scholarly journals Nonlinear sensors for biomaterials—Principles and applications

2020 ◽  
Vol 3 (5) ◽  
Author(s):  
William A. Brantley
Keyword(s):  
Nonlinear Sensors

Establishing Meaningful Limits of Detection for Ion-Selective Electrodes and Other Nonlinear Sensors

ACS Sensors ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 250-257 ◽  
Author(s):  
Peter W. Dillingham ◽  
Basim S. O. Alsaedi ◽  
Sergio Granados-Focil ◽  
Aleksandar Radu ◽  
Christina M. McGraw
Keyword(s):  
Ion Selective Electrodes ◽  
Limits Of Detection ◽  
Nonlinear Sensors

Stochastic resonance for nonlinear sensors with saturation

2003 ◽  
Vol 67 (2) ◽  
Author(s):  
David Rousseau ◽  
Julio Rojas Varela ◽  
François Chapeau-Blondeau
Keyword(s):  
Stochastic Resonance ◽  
Nonlinear Sensors

Robust Signal Recovery From Distorted Nonlinear Sensor Data

2003 ◽  
Author(s):  
Sugathevan Suranthiran ◽  
Suhada Jayasuriya
Keyword(s):  
Model Uncertainty ◽  
Signal Reconstruction ◽  
Sensor Data ◽  
Signal Recovery ◽  
Chirp Signal ◽  
Accurate Model ◽  
Design And Implementation ◽  
Novel Technique ◽  
Model Based ◽  
Nonlinear Sensors

In an attempt to facilitate the design and implementation of memory-less nonlinear sensors, the signal reconstruction schemes are analyzed and necessary modifications are proposed to improve the accuracy and minimize errors in sensor measurements. The problem of recovering chirp signal from the distorted nonlinear output is considered and an efficient reconstruction approach is developed. Model uncertainty is a serious issue with any model-based algorithms and a novel technique, which uses a norminal model instead of an accurate model and produces the results that are robust to model uncertainty, is proposed.

Distortion Analysis of Memory-Less Nonlinear Sensors

2003 ◽  
Author(s):  
Sugathevan Suranthiran ◽  
Suhada Jayasuriya
Keyword(s):  
Nonlinear Distortion ◽  
Proper Choice ◽  
Signal Recovery ◽  
Distortion Compensation ◽  
Convergence Parameter ◽  
Distortion Analysis ◽  
Nonlinear Sensors ◽  
Simulation Results ◽  
With Memory ◽  
Recovery Procedure

Distortion associated with memory-less nonlinear sensors is analyzed and several distortion compensation techniques are presented. Sensor nonlinearity is considered a defect in sensory systems because it introduces distortion into the system. Due to the fact that no efficient technique is available to deal with the issues related sensor nonlinearity, nonlinear primary sensors tend to be ignored. In this paper, we point out that there are certain advantages of using nonlinear sensor and nonlinear distortion caused by sensor nonlinearity may be completely compensated. A robust and efficient signal recovery procedure is derived to facilitate the design of nonlinear sensors. Not having an accurate sensor will result in errors and it is shown that the error can be minimized with a proper choice of a convergence parameter whereby stability of the developed algorithm is established. Simulation results are presented to validate the algorithms developed.

Binary signal transmission in nonlinear sensors: Stochastic resonance and human hand balance

2020 ◽  
Vol 23 (1) ◽  
pp. 44-49 ◽  
Author(s):  
Fabing Duan ◽  
Lingling Duan ◽  
Francois Chapeau-Blondeau ◽  
Yuhao Ren ◽  
Derek Abbott
Keyword(s):  
Stochastic Resonance ◽  
Signal Transmission ◽  
Human Hand ◽  
Binary Signal ◽  
Nonlinear Sensors

Optically S-polarized surface waves in symmetrical nonlinear sensors: Thermal effects

Optik ◽  
2009 ◽  
Vol 120 (9) ◽  
pp. 442-446
Author(s):  
H.J. El-Khozondar ◽  
G. Abu Tair ◽  
M.M. Shabat
Keyword(s):  
Surface Waves ◽  
Thermal Effects ◽  
Nonlinear Sensors ◽  
Polarized Surface

Nonlinear sensors activated by noise

2003 ◽  
Vol 325 (1-2) ◽  
pp. 152-164 ◽  
Author(s):  
L Gammaitoni ◽  
A.R Bulsara
Keyword(s):  
Nonlinear Sensors
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