EEEN40130 Advanced Signal Processing

Academic Year 2021/2022

The module builds on the foundation module in EEEN30050 (Signal Processing). While EEEN30050 focuses on deterministic signals and their transforms, this module will expose students to random signals and some fundamental statistical signal processing techniques, including adaptive filtering and its applications. Basic operations (e.g. template matching) on image signals are also covered.

The module starts with fundamentals in relation to the characterization of a random signal, followed by a brief introduction to estimation theory. In the following part, filtering of random signals, methods for power spectral density estimation, optimal linear prediction, and linear adaptive filtering are covered. Applications of linear filter in wireless communications are also discussed. The last part of the module deals with image processing. The aim is to demonstrate how signal processing techniques and the domain knowledge can be exploited to derive efficient signal processing approaches for this special class of signals.

It is expected that the delivery of the module blends pre-recorded videos, synchronous online and in-class lectures and tutorials. All module materials are available from the beginning of the trimester. Though a reading list is provided, the lecture notes are meant to be self-contained.

The treatment is a blend of the theoretical and the practical with real world applications being dealt with in some depth. Students are required to confirm some theoretical results by writing their own Matlab code. Three comprehensive signal processing tasks will be assigned. The deadlines will be communicated to students well in advance. The grading scheme of each assignment will be made clear with the handout. Each student will get individual detailed feedback for each assignment. In addition to the assignments, the assessment includes an 2 hour end-of-trimester exam.

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Curricular information is subject to change

Learning Outcomes:

Having passed this module the student will be able to:

- Expound on the theoretical foundations of a number of important signal processing operations.

- Define fundamental terms that characterise a random signal.

- Describe and analyse linear estimators.

- Specify and design algorithms for the processing of speech and images and implement those algorithms in a high level language.

- Design, implement and analyse adaptive digital filters for a variety of applications.

Indicative Module Content:

The following key topics are covered:

- Random signals: random variables, stationary random processes, auto-correlation, cross-correlation, power spectral density.

- Estimation theory: unbiased estimation, minimum variance unbiased estimation, best linear unbiased estimations (BLUE), least squares.

- Linear optimum filtering: Wiener filter, linear prediction.

- Linear adaptive filtering: steepest-descent algorithm, least-mean-square algorithm.

- Channel equalization

- Image processing: image representation, 2D-convolution, 2D-correlation, 2D discrete Fourier transform, template matching, image filtering (smoothing filters, sharpening filters)

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning






Approaches to Teaching and Learning:
-Lectures, tutorials
-Problem-based learning 
Requirements, Exclusions and Recommendations
Learning Requirements:

Signal Processing (EEEN30050) or equivalent. This course is mathematically challenging, so a strong background in university honours level mathematics in the areas of linear algebra, frequency analysis (transforms), linear time invariant systems is required.

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Channel Equalization Varies over the Trimester n/a Graded No


Assignment: Image Processing Varies over the Trimester n/a Graded No


Assignment: Spectral Power Density Estimation Varies over the Trimester n/a Graded No


Examination: End of trimester exam 2 hour End of Trimester Exam No Standard conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

- Digital Signal Processing: Principles, Algorithms, and Applications by John Proakis and Dimitris Manolakis
- Fundamentals of Statistical Signal Processing: Estimation Theory by Steven M. Kay.
- Adaptive Filter Theory by Simon Haykin
- Spectral analysis of signals by Peter Stoica and Randolph Moses
Name Role
Dr Nam Tran Lecturer / Co-Lecturer
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Lecture Offering 1 Week(s) - Autumn: All Weeks Fri 13:00 - 13:50
Lecture Offering 1 Week(s) - Autumn: All Weeks Thurs 11:00 - 11:50
Lecture Offering 1 Week(s) - 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12 Tues 12:00 - 12:50