scholarly journals Integration of a Proximity Detection Prototype into a VO Developed with PANGEA

2012 ◽  
pp. 197-204 ◽  
Author(s):  
Carolina Zato ◽  
Alejandro Sánchez ◽  
Gabriel Villarrubia ◽  
Javier Bajo ◽  
Sara Rodríguez
Keyword(s):  
Proximity Detection

Robust Device-Free Proximity Detection Using Wifi

2021 ◽  
Author(s):  
Yuqian Hu ◽  
M. Zahid Ozturk ◽  
Feng Zhang ◽  
Beibei Wang ◽  
K. J. Ray Liu
Keyword(s):  
Proximity Detection ◽  
Device Free

scholarly journals Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 38891-38906
Author(s):  
Zhuoran Su ◽  
Kaveh Pahlavan ◽  
Emmanuel Agu
Keyword(s):  
Performance Evaluation ◽  
Proximity Detection

scholarly journals How detection ranges and usage stops impact digital contact tracing effectiveness for COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Konstantin D. Pandl ◽  
Scott Thiebes ◽  
Manuel Schmidt-Kraepelin ◽  
Ali Sunyaev
Keyword(s):  
Disease Spread ◽  
Contact Tracing ◽  
Behavioral Characteristics ◽  
Policy Makers ◽  
Detection Range ◽  
Proximity Detection ◽  
Effective Technology ◽  
Different Types ◽  
Effectiveness And Efficiency ◽  
The Impact

AbstractTo combat the COVID-19 pandemic, many countries around the globe have adopted digital contact tracing apps. Various technologies exist to trace contacts that are potentially prone to different types of tracing errors. Here, we study the impact of different proximity detection ranges on the effectiveness and efficiency of digital contact tracing apps. Furthermore, we study a usage stop effect induced by a false positive quarantine. Our results reveal that policy makers should adjust digital contact tracing apps to the behavioral characteristics of a society. Based on this, the proximity detection range should at least cover the range of a disease spread, and be much wider in certain cases. The widely used Bluetooth Low Energy protocol may not necessarily be the most effective technology for contact tracing.

Development of an Adaptive Human-Machine-Interface to Minimize Driver Distraction and Workload

2013 ◽  
Author(s):  
Matthew Westin ◽  
Ronald Dougherty ◽  
Christopher Depcik ◽  
Austin Hausmann ◽  
Charles Sprouse
Keyword(s):  
Performance Metrics ◽  
Sensory Information ◽  
Driver Distraction ◽  
Test Drive ◽  
Workload Management ◽  
City Street ◽  
Proximity Detection ◽  
Test Platform ◽  
In Series ◽  
And Performance

The original use of the vehicle dashboard was to provide enough sensory information to inform the driver of the current engine and vehicle status and performance. Over time, it has evolved into an entertainment system that includes person-to-person communication, global positioning information, and the Internet, just to name a few. Each of these new features adds to the amount of information that drivers must absorb, leading to potential distraction and possible increases in the number and types of accidents. In order to provide an overview of these issues, this paper summarizes previous work on driver distraction and workload, demonstrating the importance of addressing those issues that compete for driver attention and action. In addition, a test platform vehicle is introduced which has the capability of assessing modified dashboards and consoles, as well as the ability to acquire relevant driving performance data. Future efforts with this test platform will be directed toward helping to resolve the critical tug-of-war between providing more information and entertainment while keeping drivers and their passengers safe. The long-term goal of this research is to evaluate the various technological innovations available for inclusion in the driving environment and determining how to optimize driver information delivery without excessive distraction and workload. The information presented herein is the first step in that effort of developing an adaptive distraction/workload management system that monitors performance metrics and provides selected feedback to drivers. The test platform (1973 VW Beetle converted to a plug-in series hybrid) can provide speed, location (GPS), 3-D acceleration, and rear proximity detection. The test drive route was a 2 km × 3 km city street circuit which took approximately 25 minutes to complete. Data is provided herein to demonstrate these capabilities. In addition, the platform has driver selectable layouts for the instrument cluster and console (LCD screens). The test platform is planned for use to determine driver preferences (e.g., dashboard/console configurations) and attention performance in addition to identifying optimal real-time feedback for drivers with different demographics.

scholarly journals Analysis of spatial data structures for proximity detection

2008 ◽  
Vol 13 (S1) ◽  
pp. 102-107 ◽  
Author(s):  
Anupreet Walia ◽  
Jochen Teizer
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Spatial Data Structures ◽  
Proximity Detection

Bluetooth Low-Energy-Based Applications

2018 ◽  
pp. 95-112
Author(s):  
Smita Sanjay Ambarkar ◽  
Rakhi Dattatraya Akhare
Keyword(s):  
Indoor Localization ◽  
Low Energy ◽  
Smart Devices ◽  
The Internet ◽  
Bluetooth Low Energy ◽  
Iot Applications ◽  
Proximity Detection ◽  
Battery Power ◽  
Commercial Market ◽  
The Internet Of Things

This chapter focuses on the comprehensive contents of various applications and principles related to Bluetooth low energy (BLE). The internet of things (IoT) applications like indoor localization, proximity detection problem by using Bluetooth low energy, and enhancing the sales in the commercial market by using BLE have the same database requirement and common implementation idea. The real-world applications are complex and require intensive computation. These computations should take less time, cost, and battery power. The chapter mainly focuses on the usage of BLE beacons for indoor localization. The motive behind the study of BLE devices is that it is supported by mobile smart devices that augment its application exponentially.

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