The Application of 3D Real Time VR in Web-Based Continuing Education Platform

2008 ◽  
Author(s):  
Xiaoqiang Hu ◽  
Xianmei Jiang ◽  
Ling He
Keyword(s):  
Continuing Education ◽  
Real Time ◽  
Web Based ◽  
Education Platform

scholarly journals Face-to-face Training is the Preferred Modality of Professional Continuing Education for Librarians of All Ages, but More Evidence is Needed

2011 ◽  
Vol 6 (1) ◽  
pp. 56
Author(s):  
Kathryn Oxborrow
Keyword(s):  
Continuing Education ◽  
Real Time ◽  
The United States ◽  
Web Based ◽  
Face To Face ◽  
Blended Training ◽  
Medical Library ◽  
Instructor Interaction ◽  
Library Associations ◽  
Training Modalities

A Review of: Lynn, V. A., Bose, A., & Boehmer, S. J. (2010). Librarian instruction-delivery modality preferences for professional continuing education. Journal of the Medical Library Association, 98(1), 57-64. Objective — To establish the preferred modality for professional continuing education (CE) among members of three library associations. The primary hypothesis was that face-to-face training is the preferred modality, and the secondary hypothesis was that younger librarians are more likely to favour online or blended training modalities. In addition, the authors sought to investigate which factors influence participants' decisions to take up training. Design — Online questionnaire. Setting — Three library associations based in the United States of America. These were the American Library Association (ALA), the Special Libraries Association (SLA), and the Medical Library Association (MLA). Subjects — A random sample of 328 members of the ALA (86 participants), SLA (63 participants), and MLA (291 participants). Some participants were members of more than one association. Methods — Participants were recruited to complete an online survey via direct e-mail contact (MLA), messages on email discussion lists (SLA) and social networks (ALA). The survey asked about participants' experience of, and preference for, five different training modalities for CE. These were: face-to-face (classroom instruction), web-based synchronous (with real-time participant-instructor interaction), web-based asynchronous (with instructor involvement, but not in real time), blended (a combination of different modalities), and webcasts (live online presentations with limited participant-instructor interaction). Participants were then asked to rank factors which would influence their decision to undertake CE courses. The factors were cost, opportunity to socialize/network, time away from work, learning at their own pace, and having immediate access to either the class instructor or other participants. Participants were also given space to comment on both CE modalities and influencing factors. Main Results — There was a statistically significant preference for face-to-face instruction in this sample, being preferred by at least 73.1% of participants in all age ranges. Younger librarians did not display a preference for online or blended training modalities. There was a significant difference in second preference between ALA and MLA members, who both preferred Web based asynchronous training, and SLA members, who preferred the web-based synchronous format. Participants' preferences for all modalities apart from face to face were significantly different depending on whether or not they had experienced the particular modality. Cost was ranked as the most influential factor in the decision to undertake CE by members of all three library associations (significant at P

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.

scholarly journals Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 357
Author(s):  
Dae-Hyun Jung ◽  
Na Yeon Kim ◽  
Sang Ho Moon ◽  
Changho Jhin ◽  
Hak-Jin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Model Development ◽  
Classification Model ◽  
Intelligence Analysis ◽  
Noise Filtering ◽  
Meat Industry ◽  
Mel Frequency Cepstral Coefficients ◽  
Web Based ◽  
The Status

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

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