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2,206 results for “Communications”
Dataset: On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas
<p>Dataset containing the geodata needed to replicate the analysis and the results related the research article: <a href="https://doi.org/10.1109/TNSM.2022.3190634">"On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas"</a> published on Transactions of Network and Service Management.</p> <p> </p> <p>This dataset contains all the data used in the research article: "On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas" published on Transactions of Network and Service Management.</p> <p>It is divided into three main archives:</p> <ul> <li>The first archive, called <strong>data.zip</strong>, contains the Data Surface Model (DSM) used to generate the intervisibility graphs. These maps have been aggregated from different sources and they are all released under a CC-BY-SA 4.0 license. The files follow the naming format {area}_{type}.tif, where type can be one of the followings: <ul> <li>'<strong>buildings_mask</strong>': contains the buildings' shapes in raster format. These have been obtained by rasterizing the OpenStreetMap vectorial buildings data.</li> <li>'<strong>roads_mask</strong>': contains the roads' shapes in raster format. These have been obtained by expading the OpenStreetMap road graph by a given a mount of meters and rasterizing the result.</li> <li>'<strong>dtm</strong>': the Data Terrain Model in raster format</li> <li>'': the polished version of the Data Surface Model, where the values of the DSM are used only for the buildings (to map the roofs) and outside of the buildings the values from the dtm are used. In this way trees and unmapped buildings are not considered.</li> </ul> </li> <li>The second archive, called <strong>results.zip</strong>, contains the outcome of our algorithm for the optimal BS locations. The folder format is the following: 'results/{area}/threestep/{sa_id}/{ranking_function}/{k}/{ratio}/{lambda} : <ul> <li>'area': corresponds to the macro area used for that specific run</li> <li>'sa_id': corresponds to the subarea id (from 0 to 4) of a specific block of that area</li> <li>'ranking_function': corresponds to the ranking function used by the algorithm for that specific result (see the research article for more information)</li> <li>'ratio': corresponds to the percentage of buildings used (see the research article for more information)</li> <li>'lambda': corresponds to the density of Base Stations deployed (see the research article for more information).</li> </ul> </li> </ul> <p>The data are licensed as follows:</p> <p>The DSM and DTM are licensed depending on the area:</p> <ul> <li><strong>Trento</strong>: The data are released by <a href="https://www.provincia.tn.it/">Provincia Autonoma di Trento</a> under a <a href="https://creativecommons.org/licenses/by/2.5/">CC-BY 2.5 License</a></li> <li><strong>Firenze</strong>: The data are released by <a href="https://www.regione.toscana.it/">Regione Toscana</a> under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0 License</a></li> <li><strong>Napoli</strong>: The data are released by <a href="https://cittametropolitana.na.it/">Città Metropolitana di Napoli</a> under a <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA 4.0 License</a></li> </ul> <p>The OpenStreetMap data have been obtained by <a href="https://download.geofabrik.de/">geofabrik.de</a> and are released under an <a href="https://opendatacommons.org/licenses/odbl/">Open Data Commons Open Database License</a></p> <p> </p> <p>All the results can be replicated using our code, available on <a href="https://github.com/UniVe-NeDS-Lab/TrueBS">Github</a>.</p> <p> </p> <p>In order to cite this dataset please cite the original research article:</p> <p> </p> <pre><code>@article{9828530, author={Gemmi, Gabriele and Cigno, Renato Lo and Maccari, Leonardo}, journal={IEEE Transactions on Network and Service Management}, title={On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas}, year={2022}, volume={}, number={}, pages={1-1}, doi={10.1109/TNSM.2022.3190634} } </code></pre> <p> </p>
Source Data for Xiao et al., Topological Superfluid Defects with Discrete Point Group Symmetries , Nature Communications 13, 4635 (2022).
<p>Source data for Figures 2-5. Source data for Supplementary Figures S2-S5 available upon request to David Hall (dshall@amherst.edu).</p>
Simulation outputs associated to the study: "Brief communication: Everest South Col Glacier did not thin during the last three decades" by Brun et al.
<p>This folder contains the outputs of the South Col Glacier mass balance simulations used in the study: "Brief communication: Everest South Col Glacier did not thin during the last three decades" by Brun et al., submitted to The Cryophere Journal in August 2022. The simulation outputs are obtained with two different models: COSIPY (Sauter et al., 2010) and Crocus (Vionnet et al., 2012). Note that forcing and initialization information are provided to reproduce Crocus simulations.</p>
Received Signal Modeling and BER Analysis for Molecular SISO Communications
<p>This file is to support the claim marked by footnote #2.</p>
L-DOPA functions as a plant pheromone for belowground anti-herbivory communication
<p>Dataset published in Letter for Ecology Letters</p> <p>While mechanisms of plant-plant communication for alerting neighbouring plants of an imminent insect herbivore attack have been described aboveground via the production of volatile organic compounds (VOCs), we are yet to decipher the specific components of plant-plant signalling belowground. Using bioassay-guided fractionation, we isolated and identified the non-protein amino acid , released from roots of Acyrtosiphon pisum aphid-infested Vicia faba plants, as an active compound in triggering the production of VOCs released aboveground in uninfested plants. In behavioural assays, we show that after contact with healthy plants become highly attractive to the aphid parasitoid (Aphidius ervi), as if they were infested by aphids. We conclude that neurotransmitter precursor L-DOPA, originally described as a brain can also enhance immunity in plants.</p>
KENFIN-EDURA: Explaining non-communicable disease-related behaviour in the context of urbanization, family and wealth
<p><span>Background </span></p> <p><span>The prevalence of non-communicable diseases is increasing in lower-middle-income countries as these countries transition to unhealthy lifestyles. The transition is mostly predominant in urban areas. We assessed the association between wealth and obesity in two sub-counties </span><span>in Nairobi City County, Kenya, in the context of family and poverty.</span></p> <p><span>Results </span></p> <p><span>A total of 149 households, response rate of 93%, participated, 72 from Embakasi and 77 from Langata. Most of the participants residing in Embakasi belonged to the lower income and education groups whereas participants residing in Langata belonged to the higher income and education groups. </span><span>About 30% of the pre-adolescent participants in Langata were with at least overweight, whereas the respective number in Embakasi was only 6% (p<0.001). In contrast, the prevalence of adults (mostly mothers) with overweight and obesity was high (65%) and similar in the two study areas. Wealth</span> <span>(</span><span>b</span><span> = 0.01; SE 0.0; p=0.003) and income (</span><span>b</span><span> = 0.29; SE 0.11; p=0.009) predicted higher BMI z-score in pre-adolescents. </span></p> <p><span>Conclusions</span></p> <p><span>In Nairobi, pre-adolescent overweight was already highly prevalent in the middle-income area, while the proportion of women with overweight/obesity was high also in the low-income area. These results suggest that a lifestyle promoting obesity is prevalent even in lower income areas in urban Kenya, and this is a strong justification for promoting healthy lifestyles across all socio-economic classes.</span></p>
Nokia 9000 Communicator cellular telephone
This item is a cellular phone integrated in a single housing with a pocket computer, which was designed with business applications in mind. The purpose of the device was to perform simple office tasks, such as agenda management, document editing, viewing simple webpages, or handling faxes and emails. Although the word was not in use at the time of the Nokia 9000 launch, the device may be considered as one of the first ever smartphones on the market, making it one of the most significant designs in the history of communication technology. It came into existence as a result of the development of second-generation cellular networks (GSM), which, apart from voice calls, also offered the function of digital data transfer. This possibility was usually implemented with the use of a portable (laptop) or pocket (palmtop) computer tethered to a cell phone. Manufacturer: Nokia, Finland, after 1996 Inv. no.: MIM1730/VIII-55 Model prepared on the basis of photogrammetric measurements Licence: CC BY-NC-SA Source: Objaverse 1.0 / Sketchfab
Data from the paper: D. Malko and A. Kucernak, "Kinetic isotope effect in the oxygen reduction reaction (ORR) over Fe-N/C catalysts under acidic and alkaline conditions", Electrochemistry Communications,2017, https://doi.org/10.1016/j.elecom.2017.09.004
<p>Data used to generate the figures in the paper: D. Malko and A. Kucernak, "Kinetic isotope effect in the oxygen reduction reaction (ORR) over Fe-N/C catalysts under acidic and alkaline conditions", Electrochemistry Communications,2017, https://doi.org/10.1016/j.elecom.2017.09.004</p> <p> </p>
The KISS principle in Software-Defined Networking: a framework for secure communications - dkreutz data
<p>Scripts, summarized data, plots</p> <p>type of data: raw data and processed data</p>
Data for Evaluation of altered cell-cell communication between glia and neurons in the hippocampus of 3xTg-AD mice at two time points
<p>processed_data.tar.gz contains all files from the data directory associated with the 230418_TS_AgingCCC GitHub project and includes the following:</p> <ul> <li> <p>CellRangerCounts/</p> </li> <ul> <li> <p>post_soupX/ : contains 12 directories for 12 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but the post_soupX directory contains one directory for each of the 12 samples:</p> </li> <ul> <li> <p>S01_6m_AD/</p> </li> <ul> <li> <p>barcodes.tsv</p> </li> <li> <p>genes.tsv</p> </li> <li> <p>matrix.mtx</p> </li> </ul> </ul> <li> <p>pre_soupX/ : contains 12 directories for 12 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this directory contains 1 directory for each individual sample:</p> </li> <ul> <li> <p>S01_6m_AD/outs/</p> </li> <ul> <li> <p>filtered_feature_ bc_matrix.h5</p> </li> <li> <p>raw_feature_bc_matrix.h5 </p> </li> </ul> </ul> </ul> <li> <p>PANDA_inputs/</p> </li> <ul> <li> <p>PANDA_exp_files_array.txt: Text files with files paths to expression inputs for PANDA gene regulatory networks.</p> </li> <li> <p>mm10_TFmotifs.txt: Mouse TF motif input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023 </p> </li> <li> <p>mm10_ppi.txt: Mouse protein-protein interaction information from SringDB input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023 </p> </li> </ul> <li> <p>ccc/</p> </li> <ul> <li> <p>nichenet_v2_prior/</p> </li> <ul> <li> <p>gr_network_mouse_21122021.rds : accessed in December 2023, gene regulation network – gene regulatory information from MultiNicheNet</p> </li> <li> <p>ligand_target_matrix_nsga2r_final_mouse.rds: accessed in December 2023, ligand target matrix for mouse from MultiNicheNet.</p> </li> <li> <p>ligand_tf_matrix_nsga2r_final_mouse.rds: accessed in December 2023, mouse ligand tf matrix for signaling path determination from MultiNicheNet</p> </li> <li> <p>lr_network_mouse_21122021.rds : accessed in December 2023, ligand-receptor matrix from MultiNicheNet</p> </li> <li> <p>signaling_network_mouse_21122021.rds : accessed in December 2023, signaling network – protein-protein interaction information from MultiNicheNet for mouse</p> </li> <li> <p>weighted_networks_nsga2r_final_mouse.rds : accessed in October 2023, networks weighted by literature evidence from MultiNicheNet for mouse</p> </li> </ul> <li> <p>multinichenet_output.rds : MultiNicheNet output for 3xTg-AD snRNA-seq data</p> </li> <li> <p>12m_signaling_igraph_objects.rds : list of igraph objects for 93 LRTs and their signaling mediators at 12 months</p> </li> <li> <p>6m_signaling_igraph_objects.rds :list of igraph objects for 2 LRTs and their signaling mediators at 6 months</p> </li> </ul> <li> <p>elisa/: CSV files of measured OD values for every ELISA.</p> </li> <ul> <li> <p>240319_ELISA_Ab40.csv: OD measurements for Ab40</p> </li> <li> <p>240319_ELISA_Ab42.csv: OD measurements for Ab42</p> </li> <li> <p>240319_ELISA_total_tau.csv: OD measurements for Total Tau</p> </li> </ul> <li> <p>panda/: PANDA gene regulatory networks for each time point and condition in excitatory and inhibitory neurons. Used for differential gene targeting.</p> </li> <ul> <li> <p>excitatory_neurons_AD12.Rdata</p> </li> <li> <p>excitatory_neurons_AD6.Rdata</p> </li> <li> <p>excitatory_neurons_WT12.Rdata</p> </li> <li> <p>excitatory_neurons_WT6.Rdata</p> </li> <li> <p>inhibitory_neurons_AD12.Rdata</p> </li> <li> <p>inhibitory_neurons_AD6.Rdata</p> </li> <li> <p>inhibitory_neurons_WT12.Rdata</p> </li> <li> <p>inhibitory_neurons_WT6.Rdata</p> </li> </ul> <li> <p>pseudobulk/: includes pseudo bulk matrices for every cell type which were used for downstream analyses. Each matrix also includes metadata information on condition and time point.</p> </li> <ul> <li> <p>all_counts_ls.rds: List of all the pseudo bulk matrices (below).</p> </li> <li> <p>astrocytes.rds: pseudobulk matrix for astrocytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>endothelial_cells.rds: pseudobulk matrix for endothelial cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>ependymal_cells.rds: pseudobulk matrix for ependymal cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>excitatory_neurons.rds: pseudobulk matrix for excitatory neurons. Include time point and condition information for downstream analyses.</p> </li> <li> <p>fibroblasts.rds: pseudobulk matrix for fibroblasts. Include time point and condition information for downstream analyses.</p> </li> <li> <p>inhibitory_neurons.rds: pseudobulk matrix for inhibitory neurons. Include time point and condition information for downstream analyses.</p> </li> <li> <p>meningeal_cells.rds: pseudobulk matrix for meningeal cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>microglia.rds: pseudobulk matrix for microglia. Include time point and condition information for downstream analyses.</p> </li> <li> <p>oligodendrocytes.rds: pseudobulk matrix for oligodendrocytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>opcs.rds: pseudobulk matrix for oligodendrocyte progenitor cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>percicytes.rds: pseudobulk matrix for pericytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>rgcs.rds: pseudobulk matrix for retinal ganglion cells. Include time point and condition information for downstream analyses.</p> </li> </ul> <li> <p>pseudobulk_split/: Includes pseudo bulk count matrices split by time point and condition. Used for input to PANDA for gene regulatory network construction.</p> </li> <ul> <li> <p>excitatory_neurons_AD12.Rdata</p> </li> <li> <p>excitatory_neurons_AD6.Rdata</p> </li> <li> <p>excitatory_neurons_WT12.Rdata</p> </li> <li> <p>excitatory_neurons_WT6.Rdata</p> </li> <li> <p>inhibitory_neurons_AD12.Rdata</p> </li> <li> <p>inhibitory_neurons_AD6.Rdata</p> </li> <li> <p>inhibitory_neurons_WT12.Rdata</p> </li> <li> <p>inhibitory_neurons_WT6.Rdata</p> </li> </ul> <li> <p>seurat_preprocessing/</p> </li> <ul> <li> <p>filtered_seurat.rds : merged and filtered seurat object</p> </li> <li> <p>integrated_seurat.rds : seurat object integrated using harmony</p> </li> <li> <p>clustered_seurat.rds : clustered seurat object</p> </li> <li> <p>processed_seurat.rds : processed seurat object with final cell type assignments at specified resolution</p> </li> </ul> </ul> <p> </p> <p>Raw data publicly available on GEO under series accession: GSE261596</p>
Analysis: Systematic literature review PRISMA model results about extended, virtual and augmented reality applied to Science Communication
<div> <p>The results of the analysis made after the PRISMA process of the systematic literature review carried out about results about extended, virtual and augmented reality applied to Science Communication.</p> </div>
Supporting material for Kempf, M & Denis, S (2024): Resource dependency and communication networks in Early Neolithic Central-West Europe. Quaternary Environments and Humans
<p><span>Table 1. </span><span>Site names, location, and attributes of the sample used during the analysis. Sites are organised according to stage dependency. Coordinates are in WGS84, EPSG:4326.</span></p> <p><span><span>Table 2</span><span>.</span> Description of the site status<span>, based on the stages of the CO and the frequency of the sites according to the lengths of the calculated LCP at the different chronological stages (see repository for heatmaps of the LCP lengths: 10.5281/zenodo.10617484). Sites are classified following the 4 levels presented in Figure 5 in Kempf & Denis (2024). </span></span></p> <p> </p>
Nature Communications Release
<p>Data related to 'A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors'.</p> <p>Data has been recorded using the <strong>GAIA</strong>(Global Asyncronous Intelligent Array) Multielectrode Array.</p> <p>Custom-built code to analyze and visualize the data is also included.</p> <h3>Repository Structure</h3> <p>Code: </p> <ul> <li>main</li> <li>utils folder with helper functions including: loading, filtering, and plotting.</li> </ul> <p>Data:</p> <ul> <li>Raw data (.dat)</li> <li>Filtered data (.npy) and PDFs of traces</li> </ul> <p>Microscopy:</p> <ul> <li>Microscopy images of GAIA</li> </ul> <p>DYNAPSE </p> <ul> <li>Interface with the DYNAPSE processor</li> </ul>
Dataset for: Factors associated with self-reported diagnosed asthma in urban and rural Malawi: observations from a population-based study of 3 non-communicable diseases
<p>This dataset was used in analyses reported on the in paper with the same title, published in PLOS Global Health.</p>
Dataset: Analysis of changes in inter-cellular communications during Alzheimer's Disease pathogenesis reveals conserved changes in glutamatergic transmission in mice and humans
<p>Processed data (Seurat objects) from the entorhinal cortex of 5xFAD mice and donors, analyzed and described in the correponding manuscript (https://doi.org/10.1101/2024.04.30.591802).</p>
Data for Emergent reliability in sensory cortical coding and inter-area communication
<p>The data for Ebrahimi, Sadegh, et al. "Emergent reliability in sensory cortical coding and inter-area communication." Nature 605.7911 (2022): 713-721.</p>
Covert Communication Channels based on Hardware Trojans: open-source dataset
<h1>Dataset of Hardware-Trojan (HT) based Covert Channels (HT-CCs) for the IEEE 802.11 (WiFi) standard</h1> <div> <h2>Datasets arhitecture</h2> <ul> <li>The dataset has 80 dataset elements organized in 5 classes, namely CC-free (HT0-CC) and CC-infected (HTX-CC, X={1, · · · , 4}), each class has 16 elements.</li> <li>Those 16 elements are 8 acquisitions for different SNR values ranging from 1dB to 29dB with a step of 4dB, 2 acquisitions for each SNR value. The second acquisition was obtained immediately after the first one, with no other parameters changing between one acquisition and the other. The second acquisition is marked with an underscore in the naming of the file.</li> <li>Each dataset element has 2000 frames and it is the concatenation of ten 200-frames sub-acquisitions.</li> </ul> </div> <div> <h3>Naming convention</h3> </div> <p>Files are named according to the following convention:</p> <p>First acquisition:</p> <div> <pre><code>rxSig_<SNR value>dB_HT<HT-CC attack>.mat </code></pre> <div> </div> </div> <p>Second acquisition:</p> <div> <pre><code>rxSig_<SNR value>dB_HT<HT-CC attack>_.mat </code></pre> <div> </div> </div> <ul> <li>SNR value: ranging from 1dB to 29dB with a step of 4dB, i.e., <1>=SNR of 1db, <2>=SNR of 5db, <3>=SNR of 9dB, <4>=SNR of 13dB, <5>=SNR of 17dB, <6>=SNR of 21dB, <7>=SNR of 25dB, <8>=SNR of 29dB.</li> <li>HT-CC attack: CC-free (HT0-CC) and CC-infected (HTX-CC, X={1, · · · , 4}).</li> </ul> <div> <h3>HT0-CC</h3> </div> <p>The HT0-CC is the benchmark CC-free transmission according to the WiFi standard.</p> <div> <h3>HT1-CC</h3> </div> <p>The HT1-CC is the Amplitude Modulation (AM) Short Training Sequence (STS) HT attak based on the paper</p> <blockquote> <p>A. R. Díaz-Rizo, H. Aboushady and H.-G. Stratigopoulos, "Leaking Wireless ICs via Hardware Trojan-Infected Synchronization," in IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 5, pp. 3845-3859, 1 Sept.-Oct. 2023, doi: 10.1109/TDSC.2022.3218507.</p> </blockquote> <p>The amplitude modulation value alpha is set to 10%.</p> <div> <h3>HT2-CC</h3> </div> <p>The HT2-CC is the PSK STF HT attack based on the paper</p> <blockquote> <p>J. Classen, M. Schulz and M. Hollick, "Practical covert channels for WiFi systems," 2015 IEEE Conference on Communications and Network Security (CNS), Florence, Italy, 2015, pp. 209-217, doi: 10.1109/CNS.2015.7346830.</p> </blockquote> <p>The implemented version is leaking 8 bits per OFDM PPDU.</p> <div> <h3>HT3-CC</h3> </div> <p>The HT3-CC is the Dirty Constellation attack based on paper</p> <blockquote> <p>A. Dutta, D. Saha, D. Grunwald and D. Sicker, "Secret Agent Radio: Covert Communication through Dirty Constellations," in Proceedings of the 14th international conference on Information Hiding (IH'12). Springer-Verlag, Berlin, Heidelberg, 160–175, doi: 10.1007/978-3-642-36373-3_11.</p> </blockquote> <p>The implemented version of this attack has an embedding frequency of 5 subcarriers per OFDM symbol, where each OFDM symbol comprises 48 data subcarriers. Each dirty subcarrier is leaking 2 bits; therefore, we are leaking 10 bits per OFDM symbol,</p> <div> <h3>HT4-CC</h3> </div> <p>The HT4-CC is the AM analog/RF attack based on paper</p> <blockquote> <p>K. S. Subramani, N. Helal, A. Antonopoulos, A. Nosratinia and Y. Makris, "Amplitude-Modulating Analog/RF Hardware Trojans in Wireless Networks: Risks and Remedies," in IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3497-3510, 2020, doi: 10.1109/TIFS.2020.2990792.</p> </blockquote> <p>Due to SPI limitations, the implemented version of the attack is a digitally-emulated version of the AM analog/RF attack. It leaks 8 bits per transmitted frame.</p> <div> <h2>Hardware Platform</h2> </div> <p>Acquisitions are received downconverted IQ samples using a Software Defined Radio (SDR) board bladeRF xA9.</p>
Pointing movements and eye-tracking data_Facilitated Communication Users
<p>The repository contains all the pre-sorted data used for the analysis described in the paper. Data are divided into three:</p> <ul> <li>in A, we report the movement data of each pointing gesture considered in the analysis.</li> <li>in B we report keystrokes' related data.</li> <li>in C we report the sorted eye-tracking data.</li> </ul> <h3>A. User Correct Movements:</h3> <p>Each participant's data is organized into a 1xN cell array in Matlab, where N represents the number of pointing gestures analyzed. Each cell contains an Nx8 column vector with the following information:</p> <ol> <li> <p><strong>Time Information (column 1)</strong>:</p> <ul> <li>Time associated with the pointing gesture (milliseconds).</li> </ul> </li> <li> <p><strong>Arm Coordinates (columns 2,3 and 4)</strong>:</p> <ul> <li>X-axis coordinates (millimetres).</li> <li>Y-axis coordinates (millimetres).</li> <li>Z-axis coordinates (millimetres).</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (Facilitator) (columns 5, and 6) </strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (User) (columns 7 and 8)</strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> </ol> <h3>B. Users' Keys Pressed with Probability:</h3> <p>Each participant's data is organized into an Nx6 string array, where N represents the number of pointing gestures analyzed. Each array contains the following information:</p> <ol> <li> <p><strong>Key Press Time</strong>:</p> <ul> <li>Absolute time the key is pressed (milliseconds, as recorded by the key-logger).</li> </ul> </li> <li> <p><strong>Time Between Keystrokes</strong>:</p> <ul> <li>Difference in milliseconds between two consecutive keystrokes.</li> </ul> </li> <li> <p><strong>Key Pressed</strong>:</p> <ul> <li>The key that has been pressed.</li> </ul> </li> <li> <p><strong>Pointing Time</strong>:</p> <ul> <li>Time taken by the arm to complete the forward phase of the pointing gesture (seconds).</li> </ul> </li> <li> <p><strong>Character Position</strong>:</p> <ul> <li>Position of the pressed character within the word (spacebar hits are assigned the number 300).</li> </ul> </li> <li> <p><strong>Key Selection Probability</strong>:</p> <ul> <li>Probability (percentage) of the key being selected.</li> </ul> </li> </ol> <h3><strong>C. EyeTracking data sorted</strong></h3> <p>Each participant's data is organized into an N×6 cell array, where N represents the number of pointing gestures analyzed through eye-tracking. The contents of each row are as follows:</p> <ol> <li> <p><strong>Fixation Data (Nx5 vector)</strong>:</p> <ul> <li><strong>N</strong> is the number of fixations related to one pointing gesture.</li> <li>Each vector contains: <ul> <li> <p><strong>The standardized time </strong>is determined by synchronizing the eye fixation with the arm movement. Given the movement duration is scaled from 0 to 10, we identify the moment when the eye-fixation occurs.</p> <p>This standardized time refers to the duration of the fixation relative to the duration of the pointing gesture. In <strong>column 1</strong>, we report the gross time, averaging the beginning and end of the fixation. In<strong> column 4</strong>, we provide the exact standardization at the start, and in <strong>column 5</strong>, the exact standardization at the end of the movement. During analysis, these times are synchronized with the movement duration, and we use the data from column 4.</p> </li> <li> <p><strong>Euclidean distance</strong> between the fixated key and the target key (2nd column).</p> </li> <li><strong>Duration</strong> of each fixation (3rd column).</li> </ul> </li> </ul> </li> <li> <p><strong>Sequence of Fixated Keys</strong>:</p> <ul> <li>Contains the sequence of keys fixated by the user during each pointing gesture.</li> </ul> </li> <li> <p><strong>Arm Movement Information</strong>:</p> <ul> <li>Includes details on the arm movement (same as reported in<strong> file A</strong>) corresponding to the eye-tracking fixation sequence.</li> </ul> </li> <li> <p><strong>Target Key Pressed</strong>:</p> <ul> <li>Indicates the target key pressed by the participant.</li> </ul> </li> <li> <p><strong>Probability of Key Pressed</strong>:</p> <ul> <li>Reports the likelihood of each key being pressed,<strong> as detailed in file B.</strong></li> </ul> </li> <li> <p><strong>Euclidean Distance Between Consecutive Keys</strong>:</p> <ul> <li>Measures the Euclidean distance between two consecutively pressed keys using the keyboard as a reference (refer to the paper text for more details).</li> </ul> </li> </ol> <p> </p> <p> </p>
EVALUATION OF ADVANCED VEHICLE AND COMMUNICATION TECHNOLOGIES THROUGH TRAFFIC MICROSIMULATION
<p>This folder contains products developed from STRIDE I-5 project.</p> <p>This project builds on a previously funded STRIDE project (D4) where a simulation extension was built using the micro simulator VISSIM to accurately represent vehicle autonomy and connectivity, and their operational and environmental effects.</p> <p>In this project, the research team expanded the functionality of the simulation extension to:</p> <p>i. Model human driven vehicles in the presence of AVs. An aggressive merging behavior model was implemented in VISSIM to study potential queue-jumping behavior at a freeway on-ramp. The research team also considered implementing this model in the open-source simulator SUMO.</p> <p>ii. Consider advanced vehicle dynamics and enable users to customize driver, vehicle, operating environment, and operating mode separately.</p> <p>iii. Incorporate a real-time optimization tool (RIO) previously developed with funding from the National Science Foundation (NSF). RIO jointly optimizes vehicle trajectories and signal control by taking advantage of CAV technologies.</p> <p> </p> <p>In this project, the research team conducted the following educational activities:</p> <p>i. Developed and conducted a nation-wide survey to understand the needs of the CAV education.</p> <p>ii. Developed six instructional modules for CAV education.</p> <p>iii. Developed a training module to model CAVs in microsimulation environment.</p> <p> </p> <p>The products developed from this project are:</p> <p>i. An enhanced simulation extension that can be used as a “plug and play” solution to model CAVs and human driven vehicles in presence of CAVs with options to consider advanced vehicle dynamics and incorporate signal/trajectory optimization.</p> <p>ii. Educational modules on how CAVs impact planning, design, modeling, and analysis of transportation facilities along with a training module to model CAVs using VISSIM</p>
Box model data and figure script in support of Nature Communications Comment
<p>Contained within the zip file are model outputs from the Sonke et al. 2022 box model described in the associated manuscript. File names describe the different model runs. The Science_fig.jnl file is a pyferret script to plot the data. Model data from Azimrayat Andrews et al. 2024 are found by following this link: https://iopscience.iop.org/article/10.1088/1748-9326/ad472c</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.