%0 Journal Article
%@ 17593433
%A Amir, A.
%A Amin, A.H.M.
%A Srinivasan, B.
%C Ajaccio, Corsica
%D 2011
%F scholars:2292
%I Civil-Comp Press
%J Civil-Comp Proceedings
%K Associative processing; Cloud computing; Complex networks; Image recognition; Pattern recognition; Warehouses, Associative memory; Component searching; Distributed systems; Engineering design projects; Pattern recognition algorithms; Peer to peer; Structured P2P networks; Time-consuming tasks, Engineering
%T P2P-based image recognition for component tracking in a large engineering domain
%U https://khub.utp.edu.my/scholars/2292/
%V 95
%X Large engineering design projects comprise very large and complex inventories of critical building components. Managing and searching components within multiple huge warehouses are complicated and time consuming tasks. Prompt availability of the components when there are needed is crucial to avoid the delay of the project, to guarantee the fast maintenance, and to ensure the smooth of the operation. Multi-Wheel Graph Neuron (mWGN) is a single-cycle, light-weight, and scalable associative-memory-based pattern recognition algorithm, and has been implemented over a structured P2P network. We propose the use of mWGN to connect multiple warehouse resources in order to facilitate a speedy and convenient searching of parts. For example an engineer can photograph a component and search to locate the part or obtain related information from using simple devices such as a mobile phone. In this paper, we show the accuracy of mWGN for component recognition and the efficiency of Chord network in linking the warehouses. © Civil-Comp Press, 2011.
%Z cited By 0; Conference of 2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, PARENG 2011 ; Conference Date: 12 April 2011 Through 15 April 2011; Conference Code:102667