Explore MATLAB source code curated for "学习" with clean implementations, documentation, and examples.
PCA algorithm MATLAB source code collaboratively developed with classmates - this implementation includes data standardization, covariance matrix computation, eigenvalue decomposition, and dimensionality reduction features for educational reference and technical discussion.
This MATLAB-based radar SAR imaging auto-focusing algorithm provides valuable learning material for radar signal processing students and practitioners. The implementation demonstrates key concepts in synthetic aperture radar (SAR) image formation and phase error correction techniques.
A comprehensive study on classical MIMO channel modeling with complete MATLAB source code, providing valuable insights for learning MIMO systems and channel modeling techniques.
This simulation model for PID control in control systems is developed using Simulink, designed to help students understand and apply PID control principles through visual implementation and parameter tuning.
MATLAB-based JPEG encoder/decoder implementation featuring practical code demonstrations for learning JPEG image compression algorithms and processing techniques.
This program implements genetic algorithm optimization for BP neural networks, featuring excellent performance and valuable learning content
This document simulates the Bode plot for an LCL filter, providing a learning resource for collaborative study. While the simulation doesn't cover all possible scenarios comprehensively, it offers valuable implementation approaches and demonstrates how to analyze frequency response characteristics using transfer function modeling.
Two-dimensional empirical mode decomposition analyzes images and other 2D signals, providing useful implementations for algorithm validation and technical education purposes
This MATLAB implementation of the AES encryption and decryption algorithm has been verified for functional correctness and serves as an excellent resource for learning cryptographic programming. The code demonstrates key operations including key expansion, substitution boxes (S-boxes), and round transformations using MATLAB's matrix operations and bitwise functions.
Application Background: This code is designed for robotic grasp learning, enabling robots to determine optimal grasping strategies (e.g., grasping a cup by its body vs. handle). It utilizes multimodal data including cups, remote controls, and cameras to train neural networks in grasp decision-making. Implemented in MATLAB with detailed post-extraction instructions. Key Technologies: Includes a comprehensive README file detailing usage procedures and downloadable training datasets. Core training is executed through trainGraspRecMultiSparse.m in recTraining folder, implementing sparse multimodal network architecture for grasp preference learning.