Postgraduate

EEE8129 : Intelligent Signal Processing

Semesters
Semester 1 Credit Value: 20
ECTS Credits: 10.0

Aims

To present emerging methods for the manipulation and analysis of single, multi-dimensional and random signals. And to conduct case studies in biomedical and healthcare applications.

To provide knowledge and in-depth understanding of discrete-time signal processing algorithms and approaches to measure deterministic and random signals in frequency domain. And to provide knowledge to apply and design appropriate digital filters according to the application.

Outline Of Syllabus

1.       Introduction of Machine Perception Pattern Recognition Systems Design Cycle and Learning/Adaptation.

2.       Bayesian Decision Theory, Maximum-Likelihood and Bayesian Parameter Estimation General Theory and Problem Dimensionality.

3.       Unsupervised learning, K-means clustering, GMMs based clustering by using MLE and EM Algorithm.

4.       Describing the Deterministic Signals, Transformation of Deterministic time signal into frequency domain using DFT (Discrete Fourier Transform) and FFT (Fast Fourier Transform), Comparison of DFT and FFT Computational Loads, Derivation of the DFT and Matrix Interpretation of the DFT, Determining the Spectral Leakage in FFT, and Mitigation Approaches.

5.       Importance of Digital Filter in DSP, Realisation of Digital Filters, Design of FIR Filters, FIR Filter Design by Impulse Response Truncation, Optimality of IRT Method, Gibb's Phenomenon, FIR Filter Design Using Windows.

6.       Design of IIR Filters, Frequency Transformations, Finite Word Length Effects in IIR Filters.

7.       Describing Random Sequences, Statistical Properties related to Random Sequences.

8.       Non-parametric and Parametric Techniques for Power Spectral Estimation.

9.       Describing Filtering Algorithm to Filter Random Sequences, Concept of Wiener Filter Theory and its Application, Concept of Steepest Descent Algorithm, LMS Algorithm.

10.       Case studies in biomedical and healthcare applications.

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion540:2018:00Student student time on non-synchronous pre-recorded material.
Guided Independent StudyAssessment preparation and completion120:0020:00Completion of summatively assessed individual report on MATLAB tasks, completed in final week block.
Guided Independent StudyAssessment preparation and completion16:006:00Writing a summatively assessed MATLAB Lab report
Guided Independent StudyAssessment preparation and completion12:002:00Formatively assessed online test in 2nd week of teaching block
Guided Independent StudyAssessment preparation and completion18:008:00Revision for online test
Structured Guided LearningLecture materials540:2018:0020 mins non-synchronous pre-recorded videos, replacing lecture material
Guided Independent StudyDirected research and reading115:0015:00Case Study
Structured Guided LearningStructured research and reading activities92:0018:00Reading activity to supplement knowledge of material taught in each week.
Guided Independent StudyIndependent study177:0077:00Revision, reviewing lecture notes; general reading.
Scheduled Learning And Teaching ActivitiesScheduled on-line contact time91:009:00Online synchronous sessions to be distributed into the 2 week block.
Scheduled Learning And Teaching ActivitiesScheduled on-line contact time91:009:00Online synchronous Q&A session on chatroom/discussion board to be distributed across the whole block
Total200:00
Teaching Rationale And Relationship

The online synchronous sessions provide the fundamental concepts of the course while computer based lab experience provide an opportunity to transform all theoretical learning into practical applications. Case studies will direct research and studying state-of-the-art reading.

Alternatives will be offered to students unable to be present-in-person due to the prevailing C-19 circumstances.
Student’s should consult their individual timetable for up-to-date delivery information.

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report1M75MATLAB simulations based 4000 word report to be submitted online.
Computer assessment1M25Simulations
Assessment Rationale And Relationship

The coursework will help students to demonstrate the core understanding of course material, analysis and synthesis skills to novel situations related to ISP. Students’ Matlab lab report reflect their in-depth learning related to the contents delivered during lecture, it also demonstrate the conceptual learning by the way they deal with the problems assigned to them in labs.

Reading Lists

Timetable