scholarly journals Drone proximity detection via air disturbance analysis

2020 ◽  
Author(s):  
Qian Zhao ◽  
Jason Hughes ◽  
Damian M. Lyons
Keyword(s):  
Proximity Detection ◽  
Disturbance Analysis

Inner Disturbance Analysis of RW and CMG

2008 ◽  
Author(s):  
Shigemune Taniwaki ◽  
Masaki Takahashi ◽  
Katsuhiko Izawa
Keyword(s):  
Disturbance Analysis

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

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.

scholarly journals Sleep disturbance analysis between Parkinson disease and vascular parkinsonism: PDSS-2 and PSQI based study

2021 ◽  
Vol 4 ◽  
pp. 100219
Author(s):  
Yuthim Oktiany Ranteallo ◽  
Yudy Goysal ◽  
Muhammad Iqbal Basri ◽  
Andi Kurnia Bintang ◽  
Muhammad Akbar
Keyword(s):  
Parkinson Disease ◽  
Sleep Disturbance ◽  
Vascular Parkinsonism ◽  
Disturbance Analysis

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.

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