Professional MATLAB source code with comprehensive documentation and examples
Kernel Principal Component Analysis (Kernel PCA) extends traditional PCA using kernel methods to perform nonlinear dimensionality reduction through mapping to reproducing kernel Hilbert spaces, with Python implementation examples using scikit-learn.
Implementation of comparative simulation for learning curves between basic LMS algorithm and normalized LMS algorithm, including MATLAB code implementation details and performance analysis
Standard DFT transformation compared with FFT transformation utilizing butterfly operations for enhanced computational efficiency.
Robot Vision Toolbox with extensive routine libraries, useful for video surveillance, object detection and tracking applications. Includes implementation of computer vision algorithms and pre-built processing pipelines.
An online-implemented multi-target tracking algorithm for three moving objects utilizing Kalman filter prediction, demonstrating satisfactory performance with practical code implementation insights.
Particle filtering algorithm implementation with state-space modeling, featuring system dynamics and measurement equations for robust target tracking applications
MATLAB implementation of noise cancellation with LMS adaptive filtering algorithm - complete source code and technical explanation
Simulation and implementation of the classic Chan algorithm for Time Difference of Arrival (TDOA) positioning
Principal Component Analysis (PCA) is a data dimensionality reduction method based on multivariate statistical analysis. It utilizes the correlations between process variables to establish a principal component model under normal operating conditions
This repository contains MATLAB implementations of both the standard Backpropagation (BP) algorithm and an enhanced Momentum-Adaptive Learning Rate Adjustment algorithm (Improved BP algorithm), featuring optimized gradient descent and dynamic learnin
Joint Forward Orthogonal Pursuit algorithm in distributed compressed sensing – an enhanced reconstruction method leveraging inter-sensor correlations
Implementation of Biased Kalman Filter for mitigating non-line-of-sight errors in wireless positioning systems, featuring statistical bias compensation techniques and adaptive measurement noise handling.
Full implementation of particle filter algorithm featuring advanced programming techniques for state estimation, target tracking, localization, and motion planning applications
MATLAB code for manifold learning featuring HE algorithm, one of the most classic approaches serving as a fundamental linear subspace learning method with detailed implementation insights.
The New Three-Step Search algorithm introduces appropriate enhancements to the traditional Three-Step Search, addressing TSS limitations in small motion estimation through optimized search patterns and incorporating an early termination strategy to s
A noise compression program for chaotic sequences primarily based on Principal Component Analysis (PCA) method, demonstrating excellent denoising performance by removing 100% noise from simulated signals.
Implementation of PCA, Fisher Linear Discriminant, Kernel PCA (KPCA), and 2D Discrete Wavelet Transform (DWT2) for facial recognition systems with dimensionality reduction techniques
This is a motion object detection algorithm utilizing Gaussian modeling, implemented in YUV color space for enhanced color processing and computational efficiency.
MATLAB program for simulating chaotic systems. lyapunov.m serves as the main computational routine; qi_hyper.m provides an implementation example (Qi system) using qi_hyper_lyap.m to calculate all Lyapunov exponents and qi_hyper_lyapDim.m for fractal
Implementation of power flow calculation for reactive power optimization using MATLAB programming environment, featuring detailed explanations and comprehensive source code with algorithm descriptions.