Explore MATLAB source code curated for "实验" with clean implementations, documentation, and examples.
A simple yet practical simulation framework for second-order multi-agent system consensus, suitable for beginners and university students. This model can be modified for experiments and includes code implementation details for velocity and position coordination algorithms.
This experiment focuses on stitching two images by first using RANSAC (Random Sample Consensus) to eliminate mismatched feature pairs and estimate the affine transformation matrix. One image is then transformed using the estimated affine parameters before performing the final stitching operation.
Implementing K-Means algorithm in MATLAB for data classification with practical code examples and cluster analysis
This experiment implements audio noise filtering using MATLAB, involving comprehensive analysis and selection of appropriate filters, with considerations for filter type, parameter optimization, and algorithmic approaches.
MATLAB programming for Wiener filter implementation in digital signal experiments, featuring AR model parameter estimation and excellent filtering performance using modern signal processing techniques.
Computer simulation of the newsvendor problem with key parameters: %tm - predetermined simulation days per experiment cycle, %t - cumulative simulation days per experimental cycle, %z - newspaper order quantity, %z_opt - optimal order quantity, %g - upper bound for order quantity z, %s1 - cumulative loss value, %s_min - minimum loss value, %r - generates random sales quantity samples from probability distribution
Digital Signal Processing Experiments focusing on DCT-based image compression, including comprehensive MATLAB .m files, experimental reports, algorithm explanations, and practical implementation examples for technical reference.
Experimental signal detection using wavelet transform modulus maxima method, featuring MATLAB implementation with detailed code descriptions
This experiment involves generating an ECG signal, adding noise, and then applying filtering techniques. Starting from an ECG signal data table, we first process the original signal containing known noise through filtering to obtain a clean ECG waveform. We then intentionally add high-frequency and mid-frequency noise to simulate real-world conditions. The noisy signal is displayed and filtered again to demonstrate noise removal. Each time-domain signal is visualized alongside its corresponding frequency spectrum for analysis. Different filter types are implemented and compared throughout the process to evaluate their performance characteristics.
Digital video technology experiment featuring MATLAB implementation of exhaustive block matching algorithm with detailed experimental report covering code structure and performance analysis.