Digital Signal Processing LAB VIVA Questions :-
1. Define discrete time and digital signal.
Discrete time signal is continuous in amplitude and discrete in time, where Digital signal is discrete in time and amplitude.
2. Explain briefly, the various methods of representing discrete time signal
Graphical, Tabular, Sequence, Functional representation
3. Define sampling and aliasing.
Converting a continuous time signal into discrete time signal is called as Sampling, Aliasing is an effect that causes different signals to become indistinguishable.
4. What is Nyquist rate?
Its the sampling frequency which is equal to twice of Continuous time signal which has to be sampled.
5. State sampling theorem.
It states that , To reconstruct the continuous time signal from its Discrete time signal, The sampling frequency should be more than twice of continuous time signal frequency.
6. Express the discrete time signal x(n) as a summation of impulses.
Causal and Non causal, Periodic and non periodic, even and odd, energy and power signals
8.. When a discrete time signal is called periodic?
If some set of samples repeats after a regular interval of time then its called as periodic.
9. What is discrete time system?
If a system’s excitation and responses are both discrete time signals then its called as discrete time system.
10. What is impulse response? Explain its significance.
The response of a system when the excitation is Impulse signal is called as impulse response. it also called as Natural response, free forced response.
11. Write the expression for discrete convolution.
12. classifying discrete time systems.
Causal, Non causal, time variant, time invariant, Linear, non linear, stable and unstable system.
13. Define time invariant system.
If a system’s operation is independent of time then its time invariant, i.e delayed system response is equal to system’s response for delayed input.
14. What is linear and nonlinear systems?
If a system satisfies homogeneity principle and superposition principle then it is Linear. if not Non linear.
15. What is the importance of causality?
causality states that system’s response should depend on present and past inputs only not on the future inputs. so causal systems are realizable.
16. What is BIBO stability? What is the condition to be satisfied for stability?
If a system’s response is Bounded for Bounded excitation then its BIBO stable.
For stable system the impulse response should be absolutely sum able .
17. What are FIR and IIR systems?
- FIR: system’s impulse response contains finite no. of samples
- IIR: system’s impulse response contains infinite no. of samples
18. What are recursive and non recursive systems? give examples?
A Recursive system is one in which the output depend on it,s one or more past outputs while a non recursive is one in which output is independent of output.
Ex: any system with feedback is Recursive , without feedback is non recursive.
19. Write the properties of linear convolution.
1) x(n)*y(n)= y(n)*x(n)
2) [x(n)+y(n)]*z(n)=x(n)*z(n)+y(n)*z(n)
3) [x(n)*y(n)]*z(n) =x(n)*[y(n)*z(n)]
20. Define circular convolution.
Circular convolution is same as linear convolution but circular is for periodic signals.
21. What is the importance of linear and circular convolution in signals and systems?
Convolution is used to calculate a LTI system’s response for given excitation.
22. How will you perform linear convolution via circular convolution?
circular convolution with the length of linear convolution length (l+m-1) results linear convolution.
23. What is sectioned convolution? Why is it performed?
If any one of the given two sequences length is very high then we have to go for sectioned convolution.
24. What are the two methods of sectioned convolution?
1) Over lap-Add method. 2) Over lap save method.
25. Define cross correlation and auto-correlation?
Auto correlation is a measure of similarity between signals and its delayed version as a function of time delay.
cross correlation is a measure of similarity between two signals as a function of time delay between them.
26. What are the properties of correlation?
1) R12(T)≠R21(T)
2) R12(T)=R21*(-T)
3) if R12(T)=0, both signals are orthogonal to each other
4) Fourier transform of auto correlation gives energy spectral density.