BM3025 ADVANCED DIGITAL SIGNAL PROCESSING SYLLABUS | ANNA UNIVERSITY BE MEDICAL ELECTRONICS ENGINEERING 8TH SEMESTER SYLLABUS REGULATION 2008 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY 8TH SEMESTER B.E MEDICAL ELECTRONICS ENGINEERING 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), 2008 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
BM3025 ADVANCED DIGITAL SIGNAL PROCESSING L T P C
3 0 0 3
UNIT I DISCRETE RANDOM PROCESS 9
Discrete Random Processes- Expectation- Variance- Co -Variance- Uniform- Gaussian
and Exponentially distributed noise - Hilbert space and inner product for discrete signals
-Energy of Discrete Signals- Parseval's Theorem- Wiener Khintchine Relation- Power
Spectral Density- 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 POWER SPECTRUM ESTIMATION 9
Sample auto correlation–Periodogram- Use of DFT in power spectrum estimation- Non–
parametric methods:-Bartlett- Welch and Blackman-Tukey method- Parametric
methods:- Model based Approach - AR- MA- ARMA Signal Modeling-Parameter
Estimation using Yule-Walker Method- Solutions using Durbin’s algorithm
UNIT III ADAPTIVE & MULTIRATE SIGNAL PROCESSING 9
FIR adaptive filters – steepest descent adaptive filter – LMS algorithm – convergence of
LMS algorithms – Application: noise cancellation – channel equalization – adaptive
recursive filters – recursive least squares-
Decimation by a factor D – Interpolation by a factor I – Filter Design and implementation
for sampling rate conversion: Direct form FIR filter structures – Polyphase filter
structure81
UNIT IV SPEECH SIGNAL PROCESSING 9
Digital models for speech signal : Mechanism of speech production – model for vocal
tract- radiation and excitation – complete model – time domain processing of speech
signal:- Pitch period estimation – using autocorrelation function – Linear predictive
Coding: Basic Principles – autocorrelation method – Durbin recursive solution-
UNIT V ADVANCED TRANSFORMS 9
Fourier Transform : Its power and Limitations – Short Time Fourier Transform – The
Gabor Transform - Discrete Time Fourier Transform and filter banks – Continuous
Wavelet Transform – Wavelet Transform Ideal Case – Perfect Reconstruction Filter
Banks and wavelets – Recursive multi-resolution decomposition – Haar Wavelet –
Daubec hi es Wavel et- TOTAL: 45 PERIODS
REFERENCES
1. Monson H-Hayes – Statistical Digital Signal Processing and Modeling- Wiley- 2002
2. John G-Proakis- Dimitris G-Manobakis- Digital Signal Processing- Principles-
Algorithms and Applications- Third edition- (2000) Pearson/PHI-
3. L-R-Rabiner and R-W-Schaber- Digital Processing of Speech Signals- Pearson
Education (1979)-
4. Roberto Crist- Modern Digital Signal Processing- Thomson Brooks/Cole (2004)
5. Raghuveer- M- Rao- Ajit S-Bopardikar- Wavelet Transforms- Introduction to Theory
and applications- Pearson Education- Asia- 2000-
BM3025 ADVANCED DIGITAL SIGNAL PROCESSING L T P C
3 0 0 3
UNIT I DISCRETE RANDOM PROCESS 9
Discrete Random Processes- Expectation- Variance- Co -Variance- Uniform- Gaussian
and Exponentially distributed noise - Hilbert space and inner product for discrete signals
-Energy of Discrete Signals- Parseval's Theorem- Wiener Khintchine Relation- Power
Spectral Density- 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 POWER SPECTRUM ESTIMATION 9
Sample auto correlation–Periodogram- Use of DFT in power spectrum estimation- Non–
parametric methods:-Bartlett- Welch and Blackman-Tukey method- Parametric
methods:- Model based Approach - AR- MA- ARMA Signal Modeling-Parameter
Estimation using Yule-Walker Method- Solutions using Durbin’s algorithm
UNIT III ADAPTIVE & MULTIRATE SIGNAL PROCESSING 9
FIR adaptive filters – steepest descent adaptive filter – LMS algorithm – convergence of
LMS algorithms – Application: noise cancellation – channel equalization – adaptive
recursive filters – recursive least squares-
Decimation by a factor D – Interpolation by a factor I – Filter Design and implementation
for sampling rate conversion: Direct form FIR filter structures – Polyphase filter
structure81
UNIT IV SPEECH SIGNAL PROCESSING 9
Digital models for speech signal : Mechanism of speech production – model for vocal
tract- radiation and excitation – complete model – time domain processing of speech
signal:- Pitch period estimation – using autocorrelation function – Linear predictive
Coding: Basic Principles – autocorrelation method – Durbin recursive solution-
UNIT V ADVANCED TRANSFORMS 9
Fourier Transform : Its power and Limitations – Short Time Fourier Transform – The
Gabor Transform - Discrete Time Fourier Transform and filter banks – Continuous
Wavelet Transform – Wavelet Transform Ideal Case – Perfect Reconstruction Filter
Banks and wavelets – Recursive multi-resolution decomposition – Haar Wavelet –
Daubec hi es Wavel et- TOTAL: 45 PERIODS
REFERENCES
1. Monson H-Hayes – Statistical Digital Signal Processing and Modeling- Wiley- 2002
2. John G-Proakis- Dimitris G-Manobakis- Digital Signal Processing- Principles-
Algorithms and Applications- Third edition- (2000) Pearson/PHI-
3. L-R-Rabiner and R-W-Schaber- Digital Processing of Speech Signals- Pearson
Education (1979)-
4. Roberto Crist- Modern Digital Signal Processing- Thomson Brooks/Cole (2004)
5. Raghuveer- M- Rao- Ajit S-Bopardikar- Wavelet Transforms- Introduction to Theory
and applications- Pearson Education- Asia- 2000-
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