scholarly journals ImgSensingNet: UAV Vision Guided Aerial-Ground Air Quality Sensing System

Author(s):  
Yuzhe Yang ◽  
Zhiwen Hu ◽  
Kaigui Bian ◽  
Lingyang Song
Keyword(s):  
2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Hongjie Guo ◽  
Guojun Dai ◽  
Jin Fan ◽  
Yifan Wu ◽  
Fangyao Shen ◽  
...  

This paper develops a mobile sensing system, the first system used in adaptive resolution urban air quality monitoring. In this system, we employ several taxis as sensor carries to collect originalPM2.5data and collect a variety of datasets, including meteorological data, traffic status data, and geographical data in the city. This paper also presents a novel method AG-PCEM (Adaptive Grid-Probabilistic Concentration Estimation Method) to infer thePM2.5concentration for undetected grids using dynamic adaptive grids. We gradually collect the measurements throughout a year using a prototype system in Xiasha District of Hangzhou City, China. Experimental data has verified that the proposed system can achieve good performance in terms of computational cost and accuracy. The computational cost of AG-PCEM is reduced by about 40.2% compared with a static grid method PCEM under the condition of reaching the close accuracy, and the accuracy of AG-PCEM is far superior as widely used artificial neural network (ANN) and Gaussian process (GP), enhanced by 38.8% and 14.6%, respectively. The system can be expanded to wide-range air quality monitor by adjusting the initial grid resolution, and our findings can tell citizens actual air quality and help official management find pollution sources.


Author(s):  
Chun-Ming Huang ◽  
Yi-Jun Liu ◽  
Yi-Jie Hsieh ◽  
Wei-Lin Lai ◽  
Chun-Ying Juan ◽  
...  

Sensors ◽  
2015 ◽  
Vol 15 (6) ◽  
pp. 12242-12259 ◽  
Author(s):  
Simone Brienza ◽  
Andrea Galli ◽  
Giuseppe Anastasi ◽  
Paolo Bruschi

2014 ◽  
Vol 12 (6) ◽  
pp. 1085-1092
Author(s):  
G. Fattoruso ◽  
S. De Vito ◽  
A. Buonanno ◽  
P. Di Palma ◽  
Girolamo Di Francia

2021 ◽  
Vol 2115 (1) ◽  
pp. 012012
Author(s):  
L Abhishek ◽  
J Kathirvelan

Abstract The children demise inside the borewell is expanded in nowadays, with uncovered borewell they fallen without knowing and lost their lives due to asphyxiant inside, likewise without oxygen, food and so forth The harmful gases like carbon monoxide, Methane, LPG, hydrogen sulphide inside the bore-well it will influence the children breathing and furthermore this may prompt unconsciousness, and without oxygen it might influence the brain functioning of child and child may die and furthermore explicit distance of the child at what distance child stuck isn’t know. To overcome these, we need to detect the various gases with different multiple gas sensors additionally to get the temperature and humidity condition. Alongside this ultrasonic sensor is utilized to get the distance of child at what distance child got stuck. We have utilized two Arduino Uno microcontrollers which is at the transmitter side and other at Receiver side also utilized two ZigBee’s as the communication devices. With the help of IoT involved in the proposed system. Every one of these information are sent to the cloud and we can monitor the data in the thing speak dashboard through PC or from our smart phone through Android App Usage and also, through LCD at Receiver end. We can utilize this proposed framework inside the borewell for up to 80-meter depth. Thus, we came up with this Design and Development of IoT enabled Gas sensing system for remote monitoring of Air quality in borewell Rescue operations. Based on proposed system results of real time data the Rescue specialists can make a further move by providing of oxygen, food, and so forth Save their lives.


Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 674
Author(s):  
Francesco Rundo ◽  
Ilaria Anfuso ◽  
Maria Grazia Amore ◽  
Alessandro Ortis ◽  
Angelo Messina ◽  
...  

From a biological point of view, alcohol human attentional impairment occurs before reaching a Blood Alcohol Content (BAC index) of 0.08% (0.05% under the Italian legislation), thus generating a significant impact on driving safety if the drinker subject is driving a car. Car drivers must keep a safe driving dynamic, having an unaltered physiological status while processing the surrounding information coming from the driving scenario (e.g., traffic signs, other vehicles and pedestrians). Specifically, the identification and tracking of pedestrians in the driving scene is a widely investigated problem in the scientific community. The authors propose a full, deep pipeline for the identification, monitoring and tracking of the salient pedestrians, combined with an intelligent electronic alcohol sensing system to properly assess the physiological status of the driver. More in detail, the authors propose an intelligent sensing system that makes a common air quality sensor selective to alcohol. A downstream Deep 1D Temporal Residual Convolutional Neural Network architecture will be able to learn specific embedded alcohol-dynamic features in the collected sensing data coming from the GHT25S air-quality sensor of STMicroelectronics. A parallel deep attention-augmented architecture identifies and tracks the salient pedestrians in the driving scenario. A risk assessment system evaluates the sobriety of the driver in case of the presence of salient pedestrians in the driving scene. The collected preliminary results confirmed the effectiveness of the proposed approach.


2017 ◽  
Author(s):  
T. J. Ngoy ◽  
T-H. Joubert
Keyword(s):  

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