This dataset is used for sign language emotion recognition and contains five emotions from 12 participants (6 males and 6 females) with high-positive, low-positive, high-negative, low-negative, and neutral emotions. The surface electromyography (sEMG) and inertial measurement unit (IMU) sensors were used to capture 30 sign language sentence signals. Participants' emotions were activated by film clips.
The dataset includes annotated Computed Tomography (CT) scanned images. The labels consist of three types:
The dataset contains short video clips of four shoulder exercises.
The videos are labeled as either correct or incorrect.
<p>This multilingual Twitter dataset spans over 2 years from October 2019 to the end of 2021, including 3 months before the outbreak of the COVID-19 pandemic.</p>
This dataset is a private foot pressure image dataset containing 317 images of high arches (H), 217 images of flat feet (L) and 362 images of normal feet (N).
- There are six folders corresponding to 6 types of BPPV disorders.
- Each folder has one sample.
Each class is specified by the typical movement of the eye.
+) Lt_Geo_BPPV: eye beats toward the ground, beats stronger to the left side (turn head left).
+) Rt_Geo_BPPV: eye beats toward the ground, beats stronger to the right side (turn head right).
+) Lt_Apo_BPPV: eye beats toward the sky, beats stronger to the left side (turn head right).
Multi-label event classification label of each sample-document is done with nine bits. The first bit signifies whether an event is present or absent with 1 or 0 respectively. The remaining eight bits signifies presence or absence of (i) covid, (ii) flood, (iii) storm, (iv) heavy rain, (v) cloudburst, (vi) landslide, (vii) earthquake, (viii) Tsunami with 1 or 0. The location and the impact sentence classification labeling are similar.
A synthetic data for low power (P ≤10 mW) InGaAsP MQW-DFB lasers operating at a wavelength (λ) ranging from 1.53 to 1.57 µm at a case temperature laying between -40 ℃ to 85 ℃ with side mode suppression ratio of more than 35 dB is generated and can be used for laser lifetime prediction using machine learning based approaches.
The dataset includes processed sequences of optical time domain reflectometry (OTDR) traces incorporating different types of fiber faults namely fiber cut, fiber eavesdropping (fiber tapping), dirty connector and bad splice. The dataset can be used for developping ML-based approaches for optical fiber fault detection, localization, idenification, and characterization.
Filtered NGSIM and Artificial Datasets used in the paper "A Compositional Paradigm for Read-time systems".
