Search result: Catalogue data in Autumn Semester 2014
|Electrical Engineering and Information Technology Master|
| Major Courses|
A total of 42 CP must be achieved during the Master Program. The individual study plan is subject to the tutor's approval.
| Recommended Subjects|
These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor.
|227-0102-00L||Discrete Event Systems||W||6 credits||4G||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 average-case and from a worst-case perspective. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems, Worst-Case 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 average-case 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 worst-case perspective using the theory of online algorithms and adversarial queuing.
2. Automata and Languages
3. Smarter Automata
4. Specification Models
5. Stochastic Discrete Event Systems
6. Worst-Case Event Systems
7. Network Calculus
|Literature||[bertsekas] Data Networks |
Dimitri Bersekas, Robert Gallager
Prentice Hall, 1991, ISBN: 0132009161
[borodin] Online Computation and Competitive Analysis
Allan Borodin, Ran El-Yaniv.
Cambridge University Press, 1998
[boudec] Network Calculus
J.-Y. Le Boudec, P. Thiran
[cassandras] Introduction to Discrete Event Systems
Christos Cassandras, Stéphane Lafortune.
Kluwer Academic Publishers, 1999, ISBN 0-7923-8609-4
[fiat] Online Algorithms: The State of the Art
A. Fiat and G. Woeginger
[hochbaum] Approximation Algorithms for NP-hard Problems (Chapter 13 by S. Irani, A. Karlin)
[schickinger] Diskrete Strukturen (Band 2: Wahrscheinlichkeitstheorie und Statistik)
T. Schickinger, A. Steger
Springer, Berlin, 2001
[sipser] Introduction to the Theory of Computation
PWS Publishing Company, 1996, ISBN 053494728X
|227-0103-00L||Control Systems||W||6 credits||2V + 2U||M. Morari|
|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. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz 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), multi-loop 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||G.F. Franklin, J.D. Powell, A. Emami-Naeini. Feedback Control of Dynamic Systems. 6th edition, Prentice Hall, Version 2009, Reading, ISBN 978-0-1350-150-9.Softcover student's edition ca. CHF 110.-. (Spring 2013)|
|Prerequisites / Notice||Prerequisites: Signal and Systems Theory II. |
MATLAB is used for system analysis and simulation.
|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 German, but assistants also speak English.|
|227-0166-00L||Analog Integrated Circuits||W||6 credits||2V + 2U||Q. Huang|
|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 small-signal 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 op-amps; Stability; Comparators; Second-order effects in analog circuits such as mismatch, noise and offset; A/D and D/A converters; Introduction to switched capacitor circuits.|
The exercise sessions aim to reinforce the lecture material by well guided step-by-step design tasks. The circuit simulator SPECTRE is used to facilitate the tasks. There is also an experimental session on op-amp measurments.
|Lecture notes||Handouts of presented slides. No script but an accompanying textbook is recommended.|
|Literature||Gray, Hurst, Lewis, Meyer, "Analysis and Design of Analog Integrated Circuits", 5th Ed. Wiley, 2010.|
|227-0301-00L||Optical Communication Fundamentals||W||6 credits||2V + 1U + 1P||J. Leuthold|
|Abstract||Transmitters and receivers are the basic building blocks of communication links. In this lecture we discuss the path of an analog signal in the transmitter to the digital world in an optical communication link and back to the analog world at the receiver. The lecture is organized to cover the fundamentals of all important optical and optoelectronic components in a fiber communications system.|
|Objective||Fundamentals of optical communications systems with an emphasis on transmitters and receivers.|
|Content||Chapter 1: Introduction: Analog/Digital Conversion, The Communication Channel, Shannon Channel Capacity.|
Chapter 2: The Transmitter: Components of a Transmitter, The Spectrum of a Signal, Optical Modulators, Modulation Formats.
Chapter 3: Signal-to-Noise Ratio, Intersymbol Interference, Electronic Coding.
Chapter 4: Multiplexing techniques (WDM/FDM, TDM, OFDM, Nyquist Multiplexing, OCDMA).
Chapter 5: Optical Amplifiers (Semiconductor Optical Amplifiers, Erbium Doped Fiber Amplifiers, Raman Amplifiers).
Chapter 6: The Receiver: pin-Photodiodes, Polarisation Demultiplexing, Phase Estimation, Clock Recovery.
Chapter 7: Noise: Noise Mechanisms, Photocurrent Noise, Thermal Noise, Electronic Amplifiers Noise, Optical Amplifier Noise.
Chapter 8: Receiver and Detector Errors: Detection Errors of On-Off Keying, Detection Errors of M-Ary Signals, Direct-, Heterodyne and Homodyne Reception.
|Lecture notes||Lecture notes are handed out.|
|Prerequisites / Notice||Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics (see Bsc ITET).|
|227-0377-00L||Physics of Failure and Failure Analysis of Electronic Devices and Equipment||W||3 credits||2V||U. Sennhauser|
|Abstract||Failures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.|
|Objective||Introduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.|
|Content||Summary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis|
|Lecture notes||Comprehensive copy of transparencies|
|227-0447-00L||Image Analysis and Computer Vision||W||6 credits||3V + 1U||G. Székely, O. Göksel, L. Van Gool|
|Abstract||Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.|
|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||The first part of the course starts off from 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 it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear 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 depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.|
|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 Linux and C.
The course language is English.
|227-0477-00L||Acoustics I||W||6 credits||4G||K. Heutschi|
|Abstract||Introduction to the fundamentals of acoustics in the area of sound field calculations, measurement of acoustical events, outdoor sound propagation and room acoustics of large and small enclosures.|
|Objective||Introduction to acoustics. Understanding of basic acoustical mechanisms. Survey of the technical literature. Illustration of measurement techniques in the laboratory.|
|Content||Fundamentals of acoustics, measuring and analyzing 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.|
|227-0577-00L||Network Security||W||6 credits||2V + 1U + 1P||B. Plattner, T. P. Dübendorfer, S. Frei, A. Perrig|
|Abstract||This lecture discusses fundamental concepts and technologies in the area of network security. Several case studies illustrate the dark side of the Internet and explain how to protect against such threats. A hands-on computer lab that accompanies the lecture gives a deep dive on firewalls, penetration testing and intrusion detection.|
|Objective||•Students are aware of current threats that Internet services and networked devices face and can explain appropriate countermeasures.|
•Students can identify and assess known vulnerabilities in a software system that is connected to the Internet.
•Students know fundamental network security concepts.
•Students have an in-depth understanding of important security technologies.
•Students know how to configure a real firewall and know some penetration testing tools from their own experience.
|Content||Risk management and the vulnerability lifecycle of software and networked services are discussed. Threats like denial of service, spam, worms, and viruses are studied in-depth. Fundamental security related concepts like identity, availability, authentication and secure channels are introduced. State of the art technologies like secure shell, network and transport layer security, intrusion detection and prevention systems, cross-site scripting, secure implementation techniques and more for securing the Internet and web applications are presented. Several case studies illustrate the dark side of the Internet and explain how to protect against current threats. A hands-on computer lab that accompanies the lecture gives a deep dive on firewalls, penetration testing and intrusion detection. |
This lecture is intended for students with an interest in securing Internet services and networked devices. Students are assumed to have knowledge in networking as taught in the Communication Networks lecture. This lecture and the exam are held in English.
|Prerequisites / Notice||Knowldedge in computer networking and Internet protocols (e.g. course Communication Networks (D-ITET) or Operating Systems and Networks (D-INFK).|
Due to recent changes in the Swiss law, ETH requires each student of this course to sign a written declaration that he/she will not use the information given in this for illegal purposes. This declaration will have to be signed and submitted no later than at the begining of the second lesson.
|227-0677-00L||Speech Processing I |
"Speech Processing I" takes place for the last time in fall 2014.
|W||6 credits||2V + 2U||B. Pfister|
|Abstract||Fundamentals of speech signal processing and introduction to text-to-speech synthesis and speech recognition.|
|Objective||Knowledge of the basics in speech processing. Acquisition of practical experience in this field. Comprehension of the fundamental problems of text-to-speech synthesis and speech recognition and selected solutions.|
|Content||Analysis, representation and properties of speech signals: Time and frequency domain representations, quasi-stationarity, formants, pitch, short-time analysis, spectrum, autocorrelation, linear prediction, homomorphic analysis.|
Fundamental problems of speech synthesis: Relations between text and speech; methods of speech production; prosody control.
Fundamental problems of speech recognition: Variability of speech signals, speech features for speech recognition, pattern matching (distance measures, dynamic programming), and introduction to statistical speech recognition with hidden Markov 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:|
Knowledge in digital signal processing and digital filters is helpful.
|227-0778-00L||Hardware/Software Codesign||W||6 credits||2V + 2U||L. Thiele|
|Abstract||The course provides advanced knowledge in the design of complex computer systems, in particular embedded systems. Models and methods are discussed that are fundamental for systems that consist of software and hardware components.|
|Objective||The course provides advanced knowledge in the design of complex computer systems, in particular embedded systems. Models and methods are discussed that are fundamental for systems that consist of software and hardware components.|
|Content||The course covers the following subjects: (a) Models for describing hardware and software components (specification), (b) Hardware-Software Interfaces (instruction set, hardware and software components, reconfigurable computing, heterogeneous computer architectures, System-on-Chip), (c) Application specific instruction sets, code generation and retargetable compilation, (d) Performance analysis and estimation techniques, (e) System design (hardware-software partitioning and design space exploration).|
|Lecture notes||Material for exercises, copies of transparencies.|
|Literature||Peter Marwedel, Embedded System Design, Springer, ISBN-13 978-94-007-0256-1, 2011.|
Peter Marwedel, Eingebettete Systeme, Springer, ISBN-13 978-3-540-34048-53, 2007.
Wayne Wolf. Computers as Components. Morgan Kaufmann, ISBN-13: 978-0123884367, 2012.
G. DeMicheli, R. Ernst and W. Wolf, Readings in Hw/Sw Co-design, M. Kaufmann, 2003.
|Prerequisites / Notice||Prerequisites for the course is a basic knowledge in the following areas: computer architecture, digital design, software design, embedded systems|
|252-0535-00L||Machine Learning||W||6 credits||3V + 2U||J. M. Buhmann|
|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 a practical machine learning projects.|
|Objective||Students will be familiarized with the most important 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. A machine learning project 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 pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.|
Topics covered in the lecture include:
- Bayesian theory of optimal decisions
- Maximum likelihood and Bayesian parameter inference
- Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM)
- Ensemble methods: Bagging and Boosting
- Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off
- Non parametric density estimation: Parzen windows, nearest nieghbour
- Dimension reduction: principal component analysis (PCA) and beyond
|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||Solid basic knowledge in analysis, statistics and numerical methods for|
CSE. Experience in programming for solving the project tasks.
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