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229 results for “wireless”
BeMAGIC_Deep neural (and muscle) wireless stimulation using magnetoelectric particles
<p>BeMAGIC ITN (GA861145)_Deep neural (and muscle) wireless stimulation using magnetoelectric particles. Results from ICN2 and G.TEC.</p>
Data set for Wireless SAWR sensors: FFT, EMD or wavelets for the frequency estimation in one shot?
<p>This data set is the basis for the publication "Wireless SAWR sensors: FFT, EMD or wavelets for the frequency estimation in one shot?", submitted to Journal of Sensors and Sensor Systems.<br> It contains the following data:</p> <p>- "Scipioni_JSSS23_Fig5_WaveletChoice.txt" contains results to obtain the best wavelet for this study. For this, a SAWR (Fig. 3a) signal is noised by an additive Gaussian white noise with different SNR values. The signal is then denoised by wavelets for each SNR value. Results are the new SNR values after denoising.</p> <p>- "Scipioni_JSSS23_Fig9_to_15_F_Ref.txt" contains all the frequencies around F=10700 MHz chosen to test the three methods: Fourier, wavelets, EMD.</p> <p>- 10 files "Scipioni_JSSS23_Fig10_to_14_SAW_EMDvsWavelet_FRef_XX_Occ_100.txt" contain results of the frequency and uncertainty measurement for each noisy SAWR signal versus frequencies and SNR values.</p> <p>- 3 files "Scipioni_JSSS23_Fig17_Tab2_3_Experimental SAWR signal_NoX" contain the values of 3 different experimental SAWR signals.</p>
BeMAGIC_Deep neural (and muscle) wireless stimulation using magnetoelectric particles
<p>BeMAGIC ITN (GA 861145) Deep neural (and muscle) wireless stimulation using magnetoelectric particles. Results from ICN2 and GTECM.</p>
Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO)
Open the record for dataset details and reuse information.
SIMBED+ - Replicable Real Wireless Networking Experiments using ns-3
<p>Wireless networking R&D depends on experimentation to make realistic evaluations of networking solutions, as simulation is inherently a simplification of the real-world. However, despite more realistic, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility.</p> <p>Real wireless experiments may be difficult to repeat. For the same input they can produce very different output results, since wireless communications are influenced by external phenomena such as noise, interference, and multipath. Even if repeatable, experiments may still be difficult to reproduce. Namely, other researchers may be unable to reproduce an experiment and confirm previous experimental results right away, because either the testbed is unavailable – offline or running other experiments when using a community testbed –, or inaccessible at all when a custom testbed was originally used.</p> <p>Fed4FIRE+ testbeds such as w-iLab.t, although operating in controlled environment, do not fully address the problem. This is even more evident in testbeds running in non-controlled and very dynamic environments, such as CityLab, where they may suffer from radio interference and competition from existing networks sharing the same radio spectrum.</p> <p>What if we could make any wireless experiment repeatable and reproducible? What if we could share the same Fed4FIRE+ testbed execution conditions among an "infinite" number of users? What if we could run wireless experiments faster than in real time?</p> <p>INESC TEC has developed the Offline Experimentation (OE) approach that combines the best of simulation and experimentation to achieve the above-mentioned goals. By relying on Network Simulator 3 (ns-3) and its good simulation capabilities from the MAC to the Application layer, we have been exploring how ns-3 can be used to replicate real-world wireless experiments using real traces containing 1) position of nodes and 2) the quality of each radio link.</p> <p>The previous <strong>SIMBED </strong>project validated the OE approach for controlled environments and helped identifying some of its limitations. The OE approach was then improved to support experiments using Multiple-In-Multiple-Out (MIMO) and shared radio spectrum with concurrent networks. To further validate the improved OE approach, the <strong>SIMBED+</strong> project aimed at running a set of experiments on top of the controlled and non-controlled environments of w-iLab.t and CityLab Fed4FIRE+ testbeds. For that purpose, we configured different fixed experimental scenarios, representative of Wi-Fi range of operation, subjected to controlled and non-controlled interference from concurrent experiments. For each experiment the achieved network throughput was measured. Then, we repeated each experiment using, both, Pure Simulation (PS) and OE approaches (now with MIMO and channel occupancy information) based on ns-3, also measuring the network throughput for the same set of experiments.</p> <p>To compare the network throughput of the real experiments with their PS and OE counterparts, Cumulative Distribution Function (CDF) curves were used, plotting all 1-second average throughput samples. For all the experiments performed in SIMBED+, using the improved OE approach resulted in network throughput considerably closer to real than using the PS approach. This is even more evident in non-controlled environments using MIMO and shared radio spectrum.</p> <p>These results were important for further validating the OE approach, producing two conference papers and one journal paper. The SIMBED+ results increased our confidence on the OE approach ability of reproducing past experiments, and are envisioned to foster the adoption of the OE approach by the networking community, in complement to the use of real experimentation.</p> <p> </p> <p>The following dataset presents the results of the SIMBED+ project, organized in different folders, for each subset of experiments carried on:</p> <ul> <li><strong><em>SubExp#1: </em></strong><em>Trace-based PHY rate with SISO support (802.11a, BW 20 MHz) </em></li> <li><strong><em>SubExp#2: </em></strong><em>Trace-based PHY rate with MIMO support (802.11n/ac, BW 20/40 MHz, MIMO 3x3) </em> <ul> <li><strong><em>SubExp#2.1: </em></strong><em>IEEE 802.</em><em>11n, 20 MHz, MIMO 3x3</em></li> <li><strong><em>SubExp#2.2: </em></strong><em>IEEE 802.</em><em>11n, 40 MHz, MIMO 3x3</em></li> <li><strong><em>SubExp#2.3: </em></strong><em>IEEE 802.</em><em>11ac, 40 MHz, MIMO 3x3</em></li> </ul> </li> <li><strong><em>SubExp#3: </em></strong><em>Shared radio spectrum support (occupancy at the sender)</em></li> <li><strong><em>SubExp#4: </em></strong><em>Shared radio spectrum support (occupancy at the receiver)</em></li> <li><strong><em>SubExp#5:</em></strong> <em>Trace-based PHY rate, MIMO and shared radio spectrum support</em></li> </ul> <p>Each experiment has an individual folder, named according to the date and time of the experiment and the nodes used. Inside, there’s a folder for the <strong>parsed</strong> experimental results, which contains</p> <p>This folder contains the details and parsed logs of the experiment, as follows:</p> <ul> <li><em>date_time</em><strong>.cfg </strong>– configuration details of the experiment</li> <li><em>date_time_NodeID<sup><a href="#_ftn1"><strong>[1]</strong></a></sup>_SenderID<sup><a href="#_ftn2"><strong>[2]</strong></a></sup>_ReceiverID<sup><a href="#_ftn3"><strong>[3]</strong></a></sup>_FlowType<sup><a href="#_ftn4"><strong>[4]</strong></a></sup>_Params<sup><a href="#_ftn5"><strong>[5]</strong></a></sup></em><strong>.snr </strong>– logs of the Signal/Noise ratio (1 file per node/flow) </li> <li><em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em><strong>.stats</strong> – logs of the packets received (1 file per node/flow) </li> <li><em>NodeID</em><strong>.</strong><strong>waypoints</strong> – coordinates of the static nodes</li> <li><em>date_time_MobileNodeID</em><strong>.</strong><strong>waypoints</strong> – waypoints of the mobile nodes (when applicable)</li> </ul> <p>The experiment’s folder also contains a folder for the simulations <strong>output</strong> with the simulations statistics files, for the multiple simulations approaches considered, as follows:</p> <ul> <li><em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em>.<strong>simstats </strong>– logs of the packets received (simulation)</li> </ul> <p> </p> <p><sub><a href="#_ftnref1">[1]</a> ID of the node Logging node</sub></p> <p><sub><a href="#_ftnref2">[2]</a> ID of the Sender node</sub></p> <p><sub><a href="#_ftnref3">[3]</a> ID of the Receiver node</sub></p> <p><sub><a href="#_ftnref4">[4]</a> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access</sub></p> <p><sub><a href="#_ftnref5">[5]</a> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)</sub></p>
Remcom Wireless InSite - Warehouse models and simulation configurations
<p>This dataset is provided in scope of the <a href="http://safelog-project.eu/">SafeLog</a> project. It comprises warehouse models and simulation configurations for Remcom Wireless InSite suite used in evaluating UWB signal propagation in warehouse environment.</p>
Data for Secure communication in IP-based wireless sensor networks via a trusted gateway publication
<p>This archive file contains the raw data obtained from Contiki sensor nodes during Cooja experiments in the folders e2e, terminate, terminate_1st and plaintext.</p> <p>The archive accompagnies the IEEE ISSNIP 2015 publication titled "Secure communication in IP-based wireless sensor networks via a trusted gateway" by Floris Van den Abeele, Tom Vandewinckele, Jeroen Hoebeke, Ingrid Moerman and Piet Demeester.</p> <p><br /> Also included is the data_parser python script that converts the raw data into CSV files that are parseable by R. The script contains the definitions of the contents of the raw data files.<br /> Finally, the R scripts that use the CSV files to generate the plots from the paper are also included.</p>
Wireless flow-powered miniature robot capable of traversing tubular structures
<p>Wireless millimeter-scale robots capable of navigating through fluid-flowing tubular structures hold substantial potential for inspection, maintenance, or repair use in nuclear, industrial, and medical applications. However, prevalent reliance on external power constrains their operational range and applicable environments. Alternatives with onboard powering must trade off size, functionality, and operation duration. Here, we propose a wireless millimeter-scale wheeled robot capable of using environmental flows to power and actuate its long-distance locomotion through complex pipelines. The flow-powering module can convert flow energy into mechanical energy, achieving an impeller speed of up to 9595 revolutions per minute, accompanied by an output power density of 11.7 watts per cubic meter and an efficiency of 33.7%. A miniature gearbox module can further transmit the converted mechanical energy into the robot's locomotion system, allowing the robot to move against water flow at an average rate of up to 1.05 meters per second. The robot's motion status (moving against/with flow or pausing) can be switched using an external magnetic field or an onboard mechanical regulator, contingent upon different proposed control designs. Additionally, we design kirigami-based soft wheels for adaptive locomotion. The robot can move against flows of various substances within pipes featuring complex geometries and diverse materials. Solely powered by flow, the robot can transport cylindrical payloads with a diameter of up to 55% of the pipe's diameter and carry devices, such as an endoscopic camera for pipeline inspection, a wireless temperature sensor for environmental temperature monitoring, and a leak-stopper shell for infrastructure maintenance.</p>
On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-varying Sampling Rate Offsets and Speaker Changes
<p>We present an open-source database for evaluation of time synchronization algorithms for wireless acoustic sensor networks . More Information and examples on how to use the database can be found on our GitHub page: <a href="https://github.com/fgnt/paderwasn">https://github.com/fgnt/paderwasn</a></p>
Data accompanying "Integrated Dual-Laser Photonic Chip for High-Purity Carrier Generation Enabling Ultrafast Terahertz Wireless Communications"
<p>This dataset contains measurement data for the results presented in "Integrated Dual-Laser Photonic Chip for High-Purity Carrier Generation Enabling Ultrafast Terahertz Wireless Communications".</p>
A low-cost wireless bite force measurement device - Calibration data
<p>Raw data for a low-cost wireless bite force measurement device calibration. The folder contains the data recorded during 3 different sessions for both the 1D load cell "1D-TAS606" sensor and two accurate 6 axis force transducer "3D-FT Sensors"</p>
Available Wireless Sensor Network and Internet of Things testbed facilities: dataset
<p>In this data set, we present data collected for the purpose of carrying out a systematic review of the available Wireless Sensor Network and Internet of Things testbed facilities. The data was collected through multiple stages and in each stage the pre-defined criteria were applied. We provide a dataset describing the hardware and software aspects of Wireless Sensor Network and Internet of Things testbed facilities available in the market and scientific community. The data were gathered through an extensive systematic review process of scientific articles published between the years 2011 and 2021. The review aims to obtain good quality data for people who are actively researching the Internet of Things facilities or anyone who is interested in that field.</p>
Write-only File System for Privacy-aware Wireless Sensor Networks Evaluation Dataset
<p>Evaluation dataset for the paper <strong>"WoFS: A Write-only File System for Privacy-aware Wireless Sensor Networks"</strong> published at the <em>49th IEEE Conference on Local Computer Networks (2024)</em></p>
Raw Experimental Data for work presented in 'Leveraging Chaos for Wave-Based Analog Computation: Demonstration with Indoor Wireless Communication Signals'
<p>This is the raw experimental data for the work presented in 'Leveraging Chaos for Wave-Based Analog Computation: Demonstration with Indoor Wireless Communication Signals', to be published in Physical Review X.</p> <p> </p> <p>https://journals.aps.org/prx/accepted/dc07aKdcFa91ea06d2949139dac733fa62ce1c02c</p> <p> </p> <p>See the README files and sample pieces of codes for an explanation of the data.</p>
Results of "Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits"
<p>This dataset contains the results obtained for the article "Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits", authored by Francesc Wilhelmi, Cristina Cano, Gergely Neu, Boris Bellalta, Anders Jonsson and Sergio Barrachina. The article has been sent to Elsevier Ad-hoc Networks.</p> <p>The content of this dataset has been obtained by means of the code allocated in the following GitHub repository: <a href="https://github.com/fwilhelmi/collaborative_sr_in_wns_via_selfish_mabs">https://github.com/fwilhelmi/collaborative_sr_in_wns_via_selfish_mabs</a></p> <p>Contact information: francisco.wilhelmi@upf.edu</p>
Supporting data and code for "Water vapor estimation using wireless two-way interferometry (Wi-Wi)"
<p>Notes on generating figures for Radio Science paper.<br> Nobuyasu Shiga<br> 2019/3/9</p> <p>Fig. 4<br> File name: PhaseShifter.eps<br> Matlab file: PhaseShifter.m<br> Raw Data: 20181002124143PhaseShifter.csv</p> <p>Fig. 7<br> File name: WMRain091418.eps<br> Matlab file: WaterVaporAnalysis180913paper.m<br> Raw Data: avg20180913162410.csv %WiWi<br> WXT520_M_20180914_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20180918_2350.txt<br> 01_min1_20180914_20180918Mod.csv %rain gauge</p> <p>Fig. 8<br> File name: WMRainDec.eps<br> Matlab file: WaterVaporAnalysis1202.m<br> Raw Data: avg20181130184636.csv %WiWi<br> WXT520_M_20181202_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20181208_2350.txt<br> WeatherAVG20181202-20181208.csv %meteorological equipment @SWT<br> 01_min1_20181202_20181208Mod.csv %precipitation</p> <p>Fig. 9<br> File name: WMR0917.eps<br> Matlab file: WaterVaporAnalysis180913paper.m<br> Raw Data: avg20180913162410.csv %WiWi<br> WXT520_M_20180917_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20180917_2350.txt<br> North15_MP3000.txt<br> South15_MP3000.txt</p> <p>Fig. 10<br> File name: WMGR0917.eps<br> Matlab file: WaterVaporAnalysis180913paper.m<br> Raw Data: avg20180913162410.csv %WiWi<br> WXT520_M_20180917_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20180917_2350.txt<br> KGN3_ZWD.txt<br> Zenith_MP3000.txt</p> <p> </p>
Reliable Many-to-Many Routing in Wireless Sensor Networks Using Ant Colony Optimisation
<p>Results files for testing of ACO protocol for many to many routing in wireless sensor networks. </p>
Occupancy Sensing and Activity Recognition with Cameras and Wireless Sensors
<p>This dataset contains human activity data from a wireless sensing system, which includes a Doppler motion sensor and a wireless network. The Doppler sensor is a low-cost dual Doppler sensor modified from a commercial-off-the-shelf range-controlled radar, which operates at 5.8 GHz with two directional antennas. The wireless network uses four IEEE 802.15.4 radio nodes (CC2531 from TI) to create a mesh network to measure the RSS between each pair of radio nodes operating on the 16 frequency channels at 2.4 GHz.</p> <p>For the activity experiment, we recruited human subjects to perform 42 trials of four activities (each one with two minutes duration): (1) walking in a room (10 trials), (2) sitting in a chair (10 trials), (3) lying on a bed (12 trials), and (4) body turning on a bed (10 trials). For the walking activity, the human subjects walk along different paths at different locations in the room. For the lying on bed activity, we ask human subjects to breathe normally on bed with three orientations facing upwards, right and left. Finally, for the turning on bed case, human subjects turn their bodies from one side to the other on bed with random time intervals. We also recorded two-minute data of the empty room case before and after each human subject trial. Note that each data file name has its corresponding activity in it, so it is pretty self-explanatory. </p>
Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings
<p>This is the pseudonymized data of the paper "Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings" by Hiller v. Gärtringen et al. 2024.</p> <p>Each entry represents a unique wireless M-Bus device that was captured during our field study.</p> <p>Manufacturers and serial numbers are mapped to new identifiers.<br>Payload was removed.</p> <p>The meaning of the columns in the data set are:</p> <table> <tbody> <tr> <td><strong>name</strong></td> <td><strong>type and manifestations</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>id</td> <td>integer</td> <td> <p>Unique for each wireless transmitting device.<br>Counting up from 1 to n of devices.</p> </td> </tr> <tr> <td>manufacturer</td> <td> <p>enumeration</p> <ul> <li>MAN1 - MAN16</li> </ul> </td> <td>Pseudonymized manufacturer identifier.</td> </tr> <tr> <td>device type</td> <td> <p>enumeration</p> <ul> <li>heat cost allocator</li> <li>heat meter</li> <li>temperature or humidity sensor</li> <li>warm water meter</li> <li>water meter</li> <li>radio control device</li> <li>smoke detector</li> <li>unknown type</li> </ul> </td> <td>Device types are described in EN 13757-7 Table 13</td> </tr> <tr> <td>number of telegrams</td> <td>integer</td> <td>Number of telegrams received from the device.</td> </tr> <tr> <td>has DLL Encryption</td> <td>boolean</td> <td>Indicating, if the device uses DLL encryption.</td> </tr> <tr> <td>AES mode</td> <td> <p>enumeration</p> <ul> <li>not encrypted (mode 0)</li> <li>AES-CBC static key (mode 5)</li> <li>AES-CBC dynamic key (mode 7)</li> <li>AES-CCM (mode 10)</li> </ul> </td> <td>Indicates the AES encryption mode.</td> </tr> <tr> <td>detected in 2022</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>detected in 2023</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>interpretable</td> <td>boolean</td> <td> <p>Indicates whether we identified the message as interpretable.<br>For a detailed description, see the paper.</p> </td> </tr> </tbody> </table> <p> </p>
Weighted Link Schedules in 100-node Fixed Topology Wireless Networks
<p>This is a data repo for the data samples used for learning the link scheduling in a fixed-topology placed networks. This data set contains samples for 100-node networks, and the scheduling decisions are made from delayed column generation (DCG) algorithm.</p>
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
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.