Explore MATLAB source code curated for "预处理" with clean implementations, documentation, and examples.
Comprehensive workflow for magnetotelluric data processing using Non-Linear Conjugate Gradient inversion, covering data preparation, algorithm implementation, and result validation techniques with code-level insights.
Key procedures in EEG signal processing including preprocessing, feature extraction, and classification algorithms with implementation approaches
Preprocessing EEG signals involves signal filtering, downsampling, and noise removal to prepare data for analysis, with code implementation details for digital filtering and noise reduction algorithms.
Speech signal processing and speech recognition generally involve preprocessing, feature extraction, vectorization, and matching calculation stages. This guide explains how to implement filtering, framing, windowing, and endpoint detection on speech signals using MATLAB, with specific code examples and algorithm descriptions. Note: The Voicebox toolbox (available online) provides essential functions for comprehensive speech processing implementations.
Wind speed prediction using PSO+BP algorithm begins with comprehensive data preprocessing, followed by PSOBP simulation prediction analysis, algorithm implementation considerations, and model optimization techniques.
This MATLAB-based program implements isolated word speech recognition through preprocessing, MFCC feature extraction, and DTW algorithm comparison, with potential for LabVIEW integration to enhance functionality
Face recognition implementation based on PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main function loads image files, applies preprocessing techniques, executes the face recognition algorithm with dimensionality reduction, and generates performance accuracy plots.
This process involves iteratively reading a series of DICOM dataset files using the dicomread function to create a 3D matrix, performing preprocessing with smooth3 for noise reduction, then constructing a 3D model using isosurface, isocap, isonormals, and patch functions. Finally, view, lighting, colormap, and lightangle functions are applied to configure visual properties and lighting effects for realistic rendering.
This is a Hidden Markov Model (HMM) based digital speech recognition system that accurately recognizes digits 0-9. The implementation features a MATLAB GUI interface for recording and playback operations, incorporates preprocessing modules to enhance recognition accuracy, and includes sample audio files for demonstration purposes.
Finger vein recognition code consisting of preprocessing and recognition modules, including annotated images and MATLAB scripts with detailed explanations of algorithms and key functions