MATLAB Toolbox for Radar System Simulation
A comprehensive MATLAB-based framework for simulating various radar system components including signal processing algorithms, target detection methods, and performance analysis tools.
Source Code for Calculating Zernike Invariant Moments Extracted from a Pattern Recognition Toolbox
This source code, extracted from a pattern recognition toolbox, computes n-th order m-repetition Zernike invariant moments of images
Excellent SVM Matlab Toolbox for Machine Learning
Comprehensive SVM Matlab Toolbox for Classification and Regression Tasks
CA Code GPS Toolbox: Development and Analytical Capabilities
Implementation of the CA Code GPS Toolbox utilized in U.S. corporate projects for GPS data processing and analysis
PhD Face Recognition Toolbox
PhD Face Recognition Toolbox - Advanced Algorithms for Facial Detection and Identification
Extended Kalman Filter: The Standard Nonlinear Kalman Filter
The Extended Kalman Filter serves as the standard nonlinear Kalman filter implementation; this comprehensive toolbox includes various commonly used Extended Kalman Filter variants with practical code examples and algorithm explanations.
Fingerprint Recognition Toolbox
Fingerprint Recognition Toolbox - This is an open-source toolbox developed by third-party authors, primarily designed for fingerprint processing and identification. The archive file requires no extraction password, allowing direct access to fingerprint processing algorithms and implementation resources.
EMD Toolbox and Usage Guide for Empirical Mode Decomposition
The EMD Toolbox and usage methodology for Empirical Mode Decomposition (EMD) is a signal analysis technique developed by Dr. Norden E. Huang at NASA. This method decomposes signals based on their intrinsic time-scale characteristics without requiring predefined basis functions. This represents a fundamental distinction from Fourier and wavelet decomposition methods that rely on predetermined harmonic and wavelet basis functions. Due to this characteristic, EMD method can theoretically be applied to decompose any type of signal, giving it significant advantages in processing non-stationary and nonlinear data. Upon its introduction, EMD gained rapid adoption across various engineering fields, with implementations typically involving sifting processes, envelope detection using cubic spline interpolation, and intrinsic mode function (IMF) extraction through iterative algorithms.
Support Vector Machine (SVM) MATLAB 2.51 Toolbox
MATLAB 2.51 Toolbox for Support Vector Machine (SVM) implementation, shared for community usage with enhanced code integration features.
MATLAB Code Implementation for Optimization
Optimization with MATLAB Toolbox for Various Optimization Problems including algorithm implementations and function examples