CSI
This dataset offers both Channel State Information (CSI) and Beamforming Feedback Information (BFI) data for human activity classification, featuring 20 distinct activities performed by three subjects across three environments. Collected in both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, this dataset enables researchers to explore the complementary roles of CSI and BFI in activity recognition and environmental characterization.
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Relative direction estimation among neighboring vehicles in urban environments is essential to a wide variety
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This data set contains the refractive index and reflectivity, emission intensity, and absorption length data for various materials utilitised in a SiPM-based Geant4 simulation platform. This simulation platform consists of the scintillation detector SiPM-3000 from Bridgeport Instruments, which contains a scintillator optically bonded to a silicon photomultiplier (SiPM) array within the detector housing. Scintillator crystals included in this study consist of GAGG:Ce, CLLBC:Ce, BGO, NaI:Tl, and CsI:Tl.
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This dataset is about channel state information about underwater channels, we adopt the widely-used UWA channel simulator developed by P. Qarabaqi and M. Stojanovic to make this dataset. Addtionlly, the parameters settings of UWA-Channel are as follows: the depth of sea is 100 meters, the transmitter is positioned 50 meters above the sea bottom, the height of the receiver from the bottom is 30 meters, and the distance between the two is 800 meters. The dataset consists of two parts: data and labels. Labels are the channel frequency responses generated by the simulator.
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Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities.
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To gather the dataset, we asked two participants to perform six basic knife activities. The layout of the system experiment is provided in Fig. 4. As it illustrates, we put the receiver on the right side and the ESP32 transceiver on the left side of the performing area. The performing area is a cutting board (30 x 46 cm) in this experiment. Each participant performs each activity five times in the performing area.
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This is a CSI dataset towards 5G NR high-precision positioning,
which is fine-grained, general-purpose and 3GPP R18 standards complied.
The corresponding paper is published here (https://doi.org/10.1109/jsac.2022.3157397).
5G NR is normally considered to as a new paradigm change in integrated sensing and communication (ISAC).
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This dataset contains thousands of Channel State Information (CSI) samples collected using the 64-antenna KU Leuven Massive MIMO testbed. The measurements focused on four different antenna array topologies; URA LoS, URA NLoS, ULA LoS and, DIS LoS. The users channel is collected using CNC-tables, resulting in a dataset where all samples are provided with a very accurate spatial label. The user position is sweeped across a 9 squared meter area, halting every 5 millimeter, resulting in a dataset size of 252,004 samples for each measured topology.
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The Widar3.0 project is a large dataset designed for use in WiFi-based hand gesture recognition. The RF data are collected from commodity WiFi NICs in the form of Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The dataset consists of 258K instances of hand gestures with a duration of totally 8,620 minutes and from 75 domains. In addition, two sophisticated features from raw RF signal, including Doppler Frequency Shift (DFS) and a new feature Body-coordinate Velocity Profile (BVP) are included.
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