Signal Processing
To address the challenges faced by patients with neurodegenerative disorders, Brain-Computer Interface (BCI) solutions are being developed. However, many current datasets lack inclusion of languages spoken by patients, such as Telugu, which is spoken by over 90 million people in India. To bridge this gap, we have created a dataset comprising Electroencephalograph (EEG) signal samples of commonly used Telugu words. Using the Open-BCI Cyton device, EEG samples were captured from volunteers as they pronounced these words.
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The dataset consists of 4-channeled EOG data recorded in two environments. First category of data were recorded from 21 poeple using driving simulator (1976 samples). The second category of data were recorded from 30 people in real-road conditions (390 samples).
All the signals were acquired with JINS MEME ES_R smart glasses equipped with 3-point EOG sensor. Sampling frequency is 200 Hz.
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The dataset involves two sets of participants: a group of twenty skilled drivers aged between 40 and 68, each having a minimum of ten years of driving experience (class 1), and another group consisting of ten novice drivers aged between 18 and 46, who were currently undergoing driving lessons at a driving school (class 2).
The data was recorded using JINS MEME ES_R smart glasses by JINS, Inc. (Tokyo, Japan).
Each file consists of a signals from one sigle ride.
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The AnxiECG-PPG Database contains synchronized electrocardiogram (ECG) and mobile-acquired photoplethysmography (PPG) recordings from 47 healthy participants. Moreover, the acquisition protocol assesses three distinct states: a 5-minute Baseline, a 1-minute Physical Activated State, and a Psychological Activated state provoked through emotion-induced videos (negative, positive, and neutral emotion valence).
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SeaIceWeather Dataset
This is the SeaIceWeather dataset, collected for training and evaluation of deep learning based de-weathering models. To the best of our knowledge, this is the first such publicly available dataset for the sea ice domain. This dataset is linked to our paper titled: Deep Learning Strategies for Analysis of Weather-Degraded Optical Sea Ice Images. The paper can be accessed at: https://doi.org/10.1109/jsen.2024.3376518
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This paper introduces a dataset capturing brain signals generated by the recognition of 100 Malayalam words, accompanied by their English translations. The dataset encompasses recordings acquired from both vocal and sub-vocal modalities for the Malayalam vocabulary. For the English equivalents, solely vocal signals were collected. This dataset is created to help Malayalam speaking patients with neuro-degenerative diseases.
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Achieving robust path tracking is essential for efficiently operating autonomous driving systems, particularly in unpredictable environments. This paper introduces a novel path-tracking control methodology utilizing a variable second-order Sliding Mode Control (SMC) approach. The proposed control strategy addresses the challenges posed by uncertainties and disturbances by reconfiguring and expanding the state-space matrix of a kinematic bicycle model guaranteeing Lyapunov stability and convergence of the system.
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Normal
0
false
false
false
EN-US
X-NONE
AR-SA
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This dataset is an expanded version of the CALMA engineering dataset, which assigns military value weights to stations at each requested frequency. The new dataset contains dynamic spectrum resource assignment data for ten scenarios, CELAR (01,02,03,04,11) and GRAPH (01,02,08,09,14). The complete data are compiled into four files for each scenario, including: domains.txt, requests.txt, constraints.txt, appearance_time.txt.
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An IEEE 802.15.4 backscatter communication dataset for Radio Frequency (RF) fingerprinting purposes.
It includes I/Q samples of transmitted frames from six carrier emitters, including two USRP B210 devices (labeled as c#) and four CC2538 chips (labeled as cc#), alongside ten backscatter tags (identified as tag#). The carrier emitters generate an unmodulated carrier signal, while the backscatter tags employ QPSK modulation within the 2.4 GHz frequency band, adhering to the IEEE 802.15.4 protocol standards.
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