EEEN40130 Advanced Signal Processing

Academic Year 2020/2021

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. Basic operations (e.g. noise cancellation and template matching) on speech and image signals are also covered.

Specifically, the first part of the module concerns IIR filter design, which is a continuity of FIR filter design covered in the module EEEN30050. Then fundamental terms in relation to the characterization of a random signal are defined, followed by a brief introduction to estimation theory. In the next part, filtering of random signals, methods for power spectral density estimation, optimal linear prediction, and linear adaptive filtering are covered. The last part of the module deals with speech and 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 these two special signals.

The delivery of the module includes pre-recorded videos, synchronous online lectures and tutorials. All 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. It is expected that three comprehensive signal processing tasks will be assigned.

The assessment of the module includes 5 assignments whose deadlines will communicated to students well in advance. The grading scheme of each assignment will be made clear with the handout. It is expected that each student will get individual detailed feedback for each assignment.

The main programming language used in the module for demonstrations is MATLAB but students can choose other ones of their preference to complete the assignments.

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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.

- Design and implement IIR filters to meet desired specifications.

Indicative Module Content:

The following key topics are covered:

- Advanced methods for designing IIR filters: methods of impulse invariance and bilinear transform.

- 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.

- Speech processing: Waveform coding, subband coding, transform coding, linear predictive coding

- 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
Lectures

30
Specified Learning Activities

25
Autonomous Student Learning

50
Total

105
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
Pre-requisite:
EEEN30050 - Signal Processing


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Digital IIR Filter Design Varies over the Trimester n/a Graded No

20
Assignment: Spectral Power Density Estimation Varies over the Trimester n/a Graded No

20
Assignment: Linear Prediction Varies over the Trimester n/a Graded No

20
Assignment: Noise cancellation for audio signals Varies over the Trimester n/a Graded No

20
Assignment: Image Processing Varies over the Trimester n/a Graded No

20

Carry forward of passed components
No
 
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
Assoc Professor Nam Tran Lecturer / Co-Lecturer