Question Paper Code : 91509

M.E. DEGREE EXAMINATION, JANUARY 2012.

First Semester

Applied Electronics

AP 9211 — ADVANCED DIGITAL SIGNAL PROCESSING

(Common to M.E. Communication Systems and ME. Computer and Communication)

(Regulation 2009)

Ti-me : Three hours Maximum : 100 marks

Answer ALL questions.

PART A — (10 x 2 = 20 marks)

1. Calculate the mean and variance for the auto correlation function of random

signals.

2. State Wiener- Khintchine relation.

3. What is periodogram averaging?

4. Compare parametric and non parametric methods of spectral estimation.

5. What is linear prediction?

6. What is meant by prediction?

'7. What is need of adaptivity?

8. How will you avoid echos in long distance telephone circuits?

9. What is need for multiple signal processing?

10. What is discrete wavelet transform?

11.

12.

13.

14.

(8)

(8)

(b)

(3)

(b)

PART B (5 X 16 1 80 marks)

('1) Explain the concepts of Random Process with examples.

(ii)

Or

(i) Calculate the mean and variance of the auto correlation function

of (8)

ralluulll Determine the power spectra for a random processes

generated by

= w(n) E 2).

where w(n)) is White noise process with variance 0'2 .

random Signals

(8)

(:1) Explain the AR, MA and ABMA models.

(ii)

(8)

EJXPL'CU.Ll u.1.n_, .....L_, i What is a periodogram‘? How do you

estimate the performance of a (8)

periodogram?

O1‘

Show that the Barlett estimate of the power special density is

asymptotically unbiased that the variance of the estimate decreases

with the number of data sections and the spectrum estimates are

consistant.

(T

(16)

Explain the steps in the design of an FIR wiener filter that produces

the

. . TITOCQSS1

Explain the steps in the aeslgu UL an L minimum mean square estimate

of the given process. (16)

Or

(11) Brief forward and backward prediction. ' (8) (3

ii) lime; H WM W , (ii) Explain Kalman filter. (8)

Explain in detail the LMS adaptive algorithm for FIR filters with an

(16}

Explain in detail the LLVLU auapuw WOWapplications. (16)

Or

(D

EX

plain

adapf

we noise

concell

ation

(8)

(81

ii) Jiixpraln uuaimw M. (ii) Explain adaptive echo cancellor. (8)

15. (a) Explain the process of decimation and interpolation with

examples. (16)

(b) (i) Explain the relation between the Haar Wavelet function and

scaling function. (8)

(ii) Explain how to implement multistage filter banks by wave let

decomposition. (8)