Sensors

This dataset provides wireless measurements from two industrial testbeds: iV2V (industrial Vehicle-to-Vehicle) and iV2I+ (industrial Vehicular-to-Infrastructure plus sensor).

iV2V covers 10h of sidelink communication scenarios between 3 Automated Guided Vehicles (AGVs), while iV2I+ was conducted for around 16h at an industrial site where an autonomous cleaning robot is connected to a private cellular network.

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FEA-RDC-simulation data and prototype data

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The Bluetooth 5.1 Core Specification brought Angle of Arrival (AoA) based Indoor Localization to the Bluetooth Standard. This feature is usually referred to as Bluetooth Direction Finding.Technically, the Direction Finding is done by adding a so-called Constant Tone Extension (CTE) to a Bluetooth packet. During this CTE, either transmitter or receiver can switch through several antennas of an antenna array while IQ data, i.e. amplitude and phase information, is sampled on the receiving side. Using these IQ samples, the direction(s) of the incident wavefronts can be estimated.

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This paper investigates

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All the 500 sets of original data used in theoretical fittings.

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This dataset contains information about published papers on how biological signals (ECG, EEG, EDA and MG + eye-tracking) are being used and collected in the field of video games. This dataset reflects the information published including the choice of signals, the devices used to collect them (e.g., wearables), the purposes for which they are collected, and the main results reported from their use.

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The dataset includes the device geometries and the corresponding performance. The device geometries consist of four-dimensional factors (L, W, T, H) and the device performance consists of three-dimensional parameters (R, S, δl).

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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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Human activity data based on wearable sensors, such as the Inertial Measurement Unit (IMU), have been widely used in human activity recognition. However, most publicly available datasets only collected data from few body parts and the type of data collected is relatively homogeneous. Activity data from local body parts is challenging for recognizing specific activities or complex activities. Hence, we create a new  HAR dataset which is colledted from the project named MPJA HAD: A Multi-Position Joint Angles Dataset for Human Activity Recognition Using Wearable Sensors.

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Due to the smaller size, low cost, and easy operational features, small unmanned aerial vehicles (SUAVs) have become more popular for various defense as well as civil applications. They can also give threat to national security if intentionally operated by any hostile actor(s). Since all the SUAV targets have a high degree of resemblances in their micro-Doppler (m-D) space, their accurate detection/classification can be highly guaranteed by the appropriate deep convolutional neural network (DCNN) architecture.

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