Course Title: Digital Signal Processing II
Type of Course: Optional, Theory
Offered to: EEE
Pre-requisite Course(s): None
Spectral estimation of random processes: classical methods, minimum variance method,
parametric methods: AR and ARMA spectral estimation, Levinson-Durbin algorithm, super
resolution techniques: Pisarenko, and MUSIC.
Adaptive signal processing: Applications, e.g., equalization, interference suppression, acoustic
echo cancellation. FIR and IIR adaptive filters. Recursive least squares algorithm, steepest
descent and Newton algorithm, least mean-square (LMS) algorithm, convergence analysis.
Variable step-size LMS algorithm.
Multirate DSP: Interpolation and decimation, single-stage and multistage implementation, design
of anti-aliasing and anti-imaging filters. Polyphase representation of multirate systems. Multirate
implementation of ideal LP filter, digital filter banks, narrowband filters. Perfect reconstruction
filters banks. Short time Fourier transform, subband decomposition and wavelet transform, CWT,
DWT, inter-scale relationship of DWT coefficients, multirate implementation. Applications of
wavelet transform.
To develop an in-depth understanding of advanced signal processing methods and techniques, such as power spectrum estimation, adaptive filtering, multirate signal processing and wavelet transform
To develop the skill to apply signal processing techniques in order to solve practical engineering problems, such as spectral analysis, noise cancellation, sampling-rate conversion, feature extraction, and machine learning.
To develop the ability to conduct and communicate research involving signal processing
Fundamental understanding of concepts of Digital Signal Processing I, and Continuous Signals and Linear Systems.
| CO No. | CO Statement | Corresponding PO(s)* | Domains and Taxonomy level(s)** | Delivery Method(s) and Activity(-ies) | Assessment Tool(s) |
|---|---|---|---|---|---|
| 1 | explain advanced signal processing concepts related to spectral estimation, adaptive filters, multirate systems, filter banks, wavelet transform | PO(a) | C2 | Lectures, Discussions | Assignment, Class test, Final exam |
| 2 | analyse signal using spectral analysis, time-frequency representation and wavelet transform | PO(b) | C4 | Lectures, Discussions | Assignment, Class test, Final exam |
| 3 | evaluate and compare the performance and computational complexity of different implementation techniques | PO(d) | C5 | Lectures, Discussion, Demonstration | Assignment, Class test, Final exam |
| 4 | design filters or filter banks of desired properties | PO(c) | C6 | Lectures, Discussions | Assignment, Class test, Final exam |
| 5 | apply signal processing techniques to solve practical problems having various conflicting requirements | PO(c) | C6 | Lectures, Discussion, Demonstration | Assignment, Final exam |
Cognitive Domain Taxonomy Levels: C1 – Knowledge, C2 – Comprehension, C3 – Application, C4 – Analysis, C5 – Synthesis, C6 – Evaluation, Affective Domain Taxonomy Levels: A1: Receive; A2: Respond; A3: Value (demonstrate); A4: Organize; A5: Characterize; Psychomotor Domain Taxonomy Levels: P1: Perception; P2: Set; P3: Guided Response; P4: Mechanism; P5: Complex Overt Response; P6: Adaptation; P7: Organization
Program Outcomes (PO): PO(a) Engineering Knowledge, PO(b) Problem Analysis, PO(c) Design/development Solution, PO(d) Investigation,
PO(e) Modern tool usage, PO(f) The Engineer and Society, PO(g) Environment and sustainability, PO(h) Ethics, PO(i) Individual work and team work,
PO(j). Communication, PO(k) Project management and finance, PO(l) Life-long Learning
* For details of program outcome (PO) statements, please see the departmental website or course curriculum
| K1 | K2 | K3 | K4 | K5 | K6 | K7 | K8 | P1 | P2 | P3 | P4 | P5 | P6 | P7 | A1 | A2 | A3 | A4 | A5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Week | Lectures | Topic |
|---|---|---|
| 1 | 1-3 | Spectral estimation: Periodogram, Modified Periodogram, Barlett Method |
| 2 | 4-6 | Welchs Method, Blackman-Tukey Approach, minimum variance method |
| 3 | 7-9 | Parametric methods: AR and ARMA spectral estimation, Levinson-Durbin algorithm |
| 4 | 10-12 | Super resolution techniques: Pisarenko and MUSIC |
| 5 | 13-15 | Adaptive signal processing: Overview and applications, steepest descent and Newton algorithm |
| 6 | 16-18 | Least mean-square (LMS) algorithm, convergence analysis |
| 7 | 19-21 | variable step-size LMS algorithm, Recursive least squares (RLS) algorithm |
| 8 | 20-24 | Multirate DSP: Interpolation and decimation, design of anti-aliasing and anti-imaging filters |
| 9 | 25-27 | Polyphase representation of multirate systems |
| 10 | 28-30 | Single-stage and multistage implementation, multirate implementation of ideal LP filter |
| 11 | 31-33 | Digital filter banks, perfect reconstruction filters banks |
| 12 | 34-36 | Short time Fourier transform, subband decomposition and wavelet transform, CWT |
| 13 | 37-39 | DWT, inter-scale relationship of DWT coefficients, multirate implementation, applications of wavelet transform |
Class participation will be judged by in-class evaluation; attendance will be recorded in every class.
Continuous assessment will be done in the form of quizzes, assignments, in-class evaluations.
Final Examination: A comprehensive term final examination will be held at the end of the Term following the guideline of academic Council.
Class Participation 10%
Continuous Assessment 20%
Final Examination 70%
Total 100%
Statistical Digital Signal Processing and Modeling, Hayes, M. (1996), Wiley.
Digital Signal Processing, A Computer Based Approach, Mitra, S. K. (fourth edition), Wcb/McGraw-Hill
Statistical and Adaptive Signal Processing, Manolakis, D. G., Ingle, V. K., and Kogon, S. M. (2005), Artech House Publishers
Advanced Digital Signal Processing, Proakis, J. G., Rader, C. M., Ling, F., and Nikias, C. L. (1992), Macmillan.
Modern Spectral Estimation: Theory and Application, Kay, S. M. (1999), Prentice-Hall
Adaptive Filter Theory (5th Edition), Haykin, S. (2013), Prentice-Hall
Multirate Filtering for Digital Signal Processing: MATLAB Applications, Ljiljana Milic
A Wavelet Tour of Signal Processing: The Sparse Way, Stephane Mallat
Online resources or supplementary materials will be shared with the class on a need basis
N.B. Besides going through relevant topics of the textbook, it is strongly advised that the students follow the class Lectures and discussions regularly for a thorough understanding of the topics.