Parameter Tuning in the Application of Stochastic Resonance to Redundant Sensor Systems

Nagisa Koyama; Shuhei Ikemoto; Koh Hosoda

doi:10.20965/jrm.2015.p0251

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JRM Vol.27 No.3 pp. 251-258

doi: 10.20965/jrm.2015.p0251

(2015)

Paper:

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Parameter Tuning in the Application of Stochastic Resonance to Redundant Sensor Systems

Nagisa Koyama, Shuhei Ikemoto, and Koh Hosoda

Graduate School of Engineering Science, Osaka University
1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan

Received:

October 20, 2012

Accepted:

March 15, 2015

Published:

June 20, 2015

Keywords:

stochastic resonance, biomimetics, tactile sensing

Abstract

Basic concept of proposed method

Stochastic resonance (SR) is a phenomenon by which the addition of random noise improves the detection of weak signals. Thus far, this phenomenon has been extensively studied with the aim of improving sensor sensitivity in various fields of engineering research. However, the possibility of actual application of SR has not been explored because the target signal has to be known in order to confirm the occurrence of SR. In this paper, we propose an optimization method for making SR usable in engineering applications. The underlying mechanism of the proposed method is investigated using information theory and numerical simulation. We developed a tactile sensing system based on the simulation results. The proposed method is applied to this system in order to optimize its parameters for exploiting SR. Results of the experiment show that the developed tactile sensing system successfully achieved higher sensitivity than a conventional system.

Cite this article as:

N. Koyama, S. Ikemoto, and K. Hosoda, “Parameter Tuning in the Application of Stochastic Resonance to Redundant Sensor Systems,” J. Robot. Mechatron., Vol.27 No.3, pp. 251-258, 2015.

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References

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[4] J. Collins, T. Imhoff, and P. Grigg, “Noise-enhanced information transmission in rat SA1 cutaneous mechanoreceptors via aperiodic stochastic resonance,” J. of Neurophysiology, Vol.76, No.1, pp. 642-645, 1996.
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[6] J. B. Fallon and D. L. Morgan, “Fully Tuneable Stochastic Resonance in Cutaneous Receptors,” J. Nourophysiol., Vol.94, pp. 928-933, 2005.
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[8] Y. Kurita, M. Shinohara, and J. Ueda, “Wearable sensorimotor enhancer for a fingertip based on stochastic resonance,” 2011 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 3790-3795, 2011.
[9] A. Bulsara and A. Zador, “Threshold detection of wideband signals: A noise-induced maximum in the mutual information,” Physical Review E, Vol.54, No.3, pp. 2185-2188, 1996.
[10] T. Munakata, A. Sato, and T. Hada, “Stochastic Resonance in a Simple Threshold System from a Static Mutual Information Point of View,” J. of the Physical Society of Japan, Vol.74, No.7, pp. 2094-2098, 2005.
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[13] K. Wiesenfeld, F. Moss et al., “Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs,” Nature, Vol.373, No.6509, pp. 33-36, 1995.
[14] C. Bishop and S. S. En Ligne, “Pattern recognition and machine learning,” Vol.4, springer New York, 2006.
[15] M. Matsumoto and T. Nishimura, “Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator,” ACM Trans. on Modeling and Computer Simulation (TOMACS), Vol.8, No.1, pp. 3-30, 1998.
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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] R. Benzi, A. Sutera, and A. Vulpiani, “The mechanism of stochastic resonance,” J. Phys. A: Mathemat. Gen., Vol.14, pp. 453-457, 1981.

[2] [2] D. Russell, L. Wilkens, F. Moss et al., “Use of behavioural stochastic resonance by paddle fish for feeding,” Nature, Vol.402, No.6759, pp. 291-293, 1999.

[3] [3] J. K. Douglass, L. Wilkens, E. Pantazelou, and F. Moss, “Noise enhancement of information transfer in crayfish mechanoreceptors by stochastic resonance,” Nature, Vol.365, pp. 337-340, 1993.

[4] [4] J. Collins, T. Imhoff, and P. Grigg, “Noise-enhanced information transmission in rat SA1 cutaneous mechanoreceptors via aperiodic stochastic resonance,” J. of Neurophysiology, Vol.76, No.1, pp. 642-645, 1996.

[5] [5] J. Collins, T. Imhoff, and P. Grigg, “Noise-enhanced tactile sensation,” Nature, Vol.383, No.31, p. 770, 1996.

[6] [6] J. B. Fallon and D. L. Morgan, “Fully Tuneable Stochastic Resonance in Cutaneous Receptors,” J. Nourophysiol., Vol.94, pp. 928-933, 2005.

[7] [7] M. Ohka and S. Kondo, “Stochastic resonance aided tactile sensing,” Robotica, Vol.27, No.04, pp. 633-639, 2009.

[8] [8] Y. Kurita, M. Shinohara, and J. Ueda, “Wearable sensorimotor enhancer for a fingertip based on stochastic resonance,” 2011 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 3790-3795, 2011.

[9] [9] A. Bulsara and A. Zador, “Threshold detection of wideband signals: A noise-induced maximum in the mutual information,” Physical Review E, Vol.54, No.3, pp. 2185-2188, 1996.

[10] [10] T. Munakata, A. Sato, and T. Hada, “Stochastic Resonance in a Simple Threshold System from a Static Mutual Information Point of View,” J. of the Physical Society of Japan, Vol.74, No.7, pp. 2094-2098, 2005.

[11] [11] L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni, “Stochastic resonance,” Reviews of Modern Physics, Vol.70, No.1, pp. 223-287, 1998.

[12] [12] J. Collins, C. Chow, and T. Imhoff, “Aperiodic stochastic resonance in excitable systems,” Physical Review E, Vol.52, No.4, pp. 3321-3324, 1995.

[13] [13] K. Wiesenfeld, F. Moss et al., “Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs,” Nature, Vol.373, No.6509, pp. 33-36, 1995.

[14] [14] C. Bishop and S. S. En Ligne, “Pattern recognition and machine learning,” Vol.4, springer New York, 2006.

[15] [15] M. Matsumoto and T. Nishimura, “Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator,” ACM Trans. on Modeling and Computer Simulation (TOMACS), Vol.8, No.1, pp. 3-30, 1998.

[16] [16] G. Marsaglia, “Xorshift rngs,” J. of Statistical Software, Vol.8, No.14, pp. 1-6, 2003.

[17] [17] C. Igel and M. Hüusken, “Empirical evaluation of the improved Rprop learning algorithms,” Neurocomputing, Vol.50, No.105-123, 2003.