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229 results for “wireless”
Weighted Link Schedules in sub-100 node Random Topology Wireless Networks
<p>This is a data repo for the data samples used for learning the link scheduling in a randomly placed networks. This data set contains samples for sub 100-node networks, and the scheduling decisions are made from delayed column generation (DCG) algorithm.</p>
Weighted Link Schedules in sub-100 node Fixed Topology Wireless Networks
<p>This data repo contains the link schedules in a multi-hop wireless networks that aims to maximize the weighted throughput. Each instance is represented by a numpy data file that contains the necessary data fields to reconstruct the original problem instance.</p> <p> </p> <p>Note: 100_7_new.tar.xz should be in the repo https://zenodo.org/deposit/7671940.</p>
Field Application of a High-Power Density Electromagnetic Energy Harvester to Power Wireless Sensors in Transportation Infrastructures
<p>Traffic-induced vibration of transportation infrastructures is a reliable source of kinetic energy, which can be harvested to power conventional monitoring sensors and peripherals installed on bridges, thereby reducing some dependence on non-renewable energy. The highway statistics shows that the average daily vehicles miles travelled in the US is more than 5 billion. This is a massive source of kinetic energy that lies unused in the national transportation network. This study focuses on the design and field testing of a high-power density electromagnetic energy harvester (EMEH) to convert such a kinetic energy into electrical energy for powering ubiquitous sensors installed on transportation infrastructures. The principal investigators have been investigating the design of the EMEH using analytical and finite element simulations, as well as, its laboratory prototype fabrication and testing in the first phase. The proposed EMEH utilizes the innovative concept of creating planar array of large number of small permanent magnets through certain optimization criteria to achieve strong and focused magnetic field in a particular orientation. The proposed EMEH has a compact design, such that it can be integrated into the power circuit of wireless sensor nodes (WSNs) and installed at suitable part of a transportation infrastructure without elaborate wiring. It is capable of continuously charging the rechargeable battery of a WSN, thereby extending the lifespan of the monitoring system, almost, indefinitely. For the next phase of this research, the principal investigators propose the development and field implementation of a larger scale and more compact version of the EMEH with a minimum of 500 mW output power to be installed on selected transportation infrastructures for the evaluation of its energy harvesting efficiency and capability to derive different types of monitoring sensors and peripherals. Three different highway bridges with different fundamental frequencies, ideally between 2Hz to 8Hz, will be selected for the field testing of the EMEH. An acceleration sensor will be used to record the traffic-induced vibration of each bridge during a normal daily traffic that after signal processing is used to measure the fundamental frequency of that bridge. The dynamic characteristics of the proposed EMEH (i.e. tip mass and spring stiffness) will be modified to put it into a resonant condition with the bridge by matching their natural frequencies. The output power will be monitored and used to continuously charge a rechargeable battery powering a wireless sensor. The focus is on the feasibility of the proposed EMEH to power sensors that are used to regularly monitor the structural integrity of materials and components of highway bridges such as acceleration and temperature sensors.</p>
Wireless Physiologic Monitoring in Postpartum Women
ClinicalTrials.gov study NCT04060667. IPD Sharing: NO. Countries: 1. Publications: 1.
Pilot Study Using a Wireless Motility Capsule
ClinicalTrials.gov study NCT01102894. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Wireless Disposable SpO2 Sensor Hypoxia Testing
ClinicalTrials.gov study NCT06211530. IPD Sharing: NO. Countries: 1. Publications: 5.
Perioperative Accuracy of the Raiing Wireless Axillary Thermometer
ClinicalTrials.gov study NCT02756910. IPD Sharing: NO. Countries: 1. Publications: 47.
Wirelessly Observed Therapy in Comparison to Directly Observed Therapy for the Treatment of Tuberculosis
ClinicalTrials.gov study NCT01960257. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Wireless flow-powered miniature robot capable of traversing tubular structures
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Data from: Wirelessly steerable bioelectronic neuromuscular robots adapting neurocardiac junctions
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Data from: Organic electro-scattering antenna: Wireless and multisite probing of electrical potentials with high spatial resolution
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Data from: Polyelectrolyte-based wireless and drift-free iontronic sensors for orthodontic sensing
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PODC 2020 live recording, session: wireless protocols and graph models
<p>A recording of the "wireless protocols and graph models" session in PODC 2020. This session took place on Wednesday, 5-Aug-2020, and was chaired by Andréa Richa.</p>
(In review) Evaluating portable EEG: A comparison between two wireless systems (EPOC Flex and LiveAmp) and the wired BrainAmp system
<p>This dataset contains EEG data from a study investigating the performance of two portable EEG systems (i.e., the EMOTIV EPOC Flex and the BrainProducts LiveAmp) to a lab-based system (i.e., the BrainProducts BrainAmp). The study employed a within-subjects design, divided into three sessions, each featuring a different EEG system. Participants completed all sessions, which included five tasks administered in the same order. The sessions were counterbalanced. The task order was as follows: 1) Steady State Visual Evoked Potential (SSVEP), 2) active auditory oddball, 3) passive auditory oddball, 4) face perception, and 5) resting state.</p> <p>This deposit contains the raw EEG data, the output data obtained after processing the EEG files and the scripts used for statistical analyses.</p>
IRShield: A Countermeasure Against Adversarial Physical-Layer Wireless Sensing
<p>Wi-Fi CSI datasets and Python evaluation scripts to generate plots from our paper 'IRShield: A Countermeasure Against Adversarial Physical-Layer Wireless Sensing', to appear at the 43rd IEEE Symposium on<br> Security and Privacy (S&P), 2022.</p>
Magnetically actuated gearbox for the wireless control of millimeter-scale robots
<p>The limited force or torque outputs of miniature magnetic actuators constrain the locomotion performances and functionalities of magnetic millimeter-scale robots. Here, we present a magnetically actuated gearbox with a maximum size of 3 millimeters for driving wireless millirobots. The gearbox is assembled using microgears that have reference diameters down to 270 micrometers and are made of aluminum-filled epoxy resins through casting. With a magnetic disk attached to the input shaft, the gearbox can be driven by a rotating external magnetic field, which is not more than 6.8 millitesla, to produce torque of up to 0.182 millinewton meters at 40 hertz. The corresponding torque and power densities are 12.15 micronewton meters per cubic millimeter and 8.93 microwatt per cubic millimeter, respectively. The transmission efficiency of the gearbox in the air is between 25.1 and 29.2% at actuation frequencies ranging from 1 to 40 hertz, and it lowers when the gearbox is actuated in viscous liquids. This miniature gearbox can be accessed wirelessly and integrated with various functional modules to repeatedly generate large actuation forces, strains, and speeds; store energy in elastic components; and lock up mechanical linkages. These characteristics enable us to achieve a peristaltic robot that can crawl on a flat substrate or inside a tube, a jumping robot with a tunable jumping height, a clamping robot that can sample solid objects by grasping, a needle-puncture robot that can take samples from the inside of the target, and a syringe robot that can collect or release liquids.</p>
Instances of the problem of Designing a Multi-sink Clustered Wireless Sensor Network.
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Measuring Network Latency from a Wireless ISP: Variations Within and Across Subnets - Dataset
<h1>Overview</h1> <p>This archive contains the data from a network latency measurement campaign conducted inside the wireless ISP JackRabbit Wireless between 2023-10-30 and 2023-12-01. The measurement setup and an analysis of the data is provided in the paper "Measuring Network Latency from a Wireless ISP: Variations Within and Across Subnets", accepted for the ACM Internet Measurement Conference 2024. </p> <p>Code for parsing the data and reproducing the plots can be found at <a href="https://github.com/simosund/Measuring-Network-Latency-from-a-Wireless-ISP.git">https://github.com/simosund/Measuring-Network-Latency-from-a-Wireless-ISP.git</a></p> <h2>Manifest</h2> <ul> <li><strong>The raw subfolder:</strong> contains the original data captured by epping from the measurement campaign. However, note that we have merged all subnet entries from the internal network into a single entry at each timepoint to avoid revealing details about the subnet structure on the internal network. Some internal subnet entries that only carry control traffic have also been removed. It is still possible to recreate all the results from the paper with this data.</li> <li><strong>The processed subfolder: </strong>This contains processed versions of the data that have been merged in a few different ways, allowing the results from the paper to be recreated quickly without having to parse all of the original data. <ul> <li><strong>subnets_stats_merged.json:</strong> The data from the entire measurement period have been merged into a single entry per subnet</li> <li><strong>{lan,wan}_per10s_stats_merged.json:</strong> All the internal or external data merged into a single entry per timepoint (10s).</li> <li><strong>top_100asn_stats_10min_json:</strong> The external data merged per ASN (for the 100 ASNs with the highest amount of downlink traffic during the measurement period) at a 10 minute resolution.</li> <li><strong>protocol_counters.hdf5: </strong>Counters for number of packets of each <a href="https://www.iana.org/assignments/protocol-numbers/protocol-numbers.xhtml">IP-protocol</a>. The most common protocols, such as TCP, UDP and ICMP are named, where less common ones are just numbered.</li> <li><strong>ecn_counters.hdf5:</strong> Counters of the four ECN-marks (Not-ECT, ECT(1), ECT(0) and CE).</li> </ul> </li> <li><strong>The overhead_measurements subfolder: </strong>CPU and network load measurements from a later period used to estimate the overhead of the epping tool. In the covered period, epping was running between the 19th of April and the 3rd of May. <ul> <li><strong>InfluxDB_CPU_20240413-20240513.csv:</strong> CSV with hourly CPU utilization per core on the DuT.</li> <li><strong>jackrabbit-shaper-per\_day-20240421-20240513.csv:</strong> CSV with network load metrics (throughput in bytes and packets) every 48 minutes.</li> <li><strong>proglistoutput_after_enabling_bpf_stats_24hr.txt:</strong> Output from <code>bpftool prog list</code> after enabling <code>sysctl -w kernel.bpf_stats_enabled=1</code>, containing run counts and cumulative runtime for all eBPF programs on the system.</li> </ul> </li> <li><strong>rrc14_20231115_ipasn.dat: </strong>Data from RIPE´s route collector RRC14 in the middle of the measurement campaign (2023-11-15), used to map subnets into ASNs.</li> <li><strong>asn.txt:</strong> RIPE's list of AS names (from <a href="https://ftp.ripe.net/ripe/asnames/asn.txt">https://ftp.ripe.net/ripe/asnames/asn.txt</a>), used to translate ASNs into AS names.</li> </ul> <h2>Data format for raw data</h2> <p>The raw data is organized into one subfolder per day, with each subfolder containing one compressed json-file per minute. All files have been included in a tar-archive.</p> <p>The JSON format in each file consists of a single list of entries. There are three types of entries.</p> <h3>Configuration entry</h3> <p>Each file starts with one configuration entry, containing the following fields:<br><br></p> <ul> <li><strong> timestamp:</strong> Unix timestamp in nanoseconds when entry was created</li> <li><strong>bins: </strong>The maximum number of bins in the histogram of a subnet entry. Always 250 in this dataset. Note that histograms may contain less bins as unused bins in the upper range are truncated.</li> <li><strong>bin_width_ns:</strong> The width of the bins in nanoseconds. Always 4000000 (4ms) in this dataset.</li> <li><strong>aggregation_interval_ns: </strong>How often the aggregated stats are reported in nanoseconds. Always 10000000000 (10s) in this dataset.</li> <li><strong>timeout_interval_ns: </strong>How long epping will keep a subnet entry around since it last saw traffic for it. Always 30000000000 (30s) in this dataset. Note that a subnet entry may only be deleted after its stats are reported, so no stats will be lost regardless of value.</li> <li><strong>ipv4_prefix_len:</strong> Subnet size to aggregate IPv4 traffic. Always 24 (/24) in this dataset.</li> <li><strong>ipv6_prefix_len: </strong>Subnet size to aggregate IPv6 traffic. Always /48 in this dataset.</li> </ul> <p>Example:</p> <pre><code>{<span>"timestamp"</span>: <span>1698661062033443066</span>, <span>"bins"</span>: <span>250</span>, <span>"bin_width_ns"</span>: <span>4000000</span>, <span>"aggregation_interval_ns"</span>: <span>10000000000</span>, <span>"timeout_interval_ns"</span>: <span>30000000000</span>, <span>"ipv4_prefix_len"</span>: <span>24</span>, <span>"ipv6_prefix_len"</span>: <span>48</span>}</code></pre> <h3><br>Subnet stats entry</h3> <p>The most common entry will be the subnet stats entry, which contains the aggregated stats for one /24 subnet (or /48 if IPv6) over a 10 second period. Each entry always contains the following fields:</p> <ul> <li><strong>timestamp:</strong> A unix timestamp in nanoseconds of when the entry were reported. The entry will thus contain the stats for the period [timestamp - 10s, timestamp].</li> <li><strong>ip_prefix:</strong> The subnet in standard IPv4 or IPv6 CIDR notation.</li> <li><strong>rx_stats</strong> and <strong>tx_stats:</strong> Packet and byte counts for packets received from/transmitted to the subnet. Note that <em>rx</em> and <em>tx</em> is from the perspective of the epping node, not the perspective of the subnet, so <em>rx</em> is packets epping has seen sent from the subnet, and <em>tx</em> is packets epping has seen sent towards the subnet. <ul> <li><strong>TCP_TS</strong>, <strong>TCP_noTS</strong> and <strong>other:</strong> Entries for TCP traffic that has TCP timestamps enabled, TCP traffic with TCP timestamps disabled, and non-TCP traffic, respectively. The entry will be omitted if there is no traffic of the relevant type. <ul> <li><strong>packets:</strong> Number of packets of the specific type</li> <li><strong>bytes:</strong> Sum of bytes in the observed packets, including headers (up to and including the 14-byte Ethernet header).</li> </ul> </li> </ul> </li> </ul> <p>Additionally, the following fields will be present if any RTT has been observed for the subnet. Note that the subnet will include RTTs for traffic going towards the subnet, i.e. SRC -> subnet -> SRC.</p> <ul> <li><strong>min_rtt:</strong> The minimum observed RTT in nanoseconds.</li> <li><strong>max_rtt:</strong> The maximum observed RTT in nanoseconds.</li> <li><strong>histogram:</strong>: A histogram with all observed RTTs. The bin width and maximum number of bins is specified by the configuration entry (always 4ms wide bins and max 250 bins in this dataset). Note that the histogram will truncate 0-bins after the last non-zero bin (e.g. if the maximum RTT is 7 ms, the histogram will only contain 2 bins).</li> <li><strong>count_rtt:</strong> The total number of RTTs observed (sum of histogram bin counts)</li> <li><strong>mean_rtt:</strong> The approximate mean RTT in nanoseconds.</li> <li><strong>median_rtt:</strong> The approximate median RTT in nanoseconds.</li> <li><strong>p95_rtt:</strong> The approximate 95th percentile of the RTTs in nanoseconds</li> </ul> <p>Note that <em>mean_rtt</em>, <em>median_rtt</em> and <em>p95_rtt</em> are all calculated from the histogram, assuming all RTTs are in the middle of the histogram bins, and will thus be +/- 2ms of the real value in this dataset.</p> <p>Example:</p> <div> <pre><code>{<span>"timestamp"</span>: <span>1698661072800129847</span>, <span>"ip_prefix"</span>: <span><span>"</span>100.64.0.0/24<span>"</span></span>, <span>"rx_stats"</span>: {<span>"TCP_TS"</span>: {<span>"packets"</span>: <span>64816</span>, <span>"bytes"</span>: <span>23063341</span>}, <span>"other"</span>: {<span>"packets"</span>: <span>39783</span>, <span>"bytes"</span>: <span>16074723</span>}, <span>"TCP_noTS"</span>: {<span>"packets"</span>: <span>8505</span>, <span>"bytes"</span>: <span>7403649</span>}}, <span>"tx_stats"</span>: {<span>"TCP_TS"</span>: {<span>"packets"</span>: <span>134424</span>, <span>"bytes"</span>: <span>175370347</span>}, <span>"other"</span>: {<span>"packets"</span>: <span>76267</span>, <span>"bytes"</span>: <span>86104300</span>}, <span>"TCP_noTS"</span>: {<span>"packets"</span>: <span>7946</span>, <span>"bytes"</span>: <span>5174210</span>}}, <span>"min_rtt"</span>: <span>50766</span>, <span>"max_rtt"</span>: <span>440315940</span>, <span>"histogram"</span>: [<span>622</span>, <span>596</span>, <span>835</span>, <span>1106</span>, <span>932</span>, <span>410</span>, <span>228</span>, <span>139</span>, <span>108</span>, <span>68</span>, <span>78</span>, <span>79</span>, <span>73</span>, <span>63</span>, <span>49</span>, <span>35</span>, <span>22</span>, <span>33</span>, <span>31</span>, <span>24</span>, <span>21</span>, <span>18</span>, <span>23</span>, <span>15</span>, <span>20</span>, <span>20</span>, <span>37</span>, <span>35</span>, <span>96</span>, <span>113</span>, <span>95</span>, <span>80</span>, <span>36</span>, <span>22</span>, <span>24</span>, <span>27</span>, <span>17</span>, <span>31</span>, <span>9</span>, <span>9</span>, <span>14</span>, <span>18</span>, <span>10</span>, <span>21</span>, <span>22</span>, <span>21</span>, <span>22</span>, <span>25</span>, <span>8</span>, <span>9</span>, <span>16</span>, <span>8</span>, <span>12</span>, <span>9</span>, <span>7</span>, <span>5</span>, <span>6</span>, <span>6</span>, <span>3</span>, <span>4</span>, <span>4</span>, <span>2</span>, <span>2</span>, <span>1</span>, <span>1</span>, <span>3</span>, <span>3</span>, <span>1</span>, <span>0</span>, <span>3</span>, <span>2</span>, <span>0</span>, <span>1</span>, <span>1</span>, <span>1</span>, <span>1</span>, <span>2</span>, <span>2</span>, <span>1</span>, <span>2</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>1</span>, <span>2</span>, <span>0</span>, <span>1</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>1</span>, <span>0</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>1</span>], <span>"count_rtt"</span>: <span>6568</span>, <span>"mean_rtt"</span>: <span>37529232.64311815</span>, <span>"median_rtt"</span>: <span>18000000.0</span>, <span>"p95_rtt"</span>: <span>150000000.0</span>}</code></pre> </div> <div> <h3>Global counters entry</h3> <p>Each report interval will also contain one entry with global counters, with the following fields:</p> <ul> <li><strong>timestamp:</strong> Unix timestamp in nanoseconds of when the entry was reported. The entry will thus contain stats for the period [timestamp - 10s, timestamp].</li> <li><strong>protocol_counters:</strong> A set of fields with packet and byte counters for the <a href="https://www.iana.org/assignments/protocol-numbers/protocol-numbers.xhtml" rel="nofollow">protocols indicated in the IP-header</a>. The most common protocols will be named, specifically <strong>TCP</strong>, <strong>UDP</strong>, <strong>ICMP</strong>, <strong>ICMPv6</strong> and the special <strong>non-IP</strong> for non-IP packets. All other protocols will simply use the protocol number. Protocols for which no traffic has been observed are omitted. <ul> <li><strong>packets:</strong> The number of packets of the specific type</li> <li><strong>bytes:</strong> Sum of bytes in the observed packets, including headers (up to and including the 14-byte Ethernet header). Only the list of named protocols above will include this byte counter.</li> </ul> </li> <li><strong>ecn_counters:</strong> Counters for the four distinct ECN code points in the IP header. May contain the fields <strong>no_ECT</strong> (00), <strong>ECT1</strong> (01), <strong>ECT0</strong> (10) and <strong>CE</strong> (11).</li> </ul> </div> <p>Example:</p> <div> <pre><code>{<span>"timestamp"</span>: <span>1698661072848798575</span>, <span>"protocol_counters"</span>: {<span>"TCP"</span>: {<span>"packets"</span>: <span>249032</span>, <span>"bytes"</span>: <span>242282230</span>}, <span>"UDP"</span>: {<span>"packets"</span>: <span>119778</span>, <span>"bytes"</span>: <span>108251262</span>}, <span>"ICMP"</span>: {<span>"packets"</span>: <span>1523</span>, <span>"bytes"</span>: <span>445347</span>}, <span>"ICMPv6"</span>: {<span>"packets"</span>: <span>87</span>, <span>"bytes"</span>: <span>25031</span>}, <span>"41"</span>: {<span>"packets"</span>: <span>1</span>}, <span>"47"</span>: {<span>"packets"</span>: <span>251</span>}, <span>"89"</span>: {<span>"packets"</span>: <span>22</span>}}, <span>"ecn_counters"</span>: {<span>"no_ECT"</span>: <span>347980</span>, <span>"ECT1"</span>: <span>2</span>, <span>"ECT0"</span>: <span>22709</span>, <span>"CE"</span>: <span>3</span>}, <span>"errors"</span>: {}}</code></pre> </div>
Folded wave guide TWT for 92 – 95 GHz band outdoor wireless frontend
<p>Underlying data from the conference paper: C. Paoloni, F. André, V. Krozer, R. Zimmermann, Q.T. Le, R. Letizia, S. Kohler, A. Sabaawi, G. Ulisse, “Folded wave guide TWT for 92 – 95 GHz band outdoor wireless frontend”, Workshop on Microwave Technology and Techniques (MTT), ESA/ESTEC, The Netherlands, April 2017.</p>
New key management scheme lattice-based for wireless sensor networks
<p><span>The cluster structure can effectively reduce the cost of mutual authentication of sensor nodes, which is conducive to the expansion of the network, and can guarantee the security of authentication between sensor nodes even in the post-quantum era. The size of the lattice-based authentication proposed in this paper does not change much with the continuous improvement of the security level of the RSA algorithm. The size of the certificate is kept at a stable level, which is more suitable for encrypting large data at a high-security level.</span></p>
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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.