This data set contains birefringence and optical axis maps of human dorsal hand skin tissue at a depth of approximately 400 microns below the skin surface. The maps were acquired through a depth-resolved polarisation-sensitive optical coherence tomography system.
Timeseries data of impedance measurement, created by dSPACE ControlDesk, stored in MATLAB format
This study aims to create a robust hand grasp recognition system using surface electromyography (sEMG) data collected from four electrodes. The grasps to be utilized in this study include cylindrical grasp, spherical grasp, tripod grasp, lateral grasp, hook grasp, and pinch grasp. The proposed system seeks to address common challenges, such as electrode shift, inter-day difference, and individual difference, which have historically hindered the practicality and accuracy of sEMG-based systems.
In low-altitude unmanned aerial vehicle (UAV) detection scenarios, the initial segment of radar linear frequency modulation (LFM) signals is often corrupted due to building occlusions and noise interference, making accurate range estimation difficult. To address this issue, we propose a deep learning-based framework named Deep Time-Frequency Inverse Reconstruction Network (DTFIRNet) for radar echo signal restoration and precise ranging.
This dataset provides experimental validation data supporting the proposed finite element method (FEM) for optimizing a directional eddy current testing (ECT) probe designed to detect in-plane waviness on the surface of carbon fiber prepreg. The study includes the design and evaluation of six ECT probe configurations, varying the angle and aspect ratio of the receiver coils. Sensitivity measurements were conducted to assess the probes' ability to detect fiber-related features.
Synthetic EEG Dataset for CNN-LSTM Training: Clean and Artifact-Contaminated Signals
This dataset consists of synthetically generated EEG and EMG signals designed for training Convolutional Neural Networks (CNNs) in artifact detection and removal. The dataset includes both clean EEG signals and EEG signals contaminated with simulated EMG artifacts from various sources.
High-precision, high-resolution ultra-deep field astronomical observations are essential for detecting special celestial bodies and extremely rare astronomical events. Space astronomical telescopes can achieve this by employing the fine image stabilization system (FISS) to generate line-of-sight (LOS) dithering, enabling scientific instruments to obtain higher-resolution astronomical images through resampling and fusion algorithms. To meet the requirement for sub-pixel dithering control in the FISS of space telescopes, an adaptive control algorithm based on a single neuron is proposed.
This dataset consists of images with two types of artificially added noise, intended for evaluating the robustness of machine learning models against noise perturbations. The first type of noise introduces randomly generated pixel values ranging from 0 to 255 at random positions in the image. The second type of noise adds binary noise by setting pixels at random locations to either 0 or 255. The dataset includes images with varying amounts of noisy pixels, allowing for detailed analysis under different noise intensities.
This dataset contains heartbeat and electromyography (EMG) signals recorded from the brachioradialis muscle under different conditions: rest and induced fatigue. It is intended for research in biomechanics, fatigue detection, and physiological signal processing. The data provide insights into muscle activity and heart rate variations, making it valuable for applications in biomedical engineering and human performance analysis.
