IoT
This article presents a dataset collected from a real process control network (PCN) to facilitate deep-learning-based anomaly detection and analysis in industrial settings. The dataset aims to provide a realistic environment for researchers to develop, test, and benchmark anomaly detection models without the risk associated with experimenting on live systems. It reflects raw process data from a gas processing plant, offering coverage of critical parameters vital for system performance, safety, and process optimization.
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930 Views
This dataset presents a comprehensive collection of measurements from a Thermoelectric Generator (TEG) energy harvesting prototype, equipped with nine PT100 temperature sensors and detailed recordings of voltage and current outputs. Collected over a 12-month period starting in October 2022, the data provide insights into the performance of the TEG under varying environmental conditions.
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380 Views
This dataset contains inertial measurements data (accelerometer, gyroscope and magnetometer), recorded as a part of DoorINet research. Our dataset was recorded using two types of IMUs: Memsense MS-IMU3025 [32] and Movella Xsens DOT [33]. The Memsense MS-IMU3025 was used to generate the ground-truth (GT) readings. This IMU has a gyroscope bias instability of 0.8°/h over the axis of interest Z and was recorded at 250Hz. The Movella Xsens DOT IMUs were used as units under test. It has a gyroscope bias instability of 10°/h over the axis of interest Z and was recorded at 120Hz.
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6 Views
<p>Abstract – This paper shows an overview of the recent developments in the field of Electric Vehicles (EVs), the integration of EVs and Smart Cars, the battery technology and the power electronics in EVs. Over the past decades, the automotive industry has faced growing challenges, including environmental concerns and the finite amount of fuel resources that mainly includes petrol and diesel. In response to these challenges, Electric Vehicles (EVs) have emerged as sustainable alternative, promising reduced emissions and increased energy efficiency.
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1595 Views
Our released weight dataset for fusion results in edge-cloud collaborative inference contains the corresponding weighted summation weights under 50,000 edge-cloud collaborative DNN inference tasks, listing the five heterogeneous NVIDIA edge devices they use (NVIDIA Jetson Nano, TX2, NX, Orin NX, and AGX Orin), computing power (1.9~275TOPS), DNN model type (EfficientNet-B0, ViT-B16), and network bandwidth (0.5~8Mbps).
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292 Views
Recently, a novel method was proposed to estimate the distance between a couple of wireless transceivers and a reflecting obstacle by analyzing the frequency dependence of the RSS. Although the resolution of this method is rather coarse for typical 2.4 GHz systems, a traffic monitoring system based on that novel approach has been successfully evaluated
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169 Views
Indoor intelligent perception systems have gained significant attention in recent years. However, accurately detecting human presence can be challenging in the presence of nonhuman subjects such as pets, robots, and electrical appliances, limiting the practicality of these systems for widespread use.
In this data port, we build the first comprehensive WiFi dataset of motion from various sources in real-world contexts. It includes WiFi data of humans, pets, cleaning robots, and fans.
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113 Views
The dataset is the experimental output of a 5G New Radio (NR) coverage expansion use case in the context of the NANCY project (https://nancy-project.eu/). Two experimental scenarios were carried out, namely a) a scenario where a user equipment (UE) is directly connected to a Base Station (BS) through a 5G NR link, and b) a scenario where an intermediate node is employed, which acts as a relay between the base station and the UE. To this end, two 5G BSs were deployed, using Ettus Research USRP B210 devices.
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696 Views
The Army Cyber Institute (ACI) Internet of Things (IoT) Network Traffic Dataset 2023 (ACI-IoT-2023) is a novel dataset tailored for machine learning (ML) applications in the realm of IoT network security. This effort focuses on delivering a distinctive and realistic dataset designed to train and evaluate ML models for IoT network environments. By addressing a gap in existing resources, this dataset aims to propel advancements in ML-based solutions, ultimately fortifying the security of IoT operations.
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4921 Views
We introduce a novel multi-modal dataset comprising point cloud data from a mmWave radar, RGB and depth images from an RGB-D camera, collected from 23 human subjects.
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379 Views