plot(t, signal) title('Time-Domain signal'); %Take fourier transform fftSignal = fft(signal); %apply fftshift to put it in the form we are used to (see documentation) fftSignal2 = fftshift(fftSignal); %xdft = x
假设你有一个时间序列数据timeSeriesData。 % 参数设置Fs=1000;% 采样频率 (Hz)t=(0:length(timeSeriesData)-1)/Fs;% 时间向量% 绘制时域信号figure;subplot(2,1,1);plot(t,timeSeriesData);title('Time Domain Signal');xlabel('Time (s)');ylabel('Amplitude');% 短时傅里叶变换 (STFT)windowSize...
在这个示例中,我们首先生成了一个包含两个正弦波的信号,然后对该信号进行了FFT变换,并计算了频率轴。最后,我们使用plot函数绘制了频谱图,其中横轴表示频率,纵轴表示振幅。 注意:在实际应用中,你可能需要根据信号的特性和需求对代码进行调整,例如选择合适的窗口函数、调整FFT点数等。 🚀 高效开发必备工具 🚀 🎯 ...
(Hilbert Spectrum ) % f——信号的频率向量(Frequency vector of signal) % t——信号的时间向量(Time vector of signal) % imfinsf——每个imf的瞬时频率(instantaneous frequency of each imf) % imfinse——每个imf的瞬时能量(instantaneous energy of each imf) [m,n] = size(hs); l = size(imf,2...
*pi*f*t); % 生成正弦信号 % 计算频谱 N = length(x); % 信号长度 X = abs(fft(x))/N; % 计算频谱,取绝对值并归一化 % 绘制频谱图 frequencies = (0:N-1)*(Fs/N); % 计算频率 plot(frequencies, X); xlabel('Frequency (Hz)'); ylabel('Magnitude'); title('Spectrum ...
InputDomain— Domain of input signal "time" (default) | "frequency" SpectrumType— Type of spectrum to display "power" (default) | "power-density" | "rms" ViewType— View to display "spectrum" (default) | "spectrogram" | "spectrum-and-spectrogram" SampleRate— Sample rate of input 10000...
function [X,freq]=centeredFFT(x,Fs) %this is a custom function that helps in plotting the two-sided spectrum %x is the signal that is to be transformed %Fs is the sampling rate N=length(x); %this part of the code generates that frequency axis if mod(N,2)==0 k=-N/2:N/2-1;...
y = x + 2*randn(size(t)); % Sinusoids plus noiseplot(Fs*t(1:50),y(1:50))title('Signal Corrupted with Zero-Mean Random Noise')xlabel('time (milliseconds)')NFFT = 2^nextpow2(L); % Next power of 2 from length of yY = fft(y,NFFT)/L;f = Fs/2*linspace(0,...
title('Signal');频谱分析 Y = fft(y);f = (0:length(Y)-1) * (fs / length(Y)); % fs 是采样频率 figure;plot(f, abs(Y));xlabel('Frequency (Hz)');ylabel('Magnitude');title('Frequency Spectrum');同样的，按照一下步骤也可使用现实的示波器完成波形存储：1. 连接示波器：...
plot(freq/1000, Xamphat, 'r', 'LineWidth', 3) xlim([0 max(freq/1000)]) ylim([-120 max(Xamp)+10]) set(gca, 'FontName', 'Times New Roman', 'FontSize', 14) xlabel('Frequency, kHz') ylabel('Magnitude, dB') title('Amplitude spectrum of the signals') legend('original signal',...