Global Opportunities

EEE3004 : Digital Signal Processing

Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

To develop in-depth knowledge of discrete-time signal processing algorithms, and approaches to measure deterministic and random signals in the frequency domain.

To measure the computational cost of different algorithms used in frequency transformation.

To gain proficiency in methods to distinguish the desired signals from noise using appropriate digital filters. Using research oriented learning to deal with real-world problem related to DSP.

Outline Of Syllabus

Deterministic Signals

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.

Random Signals
Describing Random Sequences, Statistical Properties Related to Random Sequences, Wienar- Khintchine Theorem.

Digital Filters
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.

Design of IIR Filters Bilinear z- transform, Frequency Transformations, Finite Word Length Effects in IIR Filters.

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

Case Studies
Focusing on real-world problems related to DSP, Multirate Digital Signal Processing, Multistage Approach, Polyphase Filters.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials270:209:00Non-synch: 20 mins pre-recorded videos consisting of theory on a DSP topic & MATLAB demonstration
Guided Independent StudyAssessment preparation and completion115:0015:00Coursework consisting of a DSP system design & verification in MATLAB.
Scheduled Learning And Teaching ActivitiesPractical112:0022:00Present in person seminar & practical lab session
Guided Independent StudyIndependent study270:209:00Student study time of Non-Synch pre-recorded material.
Guided Independent StudyIndependent study145:0045:00Reviewing lecture notes; general reading
Total100:00
Teaching Rationale And Relationship

Non-synchronous sessions provide the fundamental concepts of the course while the lab sessions and MATLAB based exercises provide an opportunity to develop skills in application and testing of DSP algorithms


COVID contingency plan

In the event of further lockdown measures preventing present in person activities, the seminar/Q&A sessions will move to synchronous online delivery with students working on MATLAB exercises remotely between sessions. (This was the successful mode of delivery during 2020/21).

Assessment Methods

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

Other Assessment
Description Semester When Set Percentage Comment
Written exercise1M100DSP Assignment in Week 9
Formative Assessments
Description Semester When Set Comment
Lab exercise1MMATLAB Exercises
Assessment Rationale And Relationship

The summative assignment will help students to demonstrate core understanding of course material, analysis/design skills applied to realistic DSP problems and their ability to simulate and verify algorithm performance in MATLAB. The formative MATLAB exercises will provide weekly feedback on their understanding of the DSP topics and their readiness to take on the assignment.

Reading Lists

Timetable