Explore MATLAB source code curated for "应用背景" with clean implementations, documentation, and examples.
Application Background: This custom-developed face detection program includes test images that can be replaced as needed. Key Technology: High detection accuracy with robust performance in blurred environments, featuring adjustable threshold parameters for optimized recognition.
Application Background: A simple gesture recognition MATLAB code that is executable and suitable for beginners. - Gestures detection Includes three gesture images (rock, paper, scissors) that match against camera-captured gestures for identification. Key Technologies: Digital image processing techniques including image segmentation, edge detection, and template matching with implementation details.
Application Background: Basic process of pulse Doppler radar signal generation and processing, including signal simulation (Gaussian white noise), signal processing (MTI processing, constant false alarm rate), etc. Key Technologies: • s3.m is the main script implementing pulse Doppler radar signal processing flow, featuring Doppler radar phase-coherent simulation and MTI (Moving Target Indicator) processing algorithms • OS-CFAR.m function performs Ordered Statistics Constant False Alarm Rate processing with configurable detection threshold calculation • Pf.m function computes false alarm probability (Pf) given parameters 2L (total data length), m (selected detection cell), and T (quality factor) - used in research papers for optimal quality factor selection
Application Background Primarily designed for LTE link-level simulation, this implementation includes fundamental modules with simplified yet complete functionality Key Technologies Contains essential components such as channel estimation and equalization, providing a straightforward yet comprehensive simulation framework
Application Background: Ant colony algorithm was initially applied to solve TSP problems and demonstrated significant advantages due to its distributed nature, strong robustness, and easy integration with other algorithms. However, it also suffers from slow convergence and tendency to fall into local optima. The Traveling Salesperson Problem (TSP), also known as the Chinese Postman Problem, is an NP-hard problem where conventional algorithms struggle to find optimal solutions. Therefore, heuristic algorithms like Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) are commonly employed. Key Technologies: ACO implementation typically involves pheromone matrix initialization, path construction using probabilistic selection, and pheromone update mechanisms with evaporation rates. Code implementation requires handling both symmetric (distance(i,j)=distance(j,i)) and asymmetric TSP variants through different distance matrix structures.
Application Background: Face recognition is a biometric technology that identifies individuals based on facial features. This technique uses cameras to capture images/video streams containing faces, automatically detects and tracks faces in images, and applies facial processing technologies. Commonly called portrait recognition or facial recognition. Key Technologies: Face recognition systems consist of four main components: face image acquisition/detection, image preprocessing, feature extraction, and matching/recognition. Typical implementations involve image capture, face localization, preprocessing, and recognition (identity verification/search).
Background: This shadow detection method was proposed by Mr. J.W. Hofstee and Mr. E.J. Hanten in their paper "Shadow Segmentation Based on Image Transformation for Illumination Changes" presented at the International Conference on Agricultural Engineering in Zurich (July 6-10). Key Technology: Implements shadow detection through illumination-invariant image transformation. The algorithm successfully detects shadows in certain images but shows varying performance across different image types, indicating potential areas for optimization in practical implementations.
Application Background: An improved version of the MeanShift algorithm known as Continuously Adaptive MeanShift (CamShift). This algorithm processes each video frame using MeanShift operations, utilizing the previous frame's results (search window center and size) as initial values for the next frame. Key technical aspects include region selection, 2D color probability distribution calculation, and adaptive tracking through iterative MeanShift convergence with MATLAB implementation demonstrating real-time object tracking capabilities.
Application Context: Stochastic Resonance (SR) utilizes noise to enhance signal detection. In bistable systems, parameters a and b in the Langevin equation critically impact system performance and require careful selection. The package includes two GA (Genetic Algorithm) implementation examples - one simplified and one advanced - demonstrating parameter optimization. Technical Innovation: Unlike conventional noise suppression methods, SR leverages environmental noise for signal amplification. The genetic algorithm systematically optimizes system parameters through fitness-based selection, crossover, and mutation operations.
Application Background Beamforming technology is a vital research area in array signal processing. The development history of array signals can be traced back to the adaptive antenna combination technology in the 1940s, which utilized phase-locked loops for antenna tracking. The core implementation of beamforming involves applying weighted summation to each array element's output, steering the antenna array beam toward a specific direction within a given time frame. The steering position that yields maximum output power for the desired signal provides the Direction of Arrival (DOA) estimation. Key Technologies The entire process can be implemented through iterative methods until predefined convergence criteria are met. Initial estimation values can be obtained using the McCulloch method. The regression estimation mentioned demonstrates consistent convergence and asymptotic unbiasedness. Simulation results from Koutrouvelis indicate that the regression method outperforms the quantile method. The regression approach requires minimal computational resources and is relatively straightforward to implement in code through matrix operations and optimization algorithms.