Cyber-physical systems clouds: A survey

Chaâri, Rihab; Ellouze, Fatma; Koubâa, Anis; Qureshi, Basit; Pereira, Nuno; Youssef, Habib; Tovar, Eduardo

doi:10.1016/j.comnet.2016.08.017

Cited by 100 publications

(46 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For evaluating the YOLO on PASCAL VOC [21], they used for values [9]: S=7 ,B=2, C=20 (as PASCAL VOC [21] has 20 classes of objects). We get finally a (7,7,30) tensor. In the final prediction, we keep only high box confidence scores and the object class with highest probability.…”

Section: B Architecture Of Yolov3mentioning

confidence: 99%

“…The evolution of Graphic Processing Units (GPUs) also significantly contributed to the adoption of CNN in computer vision overcoming the problems of real-time processing of computation intensive tasks through parallelization. In addition, latest trends in cloud robotics [6], [7], [8] have also enabled offloading heavy computations, such as video stream analysis, to the cloud. This allows to process video streams in real-time using advanced deep learning algorithms in the context of surveillance applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3

Benjdira

Khursheed

Koubâa

et al. 2019

2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS)

253

112

View full text Add to dashboard Cite

Unmanned Aerial Vehicles are increasingly being used in surveillance and traffic monitoring thanks to their high mobility and ability to cover areas at different altitudes and locations. One of the major challenges is to use aerial images to accurately detect cars and count-them in real-time for traffic monitoring purposes. Several deep learning techniques were recently proposed based on convolution neural network (CNN) for real-time classification and recognition in computer vision. However, their performance depends on the scenarios where they are used. In this paper, we investigate the performance of two state-of-the art CNN algorithms, namely Faster R-CNN and YOLOv3, in the context of car detection from aerial images. We trained and tested these two models on a large car dataset taken from UAVs. We demonstrated in this paper that YOLOv3 outperforms Faster R-CNN in sensitivity and processing time, although they are comparable in the precision metric.

show abstract

Section: B Architecture Of Yolov3mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3

Benjdira

Khursheed

Koubâa

et al. 2019

2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS)

253

112

View full text Add to dashboard Cite

show abstract

“…In the context of the Cloud Robotics [1], [2], coined by James Kuffner [3], several works since 2010 attempted to integrate robots with the cloud through the Internet [4], [5], [6], [7]. In [4], the author proposed one of the first papers that specified the concept of Robot as a Service (RaaS).…”

Section: Introductionmentioning

confidence: 99%

A service-oriented Cloud-based management system for the Internet-of-Drones

Koubâa

Qureshi

Sriti

et al. 2017

2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Self Cite

View full text Add to dashboard Cite

Deploying drones over the Cloud is an emerging research area motivated by the emergence of Cloud Robotics and the Internet-of-Drones (IoD) paradigms. This paper contributes to IoD and to the deployment of drones over the cloud. It presents, Dronemap Planner, an innovative service-oriented cloud based drone management system that provides access to drones through web services (SOAP and REST), schedule missions and promotes collaboration between drones. A modular cloud proxy server was developed; it acts as a moderator between drones and users. Communication between drones, users and the Dronemap Planner cloud is provided through the MAVLink protocol, which is supported by commodity drones. To demonstrate the effective-ness of Dronemap Planner, we implemented and validated it using simulated and real MAVLink-enabled drones, and deployed it on a public cloud server. Experimental results show that Dronemap Planner is efficient in virtualizing the access to drones over the Internet, and provides developers with appropriate APIs to easily program drones 19 applications.A Service-Oriented Cloud-Based Management System for the Internet-of-Drones Abstract-Deploying drones over the Cloud is an emerging research area motivated by the emergence of Cloud Robotics and the Internet-of-Drones (IoD) paradigms. This paper contributes to IoD and to the deployment of drones over the cloud. It presents, Dronemap Planner, an innovative service-oriented cloud based drone management system that provides access to drones through web services (SOAP and REST), schedule missions and promotes collaboration between drones. A modular cloud proxy server was developed; it acts as a moderator between drones and users. Communication between drones, users and the Dronemap Planner cloud is provided through the MAVLink protocol, which is supported by commodity drones. To demonstrate the effectiveness of Dronemap Planner, we implemented and validated it using simulated and real MAVLink-enabled drones, and deployed it on a public cloud server. Experimental results show that Dronemap Planner is efficient in virtualizing the access to drones over the Internet, and provides developers with appropriate APIs to easily program drones' applications.

show abstract

“…In the long term, the whole production and supply chain will witness communication of smart food labels so as to give in-depth insight of where exactly the food is coming from (Piramuthu & Zhou, 2016). Also, future CPS in emerging sectors like food industry will be beneficiated by cloud robotics as highlighted in (Chaâri et al, 2016).…”

Section: Serving Robotsmentioning

confidence: 99%

Prospects of robotics in food industry

2017

View full text Add to dashboard Cite

Technological advancements in various domains have broadened the application horizon of robotics to an incredible extent. Highlighting a very recent application area, this paper presents a comprehensive review of robotics application in food industry. Robots essentially have the potential to transform the processes in food processing and handling, palletizing and packing and food serving. Therefore, recent years witnessed tremendously increased trend of robots deployment in food sector. Consequently, the aspects related with robot kinematics, dynamics, hygiene, economic efficiency, human-robot interaction, safety and protection and operation and maintenance are of critical importance and are discussed in the present review. A comparison of actual robots being used in the industry is also presented. The review reveals that the food serving sector is the new potential area in which ample research opportunities exist by integrating advancements from various technology domains. It is anticipated that wider dissemination of research developments in 'robo-food' will stimulate more collaborations among the research community and contribute to further developments.Keywords: robotics in food industry; cyber physical systems; mechatronics in food handling and processing; food serving robots.Practical Application: Automation in food processing industry.

show abstract

Cyber-physical systems clouds: A survey

Cited by 100 publications

References 24 publications

Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3

Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3

A service-oriented Cloud-based management system for the Internet-of-Drones

Prospects of robotics in food industry

Contact Info

Product

Resources

About