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ELE640_1

Advanced Signal Processing

This is the study programme for 2019/2020. It is subject to change.


Signal processing meets us in very many contexts in our daily lives and in the workplace. Mobile phones, robot vision, image processing, data interpretation from sensors, radars, smart watches, medical equipment - we are surrounded by sensors, signals and data that need to be processed and interpreted to be useful. In advanced signal processing, we build on topics like Signal Processing, Image processing and Machine Learning. We learn some new fundamental theory, we learn some new techniques and "building blocks" that are useful in many contexts, and we look at some specific applications. We learn about how the jpg, mpeg and mp3 codecs are put together, and we learn to extract features from data that can be used for analyses or fed into machine learning programs.
Themes addressed: Multirate signal processing, wavelets and filterbanks. Stochastic signal processing, spectral estimation, quantization and Differential Pulsecode Modulation (DPCM). Techniques and methods for signal- and image compression. Feature extraction from signals in time and frequency domain. Sparse representation and dictionary learning.

Learning outcome

Knowledge: The student will learn some advanced signal processing techniques as a continuation of some themes from ELE500 Signal processing and ELE510 Image processing.The students will geain knowledge on some signal and image processing tools, like multi rate theroy, wavelets and filterbanks, spectral estimation etc, useful in many applications. The student will learn to use signal and image processing techniques in real world applications. Compression of signal and images will be used as an example application, and will provide insight and knowledge. The student will also learn about signal and image processing in biomedical applications, and some applications will be looked more deeply into. Analysis of ECG signals for arrhythmia detection, and segmentation and analysis of Magnetic Resonance (MR) images are examples of applications.
Skills: The student should be able to use advanced mathematical and statistical methods for analysis and contruction of signal processing systems. In addition, the student should be able to use MAtlab for simulation and programming of such systems.
Competence: After this course the student should have a general understanding of both fundamental and some advanced concepts used in signal processing, as well as an understanding in how to use such concepts in real world signal processing problems.

Contents

Multirate signal processing, wavelets and filterbanks. Stochastic signal processing, spectral estimation, quantization and Differential Pulsecode Modulation (DPCM). Techniques and methods for signal- and image compression. Feature extraction from signals in time and frequency domain. Sparse representation and dictionary learning.
Signal and Image processing used in real world applications, with examples from biomedical applications.

Required prerequisite knowledge

ELE500 Signal Processing

Recommended previous knowledge

ELE510 Image Processing and computer vision, ELE520 Machine learning

Exam

Written exam and written project rapport
Weight Duration Marks Aid
Written exam60/1004 hoursA - FNo printed or written materials are allowed. Approved basic calculator allowed.
Written project rapport40/100 A - FAll written and printed means are allowed. Calculators are allowed.

Coursework requirements

Presenting the project in class

Course teacher(s)

Course coordinator
Kjersti Engan
Head of Department
Tom Ryen
Course teacher
Sven Ole Aase

Method of work

6 hours of lectures a week. Exercises using Matlab. Project work the last 4 weeks of the semester. At least 3 of these weeks are without lecturing.

Open to

Admission to Single Courses at the Faculty of Science and Technology
Robot Technology and Signal Processing - Master's Degree Programme

Course assessment

Form and/or discussion.

Literature

- Digital Signal Processing, Proakis and Manolakis, chapter 11
- Introduction to Data Compression, K. Sayood, chapter 10,12,14
- Compendium


This is the study programme for 2019/2020. It is subject to change.

Sist oppdatert: 17.09.2019

History