Explore MATLAB source code curated for "分析" with clean implementations, documentation, and examples.
This program implements speech signal analysis through Short-Time Fourier Transform, including windowing and framing processing with detailed code implementation for spectral feature extraction
Approaches for generating Gaussian white noise commonly used in communication system simulation and analysis, including random number generation and statistical property verification
Implementation of Huffman encoding and decoding for given text files using memoryless source coding, with comprehensive analysis and algorithm explanations
In the analysis and computation of IEEE 5-bus test power systems, various network topologies and parameters are required. Prior to applying these parameters, significant time and effort are spent on data collection and organization. This process involves validating parameter consistency, handling different configurations programmatically, and preparing input files for power flow analysis algorithms.
The Runge-Kutta method for stochastic resonance provides response signals for arbitrary stochastic resonance systems, enabling comprehensive system analysis through numerical simulation of dynamic behaviors.
MATLAB-based simulation code implementation for analyzing the bit error rate performance of OFDM systems, featuring BPSK as the modulation scheme with coding implementation and performance evaluation.
In digital image processing, the grayscale histogram stands out as the simplest and most valuable tool. It plays a fundamental role throughout the entire workflow—from analyzing and observing images to developing effective processing methods. In programming terms, histograms can be efficiently computed using functions like `cv2.calcHist()` in OpenCV or `imhist()` in MATLAB, which count pixel frequencies across intensity levels to reveal critical image characteristics.
Official MATLAB code for Kernel Entropy Component Analysis (KECA) authored by R. Jenssen, published in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), May 2010
"MATLAB Image Filtering and Denoising Analysis with Applications" comprehensively studies various filtering techniques including bilinear filtering, Kirsch filtering, superluminal neighborhood filtering, inverse filtering, bilateral filtering, homomorphic filtering, wavelet filtering, six-tap filtering, constrained least squares filtering, nonlinear complex diffusion filtering, Lee filtering, Gabor filtering, Wiener filtering, Kuwahara filtering, Beltrami flow filtering, Lucy-Richardson filtering, and Non-Local Means filtering. Each method features implementation insights using MATLAB's Image Processing Toolbox functions like imfilter(), fspecial(), and wavelet denoising algorithms.
Implementation of AdaBoost algorithm (written by international authors with comprehensive analysis and code-level insights)