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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2017/2018

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DRPS : Course Catalogue : School of Engineering : Electronics

Undergraduate Course: Advanced Coding Techniques 5 (ELEE11092)

Course Outline
SchoolSchool of Engineering CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 5 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis course will cover the current topics of interest in Advanced Coding Techniques. In particular information theory fundamentals related to source coding and its extension to channel capacity are studied. Rate-distortion theory and quantisation for uncorrelated and correlated signals are of particular interest.

Syllabus:

1. Scalar quantisation,
2. Asymptotic quantisation theory,
3. Vector quantisation,
4. Rate-distortion theory,
5. Channel capacity evaluations.

Practical examples of the above concepts are presented throughout the course.
Course description 1. Introduction

2. Scalar Quantisation

3. Asymptotic Scalar Quantisation Theory and Variable Rate Encoding

4. Vector Quantisation

5. Rate Distortion Theory

6. Theoretical Channel Capacity Analysis
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2017/18, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info) Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 100%
Feedback Not entered
No Exam Information
Learning Outcomes
The students will understand fundamentals as well as advanced concepts in source coding. They will be able to quantify the bit rate that is theoretically needed to perform source coding of continuous-valued signals with some given maximum distortion. They will be able to explain the complexity-quality trade-offs in practical systems and they will be able to quantify how close practical quantisation algorithms can get to the theoretical limits given by information theory. They will be able to design scalar and vector quantisers for practical signals. They will also understand how information theory can be used to predict the data capacity of communications channels.
Reading List
A. Gersho and R. M. Gray, Vector Quantization and Signal Compression. Kluwer Academic Publishers, 8th ed., 2001.

T. Cover and J. Thomas, Elements of Information Theory. John Wiley & Sons, Inc., 1991.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsCoding,Quantisation,Rate Distortion Theory,Channel Capacity
Contacts
Course organiserDr John Thompson
Tel: (0131 6)50 5585
Email: John.Thompson@ed.ac.uk
Course secretaryMiss Megan Inch
Tel: (0131 6)51 7079
Email: M.Inch@ed.ac.uk
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