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478 results for “PAD”
Aerial Landing Pad Dataset
<p><strong>Landing Pad Dataset:</strong></p> <p>The Landing Pad Image Dataset is a collection of images containing a landing pad, along with object annotations for the landing pad found in each image. The annotations have been converted into the COCO, YOLO, and VOC formats for ease of use with various object detection frameworks. The images in the dataset were captured from a variety of angles and under different lighting conditions, making it a useful resource for training and evaluating object detection algorithms for autonomous landing of Unmanned Aerial Vehicles (UAVs). This dataset is intended for use in research and development of UAV landing systems, such as those used in industrial applications. The inclusion of the landing pad annotations allows for the detection and localization of the landing pad in real-time, enabling the UAV to safely and accurately land on the designated landing surface. Noted that some of the images are also taken from a simulation. The dataset consists of the following images and detection objects (Landing Pad and Kios Logo):</p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>Landing Pad</td> <td>Kios Logo</td> </tr> <tr> <td>Training</td> <td>961</td> <td>818</td> <td>349</td> </tr> <tr> <td>Validation</td> <td>480</td> <td>425</td> <td>153</td> </tr> <tr> <td>Testing</td> <td>480</td> <td>412</td> <td>167</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong></p> <blockquote> <p>Rafael Makrigiorgis, Panayiotis Kolios, Christos Kyrkou, & Charalambos Soteriou. (2022). Aerial Landing Pad Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.747756</p> </blockquote>
Testing 3D modelling software. Modelling charging pads for WPT of electric vehicles for EM emissions simulation.
<p>Even for the experienced 3D FEM modelers it may not be obvious which geometry discretization is the most appropriate and suitable for this type of problem. It may be a conservative approach to test the computation tool on a simplified geometry, on which the magnetic field distribution is known. As part of the “Metrology for inductive charging of electric vehicles” (MICEV) project (www.micev.eu), an axisymmetric geometry was used, with the results reported.</p>
KneE-PAD
<p><span>This repository introduces the </span>KneE-PAD <span>(<strong><span>Knee Rehabilitation Exercises for Postural Assessment D</span></strong>ataset), </span>which is a dataset consisting of knee rehabilitation exercises performed by 31 patients suffering from knee pathologies. In particular, a total of 267 patients were monitored over a 6-month period where they were asked to perform in two physiotherapy centers without any supervision 3 common lower limb rehabilitation exercises (squats, leg extension and walking). At each participant a set of 8 EMG and IMU sensors by Delsys was placed at important lower limb muscle groups. Moreover, they were asked to wear a heart rate sensor, a muscle oxygenation sensor and a goniometer to monitor their level of discomfort while an RGB camera was used to record their sessions. After curating and grouping the wrongly executed exercises, 2 common wrong variations for each exercise were identified in 31 participants.</p> <p>The goal of KneE-PAD is to be used for training machine learning algorithms for automatic postural assessment using only wearable sensors (EMG and IMU), which could become a vital part of a virtual coach to supervise the patients and provide useful feedback to them while executing their prescribed rehabilitation exercises remotely.</p>
Рис. 7–16. Carabus (Ophiocarabus) spp., генитаΛии самцов, виà сбоку (7–11 – меÃиаΛьная ÃоΛя эÃеагуса; 12–16 – энÃофаΛΛус). 7–8, 12–13 – C. ernsti ulastaiensis subsp. n., паратипы; 9–10, 14–15 – C. ernsti ernsti Kabak, 2002: 9, 14 – с гор к запаÃу от реки Сарык, 10, 15 – из ÃоΛины правого притока реки Сарык; 11, 16 – C. ernsti nilkiensis Kabak, 2014: 11 – гоAотип, 12 – паратип. ml – меÃиаΛьный бугор; pp – препуциаΛьный бугор; rbll – правый базоΛатераΛьный бугор. Figs 7–16. Carabus (Ophiocarabus) spp., male genitalia, lateral view (7–11 – medial lobe of the aedeagus; 12–16 – endophallus). 7–8, 12–13 – C. ernsti ulastaiensis subsp. n., paratypes; 9–10, 14–15 – C. ernsti ernsti Kabak, 2002: 9, 14 – from the locality in mountains to the West of the Saryk River, 10, 15 – from the locality in right tributary of the Saryk River; 11, 16 – C. ernsti nilkiensis Kabak, 2014: 11 – holotype, 12 – paratype. ml – median lobe; pp – praeputial pad; rbll – right basolateral lobe. in New data on the taxonomy of the genus Carabus Linnaeus, 1758 (Coleoptera: Carabidae) from the Ili River basin (China)
Рис. 7–16. Carabus (Ophiocarabus) spp., генитаΛии самцов, виà сбоку (7–11 – меÃиаΛьная ÃоΛя эÃеагуса; 12–16 – энÃофаΛΛус). 7–8, 12–13 – C. ernsti ulastaiensis subsp. n., паратипы; 9–10, 14–15 – C. ernsti ernsti Kabak, 2002: 9, 14 – с гор к запаÃу от реки Сарык, 10, 15 – из ÃоΛины правого притока реки Сарык; 11, 16 – C. ernsti nilkiensis Kabak, 2014: 11 – гоAотип, 12 – паратип. ml – меÃиаΛьный бугор; pp – препуциаΛьный бугор; rbll – правый базоΛатераΛьный бугор. Figs 7–16. Carabus (Ophiocarabus) spp., male genitalia, lateral view (7–11 – medial lobe of the aedeagus; 12–16 – endophallus). 7–8, 12–13 – C. ernsti ulastaiensis subsp. n., paratypes; 9–10, 14–15 – C. ernsti ernsti Kabak, 2002: 9, 14 – from the locality in mountains to the West of the Saryk River, 10, 15 – from the locality in right tributary of the Saryk River; 11, 16 – C. ernsti nilkiensis Kabak, 2014: 11 – holotype, 12 – paratype. ml – median lobe; pp – praeputial pad; rbll – right basolateral lobe.
Text-fig. 1. Dental nomenclature. Peignecyon felinoides n. gen. et n. sp. a) TU 73916 upper right M1 in occlusal view; b) TU 7391147 lower right m1, b1) lingual view, b2) occlusal view. Abbreviations: bde, basal distobuccal expansion*; bc, buccal cingulum; Ec, ectoflexus; dc, distal cingulum; dlc, distolingual cristid; end, entoconid; hyd, hypoconid; lc, lingual cingulum; ME, metacone; MEC, metaconule; mc, mesial cingulum; med, metaconid; mlc, mesiolingual cristid; mpc, mesial protoconid cristid; MS, metastyle; PA, paracone; PAC, paraconule; pad, paraconid; PR, protocone; prd, protoconid; PS, parastyle. *Not developed in Peignecyon felinoides. in A New Thaumastocyoninae (Amphicyonidae, Carnivora) From The Early Miocene Of Tuchořice, The Czech Republic
Text-fig. 1. Dental nomenclature. Peignecyon felinoides n. gen. et n. sp. a) TU 73916 upper right M1 in occlusal view; b) TU 7391147 lower right m1, b1) lingual view, b2) occlusal view. Abbreviations: bde, basal distobuccal expansion*; bc, buccal cingulum; Ec, ectoflexus; dc, distal cingulum; dlc, distolingual cristid; end, entoconid; hyd, hypoconid; lc, lingual cingulum; ME, metacone; MEC, metaconule; mc, mesial cingulum; med, metaconid; mlc, mesiolingual cristid; mpc, mesial protoconid cristid; MS, metastyle; PA, paracone; PAC, paraconule; pad, paraconid; PR, protocone; prd, protoconid; PS, parastyle. *Not developed in Peignecyon felinoides.
Support pad for a vessel carried on the head
ID no.: MOK – M.O./A/346 Museum: African Museum in Olkusz https://muzea.malopolska.pl/en/objects-list/1921 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Hockey Knee Pad Made by Karhu
This hockey knee pad was made of felt, hardened plastic and leather by Karhu in the 1950's. It was used by students at the gymnastics institute of the University of Helsinki. Karhu began to manufacture ice hockey equipment as early as the 1930's. Source: Objaverse 1.0 / Sketchfab
Pad Lock
3D model of a very well-preserved padlock made of iron. Physical object held at the National Museum of Iceland nr: 2008-36-562. Found 08/07/2008 by BB https://sarpur.is/Adfang.aspx?AdfangID=1471343 Part of the Skriðuklaustur Monastery 1550 reconstruction. Digitisation by Open Virtual Worlds, a research team within the School of Computer Science, University of St Andrews, in cooperation with the Gunnar Gunnarsson Institute at Skriðuklaustur and the National Museum of Iceland. 3D digitisation was done by Catherine Cassidy and Iain Oliver with archaeological assistance provided by Skúli Gunnarsson. Funded by the EU Northern Periphery and Arctic Programme 2014-2020 through the "Connected Culture and Natural Heritage in a Northern Environment" (CINE) project. Source: Objaverse 1.0 / Sketchfab
Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"
<p>Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"</p>
Softening the steps to gigantism in sauropod dinosaurs through the evolution of a pedal pad
<p class="01Indent"><span>How sauropod dinosaurs were able to withstand the forces associated with their immense size represents one of the most challenging biomechanical scenarios in the evolution of terrestrial tetrapods, but also one lacking robust biomechanical testing. Here, we use finite element analyses to quantify the biomechanical effects of foot skeletal postures with and without the presence of a soft tissue pad in sauropodomorphs. We find that none of the models can maintain bone stresses that fall within optimal bone safety factors in the absence of a soft tissue pad. Our findings suggest that a soft tissue pad in sauropods would have reduced bone stresses by combining the mechanical advantages of a functionally plantigrade foot and the plesiomorphic digitigrade saurischians conditions. The acquisition of a developed soft tissue pad by the Late-Triassic–Early-Jurassic may represent one of the key adaptations for the evolution of gigantism that has become emblematic of these dinosaurs. </span></p>
c-VEP-based BCI for PIN pad task on a T9 layout
<p># Participants<br> Eleven 11 volunteers (2 woman, mean age 28) with normal or corrected-to-normal vision participated in this experiment in our laboratory. The participants did not report any of the exclusion criteria (epilepsy, neurological antecedents, being under psychoactive medication). The study was approved by the ethics committee of the University of Toulouse (CER approval number 2020-334) and carried in accordance with the declaration of Helsinki. Participants gave informed written consent, prior to the experiment. The consent form included a section to approve the online data sharing.</p> <p># Experimental protocol<br> We used the BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a refresh rate of 500Hz to record the surface brain activity. The 32 electrodes followed the 10-20 international system. The ground electrode was placed at the Fpz electrode location and all electrodes were referenced to FCz electrode. The electrodes impedance were brought below 20 $k\Omega$ prior to the recording. We used a single maximum length sequence with 11 different shifts to animate the stimulation for the different targets. The presentation was rendered on an LCD monitor: LG GN 750, 1027x768 pixels, 400 cd/m^2, and 60Hz refresh rate. EEG data and markers were synchronized during recording using Lab Streaming Layer.<br> <br> Subjects were asked to follow cues on a T9 layout to emulate a PIN code typing task (11 classes problem). It was implemented in Python, using the Psychopy toolbox. The 11 stimuli were circles with radius of 150 pixels, with a margin of 250 pixels vertically and horizontally and the figure in the middle.<br> Fifteen 11-trial blocks were recorded for each subject. During a trial, one of the 11 targets was cued for 0.5s, followed by 2.2s of stimulation with a 0.7s inter-trial before next one. The cue sequence for each trial was pseudo-random and different for each block. After each block a pause was observed and subjects had to press space bar to continue.<br> <br> # Pre-processing<br> No pre-processing was applied and data are neither sliced or epoched but it is possible to do it with the help of the markers.</p>
Padding Ain't Enough: Assessing the Privacy Guarantees of Encrypted DNS – Subpage-Agnostic Domain Classification Firefox
<p>This dataset contains one part for the "Subpage-Agnostic Domain Classification" section of our FOCI 2020 paper "Padding Ain’t Enough: Assessing the Privacy Guarantees of Encrypted DNS".</p> <p><a href="https://www.usenix.org/conference/foci20/presentation/bushart">https://www.usenix.org/conference/foci20/presentation/bushart</a></p> <p>You can find the source code for this project on GitHub: <a href="https://github.com/jonasbb/padding-aint-enough">https://github.com/jonasbb/padding-aint-enough</a></p> <p>When using this software or our dataset, please cite our FOCI 20 paper.</p> <pre>@inproceedings {PaddingAintEnough, author = {Jonas Bushart and Christian Rossow}, booktitle = {10th {USENIX} Workshop on Free and Open Communications on the Internet ({FOCI} 20)}, month = aug, publisher = {{USENIX} Association}, title = {Padding Ain{\textquoteright}t Enough: Assessing the Privacy Guarantees of Encrypted {DNS}}, year = {2020}, }</pre>
Padding Ain't Enough: Assessing the Privacy Guarantees of Encrypted DNS – Web Scans
<p>This dataset contains the main data set of our FOCI 2020 paper "Padding Ain’t Enough: Assessing the Privacy Guarantees of Encrypted DNS".</p> <p><a href="https://www.usenix.org/conference/foci20/presentation/bushart">https://www.usenix.org/conference/foci20/presentation/bushart</a></p> <p>You can find the source code for this project on GitHub: <a href="https://github.com/jonasbb/padding-aint-enough">https://github.com/jonasbb/padding-aint-enough</a></p> <p>When using this software or our dataset, please cite our FOCI 20 paper.</p> <pre>@inproceedings {PaddingAintEnough, author = {Jonas Bushart and Christian Rossow}, booktitle = {10th {USENIX} Workshop on Free and Open Communications on the Internet ({FOCI} 20)}, month = aug, publisher = {{USENIX} Association}, title = {Padding Ain{\textquoteright}t Enough: Assessing the Privacy Guarantees of Encrypted {DNS}}, year = {2020}, }</pre>
Padding Ain't Enough: Assessing the Privacy Guarantees of Encrypted DNS – Subpage-Agnostic Domain Classification Tor Browser
<p>This dataset contains the second part of the "Subpage-Agnostic Domain Classification" section of our FOCI 2020 paper "Padding Ain’t Enough: Assessing the Privacy Guarantees of Encrypted DNS".</p> <p><a href="https://www.usenix.org/conference/foci20/presentation/bushart">https://www.usenix.org/conference/foci20/presentation/bushart</a></p> <p>You can find the source code for this project on GitHub: <a href="https://github.com/jonasbb/padding-aint-enough">https://github.com/jonasbb/padding-aint-enough</a></p> <p>When using this software or our dataset, please cite our FOCI 20 paper.</p> <pre>@inproceedings {PaddingAintEnough, author = {Jonas Bushart and Christian Rossow}, booktitle = {10th {USENIX} Workshop on Free and Open Communications on the Internet ({FOCI} 20)}, month = aug, publisher = {{USENIX} Association}, title = {Padding Ain{\textquoteright}t Enough: Assessing the Privacy Guarantees of Encrypted {DNS}}, year = {2020}, }</pre>
Biomechanics of the Finger Pad in Response to Torsion
<p>This dataset was used in the paper "Biomechanics of the Finger Pad in Response to Torsion". <br> The paper was not published at the time of dataset upload and no DOI was attributed to it yet. A preprint is available on bioRxiv, https://doi.org/10.1101/2022.11.07.515186.<br> We refer the reader to this publication for detailed methods.<br> <br> In brief, we have made public :<br> - The raw forces and platform kinematics files.<br> - 1DxTime variables extracted from the videos such as displayed in our Fig. 2A.<br> - 1D summary variables over trials (for example the angle at full slip for each trial).<br> </p>
Figures of IC50 of each compound by graph pad prisim
<p>This includes IC50 data results for each compound by using graph pad prisim as a pzfx format and JPG format</p>
Scanlon_Lily Pad
<p>Structural Analysis of Lily Pad</p>
Pain, Agitation and Delirium (PAD) Protocol in the Duke CICU
ClinicalTrials.gov study NCT02903407. IPD Sharing: NO. Countries: 1. Publications: 8.
Observational Study to Evaluate PAD Treatment Clinical and Economic Outcomes
ClinicalTrials.gov study NCT01855412. IPD Sharing: Not stated. Countries: 1. Publications: 13.
Platelet-Rich Plasma Versus Saline Dressings in Pediatric Heel Pad Injuries
ClinicalTrials.gov study NCT07377708. IPD Sharing: NO. Countries: 1. Publications: 2.
ScienceDex guides
Understand access before you commit
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.