5B3 – Applied Intelligent Signal Processing

This postgraduate module builds upon fundamental signal processing theory and extends it to advanced methods and their applications across a variety of fields. Students will explore modern developments in intelligent and adaptive signal processing, gaining insight into how these techniques are applied in communication, sensing, and emerging engineering domains.

Core Topics

  • Advanced Signal Processing Methods: Signal conditioning, embedded and real-time processing, and acausal or other offline techniques.
  • Nonlinear and Adaptive Processing: Nonlinear signal processing, adaptive filtering, and rank filters.
  • Pattern Recognition and Variability Treatment: Proper handling of variability, including non-Gaussian variability, and approaches to pattern recognition.
  • Sensor Fusion and Sparse-Signal Recovery: Techniques for combining data from multiple sources and recovering sparse or incomplete signals.
  • Estimation and Model-Free Methods: Model estimation, sequential estimation, and model-free approaches.
  • Spectral and Hybrid Spaces: Exploration of spectral methods beyond Fourier transformation, including wavelets and sliding spectral transformations.
  • Machine Learning and AI Connections: Adaptive methods leading toward intelligent and learning-based signal processing.

Practical Applications
Examples and exercises will illustrate these techniques in applications such as communication, ranging, computer vision and object identification, medical and health technology, neural engineering and brain–computer interfaces, industrial electronics, and electromobility.

Learning Outcomes
By the end of this module, you will:

  • Understand advanced concepts in applied and intelligent signal processing.
  • Be familiar with modern signal processing techniques, including adaptive, nonlinear, and acausal methods.
  • Recognise how advanced filtering, estimation, and spectral techniques are applied in real-world systems.
  • Gain insight into the use of intelligent and AI-based methods across diverse engineering and technological domains.