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Module

EEE3004 : Digital Signal Processing

  • Offered for Year: 2020/21
  • Module Leader(s): Mr Jeffrey Neasham
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

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

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 completion115:0015:00Coursework consisting of a DSP system design & verification in MATLAB.
Structured Guided LearningLecture materials270:209:00Non-synch: 20 mins pre-recorded videos consisting of theory on a DSP topic & MATLAB demonstration
Structured Guided LearningAcademic skills activities91:009:00MATLAB based signal processing exercises, students to work offline to reinforce understanding
Guided Independent StudyIndependent study270:209:00Student study time of Non-Synch pre-recorded material.
Guided Independent StudyIndependent study149:0049:00Reviewing lecture notes; general reading
Scheduled Learning And Teaching ActivitiesScheduled on-line contact time91:009:00Synch TT: Overview of course in wk 1, review of tutorial questions, Q&A Session & review of MATLAB
Total100:00
Teaching Rationale And Relationship

Non-synchronous sessions provide the fundamental concepts of the course while MATLAB based exercises provide an opportunity to develop skills in application and testing of DSP algorithms. Tutorial practice sheets will help students in developing design and problem solving skills.

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
Written exercise1M100DSP Assignment in Week 8
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