Framing Process for Sampled Speech Signals
Implementing frame segmentation on sampled speech signals and storing the framed signals as a matrix for further analysis
MATLAB Code for Image Encryption
Application Background: Image encryption represents a critical research domain where conventional data encryption methods face challenges due to image-specific characteristics like pixel correlation and massive data volumes. While limited algorithms exist, some demonstrate strong performance under specific parameters. Multiple chaos mapping implementation algorithms will be explored through practical MATLAB coding examples. Key Technologies: Encryption, image encryption, data encryption, matrix operations, mathematical transformations, and chaos theory implementations.
Image Compression Using Matrix Singular Value Decomposition
This method compresses images through matrix singular value decomposition by first dividing the image into blocks, computing inner products of corresponding pixels within each block to obtain correlation matrix A, performing SVD on A, selecting n (n<N) dominant eigenvectors corresponding to larger eigenvalues, and using inner products between these vectors and image blocks for compression and reconstruction.
Image Segmentation Using Watershed Threshold Method with Quadtree Decomposition
Segmenting images using watershed thresholding algorithm, performing quadtree decomposition on matrices to separate text and non-text regions, applying morphological gradient detection for binary image edge extraction. P0409: Morphology Application Example - Removing Current Lines from PCB Images while Preserving Chip Components.
MATLAB: A High-Efficiency Advanced Language for Scientific and Engineering Computing
MATLAB is a high-efficiency advanced programming language designed for scientific and engineering computations. Its fundamental data unit is the matrix, and its extensive toolbox ecosystem covers numerous fields in scientific research and engineering applications. It demonstrates unparalleled advantages over general-purpose languages in areas including numerical computation, digital signal processing, system identification, automatic control, vibration theory, time series analysis and modeling, optimization design, neural network control, dynamic simulation systems, financial management, wavelet analysis, special functions, and graphics. The language can be conveniently applied to nearly all aspects of scientific and engineering calculations through its built-in functions and matrix-based operations.
Functions for Track Initiation with Multiple Test Functions and Matrix Operations
Functions related to track initiation, containing multiple test functions and matrix operations, which perform row event formulation to determine feasible events, calculate target detection indicators, measurement association indicators, and false alarm measurement counts.
Image Block Partitioning, DCT Transformation, and Quantization for Test Images
Performing block partitioning, Discrete Cosine Transform (DCT), and quantization on test images, representing the quantized coefficients in matrix form, with implementation insights using key functions.
MATLAB Mathematics Handbook Comprehensive Edition
The MATLAB Mathematics Handbook Comprehensive Edition provides exhaustive coverage including: matrix operations and fundamental computations, eigenvalue and quadratic form numerical calculations with data analysis, interpolation, fitting and table lookup, numerical solutions for ordinary differential equations and partial differential equations, symbolic computation, integral transforms, Taylor series, probability and statistics, random number generation, probability density calculations for random variables, cumulative probability values (distribution function values) for random variables, frequency tables for positive integers, empirical cumulative distribution function plots, and least squares linear fitting. Additionally covers probability plotting for normal and Weibull distributions, box plots for sample data, adding reference lines to graphs, polynomial curve fitting to existing plots, sample probability plots, and histograms with superimposed normal density curves.
Householder Matrix QR Decomposition Implementation
Implementation of QR decomposition using Householder matrices, a matrix factorization technique that decomposes a matrix into an orthogonal matrix and an upper triangular matrix
Computing Laplacian Matting Matrix for Contour Extraction
Calculate Laplacian Matting Matrix for contour acquisition and other applications - highly effective with robust implementation