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Dataset results
24 results for “signal receivers”
A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection
<p>This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength".</p> <p>This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).</p>
Open-Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection"
<p>This archive contains three folders which are supplementary material for the paper accepted for publishing in IEEE Sensors Journal.</p> <p><strong>Contents:</strong></p> <ul> <li> The folder `open-access-data/upb/` contains the measurements acquired at UPB. The subfolders are named as `upb_ble_*`, where an asterisk masks the directory number. Whenever UPB is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/tau/` contains the measurements acquired at TAU. The subfolders are named as `tau_ble_*`, where an asterisk masks the directory number. Whenever TAU is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/wifi-on-off/` contains a sample code to read the files and plot the data from Fig. 14 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.py` and Fig. 15 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.ipynb`.</li> </ul> <p><strong>Results based on the data have been presented in the paper:</strong><br> Flueratoru, L., Shubina, V., Niculescu, D., Lohan, E.S. (2021). On the High Fluctuations of Received Signal Strength Measurements with BLE Signals for Contact Tracing and Proximity Detection, IEEE Sensors, Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation</p> <p><strong>To cite these data sets please use the following:</strong><br> Laura Flueratoru, Viktoriia Shubina, Dragoș Niculescu, & Elena Simona Lohan. (2021). Open Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4643668</p>
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
Received Signal Srength (RSS) urban measurements from GSM and UMTS networks for cellular-based positioning
<p>GSM (2G) and UMTS (3G) urban measurement data for cellular-based positioning. The data has been collected in Tampere city, Finland with a mobile phone with proprietary software. The data is given in Matlab *mat format and it contains a cell variable called BS_grid* which shows the GPS coordinates (x,y,z) (converted in local coordinates, in meter values) and the collected RSS value (in dB) per transmitter (i.e., BS or Node B). Each cell contains a N x 4 matrix, whose rows are [x y z RSS]. N is the number of measurements points in which the corresponding Base Station were heard. The size of the BS_grid* cells is equal to the number of heard Base Stations in the measured area. </p> <p>Example of research results based on these measurements can be found for example in:</p> <ul> <li>H. Nurminen, J. Talvitie, S. Ali-Loytty, P. Muller, E.S. Lohan, R. Piche, M. Renfors, "Statistical path loss parameter estimation and positioning using RSS measurements", Journal of Global Positioning Systems, vol. 12(1), 2013, ISSN 1446-3156.</li> </ul>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
<p>Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics, and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally, and identifies aspects of information processing in complex environments that remain to be investigated. </p>
Structure and dynamics of plasma irregularities over the equatorial ionospheric region: A study using spaced receiver technique employing geostationary satellites' radio signals-Data set
<p>The study investigates the characteristic features of the ionospheric irregularities using spaced receiver technique. In the spaced receiver technique, we have used a trio of receivers separated by 40 and 100 m from each other. These receivers monitor scintillations patterns of the L1 signals transmitted by the geostationary satellites. The cross-correlation of the signals and the power spectral analysis yields the measure of characteristic features of the irregularities. The data folder contains the S4 index, drift velocity of the irregularities, powerspectral slopes and size of the irregularities observed on four days. The folder also contains the gnuscript used for plotting. </p> <p> </p> <p> </p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
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Data from: Variation in sexual signals and defensive strategies elicits receiver-dependent shifts in attractiveness
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I/Q measurements with 5G SRS signals and receiver 4-port 3D Vector Antenna for positioning studies
<p>This dataset contains the I\Q data of four received signals from a 4-port 3D Vector Antenna (3D VA) as well as *fig and *png examples of the angle of arrival (AoA)/azimuth angle estimation using the MUSIC algorithm based on the raw data. The data was collected from four ports (p5, p6, p7, p8) of a 3D VA provided by ENAC. A single Yagi antenna has been used as a transmitter at 2.1GHz carrier frequency and horizontal polarization.</p>
Received Signal Modeling and BER Analysis for Molecular SISO Communications
<p>This file is to support the claim marked by footnote #2.</p>
Data from: Receiver discriminability drives the evolution of complex sexual signals by sexual selection
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Data - Sensitive optical free-space receiver architecture for coherent combining of deep-space communication signals through atmospheric turbulence
<p>This dataset contains simulation code (MATLAB), generated fiber-field data and plotting scripts to generate the results in "Sensitive optical free-space receiver architecture for coherent combining of deep-space communication signals through atmospheric turbulence". Measured data and noise signal generation scripts are also included.</p> <p>This is version 2 of the dataset which features minor changes to make the scripts simpler to run (compared to the previous version) as well as detailed instructions on how to run them and generate the figures from the paper (see README.txt).</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
Spoofing Signal Generation Algorithm using the Processing Information of a Receiver for Authentic GPS Signals
<p>Experimental results analyzation files.</p>
Fig. 7. Packets received during testing using 4 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 7. Packets received during testing using 4 nodes.
Data from: Male receiver bias for red agonistic signaling in a yellow-signaling widowbird: a field experiment
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Data from: Signaler and receiver boldness influence response to alarm calls in eastern chipmunks
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Single-cell transcriptomic analysis demonstrates decreased TNF signaling in patients receiving BAF312 during intracerebral hemorrhage
GEO Series GSE313043. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing; Other.
CXCR6 Positions Cytotoxic T Cells to Receive Critical Survival Signals in the Tumor Microenvironment
GEO Series GSE179111. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Marginal zone B cells receive robust Notch2 signals within the B cell follicle in the absense of S1pr1 signaling.
GEO Series GSE274990. Mus musculus. 42 samples. Type: Expression profiling by high throughput sequencing.
Changes in the MRI Signal in Patients With Benign Tumors of the Brain and Meninges Treated With Proton Therapy: Impact of TEL and the Biological Dose Received
ClinicalTrials.gov study NCT04584086. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.