ECG Data QRS Complex Extraction
Using MIT-BIH database data as the source for QRS complex extraction from ECG signals with implementation methodologies
Explore MATLAB source code curated for "数据库" with clean implementations, documentation, and examples.
Using MIT-BIH database data as the source for QRS complex extraction from ECG signals with implementation methodologies
Speaker Recognition System % Extract all files in the current MATLAB directory, then type "speakerrecognition" % in the MATLAB command window. A simple and intuitive graphical user interface will appear. % % GUI Functionalities: % % ONE-TO-ONE Speaker Recognition - Verification % Select two audio files. The system will determine whether the voice characteristics belong % to the same person or not (one-to-one speaker recognition, also known as verification). % Audio inputs can be loaded from disk or recorded using a microphone. % % ADD A NEW SOUND TO DATABASE % Select an audio file to add to the database with a unique positive integer ID for speaker association.
A color-based image retrieval system featuring dual image databases and implementing a block-based image retrieval approach for enhanced matching accuracy.
The ORL Face Database is a valuable resource for face recognition tasks, containing 400 images from 40 subjects with 10 images per person - suitable for implementing recognition algorithms using techniques like PCA, LDA, or deep learning approaches.
Implementation of face recognition using Linear Discriminant Analysis (LDA), including data loading from the ORL database, dataset partitioning for training and testing phases, and evaluation of classification performance metrics.
Implementation of a student grade query system using MATLAB GUI connected to SQL database, including MATLAB program files and Access database files with code-level database connectivity implementation
This speaker recognition system is a comprehensive solution featuring an embedded database for voice pattern storage and comparison.
BIRCH (Balanced Iterative Reducing and Clustering Using Hierarchies) is an unsupervised data mining algorithm designed for hierarchical clustering of exceptionally large datasets. Its key strength lies in incremental and dynamic clustering capabilities with efficient memory management through CF (Clustering Feature) trees.
Implementation of ridge regression for financial distress database analysis with error calculation, MSPE computation, and results visualization using Python/Scikit-learn
TIMIT Database 2 - Execute the following MATLAB commands to verify successful installation and ensure proper database integration with your MATLAB environment