Dynamic Compensation of Nonlinear Sensors by a Learning-From-Examples Approach

In this paper, we address the problem of nonlinear sensor dynamic compensation that will be performed on board wireless sensor network nodes. To this aim, we design suitable reduced-complexity learning-from-example algorithms and implement them on resource-constrained devices, namely, 8-bit microcon...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2008-08, Vol.57 (8), p.1689-1694
Main Authors: Marconato, A., Mingqing Hu, Boni, A., Petri, D.
Format: Article
Language:English
Subjects:
Acoustic sensors
Algorithm design and analysis
Algorithms
Compensation
Computer simulation
Dynamic compensation
Explosions
Instrumentation
Intelligent sensors
Inverse problems
low power
microcontroller implementation
Microcontrollers
Nonlinear distortion
Nonlinear dynamics
Nonlinearity
Pervasive computing
reduced-set methods
Sensors
Support vector machines
support vector machines for regression (SVRs)
Wireless communication
Wireless sensor networks
wireless sensor networks (WSNs)
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Description
Summary:In this paper, we address the problem of nonlinear sensor dynamic compensation that will be performed on board wireless sensor network nodes. To this aim, we design suitable reduced-complexity learning-from-example algorithms and implement them on resource-constrained devices, namely, 8-bit microcontrollers. The proposed approach is validated with simulations on different examples of nonlinear sensor models.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2008.922074