Explore MATLAB source code curated for "函数" with clean implementations, documentation, and examples.
This CPM coherent demodulation program features extensive functionality covering essential signal processing operations. It includes core algorithmic implementations for phase synchronization, symbol timing recovery, and decision logic. The well-structured codebase provides practical utility for digital communication systems development.
A collection of image quality assessment functions including PSNR (Peak Signal-to-Noise Ratio), RMS (Root Mean Square Error), and NMSE (Normalized Mean Square Error). These functions are particularly useful for evaluating denoised and compressed images, with implementations featuring standard mathematical calculations and normalization techniques commonly used in image processing workflows.
MATLAB implementation of the standard differential evolution algorithm featuring a ready-to-run function with parameter customization options
Multiple functions for image quality assessment including PSNR (Peak Signal-to-Noise Ratio), RMS (Root Mean Square Error), and NMSE (Normalized Mean Square Error). These functions can be implemented to evaluate the quality of denoised and compressed images using numerical comparison algorithms.
A concise overview of MATLAB's lsqcurvefit function for solving least squares problems, with enhanced code implementation details and algorithm explanations.
Custom-developed image processing functions including histogram plotting, histogram specification, and histogram equalization algorithms, with reference to Zhang Yujin's "Image Engineering (Volume 1) - Image Processing (2nd Edition)" published by Tsinghua University Press
Pattern Recognition Toolbox Functions featuring multiple recognition methods including KNN, PCA, SVM, C4.5, EM algorithm with code implementation details and technical specifications
Implementation of adaptive prediction through linear neural networks using the adapt function for online training, enabling real-time adjustment of network weights and biases to track time-varying signals and perform sequence prediction with dynamic parameter optimization.
MATLAB-implemented FFT and IFFT functions written in M-language with executable code, supporting spectral analysis and signal processing applications.
A Support Vector Machine Toolbox featuring MATLAB demonstration programs and essential functions including kernel computation functions, SVM training algorithms, and cross-validation functions for parameter selection. This toolbox provides efficient implementations for key SVM operations with practical code examples.