Syllabus

Module 1: Introduction, overview of discrete time signals and systems, discrete time system described by difference equations, convolution, cross-correlations, Z-Transform: Generalized complex exponentials as Eigen values of LTI systems, z-transform definition, region of convergence (RoC), properties of RoC, properties of the z-transform, inverse z-transform methods-pole-zero plots, time-domain responses of simple pole-zero plots.[8 hours]

Module 2: RoC implications of causality and stability Frequency Domain Analysis, Frequency analysis of continuous-time and discrete-time signals and LTI systems, LTI system as frequency selective filters, low pass, high pass, band pass, band stop, notch, comb, all pass, Pole-zero analysis, bandwidth, digital oscillators, inverse system and de-convolution.[7 hours]

Module 3: Discrete Fourier transform (DFT): Definition of the DFT and inverse DFT, DFT as the samples of the DTFT and the implied periodicity of the time-domain signal, recovering the DTFT from the DFT, circular shift of signal and the “index mod N” concept, properties of the DFT, circular convolution and its relationship with linear convolution, sectioned convolution methods: overlap add and overlap save, effect of zero padding, [7 hours]

Module 4: Introduction to the Fast Fourier Transform (FFT) algorithm, decimation-in-time and decimation-in-frequency algorithms. Sampling and reconstruction of continuous signals, ADC, quantization errors, sampling and reconstruction of discrete time signals,
[8 hours]

Module 5: Digital filter structures, FIR Lattice synthesis and digital filter design, Design of FIR filter by windowing, frequency sampling techniques, design of IIR filters by approximation of derivatives, impulse invariance technique (IIT), bilinear transformation techniques (BLT), digital to digital frequency transformation, recent developments.[10 hours]

Understand the fundamental concepts of digital signal processing, including sampling, and digital filtering.

Gain knowledge of various mathematical tools and techniques used in DSP, such as Fourier analysis, z-transforms, and discrete-time systems.

Learn the basics of signal representation and manipulation in both time and frequency domains.

Explore the design and implementation of digital filters for signal processing applications.

CO1: learn mathematical tools and techniques, such as convolution, cross-correlations, Fourier analysis and z-transforms to analyse digital signal and systems
CO2: analyze and interpret discrete-time signals and systems using the mathematical tools and techniques.
CO3: understand the impact of sampling and quantization on the quality of a signal in a digital system.
CO4: understand and solve problems on basic digital filters.
CO5: design and implement digital filters (FIR/IIR) for signal processing applications.

J.G. Proakis and D.G. Manolakis, Digital Signal Processing: Principles Algorithms and Applications, Pearson Education , 4e,2007.

S.K. Mitra, Digital Signal Processing: A computer based approach, McGraw Hill Education , 4e,2013.

A.V. Oppenheim, R.W. Schafer, Digital Signal Processing, Pearson Education , 2015

Tarun Kumar Rawat, Digital Signal Processing,, Oxford , 2014

IEEE Signal Processing

National conference on communications