Wireless sensor network microcantilever data processing using principal component and correlation analysis

Viktor Zaharov; Angel Lambertt; Ali Passian

doi:10.5220/0005933200970105

Wireless sensor network microcantilever data processing using principal component and correlation analysis

Viktor Zaharov, Angel Lambertt, Ali Passian

Quantum Sensing and Computing

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

Abstract

One of the main purpose of the wireless sensor network is an identification of unknown physical, chemical and biological agents in monitoring area. It requires the measurement of the microcantilever sensor resonance frequencies with high precision. However, resolving the weak spectral variations in dynamic response of materials that are either dominated or excited by stochastic processes remains a challenge. In this paper we present the analysis and experimental results of the resonant excitation of a microcantilever sensor system (MSS) by the ambient random fluctuations. In our analysis, the dynamic process is decomposed into the bases of orthogonal functions with random coefficients using principal component analysis (PCA) and Karhunen-Loève theorem to obtain pertinent frequency shifts and spectral peaks. We show that using the truncated Karhunen-Loève Transform helps significantly increase the resolution of resonance frequency peaks compared to those obtained with conventional Fourier Transform processing.

Original language	English
Title of host publication	ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications
Editors	Christian Callegari, Marten van Sinderen, Enrique Cabello, Pierangela Samarati, Pascal Lorenz, Mohammad S. Obaidat, Panagiotis Sarigiannidis
Publisher	SciTePress
Pages	97-105
Number of pages	9
ISBN (Electronic)	9789897581960
DOIs	https://doi.org/10.5220/0005933200970105
State	Published - 2016
Event	13th International Conference on Wireless Networks and Mobile Systems, WINSYS 2016 - Part of the 13th International Joint Conference on e-Business and Telecommunications, ICETE 2016 - Lisbon, Portugal Duration: Jul 26 2016 → Jul 28 2016

Publication series

Name	ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications
Volume	6

Conference

Conference	13th International Conference on Wireless Networks and Mobile Systems, WINSYS 2016 - Part of the 13th International Joint Conference on e-Business and Telecommunications, ICETE 2016
Country/Territory	Portugal
City	Lisbon
Period	07/26/16 → 07/28/16

Funding

This work was supported by the laboratory directed research and development (LDRD) fund at Oak Ridge National Laboratory (ORNL). Viktor Zaharov acknowledges the financial support received from the Department of Energy (DOE) Visiting Faculty Program (VFP). ORNL is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725.

Keywords

Correlation Analysis
Data Denoising
Karhunen-Loève Transform
Microcantilever
Wireless Sensor Network

Access to Document

10.5220/0005933200970105

Cite this

Zaharov, V., Lambertt, A., & Passian, A. (2016). Wireless sensor network microcantilever data processing using principal component and correlation analysis. In C. Callegari, M. van Sinderen, E. Cabello, P. Samarati, P. Lorenz, M. S. Obaidat, & P. Sarigiannidis (Eds.), ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (pp. 97-105). (ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications; Vol. 6). SciTePress. https://doi.org/10.5220/0005933200970105

Zaharov, Viktor ; Lambertt, Angel ; Passian, Ali. / Wireless sensor network microcantilever data processing using principal component and correlation analysis. ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications. editor / Christian Callegari ; Marten van Sinderen ; Enrique Cabello ; Pierangela Samarati ; Pascal Lorenz ; Mohammad S. Obaidat ; Panagiotis Sarigiannidis. SciTePress, 2016. pp. 97-105 (ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications).

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title = "Wireless sensor network microcantilever data processing using principal component and correlation analysis",

abstract = "One of the main purpose of the wireless sensor network is an identification of unknown physical, chemical and biological agents in monitoring area. It requires the measurement of the microcantilever sensor resonance frequencies with high precision. However, resolving the weak spectral variations in dynamic response of materials that are either dominated or excited by stochastic processes remains a challenge. In this paper we present the analysis and experimental results of the resonant excitation of a microcantilever sensor system (MSS) by the ambient random fluctuations. In our analysis, the dynamic process is decomposed into the bases of orthogonal functions with random coefficients using principal component analysis (PCA) and Karhunen-Lo{\`e}ve theorem to obtain pertinent frequency shifts and spectral peaks. We show that using the truncated Karhunen-Lo{\`e}ve Transform helps significantly increase the resolution of resonance frequency peaks compared to those obtained with conventional Fourier Transform processing.",

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author = "Viktor Zaharov and Angel Lambertt and Ali Passian",

note = "Publisher Copyright: Copyright {\textcopyright} 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 13th International Conference on Wireless Networks and Mobile Systems, WINSYS 2016 - Part of the 13th International Joint Conference on e-Business and Telecommunications, ICETE 2016 ; Conference date: 26-07-2016 Through 28-07-2016",

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Zaharov, V, Lambertt, A & Passian, A 2016, Wireless sensor network microcantilever data processing using principal component and correlation analysis. in C Callegari, M van Sinderen, E Cabello, P Samarati, P Lorenz, MS Obaidat & P Sarigiannidis (eds), ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications. ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications, vol. 6, SciTePress, pp. 97-105, 13th International Conference on Wireless Networks and Mobile Systems, WINSYS 2016 - Part of the 13th International Joint Conference on e-Business and Telecommunications, ICETE 2016, Lisbon, Portugal, 07/26/16. https://doi.org/10.5220/0005933200970105

Wireless sensor network microcantilever data processing using principal component and correlation analysis. / Zaharov, Viktor; Lambertt, Angel; Passian, Ali.
ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications. ed. / Christian Callegari; Marten van Sinderen; Enrique Cabello; Pierangela Samarati; Pascal Lorenz; Mohammad S. Obaidat; Panagiotis Sarigiannidis. SciTePress, 2016. p. 97-105 (ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications; Vol. 6).

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

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AU - Zaharov, Viktor

AU - Lambertt, Angel

AU - Passian, Ali

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AB - One of the main purpose of the wireless sensor network is an identification of unknown physical, chemical and biological agents in monitoring area. It requires the measurement of the microcantilever sensor resonance frequencies with high precision. However, resolving the weak spectral variations in dynamic response of materials that are either dominated or excited by stochastic processes remains a challenge. In this paper we present the analysis and experimental results of the resonant excitation of a microcantilever sensor system (MSS) by the ambient random fluctuations. In our analysis, the dynamic process is decomposed into the bases of orthogonal functions with random coefficients using principal component analysis (PCA) and Karhunen-Loève theorem to obtain pertinent frequency shifts and spectral peaks. We show that using the truncated Karhunen-Loève Transform helps significantly increase the resolution of resonance frequency peaks compared to those obtained with conventional Fourier Transform processing.

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Zaharov V, Lambertt A, Passian A. Wireless sensor network microcantilever data processing using principal component and correlation analysis. In Callegari C, van Sinderen M, Cabello E, Samarati P, Lorenz P, Obaidat MS, Sarigiannidis P, editors, ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications. SciTePress. 2016. p. 97-105. (ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications). doi: 10.5220/0005933200970105

Wireless sensor network microcantilever data processing using principal component and correlation analysis

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