scholarly journals Correction to: A web-based real-time and full-resolution data visualization for Himawari-8 satellite sensed images

2017 ◽  
Vol 11 (2) ◽  
pp. 239-240
Author(s):  
Ken T. Murata ◽  
Praphan Pavarangkoon ◽  
Atsushi Higuchi ◽  
Koichi Toyoshima ◽  
Kazunori Yamamoto ◽  
...  
Keyword(s):  
Real Time ◽  
Data Visualization ◽  
Web Based ◽  
Full Resolution ◽  
Resolution Data

scholarly journals A web-based real-time and full-resolution data visualization for Himawari-8 satellite sensed images

2017 ◽  
Vol 11 (2) ◽  
pp. 217-237 ◽  
Author(s):  
Ken T. Murata ◽  
Praphan Pavarangkoon ◽  
Atsushi Higuchi ◽  
Koichi Toyoshima ◽  
Kazunori Yamamoto ◽  
...  
Keyword(s):  
Real Time ◽  
Data Visualization ◽  
Web Based ◽  
Full Resolution ◽  
Resolution Data

A System for Web-Based Interactive Real-Time Data Visualization and Analysis

2009 ◽  
Author(s):  
Tao Yu ◽  
Qiming Chen ◽  
Qinghu Li ◽  
Rui Liu ◽  
Weihong Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Data Visualization ◽  
Time Data ◽  
Web Based ◽  
Real Time Data

scholarly journals Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Data ◽  
Video Analytics ◽  
Web Based ◽  
Real Time Analysis ◽  
Open Source Framework ◽  
Cluster Configuration ◽  
Time Requirements ◽  
High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

scholarly journals Webinar : An Initial Experience with Web-based Real Time Interactive Clinical Seminars for Japanese Medical Students

2010 ◽  
Vol 11 (2) ◽  
pp. 87-90 ◽  
Author(s):  
Gerald H. Stein ◽  
Ayako Shibata ◽  
Miho Kojima Bautista ◽  
Yasuharu Tokuda
Keyword(s):  
Medical Students ◽  
Real Time ◽  
Initial Experience ◽  
Web Based

scholarly journals Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 285
Author(s):  
Chuchart Pintavirooj ◽  
Tanapon Keatsamarn ◽  
Treesukon Treebupachatsakul
Keyword(s):  
Real Time ◽  
Vital Sign ◽  
Vital Signs ◽  
Mobile Network ◽  
Cloud Platform ◽  
Telemedicine System ◽  
Web Based ◽  
The Real ◽  
Real Time Application ◽  
Commercial Platform

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

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