52208-mt----advanced Digital Signal Processing

NR

Code No: 52208/MT

M.Tech. – II Semester Regular Examinations, September, 2008 ADVANCED DIGITAL SIGNAL PROCESSING (Common to Power Electronics & Electric Drives/ Electrical Power Systems/ Power & Industrial Drives/ Power Electronics/ Electrical Power Engineering) Time: 3hours

Max. Marks:60 Answer any FIVE questions All questions carry equal marks ---

1.a) b)

Explain with examples the design of optimum FIR filters and delay equalized elliptic filters. Determine the Least square FIR inverse of length 3 to the system with impulse response.

2.a) b)

Compare IIR and FIR filters. Using bilinear transformation method, design a low pass derived from a second-order Butterworth analog filter with a 3 dB cutoff frequency of 100Hz. The sampling rate is 1000Hz.

3.a) b)

Explain Bartlett window. Design a low-pass FIR filter length 7 with a linear phase to approximate ideal low-pass filter. H ( e j w ) = { 10 ffoo rr ||ww ||≤> 33 rroa dd // sseecc WT=9 rad/sec Use Hamming window.

4.a) b)

5.a) b)

Determine the DFT of the sequence of (n) which is a product of two sequences given as x(n)=u(n)-u(n-5), h(n)=u(n)-u(n-2). Determine the N-point DFT of the following length-N sequence defined for 0 ≤ n ≤ N − 1 xa [n]sin(2π n / N ) Explain the generalized Rome ZFIR filter design. What is a tunable FIR filter? Explain various steps involved in the design of a Tunable low pass FIR filter. Contd…2

Code No: 52208/MT

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6.a) b)

Explain the quantization of fixed-point numbers. Explain quantization noise model.

7.a)

Determine the power spectra for the random process generated by the following deference equation x(n)=-0.81 x(n-2)+w(n)-w(n-1) where w(n) is a white noise process with variance σw2. Explain how periodogram will be useful in non-parametric spectral Analysis.

b)

8.

Consider the linear system described by the difference equation: y(n)=0.8y(n-1)+x(n)+x(n-1) Where x(n) is a wide-sense stationary random process with zero mean and autocorrelation. rxx (m) = (1/ 2)( m ) a) Determine the power density spectrum of the output y(n) b) Determine the autocorrelation ryy(m) of the output c) Determine the variance σ2y of the output.

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