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Module

EEE8129 : Intelligent Signal Processing

  • Offered for Year: 2024/25
  • Module Leader(s): Dr Mohsen Naqvi
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
Semesters

Your programme is made up of credits, the total differs on programme to programme.

Semester 2 Credit Value: 20
ECTS Credits: 10.0
European Credit Transfer System

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 explanation of discrete-time signal processing algorithms and approaches to measure deterministic and random signals in frequency domain.

To provide knowledge and recognition of supervised learning and unsupervised learning. And to design and demonstrate appropriate digital filters and techniques according to the application.

Outline Of Syllabus

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

Supervised and Unsupervised Learning Techniques.

Data Dimensionality Reduction and Signal Enhancement Techniques.

Deterministic Signals, Transformation of Deterministic Time Signals into Frequency Domain.

Importance of Digital Filter in DSP, Realization of Filters, and Design of Digital Filters.

Random Sequences, Statistical Properties related to Random Sequences. Filtering Algorithms to Filter Random Sequences. Power Spectral Estimation.

Case Studies

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture122:0024:003x2hr lectures per week over 4 weeks
Guided Independent StudyAssessment preparation and completion12:002:00Final exam in assessment period
Guided Independent StudyAssessment preparation and completion110:0010:00Completion of formative and summative written exercises.
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 ActivitiesWorkshops43:0012:00MATLAB sessions either in the computer lab or virtual lab (synchronous online sessions )
Scheduled Learning And Teaching ActivitiesWorkshops42:008:00Online synchronous Q&A sessions/tutorials to be distributed across the whole block
Guided Independent StudyIndependent study182:0082:00Revision, reviewing lecture notes, general reading.
Total200:00
Teaching Rationale And Relationship

The face-to-face lectures 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

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

Exams
Description Length Semester When Set Percentage Comment
Written Examination1202A75N/A
Other Assessment
Description Semester When Set Percentage Comment
Written exercise2M25MATLAB Programming and Implementation report (1000 words max)
Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Description Semester When Set Comment
Written exercise2MTwo sets of analytical questions are provided as a homework to solve after the delivery of the related syllabus. The solutions are checked during the class and feedback provided
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.

Formative analytical questions are provided as a homework to solve after the delivery of the related syllabus. The solutions are checked during the class and feedback is provided to gauge understanding and help students prepare for the summative assessments.

Reading Lists

Timetable