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 understanding of discrete-time signal processing algorithms and approaches to measure deterministic and random signals in frequency domain. And to provide knowledge to apply and design appropriate digital filters according to the application.
Outline Of Syllabus
1. Introduction of Machine Perception Pattern Recognition Systems Design Cycle and Learning/Adaptation.
2. Bayesian Decision Theory, Maximum-Likelihood and Bayesian Parameter Estimation General Theory and Problem Dimensionality.
3. Unsupervised learning, K-means clustering, GMMs based clustering by using MLE and EM Algorithm.
4. 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.
5. 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.
6. Design of IIR Filters, Frequency Transformations, Finite Word Length Effects in IIR Filters.
7. Describing Random Sequences, Statistical Properties related to Random Sequences.
8. Non-parametric and Parametric Techniques for Power Spectral Estimation.
9. Describing Filtering Algorithm to Filter Random Sequences, Concept of Wiener Filter Theory and its Application, Concept of Steepest Descent Algorithm, LMS Algorithm.
10. Case studies in biomedical and healthcare applications.
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.