MATLAB Code Implementation of S-Transform

This article provides a MATLAB-based implementation program for S-Transform, detailing its usage methodology. Begin by installing MATLAB software and launching the program. Users can input their custom datasets or utilize the built-in demonstration data provided. The program subsequently performs S-Transform processing and outputs the transformed results. To facilitate deeper understanding of the implementation process, we elaborate on both the theoretical foundation of S-Transform and its MATLAB realization. S-Transform serves as a powerful signal analysis technique that converts time-domain signals into frequency-domain representations, enabling enhanced observation of spectral characteristics. The MATLAB implementation primarily leverages the Fast Fourier Transform (FFT) algorithm through the built-in fft function. Critical implementation considerations include: - The FFT function requires real-valued input signals - Complex-valued signals necessitate separate processing of real and imaginary components using real() and imag() functions - Proper signal windowing and zero-padding techniques should be applied to minimize spectral leakage - Frequency axis calibration requires careful consideration of sampling frequency parameters The implementation follows these key computational steps: 1. Signal preprocessing and parameter initialization 2. Hilbert transformation for analytical signal generation (if needed) 3. Windowed Fourier transform computation across time-frequency planes 4. Magnitude and phase spectrum extraction 5. Visualization using spectrogram plots and time-frequency analyses This MATLAB S-Transform implementation proves particularly valuable for: - Non-stationary signal analysis in vibration monitoring - Power quality disturbance detection in electrical systems - Biomedical signal processing for EEG/ECG applications - Time-frequency feature extraction in acoustic analysis The program includes customizable parameters for: - Window function selection (Gaussian, Hanning, etc.) - Frequency resolution adjustment - Overlap percentage configuration for successive transforms - Output visualization modes (2D/3D spectrograms) In summary, this MATLAB S-Transform implementation serves as a practical analytical tool that significantly enhances understanding of signal characteristics in the frequency domain, with robust code architecture suitable for both educational and research applications.