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Open AccessJournal ArticleDOI

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing - Part I: Fundamentals and Enabling Technologies

TLDR
This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice.
Abstract
Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.

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ReportDOI

Response to FCC 98-208 notice of inquiry in the matter of revision of part 15 of the commission's rules regarding ultra-wideband transmission systems

TL;DR: In this article, the authors consider the unique features of UWB technology and propose that the FCC should consider them in considering changes to Part 15 and take into account their unique features for radar and communications uses.
Journal ArticleDOI

A deep learning-based social distance monitoring framework for COVID-19.

TL;DR: Findings indicate that the developed framework successfully distinguishes individuals who walk too near and breaches/violates social distances; also, the transfer learning approach boosts the overall efficiency of the model.
Journal ArticleDOI

IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution

TL;DR: An up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies and the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation is provided.
Posted Content

A Vision-based Social Distancing and Critical Density Detection System for COVID-19

TL;DR: An active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest by defining a novel critical social density value and showing that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value.
Journal ArticleDOI

A Review of Mobile Applications Available in the App and Google Play Stores Used During the COVID-19 Outbreak

TL;DR: In this paper, the authors reviewed the functionalities and effectiveness of the free mobile health applications available in the Google Play and App stores used in Saudi Arabia, Italy, Singapore, United Kingdom, USA, and India during the COVID-19 outbreak.
References
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Proceedings Article

ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Posted Content

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Proceedings ArticleDOI

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Book ChapterDOI

SSD: Single Shot MultiBox Detector

TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.
Posted Content

Fast R-CNN

TL;DR: This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection that builds on previous work to efficiently classify object proposals using deep convolutional networks.
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