Abstract Developing telepresence robots is one of the most important trends in the robotics research area, where the user acts as he/she is located in a remote location. In 2010, telepresence robots became a noticeable trend after the robot “QB” that introduced by Silicon Valley start-up Anybots (Robotics trends for 2012. IEEE Robot. Autom. Mag. 19(1):119–123, 2012). Although, the availability of “QB” as a commercial telepresence robot, its cost made it unavailable for most users. In this work, a low-cost telepresence robot is presented using iRobot-Create, Microsoft Kinect sensor, and video glasses. The proposed system makes the user feels like he/she is located in a different location and acting as in the normal life (walking, stop, rotating his/her head). The user takes feedback via a streaming video from the remote location to a pair of video glasses worn by him. The remote unit consists of three components: a single iRobot-Create, a laptop, and two web-cams. In the user side, the user’s movements are recognized using Microsoft Kinect sensor. We use the RGB camera in Microsoft Kinect sensor for streaming the video of the user to the remote side. So, People in the remote side see the user, as he/she is located with them. The results of the proposed system show that the user is integrated in another environment using low-cost hardware components.

Abstract The field of Optical Character Recognition (OCR) has gained more attention in the recent years because of its importance and applications. Some examples of OCR are: video indexing, references archiving, car-plate recognition, and data entry. In this work a robust system for OCR is presented. The proposed system recognizes text in poor quality images. Characters are extracted from the given poor quality image to be recognized using chain-code representation. The proposed system uses Google online spelling to suggest replacements for words which are misspelled during the recognition process. For evaluating the proposed system, the born-digital dataset ICDAR is used. The proposed system achieves 74.02 % correctly recognized word rate. The results demonstrate that the proposed system recognizes text in poor quality images efficiently.

Abstract The field of Optical Character Recognition (OCR) has gained more attention in the recent years because of its importance and applications. Some examples of OCR are: video indexing, references archiving, car-plate recognition, and data entry. In this work a robust system for OCR is presented. The proposed system recognizes text in poor quality images. Characters are extracted from the given poor quality image to be recognized using chain-code representation. The proposed system uses Google online spelling to suggest replacements for words which are misspelled during the recognition process. For evaluating the proposed system, the born-digital dataset ICDAR is used. The proposed system achieves 74.02 % correctly recognized word rate. The results demonstrate that the proposed system recognizes text in poor quality images efficiently.

Abstract The field of Optical Character Recognition (OCR) has gained more attention in the recent years because of its importance and applications. Some examples of OCR are: video indexing, references archiving, car-plate recognition, and data entry. In this work a robust system for OCR is presented. The proposed system recognizes text in poor quality images. Characters are extracted from the given poor quality image to be recognized using chain-code representation. The proposed system uses Google online spelling to suggest replacements for words which are misspelled during the recognition process. For evaluating the proposed system, the born-digital dataset ICDAR is used. The proposed system achieves 74.02 % correctly recognized word rate. The results demonstrate that the proposed system recognizes text in poor quality images efficiently.

Abstract The field of Optical Character Recognition (OCR) has gained more attention in the recent years because of its importance and applications. Some examples of OCR are: video indexing, references archiving, car-plate recognition, and data entry. In this work a robust system for OCR is presented. The proposed system recognizes text in poor quality images. Characters are extracted from the given poor quality image to be recognized using chain-code representation. The proposed system uses Google online spelling to suggest replacements for words which are misspelled during the recognition process. For evaluating the proposed system, the born-digital dataset ICDAR is used. The proposed system achieves 74.02 % correctly recognized word rate. The results demonstrate that the proposed system recognizes text in poor quality images efficiently.

Abstract The field of Optical Character Recognition (OCR) has gained more attention in the recent years because of its importance and applications. Some examples of OCR are: video indexing, references archiving, car-plate recognition, and data entry. In this work a robust system for OCR is presented. The proposed system recognizes text in poor quality images. Characters are extracted from the given poor quality image to be recognized using chain-code representation. The proposed system uses Google online spelling to suggest replacements for words which are misspelled during the recognition process. For evaluating the proposed system, the born-digital dataset ICDAR is used. The proposed system achieves 74.02 % correctly recognized word rate. The results demonstrate that the proposed system recognizes text in poor quality images efficiently.

Mobile ad hoc network (MANET) has a distributed and uncontrolled nature in which all nodes are considered trusted and contribute in the route discovery process. Accordingly, MANET is vulnerable to many types of routing attacks. One of the most popular MANET routing protocol that is vulnerable to different attacks is ad hoc on demand distance vector (AODV) routing protocol. In this paper, a mobile backbone network of trusted nodes is proposed to enhance AODV security with minimum overhead. The proposed backbone network does not violate the MANET mobility characteristic. It is constructed from randomly moving regular MANET nodes based on their trust value, location, and power. The backbone network monitors regular nodes as well as each other to periodically estimate monitoring trust values which represent the reliability of each node in the network. The simulation results show that the backbone network is trustable, has dynamic behavior, and has good coverage.

Mobile ad hoc network (MANET) is an autonomous self-configuring infrastructure-less wireless network. MANET is vulnerable to a lot of routing security threats due to unreliability of its nodes that are highly involved in the routing process. In this paper, a new technique is proposed to enhance the security of one of the most popular MANET routing protocols that is called Ad hoc on Demand Distance Vector (AODV) with minimum routing overhead and high packet delivery ratio. The proposed technique intends to detect and remove black, gray, and cooperative black hole AODV attacks depending on a mobile backbone network constructed from randomly moving regular MANET nodes based on their trust value, location, and power. The backbone network monitors regular nodes as well as each other to periodically estimate monitoring trust values which represent the reliability of each node in the network. The drop in the monitoring trust value of any node is used as a clue to its malicious behavior. The backbone network also tries to bait the malicious nodes to reply to a request for a route to fake destination address. The proposed technique uses the control packets of the AODV to exchange its control information which highly reduces the overhead. The simulation results show that the performance of the proposed technique is more secure than AODV and the other recently introduced techniques.

Mobile ad hoc network (MANET) is an autonomous self-configuring infrastructure-less wireless network. MANET is vulnerable to a lot of routing security threats due to unreliability of its nodes that are highly involved in the routing process. In this paper, a new technique is proposed to enhance the security of one of the most popular MANET routing protocols that is called Ad hoc on Demand Distance Vector (AODV) with minimum routing overhead and high packet delivery ratio. The proposed technique intends to detect and remove black, gray, and cooperative black hole AODV attacks depending on a mobile backbone network constructed from randomly moving regular MANET nodes based on their trust value, location, and power. The backbone network monitors regular nodes as well as each other to periodically estimate monitoring trust values which represent the reliability of each node in the network. The drop in the monitoring trust value of any node is used as a clue to its malicious behavior. The backbone network also tries to bait the malicious nodes to reply to a request for a route to fake destination address. The proposed technique uses the control packets of the AODV to exchange its control information which highly reduces the overhead. The simulation results show that the performance of the proposed technique is more secure than AODV and the other recently introduced techniques.

Handheld device systems have been used as tools for teaching people with special needs due to cognitive function enhancement by utility of multimedia, attractive graphics and user-friendly navigation. Can a handheld device system, such as cellular phone, be used for teaching illiterate people? This paper explores and exploits the possibility of the development of an educational mobile system to help the illiterate people in Egypt.