E9 Signal
Processing, Acoustics and Bioengineering
E9 201 (AUG) 3:0
Digital Signal Processing
Discrete-time
signals and systems, frequency response, group delay, Z-transform, Convolution
discrete fourier transforms (DFT), fast fourier transform (FFT) algorithms, discrete Cosine
transforms (DCT), Discrete Sine
transforms (DST), relationship between DFT, DCT and DST; Design of FIR and IIR
filters, finite word length effects, Hilbert transforms, Hilbert transform relations
for Causal signals, Karhunen – Loeve
transforms, introduction to linear prediction, bandpass
sampling theorem, Bandpass signal representations.
D Narayana
Dutt and Chandra Sekhar S
Proakis and Manolakis, Digital Signal Processing, PHI.
AV Oppenheim and RW Schafer, Discrete-time
Signal Processing. Prentince Hall 1998
E9 202 (AUG) 3:0
Advanced Digital Signal Processing
Non-linear signal processing; non-linear
filters. Non-Gaussian
models, generalized Gaussian and stable distributions, robust estimation. Medium smoothers, rank-order filters,
weighted median smoother. Introduction to order statistics,
joint densities, momemts. Weighted medium filtering,
link between linear and non-linear smoothers and filters, Mallows Theorem. Generalized median filtering: L-estimator, tefilter, optimality Zero-crossing (ZC) based spectral
analysis, dominant frequency principle. ZC and level-crossing
based signal decomposition. Auditory models.
T V Sreenivas
Pre-requisite: E9-201 or equivalent.
Arce, G.R., Non-linear signal processing: A
statistical approach, Wiley, 2004.
Astola, J., and Kuosmanen,
P., Fundamentals
of non-linear digital filtering, CRC Press, 1997.
Kedem, B., Time series analysis by higher order
crossings, IEEE Press, 1994.
Maravasti, F., A unified approach
to zero-crossings and non-uniform sampling, 1990.
E9 212 (JAN) 3:0
Spectrum Analysis
Estimation of PSD from finite data: Nonparametric methods: Periodogram, Properties, Bias and Variance analysis – BT method, Window design considerations, Time-Bandwidth Product and Resolution, variance tradeoffs in window design. Modified Periodogram methods: Bartlett, Welch. Parametric methods for Rational Spectra: Covariance properties of AR, MA, ARMA processes, Yule-Walker method, Levinson-Durbin algorithm. Multivariate ARMA processes: State-space representation, subspace parameter estimation. Parametric methods for line spectra (sinusoids in noise), models of sinusoids in noise, higher-order YW method, Pisarenko, MUSIC, ESPRIT, Prony methods. Formulation of the spatial spectrum problem. Introduction to Higher-Order Spectra.
Matlab based assignments.
K V
Stoica and Moses, Introduction to Spectral Analysis, PH 1997.
E9 213(JAN) 3:0
Time-Frequency Analysis
Time-Frequency distributions: Time and frequency description of signals,
instantaneous frequency, the analytic signal, the uncertainty principle,
densities and characteristic functions, global and local averages, the
short-time fourier transform (STFT), filterbank interpretation of STFT, the Wigner
distribution , general approach and the kernel method, bilinear time-frequency
distributions, Wigner’s theorem, multi component
signals, instantaneous band width, positive distributions satisfying the marginals, gabor transform Spaces
and bases: Hilbert space, Banach space, orthogonal
bases, orthonormal bases, Riesez
bases, biorthogonal bases, shift-invariant spaces,
Shannon sampling theorem, frames, b-splines.
Wavelets: Wavelet Transform, Real Wavelets, Analytic Wavelets Dyadic
Wavelets transforms, wavelet bases, multi resolution analysis, Two-Scale
equations, conjugage Mirror filters, vanishing
moments, regularity, Lipschitz regularity, Strang-Fix conditions, The Notion of Compact support,
Shannon, Meyer, Haar and Battle-Lemarie
Wavelets, Daubechies Wavelets, Relationship between
Wavelets and Filterbands.
Chandra Sekhar S
L Cohen, Time Frequency Analysis Prentice Hall 1995
E9 231 (AUG) 3:0
Digital Array Signal Processing
Wave Fields, underlying wave equations, scalar and vector fields, spectral representation,
propagation in open and confined media.
Sensor array systems: Linear equispaced,
circular, planar, random arrays. Direction of arrival estimation: source
waveform estimation. Beam forming, subspace methods (MUSIC, ESPRIT), spatial
smoothing, performance analysis. Applications to acoustic source separation and wireless
communication.
K V
Johnson and Dudgeon, Array Signal Processing
Concepts and techniques, Prentice Hall, 1993.
Naidu, P.S., Sensor Array Signal Processing, CRC
Press, 1999.
Lecture notes and current literature.
E9 241 (AUG) 2:1
Digital Image Processing
Representation of two dimensional signals, sampling, quantization
and reconstruction. Digital images, human visual perception. Transforms: DFT, DCT, KLT, wavelet, filtering, edge detection,
image restoration, compression, segmentation, applications.
K Rajgopal
Anil K Jain, Fundamentals of Digital Image Processing,
Prentice Hall, 1989.
Gonzales, R.C., and Woods, R.E., Digital Image
Processing, Addison Wesley, 1993.
E9 242 (JAN) 3:0
Selected Topics in Image Processing
Image segmentation and clustering: mean-shift, graph cut. Image
pyramids and texture analysis. Linear/non l linear scale space theory: Scale
Invariant Feature Transform (SIFT). Visual tracking: mean-shift, particle
filters, feature-based. Background modeling, surveillance and monitoring: event
detection/recognition. Face detection and recognition, motion analysis and
segmentation, graphical models, Markov random fields and applications. Basics of 3-D vision.
K R Ramakrishnan
Forsyth, A.A., and
Paragios, N., Chen, Y., and
O. Faugeras, O. (Eds),
Handbook of Mathematical Models in Computer Vision, Springer, 2006.
Bishop, C.M., Pattern Recognition and Machine Learing, Springer, 2006.
Current Literature
E9 243 (JAN) 3:0
Computer Aided Tomographic
Imaging
Introduction to principles of tomography and applications,
tomographic imaging. Radon transform and its properties,
mathematical framework. Introduction to X-ray tomography,
emission computer tomography, magnetic resonance imaging systems.
Projection and Fourier slice theorem. Scanning geometries: translate and
rotate, translate-rotate, rotate on a circular trajectory for 2-D imaging and
helical or spiral scan trajectory for 3-D imaging. Transform domain algorithms:
Fourier inversion algorithms, filtered back projection algorithms –
reconstruction with non-diffracting sources, parallel projections and fan
projections for 2-D and cone beam projections on circular and spiral trajectory
for 3-D reconstruction. Computer implementation, iterative reconstruction
techniques: algebraic reconstruction techniques, statistical modeling of generation,
transmission and detection processes in
K Rajgopal
Kak, A.C., and Slaney, M., Principles of Computerized Tomographic Imaging, IEEE Press, 1988.
Herman, G.T., Image Reconstruction from Projections, Implementation and
Applications: Topics in Applied Physics, Vol 32,
Springer Verlag, 1979.
Natterer, F., The Mathematics of
Natterer, F., and Wubbeling, F., Mathematical Tools in Image Reconstruction,
E9 245 (AUG) 3:1
Selected Topics in Computer Vision
This course will develop the use of multiview geometry in computer vision. A theoretical basis
and estimation principles for multiview geometry,
dense stereo estimation and three-dimensional shape registration will be developed.The Use of these ideas for building real-world
solutions will be emphasised. Topics Stereo estimation: current methods in
depth estimation * 3D registration : ICP and other
approaches Multiple view geometry: projective geometry, multilinear
relationships in images, estimation.
Venu Madhav Govindu
Pre-requisites: Computer Vision (E1.216) or permission of the
instructor.
Hartley, R., and Zisserman, A., Multiple View
Geometry in Computer Vision, Second Edn,
Faugeras, O., and Luong, Q-T.,The Geometry of Multiple
Images, MIT Press 2001.
Current literature
E9 261 (JAN) 3:0
Speech Information Processing
Human speech communication: speech production, perception, phonetics. Time-varying signal analysis: short-time Fourier transform,
spectrogram, quasi-stationary analysis: cepstrum,
linear-prediction models; line spectral pair, Mel frequency cepstral
coefficients sinusoidal models, Principles of Speech
synthesis, pitch and time scale modification. VQ and Gaussian
mixture model. Speaker recognition.
A G Ramakrishnan
Pre-requisite: E9-201
Quatieri, T.F., Discrete-time
speech signal processing, Prentice-Hall, 2002.
Rabiner, L.R., and Schafer, R.W.,
Digital Speech Processing, Prentice-Hall
Douglas O’shoughnessy, Speech
Communication, IEEE Press 2000
Gold, B., and Morgan, N., Speech
and Audio Signal Processing, John Wiley, 2000.
E9 262 (JAN) 3:0
Automatic Speech Recognition
Algorithms
(Stochastic models for
speech/audio)
Human
speech communication, concept=> signal=> concept & levels of
information. Discrete and continuous representations, signal representation as a
pattern; structure representation through lexicon, grammar. ASR: text
recognition, speaker recognition, language identification, keyword spotting. Gaussian models and Bayesian inference; maximum likelihood
parameter estimation. Mixture Gaussian models, EM
algorithm derivation; relation to K-means algorithm, LBG algorithm and EM
generalization. Application to speaker-ID. Units of speech: linguistic, acoustic and
stochastic; segmentation problem. Dynamic programming
introduction. maximum-likelihood segmentation;
segment clustering and automatic sub-word units. Graphical models and Markov
models; Language modeling, N-grams and their estimation. Tree structured language model, minimum
entropy decision tree algorithm; language perplexity measure. Application to spoken language-ID. Hidden Markov model (HMM):
Markov structure for latent variables; Gaussian density, discrete density,
mixture Gaussian and semi-continuous density models. HMM evaluation,
training and decoding problems: forward-backward algorithm, Baum-Welch
algorithm, Viterbi algorithm, segmental K-means (SKM)
algorithm. HMM duration density and explicit duration modeling and modified EM
algorithm. Finite
state network (FSN) of HMMs and lexicon building. Continuous
speech recognition (CSR) through FSN decoding using time-synchronous Viterbi algorithm. Viterbi
beam search for large vocabulary CSR. Speaker adaptation: maximum likelihood
linear regression (MLLR) and eigen-voice approaches.
Not checked. Unable to understand.
T. V. Sreenivas
Pre-requisite:
E2-202 Random Processes or equivalent.
Huang,
X., Acero, A., and Hon, H., Spoken Language
Processing, Prentice Hall, 2001.
Bishop, C.M., Pattern Recognition and Machine laearning,
Springer, 2006.
Rabiner, L.R., and Juang, B.H., Fundamentals of Speech
Recognition, Prentice Hall, 1993.
E9 271 (JAN) 3:0
Space-Time Signal Processing and Coding
Brief review of single-input single-output
(SISO) communication systems. Performance of SISO systems
in fading channels. Motivation for Space-Time (or Multiple-Input Multiple-Output (MIMO)
communication systems. Capacity of MIMO systems.
Space-Time codes: Space-Time Trellis codes and Space-Time Block codes;
Design Criteria for code constructions; Constructions using Orthogonal designs
and their variations; Algebraic techniques for space-time codes; Decoding
algorithms for Space-Time Codes. Distributed Space Time Coding.
B Sundar Rajan
Pre requisites: Digital Communication
Current literature
E9 281 (JAN) 3:0
Biomedical Signal Processing
EEG and ECG signals: Genesis, monitoring, measurement and uses. Time and
frequency domain analysis of signals: Morphological studies, correlation,
spectral analysis. Linear prediction technique: AR model and its
implementation, inverse filter. Homomorphic
processing: generalized superposition, complex cepstrum,
minimum phase component. Nonlinear dynamics and chaos: fractal dimension,
correlation dimension, Lyapunov exponent. Artifacts:
Types, detection and minimization. Applications to biomedical
signals.
D Narayana Dutt
Tompkins, W.J. (ed.), Biomedical Signal
Processing, Prentice Hall, 1993.
Rangayyam, R.M., Biomedical Signal Analysis – A Case-Study Approach, John Wiley,
2002.
Chellis, R.E., and
Current Published literature.
E9 291 (AUG) 2:1
DSP System Design
Need for special digital signal processors. DSP architecture: Von Newmann vs Harvard architecture,
architectures of superscalar and VLIW fixed and floating point processors, on
chip components: timers, boot ROM, RAM, CODECS, McBSP,
DSP bias. Design aspects: memory, host port interface,
CODECS and RTDX. DSP development tools: assembler, C-compiler, linker and
loader. DSP algorithms: FIR/IIR filters, FFT, adaptive filters, sampling rate
converters and DCT. DSP applications: FIR and FFT with RTDX and MATLAB, voice
scrambler, multirate filter. Weekly laboratory
exercises involving DSP starter kits, interfacing, coding, and use of software
tools.
G
Rulph Chassaing, Digital signal
processing and applications with the C6713 and C6416 DSK, Wiley, 2005.
User’s manuals of various fixed and floating point
DSPs.
Application Guides from DSP manufacturers.
Current literature.
E9 292 (JAN) 2:1
Real-Time Signal Processing with DSP
Implementation of discrete-time systems, DSP device architecture and
programming (TMS320C6x), FIR/IIR digital filter design, multirate
DSP, power spectrum estimation, linear prediction and adaptive filtering,
real-time system development, DSP programming, code composer studio and DSP
BIOS, spawning and controlling tasks and data I/O, real-time scheduling
analysis, load analysis, queues, semaphores and mailboxes, real-time data
exchange using Lab View.
Mini project.
K Rajgopal and G
Pre-requisite: Knowledge of Digital Signal Processing
Nasser Kehtarnawaz, Real–Time Digital Signal
Processing based on TMS320C6000, Elsevier, 2004.
TMS320C6x Data Sheets from TI