DCSP 2024

This document describes the course objectives and organisation, and contains a course timetable. You could download Lecture notes (HERE)

1. Objectives

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
 
                         Thu        13:00-14:00               R2.41
 
                         Fri          12:00-13:00               R0.14
 

               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).

2.3 Exercise Classes

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. 

2.4 Practical work

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.

2.5 Background Reading

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.

2.6 Assessment

Assessment for this course will be based:

 

 Three-hour examination (80%) Coursework (20%)

3. Course Outline

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                                      [slides_dcsp_2024_1 (ppt)] [slides_dcsp_2024_2 (ppt)]

                           Lecture video:                                   [Video01_2021 (mp4)] [Video02_2021 (mp4)] [Video03_2021 (mp4)] [Video04_2021 (mp4)]                                 

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Week 2              Information Theory                          [slides_dcsp_2024_3 (ppt)] [slides_dcsp_2024_4 (ppt)]

                           Lecture video:                                   [Video01_2021 (mp4)] [Video02_2021 (mp4)] [Video03_2021 (mp4)] [Video04_2021 (mp4)]

                           Seminar I: Matlab                            [slides_dcsp_seminar_2024_1 (ppt)] [dcsp_seminar_2024_Exercise_Answers] [Fibonacci.m]

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Week 3              Fourier Transform                            [slides_dcsp_2024_5 (ppt)]  [slides_dcsp_2024_6 (ppt)]

                           Lecture video:                                   [Video01_2021 (mp4)] [Video02_2021 (mp4)] [Video03_2021 (mp4)] [Video04_2021 (mp4)]

                           Seminar II: Coding                           [dcsp_seminar_Exercise_2024_2 (pdf)]  [midsummer.mat]  [message.mat] [slides_dcsp_seminar_2024_2 (ppt)]

                                                                               [dcsp_seminar_2024_2_Exercise1_Answer] [dcsp_seminar_2024_2_Exercise2_Answer]

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Week 4              Noise & Signal Representation        [slides_dcsp_2024_7 (ppt)]  [slides_dcsp_2024_8 (ppt)] 

                           Lecture video:                                   [Video01_2021 (mp4)] [Video02_2021 (mp4)] [Video03_2021 (mp4)] [Video04_2021 (mp4)]

                           Seminar III: FT                                [dcsp_seminar_Exercise_2024_3 (pdf)]  [slides_dcsp_seminar_2024_3 (ppt)]

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Week 5              DFT, PSD and Applications             [slides_dcsp_2024_9 (ppt)]  [slides_dcsp_2024_10 (ppt)]

                           Lecture video:                                   [Video01_2021 (mp4)] [Video02_2021 (mp4)] [Video03_2021 (mp4)] [Video04_2021 (mp4)]

                           Seminar IV: Noise                             [dcsp_seminar_Exercise_2024_4 (pdf)]  [slides_dcsp_seminar_2024_4 (ppt)]  [dcsp_seminar_2024_4_Exercise_Answers] 

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Week 6              Filter I                                                [slides_dcsp_2024_11 (ppt)]  [slides_dcsp_2024_12 (ppt)]

                           Lecture video:                                   [Video01_2021 (mp4)]  [Video02_2021 (mp4)] [Video03_2021 (mp4)]  [Video04_2021 (mp4)]

                           Seminar V: DFT I                             [dcsp_seminar_Exercise_2024_5 (pdf)]  [dcsp_seminar_2024_5 (ppt)]

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Week 7              Filter II                                               [slides_dcsp_2024_13 (ppt)]

                           Lecture video:                                   [Video01_2021 (mp4)]  [Video02_2021 (mp4)] [Video03_2021 (mp4)]  [Video04_2021 (mp4)]

                           Seminar VI: DFT Matlab                 [dcsp_seminar_Exercise_2024_6 (pdf)]  [dcsp_seminar_2024_6 (ppt)]  [dcsp_seminar_2024_6_Exercise1_Answer]  [dcsp_seminar_2024_6_Exercise2_Answer]

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Week 8              Kalmann Filter                                 [slides_dcsp_2024_14 (ppt)]  [slides_dcsp_2024_15 (ppt)]  [Guest_Lecture_Notes_2024_16 (pdf)] 

                           Lecture video:                                   [Video03_2021 (mp4)] [Video04_2021 (mp4)]

                           Seminar VII: Filter Design              [dcsp_seminar_Exercise_2024_7 (pdf)]  [dcsp_seminar_2024_7 (ppt)]

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Week 9              Wiener Filter & Revision                 [slides_dcsp_2024_17 (ppt)]  [slides_dcsp_2024_18 (ppt)] 

                           Lecture video:                                   [Video01_2021 (mp4)]

                           Seminar VIII: Wiener Filter            [dcsp_seminar_Exercise_2024_8 (pdf)]  [dcsp_seminar_2024_8 (ppt)]

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Week 10            Assignment Help                                 

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Term 3              Revision Class                                    [dcsp_revision_2024_Term3 (ppt)]  [dcsp_revision_lecture_2024_Term3 (mp4)]

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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.

                 Signal 1 (.wav)

                 Signal 2 (.wav)

                 Signal 3 (.wav)

                 Signal 4 (.wav)

                 Signal 5 (.wav)

                 Signal 6 (.wav)

                 Signal 7 (.wav)

                 Signal 8 (.wav)

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.

                 TuneJazz.mat

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.

                 NoiseJazz.mat

                                

4 Reading List

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

                 [5].   DCSP on line course in 2013