ANNA UNIVERSITY ECE SYLLABUS
EC6002 ADVANCED DIGITAL SIGNAL PROCESSING SYLLABUS
6TH/8TH SEM ECE/EEE SYLLABUS
REGULATION 2013
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| EC6002 ADVANCED DIGITAL SIGNAL PROCESSING SYLLABUS |
OBJECTIVES:
To bring out the concepts related to stationary and non-stationary random signals
To emphasize the importance of true estimation of power spectral density
To introduce the design of linear and adaptive systems for filtering and linear prediction
To introduce the concept of wavelet transforms in the context of image processing
To emphasize the importance of true estimation of power spectral density
To introduce the design of linear and adaptive systems for filtering and linear prediction
To introduce the concept of wavelet transforms in the context of image processing
UNIT I DISCRETE-TIME RANDOM SIGNALS
Discrete random process – Ensemble averages, Stationary and ergodic processes, Autocorrelation and Autocovariance properties and matrices, White noise, Power Spectral Density, Spectral Factorization, Innovations Representation and Process, Filtering random processes, ARMA, AR and MA processes.
UNIT II SPECTRUM ESTIMATION
Bias and Consistency, Periodogram, Modified periodogram, Blackman-Tukey method, Welch method, Parametric methods of spectral estimation, Levinson-Durbin recursion
UNIT III LINEAR ESTIMATION AND PREDICTION
Forward and Backward linear prediction, Filtering - FIR Wiener filter- Filtering and linear prediction, noncausal and causal IIR Wiener filters, Discrete Kalman filter.
UNIT IV ADAPTIVE FILTERS
Principles of adaptive filter – FIR adaptive filter – Newton‟s Steepest descent algorithm – LMS algorithm – Adaptive noise cancellation, Adaptive equalizer, Adaptive echo cancellers.
UNIT VWAVELET TRANSFORM
Multiresolution analysis, Continuous and discrete wavelet transform, Short Time Fourier Transform, Application of wavelet transform, Cepstrum and Homomorphic filtering.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, students will be able to:
Explain the parametric methods for power spectrum estimation.
Discuss adaptive filtering techniques using LMS algorithm and the applications of adaptive filtering.
Analyze the wavelet transforms.
TEXTBOOKS:
1. Monson H, Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley and Sons Inc., New York, Indian Reprint, 2007.
2. John G.Proakis, Dimitris G. Manolakis, “Digital Signal Processing”, Pearson, Fourth 2007.
3. Dwight F. Mix, “Random Signal Processing”, Prentice Hall, 1995.
REFERENCE:
1. Sophocles J. Orfanidis, “Optimum Signal Processing, An Introduction”, Mc Graw Hill, 1990.
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