EventoWeb
Zürcher Hochschule für Angewandte Wissenschaften
[
German (Switzerland)
German (Switzerland)
] [
English
English
]
Not registered
[home]
[Login]
[Print]
Navigation
Kontakt zu Service Desk
Online-Dokumentation
Allgemeiner Zugriff
Module suchen
t.BA.WV.DSV2.19HS (Digital Signal Processing 2)
Module: Digital Signal Processing 2
This information was generated on: 26 April 2024
No.
t.BA.WV.DSV2.19HS
Title
Digital Signal Processing 2
Organised by
T ISC
Credits
4
Description
Version: 4.0 start 01 February 2024
Short description
Digital signal processing (DSV) plays a central role in many applications in wireless communication, control engineering, sensor, measurement and medical technology, speech and audio signal processing, ...). In this module you will learn about classical estimation methods, learning systems and classification with deep neural networks
Module coordinator
Rupf Marcel (rumc)
Learning objectives (competencies)
Objectives
Competences
Taxonomy levels
Becoming familiar with advanced DSV algorithms and their applications
F, M
3-5
Deepening the knowledge of implementation of DSV algorithms
F, M
3-5
Learn about stochastic DSP and Deep Neural Networks
F, M
3-5
Module contents
- description of random signals
(all interesting signals are stochastic! description in the time and
frequency domain. Applications: search for similarities / features, measurement of the room-impulse-response with pseudo-noise-signals ...)
- adaptive LMS filtering
(time-variant filters,
which can adapt themselves to "unknown" requirements and follow changes with recursive algorithms
, Minimum Mean Squared Error- (MMSE) Wiener-Filters, LMS algorithm)
- Least-Squares estimation filters
(method of least squares, principle of orthogonality, linear regression,
LS-FIR-filter, Rekursiv Least Squares (RLS), applications like prediction, echo-cancellation
, active noise-cancellation, system identifikation, ...)
- Kalman-Filtering
(state equations, system model, Kalman filter equations, simple kinematic tracking-examples, data fusion e.g.
of Inertial Measurement Unit IMU
)
- Machine Learning Fundamentals
(training- and test data, overfitting, cost function, regression and classification, decision boundaries)
- Deep Learning
(artifical neuron, deep neural networks, backpropagation, convolutional neural networks CNN, recurrent neural networks RNN, Applications: speech recognition, TinyML, ...)
Teaching materials
Script, slides, exercises, labs and test-exams
Supplementary literature
A.V. Oppenheim, G.C. Verghese, "Signals, Systems & Inference", Pearson, 2016.
CH.M. Bishop with H. Bishop, "Deep Learning: Foundations and Concepts", Springer, 2023.
Prerequisites
DSV1 recommended
Teaching language
(X) German ( ) English
Part of International Profile
( ) Yes (X) No
Module structure
Type 3a
For more details please click on this link:
T_CL_Modulauspraegungen_SM2025
Exams
Description
Type
Form
Scope
Grade
Weighting
Graded assignments during teaching semester
exam
Lab
written
Testat
90'
yes
20%
20%
End-of-semester exam
exam
written
90'
yes
60%
Remarks
Legal basis
The module description is part of the legal basis in addition to the general academic regulations. It is binding. During the first week of the semester a written and communicated supplement can specify the module description in more detail.
Note
Additional available versions:
1.0 start 01 February 2021
,
2.0 start 01 August 2022
,
3.0 start 01 February 2023
Course: Digitale Signalverarbeitung 2 - Praktikum
No.
t.BA.WV.DSV2.19HS.P
Title
Digitale Signalverarbeitung 2 - Praktikum
Note
No module description is available in the system for the cut-off date of 26 April 2024.
Course: Digitale Signalverarbeitung 2 - Vorlesung
No.
t.BA.WV.DSV2.19HS.V
Title
Digitale Signalverarbeitung 2 - Vorlesung
Note
No module description is available in the system for the cut-off date of 26 April 2024.