Course Catalogue

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Information theory is the foundation theory of modern digital communication systems and it have also been extended to many applications including natural language processing, biology, economics, statistics and data analysis. This course is a basic course for majors in Communications Engineering, and Electronic Engineering. After completion of the course, students will have a preliminary understanding of the basic concepts, theories, methods, and applications of information theory, including but not limited to source entropy, lossless source coding, capacity of discrete and continuous channel, basic channel coding methods, and rate distortion functions.

Course will be delivered continuously in the traditional manner at UESTC.

Mandatory Entry Requirements

None

Recommended Entry Requirements

None

None

None

Assessment

Total ＝ Reports（45%）+ Presentation（5%）+ Final Examination（50%）

The reports are produced from practical work completed by students in the field of information theory as stated in the course handbook.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.

Due to the nature of the coursework and sequencing of courses, it is not possible to reassess the coursework and projects.

The initial grade on coursework and project will be used when calculating the resit grade.

Reassessment is offered only to meet the special requirement that all courses must be passed for graduation in this programme.

This course introduces the fundamentals of information theory, the basic limitations of information compression and transmission in communication systems, and the analytical methods to apply the concepts of information theory for the analysis of related disciplines in information science.

By the end of this course students will be able to:

■ Apply the concept of entropy to solve complex problems for both discrete and continuous source in communications systems, such as discrete memoryless and Markov source, continuous source.

■ Evaluate the notion of average mutual information and channel capacity, apply the Shannon capacity formula to analyse complex problems in typical discrete or continuous channel and to have a deeper understanding of basic limitations in typical communication systems,

■ Evaluate the characteristics of lossless source coding and channel coding including linear block codes, and apply both source and channel coding techniques to recognise the limitations of information compression and communications systems, respectively.

■ Evaluate lossy compression by the rate distortion theory for single/multiple-symbol information source with Shannon's Third theorem, and apply it to discuss its application for complex communications engineering problems.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment. In addition students must submit work for assessment for the course project or a grade of credit withheld will be given.

Students must attend the timetabled laboratory classes.

Note that these are minimum requirements: good students will achieve far higher participation/submission rates. Any student who misses an assessment or a significant number of classes because of illness or other good cause should report this by completing a MyCampus absence report.