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EEE3004 : Digital Signal Processing

  • Offered for Year: 2019/20
  • Module Leader(s): Dr Charalampos Tsimenidis
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
Semester 1 Credit Value: 10
ECTS Credits: 5.0


To provide knowledge and in-depth understanding of discrete-time signal processing algorithms, and approaches to measure deterministic and random signals in frequency domain.
To measure the computational cost of different algorithms used in frequency transformation.
To provide knowledge to distinguish the desired signals from noise using appropriate digital filters.
To provide 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
Guided Independent StudyAssessment preparation and completion160:308:00Revision for final exam
Guided Independent StudyAssessment preparation and completion12:002:00Final exam
Scheduled Learning And Teaching ActivitiesLecture122:0024:00N/A
Scheduled Learning And Teaching ActivitiesPractical81:008:00Computer practical
Guided Independent StudyIndependent study158:0058:00Reviewing lecture notes; general reading
Jointly Taught With
Code Title
EEE8129Intelligent Signal Processing
Teaching Rationale And Relationship

Lectures provide the fundamental concepts of the course while computer based lab experience provide an opportunity to transform all theoretical learning into practical applications. Tutorials practices will help students in developing real problem solving skills.

Assessment Methods

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

Description Length Semester When Set Percentage Comment
Written Examination1201A80N/A
Exam Pairings
Module Code Module Title Semester Comment
EEE8129Intelligent Signal Processing1N/A
Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report1M10Development of Matlab-based program to analyse deterministic, random signals and generating lab report. 1200 words max.
Practical/lab report1M10Designing filter coefficient, its implementation to get desired signals from signal with noise, assessment is report based. 1200 max
Assessment Rationale And Relationship

The examination will help students to demonstrate the core understanding of course material, analysis and synthesis skills to novel situations related to DSP. Students’ 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