# EC8091 Advanced Digital Signal Processing Important Questions

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RAJAS ENGINEERING COLLEGE
III YEAR
REGULATION 2017
IMPORTANT QUESTIONS (PART-B & C)

PREPARED BY
N.SATHASIVAN M.E,
AP/ECE
RAJAS ENGINEERING COLLEGE

Unit1: DISCRETE-TIME RANDOM PROCESSES

1. Explain the following parametric model equations
(i) ARMA (ii) AR (iii) MA
2. Explain the concept of spectral factorization theorem in details.
Or(Enumerate the physical significance of spectral factorization.)
3. Explain the concepts of Random Process with examples.
4. State and prove the parsevals Theorem.
5. Explain the step in the determination of autocorrelation and power spectrum of a random process.
6. Explain briefly about Ergodic process with necessary equations and also state the mean Ergodic theorems.
7. Explain how power spectrum can be estimated from the AR model.
8. Explain filtering random processes.

UNIT 2: SPECTRUM ESTIMATION

1. What is Levinsion Durbin algorithm? Explain it with its applications.
2. Explain the following parametric methods to measure be spectrum of long duration signals. (1) ARMA model (2) MA model
3. Explain the periodogram method of spectrum estimation in detail and also obtain the variance of the periodogram.
4. Compare the Barlett method of signal modeling with Welch method in detail.
5. What is a periodogram? How do you estimate the performance of a periodogram?
6. Explain how power spectrum can be estimated from the AR model.
7. Discuss the Welch method of Periodogram averaging.
8. Explain in detail about Bartlett’s method.

UNIT-3:OPTIMUM FILTERS

1. Derive Wiener-Hopf equation for FIR Wiener-filter and also obtain the Expression for minimum mean square error.
2. Explain in detail about causal IIR Wiener filter.
3. Explain in detail about Non causal IIR Wiener filter.
4. Brief forward and backward prediction.
5. Describe the basics of forward linear prediction .Give the schematic of FIR filter.
6. With necessary expressions, briefly explain about the discrete Kalman filter.

1. Explain the method of working of adaptive filters based on steepest descent algorithm.
2. Explain the adaptive channel equalization with an example.
3. Explain the implementation of the normalized LMS algorithm.
4. Explain the adaptive echo cancellation with an example.
5. Draw and explain the adaptive echo canceller and noise cancellation in detail.
6. Explain in detail the LMS adaptive algorithm for FIR filters with an application.