Explore MATLAB source code curated for "频域" with clean implementations, documentation, and examples.
Implemented the Runge-Kutta method to solve 6-degree-of-freedom dynamic equations of gear systems, with visualization of time-domain responses and bifurcation diagrams in the frequency domain using numerical analysis techniques.
MATLAB-based scrambling and descrambling simulation system enabling time-domain and frequency-domain waveform analysis of signals before and after scrambling/descrambling processes. Includes implementation details of scrambling algorithms and visualization techniques for understanding digital signal scrambling concepts.
MATLAB implementation of frequency domain beamforming with linear arrays, featuring algorithm explanation and code structure
A frequency domain approach for modal parameter identification that analyzes vibration signals to extract structural natural frequencies, damping ratios, and mode shape coefficients, featuring robust implementation with complete algorithmic procedures
Deepen understanding of image transformation techniques. Compute Fourier transform J of an image, perform spectral shifting and visualization; calculate inverse FFT of J and display results. Implementation approaches include using fft2() for 2D Fourier transform, fftshift() for spectrum centering, and ifft2() for inverse transformation.
A frequency-domain modal parameter identification technique for vibration signal analysis that extracts natural frequencies, damping ratios, and mode shape coefficients. The method employs robust signal processing algorithms with complete program implementation including FFT-based spectral analysis and curve-fitting routines.
Frequency domain and subband adaptive filters implement filter parameter optimization in the frequency domain with efficient algorithm implementations
An approach for eliminating image textures using Fourier Transform by removing high-frequency components while preserving low-frequency information, with implementation details including frequency domain filtering and spectral analysis.
This MATLAB simulation project contains four programs that compare transmission signal frequency domain characteristics under two different scenarios, featuring algorithms for signal generation, multipath modeling, and frequency domain analysis.
The five-point cubic smoothing method serves as an effective technique for smoothing signals in both time and frequency domains. In the time domain, it reduces high-frequency random noise mixed into vibration signals. For frequency-domain applications, it enables smoother spectral curves by applying a cubic polynomial fit over five consecutive data points.