Design and Implementation of a Social Distancing and Contact Tracing Wearable

Verbelen, Yannick; Kaluvan, Suresh; Haller, Ulrike; J.Boardman, Morgan; Scott, Tom

doi:10.1109/cist49399.2021.9357203

Cited by 6 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Penelitian ini menggunakan metode OpenCV dan CNN, diperoleh hasil akurasi sebesar 98,2% [10]. CNN merupakan salah satu metode yang memiliki perkembangan sangat pesat yang mampu melakukan pengolahan, analisis dan pendeteksian suatu objek, baik gambar maupun video [11] [12] [13]. Selanjutnya penelitian terkait keputusan tentang jarak sosial menggunakan metode LSTM-RNN dengan model Regresi JST.…”

Section: Pendahuluanunclassified

Implementasi Deep Learning Dalam Pendeteksian Kerumunan Yang Berpotensi Melanggar Protokol Kesehatan Covid-19

Naf’an

Islami

Gushelmi

2022

mib

View full text Add to dashboard Cite

This study aims to propose a system that is able to detect crowds that have the potential to violate the Covid-19 health protocol. In this case the camera is used to capture objects in the form of images (image). Furthermore, the obtained images are processed using Deep Learning. In this case, the Convolutional Neural Network (CNN) is used. The criteria used are the classification of the number of people in the picture and the distance between each person in the picture. The distance allowed between each person is 1 meter. If there are more than 5 people in the image that have a distance between each of them less than 1 meter, then this is classified as a crowd that has the potential to violate the Covid-19 health protocol. In this study, 2 classifications were used, namely crowd and non-crowded. From the test results obtained the average value of accuracy, precision, and recall are 91.71, 91.25, and 92.65, respectively.

show abstract

Section: Pendahuluanunclassified

Implementasi Deep Learning Dalam Pendeteksian Kerumunan Yang Berpotensi Melanggar Protokol Kesehatan Covid-19

Naf’an

Islami

Gushelmi

2022

mib

View full text Add to dashboard Cite

show abstract

“…[14][15][16][17] However, the separated beta source and energy converting device is unable to solve the self-absorption problem, while the application of radioactive semiconductor is a promising way to enhance the output performance and radiation resistance of betavoltaic batteries. [18][19][20][21][22] For SiC, the main radioactive isotopes with pure beta emission are 32 Si and 14 C, which are heavier than the relative stable isotopes (i.e., 28 Si, 29 Si, 30 Si, 12 C, and 13 C). 32 Si is a cosmic ray produced beta emitting isotope with a half-life of about 153 years.…”

mentioning

confidence: 99%

First principles study on the time-related properties of 4H-³²SiC as an energy converting material of betavoltaic batteries

Li,

Lu,

Liu

et al. 2024

Appl. Phys. Express

View full text Add to dashboard Cite

The radioactive 4H-32SiC is applied as energy converting material to fabricate high performance betavoltaic batteries. The time-related component change is considered, and the structural, stability and electrical property changes are calculated by Density Functional Theory (DFT). As time goes by, the number of 32Si atoms decrease exponentially while the concentration of 32S increases gradually. The Si63PC64 configurations have smaller lattice constants, while the lattices of Si62PSC64 configurations are larger. All Si63PC64 and Si62PSC64 configurations have very small bandgaps indicating the metallic behavior. This suggests that the betavoltaic battery with 4H-32SiC is likely to transform into a Schottky diode over time.

show abstract

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

Majeed

Hwang

2021

IEEE Access

View full text Add to dashboard Cite

Since the emergence of coronavirus disease-2019 (COVID-19) outbreak, every country has implemented digital solutions in the form of mobile applications, web-based frameworks, and/or integrated platforms in which huge amounts of personal data are collected for various purposes (e.g., contact tracing, suspect search, and quarantine monitoring). These systems not only collect basic data about individuals but, in most cases, very sensitive data like their movements, spatio-temporal activities, travel history, visits to churches/clubs, purchases, and social interactions. While collection and utilization of person-specific data in different contexts is essential to limiting the spread of COVID-19, it increases the chances of privacy breaches and personal data misuse. Recently, many privacy protection techniques (PPTs) have been proposed based on the person-specific data included in different data types (e.g., tables, graphs, matrixes, barcodes, and geospatial data), and epidemic containment strategies (ECSs) (contact tracing, quarantine monitoring, symptom reports, etc.) in order to minimize privacy breaches and to permit only the intended uses of such personal data. In this paper, we present an extensive review of the PPTs that have been recently proposed to address the diverse privacy requirements/concerns stemming from the COVID-19 pandemic. We describe the heterogeneous types of data collected to control this pandemic, and the corresponding PPTs, as well as the paradigm shifts in personal data handling brought on by this pandemic. We systemically map the recently proposed PPTs into various ECSs and data lifecycle phases, and present an in-depth review of existing PPTs and evaluation metrics employed for analysis of their suitability. We describe various PPTs developed during the COVID-19 period that leverage emerging technologies, such as federated learning, blockchain, privacy by design, and swarm learning, to name a few. Furthermore, we discuss the challenges of preserving individual privacy during a pandemic, the role of privacy regulations/laws, and promising future research directions. With this article, our aim is to highlight the recent PPTs that have been specifically proposed for the COVID-19 arena, and point out research gaps for future developments in this regard.

show abstract

Design and Implementation of a Social Distancing and Contact Tracing Wearable

Cited by 6 publications

References 18 publications

Implementasi Deep Learning Dalam Pendeteksian Kerumunan Yang Berpotensi Melanggar Protokol Kesehatan Covid-19

Implementasi Deep Learning Dalam Pendeteksian Kerumunan Yang Berpotensi Melanggar Protokol Kesehatan Covid-19

First principles study on the time-related properties of 4H-³²SiC as an energy converting material of betavoltaic batteries

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

Contact Info

Product

Resources

About

Design and Implementation of a Social Distancing and Contact Tracing Wearable

Cited by 6 publications

References 18 publications

Implementasi Deep Learning Dalam Pendeteksian Kerumunan Yang Berpotensi Melanggar Protokol Kesehatan Covid-19

Implementasi Deep Learning Dalam Pendeteksian Kerumunan Yang Berpotensi Melanggar Protokol Kesehatan Covid-19

First principles study on the time-related properties of 4H-32SiC as an energy converting material of betavoltaic batteries

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

Contact Info

Product

Resources

About

First principles study on the time-related properties of 4H-³²SiC as an energy converting material of betavoltaic batteries