Authors:
Viktor Zaharov
1
;
Angel Lambertt
2
and
Ali Passian
3
Affiliations:
1
Polytechnic University of Puerto Rico, United States
;
2
Universidad Anahuac Norte, Mexico
;
3
Oak Ridge National Laboratory, United States
Keyword(s):
Wireless Sensor Network, Microcantilever, Karhunen-Lo`eve Transform, Correlation Analysis, Data Denoising.
Related
Ontology
Subjects/Areas/Topics:
Ad Hoc Networks of Autonomous Intelligent Systems
;
Measurements and Experimental Research
;
Sensor, Mesh and Ad Hoc Communications and Networks
;
Telecommunications
;
Wireless Information Networks and Systems
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`eve theorem to obtain pertinent frequency shifts and spectral peaks. We show that using the truncated
Karhunen-Lo`eve Transform helps significantly increase the resolution of resonance frequency peaks compared
to those obtained with conventional
Fourier Transform processing.
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