DCSP 2024
This document describes the course objectives and
organisation, and contains a course timetable. You could download Lecture notes (HERE)
The course will teach a variety of contemporary
approaches to the
theory and implementation of DCSP, with emphasis
on theory, software and algorithms. It equips students with sufficient
knowledge to enable employment or postgraduate
study involving data, in particular digital and Big Data.
2. General Information
2.1 Tutors for this module
Jianfeng
Feng
email: jianfeng.feng@warwick.ac.uk
Ruohan
Zhang email:
Ruohan.Zhang.1@warwick.ac.uk
2.2 Teaching methods
2 Lectures + 1 Senimar per week, held at the following times
Day
Time
Place
Lectures:
Mon 16:00-17:00 Teams Online
Seminar: Mon
10:00-11:00 MB3.17
These lectures will cover the introductory theory
behind the topics above, as well as various topics related to these (e.g.
implementation issues).
All students on this course will have a supervised
exercise class every week, starting from the second week. Details of times, and
which exercise class you should attend are given during lectures.
A lot of the work on the course is based on exercises
that you should do with or without the computer. In addition to the
timetabled exercise classes you are expected to spend many hours a
week both using the computer, and reading supporting material from the
reading list in order to deepen your understanding of the topics being
covered. All students experience problems learning new concepts and
skills and it is important not to be discouraged and give up. If you get stuck
ask other students, a demonstrator, or a tutor for help. As stated
already, various exercises may be set during the course.
Remember that the lectures for this (and any other)
course really aim to provide you with the minimal essential information on a
subject. To get a deeper understanding (and in order to do well in the exams,
and in subsequent courses which build on these) you MUST read around the
subject. The reading list at the end of this handout should provide some
good staring points.
Assessment for this course will be based:
Three-hour examination (80%) Coursework
(20%)
The list below gives a provisional week-by-week list
of topics (note these may change depending on how the course progresses).
Some lectures will incorporate important announcements, including changes in
later lectures, so if you ever miss a lecture make sure you find out from
another student exactly what was said.
Note: I will update the materials below before the lecture. Considering some students might be self-isolating, I will also upload last year's lecture videos after the lecture.
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Week
1
Introduction
Lecture video:
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Week
2
Information Theory
Lecture video:
Seminar I: Matlab
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Week
3
Fourier Transform
Lecture video:
Seminar II: Coding
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Week
4
Noise & Signal Representation
Lecture video:
Seminar III: FT
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Week
5
DFT, PSD and Applications
Lecture video:
Seminar IV: Noise
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Week
6
Filter I
Lecture video:
Seminar V: DFT I
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Week
7
Filter II
Lecture video:
Seminar VI: DFT Matlab
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Week
8
Kalmann Filter
Lecture video:
Seminar VII: Filter Design
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Week
9
Wiener Filter & Revision
Lecture video:
Seminar VIII: Wiener Filter
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Week
10
Assignment Help
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Term
3
Revision Class
Lecture video:
Seminar: Classic exercises
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Assignment
Download .pdf file here
Assignment 2024;
Files for question 2;
You could pick up one of these files for question 2.
Files for question 8:
Download TuneJazz.mat below and try to remove noise as much as you can. The original signal is an excerpt from a piece of Jazz music.
The sampling frequency is 44100 Hz.
Files for question 8:
You can also try and clean up the following one for fun. This time, the original signal has been contaminated by white noise.
Many general DCSP
textbooks have some sections devoted to some of the material covered in this
course.Furthermore, there
are many on-line materials such as lecture notes, and in particular public lectures, for example, the one by
Paolo Prandoni and Martin Vetecli in 2013 from EPFL [5] on DCSP. Here are some advanced examples.
[1]. Power Spectrum Estimating
[2]. David J. C. MacKay. Information Theory, Inference,
and Learning Algorithms Cambridge: Cambridge University Press, 2003.
[3]. R. Cristi, Modern digital signal processing; Brooks/Cole; 2004
[4]. On-line materials: Intuitive Guide to Principles of Communications
(http://www.complextoreal.com/); slightly advanced but very good
4 Reading List