Market-Based Approach to Mobile Surveillance Systems

The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is, therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given area of interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This paper proposes a market-based approach that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target tracking are studied using the proposed approach as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively

Remote interaction with mobile robots

This paper describes an architecture, which can be used to build remote laboratories to interact remotely via Internet with mobile robots using different interaction devices. A supervisory control strategy has been used to develop the remote laboratory in order to alleviate high communication data rates and system sensitivity to network delays. The users interact with the remote system at a more abstract level using high level commands. The local robot's autonomy has been increased by encapsulating all the robot's behaviors in different types of skills. User interfaces have been designed using visual proxy pattern to facilitate any future extension or code reuse. The developed remote laboratory has been integrated into an educational environment in the field of indoor mobile robotics. This environment is currently being used as a part of an international project to develop a distributed laboratory for autonomous and teleoperated systems (IECAT, 2003).Publicad

Intelligent Systems in Context-Based Distributed Information Fusion

Publicad

Drone deep reinforcement learning: A review

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios

The diagnostic value of sonoelastographic strain ratio in discriminating malignant from benign solid breast masses

Objective: To detect the diagnostic efficiency of sono elastographic strain ratio in discriminating malignant from benign solid breast masses and compare it with the sono elastographic elasticity score method. Patients and methods: This study included 120 histopathologically diagnosed solid breast masses from 120 females (mean age 38.2 years). Elastography score and strain ratio (SR) were performed for each mass. Receiver operating characteristic (ROC) curve was plotted for both methods. Results: The benign lesions had significant lower SR (mean 2.12 ± 1.72) than that of malignant lesions (mean 6.91 ± 3.96). The AUC from ROC curve was 0.98 for elasticity score and 0.99 for SR. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the elasticity score in the diagnosis of solid breast masses were 100%, 88%, 83.3%, 100% and 92.5% respectively, and of the strain ratio were 93.3%, 97.3%, 95.5%, 96.1% and 95.8% respectively (when cutoff value 3.77 was used). There is no statistically significant difference found between both methods. Conclusion: SR has high diagnostic performance in differentiating malignant from benign solid breast masses, however there is no statistically significant difference between SR and elasticity score

Intelligent Systems in Context-Based Distributed Information Fusion

Publicad

Additional value of qualitative strain ultrasound elastography and strain ratio in predicting thyroid malignancy

Objective: To detect if strain ultrasound elastography and strain ratio have additional value to the conventional grey scale ultrasound in predicting thyroid malignancy. Patients and methods: This study included 92 thyroid nodules from 62 patients (the mean age was 40.64 ± 13.93). Morphologic aspects of the thyroid nodule in conventional grey scale ultrasonography and elastographic examinations with elastography score and strain ratio (SR) were performed for all nodules. The final diagnosis was confirmed by fine needle aspiration biopsies in 72 nodules and by excisional biopsies in 20 nodules. Results: We found that combination of both conventional ultrasound and strain elastography score have the best diagnostic performance with sensitivity, specificity, PPV, NPV and accuracy accounting for 80%, 97%, 57%, 99% and 96% respectively. The means SR for benign nodules (1.37 ± 0.56) was significantly lower than that for malignant nodules (3.0 ± 0.71) [p-value .003].The optimal SR cutoff is 2.5 with estimated 80% sensitivity, 98% specificity, PPV 67%, NPV 99% and accuracy 97%. Conclusion: The clinical application of elastography score and SR should be carried out hand in hand with conventional sonographic assessment of thyroid nodules to achieve the best diagnostic performance

Requirements for building an ontology for autonomous robots

Purpose - IEEE Ontologies for Robotics and Automation Working Group were divided into subgroups that were in charge of studying industrial robotics, service robotics and autonomous robotics. This paper aims to present the work in-progress developed by the autonomous robotics (AuR) subgroup. This group aims to extend the core ontology for robotics and automation to represent more specific concepts and axioms that are commonly used in autonomous robots. Design/methodology/approach - For autonomous robots, various concepts for aerial robots, underwater robots and ground robots are described. Components of an autonomous system are defined, such as robotic platforms, actuators, sensors, control, state estimation, path planning, perception and decision-making. Findings - AuR has identified the core concepts and domains needed to create an ontology for autonomous robots. Practical implications - AuR targets to create a standard ontology to represent the knowledge and reasoning needed to create autonomous systems that comprise robots that can operate in the air, ground and underwater environments. The concepts in the developed ontology will endow a robot with autonomy, that is, endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance. Originality/value - Creating a standard for knowledge representation and reasoning in autonomous robotics will have a significant impact on all R&A domains, such as on the knowledge transmission among agents, including autonomous robots and humans. This tends to facilitate the communication among them and also provide reasoning capabilities involving the knowledge of all elements using the ontology. This will result in improved autonomy of autonomous systems. The autonomy will have considerable impact on how robots interact with humans. As a result, the use of robots will further benefit our society. Many tedious tasks that currently can only be performed by humans will be performed by robots, which will further improve the quality of life. To the best of the authors'knowledge, AuR is the first group that adopts a systematic approach to develop ontologies consisting of specific concepts and axioms that are commonly used in autonomous robots

Requirements for building an ontology for autonomous robots

Purpose IEEE Ontologies for Robotics and Automation Working Group were divided into subgroups that were in charge of studying industrial robotics, service robotics and autonomous robotics. This paper aims to present the work in-progress developed by the autonomous robotics (AuR) subgroup. This group aims to extend the core ontology for robotics and automation to represent more specific concepts and axioms that are commonly used in autonomous robots. Design/methodology/approach For autonomous robots, various concepts for aerial robots, underwater robots and ground robots are described. Components of an autonomous system are defined, such as robotic platforms, actuators, sensors, control, state estimation, path planning, perception and decision-making. Findings AuR has identified the core concepts and domains needed to create an ontology for autonomous robots. Practical implications AuR targets to create a standard ontology to represent the knowledge and reasoning needed to create autonomous systems that comprise robots that can operate in the air, ground and underwater environments. The concepts in the developed ontology will endow a robot with autonomy, that is, endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance. Originality/value Creating a standard for knowledge representation and reasoning in autonomous robotics will have a significant impact on all R\&A domains, such as on the knowledge transmission among agents, including autonomous robots and humans. This tends to facilitate the communication among them and also provide reasoning capabilities involving the knowledge of all elements using the ontology. This will result in improved autonomy of autonomous systems. The autonomy will have considerable impact on how robots interact with humans. As a result, the use of robots will further benefit our society. Many tedious tasks that currently can only be performed by humans will be performed by robots, which will further improve the quality of life. To the best of the authors’knowledge, AuR is the first group that adopts a systematic approach to develop ontologies consisting of specific concepts and axioms that are commonly used in autonomous robots