t.BA.WV.DSV2.19HS (Digital Signal Processing 2) 
Module: Digital Signal Processing 2
This information was generated on: 24 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

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 24 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 24 April 2024.