A Crowd-Based Explosive Detection System with Two-Level Feedback Sensor Calibration

Large, open, public events, such as marathons and festivals, have always presented a unique safety challenge. These sprawling events, which can take up entire city blocks or stretch for many miles, can draw tens to hundreds of thousands of spectators and in some cases have open admission. As it is i...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design s. 1 - 9
Hlavní autori: Yang, Chengmo, Cronin, Patrick, Agambayev, Agamyrat, Ozev, Sule, Cetin, A. Enis, Orailoglu, Alex
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Association on Computer Machinery 02.11.2020
Predmet:
calibration
chemFET sensors
chemical sensors
chemical variables measurement
crowd-based explosive detection system
decision making
Detection algorithms
explosive detection
Explosives
field effect transistors
Light emitting diodes
Peer-to-peer computing
safety challenge
Sensitivity
server-based decision-making framework
Servers
sprawling events
two-level feedback loop
two-level feedback sensor calibration
ISSN:1558-2434
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Large, open, public events, such as marathons and festivals, have always presented a unique safety challenge. These sprawling events, which can take up entire city blocks or stretch for many miles, can draw tens to hundreds of thousands of spectators and in some cases have open admission. As it is impracticable to guarantee the subjection of every event-goer to a security screening, we propose a crowd-based explosive detection system that uses a multitude of low-cost ChemFET sensors which are distributed to attendees. As the sensors offer limited accuracy, we further propose a server-based decision-making framework that utilizes a two-level feedback loop between the sensors and the server and explores spatial and temporal locality of the collected data to overcome the inherent low-accuracy of individual sensors. We thoroughly explore two distinct detection schemes, stressing their performance under a myriad of conditions, thus showing that such a crowd-based detection system comprised of low-cost and low-accuracy sensors can deliver high detection accuracy with minimal false positives.
ISSN:1558-2434
DOI:10.1145/3400302.3415670