This dataset contains the raw time-series data acquired during wrist perturbation experiments performed with the proposed 2-DoF wearable robotic device. Data are organized in column format and include timestamp, motor currents measured from the ESCON modules (digitally converted), desired motor torques, measured motor angular displacements from encoders, desired angular trajectories, and measured neutral motor positions for both actuation axes.
A novel quantum algorithm for use in Biological Sequence Alignment is presented and analyzed. The large amounts of data extracted from genome sequencing, de novo assembly sequencing, resequencing, and transcriptome sequencing at the DNA or RNA level, foreshadow the need for higher computing power as well as more sophisticated alignment methods. Modern and faster sequencing techniques in genomics have led to the reconsideration of current methods of designing or implementing alignment protocols.
# README
## 1. Overview
This package contains two datasets used in our experiments:
1. **Dataset 1**\
A self-collected blood-classification dataset pre-processed into a CIFAR-style format.
2. **Dataset 2**\
A publicly available blood-segmentation dataset.\
Please refer to its official GitHub repository for detailed
documentation and download instructions.
This README describes how to use both datasets.
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This dataset contains sEMG+IMU multimodal data from 12 healthy young subjects (9 males and 3 females, aged 21–23 years) and 3 elderly subjects (one 84-year-old female, one 84-year-old male, and one 94-year-old male).
Magnetic sensors are popular for rotation sensing in hinges and knobs because they do not require a mechanical or electrical connection between moving parts. However, the straightforward solution of spinning a permanent magnet about the rotation axis puts components directly on the hinge line, preventing its use in thin folded structures.
Dataset is derived from recordings of internal signals from a RISC-V processor during execution of various workloads, along with spike trains generated from the same, created to investigate the application of spiking neural networks (SNNs) to the task of detecting faults in program execution.
Accurate electric and magnetic head phantoms are important tools for validating electroencephalography (EEG) and magnetoencephalography (MEG) devices and signal processing techniques. A combined EEG and MEG (M/EEG) phantom would be particularly useful for its ability to test and validate multimodal imaging methods. Although MEG phantoms are commercially available, combined M/EEG and even pure EEG phantoms must be made from raw materials by the researchers who need it, but the materials research and development process required are often difficult to complete or replicate.
The dataset in `./data` contains the Twitter, Weibo, Zhihu and Douban datasets that we used in the experiments for evaluation.
The present dataset reports hand- and eye-tracking data of People with Multiple Sclerosis (PwMS) recording with Microsoft HoloLens2. PwMS were requested to perform nine pick-and-place movements involving three plastic boxes. The kinematic trajectories were exploited to extract features related to hand movements and hand-eye coordination. Subsequently, such features were compared to unimpaired motor behavior (not present in this dataset) to asses whether HoloLens2 could be used as a marker-less solution to evaluate the severity of Multiple Sclerosis upper-limb impairments.
TCHAD-100 is a behavioral biometric dataset dedicated to keystroke dynamics analysis, collected from 100 Chadian participants in a real-world academic environment. The dataset includes dwell times, flight times, demographic metadata, and typing preferences in a cross-cultural African context.
The data were collected using a custom web-based acquisition platform and stored in a PostgreSQL database before anonymization and export. Each participant provided up to 60 keystroke samples using fixed and free-text phrases under controlled conditions.
