SY9313 ADVANCED DIGITAL SIGNAL PROCESSING SYLLABUS | ANNA UNIVERSITY ME EMBEDDED SYSTEMS 1ST SEM SYLLABUS REGULATION 2009 2011 2012-2013

Latest: TNEA 2014 Engineering Application Status, Counselling Date, Rank List

SY9313 ADVANCED DIGITAL SIGNAL PROCESSING SYLLABUS | ANNA UNIVERSITY ME EMBEDDED SYSTEMS 1ST SEM SYLLABUS REGULATION 2009 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY FIRST SEMESTER M.E EMBEDDED SYSTEMS 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

SY9313 ADVANCED DIGITAL SIGNAL PROCESSING L T P C
[Review of discrete-time signals and systems- DFT and FFT, 3 1 0 4
Z-Transform, Digital Filters is recommended]
UNIT – I DISCRETE RANDOM SIGNAL PROCESSING (9)
Discrete Random Processing – Expectations – Variance – Co-Variance – Scalar Product –
Energy of Discrete Signals – Parseval’s Theorem – Wiener Khintchine Relation – Power
Spectral Density – Periodogram. Autocorrelation – Sum Decomposition Theorem – Spectral
Factorization Theorem – Discrete Random Signal Processing by Linear Systems – Simulation of
White Noise – Low Pass Filtering of White Noise.
UNIT – II LINEAR ESTIMATION AND PREDICTION (9)
Maximum likelihood criterion – Efficiency of estimator – Least Mean Squared Error Criterion –
Wiener Filter – Discrete Wiener Hoff Equations – Recursive estimators – Kalman filter – Linear
prediction – Prediction error – Whitenign fliter – Inverse filter – Levinson recursion – Lattice
realization and Levinson recursion algorithm for solving Toeplitz system of equations.
UNIT – III ADAPTIVE FILTERS (9)
FIR adaptive filters – Newton’s steepest descent method – Adaptive filter based on steepest
descent method – Widrow Hoff LMS adaptive algorithm – Adaptive channel equalization –
Adaptive echo chancellor – Adaptive noise cancellation – RLS Adaptive filters – Exponentially
weighted RLS – Sliding window RLS – Simplified HR LMS adaptive filter.
UNIT – IV MULTIRATE DIGITAL SIGNAL PROCESSING (9)
Mathematical description of change of sampling rate – Interpolation and Decimation –
Continuous time model – Direct digital domain approach – Decimation by an integer factor –
Interpolation by an integer factor – Single and multistage realization – Poly phase realization –
Application to sub band coding – Wavelet transform and filter bank implementation of wavelet
expansion of signals.
UNIT – V DIGITAL SIGNAL PROCESSORS (9)
Fundamentals of Fixed – Point DSP Architecture – Fixed Point Representation of Numbers –
Arithmetic Computation – Memory Accessing – Pipelining of Instructions – Features of Example
Processors – TMS320C25 – DSP16A and DSP 56001 – Floating Point DSPs – Floating-Point
Representation of Numbers – TMS320C30 – Comparison of DSPs – Development Tools for
DSP Programming – TMS320C30 Evaluation Module.
L:45 T:15 TOTAL :60 PERIODS
6
REFERENCES:
1. Monson H. Hayes, ‘Statistical Digital Signal Processing and Modeling”, John Wiley and Sons
Inc., New York, 1996.
2. Sopocles J. Orfanidis, “Optimum Signal Processing”, McGraw Hill, 1990.
3. John G. Proakis, Dimitirs G. Monolakis, “Digital Signal Processing”, Pearson Education,
1995.
4. Sanjit K. Mitra, “Digital Signal Processing – A Computer based approach”, Tata McGraw Hill
– 1998.
5. Rabiner and Gold, “Theory and Applications of Digital Signal Processing, A Comprehensive,
Industrial – Strength DSP reference book”
6. TMS320C5X User’s Guide, Texas Instruments, 1995.