The given Dataset is record of different age group people either diabetic or non diabetic for theie blood glucose level reading with superficial body features like body temperature, heart rate, blood pressure etc.
The main purpose of the dataset is to understand the effect of blood glucose level on human body.
The different superficial body parameters show sifnificant variation according to change in blood glucose level.
The use of dataset to be done for machine learning analysis or study purpose only. No medical implementations to be claimed using the given dataset.
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3148 Views
Context
This dataset consists of subject wise daily living activity data, which is acquired from the inbuilt accelerometer and gyroscope sensors of the smartphones.
Content
The smartphone was mounted on the waist and front pockets of the users. All the different activities were performed in a laboratory except Running, which was performed on a Football Playground.
Smartphone used: Poco X2 and Samsung Galaxy A32s
Inbuild Sensors used: Accelerometer and Gyroscope
Ages: All subjects are Above 23 years
Smartphone used: Poco X2 and Samsung Galaxy A32s Inbuild Sensors used: Accelerometer and Gyroscope Ages: All subjects are Above 23 years Weight: All subjects are above 50 kgs No. of Subjects= 1
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905 Views
Packet delivery ratio data collected for the article Wireless-Sensor Network Topology Optimization in Complex Terrain: A Bayesian Approach. Published in the IEEE Internet of Things Journal.
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112 Views
The cell characterization scripts and ultra low voltage flip-flop design information including 320-bit (16x20) parallel shift register design....
If you use this data, please add the citation to the following paper :
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75 Views
The dataset corresponds the measurement that was implemented on the control accuracy of a mixed reality application for digital twin based crane.
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187 Views
The .zip archive contains a folder ‘tasks’, and a .csv file, “analysis_results.csv” which is a table with 4077 entries. The .csv table is delimeted by comma. Each subfolder of the ‘tasks’ folder represents an analysis task of a unique sample. The association between tasks and samples is shown in the analysis_results.csv table, which contains the analysis results per sample. Each row in the table represents a botnet sample and holds information such as analysis task id, file hash, URL of the server where the sample was captured from, as well as the analysis results for that sample. For each task id, the corresponding folder contains: 1) the results of the analysis (analysis_result.json); 2) the captured traffic (capture.pcap); 3) the recorded system calls (syscalls.json) and 4) the botnet sample file (ELF binary) with the original filename. Depending on the IoT botnet sample analysed, the network traffic may include port scanning, exploitation, C2 communications and DDoS traffic.
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825 Views
The channel path data (signal strength, delay, angle of departure and angle of arrival, etc. of each path for each Tx-Rx pair) calculated by a ray-tracing model in an industrial warehouse, via Wireless InSite
https://www.remcom.com/wireless-insite-em-propagation-software
For more details, please download the scripts and .zip to access the instructions and the data files that contain the path information.
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418 Views
The Internet of Things (IoT) technology has revolutionized every aspect of everyday life by making everything smarter. IoT became more popular in recent years due to its vast applications in many fields such as smart cities, agriculture, healthcare, ambient assisted living, animal tracking, etc. Localization of a sensor node refers to knowing a sensor node's geographical location in the IoT network.
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145 Views
This dataset consists of temporal and temperature drift characteristics of Si3N4-gate iSFET andsupplementary files
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256 Views