Video Sequence Motion Estimation and Compensation Algorithms
MATLAB implementation of motion estimation and compensation algorithms for video sequences, featuring block matching techniques and motion vector optimization
MATLAB Implementation of Fuzzy Neural Networks
MATLAB code for fuzzy neural network implementation with practical examples and algorithm explanations
MATLAB Implementation of Genetic Algorithm
This MATLAB genetic algorithm code provides a well-structured implementation with customizable parameters including selection, crossover, and mutation operations - simply download, extract, and integrate into your projects
MATLAB Implementation of Digital Watermarking with Code Examples
Digital watermarking implementation using MATLAB, including algorithm explanations and practical application scenarios with technical descriptions
MATLAB Implementation of JPEG2000 Codec
MATLAB-based implementation of JPEG2000 encoder and decoder, covering the complete compression pipeline with algorithm explanations and key function descriptions.
Implementing a CIC Filter with Decimation Factor of 4 in MATLAB
Implementation of a CIC filter with a decimation factor of 4 using MATLAB, including code structure, algorithm details, and performance considerations.
MATLAB Implementation of Artificial Fish Swarm Algorithm
MATLAB implementation of Artificial Fish Swarm Algorithm, an intelligent optimization technique increasingly applied across various domains with swarm behavior simulation capabilities for solving complex problems.
MATLAB Implementation of Nearest Neighbor Algorithm
Reference implementation of nearest neighbor algorithm using MATLAB with code structure and implementation insights
MATLAB Implementation for Voice Activity Detection
A comprehensive MATLAB program for voice activity detection, featuring complete source files, thoroughly debugged and fully functional
MATLAB Implementation of Tabu Search Algorithm
Development Background Tabu Search (TS), first proposed by Glover in 1986, extends local neighborhood search as a global stepwise optimization algorithm that simulates human intellectual processes. Key Technology The TS algorithm employs a flexible memory structure and corresponding tabu criteria to avoid cyclical searches, while incorporating aspiration criteria to override tabu restrictions for promising solutions. This ensures diversified exploration and ultimately achieves global optimization. Compared to simulated annealing and genetic algorithms, TS represents another meta-heuristic approach with distinct search characteristics. To date, TS has achieved significant success in combinatorial optimization, production scheduling, machine learning, circuit design and neural networks.