Lehrinhalte
This course extends the signal processing fundamentals taught in DSP towards advanced topics that are the subject of current research. It is aimed at those with an interest in signal processing and a desire to extend their knowledge of signal processing theory in preparation for future project work (e.g. Diplomarbeit) and their working careers. This course consists of a series of five lectures followed by a supervised research seminar during two months approximately. The final evaluation includes students seminar presentations and a final exam.
The main topics of the Seminar are:
[list]
[*]Estimation Theory
[*]Detection Theory
[*]Robust Estimation Theory
[*]Seminar projects: e.g. Microphone array beamforming, Geolocation and Tracking, Radar Imaging, Ultrasound Imaging, Acoustic source localization, Number of sources detection.
[/list]
Literatur
[list]
[*]L. L. Scharf, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis (New York: Addison-Wesley Publishing Co., 1990).
[*]S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory (Book 1), Detection Theory (Book 2).
[*]R. A. Maronna, D. R. Martin, V. J. Yohai, Robust Statistics: Theory and Methods, 2006.
[/list]
Voraussetzungen
DSP, general interest in signal processing is desirable.
This course extends the signal processing fundamentals taught in DSP towards advanced topics that are the subject of current research. It is aimed at those with an interest in signal processing and a desire to extend their knowledge of signal processing theory in preparation for future project work (e.g. Diplomarbeit) and their working careers. This course consists of a series of five lectures followed by a supervised research seminar during two months approximately. The final evaluation includes students seminar presentations and a final exam.
The main topics of the Seminar are:
[list]
[*]Estimation Theory
[*]Detection Theory
[*]Robust Estimation Theory
[*]Seminar projects: e.g. Microphone array beamforming, Geolocation and Tracking, Radar Imaging, Ultrasound Imaging, Acoustic source localization, Number of sources detection.
[/list]
Literatur
[list]
[*]L. L. Scharf, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis (New York: Addison-Wesley Publishing Co., 1990).
[*]S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory (Book 1), Detection Theory (Book 2).
[*]R. A. Maronna, D. R. Martin, V. J. Yohai, Robust Statistics: Theory and Methods, 2006.
[/list]
Voraussetzungen
DSP, general interest in signal processing is desirable.
- Lehrende: Abdelhak Zoubir
Semester: Verão 2018
- Lehrende: Abdelhak Zoubir
Semester: Verão 2018
Lehrinhalte
The module provides an introduction to the emerging field of machine learning from an engineering perspective. Important models and learning methods are presented and exemplified through problems from information and communication technology.
[list]
[*]Fundamentals of probability theory and multivariate statistics
[*]Taxonomy of machine learning problems and models (supervised, unsupervised, generative, discriminative)
[*]Regression and classification: theory, methods and ICT applications
[*]Dimensionality reduction, clustering and big data analytics: methods and application in communications and signal processing
[*]Probabilistic graphical models: categories, inference and parameter estimation
[*]Fundamentals of Bayesian inference, Monte Carlo methods, Bayesian non-parametrics
[*]Fundamentals of convex optimization: Solution methods and application in communications
[*]Approximate algorithms for scalable Bayesian inference; application in signal processing and information theory (e.g. decoding of LDPC codes)
[*]Hidden Markov models (HMM): Theory, Algorithms and ICT applications (e.g. Viterbi decoding of convolutional codes)
[*]High-dimensional statistics (large p small n setting), learning dependency structure in high-dimensional data, learning causality relations from observational data.
[*]Sparse estimation, random projections, compressive sensing: Theory and applications in signal processing
[*]Deep neural networks (deep learning): Models, learning algorithms, libraries and ICT applications
[/list]
Literatur
[list]
[*]Kevin P. Murphy. Machine Learning A probabilistic perspective, MIT Press, 2012
[*]Christopher M. Bishop. Pattern recognition and Machine Learning, Springer, 2006
[*]Peter Bühlmann und Sara van de Geer. Statistics of high-dimensional data Methods, theory and applications, Springer, 2011
[/list]
Voraussetzungen
Good command of Matlab (for instance knowledge from course 18-st-2030 Matlab Grundkurs) and engineering mathematics
The module provides an introduction to the emerging field of machine learning from an engineering perspective. Important models and learning methods are presented and exemplified through problems from information and communication technology.
[list]
[*]Fundamentals of probability theory and multivariate statistics
[*]Taxonomy of machine learning problems and models (supervised, unsupervised, generative, discriminative)
[*]Regression and classification: theory, methods and ICT applications
[*]Dimensionality reduction, clustering and big data analytics: methods and application in communications and signal processing
[*]Probabilistic graphical models: categories, inference and parameter estimation
[*]Fundamentals of Bayesian inference, Monte Carlo methods, Bayesian non-parametrics
[*]Fundamentals of convex optimization: Solution methods and application in communications
[*]Approximate algorithms for scalable Bayesian inference; application in signal processing and information theory (e.g. decoding of LDPC codes)
[*]Hidden Markov models (HMM): Theory, Algorithms and ICT applications (e.g. Viterbi decoding of convolutional codes)
[*]High-dimensional statistics (large p small n setting), learning dependency structure in high-dimensional data, learning causality relations from observational data.
[*]Sparse estimation, random projections, compressive sensing: Theory and applications in signal processing
[*]Deep neural networks (deep learning): Models, learning algorithms, libraries and ICT applications
[/list]
Literatur
[list]
[*]Kevin P. Murphy. Machine Learning A probabilistic perspective, MIT Press, 2012
[*]Christopher M. Bishop. Pattern recognition and Machine Learning, Springer, 2006
[*]Peter Bühlmann und Sara van de Geer. Statistics of high-dimensional data Methods, theory and applications, Springer, 2011
[/list]
Voraussetzungen
Good command of Matlab (for instance knowledge from course 18-st-2030 Matlab Grundkurs) and engineering mathematics
- Lehrende: Anja Klein
- Lehrende: Heinz Köppl
- Lehrende: Marius Pesavento
- Lehrende: Abdelhak Zoubir
Semester: Verão 2018
Lehrinhalte
1) Introduction to MATLAB
2) Discrete-Time Signals and Systems
3) Frequency-Domain Analysis using the DFT
4) Digital FIR Filter Design
5) IIR Filter Design using Analog Prototypes
6) Nonparametric Spectrum Estimation
7) Parametric Spectrum Estimation.
Literatur
Lab manual
Voraussetzungen
Deterministic signals and systems theory
Further Grading Information
The course can be done in parallel or after the course DSP.
Additional Information
[url]http://www.spg.tu-darmstadt.de/[/url]
1) Introduction to MATLAB
2) Discrete-Time Signals and Systems
3) Frequency-Domain Analysis using the DFT
4) Digital FIR Filter Design
5) IIR Filter Design using Analog Prototypes
6) Nonparametric Spectrum Estimation
7) Parametric Spectrum Estimation.
Literatur
Lab manual
Voraussetzungen
Deterministic signals and systems theory
Further Grading Information
The course can be done in parallel or after the course DSP.
Additional Information
[url]http://www.spg.tu-darmstadt.de/[/url]
- Lehrende: Gelöschter User (TU-ID gelöscht)
- Lehrende: Abdelhak Zoubir
Semester: Verão 2018