EC2314 Digital Signal Processing Anna University Question Bank Question Paper Model Question Paper Important Questions 2 marks and 16 marks questions

EC 2314 Digital Signal Processing Anna University Question Bank Question Paper Model Question Paper Important Questions 2 marks and 16 marks questions

**UNIT – I**

**PART – A**

- What is signal and Signal processing?
- List the advantages of Digital Signal processing.
- Mention few applications of Digital Signal processing.
- Classify discrete time signals.
- What are Energy and Power signals?
- What do you mean by periodic and Aperiodic signals?
- When a signal is said to be symmetric and Anti symmetric?
- What are deterministic and random signals?
- What are the elementary signals?
- What are the different types of representation of discrete time signals?
- Draw the basic block diagram of digital signal processing of analog signals.
- What are the basic time domain operations of discrete time signal?
- What is the significance of unit sample response of a system?
- Classify discrete time systems.
- Whether the system defined by the impulse response h(n) = 2
^{n}u(-n) + 2^{-n}u(n) is causal ? Justify your answer. - Compute the energy of the signal x(n) = 2
^{-n}u(n) - Compute the energy of the signal x(n) = (0.5)
^{n }u(n) - Define convolution.
- List out the properties of convolution.
- What do you mean by BIBO stable?
- What is linear time invariant system?
- Compute the convolution of x(n) = {1,2,1,-1} and h(n) = {1,2,1,-1} using

↑ ↑

tabulation method.

- Check whether the system defined by h (n) = [5 (1/2)
^{n}+ 4 (1/3)^{n}] u(n) is stable. - Differentiate between analog, discrete, quantized and digital signals.
- Differentiate between analog
- and digital signals.
- Differentiate between one dimensional and two dimensional signal with an example for each.
- Name any four elementary time domain operations for discrete time signals.
- For the signal f (t) = 5 cos (5000πt) + sin
^{2}(3000πt), determine the minimum sampling rate for recovery without aliasing.

Ω

_{1}= 5000π = 2πF_{1}Ω_{2}= 3000π = 2πF2F

_{1}= 2.5 kHz F_{2}=1.5 kHzF

_{max }= 2.5 kHzAccording to sampling theorem Fs ≥ 2 F

_{max}So, Fs

_{ }= 5 kHz- For the signal f (t) = cos
^{2}(4000πt) + 2 sin (6000πt), determine the minimum sampling rate for recovery without aliasing.

Ω

_{1}= 4000π = 2πF_{1}Ω_{2}= 6000π = 2πF2F

_{1}= 2 kHz F_{2 }=3 kHzF

_{max }= 3 kHzAccording to sampling theorem Fs ≥ 2 F

_{max}So, Fs

_{ }= 6 kHz- What is sampling?
- State sampling theorem and what is Nyquist frequency?

Sampling theorem - Fs ≥ 2 F

_{max}Nyquist frequency or Nyquist rate F

_{N}= 2 F_{max}- What is known as aliasing?
- Define the criteria to perform sampling process without aliasing.
- Differentiate between anti aliasing and anti imaging filters.
- What are the effects of aliasing?
- What is anti aliasing filter? What is the need for it?
- Draw the basic block diagram of a digital processing of an analog signal.
- Draw the basic structure of linear constant difference equation.
- What is sample and Hold circuit?
- If a minimum signal to noise ratio (SQR) of 33 dB is desired, how many bits per code word are required in a linearly quantized system?

SQR =1.76 +6.02b

SQR given is 33 dB

1.76+6.02b = 33 dB

6.02 b = 31.24

b = 5.18 = 6 bits

- Determine the number of bits required in computing the DFT of a 1024 – point sequence with an SQR of 30 dB

The size of the sequence is N = 1024 = 2

^{10} SQR is σ

_{X}^{2 }/ σ_{q}^{2}= 2^{2b}/ N^{2}10 log [σ

_{X}^{2 }/ σ_{q}^{2}] = 10 log [2^{2b}/ N^{2}]N

^{2}= 2^{20}10 log [σ

_{X}^{2 }/ σ_{q}^{2}] = 10 log [2^{2b}/ 2^{20}]SNR = 10 log [2

^{2b}^{- 20}] = 30 dB 3(2b-20) =30

b=15 bits is the precision for both multiplication and addition

- Determine the system described by the equation y (n) = n x (n) is linear or not.
- What is the total energy of the discrete time signal x(n) which takes the value of unity at n =-1,0,1?
- Draw the signal x(n) = u(n) – u(n-3)

**PART - B**

- For each of the following systems, determine whether the system is static stable, causal, linear and time invariant
- y(n) = e
^{x(n)} - y(n) = ax(n) +b
- y(n) = Σ
^{n}_{k=n0}x(k) - y(n) = Σ
^{n+1}_{k= -∞}x(k - y(n) = n x
^{2}(n) - y(n) = x(-n+2)
- y(n) = nx(n)
- y(n) = x(n) +C
- y(n) = x(n) – x(n-1)
- y(n) = x(-n)
- y(n) = Δ x(n) where Δ x(n) = [x(n+1) – x(n)]
- y(n) = g(n) x(n)
- y(n) = x(n
^{2}) - y(n) = x
^{2}(n) - y(n) = cos x(n)
- y(n) = x(n) cos ω
_{0}n - Compute the linear convolution of h(n) = {
**1,**2,1} and x(n) ={**1**,-3,0,2,2} - Explain the concept of Energy and Power signals and determine whether the following are energy or power signals
- x(n) = (1/3)
^{n}u(n) - x(n) = sin (π / 4)
^{n} - The unit sample response h(n) of a system is represented by

h (n) = n

^{2}u(n+1) – 3 u(n) +2n u(n-1) for -5≤ n ≤5. Plot the unit sample response.- State and prove sampling theorem. How do you recover continuous signals from

its samples? Discuss the various parameters involved in sampling and

reconstruction.

- What is the input x(n) that will generate an output sequence

y(n) = {1,5,10,11,8,4,1} for a system with impulse response h(n) = {1,2,1}

- Check whether the system defined by h(n) = [5 (1/2)
^{n}+4(1/3)^{n}] u(n) is stable? - Explain the analog to digital conversion process and reconstruction of analog signal from digital signal.
- What are the advantages and disadvantages of digital signal processing compared with analog signal processing?
- Classify and explain different types of signals.
- Explain the various elementary discrete time signals.
- Explain the different types of mathematical operations that can be performed on a discrete time signal.
- Explain the different types of representation of discrete time signals.
- Determine whether the systems having the following impulse responses are causal and stable
- h(n) = 2
^{n}u(-n) - h(n) = sin nπ / 2
- h(n) = sin nπ + δ (n)
- h(n) = e
^{2n}u(n-1) - For the given discrete time signal

x (n) = { -0.5,0.5, for n = -2, -1

1, n = 0

3, 2, 0.4 n > 0}

Sketch the following a) x (n-3), b) x (3-n) c) x (2n) d) x (n/2) e) [x (n) + x (-n)] / 2

- Find the convolution of x (n) = a
^{n}u (n), a < 1 with h(n) = 1for 0 ≤ n ≤ N-1 - Draw the analog, discrete, quantized and digital signal with an example.
- Explain the properties of linearity and stability of discrete time systems with examples.
- The impulse response of a linear time invariant system is h (n) = {1, 2, 1,-1}.

_{ ↑}

_{ }Determine the response of the system to the input signal x (n) = {1, 2, 3, 1}.

_{↑}

_{ }

- Determine whether or not each of the following signals are periodic. If a signal is

Periodic specify its fundamental time period.

i. x(t) = 2 cos 3 πt

ii. x(t) = sin 15 πt + sin 20 πt

iii. x(n) = 5 sin 2n

iv. x(n) = cos (n/8) cos (πn / 8)

**UNIT –II**

**PART - A**

- Define Z-transform
- Define ROC in Z-transform
- Determine Z-transform of the sequence x(n)= {2,1,-1,0, 3}

↑

- Determine Z-transform of x(n) = - 0.5 u (-n-1)
- Find Z- transform of x(n) = - b
^{n}u(-n-1) and its ROC - Find Z- transform of x(n) = a
^{n}u(n) and its ROC - What are the properties of ROC in Z- transform?
- State the initial value theorem of Z- transforms.
- State the final value theorem of Z- transforms.
- Obtain the inverse Z – transform of X(Z) = log ( 1 + Z
^{-1}) for │Z │ < 1 - Obtain the inverse Z – transform of X(Z) = log ( 1-2z) for │Z │ < 1/2
- What is the condition for stability in Z-domain?
- Mention the basic factors that affect thr ROC of z- transform.
- Find the z- transform of a digita limpulse signal and digital step signal

**PART - B**

1. Determine the Z-transform and ROC of

a. x(n) = r

^{n}cos ωn u(n)b. x(n) = n

^{2}a^{n}u(n)c. x(n) = -1/3 (-1/4)

^{n}u(n) – 4/3 (2)^{n}u(-n-1)d. x(n) = a

^{n}u(n) + b^{n}u(n) + c^{n}u(-n-1) , |a | < | b| < |c|e. x(n) = cos ωn u(n)

f. x(n) = sin ω

_{0}n . u(n)g. x(n) = a

^{n}u(n)h. x(n) = [ 3 (2

^{n}) – 4 (3^{n})] u(n)2. Find the inverse Z-transform of

a. X(z) = z (z+1) / (z-0.5)

^{3}b. X(z) = 1+3z

^{-1}/ 1 + 3z^{-1}+ 2z^{-2}c. H(z) = 1 / [1 - 3z

^{-1}+ 0.5z^{-2}] |z | > 1d. X(z) = [z (z

^{2}- 4z +5)] / [(z-3) (z-2) ( z-1)] for ROC |2 | < | z| < |3|, |z| > 3, |z|< 13. Determine the system function and pole zero pattern for the system described by

difference equation y (n) -0.6 y (n-1) +0.5 y (n-2) = x (n) – 0.7 x (n-2)

4. Determine the pole –zero plot for the system described by the difference

equation y (n) – 3/4 y (n-1) +1/8 y (n-2) = x(n) – x(n-1)

5. Explain the properties of Z-transform.

6. Perform the convolution of the following two sequences using Z-transforms.

x(n) = 0.2

^{n}u(n) and h(n) = (0.3)^{n}u(n)7. A causal LTI system has an impulse response h(n) for which the Z-transform is

given by H(z) = (1+z

^{-1}) / [(1 + 1/2z^{-1}) (1 + 1/4z^{-1}). What is the ROC of H (z)? Is the system stable? Find the Z-transform X (z) of an input x (n) that will produce

the output y(n) = -1/3 (-1/4)

^{n}u(n) – 4/3 (2)^{n}u(-n-1).Find the impulse response h (n) of the system.

8. Solve the difference equation y(n) -3y(n-1) – 4y(n-2) = 0, n ≥ 0 ,y(-1) = 5

9. Compute the response of the system y(n) = 0.7 y(n-1)-0.12y(n-2) +x(n-1)+

x (n-2)to the input x(n) = n u(n)

10. What is ROC? Explain with an example.

11. A causal LTI IIR digital filter is characterized by a constant co-efficient difference equation given by y(n) = x(n-1)-1.2x(n-2)+x(n-3)+1.3 y(n-1) – 1.04 y(n-2)+0.222y(n-3),obtain its transfer function.

12. Determine the system function and impulse response of the system described by the difference equation y(n) = x(n) +2x(n-1)- 4x(n-2) + x(n-3)

13. Solve the difference equation y(n) - 4y(n-1) - +4 y(n-2) = x(n) – x(n-1) with the initial condition y(-1) = y(-2) = 1

14. Find the impulse response of the system described by the difference equation y(n) = 0.7 y(n-1) -0.1 y(n-2) +2 x(n) – x(n-2)

15. Determine the z- transform and ROC of the signal x (n) = [3 (2

^{n}) – 4 (3^{n})] u(n).16. State and prove convolution theorem in z-transform.

17. Given x(n) = δ(n) + 2 δ(n-1) and y(n) = 3 δ(n+1) + δ(n)- δ(n-1). Find x(n) * y(n) and X(z).Y(z).

**UNIT - III**

**PART – A**

- Compute the DFT of x(n) = δ(n – n
_{o}) - State and prove the Parseval’s relation of DFT.
- What do you mean by the term bit reversal as applied to DFT?
- Define discrete Fourier series.
- Draw the basic butterfly diagram of DIF –FFT algorithm.
- Compute the DFT of x(n) = a
^{n} - State the time shifting and frequency shifting properties of DFT.
- What is twiddle factor? What are its properties?
- Draw the basic butterfly diagram of DIT –FFT algorithm.
- Determine the 3 point circular convolution of x(n) = {1,2,3} and h(n) = {0.5,0,1}
- If an N-point sequence x(n) has N-point DFT of X(K) then what is the DFT of the following i) x
^{*}(n) ii) x^{*}(N-n) iii) x((n-l))_{N }iv) x(n) e^{j2πln/N} - What is FFT and what are its advantages?
- Distinguish between DFT and DTFT (Fourier transform)
- What is the basic operation of DIT –FFT algorithm?
- What is zero padding? What are its uses?
- State and prove Parseval’s relation for DFT.
- Draw the flow graph of radix – 2 DIF - FFT algorithm for N= 4
- What do you mean by bit reversal in DFT?

20. Write the periodicity and symmetry property of twiddle factor.

21. Give the relationship between z-domain and frequency domain.

22. Distinguish between discrete Fourier series and discrete Fourier transform.

23. What is the relationship between Fourier series co-efficient of a periodic

sequence and DFT?

24. What is the circular frequency shifting property of DFT?

25. Establish the relation between DFT and z-transform.

26. Define DFT pair.

27. Define overshoot.

28. Define Gibbs phenomenon.

29. How many multiplications and additions are required to compute N-point DFT

using radix – 2 FFT?

**PART – B**

- Perform circular convolution of the sequence using DFT and IDFT technique

x

_{1}(n) = {2, 1,2,1} x_{2}(n) = {0,1,2,3} (8)2. Compute the DFT of the sequence x(n) = {1,1,1,1,1,1,0,0}

^{ }(8)- From the first principles obtain the signal flow graph for computing 8 – point DFT using radix-2 DIT FFT algorithm. Using the above compute the DFT of sequence x(n) = {0.5,0.5,0.5,0.5,0,0,0,0} (16)
- State and prove the circular convolution property of DFT.Compute the circular

convolution of x(n) = {0,1,2,3,4} and h(n) = {0,1,0,0,0} (8)

- Perform circular convolution of the sequence using DFT and IDFT technique

x

_{1}(n) = {1,1,2,1} x_{2}(n) = {1,2,3,4} (8)- Compute the DFT of the sequence x(n) = {1,1,1,1,1,1,0,0}
^{ }(8) - From the first principles obtain the signal flow graph for computing 8 – point DFT using radix-2 DIF-FFT algorithm. An 8 point sequence is given by x(n)={2,2,2,2,1,1,1,1} compute its 8 point DFT of x(n) by radix-2 DIF-FFT (16)
- Compute 5 point circular convolution of x
_{1}(n) = δ (n) +δ (n-1)-δ (n-2) - δ (n-3) and x_{2}(n) = δ (n) – δ (n-2)+ δ (n-4) (8) - Explain any five properties of DFT. (10)
- Derive DIF – FFT algorithm. Draw its basic butterfly structure and compute the DFT x(n) = (-1)
^{n }using radix 2 DIF – FFT algorithm. (16) - Perform circular convolution of the sequence using DFT and IDFT technique

x

_{1}(n) = {0,1,2,3} x_{2}(n) = {1,0,0,1} ( 8 )- Compute the DFT of the sequence x (n) = 1/3 δ (n) – 1/3 δ (n-1) +1/3 δ (n -2)
^{ }(6) - From the first principles obtain the signal flow graph for computing 8 – point DFT using radix-2 DIT - FFT algorithm. Using the above compute the DFT of sequence x(n) = 2 sin nπ / 4 for 0 ≤ n ≤ 7 (16)
- What is circular convolution? Explain the circular convolution property of DFT and compute the circular convolution of the sequence x(n)=(2,1,0,1,0) with

itself (8)

- Perform circular convolution of the sequence using DFT and IDFT technique

x

_{1}(n) = {0,1,2,3} x_{2}(n) = {1,0,0,1} ( 8 )- i) Compute the DFT of the sequence x (n) = (-1)
^{n}^{ }(4)

ii) What are the differences and similarities between DIT – FFT and DIF – FFT

algorithms? (4)

- From the first principles obtain the signal flow graph for computing 8 – point DFT using radix-2 DIT - FFT algorithm. Using the above compute the DFT of sequence x(n) = cos nπ / 4 for 0 ≤ n ≤ 7 (16)

18. Compute 4-point DFT of the sequence x (n) = (0, 1, 2, 3) (6)

19. Compute 4-point DFT of the sequence x (n) = (1, 0, 0, 1) (6)

20. Explain the procedure for finding IDFT using FFT algorithm (6)

21. Compute the output using 8 point DIT – FFT algorithm for the sequence

x(n) = {1,2,3,4,5,6,7,8} (16)

22.Determine the 8-point DFT of the sequence x(n) = {0,0,1,1,1,0,0,0}

23. Find the circular convolution of x(n) = 1,2,3,4} and h(n) = {4,3,2,1}

24. Determine the 8 point DFT of the signal x(n) = {1,1,1,1,1,1,0,0}. Sketch its

magnitude and phase.

**UNIT IV**

**PART – A**

- An analog filter has a transfer function H(s) = 1 / s+2. Using impulse invariance

method, obtain pole location for the corresponding digital filter with T = 0.1s.

- What is frequency warping in bilinear transformation?
- If the impulse response of the symmetric linear phase FIR filter of length 5 is

h (n) = {2,3,0,x,y}, find the values of x and y.

- What is prewarping? Why is it needed?
- Find the digital transfer function H (z) by using impulse invariance method for the analog transfer function H(s) = 1 / s+2.
- What are the different structures of realization of FIR and IIR filters?
- What are the methods used to transform analog to digital filters?
- State the condition for linear phase in FIR filters for symmetric and anti symmetric response.
- Draw a causal FIR filter structure for length M= 5.
- What is bilinear transformation? What are its advantages?
- Write the equation of Barlett (or) triangular and Hamming window.
- Write the equation of Rectangular and Hanning window.
- Write the equation of Blackman and Kaiser window.
- Write the expression for location of poles of normalized Butter worth filter.

P

_{k}= ± Ω_{c}e^{j (N+2k +1) π / 2N} Where k = 0, 1, ……. (N-1) and for a normalized filter Ω

_{c}= 1 rad / sec- Write the expression for location of poles of normalized Chebyshev filter.
- Draw the magnitude response of 3
^{rd}order Chebyshev filter. - Draw the magnitude response of 4th order Chebyshev filter.
- Draw the basic FIR filter structure.
- Draw the direct form – I structure of IIR filter.
- Draw the direct form – II structure of IIR filter.
- Draw the cascade form realization structure of IIR filter.
- Draw the parallel form realization structure of IIR filter.
- When cascade form realization structure is preferred in filters?
- Distinguish between FIR and IIR filters.
- Compare analog and digital filters.
- Why FIR filters are always stable?

Because all its poles are located at the origin.

- State the condition for a digital filter to be causal and stable.
- What are the desirable characteristics of windows?
- Give the magnitude function Butterworth filter. What is the effect of varying the order of N on magnitude and phase response?
- List out the properties of Butterworth filter.
- List out the properties of Chebyshev filter.
- Give the Chebyshev filters transfer function and draw its magnitude response.
- Give the equation for the order ‘N’ and cut off frequency Ω
_{c }of Butterworth filter - Why impulse invariance method is not preferred in the design of IIR filter other than low pass filters?
- What are the advantages and disadvantages FIR filters?
- What are the advantages and disadvantages IIR filters?
- What is canonic structure?

If the number of delays in the structure is equal to the order of the difference equation or order of transfer function, then it is called canonic form of realization.

- Compare Butterworth and Chebyshev filters.
- What are the desirable and undesirable features of FIR filters?
- What are the design techniques of designing FIR filters?

Fourier series method

Windowing technique

Frequency sampling method

**PART –B**

1. With suitable examples, describe the realization of linear phase FIR filters (8)

2. Convert the following analog transfer function H(s) = (s+0.2) / [(s+0.2)

^{2}+ 4] into equivalent digital transfer function H (z) by using impulse invariance method assuming

T= 1 sec. (8)

3. Convert the following analog transfer function H(s) = 1 / (s+2) (s+4) into

equivalent digital transfer function H (z) by using bilinear transformation with T = 0.5

sec.

4. Convert the following analog transfer function H(s) = (s+0.1) / [(s+0.1)

^{2}+ 9] into equivalent digital transfer function H (z) by using impulse invariance method assuming

T= 1 sec. (8)

5. Convert the following analog transfer function H(s) = 2/ (s+1) (s+3) into

equivalent digital transfer function H (z) by using bilinear transformation with T = 0.1

sec.Draw the diect form – II realization of digital filter. (8)

6. Design a high pass filter of length 7 samples with cut off frequency of 2 rad / sec

using Hamming window. Plot its magnitude and phase response. (16)

7. For the constraints

0.8 ≤ │H(ω)│≤ 1.0 , 0 ≤ ω ≤ 0.2π

│ H(ω)│ ≤ 0.2, 0.6 π ≤ ω ≤ π

With T= 1 sec determine the system function H(z) for a Butterworth filter using

bilinear transformation. (16)

8. Describe the effects of quantization in IIR filter. Consider a first order filter with

difference equation y (n) = x (n) + 0.5 y (n-1).Assume that the data register length is 3

bits plus a sign bit. The input x (n) = 0.875δ (n). Explain the limit cycle oscillations in

the above filter, if quantization is preferred by means of rounding and signed

magnitude representation is used. (16)

9. With a neat sketch explain the architecture of TMS 320 C54 processor. (16)

10. For the constraints

0.7 ≤ │H(ω)│≤ 1.0 , 0 ≤ ω ≤ π/2

│ H(ω)│ ≤ 0.2, 3π/4 ≤ ω ≤ π

With T= 1 sec, design a Butterworth filter. (16)

11. Explain the quantization effects in design of digital filters. (16)

12. Discuss about the window functions used in design of FIR filters (8)

13. Obtain the cascade and parallel realization of system described by difference equation

y(n) = -0.1 y(n-1) + 0.2 y(n-2) + 3x(n) +3.6 x(n-1) + 0.6 x(n-2) (10)

14. Design a digital Butterworth filter satisfying the following constraints with T= 1 sec,

using Bilinear transformation.

0.707 ≤ │H (ω) │≤ 1.0, 0 ≤ ω ≤ π/2

│ H (ω) │ ≤ 0.2, 3π/4 ≤ ω ≤ π (16)

15. Design a digital Chebyshev filter satisfying the following constraints with T= 1 sec,

using Bilinear transformation.

0.707 ≤ │H (ω) │≤ 1.0, 0 ≤ ω ≤ π/2

│ H (ω) │ ≤ 0.2, 3π/4 ≤ ω ≤ π (16)

18. Draw and explain cascade form structure for a 6

^{th}order FIR filter. (6)19. Explain impulse invariance method of digital filter design. (10)

20. Derive an expression between s- domain and z- domain using bilinear transformation.

Explain frequency warping. (10)

21. Draw the structure for IIR filter in direct form – I and II for the following transfer

Function H (z) = (2 + 3 z

^{-1}) (4+ 2 z^{-1}+3 z^{-2}) / (1+0.6 z^{-1}) (1+ z^{-1}+0.5 z^{-2}) (10)22. Design a filter with

H

_{d}(ω) = e^{-j2ω}- π/4 ≤ ω ≤ π/4 = 0 π/4 ≤ ω ≤ π

Using a Hamming window with N= 7 (16)

23. Discuss about frequency transformations in detail. (8)

24. Design a LPF with

H

_{d}(ω) = e^{-j3ω}- 3π/4 ≤ ω ≤3π/4 = 0 3π/4 ≤ ω ≤ π

Using a Hamming window with N= 7 (16)

25. Using the bilinear transformation and a low pass analog Butterworth prototype,

design a low pass digital filter operating at a rate of 20 KHz and having pass band

extending to a 4 KHz with a maximum pass band attenuation of 0.5 dB and stop band

starting at 5KHzwith a minimum stop band attenuation of 10 dB. (16)

26. Using the bilinear transformation and a low pass analog Chebyshev type I prototype,

design a low pass digital filter operating at a rate of 20 KHz and having pass band

extending to a 4 KHz with a maximum pass band attenuation of 0.5 dB and stop band

starting at 5KHzwith a minimum stop band attenuation of 10 dB. (16)

27. Obtain the cascade realization of linear phase FIR filter having system function

H(z) = ( 1+1/2 z

^{-1}+ z^{-2}) (2 + ¼ z^{-1}+2z^{-2}) using minimum number of multipliers.(8)28. Design an ideal Hilbert transformer having frequency response

H(e

^{jω}) = j for -π ≤ ω ≤ 0 = -j for 0≤ ω ≤ π

for N=11, using i. rectangular window

ii. Blackmann window

29. Obtain the direct form – I, direct form – II, cascade and parallel form of realization

for the system y(n) = -0.1 y9n-1) + 0.2 y(n-2) + 3 x(n) + 3.6 x (n-1) + 0.6 x(n-2)

30. Using Bilinear transformation and a low pass analog Butterworth prototype, design a

low pass digital filter operating at the rate of 20k Hz and having pass band extending

to 4 kHz with maximum pass band attenuation of 10 dB and stop band starting at 5

kHz with a minimum stop band attenuation of 0.5 dB

31. Using Bilinear transformation and a low pass analog Chebyshev type I prototype,

design a low pass digital filter operating at the rate of 20k Hz and having pass band

extending to 4 kHz with maximum pass band attenuation of 10 dB and stop band

starting at 5 kHz with a minimum stop band attenuation of 0.5 dB

32. Design a low pass filter using Hamming window for N=7 for the desired frequency

Response D (ω) = e

^{j3ω}for -3π / 4 ≤ ω ≤3π / 4 = 0 for 3π / 4 ≤ ω ≤ π

33. Design an ideal differentiator for N=9 using Hanning and triangular window

**UNIT – V**

**PART - A**

1. Compare fixed point arithmetic and floating point arithmetic.

2. What is product quantization error or product round off error in DSP?

3. What are the quantization methods?

4. What is truncation and what is the error that arises due to truncation in floating

point numbers?

- What is meant by rounding? Discuss its effects?
- What are the two kinds of limit cycle oscillations in DSP?
- Why is rounding preferred to truncation in realizing digital filters?
- What are the 3 quantization errors due to finite word length registers in digital filters?
- List out the features of TMS 320 C54 processors.
- What are the various interrupt types supported by TMS 320 C54?
- Mention the function of program controller of DSP processor TMS 320 C54.
- List the elements in program controller of TMS 320C54.
- What do you mean by limit cycle oscillations?
- What is pipelining? What is the pipeline depth of TMS 320 C54 processor?
- What are the different buses of TMS 320 C54 processor?
- What are quantization errors due to finite word length registers in digital filters?
- Differentiate between Von Neumann and Harvard architecture.
- Define limit cycle oscillations in recursive systems.
- How to prevent overflow in digital filters?

**PART –B**

1. Describe the function of on chip peripherals of TMS 320 C54 processor. (12)

2. What are the different buses of TMS 320 C54 processor? Give their functions. (4)

3. Explain the function of auxiliary registers in the indirect addressing mode to point the

data memory location. (8)

4. Explain about the MAC unit. (8)

5. What is meant by instruction pipelining? Explain with an example how pipelining

increases through put efficiency. (8)

6. Explain the operation of TDM serial ports in P-DSPs (8)

7. Explain the characteristics of a limit cycle oscillation with respect to the system

described by the equation y (n) = 0.95 y (n-1) + x (n).Determine the dead band of the

filter. (10)

8. Draw the product quantization noise model of second order IIR filter. (6)

9. In a cascaded realization of the first order digital filter, the system function of the

individual section are H19z) = 1 / (1-0.9 z

^{-1}) and H2(z) = 1 / (1-0.8z^{-1}). Draw the product quantization noise model of the system and determine the output noise

power. (16)

10. Explain the statistical characterization of quantization effects in fixed point

realization of digital filter. (16)

11. Give a detailed note on Direct memory Access controller in TMS 320 C54x

processor.

12. Find the effect of quantization on the pole locations of the second order IIR filter

Given by H(z) = 1 / (1-0.5z

^{-1}) (1- 0.45 z^{-1)}when it is realized in direct form – I and in cascade form. Assume a word length of 3 bits.

13. Determine the variance of the round off noise at the output of the two cascade

realizations of the filters with system functions H1 (z) = 1 / 1-0.5 z

^{-1} H2 (z) = 1 / 1- 0.25 z

^{-1}^{ }Cascade I, H (z) = H1 (Z) H2 (z)

Cascade II, H (z) = H2 (z) H1 (z)

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