Explore MATLAB source code curated for "卷积" with clean implementations, documentation, and examples.
A nonlinear convolutional blind source separation program designed for students beginning to learn blind source separation concepts, featuring algorithm explanations and implementation insights for educational purposes
Blind source separation under convolutional conditions is extremely valuable for beginners, providing foundational knowledge in signal processing algorithms and their practical implementations.
In MATLAB, signal S convolved with channel H produces received signal V using V = conv(S,H), followed by deconvolution from V and H to recover original parameters.
1. Deepen comprehension of Fast Fourier Transform (FFT) fundamental principles through practical experiments. 2. Explore the relationship between FFT point count and spectral resolution, and analyze connections between zero-padded sequence FFT and original sequence FFT implementations. Discrete Fourier Transform (DFT) and convolution represent two fundamental operations in signal processing, involving extensive applications in signal and system analysis/synthesis. These operations are interconnected: convolution can be implemented via DFT, while numerous algorithms including correlation, filtering, and spectral estimation can be DFT-based. Conversely, DFT can also be realized through convolution operations, demonstrating their mathematical equivalence in digital signal processing.
This article covers fundamental signal forms, convolution, Fourier transform, s-transform, and z-transform, with code implementation insights for signal processing techniques.
This collection focuses entirely on algorithms for blind source separation of linear and convolutional mixed signals. These implementations are complex and require careful study - we advise downloading only if you're prepared for intensive technical reading. All materials are freely available.
Implementation of fundamental digital signal processing algorithms using MATLAB GUI, including convolution, FFT, FIR/IIR digital filter design, optimal filters, adaptive filtering, Kalman filtering with interactive visualization and parameter configuration capabilities.
Creating synthetic seismic records through convolution of seismic wavelets with subsurface reflectivity series
Edge Detection in Digital Image Processing with Algorithm Implementation Details