Short-Time Fourier Transform of Signals

First, we perform a short-time Fourier transform (STFT) on the signal, which helps analyze the spectral characteristics of the signal. The STFT implementation typically involves dividing the signal into overlapping segments using a window function (such as Hamming or Hanning window) and applying Fourier transform to each segment. This provides time-frequency localization crucial for non-stationary signal analysis. Next, we apply cyclostationary processing to the output sequence to ensure signal stationarity. This processing involves statistical analysis of periodic characteristics in the signal's properties, which can be implemented using algorithms that detect and analyze periodic correlations in the frequency domain. Finally, we use the mesh function for 3D visualization, allowing intuitive observation of signal variation trends. The mesh function creates a 3D surface plot where the x-axis represents time, y-axis represents frequency, and z-axis represents magnitude, providing comprehensive time-frequency-amplitude representation. Through these steps, we can comprehensively analyze and understand signal characteristics with proper code implementation including parameter optimization for window size, overlap percentage, and spectral resolution.