IoT
Federated learning (FL), as a privacy-preserving distributed machine learning algorithm is being rapidly applied in wireless communication networks, which enables IoT clients to obtain well-trained models while keeping data local.
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The dataset contains the data collected using an Arduino Nano 33 BLE Sense for several classification tasks: color detection, keyword spotting, sound frequency recognition, vibration pattern detection, hand-gesture recognition, and vibration intensity detection.
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This cherry tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of cherry trees, including stress analysis and prediction. An orchard of cherry trees is considered in the area of Western Macedonia, where 577 cherry trees were recorded in a full crop season starting from Jul. 2021 to Jul. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos.
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The evolution of the Industrial Internet of Things (IIoT) introduces several benefits, such as real-time monitoring, pervasive control and self-healing. However, despite the valuable services, security and privacy issues still remain given the presence of legacy and insecure communication protocols like IEC 60870-5-104. IEC 60870-5-104 is an industrial protocol widely applied in critical infrastructures, such as the smart electrical grid and industrial healthcare systems.
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This dataset containg EMG data recorded with a sample rate of 1kHz. Data was collected from the brachioradialis, flexor carpi ulnaris and common extensor digitorum muscles.
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The dataset reflects a single household (home) power profile related to the grid. Households include typical appliances, two air-to-air heat pumps, a 3-phase 18 kW through-flow water heater, 6 kW solar panels and a 2,5 kW charger for the electric car.
For five-month (April_August) in 2022, every 0.2 sec took each 3-phase voltage and current measurement, calculate power and harmonics (up to 15th) for power profile registration.
Positive value reflects energy flow from the grid to a household, and negative values are energy flow to the grid.
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This dataset has 2 different RFID manufacturer tags (2 of each) with different EPC content. Signal Strength data of all 4 tags were taken in the frequency spectrum in the US UHF (900-910 MHz) range. There are 100 data readings per tag, so 400 files total.
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185 Views
Information Collected by Green Care
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334 Views
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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1718 Views