with hands-on, practical experience. | |||||

Objective | The students will understand the fundamental concepts of communication networks, with a focus on computer networking. They will learn to identify relevant mechanisms that are used to networks work, and will see a reasonable set of examples implementing such mechanisms, both as seen from an abstract perspective and with hands-on, practical experience. | |||||

Prerequisites / Notice | Prerequisites: A layered model of communication systems (represented by the OSI Reference Model) has previously been introduced. | |||||

252-0286-00L | System Construction Does not take place this semester. The course will be offered again in the autumn semester 2015. | W | 4 credits | 2V + 1U | not available | |

Abstract | Main goal is teaching knowledge and skills needed for building custom operating systems and runtime environments. Relevant topics are studied at the example of sufficiently simple systems that have been built at our Institute in the past, ranging from purpose-oriented single processor real-time systems up to generic system kernels on multi-core hardware. | |||||

Objective | The lecture's main goal is teaching of knowledge and skills needed for building custom operating systems and runtime environments. The lecture intends to supplement more abstract views of software construction, and to contribute to a better understanding of "how it really works" behind the scenes. | |||||

Content | Case Study 1: Embedded System - Safety-critical and fault-tolerant monitoring system - Based on an auto-pilot system for helicopters Case Study 2: Multi-Processor Operating System - Universal operating system for symmetric multiprocessors - Shared memory approach - Based on Language-/System Codesign (Active Oberon / A2) Case Study 3: Custom designed Single-Processor System - RISC Single-processor system designed from scratch - Hardware on FPGA - Graphical workstation OS and compiler (Project Oberon) Case Study 4: Custom-designed Multi-Processor System - Special purpose heterogeneous system on a chip - Masssively parallel hard- and software architecture based on message passing - Focus: dataflow based applications | |||||

Lecture notes | Printed lecture notes will be delivered during the lecture. Slides will also be available from the lecture homepage. |

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