MP9214 ADVANCED DIGITAL SIGNAL PROCESSING SYLLABUS | ANNA UNIVERSITY ME MOBILE AND PERVASIVE COMPUTING 1ST SEM SYLLABUS REGULATION 2009 2011 2012-2013

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MP9214 ADVANCED DIGITAL SIGNAL PROCESSING SYLLABUS | ANNA UNIVERSITY ME MOBILE AND PERVASIVE COMPUTING 1ST SEM SYLLABUS REGULATION 2009 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY FIRST SEMESTER M.E MOBILE AND PERVASIVE COMPUTING DEPARTMENT SYLLABUS, TEXTBOOKS, REFERENCE BOOKS,EXAM PORTIONS,QUESTION BANK,PREVIOUS YEAR QUESTION PAPERS,MODEL QUESTION PAPERS, CLASS NOTES, IMPORTANT 2 MARKS, 8 MARKS, 16 MARKS TOPICS. IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011 2012-2013 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI, TIRUNELVELI,COIMBATORE), 2009 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009

MP9214 ADVANCED DIGITAL SIGNAL PROCESSING L T P C
3 0 0 3
OBJECTIVE
Presents a comprehensive introduction to important emerging DSP technologies with a focus on
wavelets/sub band and applications in multimedia manipulation and computer graphics.
Provides students with backgrounds for pursuing independent research in DSP, audio/video
compression, processing, and related application
PREREQUISITE
Essential
Knowledge of signals, systems and Random behavior of signals & Systems
Optional
Exposure to Filters and Various Transforms
UNIT I BASIC SYSTEMS AND TRANSFORMS 10
Basic multirate operations, efficient structures for decimation and interpolation, a simple aliasfree
QMF system, two dimensional filter banks.
Review of various transforms – DTFT, DFT, ZT, FIR and IIR filter design (any one method)
UNIT II SPECTRAL ESTIMATION 9
Spectral analysis and Estimation – Classical spectral estimation, parametric models of random
processes, Autoregressive processes and spectral properties.
Higher order power spectral estimation – Bispectrum, Trispectrum, nth order spectrum.
8
UNIT III WAVELET TRANSFORM 9
Wavelet theory – wavelet theory based signal and image processing, Extensions to wavelet
packets applications in image compression, EZW code, Spatial oriented tree.
Finer time-scale resolution and fast integral transforms, Signal analysis applications.
UNIT IV ADAPTIVE FILTERS 9
Adaptive filters – FIR adaptive filters, Newton’s steepest decent method, adaptive filter based on
Steepest descent method, Widow Hopf LMS adaptive algorithm, adaptive channel equalization,
Adaptive echo canceller, RLS, Sliding window RLS
UNIT V APPLICATIONS 8
Applications – Multicarrier Communications, Computer graphics, image query, Location aware
computing
TOTAL : 45 PERIODS
REFERENCES
1. J.G. Proakis, C.M. Rader, F. Ling and C.L. Nikias, Advanced Digital Signal Processing,
Macmillan, 1992.
2. S. Haykin, Adaptive Filter Theory, Prentice-Hall, 2002.
3. P.P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice-Hall, 1993.
4. J. Stollnitz, Tony D. Derose, and David Salesin, Wavelets and Computer Graphics: Theory
and Applications, Morgan Kaufmann Pub.: 1996.
SOURCES
1. Digital Signal Processing II, University of Illinois at Urbana Champaign.
http://courses.ece.uiuc.edu/ece551/
2. Advanced Digital Signal Processing
http://bme.iust.ac.ir/courses/adsp.html
Iran University of Science and Technology
3. Advanced Digital Signal Processing, University of Surrey, UK
http://www.ee.surrey.ac.uk/CE/technical/advdsp.html
3. Advanced Digital Signal Processing
http://www.cvn.columbia.edu/courses/Fall1999/ELENE6860.html
Columbia University
4. Advanced Digital Signal Processing
http://www-lns.tf.uni-kiel.de/staff/jkl/adsv_syl.htm
University of Kiel