# Advanced Digital Signal Processing AP9211 ND2010 Question Paper

Anna University Question Paper
M.E. DEGREE EXAMINATION, NOVEMBER/DECEMBER 2010
First semester
Applied Electronics
(Common to M.E Communication Systems and M.E. Computer and Communication)
(Regulation 2009)

PART A—(10 × 2 = 20 marks)

1. Differentiate between power density spectrum and cross power density spectrum.
2. Compare the correlation of the sequence x(n)=(0.5) n u(n).
3. What are the difficulties in FFT based power spectral estimation methods?
4. Compare Non-parametric method with parametric method for spectrum estimation.
5. Draw the structure of the forward prediction error filter.
6. What is Lattice structure? What is the advantage of such structure?
7. List some applications of Adaptive filters.
8. What is the principle used in LMS algorithm?
9. What is the effect on power spectrum due to up sampling and down sampling?
10. What is the need for anti-imaging filter in multirate digital signal processing?

PART B—(5 × 16 = 80 marks)

11. (a) (i) List the properties of auto correlation matrix and power spectrum and explain.
(ii) Explain the following parametric model equations
(1) ARMA
(2) AR.
Or
(b) (i) Explain the difference between power spectral density and periodogram.
(ii) A sinusoidal signal of 1 V p-p and 100 Hz frequency is sampled at 800
Hz and is mixed with AWGM of ‘‘0 ’’ mean and ‘‘0.1” Varience. This is filtered by a second order butter worth IIR digital filter. Find the PSD of input and output sequences and plot the magnitude and phase response.

12. (a) (i) Explain the various pit falls found in spectral analysis.
(ii) Explain briefly the following non-parametric methods for spectral estimation.
(1) Periodogram
(2) Welch method.
Or
(b) (i) What are the difficulties in non-parametric methods for measuring the spectral density?
(ii) Explain the following parametric methods to measure be spectrum of long duration signals.
(1) ARMA model
(2) MA model

13. (a) (i) Explain how the desirable Kalman filter is different from Wiener filter in estimation?
(ii) Explain the Kalman- filtering using signal flow graph.
Or
(b) (i) Explain the difference between forward prediction and backward prediction.
(ii) Explain the process of finding the FIR Wiener filter co-efficient that minimize the mean square error and derive the necessary equations.

14. (a) (i) Draw the block diagram of an adaptive filter as a noise canceller and explain.
(ii) Compare LMS algorithm with RLS adaptive algorithm.
Or
(b) (i) Explain the implementation of the normalized LMS algorithm.
(ii) Explain the adaptive echo cancellation with an example.

15. (a) Explain the concept of multirate signal processing with spectral interpretation of decimation of a signal from 6 KHz to 2 KHz and spectral
interpretation of interpretation of signal from 2 KHz to 6 KHz.
Or
(b) (i) Explain the Realization of an FIR filter based on Type I and Type II poly phase decomposition.
(ii) Explain the Encoder and decoder-operation of sub-band coding Technique.