Lehrinhalte
The course covers the following topics:
[list]
[*]The basic concepts of stochastic
[*]The sampling theorem
[*]Discrete-time noise processes and their properties
[*]Description of noise processes in the frequency domain
[*]Linear time-invariant systems: FIR and IIR filters
[*]Filtering of noise processes: AR, MA, and ARMA models
[*]The Matched filter
[*]The Wiener filter
[*]Properties of estimators
[*]The method of least squares
[/list]
Literatur
Lecture notes and slides can be downloaded here:
[list]
[*][url]http://www.spg.tu-darmstadt.de[/url]
[*]Moodle platform
[/list]
Further reading:
[list]
[*]A. Papoulis: Probability, Random Variables and Stochastic Processes. McGraw-Hill, Inc., third edition, 1991.
[*]P. Z. Peebles, Jr.: Probability, Random Variables and Random Signal Principles. McGraw-Hill, Inc., fourth edition, 2001.
[*]E. Hänsler: Statistische Signale; Grundlagen und Anwendungen. Springer Verlag, 3. Auflage, 2001.
[*]J. F. Böhme: Stochastische Signale. Teubner Studienbücher, 1998.
[*]A. Oppenheim, W. Schafer: Discrete-time Signal Processing. Prentice Hall Upper Saddle River,1999.
[/list]
The course covers the following topics:
[list]
[*]The basic concepts of stochastic
[*]The sampling theorem
[*]Discrete-time noise processes and their properties
[*]Description of noise processes in the frequency domain
[*]Linear time-invariant systems: FIR and IIR filters
[*]Filtering of noise processes: AR, MA, and ARMA models
[*]The Matched filter
[*]The Wiener filter
[*]Properties of estimators
[*]The method of least squares
[/list]
Literatur
Lecture notes and slides can be downloaded here:
[list]
[*][url]http://www.spg.tu-darmstadt.de[/url]
[*]Moodle platform
[/list]
Further reading:
[list]
[*]A. Papoulis: Probability, Random Variables and Stochastic Processes. McGraw-Hill, Inc., third edition, 1991.
[*]P. Z. Peebles, Jr.: Probability, Random Variables and Random Signal Principles. McGraw-Hill, Inc., fourth edition, 2001.
[*]E. Hänsler: Statistische Signale; Grundlagen und Anwendungen. Springer Verlag, 3. Auflage, 2001.
[*]J. F. Böhme: Stochastische Signale. Teubner Studienbücher, 1998.
[*]A. Oppenheim, W. Schafer: Discrete-time Signal Processing. Prentice Hall Upper Saddle River,1999.
[/list]
- Lehrende: ZoubirAbdelhak
Semester: ST 2019
Lehrinhalte
The course covers the following topics:
[list]
[*]The basic concepts of stochastic
[*]The sampling theorem
[*]Discrete-time noise processes and their properties
[*]Description of noise processes in the frequency domain
[*]Linear time-invariant systems: FIR and IIR filters
[*]Filtering of noise processes: AR, MA, and ARMA models
[*]The Matched filter
[*]The Wiener filter
[*]Properties of estimators
[*]The method of least squares
[/list]
Literatur
Lecture notes and slides can be downloaded here:
[list]
[*][url]http://www.spg.tu-darmstadt.de[/url]
[*]Moodle platform
[/list]
Further reading:
[list]
[*]A. Papoulis: Probability, Random Variables and Stochastic Processes. McGraw-Hill, Inc., third edition, 1991.
[*]P. Z. Peebles, Jr.: Probability, Random Variables and Random Signal Principles. McGraw-Hill, Inc., fourth edition, 2001.
[*]E. Hänsler: Statistische Signale; Grundlagen und Anwendungen. Springer Verlag, 3. Auflage, 2001.
[*]J. F. Böhme: Stochastische Signale. Teubner Studienbücher, 1998.
[*]A. Oppenheim, W. Schafer: Discrete-time Signal Processing. Prentice Hall Upper Saddle River,1999.
[/list]
The course covers the following topics:
[list]
[*]The basic concepts of stochastic
[*]The sampling theorem
[*]Discrete-time noise processes and their properties
[*]Description of noise processes in the frequency domain
[*]Linear time-invariant systems: FIR and IIR filters
[*]Filtering of noise processes: AR, MA, and ARMA models
[*]The Matched filter
[*]The Wiener filter
[*]Properties of estimators
[*]The method of least squares
[/list]
Literatur
Lecture notes and slides can be downloaded here:
[list]
[*][url]http://www.spg.tu-darmstadt.de[/url]
[*]Moodle platform
[/list]
Further reading:
[list]
[*]A. Papoulis: Probability, Random Variables and Stochastic Processes. McGraw-Hill, Inc., third edition, 1991.
[*]P. Z. Peebles, Jr.: Probability, Random Variables and Random Signal Principles. McGraw-Hill, Inc., fourth edition, 2001.
[*]E. Hänsler: Statistische Signale; Grundlagen und Anwendungen. Springer Verlag, 3. Auflage, 2001.
[*]J. F. Böhme: Stochastische Signale. Teubner Studienbücher, 1998.
[*]A. Oppenheim, W. Schafer: Discrete-time Signal Processing. Prentice Hall Upper Saddle River,1999.
[/list]
- Lehrende: ZoubirAbdelhak
Semester: ST 2019
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
Online-Angebote
Moodle
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
Online-Angebote
Moodle
- Lehrende: KleinAnja
- Lehrende: KöpplHeinz
- Lehrende: PesaventoMarius
- Lehrende: ZoubirAbdelhak
Semester: ST 2019
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: (TU-ID gelöscht)Gelöschter User
- Lehrende: ZoubirAbdelhak
Semester: ST 2019
Lehrinhalte
The practicum of the module consists of five experiments which are time closely matched in time to the lecture:
[list]
[*]Measuring of signals in the time range with digital storage oscilloscope, trigger conditions
[*]Measuring of signals in the frequency range with digital storage oscilloscope, error of measurement (aliasing / subsampling, leackage) and window functions
[*]Measuring of mechanical dimensions with suitable primary sensors, sensor electronics / amplifier circuits
[*]computer-based measuring
[*]Importing of sensor signals, whose processing and the resulting automated control of a process using a programmable logic controller (PLC)
[/list]
Literatur
[list]
[*]Slide set of lecture
[*]Textbook and exercise book Lerch: Elektrische Messtechnik, Springer
[*]Exercise documents
[*]Practical experiment manuals
[/list]
Voraussetzungen
Basics of ETiT I-III, Math I-III, Electronic
The practicum of the module consists of five experiments which are time closely matched in time to the lecture:
[list]
[*]Measuring of signals in the time range with digital storage oscilloscope, trigger conditions
[*]Measuring of signals in the frequency range with digital storage oscilloscope, error of measurement (aliasing / subsampling, leackage) and window functions
[*]Measuring of mechanical dimensions with suitable primary sensors, sensor electronics / amplifier circuits
[*]computer-based measuring
[*]Importing of sensor signals, whose processing and the resulting automated control of a process using a programmable logic controller (PLC)
[/list]
Literatur
[list]
[*]Slide set of lecture
[*]Textbook and exercise book Lerch: Elektrische Messtechnik, Springer
[*]Exercise documents
[*]Practical experiment manuals
[/list]
Voraussetzungen
Basics of ETiT I-III, Math I-III, Electronic
- Lehrende: Ben DaliOmar
- Lehrende: KupnikMario
- Lehrende: SuppeltSven
Semester: ST 2019
Lehrinhalte
Signal detection and parameter estimation are fundamental signal processing tasks. In fact, they appear in many common engineering operations under a variety of names. In this course, the theory behind detection and estimation will be presented, allowing a better understanding of how (and why) to design "good" detection and estimation schemes.
These lectures will cover: Fundamentals
Detection Theory Hypothesis Testing Bayesian Tests
Ideal Observer Tests
Neyman-Pearson Tests
Receiver Operating Characteristics
Uniformly Most Powerful Tests
The Matched Filter Estimation Theory Types of Estimators
Maxmimum Likelihood Estimators
Sufficiency and the Fisher-Neyman/Factorisation Criterion
Unbiasedness and Minimum variance
Fisher Information and the CRB
Asymptotic properties of the MLE
Literatur
[list]
[*]Lecture slides
[*]Jerry D. Gibson and James L. Melsa. Introduction to Nonparametric Detection with Applications. IEEE Press, 1996.
[*]S. Kassam. Signal Detection in Non-Gaussian Noise. Springer Verlag, 1988.
[*]S. Kay. Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall,
1993.
[*]S. Kay. Fundamentals of Statistical Signal Processing: Detection Theory. Prentice Hall, 1998.
[*]E. L. Lehmann. Testing Statistical Hypotheses. Springer Verlag, 2nd edition, 1997.
[*]E. L. Lehmann and George Casella. Theory of Point Estimation. Springer Verlag, 2nd edition, 1999.
[*]Leon-Garcia. Probability and Random Processes for Electrical Engineering. Addison Wesley, 2nd edition, 1994.
[*]P. Peebles. Probability, Random Variables, and Random Signal Principles. McGraw-Hill, 3rd edition, 1993.
[*]H. Vincent Poor. An Introduction to Signal Detection and Estimation. Springer Verlag, 2nd edition,
1994.
[*]Louis L. Scharf. Statistical Signal Processing: Detection, Estimation, and Time Series Analysis. Pearson Education POD, 2002.
[*]Harry L. Van Trees. Detection, Estimation, and Modulation Theory, volume I,II,III,IV. John Wiley & Sons, 2003.
[*]A. M. Zoubir and D. R. Iskander. Bootstrap Techniques for Signal Processing. Cambridge University Press, May 2004.
[/list]
Voraussetzungen
DSP, general interest in signal processing
Signal detection and parameter estimation are fundamental signal processing tasks. In fact, they appear in many common engineering operations under a variety of names. In this course, the theory behind detection and estimation will be presented, allowing a better understanding of how (and why) to design "good" detection and estimation schemes.
These lectures will cover: Fundamentals
Detection Theory Hypothesis Testing Bayesian Tests
Ideal Observer Tests
Neyman-Pearson Tests
Receiver Operating Characteristics
Uniformly Most Powerful Tests
The Matched Filter Estimation Theory Types of Estimators
Maxmimum Likelihood Estimators
Sufficiency and the Fisher-Neyman/Factorisation Criterion
Unbiasedness and Minimum variance
Fisher Information and the CRB
Asymptotic properties of the MLE
Literatur
[list]
[*]Lecture slides
[*]Jerry D. Gibson and James L. Melsa. Introduction to Nonparametric Detection with Applications. IEEE Press, 1996.
[*]S. Kassam. Signal Detection in Non-Gaussian Noise. Springer Verlag, 1988.
[*]S. Kay. Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall,
1993.
[*]S. Kay. Fundamentals of Statistical Signal Processing: Detection Theory. Prentice Hall, 1998.
[*]E. L. Lehmann. Testing Statistical Hypotheses. Springer Verlag, 2nd edition, 1997.
[*]E. L. Lehmann and George Casella. Theory of Point Estimation. Springer Verlag, 2nd edition, 1999.
[*]Leon-Garcia. Probability and Random Processes for Electrical Engineering. Addison Wesley, 2nd edition, 1994.
[*]P. Peebles. Probability, Random Variables, and Random Signal Principles. McGraw-Hill, 3rd edition, 1993.
[*]H. Vincent Poor. An Introduction to Signal Detection and Estimation. Springer Verlag, 2nd edition,
1994.
[*]Louis L. Scharf. Statistical Signal Processing: Detection, Estimation, and Time Series Analysis. Pearson Education POD, 2002.
[*]Harry L. Van Trees. Detection, Estimation, and Modulation Theory, volume I,II,III,IV. John Wiley & Sons, 2003.
[*]A. M. Zoubir and D. R. Iskander. Bootstrap Techniques for Signal Processing. Cambridge University Press, May 2004.
[/list]
Voraussetzungen
DSP, general interest in signal processing
- Lehrende: ZoubirAbdelhak
Semester: ST 2019