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9,152 results for “Networking”
Figure 6b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 6b "Network Direction" features. - Manual addition of traffic flow to the network by using the options "Add flow" and "Add long flow"
Figure 3a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 3a "Network Edition" example of use (I). - Use of the "Join vertex" (in green), "Remove edge" (in red) and "Add point" options (in green) in the SpNetPrep application
Figure 8a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 8a "Point Pattern Edition" features. - An example of a point pattern that lies on a road network as it can be visualized in SpNetPrep
Figure 4b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 4b "Network Edition" example of use (II). - Network resulting from clicking on "Rebuild linear network" in the situation of a
Figure 7 from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 7 Example of a linear road network following usual notation for the edges (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} e_{i} \end{equation*} \end{varwidth} \end{document} ) and vertex (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} v_{i} \end{equation*} \end{varwidth} \end{document} ). Arrows represent the direction of traffic flow.
Figure 6a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 6a "Network Direction" features. - A zone of a road network introduced as an input in the "Network Direction" section of the SpNetPrep application
Figure 4a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 4a "Network Edition" example of use (II). - Another use of the "Join vertex" (in green) option of the "Network Edition" section
Figure 5b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 5b Example of use of the SimplifyLinearNetwork function. - Simplified version of the network in a after the application of the SimplifyLinearNetwork function with parameters Angle = 25 and Length = 65
Image Synthesis with a Convolutional Capsule Generative Adversarial Network- Prepared Data
<p>A set of prepared datasets for running experiments to replicate paper (see below).</p> <p>List of data:</p> <p>Training capspix2pix:</p> <ul> <li>crops256.zip - folder containing 256x256 crops from the original dataset for training capspix2pix. Images are in the "train/original" folder, and labels are in the "train/mask" folder.</li> <li>syn256_x_data_val.npy + syn256_y_data_val.npy + syn256_y_points_data_val.npy (images + labels + centrelines) - validation synthetic dataset, used while training capspix2pix for plotting</li> </ul> <p>Training u-net:</p> <ul> <li>capspix2pix_AR_data_train.npy + capspix2pix_AR_mask_train.npy (images + labels) - data generated from a capspix2pix model from real labels</li> <li>capspix2pix_SSM_data_train.npy + capspix2pix_AR_mask_train.npy (images + labels) - data generated from a capspix2pix model from synthetic labels</li> <li>PBAM_SSM_data_train.npy + PBAM_SSM_mask_train.npy (images + labels) - data generated from PBAM (Physics-based model) for training u-net</li> <li>pix2pix_AR_data_train.npy + pix2pix_AR_mask_train.npy (images + labels) - data generated from a pix2pix model from real labels for training u-net</li> <li>pix2pix_SSM_data_train.npy + pix2pix_SSM_mask_train.npy (images + labels) - data generated from a pix2pix model from synthetic labels for training u-net</li> <li>real_data_data_train.npy + real_data_mask_train.npy (images + labels) - augmented real dataset for training u-net</li> </ul> <p>Testing u-net:</p> <ul> <li>org64_data_test.npy + org64_mask_test.npy (images + labels) - crops from original test dataset for testing u-net</li> </ul> <p>Interpolation:</p> <ul> <li>crops256_inter_data_train.npy + crops256_inter_mask_train.npy (images + labels) - example data for interpolation</li> </ul> <p><strong>Please cite the following paper when using this dataset:</strong></p> <p>Bass, C., Dai, T., Billot, B., Arulkumaran, K., Creswell, A., Clopath, C., De Paola, V., and Bharath, A. A., 2019. “Image synthesis with a convolutional capsule generative adversarial network,” <em>Medial Imaging with Deep Learning.</em></p> <p><strong>See Github page for further instructions:</strong></p> <p>https://github.com/CherBass/CapsPix2Pix</p> <p> </p>
Predicting and containing epidemic risk using on-line friendship networks - data and code
<p>Contains code and data I used for the paper</p>
Training and test datasets for dual-layer network model
<p>Training and test datasets for dual-layer network model</p>
Online machine learning algorithms to predict link quality in community wireless mesh networks
<p>FunkFeuer raw topology data from 15-01-2016 to 28-01-2016 retrieved on 17-02-2016 from http://opendata.confine-project.eu/dataset/funkfeuer-topology-data. This dataset was employed in "Miguel L. Bote-Lorenzo, Eduardo Gómez-Sánchez, Carlos Mediavilla-Pastor, Juan I. Asensio-Pérez, Online machine learning algorithms to predict link quality in community wireless mesh networks, Computer Networks, Volume 132, 2018, Pages 68-80, https://doi.org/10.1016/j.comnet.2018.01.005."</p>
DeepFruits: A Fruit Detection System Using Deep Neural Networks
<p>This is the dataset associated with MDPI Sensors paper entitled "DeepFruits: A Fruit Detection System Using Deep Neural Networks".</p> <p>This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.</p>
Multipath aware Scheduling for High Reliability and Fault Tolerance in Low Power Industrial Networks
<p>The Industrial Internet of Things is expected to enable the Industry 4.0 through the large deployment of low-<br> power devices. However, industrial applications require most of the time high reliability close to 100%, and<br> low end-to-end delays. Thus, most industrial wireless networks rely on a strict schedule of the transmissions<br> to avoid collisions, and implement frequency hopping to combat external interference. In multihop topologies,<br> the network has to decide both when the transmissions have to be scheduled, and which router can forward<br> the packets. To be fault-tolerant, multipath routing consists in exploiting several paths in parallel. We<br> exploit here a braided path routing structure, where each router has several possible next hops. Thus, we<br> can cope with any fault along the path, while still providing a remaining operational path. We propose<br> also a scheduling algorithm, where multiple transmitters are attached to a single cell, to the same receiver.<br> The schedule is constructed such that only one transmitter is active at a time, and is consequently collision-<br> free. Mutualizing the same cell for several transmitters reduces the energy consumption and increases the<br> network capacity.</p>
Supplementary material 1 from: Matsuoka S, Sugiyama Y, Sato H, Katano I, Harada K, Doi H (2019) Spatial structure of fungal DNA assemblages revealed with eDNA metabarcoding in a forest river network in western Japan. Metabarcoding and Metagenomics 3: e36335. https://doi.org/10.3897/mbmg.3.36335
: Data type: multimedia
Supplementary material 2 from: Matsuoka S, Sugiyama Y, Sato H, Katano I, Harada K, Doi H (2019) Spatial structure of fungal DNA assemblages revealed with eDNA metabarcoding in a forest river network in western Japan. Metabarcoding and Metagenomics 3: e36335. https://doi.org/10.3897/mbmg.3.36335
: Data type: molecular data
STOC indicator trend - Galaxy Training Network
<p>Example file for the tutorial STOC indicator trend</p>
Figure 1 in Herbarium specimens reveal the exchange network of British and Irish botanists, 1856-1932
Figure 1 The schema used for cleaning the individual's names in the dataset.
The "lastfm" data set used in the article "A comparative study of social network models: Network evolution models and nodal attribute models"
<p>This is the "lastfm" network used in the article:</p> <p>Toivonen, R., Kovanen, L., Kivelä, M., Onnela, J. P., Saramäki, J., & Kaski, K. (2009). A comparative study of social network models: Network evolution models and nodal attribute models. Social networks, 31(4), 240-254.</p> <p>doi:10.1016/j.socnet.2009.06.004</p> <p>The data set is described in the article. Please cite the original article when using this data set.</p> <p>Format of the data set is an edge list, where row in the file is an edge connecting the two nodes indicated by the two numbers separated by a whitespace. Each node number corresponds to a single account in the website.</p> <p>The original data in which this network is based on was licensed under the "Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England & Wales" licese, and accordinly this data set uses the same license. License available at https://creativecommons.org/licenses/by-nc/2.0/uk/</p>
Data: Imitation learning of fish and swarm behavior with Recurrent Neural Networks
<p>Data for the Master's Thesis <em>Imitation learning of fish and swarm behavior with Recurrent Neural Networks</em></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.