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
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.
Describing Random Sequences, Statistical Properties Related to Random Sequences, Wienar- Khintchine Theorem.
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.
Describing Filtering Algorithm to Filter Random Sequences, Concept of Wiener Filter Theory and its Application, Concept of Steepest Descent Algorithm, LMS Algorithm.
Focusing on real-world problems related to DSP, Multirate Digital Signal Processing, Multistage Approach, Polyphase Filters.
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.