04-12-2012, 12:14 PM
Syllabus M.TECH. FIRST YEAR COURSES
Objective is to provide students with:
· Understanding of the theory of A/D and D/A signal conversion, digital filtering and
spectral analysis.
· Experience in the design and implementation of digital filters and spectral analyzers,
and in their application to real signals (e.g., speech, images).
· Broad overview of the use of digital signal processing in consumer and business
products
Discrete Time Signals: Sequences; representation of signals on orthogonal basis; Sampling and
Reconstruction of signals.
Discrete systems: attributes, Z-Transform, Analysis of LSI systems, Frequency Analysis,
Inverse Systems, Discrete Fourier Transform (DFT), Fast Fourier Transform algorithm,
Implementation of Discrete Time Systems.
Design of FIR Digital filters: Window method, Park-McClellan's method.
Design of IIR Digital Filters: Butterworth, Chebyshev and Elliptic Approximations; Low pass,
Band pass, Band stop and High pass filters.
Effect of finite register length in FIR filter design.
Parametric and non-parametric spectral estimation. Introduction to multirate signal
processing. Application of DSP to Speech and Radar signal processing.
EC-912: DETECTION AND ESTIMATION
Classical Detection and Estimation Theory: Introduction, simple binary hypothesis tests, M
Hypotheses, estimation theory, composite hypotheses, general Gaussian problem, performance
bounds and approximations.
Representations of Random Processes: Introduction, orthogonal representations, random
process characterization, homogenous integral equations and eigen-functions, periodic
processes, spectral decomposition, vector random processes.
Detection of Signals – Estimation of Signal Parameters: Introduction, detection and estimation in
white Gaussian noise, detection and estimation in nonwhite Gaussian noise, signals with
unwanted parameters, multiple channels and multiple parameter estimation.
Estimation of Continuous Waveforms: Introduction, derivation of estimator equations, a lower
bound on the mean-square estimation error, multidimensional waveform estimation,
nonrandom waveform estimation.
Linear Estimation: Properties of optimum processors, realizable linear filters, Kalman-Bucy
filters, fundamental role of optimum linear filters.
Objective is to provide students with:
· Understanding of the theory of A/D and D/A signal conversion, digital filtering and
spectral analysis.
· Experience in the design and implementation of digital filters and spectral analyzers,
and in their application to real signals (e.g., speech, images).
· Broad overview of the use of digital signal processing in consumer and business
products
Discrete Time Signals: Sequences; representation of signals on orthogonal basis; Sampling and
Reconstruction of signals.
Discrete systems: attributes, Z-Transform, Analysis of LSI systems, Frequency Analysis,
Inverse Systems, Discrete Fourier Transform (DFT), Fast Fourier Transform algorithm,
Implementation of Discrete Time Systems.
Design of FIR Digital filters: Window method, Park-McClellan's method.
Design of IIR Digital Filters: Butterworth, Chebyshev and Elliptic Approximations; Low pass,
Band pass, Band stop and High pass filters.
Effect of finite register length in FIR filter design.
Parametric and non-parametric spectral estimation. Introduction to multirate signal
processing. Application of DSP to Speech and Radar signal processing.
EC-912: DETECTION AND ESTIMATION
Classical Detection and Estimation Theory: Introduction, simple binary hypothesis tests, M
Hypotheses, estimation theory, composite hypotheses, general Gaussian problem, performance
bounds and approximations.
Representations of Random Processes: Introduction, orthogonal representations, random
process characterization, homogenous integral equations and eigen-functions, periodic
processes, spectral decomposition, vector random processes.
Detection of Signals – Estimation of Signal Parameters: Introduction, detection and estimation in
white Gaussian noise, detection and estimation in nonwhite Gaussian noise, signals with
unwanted parameters, multiple channels and multiple parameter estimation.
Estimation of Continuous Waveforms: Introduction, derivation of estimator equations, a lower
bound on the mean-square estimation error, multidimensional waveform estimation,
nonrandom waveform estimation.
Linear Estimation: Properties of optimum processors, realizable linear filters, Kalman-Bucy
filters, fundamental role of optimum linear filters.