Traffic Sign Detection

Application Background Traffic signs play a crucial role in road traffic systems by displaying current road conditions, alerting drivers to potential hazards, enforcing speed regulations, and prohibiting specific maneuvers like turning or parking in special zones. These functions significantly contribute to road safety. Therefore, traffic sign detection and recognition represent vital research areas for preventing accidents and ensuring driver safety. Among traffic signs, prohibition signs (43 types) hold particular importance by restricting specific behaviors. Speed limit signs and no-turn signs are especially critical for safe driving and remain focal points in current traffic sign recognition research.

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Small-Signal Stability Analysis for Multi-Machine Power Systems

Small-signal stability analysis for multi-machine power systems involves eigenvalue and eigenvector computation to assess system dynamic behavior under small disturbances

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Jacobi Method for Computing Matrix Eigenvalues and Eigenvectors

Implementation of Jacobi method for calculating matrix eigenvalues and eigenvectors using MATLAB, thoroughly debugged and ready for use

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MATLAB Code Implementation of PCA Steps

PCA Implementation Steps: 1. Center the data (mean normalization); 2. Compute the covariance matrix; 3. Calculate eigenvalues and eigenvectors of the covariance matrix; 4. Sort eigenvalues and corresponding eigenvectors; 5. Determine projection direction based on target dimensionality d'; 6. Compute dimensionally reduced data

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Feature Extraction of Fault Signals Using Wavelet Transform

Feature extraction from fault signals using wavelet transform with feature vector construction methodology

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PCA Algorithm Programming Design Steps

PCA Algorithm Programming Design Steps: 1. Mean Centering 2. Compute Covariance Matrix and its Eigenvalues/Eigenvectors 3. Count Eigenvalues Exceeding Threshold 4. Sort Eigenvalues in Descending Order 5. Remove Small Eigenvalues 6. Remove Large Eigenvalues (Typically Omitted) 7. Combine Selected Eigenvalues 8. Select Corresponding Eigenvalues/Eigenvectors 9. Compute Whitening Matrix 10. Extract Principal Components

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Random Sampling of the Above Image Using a 9×9 Window

(1) Apply a 9×9 window to randomly sample the above image, extracting a total of 200 sub-images; (2) Convert each sub-image into an 81-dimensional row vector by concatenating all columns; (3) Perform KL transformation on all 200 row vectors, compute the eigenvectors and eigenvalues of the corresponding covariance matrix, and arrange eigenvalues and corresponding eigenvectors in descending order; (4) Select the eigenvectors corresponding to the top 40 largest eigenvalues as principal components, project the original image blocks onto these 40 eigenvectors, and use the obtained projection coefficients as the feature vector for each sub-block; (5) Compute the feature vectors for all sub-blocks.

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Extraction of Eigenvalues and Eigenvectors, Training Samples, and Final Recognition

This implementation features eigenvalue/eigenvector extraction, training sample processing, and final recognition stages. The program achieves high-performance levels capable of handling classification and regression tasks in pattern recognition domains.

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Extracting Image Texture Features Using Gray-Level Co-occurrence Matrix

Image texture feature extraction based on Gray-Level Co-occurrence Matrix, where FC represents the generated feature vector with implementation insights

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AR Parameter Model Analysis Method for Surface Electromyography Signals

AR parameter model analysis method for surface EMG signals with feature vector extraction techniques

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