EC2030 ADVANCED DIGITAL SIGNAL PROCESSING L T P C
3 0 0 3
To introduce the student to advanced digital signal processing techniques.
· To study the parametric methods for power spectrum estimation.
· To study adaptive filtering techniques using LMS algorithm and to study the applications of adaptive filtering.
· To introduce the student to wavelet transforms.
UNIT I DISCRETE RANDOM PROCESS 9
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 SPECTRAL ESTIMATION 9
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 9
Forward and Backward linear prediction, Filtering - FIR Wiener filter- Filtering and linear prediction, non-causal and causal IIR Wiener filters, Discrete Kalman filter.
UNIT IV ADAPTIVE FILTERS 9
Principles of adaptive filter – FIR adaptive filter – Newton’s Steepest descent algorithm – Derivation of first order adaptive filter – LMS adaptation algorithms – Adaptive noise cancellation, Adaptive equalizer, Adaptive echo cancellors.
UNIT V ADVANCED TRANSFORM TECHNIQUES 9
2-D Discrete Fourier transform and properties– Applications to image smoothing and sharpening – Continuous and Discrete wavelet transforms – Multiresolution Analysis – Application to signal compression.
TOTAL : 45 PERIODS
1. Monson H Hayes,” Statistical Digital Signal processing and Modeling”, Wiley
Student Edition, John Wiley and Sons, 2004.
2. R.C. Gonzalez and R.E. Woods, “ Digital Image Processing”, Pearson, Second
1. John G Proakis and Manolakis, “ Digital Signal Processing Principles, Algorithms and
Applications”, Pearson, Fourth Edition, 2007.
2. Sophocles J. Orfanidis, Optimum Signal Processing, An Introduction, McGraw Hill,