Artificial Intelligence methods for large microsensor arrays: feasibility and design

Elena Gaura; R. M. Newman; N Steele

doi:10.1007/978-3-7091-0646-4

Artificial Intelligence methods for large microsensor arrays: feasibility and design

Elena Gaura, R. M. Newman, N Steele

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

Recently several authors have turned their attention to the design problems of very large arrays of intelligent sensors, such as would be suitable for inclusion as an integral part of a,smart structure'. Such systems have architectural and data throughput advantages, but to be used effectively need to be designed so as to be 'controllerless' and 'decentralised'. This paper explores some of these design issues, and illustrates how artificial intelligence (in particular Neural Networks techniques) could be effectively incorporated within the design of large intelligent microsystems with the scope of enhancing their functionality. Micromachined acceleration sensors are considered as a case study.

The paper opens with a discussion on the ever-increasing appropriateness of local sensor health monitoring, fault diagnosis and measurement confidence indices. The use of Artificial Intelligence techniques is suggested for implementing on-chip sensor diagnosis. Design issues for large, intelligent arrays of microsensors are brought forward.

Original language	English
Title of host publication	Artificial Neural Nets and Genetic Algorithms
Subtitle of host publication	Proceedings of the International Conference in Roanne, France, 2003
Editors	David W. Pearson, Nigel C. Steele, Rudolf F. Albrecht
Place of Publication	Berlin; New York
Publisher	Springer, Springer Nature
Pages	154-160
Number of pages	7
Edition	1
ISBN (Electronic)	9783709106464
ISBN (Print)	3211007431, 9783211007433
DOIs	https://doi.org/10.1007/978-3-7091-0646-4
Publication status	Published - 2003
Externally published	Yes
Event	International Conference on Artificial Neural Nets and Genetic Algorithms - Roanne, France Duration: 23 Apr 2003 → 25 Apr 2003

Publication series

Name	Springer Computer Science
Publisher	Springer-Verlag Wien

Conference

Conference	International Conference on Artificial Neural Nets and Genetic Algorithms
Country/Territory	France
City	Roanne
Period	23/04/03 → 25/04/03

Access to Document

10.1007/978-3-7091-0646-4

Cite this

Gaura, E., Newman, R. M., & Steele, N. (2003). Artificial Intelligence methods for large microsensor arrays: feasibility and design. In D. W. Pearson, N. C. Steele, & R. F. Albrecht (Eds.), Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Roanne, France, 2003 (1 ed., pp. 154-160). (Springer Computer Science). Springer, Springer Nature. https://doi.org/10.1007/978-3-7091-0646-4

Gaura, Elena ; Newman, R. M. ; Steele, N. / Artificial Intelligence methods for large microsensor arrays : feasibility and design. Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Roanne, France, 2003. editor / David W. Pearson ; Nigel C. Steele ; Rudolf F. Albrecht. 1. ed. Berlin; New York : Springer, Springer Nature, 2003. pp. 154-160 (Springer Computer Science).

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title = "Artificial Intelligence methods for large microsensor arrays: feasibility and design",

abstract = "Recently several authors have turned their attention to the design problems of very large arrays of intelligent sensors, such as would be suitable for inclusion as an integral part of a,smart structure'. Such systems have architectural and data throughput advantages, but to be used effectively need to be designed so as to be 'controllerless' and 'decentralised'. This paper explores some of these design issues, and illustrates how artificial intelligence (in particular Neural Networks techniques) could be effectively incorporated within the design of large intelligent microsystems with the scope of enhancing their functionality. Micromachined acceleration sensors are considered as a case study.The paper opens with a discussion on the ever-increasing appropriateness of local sensor health monitoring, fault diagnosis and measurement confidence indices. The use of Artificial Intelligence techniques is suggested for implementing on-chip sensor diagnosis. Design issues for large, intelligent arrays of microsensors are brought forward.",

author = "Elena Gaura and Newman, {R. M.} and N Steele",

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language = "English",

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series = "Springer Computer Science",

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booktitle = "Artificial Neural Nets and Genetic Algorithms",

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note = "International Conference on Artificial Neural Nets and Genetic Algorithms ; Conference date: 23-04-2003 Through 25-04-2003",

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Gaura, E, Newman, RM & Steele, N 2003, Artificial Intelligence methods for large microsensor arrays: feasibility and design. in DW Pearson, NC Steele & RF Albrecht (eds), Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Roanne, France, 2003. 1 edn, Springer Computer Science, Springer, Springer Nature, Berlin; New York, pp. 154-160, International Conference on Artificial Neural Nets and Genetic Algorithms, Roanne, France, 23/04/03. https://doi.org/10.1007/978-3-7091-0646-4

Artificial Intelligence methods for large microsensor arrays: feasibility and design. / Gaura, Elena; Newman, R. M.; Steele, N.
Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Roanne, France, 2003. ed. / David W. Pearson; Nigel C. Steele; Rudolf F. Albrecht. 1. ed. Berlin; New York: Springer, Springer Nature, 2003. p. 154-160 (Springer Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Artificial Intelligence methods for large microsensor arrays

T2 - International Conference on Artificial Neural Nets and Genetic Algorithms

AU - Gaura, Elena

AU - Newman, R. M.

AU - Steele, N

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Y1 - 2003

N2 - Recently several authors have turned their attention to the design problems of very large arrays of intelligent sensors, such as would be suitable for inclusion as an integral part of a,smart structure'. Such systems have architectural and data throughput advantages, but to be used effectively need to be designed so as to be 'controllerless' and 'decentralised'. This paper explores some of these design issues, and illustrates how artificial intelligence (in particular Neural Networks techniques) could be effectively incorporated within the design of large intelligent microsystems with the scope of enhancing their functionality. Micromachined acceleration sensors are considered as a case study.The paper opens with a discussion on the ever-increasing appropriateness of local sensor health monitoring, fault diagnosis and measurement confidence indices. The use of Artificial Intelligence techniques is suggested for implementing on-chip sensor diagnosis. Design issues for large, intelligent arrays of microsensors are brought forward.

AB - Recently several authors have turned their attention to the design problems of very large arrays of intelligent sensors, such as would be suitable for inclusion as an integral part of a,smart structure'. Such systems have architectural and data throughput advantages, but to be used effectively need to be designed so as to be 'controllerless' and 'decentralised'. This paper explores some of these design issues, and illustrates how artificial intelligence (in particular Neural Networks techniques) could be effectively incorporated within the design of large intelligent microsystems with the scope of enhancing their functionality. Micromachined acceleration sensors are considered as a case study.The paper opens with a discussion on the ever-increasing appropriateness of local sensor health monitoring, fault diagnosis and measurement confidence indices. The use of Artificial Intelligence techniques is suggested for implementing on-chip sensor diagnosis. Design issues for large, intelligent arrays of microsensors are brought forward.

U2 - 10.1007/978-3-7091-0646-4

DO - 10.1007/978-3-7091-0646-4

M3 - Conference proceeding contribution

SN - 3211007431

SN - 9783211007433

T3 - Springer Computer Science

SP - 154

EP - 160

BT - Artificial Neural Nets and Genetic Algorithms

A2 - Pearson, David W.

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PB - Springer, Springer Nature

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Y2 - 23 April 2003 through 25 April 2003

ER -

Gaura E, Newman RM, Steele N. Artificial Intelligence methods for large microsensor arrays: feasibility and design. In Pearson DW, Steele NC, Albrecht RF, editors, Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Roanne, France, 2003. 1 ed. Berlin; New York: Springer, Springer Nature. 2003. p. 154-160. (Springer Computer Science). doi: 10.1007/978-3-7091-0646-4

Artificial Intelligence methods for large microsensor arrays: feasibility and design

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