# DSP_Multiple_Choice_Questions

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Mewar University Gangrar Chaittorgarh Electronics and Communication Engineering Department Multiple Choice Question Bank Subject: DSP 5 th Semester ECE Unit-1 1. DSP stands a Digital signal processing b Discrete signal processing c Double signal processor d None of the above 2. Given that N i e W 2 where 3 N . Then N W F can be computed as F a 0 b 1 c -1 d e 3. Given that N i e W 2 where 3 N . 2 N W F can be computed as F a 0 b 1 c -1 d e 4. Determine the convolution sum of two sequences xn 3 2 1 2 and hn 1 2 1 2 a yn 38812944 b yn 38312944 c yn 38812914 d yn 3881944 5. Sampling theorem: a fmfs b fsfm

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c fs2fm d fs2fm 6. Application of Convolution: a FIR Filtering b Addition c Manipulation d None of these 7. Condition for aliasing problem: a fsfm b fs2fm cfsfm d all of these 8. DFT stands as: a. Discrete Fourier transform b. digital function transform c. digital frequency transform d. none 9. FFT stands as: a. Fast Fourier transform b. Fourier function transform c. Fast frequency transform d. none 10. Twiddle factor: a. N 2 i e W b. N 2 i e W

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c. N 2 i e W d. none 11. Phase factor: a. N 2 i e W b. N 2 i e W c. N 2 i e W d. none 12. Calculate DFT of x n 1 0 1 0. a. x k 2 0 2 0 b. x k 1 0 1 0 c. x k 2 0 1 0 d. none 13. Calculate DFT of x n n . a. 1 b. 0 c. N 2 i e W d. none 14. Calculate DFT of x n 0 n n where 0n0N. a. 0 jwn e b. 0 jwn e c. 1

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d. none 15. The FFT algorithms: a. eliminate the redundant calculation and enable to analyze the spectral properties of a signal. b. enable the redundant calculation and redundant to analyze the spectral properties of a signal. c. a b d. none 16. The relation between DFT and Fourier series coefficients of a periodic sequence is a. XK C k /N b. XK C k c. XK NC k d. XK1/C k 17. If xn ------N pt DFT------  XK Then x-n mod N -------------N pt DFT--------  ___________ a. X-K b. XK c. X-k d. XK 18. If the Nyquist rate for x a t is Ω s what is the Nyquist rate for d x a t/dt a. dΩ s /df b. Ω s c. Ω s/2 d. 2Ω s 19. If the Nyquist rate for x a t is Ω s what is the Nyquist rate for x a 2t a. 2Ω s b. Ω s /2 c. Ω s d. Ω s/4 20. If the Nyquist rate for x a t is Ω s what is the Nyquist rate for x a 2 t a. 2Ω s b. Ω s /2 c. Ω s d. Ω s/4 21. If the Nyquist rate for x a t is Ω s what is the Nyquist rate for x a tCosΩ 0 t a. Ω s + 2Ω 0 b. Ω s 2Ω 0 c. Ω s /2Ω 0

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d. Ω s - 2Ω 0 22. The minimum sampling frequency for x a t is real with X a f non-zero only for 9 KHz |f| 12 KHz is a. 4.5 KHz b. 6 KHz c. 9 KHz d. 12 KHz 23. The minimum sampling frequency for x a t is real with X a f non-zero only for 18 KHz |f| 22 KHz is a. 8.8 KHz b. 9 KHz c. 11 KHz d. 17.6 KHz 24. The minimum sampling frequency for x a t is complex with X a f non-zero only for 30 KHz |f| 35 KHz is a. 6 KHz b. 5 KHz c. 15 KHz d. 17.5 KHz 25. Find two different continuous-time signals that will produce the sequence xn cos 0.15 nπ when sampled with a sampling frequency of 8 KHz. a. sine1200πt and Cos17200πt b. Cos1200πt and Sine17200πt c. Cos1200πt and Cos17200πt d. Sine1200πt and Sine17200πt 26. A continuous-time signal x a t is known to be uniquely recoverable from its samples xanTs when T s 1 ms. What is the highest frequency in X a f a. 500 Hz b. 1000 Hz c. 700 Hz d. 5 KHz 27. Suppose that x a t is bandlimited to 8 kHz that is Xa f 0 for |f| 8000 then what is the Nyquist rate for x a t a. 16 KHz b. 4 KHz c. 8 KHz d. 12 KHz 28. Suppose that x a t is bandlimited to 8 kHz that is Xa f 0 for |f| 8000 then what is the Nyquist rate for x a tcos2π . 1000t a. 16 KHz b. 4 KHz c. 18 KHz d. 5 KHz

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28. If a continuous-time filter with an impulse response h a t is sampled with a sampling frequency of f s what happens to the cutoff frequency w c of the discrete-time filter as f s is increased a. w c increases b. w c decreases c. w c remains constant d. w c depends upon f s 29. A complex bandpass signal xat with X a f nonzero for 10 kHz f 12 kHz is sampled at a sampling rate of 2 kHz. The resulting sequence is xn δn then x a t will be a. x a t 1/2000 Sine2000πt/πte j2π11000t b. x a t 1/2000 Sine2000πt/πte -j2π11000t c. x a t 1/2000 Cos2000πt/πte j2π11000t d. x a t 1/2000 Cos2000πt/πte -j2π11000t 30. If the highest frequency in x a t is f 8 kHz then the minimum sampling frequency for the bandpass signal y a t x a t CosΩ 0 t if Ω 0 2π.20.10 3 will be a. 56 KHz b. 64 KHz c. 16 KHz d. 32 KHz 31. Drawbacks of DSP is a. Digital processing needs pre and post processing devices b. high cost c. No memory storage d. none of above 32. Drawbacks of DSP is a. Digital processing needs A/D and D/A converters and associated reconstruction filters b. high cost c. No reliable d. none of above 33. Advantages of DSP are: a. low cost b. stable c. reliable d. all of above 34. Advantages of DSP are: a. predictable b. repeatable c. Sharing a single processor among a number of signals by time sharing d. all of above

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35. Advantages of DSP are: a. low cost b. repeatable c. storage of data is very easy d. all of above 36. Application of DSP: a. Military b. telecommunication c. consumer electronics d. all of above 37. Application of DSP: a. medicine b. seismology c. signal filtering d. all of above 38. Fast convolution techniques: a. overlap save b. overlap add c. a b d. none of above 39. Correlation a. It gives a measure of similarity between two data sequences. b. It gives a measure of dis-similarity between two data sequences c. a b d. none of above 40. Find the response of an FIR filter with impulse response hn 124 to the input sequence xn12. a. yn1488 b. yn1466 c. yn1288 d. none of above Answer Key Unit-1: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

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Unit-2 1. IIR filters a Use feedback b Are sometimes called recursive filters c Can oscillate if not properly designed d all of the above 2. A Blackman window can eliminate ripple in FIR filters. The tradeoff is a larger transition bandwidth b smaller transition bandwidth c a non-linear phase response d possible instability 3. The output of two digital filters can be added. Or the same effect can be achieved by a adding their coefficients b subtracting their coefficients c convolving their coefficients d averaging their coefficients and then using a Blackman window 4. The letter A below indicates the filter a stopband b transition band c passband

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d ripple 5. A DSP convolves each discrete sample with four coefficients and they are all equal to 0.25. This must be a a low-pass filter b high-pass filter c band-pass filter d band-stop filter 6. The inverse Fourier transform a converts from the frequency domain to the time domain b converts from the time domain to the frequency domain c converts from the phasor domain to the magnitude domain d is used to make real-time spectrum analyzers 7. This is the impulse response for a an IIR highpass filter b an FIR bandpass filter c an IIR lowpass filter d an FIR lowpass filter 8. Coefficient symmetry is important in FIR filters because it provides a a smaller transition bandwidth b less passband ripple

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c less stopband ripple d a linear phase response 10. This time graph shows the a frequency response of an IIR filter b amplitude response of an IIR filter c impulse response of an IIR filter d none of the above 11. Curve A is the

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a phase response of a lowpass filter b amplitude response of a lowpass filter c both of the above d none of the above 12. This windowed sinc FIR filter has ripple caused by a non-symmetrical coefficients b Gibbs phenomenon c too few taps d a defective accumulator 13. Two digital filters can be operated in cascade. Or the same effect can be achieved by a adding their coefficients b subtracting their coefficients c convolving their coefficients d averaging their coefficients and then using a Blackman window 14. A DSP convolves each discrete sample with four coefficients and they are all equal to 0.25. This must be an

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a IIR filter b FIR filter c RRR filter d All of the above 15. This frequency response graph is for a a lowpass filter b highpass filter c bandpass filter d bandstop filter 16. The letter C below indicates the filter a stopband b passband c transition band d ripple 17. A quantizer operates at a sampling frequency of 16 kHz. What is its Nyquist limit a 4 kHz

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b 8 kHz c 16 kHz d 32 kHz 18. Curve B is the a phase response of a lowpass filter b amplitude response of a lowpass filter c both of the above d none of the above 19. After point D as frequency is increasing a the phase response is linear b the phase response is non-linear c the stopband is infinite d the Nyquist limit has been exceeded 20. The letter B below indicates the filter

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a stopband b passband c transition band d ripple 21. If a linear phase filter has a phase response of 40 degrees at 200 Hz what will its phase response be at a frequency of 400 Hz assuming that both frequencies are in the passband of the filter a 35 degrees b 40 degrees c 45 degrees d 80 degrees 22. Which of the following is used to alter FIR filter coefficients so they smoothly approach zero at both ends a rectangular window b Blackman window c Laplace window d Hilbert window 23. Point C is called

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a a phase reversal b the half-power point c a phase discontinuity d a phase wrap 24. A DSP convolves each discrete sample with these coefficients: -0.25 -0.25 1.0 -0.25 and -0.25. This must be a a low-pass filter b high-pass filter c band-pass filter d band-stop filter 25. The basic process thats going on inside a DSP chip is a quantization b MAC c logarithmic transformation d vector calculations 26. For the rectangular window function the transition width of the main lobe is approximately here M is the length of the filter a 4piM b pi/4M c piM/4 d 4pi/M

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27. For the rectangular window function the first sidelobe will be __________ dB down the peak of the main lobe. a 12 dB b 11 dB c 13 dB d 14 dB 28. For the rectangular window function the roll-off will be __________ dB/decade. a 25 dB b 20 dB c 15 dB d 10 dB 29. For the hamming window function the width of the main lobe is approximately here M is the length of the filter a 8piM b pi/8M c piM/8 d 8pi/M 30. For the hamming window function the peak of the first sidelobe will be at __________ dB. a -40 dB b -48 dB c -43 dB d -45 dB 31. For the hamming window function the side lobe roll-off will be __________ dB/decade. a 25 dB b 20 dB c 15 dB d 10 dB 32. For the hanning window function the width of the main lobe is approximately here M is the length of the filter a 8piM b pi/8M c piM/8 d 8pi/M 33. For the hamming window function the peak of the first sidelobe will be at __________ dB.

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a -35 dB b -32 dB c -40 dB d -43 dB 34. For the Blackmann window function the width of the main lobe is approximately here M is the length of the filter a 12piM b pi/8M c piM/8 d 12pi/M 35. For the hamming window function the peak of the first sidelobe will be at __________ dB. a -58 dB b -48 dB c -45 dB d -43 dB 36. What is a delay a. Delay a copy of the output signal by x number of samples and combine it with the new input signal. b. Delay a copy of the input signal by x number of samples and combine it with the new output signal. c. a b d. none of above 37. What is FIR filter a. FIR filters are “finite” there is a specific limit to the number of times that any delayed sample is added to a new input sample. b. FIR filters are “finite” there is a specific limit to the number of times that any delayed sample is added to a new output sample. c. a b d. none of above 38. The output of a filter is a function not only of the input at the present time:

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a. but also of previous events. b. but also of future events. c. a b d. none of above 39. FIR filters have ……. and IIR filters have ………. a. Zeros poles zeros b. poles zeros Zeros c. Zeros zeros d. none of above 40. How to define IIR filters term as infinite: a. As with any feedback device create a loop hence the term infinite. b. As with any non-feedback device create a loop hence the term infinite. c. As with any feedback device create a open loop hence the term infinite. d. None of above Answers-Key Unit-2: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

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Unit-3 1. The filter coefficients are stored in: a. binary registers b. digital system c. binary memory d. none 2. Truncation or rounding of the data results in a. degradation of system performance b. increase system performance c. grow power d. none 3. the process of quantization is introduce a. error b. noise c. power d. none 4. Issue connected with finite word length effects: a. quantization effects in A/D conversion b. product quantization and coefficient quantization errors in digital filters c. a b d. none. 5. Issue connected with finite word length effects: a. limit cycles in IIR filters b. product quantization and coefficient quantization errors in digital filters c. a b d. none. 6. Issue connected with finite word length effects: a. finite word length effects in FFTs b. product quantization and coefficient quantization errors in digital filters c. limit cycles in IIR filters d. all of above 7. Rounding or truncation introduces an error whose magnitude depends a. On the number of bits truncated or rounded bits. b. On the number of bits rounded bits.

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c. On the number of bits truncated bits. d. all of above. 8. The range for negative truncation error for sign magnitude representation is a. 0 2 2 T L B b. 2 2 0 L B T c. 0 2 2 T L B d. none of above 9. The range for positive truncation error for sign magnitude representation is a. 0 2 2 T L B b. 2 2 0 L B T c. 0 2 2 T L B d. none of above 10. The range for truncation error for two’s complement representation is a. 0 2 2 T L B b. 2 2 0 L B T c. 0 2 2 T L B d. none of above 11. The range for round off error for sign magnitude representation is a. 2 / 2 2 2 / 2 2 L B R L B b. 2 2 0 L B T c. 0 2 2 T L B d. none of above 12. The range for round off error for two’s complement representation is a. 2 / 2 2 2 / 2 2 L B R L B b. 2 2 0 L B T c. 0 2 2 T L B d. none of above 13. The dynamic range is a. DR6B + 10.8 b. DR3B + 10.8

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c. DR6B + 1.8 d. none of above 14. The dynamic range is a. DR -2logP en b. DR -logP en c. DR -10logP en d. none of above 15. 0 n 2 n x a. dz z z X z X j 2 1 1 c 1 b. dz z z X j 2 1 1 c c. dz z z X z X 2 1 1 c 1 d. none of above 16. Coefficient quantization effects in Direct form realization of IIR filters is a. Y ’ z H ideal z Xz + Ez b. Y ’ z H ideal z + Ez c. Y ’ z H ideal z Xz d. none of above 17. Stray filter a. coefficient quantization error in Direct form realization of IIR filters b. coefficient quantization error in cascaded-Direct form realization of IIR filters c. coefficient quantization error in ladder form realization of IIR filters d. none of above 18. Limit cycle is a. zero input limit cycle b. overflow limit cycle

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c. a b d. none of above 19. The effects of limit cycles in first and second order systems were studied by a. Hendy using an effective value model b. Thomson using an effective value model c. Jackson using an effective value model d. none of above 20. If a is positive the limit cycle will have a. variable magnitude by alternating sign. b. constant magnitude by alternating sign. c. constant phase by alternating sign. d. none of above 21. What is scaling a. Scaling must be done in such a way that no overflow occurs at the summing point. b. Scaling must be done in such a way that overflow occurs at the summing point. c. Scaling must be done in such a way that no underflow occurs at the summing point. d. none of above 22. The necessary and sufficient condition for preventing overflow in a IIR digital filter. a. n k i k h 1 X b. k i k h 1 X

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c. n k i k h 1 X d. none of above 23. The necessary and sufficient condition for preventing overflow in a FIR digital filter. a. n k i k h 1 X b. n k i k h 1 X c. 1 M 0 k i k h 1 X d. none of above 24. The band of integers is known as the "deadband". a. true b. false c. either true or false d. none of above 25. In the second order system under rounding the output assumes a cyclic set of values of the deadband. a. limit-cycle. b. band-cycle. c. dead-cycle. d. none of above

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26. With finite precision the reponse does not converge to the origin but assumes cyclically a set of values: a. the limit-cycle. b. band-cycle. c. dead-cycle. d. none of above 27. With infinite precision the response converges to the .......... a. origin. b. center. c. mid. d. none of above 28. below figure shows: a. Quantisation error in rounding. b. Quantisation error in truncation in 2’s complement. c. Quantisation error in truncation in sign magnitude. d. none of above 29. below figure shows:

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a. Quantisation error in rounding. b. Quantisation error in truncation in 2’s complement. c. Quantisation error in truncation in sign magnitude. d. none of above 30. Below figure shows: a. probabilistic characteristics of Quantisation error in round-off. b. probabilistic characteristics of Quantisation error in truncation in 2’s complement. c. probabilistic characteristics of Quantisation error in truncation in sign magnitude. d. none of above

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31. Below figure shows: a. probabilistic characteristics of Quantisation error in round-off. b. probabilistic characteristics of Quantisation error in truncation in 2’s complement. c. probabilistic characteristics of Quantisation error in truncation in sign magnitude. d. none of above 32. This is a deterministic frequency response error is referred to as…………... a. coefficient quantization error b. product quantization error c. a b d. none of above 33. A digital system is characterized by the difference equation yn0.9 yn-1 + xn with xn0 and initial condition y-112. Determine deadband of the system. a. -55 b. -3 3 c. -11

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d. none of above 34. FIR filters are ….. generally as sensitive to coefficient roundoff. a. not b. less c. most d. none of above 35. FIR filters often require more computation because you must do ………… for each term in the impulse response. a. a multiply-add b. add c. multiply d. all of above 36. FIR filters can be ………. delay IIR filters can…………. a. constant not b. not constant c. not not d. none of above 37. “Linear Phase” constant delay If a filter has a ………….delay the phase shift of the filter will be tw where t is the time delay and w the natural frequency 2pif. a. a. constant b. variable c. equal d. none of above

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38. Non-linear delay This is the part of the phase shift in and around the filter’s passband that is not modeled by a ………... a. straight line b. circle c. square d. none of above 39. Power of quantization noise a. 12 2 p B 2 n e b. 2 2 p B 2 n e c. 12 2 p B n e d. none of above 40. If you don’t want a zero at pi you can’t use a symmetric ……-length filter. You can use an antisymmetric even length filter if you want a highpass filter but then you’ll have a zero at DC. This means that symmetric high pass filters are of …… length. a. even odd b. odd even c. even even d. none of above Answers-Key Unit-3: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

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Unit-4 1. Used to increase the sampling rate by an integer factor a. Up-sampler b. down sampler c. a b d. none of above 2. Used to decrease the sampling rate by an integer factor a. Up-sampler b. down sampler c. a b d. none of above 3. This block represents a. Up-sampler b. down sampler c. a b d. none of above 4. Up-sampling operation is implemented by inserting L-1 equidistant ……..-valued samples between two consecutive samples of xn. a. zero b. one c. two d. none of above

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5. Input-output relation for ……….. a. Up-sampler b. down sampler c. a b d. none of above 6. In practice the zero-valued samples inserted by the up-sampler are replaced with appropriate nonzero values using some type of filtering process Process is called……. a. interpolation b. decimation c. a b d. none of above 7. ………operation is implemented by keeping every M-th sample of xn and removing M-1 in- between samples to generate yn. a. Up-sampling b. Down-sampling c. a b d. none of above 8. Input-output relation for ……… yn xnM a. Up-sampler b. down sampler c. a b d. none of above 9. The up-sampler and the down-sampler are ……..but time-varying discrete-time systems: otherwise 0 L 2 L 0 n L / n x n x u 

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a. linear b. none linear c. a b d. none of above 10. A factor-of-2 sampling rate expansion leads to a compression of by a factor of 2 and a 2-fold repetition in the baseband 0 2 . This process is called……… a. imaging b. sampling c. decimation d. none of above 11. A ……..is formed by an interconnection of the up-sampler the down-sampler and the components of an LTI digital filter. a. complex multirate system b. complex single-rate system c. a b d. none of above 12. An interchange of the positions of the branches in a cascade often can lead to a computationally ……….realization. a. efficient b. non-efficient c. neither efficient nor non- efficient d. none of above 13. To implement a ……..in the sampling rate we need to employ a cascade of an up-sampler and a down-sampler. a. fractional change e X j

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b. constant change c. variable change d. none of above 14. A cascade of a factor-of-M down-sampler and a factor-of-L up-sampler is interchangeable with no change in the input-output relation: if and only if M and L are relatively ….. a. prime b. non prime c. natural number d. none of above 15. From the sampling theorem it is known that a the sampling rate of a critically sampled discrete-time signal with a spectrum occupying the full Nyquist range cannot be reduced any further since such a reduction will introduce……….. a. aliasing b. quantization c. error d. none of above 16. The bandwidth of a critically sampled signal must be reduced by ………filtering before its sampling rate is reduced by a down-sampler. a. lowpass b. highpass c. a b d. none of above 17. The zero-valued samples introduced by an up-sampler must be interpolated to more appropriate values for an effective sampling rate………... a. decrease n y n y 2 1

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b. increase c. a b d. none of above 18. Since up-sampling causes periodic repetition of the basic spectrum the unwanted images in the spectra of the up-sampled signal must be removed by using a lowpass filter Hz called………………….. a. the interpolation filter b. the decimation filter c. the Low pass filter d. all of above 19. Down-sampling the signal vn should be bandlimited to by means of a lowpass filter called ……………... a. the interpolation filter b. the decimation filter c. the Low pass filter d. all of above 20. In the case of single-rate digital signal processing IIR digital filters are in general computationally more efficient than equivalent FIR digital filters and are therefore preferred where computational ………….needs to be minimized. a. cost b. memory c. speed d. none of above 21. Below figure shows: n x u M /

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Decimation a. Aliasing Step b. Anti-Aliasing Step c. a b d. all of above 22. Below figure shows: Interpolation a. Imaging Step b. Anti-Imaging Step c. a b d. all of above 23. To prevent…………… the down-sampled signal should be band-width limited to fs/2N by low-pass filtering prior to sample removal. a. aliasing b. quantization c. a b d. all of above 24. A signal can be restored to a higher sampling frequency by the processes of……………. a. up sampling and interpolation

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b. down sampling and decimation c. a b d. all of above 25. ………….have the property of noise-shaping which allows the elimination of quantization noise by low-pass filtering. a. Delta-Delta quantizers b. Delta-sigma quantizers c. a b d. all of above 26. Care has to be taken with any feedback system. Feedback coefficients have to remain below…….. a. 1.0 b. 2.0 c. 1.5 d. all of above 27. Drawbacks of IIR filters are: a. phase distortion and ringing. b. prevent phase distortion c. more computation d. all of above 28. Drawbacks FIR filters are: a. more computation than an IIR with similar effect. b. prevent phase distortion c. less computation

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d. all of above 29. Which of the Following is not a Filter a. Graphic Equalizer on a stereo system b. Tone control on a stereo system c. Ear d. all of above 30. Finite Impulse Response FIR is a a. feedforward filter b. feedback filter c. a b d. all of above 31. The direct form creates a number of difficulties: a. It increases the size in terms of bit depth of numerical coefficients b. It increases the depth required for accumulators mantissa for floating point c. a b d none of above 32. A ……….is nothing but a way to implement a set of filters generally strongly mathematically related in one operation. a. filterbank b. summing point bank c. node bank d. none of above 33. A ………can always be decomposed into a set of individual filters this is usually a lot more work that it’s worth but not always.

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a. filterbank b. summing point bank c. node bank d. none of above 34. The “MDCT” or “Modified Discrete Cosine Transform” it’s a …………… a. filter-bank. b. transform c. a b d. none of above 35. Programmable DSP with …….can be used to implement digital filters. a. MAC b. MAA c. ADD d. none of above 36. For high-bandwidth signal processing applications………can provide multiple MACs to achieve the desired thoughput. a. FPGA technology b. Nanotechnology c. MEMS technology d. none of above 37. The roots of polynomial Fz define the zeros of the filter. FIRs are also called........... a. all zero filters b. all poles filters c. all poles and zero filters

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d. none of above 38. A variation of the direct FIR model is called the transposed FIR filter. It can be constructed from the direct form FIR filter by a. Exchanging the input and output b. Inverting the direction of signal flow c. Substituting an adder by a fork and vice versa d. all of above 39. The direct form FIR filter needs ................ between the adders to reduce the delay of the adder tree and to achieve high throughput. a. extra pipeline registers b. less pipeline registers c. Not pipeline registers d. all of above 40. The FIR filter with transposed structure has registers between the adders and can achieve high ........without adding any extra pineline registers. a. throughput b. speed c. memory d. all of above Answers-Key Unit-4: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40