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A random signal with autocorrelation function is filtered by a filter with transfer function H(f) =...
Filter this signal with the Butterworth filter (i.e., compute (f * h)(i) forO š I < π . Try vario...
Consider the signal f(t)=
ps:
We were unable to transcribe this imageFilter this signal with the Butterworth filter (i.e., compute (f * h)(i) forO š I < π . Try various values of A-: α (starting with A α-10). Compare the filtered signal with the original signal. We were unable to transcribe this image
Filter this signal with the Butterworth filter (i.e., compute (f * h)(i) forO š I
1) Random Processes: Suppose that a wide-sense stationary Gaussian random process X (t) is input to the filter shown below. The autocorrelation function of X(t) is 2xx (r) = exp(-ary Y(t) X(t) Delay...
1) Random Processes: Suppose that a wide-sense stationary Gaussian random process X (t) is input to the filter shown below. The autocorrelation function of X(t) is 2xx (r) = exp(-ary Y(t) X(t) Delay a) (4 points) Find the power spectral density of the output random process y(t), ΦΥΥ(f) b) (1 points) What frequency components are not present in ΦYYU)? c) (4 points) Find the output autocorrelation function Фуу(r) d) (1 points) What is the total power in the output process...
A 250Hz continuous time signal is sampled at Fs = 700Hz and then filtered by the FIR filter with ...
A 250Hz continuous time signal is sampled at Fs = 700Hz and then filtered by the FIR filter with impulse response h [ n ] = [ 1 3 − 2 − 4 ] . What is the magnitude of the gain imparted by the filter at that frequency? Your answer should have one decimal place of accuracy.
3. A white Gaussian noise signal W (t) with autocorrelation function it passes through a linear filter invariant in time h (t). Calculate the average power of the W(T) J-oo h2 (t) dt = 1 exit process...
3. A white Gaussian noise signal W (t) with autocorrelation function it passes through a linear filter invariant in time h (t). Calculate the average power of the W(T) J-oo h2 (t) dt = 1 exit process Y (t) knowing that
3. A white Gaussian noise signal W (t) with autocorrelation function it passes through a linear filter invariant in time h (t). Calculate the average power of the W(T) J-oo h2 (t) dt = 1 exit process Y (t)...
4.5-1 A vestigial filter H; (f) shown in the transmitter of Fig. 4.21 has a transfer...
4.5-1 A vestigial filter H; (f) shown in the transmitter of Fig. 4.21 has a transfer function as shown in Fig. P4.5-1. The carrier frequency is fc = 10 kHz and the baseband signal bandwidth is 4 kHz. Find the corresponding transfer function of the equalizer filter Ho(f) shown in the receiver of Fig. 4.21. Hint: Use Eq. (4.25). --- Figure P.4.5-1 HO 0 ON 10 11 12 14 f kHz
10. An input signal x(t) is processed by a filter with an amplitude | H(f) |...
10. An input signal x(t) is processed by a filter with an amplitude | H(f) | and phase θ(f) response given below H(f) 90 70 50 30 10 10 θ(f) 25 -50 70 05 35 -3-252 -15105 0 05 115 2 25 3 35 -35-3-25-2-15-1-05 0 0.5 15 2 25 35 frequency (kHz) frequency (kHz) a) For x,(t)-2cos(22500t) find output signal ya(t) b) For x,(t) 4cos(27750t) find output signal yb(t) c) For x,(t)=2cos(2π500t) +4cos(2π750t) find output signal ye(t) d) For...
1- The signal x(t) is applied to a low pass filter with cutoff frequency equal to...
1- The signal x(t) is applied to a low pass filter with cutoff frequency equal to 1; write a MATLAB code to find and plot X(f), H(f) and the output of the filter Y(f), where x(t) is given below: x(t) -0.5 0.5 2- Apply the signal x(t) in the previous example to a HPF, BPF and BSF and draw the output signal Y(f). 3- Find and Plot the transfer function of BSF.
I. The autocorrelation function of a random signal is R(r) !-ⓞrect rect a. Find the power...
I. The autocorrelation function of a random signal is R(r) !-ⓞrect rect a. Find the power spectral density of the signal. b. Plot the amplitude of the power spectral density with Matlab (Let Ts -2) c. Find the null-to-null bandpass bandwidth, and the 0-to-null baseband bandwidth (in terms of Ts).
3. A white Gaussian noise signal W (t) with autocorrelation function it passes through a linear filter invariant in time h (t). Calculate the average power of the exit process Y (t) knowing that We w...
3. A white Gaussian noise signal W (t) with autocorrelation function
it passes through a linear filter invariant in time h (t). Calculate the average power of the
exit process Y (t) knowing that We were unable to transcribe this imager. h2 (t)dt = 1.
r. h2 (t)dt = 1.
6.(20%) Given a filter with frequency response function 5 F[h(t)=H=4+j(2f) 3 and given an input x(t)...
6.(20%) Given a filter with frequency response function 5 F[h(t)=H=4+j(2f) 3 and given an input x(t) eu(t) with its Fourier transform by 1 = *U)-3+ j(27f) F[x()] (10%) (a) Obtain the energy spectral density G,(f) for the input signal x(t) (10%) (b) Obtain the energy spectral density G.(f) for the output signal y(t)
6.(20%) Given a filter with frequency response function 5 F[h(t)=H=4+j(2f) 3 and given an input x(t) eu(t) with its Fourier transform by 1 = *U)-3+ j(27f) F[x()]...
Consider the signal f(t)=
ps:
We were unable to transcribe this imageFilter this signal with the Butterworth filter (i.e., compute (f * h)(i) forO š I < π . Try various values of A-: α (starting with A α-10). Compare the filtered signal with the original signal. We were unable to transcribe this image
Filter this signal with the Butterworth filter (i.e., compute (f * h)(i) forO š I
1) Random Processes: Suppose that a wide-sense stationary Gaussian random process X (t) is input to the filter shown below. The autocorrelation function of X(t) is 2xx (r) = exp(-ary Y(t) X(t) Delay a) (4 points) Find the power spectral density of the output random process y(t), ΦΥΥ(f) b) (1 points) What frequency components are not present in ΦYYU)? c) (4 points) Find the output autocorrelation function Фуу(r) d) (1 points) What is the total power in the output process...
3. A white Gaussian noise signal W (t) with autocorrelation function it passes through a linear filter invariant in time h (t). Calculate the average power of the W(T) J-oo h2 (t) dt = 1 exit process Y (t) knowing that
3. A white Gaussian noise signal W (t) with autocorrelation function it passes through a linear filter invariant in time h (t). Calculate the average power of the W(T) J-oo h2 (t) dt = 1 exit process Y (t)...
4.5-1 A vestigial filter H; (f) shown in the transmitter of Fig. 4.21 has a transfer function as shown in Fig. P4.5-1. The carrier frequency is fc = 10 kHz and the baseband signal bandwidth is 4 kHz. Find the corresponding transfer function of the equalizer filter Ho(f) shown in the receiver of Fig. 4.21. Hint: Use Eq. (4.25). --- Figure P.4.5-1 HO 0 ON 10 11 12 14 f kHz
10. An input signal x(t) is processed by a filter with an amplitude | H(f) | and phase θ(f) response given below H(f) 90 70 50 30 10 10 θ(f) 25 -50 70 05 35 -3-252 -15105 0 05 115 2 25 3 35 -35-3-25-2-15-1-05 0 0.5 15 2 25 35 frequency (kHz) frequency (kHz) a) For x,(t)-2cos(22500t) find output signal ya(t) b) For x,(t) 4cos(27750t) find output signal yb(t) c) For x,(t)=2cos(2π500t) +4cos(2π750t) find output signal ye(t) d) For...
1- The signal x(t) is applied to a low pass filter with cutoff frequency equal to 1; write a MATLAB code to find and plot X(f), H(f) and the output of the filter Y(f), where x(t) is given below: x(t) -0.5 0.5 2- Apply the signal x(t) in the previous example to a HPF, BPF and BSF and draw the output signal Y(f). 3- Find and Plot the transfer function of BSF.
I. The autocorrelation function of a random signal is R(r) !-ⓞrect rect a. Find the power spectral density of the signal. b. Plot the amplitude of the power spectral density with Matlab (Let Ts -2) c. Find the null-to-null bandpass bandwidth, and the 0-to-null baseband bandwidth (in terms of Ts).
3. A white Gaussian noise signal W (t) with autocorrelation function
it passes through a linear filter invariant in time h (t). Calculate the average power of the
exit process Y (t) knowing that We were unable to transcribe this imager. h2 (t)dt = 1.
r. h2 (t)dt = 1.
6.(20%) Given a filter with frequency response function 5 F[h(t)=H=4+j(2f) 3 and given an input x(t) eu(t) with its Fourier transform by 1 = *U)-3+ j(27f) F[x()] (10%) (a) Obtain the energy spectral density G,(f) for the input signal x(t) (10%) (b) Obtain the energy spectral density G.(f) for the output signal y(t)
6.(20%) Given a filter with frequency response function 5 F[h(t)=H=4+j(2f) 3 and given an input x(t) eu(t) with its Fourier transform by 1 = *U)-3+ j(27f) F[x()]...
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