Suppose you have this signal x below: [ ] # sampling rate sr = 128 # sampling interval ts = 1.0/sr t = np.arange(0,2, ts) freq = 5. X = 3*np.sin(2*np.pi*freq*t + 3) freq = 2 x += 2*np.sin(2*np.pi*freq*t - 2) Task1 • Add a random normal distribution noise (mean=0, std=2) into the signal above using numpy and • plot the given signal. ● • plot the signal + noise. plot the FFT of the signal. ● Task 2 • filter-out the added noise (to get back the original data) • plot the frequencies of the filtered signal. • plot the filtered signal in time domain.

Computer Networking: A Top-Down Approach (7th Edition)
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Author:James Kurose, Keith Ross
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Chapter1: Computer Networks And The Internet
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Language : Python Topics covered : Numerical Integration Numerical Differentiation Fourier Transform Analysis
Suppose you have this signal x below:
[ ] # sampling rate
sr = 128
# sampling interval
ts=1.0/sr
t = np.arange(0,2, ts)
freq = 5.
X = 3*np.sin(2*np.pi*freq*t + 3)
freq = 2
x += 2*np.sin(2*np.pi*freq*t 2)
Task1
• Add a random normal distribution noise
(mean=0, std=2) into the signal above using
numpy and
plot the given signal.
plot the signal + noise.
plot the FFT of the signal.
●
Task 2
.filter-out the added noise (to get back the
original data)
●
plot the frequencies of the filtered signal.
plot the filtered signal in time domain.
Transcribed Image Text:Suppose you have this signal x below: [ ] # sampling rate sr = 128 # sampling interval ts=1.0/sr t = np.arange(0,2, ts) freq = 5. X = 3*np.sin(2*np.pi*freq*t + 3) freq = 2 x += 2*np.sin(2*np.pi*freq*t 2) Task1 • Add a random normal distribution noise (mean=0, std=2) into the signal above using numpy and plot the given signal. plot the signal + noise. plot the FFT of the signal. ● Task 2 .filter-out the added noise (to get back the original data) ● plot the frequencies of the filtered signal. plot the filtered signal in time domain.
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