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6 results for “Cloud Continuum”
Cloud to Thing Continuum based Sports Monitoring System using Machine Learning and Deep Learning Model
<p><span>Sports monitoring and analysis have seen significant advancements with the integration of cloud computing and continuum paradigms, facilitated by machine learning and deep learning techniques. In this study, we present a novel approach for sports monitoring that seamlessly transitions from traditional cloud-based architectures to a continuum paradigm, enabling real-time analysis and insights into player performance and team dynamics. Leveraging machine learning and deep learning algorithms, our framework offers enhanced capabilities for player tracking, action recognition, and performance evaluation in various sports scenarios. This research proposes a Cloud-to-Thing Continuum based Sports Monitoring System utilizing Machine Learning (ML) and Deep Learning (DL) models. The system integrates data acquisition, preprocessing, feature extraction, cloud-based processing, continuum paradigm integration, and decision-making stages. It leverages innovative techniques such as Improved Mask R-CNN for pose estimation, hybrid metaheuristic algorithms with Generative Adversarial Network (GAN) for classification, and fuzzy decision-making Based on the integrated analysis, decisions are made regarding player performance, team strategies, and tactical adjustments. The continuum approach ensures a balance between centralized cloud processing and distributed edge processing, optimizing resource utilization and reducing latency. Through this system, real-time analysis of sports events is achieved, enabling immediate feedback for time-sensitive applications.</span></p>
Datasets on approved and ongoing standards for Cloud, Edge and IoT computing in the continuum and analysis and assessment of relevance
<p>Two datasets:</p> <ol> <li><span>the database of standards relevant to the Cloud-Edge-IoT continuum and to the ACES-EDGE Research and Innovation Action funded under the grant agreement No. 101093126 call HORIZON-CL4-2022-DATA-01-02.</span></li> <li><span>the Excel workbook with the analysis of the assessment of the standards in relation to the needs of the ACES-EDGE implementation.</span></li> </ol> <p><span>Both datasets will be used for a more deep assessment of the standardisation requirements of the ongoing technolgical developments.</span></p>
D1.3.2 Lab demonstrator of 6GSMART Cloud continuum Orchestrator-MNO
<p><em>D1.3.2 Lab demonstrator of 6GSMART Cloud continuum Orchestrator-MNO</em></p> <p>Video</p> <p>Developed by TELEFÓNICA-MINSAIT</p>
Resource Management - Cloud Continuum
<p>Dataset and classification of articles and proceedings for Resource Management in the Edge-Fog and Cloud Continuum. Resource management aspects include data aggregation, offloading, P2P data sharing, scheduling (auto-scaling) and load-balancing.</p>
Elastic Data Analytics for the Cloud-to-Things Continuum - Demo Video
<p>The massive deployment of Internet-connected devices has led to an increase in the collection of data that are then used by companies to improve their decision-making processes. This growing trend demands more and more cloud and communications infrastructure. The limited resources, the need of sharing them, and the fact that many consumers are interested in the same data call for an efficient management of the available resources. The cloud-to-thing continuum can be used to execute different analytics closer to the data source so infrastructure consumption and data circulation can be optimized. In this paper, different dimensions for achieving elastic analytics and a framework for dynamically modifying their behavior, is proposed.</p> <p>This artifact corresponds with a descriptive video of the framework presented in the paper. </p>
Small Magellanic Cloud ATCA Radio Continuum Sources
This table contains the classification of 717 radio-continuum sources from the Australia Telescope Compact Array (ATCA) Catalog of the Small Magellanic Cloud (SMC). All 717 sources have been categorised into one of three groups: supernova remnants (SNRs), HII regions and background sources. In total, 71 sources are named as HII regions (or candidates) and 21 sources are named as SNRs (or candidates). Six sources are named as either HII regions or background sources and two are candidate radio planetary nebulae. One source is coincident with an X-ray binary. 616 objects are classified as background sources and their statistics are presented in the published paper II from which this table is taken. This table was created by the HEASARC in March 2005 based on CDS table J/MNRAS/355/44/table2.dat . This is a service provided by NASA HEASARC .
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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.