Conference Paper
Enhancement of stochastic resonance by tuning system parameters and adding noise simultaneously
Dept. of Electr. and Comput. Eng., Polytech. Univ. of Brooklyn, NY
DOI: 10.1109/ACC.2006.1657196 Conference: American Control Conference, 2006 Source: IEEE Xplore

Conference Paper: Nonlinear Bistable Stochastic Resonance Filters for Image Processing
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ABSTRACT: Nonlinear bistable doublewell stochastic resonance systems have been successfully used for onedimensional signal processing, based on the concept of parametertuning stochastic resonance. This paper investigates the applications of parametertuning stochastic resonance in image processing. First, a twodimensional stochastic resonance system is introduced as a nonlinear filter for image processing. The equation satisfied by the dynamic probability density function of the images processed by this stochastic resonance filter and its solutions are then discussed. Finally, this nonlinear filter is used to process a blackwhite image corrupted by additive white Gaussian noise to reveal the possibility to extend the concept of parametertuning stochastic resonance to twodimensional cases. This provides an innovative approach for image processingAcoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on; 05/2007  [Show abstract] [Hide abstract]
ABSTRACT: The traditional stochastic resonance is realized by adding an optimal amount of noise, while the parametertuning stochastic resonance is realized by optimally tuning the system parameters. This paper reveals the possibility to further enhance the stochastic resonance effect by tuning system parameters and adding noise at the same time using optimization theory. The further improvement of the maximal normalized power norm of the bistable doublewell dynamic system with white Gaussian noise input can be converted to an optimization problem with constraints on system parameters and noise intensity, which is proven to have one and only one local maximum for the Gaussiandistributed weak input signal. This result is then extended to the arbitrary weak input signal case. For the purpose of practical implementation, a fastconverging optimization algorithm to search the optimal system parameters and noise intensity is also proposed. Finally, computer simulations are performed to verify its validity and demonstrate its potential applications in signal processing.01/2006; 
Conference Paper: Nonlinear Enhancement of Weak Signals Using Optimization Theory
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ABSTRACT: Stochastic resonance (SR) is a phenomenon that performance of the nonlinear system can be improved with the addition of optimal amount of noise. Stochastic resonance has been increasingly used for signal processing. The output of the nonlinear bistable dynamic system with white Gaussian noise input can be used to restore the weak input signal, if the similarity between the input signal and the output can be maximized. This paper first use the optimization theory to show that the normalized power norm describing the similarity will reach a larger maximum when tuning both system parameters and noise intensity, compared with that of only adjusting noise intensity (classical stochastic resonance) or only adjusting system parameters. Then, computer simulations are performed to verify this proposal and demonstrate its application in signal processingMechatronics and Automation, Proceedings of the 2006 IEEE International Conference on; 07/2006
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