Course Catalogue

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This course introduces the basic concepts, analysis methods and applications of random signals. It includes the power spectrum analysis of random signals, the stationary and ergodic random processes, and the Gaussian random processes. It also includes basic LTI systems, including the low-pass, band-pass, Wiener and match filters. The Hilbert transform and related applications, the Markov chain are also introduced.

Courses will be delivered continuously for 16 weeks and 2 consecutive courses per week.

Mandatory Entry Requirements

English, Calculus I and II, Linear Algebra, Fourier Transform, Probability Theory

Recommended Entry Requirements

Circuit Analysis and Design, Signals and Systems, Digital Circuit Design

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Assessment

Total ＝ Coursework (25%) + Report (10%) + Oral Assessment & Presentation (10%) + Examination (55%)

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 laboratory and/or project.

The initial grade on coursework laboratories 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 aims to explain the basic theory, characteristics and analysis methods of random signals, to introduce basic LTI systems and applications, and to develop the ability to solve problems that involve random signals.

By the end of this course, through coursework assignments, projects and final exams, students (alone and in team) will be able to:

■ describe typical distributions of characteristic function using concepts from probability theory; calculate the characteristic of a system using Multi-dimensional Gaussian distribution and Gaussian signals; 

■ analyse the response of linear systems to both deterministic and random input processes; described the relationship between correlation function and power spectrum of generalized stationary random signal;

■ determine the mean ergodic and correlation ergodic of stochastic processes; analyse the response of a system with a random signal input and the output characteristics of system with the injection of a white noise input though linear time-invariant systems;

■ design system structures to meet desired performance objectives for both continuous and discrete time applications.

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 laboratory 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.