Recognizing emotions objectively and accurately remains challenging because of the limited ecological validity, informational incompleteness, and constrained model performance of conventional ...
This project implements an end-to-end deep learning pipeline for automated heartbeat classification using the MIT-BIH Arrhythmia Dataset. The system performs ECG signal preprocessing, heartbeat ...
Ultra-wide band (UWB) positioning technology has attracted increasing attention due to its high ranging accuracy. However, in indoor environments, non-line-of-sight (NLOS) signals significantly ...
For neural prosthetic devices, accurate classification of high dimensional electroencephalography (EEG) signals is significantly impaired by the existence of redundant and irrelevant features that ...
The new iPhone 17 series has finally landed in stores, and this year, it’s more noticeable than ever for one key reason: the switch to aluminum. After two years of extolling the benefits of titanium, ...
Abstract: In recent years, the demand for smart healthcare solutions have heightened the need for accuracy, reliability, and comfort in bedside ECG recording and analysis. This study presents a ...
Abstract: We propose a new cognitive technique for blind adaptive beamforming which uses a pre-trained deep learningbased signal classifier to protect a signal of interest (SOI) from interference. The ...
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