Artificial Intelligence
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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Solar Insecticidal Lamp, as a professional device for smart phytoprotection, can kill the insects to calculate the insect density, further guiding the famers to spray pesticide accurately. Various experiments were performed by a testbed, combined Solar Insecticidal Lamp with two cameras, to get the dataset including time, Pulse Number of Insecticidal Sounds, Pulse Number of Insecticidal Discharges, insecticidal status, abnormal value, and insecticidal quantity. The dataset can be used for a variety of methods related to the research of insecticidal counting.
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The Framingham Township Heart Institute offers a 10-year data set on coronary heart disease
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This dataset is Bill Proposal History of the Republic of Korea 20th National Assembly(2016-2020) Lawmakers. This dataset consists of 21,594 rows with 37 columns. Contents are mainly written in korean, so translation will be needed for foreign researchers. If any further instruction or information is needed, contact by minhan.nick.cho@gmail.com.
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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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The files contain the results and control software associated with our research paper.
We address the problem of designing a modular method for the automatic design of robot swarms, which involves defining the modules that will be then automatically selected and assembled into an appropriate architecture (e.g., a finite-state machine or a behavior tree).
This data is associated with a paper in which we propose a method based on repertoires of neural networks automatically generated via a quality-diversity evolutionary algorithm.
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