Several interesting and important natural processes are the manifestation of the interplay of nonlinearity and fluctuations. Stochastic resonance is one such mechanism and is crucial to explain many physical, chemical, and biological processes, as well as having huge technological importance. The general setup to describe stochastic resonance considers two states. Recently, it has been unveiled that it is necessary to consider the intrinsic fluctuations related to the two different states of the system are different in interpreting certain fundamental natural processes, such as glacial-interglacial transitions in Earth's ice age. This also has significance in developing advantageous technologies. However, until now, there has been no general theory describing stochastic resonance in terms of the transition rate between the two states and their probability distribution function while considering different noise amplitudes or fluctuation characteristics of these two states. The development of this fundamental theory is attempted in the present research work. As a first step, a relevant approximation is used in which the system is considered within the adiabatic limit. The analytical derivations are corroborated by numerical simulation results. Furthermore, a semianalytical theory is proposed for the definite system without any approximations as the exact analytical solution is not achievable. This semianalytical theory is found to replicate the results obtained from the Brownian dynamics simulation study for previously known quantifiers for stochastic resonance which are estimated in the present context for the system with state-dependent diffusion.
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http://dx.doi.org/10.1103/PhysRevE.111.014125 | DOI Listing |
Phys Rev Lett
February 2025
Imperial College London, Quantum Measurement Lab, Blackett Laboratory, London SW7 2BW, United Kingdom.
Throughout quantum science and technology, measurement is used as a powerful resource for nonlinear operations and quantum state engineering. In particular, single-photon detection is commonly employed for quantum-information applications and tests of fundamental physics. By contrast, and perhaps counterintuitively, measurement of the absence of photons also provides useful information, and offers significant potential for a wide range of new experimental directions.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2024
Tactile loss caused by diabetic peripheral neuropathy (DPN) might result in foot ulceration and lower extremity amputation. Neurorehabilitation with a vibrating insole is one of the novel therapies for improving tactile sensibility. In this preliminary study, a vibratory foot orthosis (VFO) in conjunction with a random square wave pulse stimulus and pseudorandom white noise (PRWN) via a stochastic resonance (SR) method was newly designed for tactile stimulation.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2024
Functional Magnetic Resonance Imaging (fMRI) serves as a unique non-invasive tool for investigating brain function by analyzing blood oxygenation level-dependent (BOLD) series. These signals result from the complex interplay between deterministic and stochastic components underpinning biological brain activity. In this context, the quantification of the stochastic component, here defined as brain noise, is challenging without making assumptions on the deterministic dynamics.
View Article and Find Full Text PDFRev Sci Instrum
March 2025
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
A method is proposed for estimating the power spectral density (PSD) of time series that uses median smoothing in the frequency domain. The "Median method" for PSD estimation rejects deterministic noise peaks in the PSD while preserving stochastic signals and noise sources. For a PSD averaging factor M, deterministic noise sources are suppressed by a factor of ∼M in power when applying the Median method.
View Article and Find Full Text PDFFront Aging Neurosci
February 2025
Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China.
Objectives: To propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic characteristics to diagnose mild cognitive impairment (MCI) in patients with unilateral middle cerebral artery (MCA) steno-occlusive disease.
Methods: The performances of different algorithms on the MCI dataset were evaluated using 5-fold cross-validation. The diagnostic results of the multimodal performance were evaluated using t-distributed stochastic neighbor embedding (t-SNE) analysis.
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