Explore MATLAB source code curated for "仿真结果" with clean implementations, documentation, and examples.
This implemented blind multiuser detection program using Kalman filtering simulates bit error rates under varying signal-to-noise ratio conditions, providing performance analysis capabilities through Monte Carlo simulations with adaptive filtering techniques.
Multi-beamforming in array signal processing using FFT-based methods, featuring comprehensive reports, source code implementations, and simulation result screenshots with detailed technical documentation
Implementation of image denoising using Bayesian threshold method, with simulation results based on the research paper "Chang: Adaptive Wavelet Thresholding for Image Denoising and Compression" including algorithm explanations and code implementation details.
A PID controller was developed using Genetic Algorithm (GA), demonstrating excellent performance in simulation results with detailed implementation insights.
Simulation program and results for single-user Multi-Carrier Code Division Multiple Access (MC-CDMA) system, with detailed implementation insights to assist researchers in wireless communication studies
GO-CFAR detection is a highly utilized method in signal processing. This program compares simulation results between theoretical and actual values, incorporating Swerling I type radar signal fluctuations. The implementation includes threshold calculation algorithms, noise estimation techniques, and detection probability analysis.
MATLAB simulation source code for digital watermarking algorithms with implementation examples, suitable for graduation projects and course design reference.
Simulation outcomes of a PCM encoder-decoder system developed using MATLAB's Simulink environment
A Word document analyzes the theoretical principles and simulation results of sphere decoding. The document presents two main implementations: 1) sphereandML as the main program, which achieves performance close to Maximum Likelihood (ML) detection by calling spheredecode and spheredecodeinf subroutines; 2) main_spheretoML as the main program, which achieves full Maximum Likelihood (NL) detection performance by calling spheredecodetoML and spheredecodeinftoML subroutines.
Support Vector Machine automatically extracts fuzzy rules with reliability verified through simulation results, demonstrating robust pattern recognition capabilities through margin maximization and kernel functions.