Dynamic Matrix Control (DMC) MATLAB Implementation
A robust MATLAB program for Dynamic Matrix Control (DMC) featuring advanced control algorithms suitable for diverse industrial applications and system optimization scenarios
Target Tracking as a Primary Application Domain of Kalman Filtering
Target tracking represents one of the principal application domains for Kalman filtering. Through this assignment or exploration, you will deepen your understanding of the Kalman filter algorithm, grasp its fundamental characteristics, and master the essential steps and methods for applying and researching the Kalman filter algorithm in practical scenarios. Key considerations include system modeling, state prediction employing transition matrices, measurement update steps leveraging observation matrices, and real-time recursive computation for optimal state estimation.
MP-Based Signal Sparse Decomposition Algorithm
The MP-based signal sparse decomposition algorithm features straightforward implementation and computational simplicity, with broad applications across multiple technical domains. The algorithm operates by iteratively selecting optimal dictionary atoms to approximate signals through greedy pursuit strategies.
Multilinear Principal Component Analysis
MATLAB source code for Multilinear Principal Component Analysis, initially developed for face recognition and gait recognition applications, with subsequent extensions to various other domains
MATLAB Image Fusion: Algorithms and Implementation Techniques
While image fusion applications continue to expand across various fields, current research remains insufficiently systematic, lacking a complete theoretical framework. This repository provides comprehensive MATLAB implementations of multiple image fusion algorithms, offering practical reference solutions including wavelet transform-based fusion, pyramid decomposition methods, and principal component analysis techniques. The code demonstrates key functions for image registration, feature extraction, and fusion rule optimization.
Ant Colony Optimization Algorithm Paper with MATLAB Source Code for Beginners
Comprehensive guide to Ant Colony Optimization algorithm with MATLAB source code, designed for beginners. Explores application domains, implementation methodology, and includes executable simulation code with detailed comments. Features practical examples covering path planning, data clustering, and image segmentation applications.