Hans-Andrea Loeliger: Katalogdaten im Herbstsemester 2021 |
| Name | Herr Prof. Dr. Hans-Andrea Loeliger |
| Lehrgebiet | Signalverarbeitung |
| Adresse | Inst. f. Signal-u.Inf.verarbeitung ETH Zürich, ETF E 101 Sternwartstrasse 7 8092 Zürich SWITZERLAND |
| Telefon | +41 44 632 27 65 |
| loeliger@isi.ee.ethz.ch | |
| URL | http://people.ee.ethz.ch/~loeliger/ |
| Departement | Informationstechnologie und Elektrotechnik |
| Beziehung | Ordentlicher Professor |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 227-0085-12L | Projekte & Seminare: Electronic Circuits & Signals Exploration Laboratory   Nur für Elektrotechnik und Informationstechnologie BSc. Die Lerneinheit kann nur einmal belegt werden. Eine wiederholte Belegung in einem späteren Semester ist nicht anrechenbar. | 2 KP | 1P | H.‑A. Loeliger | |
| Kurzbeschreibung | Der Bereich Praktika, Projekte, Seminare umfasst Lehrveranstaltungen in unterschiedlichen Formaten zum Erwerb von praktischen Kenntnissen und Fertigkeiten. Ausserdem soll selbstständiges Experimentieren und Gestalten gefördert, exploratives Lernen ermöglicht und die Methodik von Projektarbeiten vermittelt werden. | ||||
| Lernziel | As everyday electronic circuits have transitioned into integrated circuits, they have become increasingly difficult to examine and to tinker with. As a result, students become less exposed to basic analog electronic circuits and their fundamental operating principles. At university level, bachelor classes in analog circuits and electronics provide rigorous theoretical insights but are typically focused on linearised operating behaviour. The goal of this lab course is for the students to enhance their understanding on how basic analog electronic circuits work, or perhaps don't work, and provide enough practical experience for the students to feel at ease using transistors, resistors, capacitors, diodes, etc., to create working circuits. For example, students create circuits that make physical quantities audible. Students are encourage to realise their own circuit ideas. | ||||
| 227-0085-22L | Projekte & Seminare: Programmierung eines Blackfin DSP Findet dieses Semester nicht statt. Nur für Elektrotechnik und Informationstechnologie BSc. Die Lerneinheit kann nur einmal belegt werden. Eine wiederholte Belegung in einem späteren Semester ist nicht anrechenbar. | 4 KP | 4P | H.‑A. Loeliger | |
| Kurzbeschreibung | Der Bereich Praktika, Projekte, Seminare umfasst Lehrveranstaltungen in unterschiedlichen Formaten zum Erwerb von praktischen Kenntnissen und Fertigkeiten. Ausserdem soll selbstständiges Experimentieren und Gestalten gefördert, exploratives Lernen ermöglicht und die Methodik von Projektarbeiten vermittelt werden. | ||||
| Lernziel | Zahlreiche praktische Anwendungen erfordern die Echtzeitverarbeitung von digitalen Signalen (zum Beispiel digitale Kommunikation, Audio- und Videoverarbeitung, und Radar, etc.). Digitale Signalprozessoren ("Digitial Signal Processors/DSPs") sind eine Familie von Mikroprozessoren, die spezifisch für solche Aufgaben gebaut und opimiert sind. In diesem Praktikum erlernen Studenten die Grundlagen der digitalen Signalverarbeitung sowie deren Implementation auf DSPs mittels Assembler. Schritt für Schritt werden die relevanten theoretischen Grundlagen erarbeitet und die nötigen Kenntnisse in der Programmierung mit Assembler vermittelt. Den Abschluss des Praktikums bildet ein individuelles kleines Projekt, das die Studenten in Zweiergruppen verwirklichen. Für die Implementierung wird ein für dieses P&S entwickeltes Board mit Komponenten verwendet, wie sie auch in der Industrie verbreitet sind. Es ist bestückt mit Ein- und Ausgängen für analoge Audiosignale, einem analog/digital-digital/analog Codec, einem DSP der Familie "Blackfin" von Analog Devices (BF532), sowie 32MB Arbeitsspeicher. | ||||
| 227-0101-AAL | Discrete-Time and Statistical Signal Processing Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben. Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen. | 6 KP | 8R | H.‑A. Loeliger | |
| Kurzbeschreibung | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm. | ||||
| Lernziel | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter. | ||||
| Inhalt | 1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm. | ||||
| Skript | Lecture Notes. | ||||
| 227-0101-00L | Discrete-Time and Statistical Signal Processing | 6 KP | 4G | H.‑A. Loeliger | |
| Kurzbeschreibung | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm. | ||||
| Lernziel | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter. | ||||
| Inhalt | 1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm. | ||||
| Skript | Lecture Notes | ||||
| 227-0105-00L | Introduction to Estimation and Machine Learning | 6 KP | 4G | H.‑A. Loeliger | |
| Kurzbeschreibung | Mathematical basics of estimation and machine learning, with a view towards applications in signal processing. | ||||
| Lernziel | Students master the basic mathematical concepts and algorithms of estimation and machine learning. | ||||
| Inhalt | Review of probability theory; basics of statistical estimation; least squares and linear learning; Hilbert spaces; Gaussian random variables; singular-value decomposition; kernel methods, neural networks, and more | ||||
| Skript | Lecture notes will be handed out as the course progresses. | ||||
| Voraussetzungen / Besonderes | solid basics in linear algebra and probability theory | ||||
| 227-0427-00L | Signal Analysis, Models, and Machine Learning Findet dieses Semester nicht statt. This course was replaced by "Introduction to Estimation and Machine Learning" and "Advanced Signal Analysis, Modeling, and Machine Learning". | 6 KP | 4G | H.‑A. Loeliger | |
| Kurzbeschreibung | Mathematical methods in signal processing and machine learning. I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity. II. Learning linear and nonlinear functions and filters: neural networks, kernel methods. III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events. | ||||
| Lernziel | The course is an introduction to some basic topics in signal processing and machine learning. | ||||
| Inhalt | Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis. Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods. Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events. | ||||
| Skript | Lecture notes. | ||||
| Voraussetzungen / Besonderes | Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory | ||||