TY - GEN
T1 - Wireless sensor network microcantilever data processing using principal component and correlation analysis
AU - Zaharov, Viktor
AU - Lambertt, Angel
AU - Passian, Ali
N1 - Publisher Copyright:
Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
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.
KW - Correlation Analysis
KW - Data Denoising
KW - Karhunen-Loève Transform
KW - Microcantilever
KW - Wireless Sensor Network
UR - http://www.scopus.com/inward/record.url?scp=85006062993&partnerID=8YFLogxK
U2 - 10.5220/0005933200970105
DO - 10.5220/0005933200970105
M3 - Conference contribution
AN - SCOPUS:85006062993
T3 - ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications
SP - 97
EP - 105
BT - ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications
A2 - Callegari, Christian
A2 - van Sinderen, Marten
A2 - Cabello, Enrique
A2 - Samarati, Pierangela
A2 - Lorenz, Pascal
A2 - Obaidat, Mohammad S.
A2 - Sarigiannidis, Panagiotis
PB - SciTePress
T2 - 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
Y2 - 26 July 2016 through 28 July 2016
ER -