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Name of the College : Valliammai Engineering College
University : Anna University
Department : Electronics and Communication Engineering
Subject Code/Name : AP7101-Advanced Digital Signal Processing
Degree : M.E Communication Systems
Year : I
Semester : I
Document Type : Question Bank
Website : valliammai.co.in

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Valliammai Digital Signal Processing Question Bank

Unit I

Part A :

Related / Similar Question Paper :
Valliammai College ME Advanced Concrete Technology Question Paper

1. Define statistical variance and covariance.
2. Define wide sense stationary process.
3. Write Yule-Walker equation.
4. What are the three cases of interest to derive ap(k) of p x p non-symmetric toeplitz matrix?
5. What are the minimum error of all-pole and all-zero modeling.
6. Compute autocorrelation of sequence. x(n) = (0.5)nu(n).

7. Define ensemble average.
8. Define all modeling using covariance method.
9. Find the power spectrum of wide sense stationary random process for given autocorrelation sequence. rx(k)=2d(k)+j d(k-1)-j d(k+1).
10. Define periodicity of WSS random process.
11. Write the modified Yule-Walker equation.
12. Define all pole modeling using autocorrelation method.
13. Define linear prediction error and linear prediction coefficient
14. Find the autocorrelation sequence for given power spectral density. Px(ejw)=3+2cosw
15. How do you compute the energy of a discrete signal in time and frequency domain?
16. Check whether given matrix is a valid autocorrelation matrix.

Unit II

Part A :
1. Compare Parametric and Non-Parametric methods of spectral estimation.
2. Define Periodogram? How can it be smoothed?
3. What are the difficulties in FFT based power spectral estimation methods?
4. List the disadvantages of non-parametric methods.
5. Mention the three models used for parametric power estimation.
6. What are the demerits of the periodogram?
7. Define bias and consistency.
8. Find the mean and variance of the periodogram of white noise power spectral density is unity.
9. Compare performance measures for the Parametric and Non-Parametric methods of spectral estimation.
10. Determine the frequency resolution of the Barlett, Welch methods of power spectrum estimates for a quality factor Q=15. Assume that overlap in Welch’s method in 50% and length of the sample sequence is 1000.
11. Compare AR, MA, ARMA models with respect to complexity.
12. Write the bias equation of modified periodogram.
13. Define sample autocorrelation function. Give the mean value of this estimate.
14. Define Biasing of periodogram and modified periodogram.
15. What are the variance of Barlett method and Welch’s method of periodogram?
16. What are the advantages of parametric methods.
17. Define Autoregressive Spectrum estimation using autocorrelation method
18. Define Autoregressive Spectrum estimation using covariance method
19. Define Autoregressive moving average spectrum estimation.
20. Define moving average spectrum estimation.

Unit III

Part A :
1. How will you find the ML estimate?
2. Give the basic principle of Levinson recursion.
3. What are FIR systems?
4. Compare IIR and FIR wiener filters.
5. Write the error criterion for LMS algorithm.
6. Draw the structure of the forward prediction error filters.
7. What is Lattice structure? What is the advantage of such structure?
8. What are the properties of prediction error filters?
9. Mention the advantages of Wiener filter.
10. Name any one application of the AR model.
11. What is a whitening filter?
12. What is meant by linear prediction?
13. How wiener filter can be modified as linear predictor?
14. Define maximum likelihood criterion.
15. Define discrete Wiener Hoff equations.
16. Write the applications of Kalman filter.
17. How FIR wiener filter can be used as noise cancellation?
18. What is the minimum error for a noncausal filter?
19. What is the minimum error for a causal wiener filter?
20. How causal IIR wiener filter can be used as noise cancellation?

Unit IV

Part A :
1. Why are FIR filters used in adaptive filter application?
2. What is adaptive noise cancellation?
3. Define misadjustment of adaptive filter
4. What is need for adaptivity?
5. How will you avoid echos in long distance telephonic circuits?
6. Express the LMS adaptive algorithm. State its properties.
7. What is the need for adaptive filters?
8. What is meant by channel equalization?
9. State the properties of Widrow-Hopf LMS adaptive algorithm.
10. List some applications of Adaptive filters.
11. What is the principle used in LMS algorithm?
12. What are the advantages of FIR adaptive filters?
13. Why LMS is normally preferred over RLS?
14. What is the relationship between the order of the filter with the step size in LMS adaptive filter?
15. Write the difference between LMS algorithm and RLS algorithm.
16. What is the principle of steepest descent adaptive FIR filter?
17. What is the advantage of normalized LMS over LMS adaptive filter?
18. Define error function of exponentially weighted RLS
19. Define error function of sliding window RLS
20. Define time constant for the steepest descent FIR adaptive filter