Course Catalogue

Stochastic Signal Analysis (UESTC) UESTCHN3016

  • Academic Session: 2026-27
  • School: School of Engineering
  • Credits: 8
  • Level: Level 3 (SCQF level 9)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

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.

Timetable

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

Requirements of Entry

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

Excluded Courses

None

Co-requisites

None

Assessment

Assessment

 

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

Main Assessment In: December

Course Aims

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.

Intended Learning Outcomes of Course

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.

Minimum Requirement for Award of Credits

No exceptions