Postgraduate

EEE8129 : Intelligent Signal Processing

Semesters
Semester 2 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.       Supervised and unsupervised learning, K-means clustering, GMMs based clustering by using Maximum-Likelihood Estimation and EM Algorithm.

3.       Unsupervised learning based data dimensionality reduction and blind signal enhancement techniques e.g. PCA, ICA and IVA.

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 and Realization of FIR and IIR Filters.

6.       Design of FIR Filters, FIR Filter Design by Impulse Response Truncation, Optimality of IRT Method, Gibb's Phenomenon, FIR Filter Design Using Windows. Design of IIR Filters.

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

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

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

10.       Case studies in 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
Scheduled Learning And Teaching ActivitiesLecture122:0024:003x2hr lectures per week over 4 weeks or non-synchronous pre-recorded videos, replacing lecture mater
Guided Independent StudyAssessment preparation and completion17:307:30Completion of formatively assessed individual report on MATLAB tasks
Guided Independent StudyAssessment preparation and completion12:302:30Final exam in assessment period
Guided Independent StudyAssessment preparation and completion241:0024:00Revision for final exam
Guided Independent StudyDirected research and reading114:0014:00Case study
Structured Guided LearningStructured research and reading activities122:0024:00Reading activity to supplement knowledge of material taught in each week.
Scheduled Learning And Teaching ActivitiesWorkshops42:008:00Online synchronous Q&A sessions/tutorials to be distributed across the whole block
Scheduled Learning And Teaching ActivitiesWorkshops62:0012:00MATLAB sessions either in the computer lab or virtual lab (synchronous online sessions )
Guided Independent StudyIndependent study184:0084:00Revision, reviewing lecture notes, general reading, preparation for final exam
Total200:00
Teaching Rationale And Relationship

The face-to-face lectures (or recorded videos in case of public health restrictions) and online synchronous sessions provide the fundamental concepts of the course.

The computer-based lab experience provides an opportunity to transform all theoretical learning into practical applications.

Case study provides research and studying state-of-the-art reading.

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

Exams
Description Length Semester When Set Percentage Comment
Written Examination1502A100Closed-Book Exam (It will be replaced with a summative assessment in case public health related restrictions)
Formative Assessments
Description Semester When Set Comment
Lab exercise2MMATLAB report with a maximum of 2000 words
Assessment Rationale And Relationship

The examination allows students to demonstrate their ability to solve engineering problems focused on intelligent signal processing based on the knowledge and methodology presented in the course material. The laboratory report assesses technical writing skills and provides the opportunity for the students to apply programming skills to validate the theory taught on the course.

Reading Lists

Timetable