Explore MATLAB source code curated for "方差" with clean implementations, documentation, and examples.
This experimental program computes various parameters of vibration signals, implementing key statistical and signal processing calculations including mean value, standard deviation, variance, skewness, kurtosis, peak-to-peak value, root mean square amplitude, average amplitude, peak value, waveform factor, crest factor, impulse factor, and margin indicator using efficient numerical algorithms.
Reading 2D images, converting color images to grayscale, and performing image calculations including mean, variance, and FFT transformations
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.
Compute image characteristics including average gradient, edge strength, information entropy, and variance for both single-band and multi-band images, with implementation insights using computer vision algorithms.
MATLAB-based function for computing image mean and variance values, designed for seamless integration into custom image processing workflows with clear implementation structure
Image quality assessment using entropy, average gradient, edge strength, variance, and other metrics with implementation insights
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.
MATLAB implementation for extracting image texture features including variance, entropy, angular second moment, with code demonstrations and algorithm explanations.
This project implements speech recognition for passwords composed of digits 0-9 by calculating the cross-correlation function between two signals and their corresponding variance, using maximum variance as the discrimination threshold. Signal preprocessing is essential and includes FIR filter-based pre-emphasis and endpoint detection using short-term average energy and zero-crossing rate methods to extract useful signals. These preprocessing steps maximize recognition accuracy. The cross-correlation function, which quantifies signal similarity, helps differentiate test signals from template signals. In MATLAB implementation, key functions like xcorr() for cross-correlation calculation and var() for variance computation are utilized, while preprocessing involves designing FIR filters with fir1() and implementing energy/zcr-based endpoint detection algorithms.
A functional MATLAB program for calculating the variance of OFDM frequency offset estimation error, complete with result visualization capabilities and algorithm analysis