Signal Processing
Although several databases of handwriting movements have been created so, none of them has been specifically designed for studying the effect of age during ellipse drawing. Ninety subjects voluntarily participated in the database construction. Their age ranged from 19 to 85 years: 30 participants in the range [19, 39] years, 30 in the range [40, 59] and 30 subjects in the range [60, 85]. Twenty-six women (range 19-72 years) and sixty-four men (range 25-85 years) participated.
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This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.
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Magnetotellurics forward modeling synthesizing time series
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5G-NR is beginning to be widely deployed in the mmWave frequencies in urban areas in the US and around the world. Due to the directional nature of mmWave signal propagation, improving performance of such deployments heavily relies on beam management and deployment configurations.
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Please cite the following paper when using this dataset:
N. Thakur and C.Y. Han, “An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection,” Journal of COVID, 2022, Volume 5, Issue 3, pp. 1026-1049
Abstract
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It contains two parts of sound and video, and they are in one-to-one correspondence.It is used for emotion recognition for speech and video and contains nine emotions.
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This dataset contains Wi-Fi sensing data using Channel State Information (CSI) for respiration rate measurements in a standard 3m x 3m room. The Wi-Fi CSI data was collected using the Wi-Fi module on the ESP32 Microcontroller units using the esp32-csi-tool. The Wi-Fi CSI data is accompanied by respiration belt data taken with the Wi-Fi measurements simultaneously using the Neulog NUL-236 respiration belt logger as ground truth.
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226 Views
In this paper, we develop an internet of medical things (IoMT)-based electrocardiogram(ECG) recorder for monitoring heart conditions in practical cases. To remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that utilizes deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for the noise cancellation.
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The EegDot data set (EEG data evoked by Different Odor Types established by Tianjin University) collected using a Cerebus neural signal acquisition equipment involved thirteen odor stimulating materials, five of which (smelling like rose (A), caramel (B), rotten (C), canned peach (D), and excrement (E)) were selected from the T&T olfactometer (from the Daiichi Yakuhin Sangyo Co., Ltd., Japan) and the remaining eight from essential oils (i.e., mint (F), tea tree (G), coffee (H), rosemary (I), jasmine (J), lemon (K), vanilla (L) and lavender (M)).
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1700 Views