Search result: Catalogue data in Spring Semester 2009
Electrical Engineering and Information Technology Master  | ||||||
 Courses of the specialisation | ||||||
| Communication 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. | ||||||
| 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 | |
|---|---|---|---|---|---|---|
| 227-0434-00L | Harmonic Analysis: Theory and Applications in Advanced Signal Processing | W | 6 credits | 2V + 2U | H. Bölcskei | |
| Abstract | Introduction to basic concepts in harmonic analysis with applications in signal processing and information theory. | |||||
| Objective | Introduction to basic concepts in harmonic analysis with applications in signal processing and information theory. | |||||
| Content | Elements of linear algebra, Fourier theory and sampling, Hilbert spaces, linear operator theory, frame theory, approximation theory, wavelets, short-time Fourier transform, Gabor expansion, filter banks, transform coding, sparse signals, uncertainty principles, compressed sensing. | |||||
| Lecture notes | Lecture notes, problem sets with documented solutions. | |||||
| Literature | S. Mallat, "A wavelet tour of signal processing", 2n ed., Academic Press, 1999 M. Vetterli and J. Kovacevic, "Wavelets and subband coding", Prentice Hall, 1995 I. Daubechies, "Ten lectures on wavelets", SIAM, 1992 O. Christensen, "An introduction to frames and Riesz bases", Birkhäuser, 2003 M. A. Pinksy, "Introduction to Fourier analysis and wavelets", Brooks/ Cole Series in Advanced Mathematics, 2002. | |||||
| 227-0468-00L | Analog Signal Processing and Filtering | W | 6 credits | 2V + 2U | H. Schmid | |
| Abstract | This lecture provides a wide overview over analogue (integreted) filters (continuous-time and discrete-time), amplifiers, and sigma-delta converters, by treating all these circuits using signal-flow considerations. The lecture is suitable for both analog and digital designers. The exam allows for the different interests of the two groups. | |||||
| Objective | This lecture provides a wide overview over analogue (integreted) filters (continuous-time and discrete-time), amplifiers, and sigma-delta converters, by treating all these circuits using signal-flow considerations. The lecture is suitable for both analog and digital designers. The exam allows for the different interests of the two groups. The learning goal is that the students understand the signal flow in such circuits and also non-ideal effects well enough to enable them to gain an understanding of further circuits by themselves. | |||||
| Content | The theory and CMOS implementation of active Filters is discussed in detail using the example of Gm-C filters. A 1xDVD read channel filter is designed in a computer exercise using Cadence design tools. The theory of active filters is taken up again by discussing single-amplifier biquadratic filters. Theory and implementation of opamps, current conveyors, and inductor simulators follow. 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 two or three published scientific papers. From these papers we will together develop the technical content. Support material can be found in the book "Analog Integrated Circuit Design", David Johns und Ken Martin, John Wiley & Sons, 1997. This book is offered in the lecture at the student price, which is much lower than the market price. Further course material is provided free of charge. | |||||
| Literature | Contents and Material of the 2008 lecture series: Link (The contents change slightly each year.) | |||||
| Prerequisites / Notice | Prerequisites: Recommended (but not required): Stochastic models and signal processing, Communication Electronics, Analog Integrated Circuits, Transmission Lines and Filters. | |||||
| 227-0448-00L | Image Analysis and Computer Vision II 4 credit points under the programme regulations 2001 | W | 6 credits | 4G | V. Ferrari, L. Van Gool | |
| Abstract | Introduction into the basic procedures for the interpretation of image content and object recognition. Demonstrating the current capabilities of computer vision systems through selected applications. Gaining own experience through practical computer and programming exercises. | |||||
| Objective | Overview of the basic concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
| Content | Basics of visual perception. Usage of unitary transforms, Principal and Independent Component Analysis for representing image information. Colour perception and representation. Object description based on surface features. Texture characterization and analysis, including stochastic methods. Deformable contour models, snakes and thin plate splines. Tracking based on local features. Particle filters. Shape characterization using invariant descriptors, geometric invariants. Combination of shape and surface features using moment invariants. Object recognition for specific objects and object classes, image and model based schemes. | |||||
| Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions. | |||||
| Prerequisites / Notice | Prerequisites: Bildatenanalyse und Computer Vision I. Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on UNIX and C. The course will be held in English. | |||||
| 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 | |||||
| 227-0148-00L | VLSI III: Test and Fabrication of VLSI Circuits | W | 6 credits | 4G | W. Fichtner, N. Felber, H. Kaeslin | |
| Abstract | 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. Being familiar with decision criteria of economic nature and with models of industrial cooperation. | |||||
| 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. Being familiar with decision criteria of economic nature and with models of industrial cooperation. | |||||
| Content | This final course in a series of three focusses on manufacturing, testing, physical analysis, and packaging of VLSI circuits. Topics include: Effects of fabrication defects, abstraction from physical to transistor- and gate-level fault models, fault grading 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. The course further addresses: Models of industrial cooperation, the caveats of virtual components, the cost structures of ASIC development and manufacturing, market requirements, decision criteria, and case studies. Today's deep-submicron CMOS fabrication processes, outlook on the future evolution of semiconductor technology. 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 (Dr. N. Felber). | |||||
| Literature | "Digital Integrated Circuit Design, from VLSI Architectures to CMOS Fabrication" Cambridge University Press, 2008, ISBN 9780521882675 (Dr. H. Kaeslin). | |||||
| Prerequisites / Notice | Prerequisites: Basic knowledge of digital design. | |||||
| 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. | |||||
| 263-9000-00L | Information Processing with Neural Networks | W | 4 credits | 2V + 1U | J. Bernasconi | |
| Abstract | Information processing with artificial neural networks (Basic principles and applications) | |||||
| Objective | The course gives an introduction to the different methods and techniques of information processing with artificial neural networks. Its aim is to provide the necessary background for an efficient use of these new information processing techniques. | |||||
| Content | Artificial neurons, different types of neural network paradigms (feedforward networks, Hopfield networks, winner-take-all networks), learning procedures (error backpropagation, stochastic learning, reinforcement learning, competitive learning), analysis and optimization of learning and generalization behavior, discussion and analysis of applications. | |||||
| Lecture notes | Course script (including a list of further references). | |||||
| Literature | Course script (with additional literature references) | |||||
| 227-0678-00L | Speech Processing II | W | 6 credits | 4G | 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. | |||||
| 402-0804-00L | Neuromorphic Engineering II | W | 6 credits | 5G | T. Delbrück, R. J. Douglas, 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 | |||||
| 227-0366-00L | Introduction to Computational Electromagnetics | W | 6 credits | 4G | C. Hafner, R. Vahldieck | |
| 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: Link | |||||
| Prerequisites / Notice | First half of the semester: lectures; second half of the semester: exercises in form of small projects | |||||
| 251-0834-00L | Information Systems for Engineers | W | 4 credits | 2V + 1U | R. Marti | |
| Abstract | Foundations and concepts of information systems from a user's viewpoint. The focus is on structured data: relational databases, the query language SQL, and designing relational data structures. Additional topics: search in document collections and in the Web (Information Retrieval) by estimating relevance and importance of documents with respect to a free-text query; data exchange using XML. | |||||
| Objective | Following the course should enable students to 1. answer non-trivial queries on existing relational databases by formulating (entry-level) SQL statements, as well as to add new database content and to update or delete existing content, 2. formalize real-world facts in the form of a so-called entiity-relationship data model and implement this data model in the form of normalized relations (tables) 3. explain how a database management system (DBMS) essentially works and what kind of services it provides 4. explain how a search engine such as Google basically works | |||||
| Content | Die Lehrveranstaltung vermittelt Grundlagen und Konzepte von Informationssystemen aus der Sicht eines Anwenders. Im Zentrum stehen relationale Datenbanksysteme, die Abfrage- und Datenmanipulationssprache SQL, sowie der Entwurf bzw. die Strukturierung relationaler Datenbanken. Dieser Stoff wird auch in praktischen Übungen vertieft. Weitere Themen sind der Umgang mit unstrukturierten und semistrukturierten Daten, die Integration von Daten aus verschiedenen autonomen Informationssystemen, sowie eine Übersicht der Architektur von Datenbanksystemen. Inhalt: 1. Einleitung. 2. Das Relationenmodell. 3. Die Abfrage- und Datenmanipulationssprache SQL. 4. Entwurf relationaler Datenbanken mit Hilfe von Entity-Relationship Diagrammen. Grundideen der Normalisierung von Relationen. 5. Architektur relationaler Datenbanksysteme. 6. Information Retrieval: Suche von (Text-) Dokumenten. Indexing, Stopwort-Elimination und Stemming. Boole'sches Retrieval und das Verktorraum-Modell. 7. Web Information Retrieval: Web-Crawling. Ausnutzen der Web-Links zwischen Web-Seiten (Page Ranking). Das Zusammenspiel von Crawling, klassischem Information Retrieval und Page Ranking. 8. Modellierung semi-strukturierter Daten mit XML und einfache Anfragen mit XPath und XQuery. 9. Zugriff auf SQL-Datenbanken aus Programmen, Transaktionen. | |||||
| Literature | Vorlesungsunterlagen (PowerPoint Folien, teilweise auch zusätzlicher Text) werden auf der Web-Site publiziert. Der Kauf eines Buches wird nicht vorausgesetzt. Das Buch "Informationssysteme und Datenbanken, 7. Auflage" von C.A. Zehnder, erschienen im vdf-Verlag/Teubner-Verlag, 2002, umfasst in etwa den gleichen Stoff. Die Vorlesung ist aber nicht auf das Buch abgestimmt. Als weiterführende Literatur kann z.B. folgendes Standardwerk (ca. 1150 Seiten!) empfohlen werden: A. Silberschatz, H.F. Korth, S. Sudarshan: Database System Concepts, 5th Edition, McGraw-Hill, 2006. | |||||
| Prerequisites / Notice | Voraussetzung: Elementare Kenntnisse von Mengenlehre und logischen Ausdrücken. Kenntnisse und minimale Programmiererfahrung in einer Programmiersprache wie z.B. Pascal, C, oder Java. | |||||
| 251-0526-00L | Statistical Learning Theory | W | 5 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 Advanced Topics in Machine Learning can be followed without the introductory course. | |||||
| 251-0222-00L | Compiler Design I | W | 6 credits | 2V + 2U | T. Gross, F. T. Schneider | |
| Abstract | This course uses compilers as example to expose modern software development techniques. Compiler organization. Lexical analysis. Top-down parsing via recursive descent, table-driven parsers, bottom-up parsing. Symboltables, semantic checking. Code generation for a simple RISC machine: conditionals, loops, procedure calls, simple register allocation techniques. | |||||
| Objective | Learn principles of compiler design, gain practical experience designing and implementing a medium-scale software system. | |||||
| Content | This course uses compilers as example to expose modern software development techniques. The course introduces the students to the fundamentals of compiler construction. Students will implement a simple yet complete compiler for an object-oriented programming language for a realistic target machine. Students will learn the use of appropriate tools (parser generators); the implementation language is Java. Throughout the course, students learn to apply their knowledge of theory (automata, grammars, stack machines, program transformation) and well-known programming techniques (module definitions, design patterns, frameworks, software reuse) in a software project. Specific topics: Compiler organization. Lexical analysis. Top-down parsing via recursive descent, table-driven parsers, bottom-up parsing. Symboltables, semantic checking. Code generation for a simple RISC machine: expression evaluation, straight line code, conditionals, loops, procedure calls, simple register allocation techniques. Storage allocation on the stack, parameter passing, runtime storage management, heaps. Special topics as time permits: introduction to global dataflow and its application to register allocation, instruction scheduling. | |||||
| Literature | Aho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition) Muchnick, Advanced Compiler Design and Implementation, Morgan Kaufmann Publishers, 1997 | |||||
| Prerequisites / Notice | Prerequisites: Prior exposure to modern techniques for program construction, knowledge of at least one processor architecture at the assembly language level. | |||||
| 251-0532-00L | Bio-Inspired Optimization and Design | W | 5 credits | 2V + 1U | E. Zitzler, P. Koumoutsakos | |
| Abstract | This lecture focuses on the foundations of bio-inspired optimization. The exercises will be oriented towards the implementation of these concepts to design applications. | |||||
| Objective | You are familiar with the foundations of optimization and with different randomized search algorithms, in particular bio-inspired ones. You will be able to design, implement, and tune basic and advanced bio-inspired optimization techniques for tackling complex, large-scale applications. You will be able to evaluate different search algorithms and implementations. You are aware of the theoretical foundations of bio-inspired optimization, know the limitations as well as potential advantages and disadvantages of specific design concepts. | |||||
| Content | Biologically-inspired computation is an umbrella term for different computational approaches that are based on principles or models of biological systems. This class of methods such as evolutionary algorithms, ant colony optimization, and swarm intelligence complements traditional techniques in the sense that the former can be applied to large-scale applications where little is known about the underlying problem and where the latter approaches encounter difficulties. Therefore, bio-inspired methods are becoming increasingly important in face of the complexity of today's demanding applications, and accordingly they have been successfully used in various fields ranging from computer engineering and mechanical engineering to chemical engineering and molecular biology. This lecture focuses on the foundations of bio-inspired computation with an emphasis on their application to optimization. The exercises will be oriented towards the implementation of these concepts to realistic applications. | |||||
| Lecture notes | Lecture notes will be provided in the course of the semester. | |||||
| 227-0216-00L | Control Systems II | W | 6 credits | 4G | M. Morari | |
| 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" (Feedback Control). 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 to treat 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 | Copy of transparencies | |||||
| Literature | Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005. | |||||
| Prerequisites / Notice | Prerequisites: Control Systems or equivalent | |||||
| 227-0120-00L | Communication Networks | W | 6 credits | 4G | B. Plattner | |
| 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. | |||||
| 227-0115-00L | Optoelectronics and Optical Communications | W | 7 credits | 5G | H. Jäckel | |
| Abstract | Introduction to the physics of optical processes in dielectrics and semiconductor required to understand the concepts, the design and characterization of all basic devices and components for fiberoptic links, such as optical fibers, waveguides, LED, LASERS, optical amplifiers, modulators and photodetectors. Basic system design and performance characteristics of simple fiberoptic links. | |||||
| Objective | Enable students to understand and master the concepts and physics of optical processes in solids. Apply these concepts to the design of basic passive and active optical components and devices required in optical communication systems. Provide the basic know-how to design and characterize fiberoptic links und understand the performance trade-offs and limitations. | |||||
| Content | Optoelectronics is the enabling technology for future Tb/s fiberoptic communication systems serving also as backbones for RF- and Mobile Communication networks. The basic concepts, evolution and significance of optoelectronics are briefly reviewed. The basic nonresonant interactions and propagation properties of lightwaves in dielectrics are analysed. Next the wavepropagation and mode structures in dispersive dielectric waveguides, such as planar waveguides and optical fibers are discussed. The concepts of waveguide coupling, rep. coupled mode theory is used for the design of coupler structures and Bragg-reflectors. A detailed discussion of resonant optical processes leading to optical gain and absorption mainly for semiconductors in the semi-classical and the quantummechanical formalism follows. These basics form the foundation to develop the concepts and design of active optical devices, such as the Light Emitting Diode (LED), the semiconductor and fiber optical amplifier and finally the diode LASER. In the area of photodetector emphasis is put on PIN- and Avalanche photodiodes and their basic high speed and noise properties. Optical modulators form the final device chapter. The analysis, the design considerations and the performance characteristics and limitations of simple point-to-point fiberoptic links with respect to dispersion, attenuation and noise conclude the course. | |||||
| Lecture notes | Script and books: G.P. Agrawal: Fiber-Optic Communication Systems, Wiley, 1992 L. Coldren and S. Corzine: Diode Lasers and Photonic Integrated Circuits, Wiley, 1995 K.H. Ebeling: Integrated Optoelectronics, Springer, 1993 | |||||
| Prerequisites / Notice | Prerequisites: Physik I+II, Halbleiterbauelemente, Felder und Komponenten I+II. | |||||
| 227-0116-00L | VLSI I: From Architectures to VLSI Circuits and FPGAs | W | 7 credits | 5G | W. Fichtner, N. Felber, H. Kaeslin | |
| Abstract | Understand Very-Large-Scale Integrated Circuits, Application-Specific Integrated Circuits, and Field-Programmable Gate-Arrays. Become fluent in their front-end design from architectural conception down to gate-level netlists. How to model and simulate digital circuits with VHDL. How to take advantage of automatic synthesis tools to produce 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. 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: terminology, overview on design methodologies and fabrication depths, levels of abstraction for circuit modeling, VLSI design flow, dedicated VLSI architectures, how to obtain an architecture for a given processing algorithm, architectural transformations for meeting throughput, area, and power requirements. Hardware Description Languages (HDL) and the underlying concepts, VHDL (IEEE standard 1076) for simulation and synthesis, a suitable nine-valued logic system (IEEE standard 1164), Register Transfer Level (RTL) synthesis. Timing models, evaluation of synchronous and asynchronous design techniques, Anceau diagrams. Functional verification of digital circuits, reusable testbenches, assertion-based checks, building blocks of digital VLSI circuits, case studies of actual circuits, comparison with microprocessors and DSPs. 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. | |||||
| Literature | "Digital Integrated Circuit Design, from VLSI Architectures to CMOS Fabrication" Cambridge University Press, 2008, ISBN 9780521882675. | |||||
| 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. General overview with follow-up courses VLSI II and III: Link | |||||
| 251-0286-00L | System Construction | W | 5 credits | 2V + 1U | J. Gutknecht | |
| Abstract | The lecture's goal is teaching of knowledge and skills needed for building custom operating systems and runtime environments. Relevant topics will be studied in detail at the example of sufficiently simple systems that have been built at the Institute in the past, ranging from purpose-oriented single processor real-time systems up to generic system kernels for Symmetric Multi Processors. | |||||
| Objective | The lecture's goal is teaching of knowledge and skills needed for building custom operating systems and runtime environments. | |||||
| Content | This lecture revives a tradition of the Computer Systems Institute. Its main goal is teaching of knowledge and skills needed for building custom operating systems and runtime environments. Relevant topics will be studied in detail at the example of sufficiently simple systems that have been built at the Institute in the past, ranging from purpose-oriented single processor real-time systems up to generic system kernels for SMPs (Symmetric Multi Processors). The lectuire intends to supplement more abstract views of software construction, and to contribute to a better understanding of "how it really works" behind the scenes. | |||||
| 227-0441-00L | QoS in Wireless Networks | W | 3 credits | 2G | M. Kuhn | |
| Abstract | In wireless networks the support of QoS is an important issue with respect to user satisfaction, quality assurance and benchmarking - in cellular networks as well as in access networks (e.g. WLAN 802.11). Based on a review of wireless networks, the lecture gives an introduction to: QoS, mechanisms supporting QoS in wireless networks, QoS measurements (and statistical evaluation), benchmarking. | |||||
| Objective | Understanding mechanisms supporting QoS as well as QoS measurements and their evaluation in wireless networks | |||||
| Content | - Introduction - Wireless networks (cellular networks, access networks) - Wireless standards (e.g. GSM, UMTS, IEEE 802.11) - Services in wireless networks (e.g. VoIP, VoWLAN, GPRS) - Quality of service (QoS) in wireless networks (definitions, Key Performance Indicators, mechanisms used to support QoS) - QoS measurements (e.g. voice quality, coverage, delay) - Statistical evaluation of QoS measurements - 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 | German or English, all slides are in English. | |||||
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