P2P-based image recognition for component tracking in a large engineering domain

A. Amir; A. H.M. Amin; B. Srinivasan

doi:10.4203/ccp.95.73

P2P-based image recognition for component tracking in a large engineering domain

A. Amir, A. H.M. Amin, B. Srinivasan

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (PARENG 2011)
Editors	P. Iványi, B.H.V. Topping
Place of Publication	Stirlingshire, UK
Publisher	Civil-Comp Press
Number of pages	14
Volume	95
ISBN (Electronic)	9781905088430, 9781905088447
ISBN (Print)	9781905088423
DOIs	https://doi.org/10.4203/ccp.95.73
Publication status	Published - 2011
Event	International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering 2011 - Ajaccio, Corsica, France Duration: 12 Apr 2011 → 15 Apr 2011 Conference number: 2nd

Conference

Conference	International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering 2011
Abbreviated title	PARENG 2011
Country/Territory	France
City	Ajaccio, Corsica
Period	12/04/11 → 15/04/11

Keywords

Associative memory
Component searching
Component tracking
Distributed system
Pattern recognition
Peer-to-peer

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10.4203/ccp.95.73

Cite this

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title = "P2P-based image recognition for component tracking in a large engineering domain",

abstract = "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.",

keywords = "Associative memory, Component searching, Component tracking, Distributed system, Pattern recognition, Peer-to-peer",

author = "A. Amir and Amin, {A. H.M.} and B. Srinivasan",

note = "From proceedings ~ Paper 73; Preface: This volume comprises the summaries of contributed papers presented at The Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (PARENG 2011) held in Ajaccio, Corsica, France, 12-15 April 2011. The full papers from the conference are available on the accompanying CD-ROM.; International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering 2011, PARENG 2011 ; Conference date: 12-04-2011 Through 15-04-2011",

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language = "English",

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volume = "95",

editor = "Iv{\'a}nyi, {P. } and Topping, {B.H.V. }",

booktitle = "Proceedings of the 2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (PARENG 2011)",

publisher = "Civil-Comp Press",

address = "United Kingdom",

}

Amir, A, Amin, AHM & Srinivasan, B 2011, P2P-based image recognition for component tracking in a large engineering domain. in P Iványi & BHV Topping (eds), Proceedings of the 2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (PARENG 2011). vol. 95, Civil-Comp Press, Stirlingshire, UK, International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering 2011, Ajaccio, Corsica, France, 12/04/11. https://doi.org/10.4203/ccp.95.73

P2P-based image recognition for component tracking in a large engineering domain. / Amir, A.; Amin, A. H.M.; Srinivasan, B.

Proceedings of the 2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (PARENG 2011). ed. / P. Iványi; B.H.V. Topping. Vol. 95 Stirlingshire, UK : Civil-Comp Press, 2011.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

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AU - Amir, A.

AU - Amin, A. H.M.

AU - Srinivasan, B.

N1 - Conference code: 2nd

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - Associative memory

KW - Component searching

KW - Component tracking

KW - Distributed system

KW - Pattern recognition

KW - Peer-to-peer

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BT - Proceedings of the 2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (PARENG 2011)

A2 - Iványi, P.

A2 - Topping, B.H.V.

PB - Civil-Comp Press

CY - Stirlingshire, UK

T2 - International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering 2011

Y2 - 12 April 2011 through 15 April 2011

ER -

P2P-based image recognition for component tracking in a large engineering domain

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Keywords

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