## EC6002 ADVANCED DIGITAL SIGNAL PROCESSING

Subject Informations :

University        :  Anna University
Department     :  B.E. ELECTRONICS AND COMMUNICATION ENGINEERING
Semester          :  06 th sem / ELECTIVE – I
Year                  :  03 rd year
Regulation       :  R2013
Subject Code   :  EC6002
Subject Name  :  ADVANCED DIGITAL SIGNAL PROCESSING

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EC6002 ADVANCED DIGITAL SIGNAL PROCESSING  | Important  Questions

EC6002 ADVANCED DIGITAL SIGNAL PROCESSING  – SYLLABUS – R- 2013

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

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,
non-causal and causal IIR Wiener filters, Discrete Kalman filter.

Principles of adaptive filter – FIR adaptive filter – Newton‟s Steepest descent algorithm – LMS

UNIT V WAVELET TRANSFORM

Multiresolution analysis, Continuous and discrete wavelet transform, Short Time Fourier Transform,
Application of wavelet transform, Cepstrum and H
omomorphic 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.

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