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$ cat /etc/passwd | grep home | wc -l
def quick_sort(arr):
less = []
pivot_list = []
more = []
if len(arr) <= 1:
return arr
else:
pass
%% Signal in Time Domain
% Use Fourier transforms to find the frequency components of a signal buried in noise.
% Specify the parameters of a signal with a sampling frequency of 1 kHz and a signal duration of 1.5 seconds
Fs = 1000; % Sampling frequency
T = 1/Fs; % Sampling period
L = 1500; % Length of signal
t = (0:L-1)*T; % Time vector
% Form a signal containing a 50 Hz sinusoid of amplitude 0.7 and a 120 Hz sinusoid of amplitude 1.
S = 0.7*sin(2*pi*50*t) + sin(2*pi*120*t);
% Corrupt the signal with zero-mean white noise with a variance of 4.
X = S + 2*randn(size(t));
% Plot the noisy signal in the time domain. It is difficult to identify the frequency components by looking at the signal X(t).
subplot(311);
plot(1000*t(1:200),X(1:200), 'b')
title('Signal Corrupted with Zero-Mean Random Noise')
xlabel('t (milliseconds)')
ylabel('X(t)')
hold on
plot(1000*t(1:200),S(1:200),'r')
hold off