Explore MATLAB source code curated for "应用背景" with clean implementations, documentation, and examples.
Application Background: This code demonstrates the DV Hop localization process. Key Technology: Utilizing hop count measurements during localization, which calculates unknown node positions based on the number of hops through network nodes.
Application Background: Fingerprint information processing using MATLAB has become one of the most prominent technologies. Key Technology: MATLAB source code for fingerprint recognition featuring advanced image processing algorithms and feature extraction methods.
Application Context This code provides an extremely detailed simulation of an OFDM system. To facilitate better understanding for learners, the simulation includes both real and imaginary components of defined functions. It covers pre-demodulation processing, post-demodulation analysis, normalized power calculations, and other critical aspects. This comprehensive simulation approach offers significant educational value for understanding OFDM system implementation. Key Technology OFDM (Orthogonal Frequency Division Multiplexing) is essentially a multicarrier modulation technique derived from MCM (Multi-Carrier Modulation). The modulation and demodulation processes are implemented using IFFT and FFT operations respectively, making it one of the most efficient and widely adopted multicarrier transmission schemes with minimal implementation complexity.
Application Background: This project implements handwritten character recognition using artificial neural networks, specifically targeting digits ranging from 0 to 9. Technical Approach: The solution employs backpropagation algorithm as the core learning mechanism, enhanced with image preprocessing and data augmentation techniques to improve recognition accuracy and model generalization.
This program has been tested and verified on MATLAB 2009a and 2012b versions. Some functions may not exist or have different calling formats in older versions, modifications can be made by referring to the corresponding version's help documentation. The program implements an ensemble classifier design following the random forest methodology, utilizing bootstrap aggregation and feature randomness for robust classification performance.
Application Context: Face detection code demonstrating how to detect faces, noses, mouths, and eyes using MATLAB's built-in classes and functions. Based on the Viola-Jones face detection algorithm, the Computer Vision System Toolbox includes the vision.CascadeObjectDetector system for object detection. Prerequisites: Computer Vision System Toolbox must be installed. Key Technology: MATLAB enables face detection through various techniques including boundary setting, edge detection, and utilizing signal processing and image processing tools. This technology serves security purposes by allowing authorized personnel access through comparison with pre-stored facial data.
Application Context: IADM_NNLS (Inexact Alternating Direction Method for Nuclear Norm Regularized Least Squares) solves optimization problems where the regularization term uses the nuclear norm, such as low-rank representation algorithms. Key Techniques: Matrix rank is always less than or equal to the number of rows. From a definition perspective, for a set of vectors A, the rank represents the size of the maximum linearly independent subset. This implementation enhances rank computation accuracy through nuclear norm minimization.