Explore MATLAB source code curated for "Matlab实现" with clean implementations, documentation, and examples.
Spatial domain visible watermarking algorithm implemented in MATLAB, incorporating Human Visual System (HVS) characteristics. The watermark embedding process considers both the host image and watermark image's visual properties to achieve optimal visibility and perceptual quality.
MATLAB implementation of fuzzy clustering analysis with graphical visualization of clustering results, featuring data preprocessing, algorithm parameter tuning, and result interpretation.
Beamforming algorithm implementation in MATLAB with two source files: shizhizhen.m (time-domain beamforming) and xianliezhen.m (array signal processing)
This source code provides a MATLAB implementation of the Total Least Squares (TLS) method, allowing developers to understand both the fundamental principles of least squares methodology and its practical programming implementation, including key algorithms and matrix operations.
Implementation of Fisher algorithm for multi-class classification in MATLAB, where the main program can be executed by calling multiclassfisher function with appropriate data input parameters.
Learn how to implement gray-level co-occurrence matrix (GLCM) and extract its statistical features using MATLAB, including detailed code implementation and function explanations for texture analysis applications.
MATLAB implementation of gradient-based optical flow estimation incorporating global smoothness constraints, including algorithm explanation and code structure breakdown
MATLAB Implementation of Feature Detection Using the Kanade-Lucas-Tomasi (KLT) Algorithm
This article explores multirate sampling rate conversion with MATLAB implementations and fundamental theoretical concepts, including key algorithms, signal processing functions, and practical code demonstrations for digital signal processing applications.
Backpropagation (BP) Neural Network is a supervised learning algorithm for neural networks. It is a hierarchical network structure consisting of an input layer, one or more hidden layers (middle layers), and an output layer. Each neuron in a layer is fully connected to all neurons in the adjacent layers, while there are no connections between neurons within the same layer. The network learns by comparing the actual output with the desired output (teacher signal). When a learning pattern is presented to the network, neurons generate connection weights based on the input response. The algorithm then propagates the error backwards from the output layer through the hidden layers, adjusting the connection weights to minimize the difference between the expected and actual outputs. This iterative process continues until the global error converges to a predetermined minimum value, completing the learning phase. This chapter focuses on applying BP neural networks for PID parameter tuning and digital recognition technology.