Urban Search and Rescue (USAR) involves having to enter and explore partially collapsed buildings in search for victims trapped by the collapse. There are many hazards in doing this, because of the possibility of additional collapses, explosions, fires, or flooding of the area being searched. The use of robots for USAR would increase the safety of the operation for the humans involved, and make the operation faster, because the robots could penetrate areas inaccessible to human beings. Teleoperated robots have been deployed in USAR situations to explore confined spaces in the collapsed buildings and send back images of the interior to rescuers. These deployments have resulted in the identification of several problems found during the operation of these robots. This thesis addresses a problem that has been encountered repeatedly in these robots: the determination of the scale of unrecognizable objects in the camera views from the robot. A procedure that would allow the extraction of size using a laser pointer mounted on the robot's camera is described, and an experimental setup and results that verify this procedure have been shown. Finally, ways to extend the procedure have been explored

P2P search system usually be integrated to many systems in wired network. As the result of technology development, wireless networks are more and more popular. Many system in wired networks are be implemented in wireless network environment, too. In virtue of diverseness between wired and wireless networks, there are some modify for characteristic of wireless network. In this paper, we demonstrate KAdHoc, a peer-to-peer search system for Mobile Ad Hoc Network (MANET).There are problems when P2P systems being used in MANET such as physical locality, power exhaustion and nodes information outdated when nodes move. KAdHoc consider these problems and design a mechanism for P2P search system in MANET. The simulation result show that KAdHoc has fewer lookup time and has high success rate and low total traffic even in High Node density. KAdHoc even has higher success rate in higher node velocity.

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (p. 123-130). Also available in print.

Thesis (M.A.)--Lancaster Bible College, Graduate School, 2000. / Includes bibliographical references (leaves 59-61).

The Assembly sequencing problem (ASP) is part of the assembly planning process. The ASP is basically a large scale, combinatorial problem which is highly constrianed. The aim of this thesis is to automatically generate assembly sequence(s) for mechanical products. In this thesis, the CAD model of an assembly is represented or modeled as a label-rich graph. The assembly sequences are generated using graph grammar rules that are applied on the graph. The sequences are stored in a search tree and to find an optimal sequence multiple evaluation criteria like time, subassembly stability and accessibility measures are used. This research implements a novel tree search algorithm called "Ordered Depth First Search" (ODFS) to find an optimal assembly sequence in very low processing time. The software has successfully generated an optimized assembly sequence for an assembly with 14 parts. / text

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

In this thesis two case studies are performed about solving two design problems we face during the design phase of new Volvo truck. One is to solve the frame packing problem on CAN bus. The other is to solve the LDC allocation problem. Both solutions are targeted to meet as many end-to-end latency requirements as possible. Now the solution is obtained through manually approach and based on the designer experience. But it is still not satisfactory enough. With the development of artificial intelligence method we propose two methods based on genetic algorithm to solve our design problem we face today. In first case study about frame packing we perform one single genetic algorithm process to find the optimal solution. In second case study about LDC allocation we proposed how to handle two genetic algorithm processes together to reach the optimal solution. In this thesis we show the feasibility of adopting artificial intelligence concept in some activities of the truck design phases like we do in both case studies.

Peer-to-Peer(P2P) systems have emerged as a promising paradigm to structure large scale distributed systems. They provide a robust, scalable and decentralized way to share and publish data.The unstructured P2P systems have gained much popularity in recent years for their wide applicability and simplicity. However efficient resource discovery remains a fundamental challenge for unstructured P2P networks due to the lack of a network structure. To effectively harness the power of unstructured P2P systems, the challenges in distributed knowledge management and information search need to be overcome. Current attempts to solve the problems pertaining to knowledge management and search have focused on simple term based routing indices and keyword search queries. Many P2P resource discovery applications will require more complex query functionality, as users will publish semantically rich data and need efficiently content location algorithms that find target content at moderate cost. Therefore, effective knowledge and data management techniques and search tools for information retrieval are imperative and lasting. In my dissertation, I present a suite of protocols that assist in efficient content location and knowledge management in unstructured Peer-to-Peer overlays. The basis of these schemes is their ability to learn from past peer interactions and increasing their performance with time.My work aims to provide effective and bandwidth-efficient searching and data sharing in unstructured P2P environments. A suite of algorithms which provide peers in unstructured P2P overlays with the state necessary in order to efficiently locate, disseminate and replicate objects is presented. Also, Existing approaches to federated search are adapted and new methods are developed for semantic knowledge representation, resource selection, and knowledge evolution for efficient search in dynamic and distributed P2P network environments. Furthermore,autonomous and decentralized algorithms that reorganizes an unstructured network topology into a one with desired search-enhancing properties are proposed in a network evolution model to facilitate effective and efficient semantic search in dynamic environments.

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