IoT

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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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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This dataset supports a review and an in-depth analysis on the environmental impacts of integrated circuits (ICs). The paper is currently under review.
We gathered data from foundry reports, industry roadmaps, scientific literature, and commercial state-of-the-art LCA databases. All assumptions are detailed.
More information can be found on the GitHub repository : https://github.com/ThibaultPirson/environmental-footprint-IC.
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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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Supplementary material for the article "A Sensor Network Utilizing Consumer Wearables for Telerehabilitation of Post-acute COVID-19 Patients": (1) TERESA (TEleREhabilitation Self-training Assistant) back-end application API documentation and (2) anonymous details of the Wristband protocols used in the study.
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This data set contains data collected from an overhead crane (https://doi.org/10.1109/WF-IoT.2018.8355217) OPC UA server when driving an L-shaped path with different loads (0kg, 120kg, 500kg, and 1000kg). Each driving cycle was driven with an anti-sway system activated and deactivated. Each driving cycle consisted of repeating five times the process of lifting the weight, driving from point A to point B along with the path, lowering the weight, lifting the weight, driving back to point A, and lowering the weight.
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Our data set has 5136 records collected in 214 days. The sampling rate of the sensors is 1 hour. Each record includes the number of vehicles entering and leaving the parking lot in an hour, the CO2 concentration of every building floor at the recording time, and the power consumption of each floor in an hour.
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This data set contains measurements on reading and writing data to OPC UA servers directly and via REST and GraphQL interfaces. Each measurement is conducted 1000 times. Measurements include reading a single value and reading 50 values. Measurements using cache server were also performed. Measurement data is collected with Wireshark and the .csv files are exported from it. in addition, .txt files contain request execution times recorded by the client.
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The Internet of Things and edge computing are fostering a future of ecosystems
hosting complex decentralized computations, deeply integrated with our very dynamic
environments. Digitalized buildings, communities of people, and cities will be the
next-generation “hardware and platform”, counting myriads of interconnected devices, on top of
which intrinsically-distributed computational processes will run and self-organize. They will
spontaneously spawn, diffuse to pertinent logical/physical regions, cooperate and compete,
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