Mudanças entre as edições de "Teste do highlight de códigos"
Ir para navegação
Ir para pesquisar
(Uma revisão intermediária pelo mesmo usuário não está sendo mostrada) | |||
Linha 1: | Linha 1: | ||
+ | <syntaxhighlight lang=bash> | ||
+ | $ cat /etc/passwd | grep home | wc -l | ||
+ | </syntaxhighlight> | ||
+ | |||
<syntaxhighlight lang=python> | <syntaxhighlight lang=python> | ||
def quick_sort(arr): | def quick_sort(arr): | ||
Linha 8: | Linha 12: | ||
else: | else: | ||
pass | pass | ||
+ | </syntaxhighlight> | ||
+ | |||
+ | <syntaxhighlight lang=matlab> | ||
+ | %% 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 | ||
+ | |||
</syntaxhighlight> | </syntaxhighlight> |
Edição atual tal como às 16h08min de 9 de março de 2020
$ 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