<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The application of quadcopter and intelligent learning techniques in urban monitoring systems can improve flexibility and efficiency features. This paper proposes a cloud-based urban monitoring system that uses deep learning, fuzzy system, image processing, pattern recognition, and Bayesian network. The main objectives of this system are to monitor climate status, temperature, humidity, and smoke, as well as to detect fire occur-rences based on the above intelligent techniques. The quadcopter transmits sensing data of the temperature, humidity, and smoke sensors, geographical coordinates, image frames, and videos to a control station via RF communications. In the control station side, the monitoring capabilities are designed by graphical tools to show urban areas with RGB colors according to the predetermined data ranges. The evaluation process illustrates simulation results of the deep neural network applied to climate status and effects of the sensors’ data changes on climate status. An illustrative example is used to draw the simulated area using RGB colors. Furthermore, circuit of the quadcopter side is designed using electric devices.</p>
</abstract>

The application of quadcopter and intelligent learning techniques in urban monitoring systems can improve flexibility and efficiency features. This paper proposes a cloud-based urban monitoring system that uses deep learning, fuzzy system, image processing, pattern recognition, and Bayesian network. The main objectives of this system are to monitor climate status, temperature, humidity, and smoke, as well as to detect fire occur-rences based on the above intelligent techniques. The quadcopter transmits sensing data of the temperature, humidity, and smoke sensors, geographical coordinates, image frames, and videos to a control station via RF communications. In the control station side, the monitoring capabilities are designed by graphical tools to show urban areas with RGB colors according to the predetermined data ranges. The evaluation process illustrates simulation results of the deep neural network applied to climate status and effects of the sensors’ data changes on climate status. An illustrative example is used to draw the simulated area using RGB colors. Furthermore, circuit of the quadcopter side is designed using electric devices.

A Cloud-Based Urban Monitoring System by Using a Quadcopter and Intelligent Learning Techniques

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The application of quadcopter and intelligent learning techniques in urban monitoring systems can improve flexibility and efficiency features. This paper...

A Cloud-Based Urban Monitoring System by Using a Quadcopter and Intelligent Learning Techniques

Publié en ligne: 29 mai 2023

Pages: 11 - 19

Reçu: 21 déc. 2021

Accepté: 24 févr. 2022

DOI: https://doi.org/10.14313/jamris-2-2022-11

Mots clés
urban monitoring, cloud computing, quad-copter, deep learning, fuzzy system, image processing, pattern recognition, bayesian network, intelligent techniques, learning systems

© 2022 Sohrab Khanmohammadi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Cloud-Based Urban Monitoring System by Using a Quadcopter and Intelligent Learning Techniques

Publié en ligne: 29 mai 2023

Pages: 11 - 19

Reçu: 21 déc. 2021

Accepté: 24 févr. 2022

DOI: https://doi.org/10.14313/jamris-2-2022-11

Mots clésurban monitoring, cloud computing, quad-copter, deep learning, fuzzy system, image processing, pattern recognition, bayesian network, intelligent techniques, learning systems

© 2022 Sohrab Khanmohammadi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
urban monitoring, cloud computing, quad-copter, deep learning, fuzzy system, image processing, pattern recognition, bayesian network, intelligent techniques, learning systems