|13040515||Advanced Signal Processing||L||T||P||C|
|Version1.0||Date of Approval: —||3||1||0||3|
|Pre-requisites//Exposure||Signal & System|
- Students learn the essential advanced topics in digital signal processing that are necessary for successful graduate-level research.
- The course includes a review of the linear constant-coefficient system properties covered in an undergraduate DSP course, and then examines a variety of multirate filter structures, time-varying and adaptive systems, fast algorithms, and other topics relevant to the research areas of the students.
On completion of this course, you should be able to:
- Master modern signal processing tools including vector spaces, bases and frames, operators, signal expansions and approximation, as well as classical signal processing tools including Fourier and z transforms, filtering, and sampling.
- Apply the above tools to real-world problems including spectral analysis, filter design, noise cancellation, signal compression, rate conversion, feature extraction, inverse problems, machine learning and justify why these are appropriate tools.
- Think critically, ask questions, and apply problem-solving techniques to homework and midterm exams.
- Research, present, and report on a selected topic that is of current interest within a specified time.
Basic concept review of digital signals and systems; computer-aided digital filter design, quantization effects, decimation and interpolation, and fast algorithms for convolution and the DFT; introduction to adaptive signal processing.
- “Digital Signal Processing, 4th Edition” by Proakis and Manolakis, Prentice Hall, 2007 (ISBN: 0-13-187374-1).
- Vetterli, J. Kovacevic, and V. K. Goyal, “Foundation of Signal Processing”, Cambridge University Press, 2014
- Rabiner & Gold, “Theory & application of digital Signal Processing”, PHI 1992.
- Roman kuc, “Introduction to Digital Signal Processing,” McGraw hill Edition.
- Openheim AV & Schafer RW, Discrete Time Signal Processing PHI.
Unit I: Introduction of DSP
8 lecture hours
Introduction to Signal Processing, Discrete Linear Systems, superposition Principle, Unit-Sample response, stability & causality Criterion.
Unit II: Fourier Transform & inverse Fourier transform
12 lecture hours
Fourier Transform & inverse Fourier transform: Frequency domain design of digital filters, Fourier transform, use of Fourier transform in Signal processing. The inverse fourier transform, Sampling continuous function to generate a sequence, Reconstruction of continuous -time signals from Discrete-time sequences.
Unit III: DFT & FFT & Z transform with Applications
14 lecture hours
Discrete Fourier transforms, properties of DFT, Circular Convolution, Fast Fourier Transform, and Realizations of DFT. The Z-transform, the system function of a digital filter, Digital Filter implementation from the system function, the inverse Z- transform, properties & applications, Special computation of finite sequences, sequence of infinite length & continuous time signals, computation of fourier series & time sequences from spectra.
Unit IV: Digital Filter Structure & Implementation
14 lecture hours
Linearity, time invariance & causality, the discrete convolution, the transfer function, stability tests, steady state response, Amplitude & Phase characteristics, stabilization procedure, Ideal LP Filter, Physical reliability & specifications.FIR Filters, Truncation windowing & Delays, design example, IIR Filters: Review of design of analog filters & analog frequency transformation. Digital frequency transformation. Design of LP filters using impulse invariance method, Bilinear transformation, Phase equalizer, digital all pass filters.
Unit V: Implementation of Filters
6 lecture hours
Realization block diagrams, Cascade & parallel realization, effect of infinite-word length, transfer function of degree 1&2, Sensitivity comparisons, effects of finite precision arithmetic on Digital filters.
Mode of Evaluation: The theory and lab performance of students are evaluated separately.
|Theory||Laboratory||Theory and laboratory|