Explore MATLAB source code curated for "均值" with clean implementations, documentation, and examples.
Comprehensive Signal Feature Extraction Using 16 Statistical Metrics: Mean, Standard Deviation, Variance, Skewness, Kurtosis, Peak-to-Peak, Peak Value, RMS Amplitude, Average Amplitude, Root Amplitude, Waveform Factor, and Crest Factor with Code Implementation Insights
Reading 2D images, converting color images to grayscale, and performing image calculations including mean, variance, and FFT transformations
Genetic Algorithm Implementation for TDOA Localization with Multiple Test Analysis - This project contains several core modules: program (main controller for multiple test iterations), definition_constant (parameter configuration), main_program (single trial execution), all_Noise (noise-corrupted TDOA calculation), and gen_ini_pop_arr (chromosome population initialization). The system performs account_test trials to find optimal chromosomes for each test while computing mean value (MV) and mean squared error (MSE) metrics.
White Noise Testing in Random Signal Analysis Experiments: Characteristics of white noise signals including mean, variance, correlation function, probability density, frequency spectrum, and power spectral density, with implementation approaches using signal processing functions.
Quantitative assessment of image fusion outcomes using statistical and information-theoretic metrics including mean, standard deviation, entropy, gradient, correlation coefficient, and spectral distortion
Binarizing images with adaptive threshold method involves dividing the image into sub-images, calculating local mean values for each sub-image, and using these means as thresholds for binarization
Divide an image into blocks, compute the mean and variance for each block, and provide visualized results for easy observation and analysis. Includes code implementation details for image segmentation and statistical computation.
Implementation of texture descriptors derived from regional brightness histograms, including mean, standard deviation, smoothness, third moment, uniformity, and entropy - with code-level explanations for algorithm integration
This program implements an image texture feature extraction algorithm using Gray-Level Co-occurrence Matrix (GLCM), calculating GLCM across four distinct directions to derive texture feature vectors, followed by averaging to significantly enhance computational efficiency
Processing an analog strong noise random signal within audio frequency range (containing strong noise + periodic signal) to extract the target signal, with calculations of mean, average power, variance, frequency spectrum, power spectral density, and cross-correlation for both original and extracted signals, accompanied by graphical visualizations.