应用背景 Resources

Showing items tagged with "应用背景"

Application Background: Particle Swarm Optimization (PSO) is a prominent swarm intelligence algorithm that has become a research hotspot in stochastic optimization. Quantum-behaved Particle Swarm Optimization (QPSO) introduces quantum mechanical principles to probabilistically enhance traditional PSO. Key Technology: By incorporating quantum behavior, QPSO achieves superior convergence compared to basic PSO, demonstrating excellent performance across various applications. Code implementation typically involves quantum state probability distributions for position updates and delta potential well models for particle trajectory control.

MATLAB 255 views Tagged
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Application Context: Shearlet transform is an effective method for achieving localized and optimally sparse representations with simple mathematical construction and fast algorithmic implementation. These advantages make shearlet transform an attractive candidate for image representation. Key Technologies: (a) Decomposition of noisy images. (b) Obtaining shearlet coefficients through different subbands and directional filtering orientations using adaptive directional selection.

MATLAB 171 views Tagged
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Application Background: Mutual Information (MI)-based registration methods utilize the statistical information of image grayscale to measure similarity between images. By employing search algorithms to find optimal spatial transformations that maximize similarity, these methods achieve accurate image alignment. MI measures the generalized distance between the joint probability distribution of two variables and their completely independent distributions as a similarity metric. Key Technology: This approach adopts rigid body transformation in 2D image space, primarily determining the rotation angle and x/y-axis displacements of floating images that maximize mutual information.

MATLAB 235 views Tagged
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Application Background: The fusion process integrates two or more distinct images using similar or different modalities. Through fusion techniques, we can consolidate all useful information from multiple sources into a single comprehensive image. Key Technology: Evaluation of fusion quality through various similarity metrics.

MATLAB 211 views Tagged
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Application Background: This MATLAB experiment focuses on multi-focus image fusion, utilizing source images such as "pepsi" and "clock". The program has been modified with improvements to high/low-frequency algorithms, ensuring stability for graduation thesis use. Key Technologies: Implements image fusion through point-wise NSCT transformation using the NSCT toolbox, employing maximum pixel method for low-frequency components and maximum variance method for high-frequency components. Enhanced algorithm incorporates pixel correlation-based fusion methodology.

MATLAB 219 views Tagged
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Implementation Background: LDPC (Low-Density Parity Check) codes are specialized linear block codes characterized by sparse parity-check matrices where the number of "1"s is significantly fewer than "0"s. Key technical requirements include row/column weight constraints and minimal overlap between rows/columns. This MATLAB implementation involves constructing sparse parity matrices and features multiple decoding approaches including message-passing algorithms with both MATLAB-native and optimized C-based implementations.

MATLAB 201 views Tagged
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Application Context: Designed for tracing simple closed contours using various segmentation factors to detect and simulate boundary outlines. The implementation is tested and validated in MATLAB. Key Technology: Focuses on contour boundary tracing algorithms in image processing, delivering effective results for objects with well-defined edges. The MATLAB code structure is clear and well-commented, facilitating understanding of fundamental boundary tracing principles and implementation approaches.

MATLAB 231 views Tagged
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The application background of target tracking lies in radar data processing, where radar systems detect targets, record positional data (called plots), and automatically form tracks while predicting targets' future positions. This article briefly discusses using Kalman filtering for single-target trajectory prediction and evaluates experimental results through MATLAB simulation. The implementation includes state-space modeling, prediction-correction cycles, and performance metrics calculation using MATLAB's built-in functions like "kalman" or custom implementations with matrix operations for state estimation.

MATLAB 286 views Tagged
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Application Background: Particle Swarm Optimization (PSO) is an optimization algorithm inspired by natural phenomena such as bird flocking and fish schooling. It simulates particle movement and cooperation within a search space to find optimal solutions. PSO has been widely applied across various fields including engineering optimization, machine learning, and image processing. Its simplicity and efficiency make it a popular choice for solving complex optimization problems. Key Technologies: For each particle i = 1, 2, ...: Initialize particle position with uniformly distributed random vector: x_i ~ U(blo, bup), where blo and bup represent lower and upper search space bounds. Initialize particle's best-known position to its initial position: p_i ← x_i. Update swarm's best-known position if f(p_i) < f(g): g ← p_i. Initialize particle velocity: v_i ~ U(-|bup-blo|, |bup-blo|). The algorithm iteratively updates velocities and positions using social and cognitive components until meeting termination criteria.

MATLAB 213 views Tagged
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Application Background: Designed for fault diagnosis applications and endpoint effect processing, this implementation provides a robust Hilbert-Huang Transform (HHT) program with practical utility. The code implements Empirical Mode Decomposition (EMD) for signal analysis, incorporates boundary extension techniques to minimize endpoint effects, and enables Hilbert spectral analysis for time-frequency characterization. Key Technologies: Hilbert-Huang Transform (HHT), Empirical Mode Decomposition (EMD), Signal Extension Methods

MATLAB 216 views Tagged
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