Abstract

Abstract
Hand gesture recognition is a natural way of human computer interaction and an area of very active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research applied to Human-Computer Interaction (HCI) is to create systems, which can identify specific human gestures and use them to convey information or controlling devices. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. This paper presents a solution, generic enough, with the help of machine learning algorithms, allowing its application in a wide range of human-computer interfaces, for real-time gesture recognition. Experiments carried out showed that the systemwas able to achieve an accuracy of 99.4%in terms of hand posture recognition and an average accuracy of 93.72%in terms of dynamic gesture recognition. To validate the proposed framework, two applications were implemented. The first one is a real-time system able to help a robotic soccer referee judge a game in real time. The prototype combines a vision-based hand gesture recognition system with a formal language definition, the Referee CommLang, into what is called the Referee Command Language Interface System (ReCLIS). The second one is a real-time system able to interpret the Portuguese Sign Language. Sign languages are not standard and universal and the grammars differ from country to country. Although the implemented prototype was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system. © Springer International Publishing Switzerland 2015

Abstract
This paper describes Cybi, an inexpensive smart companion mobile robot for elderly and disabled people. The software architecture that enables users to perform telepresence and teleoperation tasks with Cybi is based on combining Cloud Computing technologies and Fuzzy Logic. On one hand, the robot can be remotely teleoperated from a thin client by executing a specific module of a distributed software component that uses ROS, WebRTC and Google App Engine. On the other hand, Cybi is capable of understanding simple spoken orders that allow it to navigate through an unknown domestic indoor environment. In both cases, a fuzzy reactive navigation software component is used with the aim of making the teleoperation procedure as easy and safe as possible for both elders' caregivers and elderly users themselves. Finally, several real tests are also presented, demonstrating that Cybi could be considered as a suitable robotic companion, since the proposed methods enhance the performance of teleoperation and telepresence tasks. © 2015 IEEE.

Abstract
This paper presents a comparative study on various implementation techniques of Unified Power Flow Controller (UPFC) in load flow algorithms. The comparison is done among; decoupled technique, comprehensive technique, load injection technique, pi load injection technique, indirect technique, and matrix partitioning technique. The merits and demerits of these techniques are presented. Beside these techniques, the paper presents a developed UPFC model based only on current injection approach. The model is implemented in Newton-Raphson current injection load flow method (NR-CIM). This model addresses the main drawbacks of previous techniques. Test results are presented using IEEE standard systems which demonstrate the effectiveness of the developed model.

Abstract

Abstract