Stochastic Resonance
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Xplore Articles related to Stochastic Resonance
Back to TopExistence of a density for the filter associated to Hilbertspace valued systems
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2000
The purpose of this paper is to prove that the unnormalized filter associated to a Hilbert space valued nonlinear filtering problem with correlated noises, which solves a parabolic stochastic partial differential equation, the Zakai equation, admits a density with respect to a given measure on the Hilbert space. We also compute the stochastic differential equation solved by this density.
Electronics, Robotics and Automotive Mechanics Conference (CERMA'06), 2006
A new algorithm of two stage vector quantization for joint sourcechannel speech coding for any transmission channels is presented. The computational complexity is only slightly higher than the most widely used multi stage vector quantization algorithm (MSVQ). This new algorithm improves the characteristics and the results of a sequential quantizer of two stages. The base of this algorithm is the ...
Noise suppressing sensor encoding and neural signal orthonormalization
IEEE Transactions on Neural Networks, 1998
In this paper we regard first the situation where parallel channels are disturbed by noise. With the goal of maximal information conservation we deduce the conditions for a transform which "immunizes" the channels against noise influence before the signals are used in later operations. It shows up that the signals have to be decorrelated and normalized by the filter which ...
Chaotic noise and iterative simulated annealing for TSP
Proceedings of the 41st SICE Annual Conference. SICE 2002., 2002
We investigate the solving ability of TSP by using a Hopfield neural network with chaotic noise and iterative simulated annealing noise. From several numerical experiments, we can conclude that the ability of iterative simulated annealing noise is almost the same as chaotic noise in searching ability of a global minimum, and is superior to the chaotic noise in performance for ...
Identification of unfalsified plant model sets based on lowcorrelated bounded noise model
Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), 1999
We propose a new model set identification method for robust control, which determines both nominal models and uncertainty bounds in frequencydomain using periodgrams obtained from experimental data. This method also gives less conservative model sets when we have more experimental data, which is one of the distinguished features compared with the existing model set identification methods. We construct a new ...
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Educational Resources on Stochastic Resonance
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Stochastic Sampling Machine for Bayesian Inference  Raphael Frisch at INC 2019
IMS 2015: Robert H. Caverly  Aspects of Magnetic Resonance Imaging
Generating Stochastic Bits Using Tunable Quantum Systems  Erik Blair at INC 2019
SCSD: Towards Low Power Stochastic Computing using Sigma Delta Streams  Patricia GonzalezGuerrero  ICRC 2018
Alan S. Willsky  IEEE Jack S. Kilby Signal Processing Medal, 2019 IEEE Honors Ceremony
Stochastic Single Flux Quantum Neuromorphic Computing using Magnetically Tunable Josephson Junctions  Stephen Russek: 2016 International Conference on Rebooting Computing
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson  4 of 7  MRI at 130 Microtesla
Niobium Manufacturing for Superconductivity  ASC2014 Plenary series  5 of 13  Tuesday 2014/8/12
Ponnuthurai Nagaratnam Suganthan  Differential Evolution
Generation of Models for Wireless Sensor Network Assessment
IEEE Themes  DistanceDependent Kronecker Graphs For Modeling Social Networks
Asynchronous Design for New Device Development  Laurent Fesquet at INC 2019
IEEE Medal for Innovations in Healthcare Technology  Thomas F. Budinger  2018 IEEE Honors Ceremony
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson  3 of 7  MEG and ULFMRI
Probabilistic AI: Quantum & Probabilistic Computing  Jean Simatic at INC 2019
A Bayesian Approach for Spatial Clustering  IEEE CIS Webinar
Validating CyberPhysical Energy Systems, Part 4: IECON 2018
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson  5 of 7  SQUID Instrumentation for Early Cancer Diagnostics
Charles A. Mistretta accepts the IEEE Medal for Innovations in Healthcare Technology  Honors Ceremony 2016
IEEEUSA EBooks

Existence of a density for the filter associated to Hilbertspace valued systems
The purpose of this paper is to prove that the unnormalized filter associated to a Hilbert space valued nonlinear filtering problem with correlated noises, which solves a parabolic stochastic partial differential equation, the Zakai equation, admits a density with respect to a given measure on the Hilbert space. We also compute the stochastic differential equation solved by this density.

A new algorithm of two stage vector quantization for joint sourcechannel speech coding for any transmission channels is presented. The computational complexity is only slightly higher than the most widely used multi stage vector quantization algorithm (MSVQ). This new algorithm improves the characteristics and the results of a sequential quantizer of two stages. The base of this algorithm is the modification of the wellknown GSRGSKAepsiv algorithm (reduced complexity generalized stochastic Kmeans algorithm of great speed) for a nonstationary channel. This new algorithm is optimal for the joint construction of two stages. The main features of the proposed algorithm are as follows: 1) Due to its stochastic nature it avoids being trapped in poor local minimums. 2) Initial codebooks are not needed; the codevectors move away from the gravity center of the training vectors towards their final position. 3) Source coding and channel coding are jointly optimized to obtain robust codebooks for different levels of noise in transmission channels. 4) The reduction of calculation time is based on geometric considerations and memory management. This algorithm allows to design the codebook orderly due to its advantageous convergence properties. The results showed that the algorithm needs only 8 to 16% of the number of mathematical operations in comparison with the operations required by others propositions for full search of MSQV, either stationary or nonstationary channels

Noise suppressing sensor encoding and neural signal orthonormalization
In this paper we regard first the situation where parallel channels are disturbed by noise. With the goal of maximal information conservation we deduce the conditions for a transform which "immunizes" the channels against noise influence before the signals are used in later operations. It shows up that the signals have to be decorrelated and normalized by the filter which corresponds for the case of one channel to the classical result of Shannon. Additional simulations for image encoding and decoding show that this constitutes an efficient approach for noise suppression. Furthermore, by a corresponding objective function we deduce the stochastic and deterministic learning rules for a neural network that implements the data orthonormalization. In comparison with other already existing normalization networks our network shows approximately the same in the stochastic case, but by its generic deduction ensures the convergence and enables the use as independent building block in other contexts, e.g., whitening for independent component analysis.

Chaotic noise and iterative simulated annealing for TSP
We investigate the solving ability of TSP by using a Hopfield neural network with chaotic noise and iterative simulated annealing noise. From several numerical experiments, we can conclude that the ability of iterative simulated annealing noise is almost the same as chaotic noise in searching ability of a global minimum, and is superior to the chaotic noise in performance for detecting local minima.

Identification of unfalsified plant model sets based on lowcorrelated bounded noise model
We propose a new model set identification method for robust control, which determines both nominal models and uncertainty bounds in frequencydomain using periodgrams obtained from experimental data. This method also gives less conservative model sets when we have more experimental data, which is one of the distinguished features compared with the existing model set identification methods. We construct a new noise model set in terms of periodgrams, which consists of hardbounded (or deterministic) noises but takes into account of a low correlation property of noise signals, simultaneously. Then, based on the noise model, we show how to compute the nominal models and the upper bounds of modeling error via convex optimization, which minimize the given cost functions. Numerical examples show the effectiveness of the proposed method.

Zakai equation of nonlinear filtering in infinite dimension
It is proved that the unnormalized filter associated with a nonlinear filtering problem with independent noises and bounded coefficients such that the signal process is infinitedimensional solves a stochastic partial differential equation, the Zakai equation.<>

Control of DiscreteTime Nonlinear Stochastic Systems with Incomplete State Information
In this paper, a method is given for stabilizing compensation design for a large class of discrete time nonlinear stochastic systems with incomplete state information. The nonlinearity is contained either in the state or the output equation and it involves a zero mean white noise sequence. The proposed compensator is a constant gain observer together with a constant feedback control gain. The stabilizability conditions depend on characteristics of system parameters and are for both mean square and almost surely stochastic stability of the controlled system.

On zero locations for sampled stochastic systems
Zero locations of stochastic models have a significant influence on optimal prediction algorithms as well as on the convergence speed of identification methods. The possible zero locations for a sampled stochastic system are examined. In particular, it is shown and numerically illustrated that the sampled zeros can appear only in a restricted set within the unit circle. A complete analysis is given for secondorder systems.<>

Modeling and Simulation of Digital Closedloop Fiber Optic Gyroscope
Computer modeling and simulation are important for the researches and applications of the fiber optic gyroscope (FOG). In the paper, the dynamic model and stochastic model of digital closedloop FOG are developed. With a number of reasonable approximations, the nonlinear dynamic model is simplified into a linear discrete dynamic model. The digital control algorithm in the closedloop control is analyzed, and the dynamic simulation is conducted. In the stochastic modeling, different stochastic processes are built to simulate the various types of random gyro noise. A novel method is investigated to simulate 1/f noise with the orthogonal wavelet transform. The stochastic simulations are conducted and the effectiveness of the stochastic model is validated with Allan variance analysis

In the Spotlight: Biomedical Signal Processing
This article presents a review on biomedical signal processing. Discussions on traditional approaches, nonstationary and nonlinear systems, signal fusion, physiological modeling, and the MMM (multivariate, multiorgan and multiscale) paradigm are included.
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