Two-photon correlation phenomena, including the historical experiment of Hanbury Brown and Twiss, may have to be described quantum mechanically, regardless of whether the source of radiation is classical or quantum. Supporting this point, we present a ghost imaging type of second-order spatial correlation experiment of chaotic light to show that the classical understanding based on the concept of statistical intensity fluctuations does not give a correct interpretation for the observation. From a practical point of view, this experiment demonstrates the possibility of having high contrast lensless two-photon imaging with chaotic light, suggesting imaging applications for radiations for which no effective lens is available.
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http://dx.doi.org/10.1103/PhysRevLett.96.063602 | DOI Listing |
Sci Rep
December 2024
College of Electric Engineering, Naval University of Engineering, Wuhan, 430033, China.
To address the challenges related to active power dissipation and node voltage fluctuation in the practical transformation of power grids in the field of new energy such as wind and photovoltaic power generation, an improved Dung Beetle Optimization Algorithm Based on a Hybrid Strategy of Levy Flight and Differential Evolution (LDEDBO) is proposed. This paper systematically addresses this issue from three aspects: firstly, optimizing the DBO algorithm using Chebyshev chaotic mapping, Levy flight strategy, and differential evolution algorithm; secondly, validating the algorithm's feasibility through real-time network reconfiguration at random time points within a 24-h period; and finally, applying the LDEDBO to address the dynamic reconfiguration problems of the IEEE-33 and IEEE-69 node bus. The simulation indicates that the power dissipation of the IEEE-33 node bus is decreased by 28.
View Article and Find Full Text PDFCogn Neurodyn
December 2024
School of Information Science and Engineering, Dalian polytechnic University, Dalian, 116034 China.
Two types of neuron models are constructed in this paper, namely the single discrete memristive synaptic neuron model and the dual discrete memristive synaptic neuron model. Firstly, it is proved that both models have only one unstable equilibrium point. Then, the influence of the coupling strength parameters and neural membrane amplification coefficient of the corresponding system of the two models on the rich dynamical behavior of the systems is analyzed.
View Article and Find Full Text PDFWiad Lek
December 2024
MUNICIPAL ENTERPRISE "CENTRAL CITY HOSPITAL OF CHERVONOGRAD CITY COUNCIL ", CHERVONOGRAD, UKRAINE.
Objective: Aim: Installation the changes of the microstructural rearrangement of the layers of the rat cornea at the end of the second week of experimental streptozotocin- induced diabetes.
Patients And Methods: Materials and Methods: The research was conducted on 15 sexually mature, outbred white male rats, weighing 120-130 g. Two groups of animals were used in the work: the first group with developing diabetes (2 weeks after administration of streptozotocin); the second group served as control and received injections of 0.
Sci Rep
December 2024
School of Computer Science, Hubei University of Technology, Wuhan, 430068, China.
The quality of underwater images is often affected by light scattering and attenuation, resulting in a loss of contrast and brightness. To address this issue, this paper proposes an underwater image enhancement method: improved Fick's law algorithm-based optimally weighted histogram framework (IFLAHF). The method incorporates the bi-histogram equalization-based three plateau limits (BHE3PL) technique to enhance image contrast and details while maintaining brightness.
View Article and Find Full Text PDFNat Commun
December 2024
Physical Institute, University of Münster, Münster, 48149, Germany.
Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as "point estimates" that do not capture the uncertainty of prediction, which is an inherently probabilistic process.
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