利用相邻相关像素进行噪音抑制的模糊小目标检测算法

提出了一种利用相邻相关像素对红外数字图像中的可疑小目标进行检测的算法. 该算法首先利用自适应全局阈值检测图像中的亮像素,并借助相邻相关像素信息滤除结果中的亮噪音点;然后依据亮像素的相关性,对剩余的亮像素进行加强,并再次抑制噪音,获得可观的信噪比增益.相对于传统的Top-Hat变换,该算法能够在有效提高待检测目标信号强度的同时很好地抑制噪音,有效地保留了目标图像的边缘细节.     

Editorial Board: Electrophoresis 8'14

Electrophoresis 2014, 35, 1144–1151. DOI: 10.1002/elps.201300501 The center stage of nanopore sequencing is to extract gene information from the translocation of DNA through a nanopore. Graphene nanopore technology has been promising ultra‐high resolution for gene sequencing owing to the atomic thickness and excellent electronic properties of the graphene monolayer. By filtering out the thermal noise of ionic current, the instantaneous conformational variations of DNA in a graphene nanopore could be unveiled from undulates of the blocked ionic current, because of the spatial blockage effect of DNA against ionic migration. It supplies a theoretical basis for the monitor of dynamical information of DNA in a graphene nanopore during sequencing from the ionic current fluctuation.

     

Spatial blockage of ionic current for electrophoretic translocation of DNA through a graphene nanopore

Graphene nanopore has been promising the ultra‐high resolution for DNA sequencing due to the atomic thickness and excellent electronic properties of the graphene monolayer. The dynamical translocation phenomena and/or behaviors underneath the blocked ionic current, however, have not been well unveiled to date for the translocation of DNA electrophoretically through a graphene nanopore. In this report, the assessment on the sensitivity of ionic current to instantaneous statuses of DNA in a 2.4 nm graphene nanopore was carried out based on the all‐atom molecular dynamics simulations. By filtering out the thermal noise of ionic current, the instantaneous conformational variations of DNA in a graphene nanopore have been unveiled from the fluctuations of ionic current, because of the spatial blockage effect of DNA against ionic current. Interestingly, the neighborhood effect of DNA against ionic current was also observed within a distance of 1.5 nm nearby the graphene nanopore, suggesting the further precise control for DNA translocation through a graphene nanopore in gene sequencing. Moreover, the sensitivity of the blocked ionic current toward the instantaneous conformations of DNA in a graphene nanopore demonstrates the great potential of graphene nanopores in the dynamics analysis of single molecules.     

Inside Back Cover: Retina-Inspired Organic Photonic Synapses for Selective Detection of SWIR Light (Angew. Chem. Int. Ed. 6/2023)

Photonic synapses with the dual function of optical signal detection and information processing can simulate human visual system. However, photonic synapses with selective detection of short-wavelength infrared (SWIR) light have never been reported, which can not only broaden the human vision region but also integrate neuromorphic computation and infrared optical communication. Here, organic photonic synapses based on a new donor-acceptor copolymer P1 are fabricated, which exhibit excellent synaptic characteristics with selective detection for SWIR and extremely low energy consumption (2.85 fJ). The working mechanism is rooted in energy level barriers and unbalanced charge transportation. Moreover, these photonic synapses demonstrate excellent performance in multi-signal logic editing, letter imaging and memory with noise reduction function. This contribution provides ideas of constructing selective-response synapses for artificial visual system and neuromorphic computing.     

Friction memory in the stick-slip of a sheared granular bed

We investigate friction memory effects in the chaotic stick-slip motion of a plate shearing a granular bed. By analyzing separately trajectories' portions having increasing or decreasing instantaneous velocity, it is found that there are two quantitatively distinct granular friction-velocity curves for positive or negative acceleration, which cross one another in the weakening region. Interpreting acceleration dependence as an indirect consequence of contact ageing, we try to explain these effects by including rate-and-state (RS) friction equations in the stochastic model describing the plate motion. Preliminary results on a study case show that the main experimental features can be reproduced in this way, although quantitative agreement is partial. From the value obtained for the RS parameters we conclude that sliding friction decorrelation takes place at the length-scale of the solid-on-solid micro-contacts between grains and plate. In addition, the contemporary presence of noise and RS effects influences the average friction curve at large shear rate.     

Van Kampen's expansion approach in an opinion formation model

We analyze a simple opinion formation model consisting of two parties, A and B, and a group I, of undecided agents. We assume that the supporters of parties A and B do not interact among them, but only interact through the group I, and that there is a nonzero probability of a spontaneous change of opinion ( , ). From the master equation, and via van Kampen's Ω-expansion approach, we have obtained the “macroscopic” evolution equation, as well as the Fokker-Planck equation governing the fluctuations around the deterministic behavior. Within the same approach, we have also obtained information about the typical relaxation behavior of small perturbations. An erratum to this article is available at .     

Weberized Mumford-Shah Model with Bose-Einstein Photon Noise

Human vision works equally well in a large dynamic range of light intensities, from only a few photons to typical midday sunlight. Contributing to such remarkable flexibility is a famous law in perceptual (both visual and aural) psychology and psychophysics known as Weber's Law. The current paper develops a new segmentation model based on the integration of Weber's Law and the celebrated Mumford-Shah segmentation model (Comm. Pure Appl. Math., vol. 42, pp. 577-685, 1989). Explained in detail are issues concerning why the classical Mumford-Shah model lacks light adaptivity, and why its "weberized" version can more faithfully reflect human vision's superior segmentation capability in a variety of illuminance conditions from dawn to dusk. It is also argued that the popular Gaussian noise model is physically inappropriate for the weberization procedure. As a result, the intrinsic thermal noise of photon ensembles is introduced based on Bose and Einstein's distributions in quantum statistics, which turns out to be compatible with weberization both analytically and computationally. The current paper focuses on both the theory and computation of the weberized Mumford-Shah model with Bose-Einstein noise. In particular, Ambrosio-Tortorelli's Γ-convergence approximation theory is adapted (Boll. Un. Mat. Ital. B, vol. 6, pp. 105-123, 1992), and stable numerical algorithms are developed for the associated pair of nonlinear Euler-Lagrange PDEs.     

A new probabilistic fuzzy model: Fuzzification–Maximization (FM) approach

Over the past few decades, fuzzy logic systems have been used for nonlinear modeling and approximation in many fields ranging from engineering to science. In this paper, a new fuzzy model is developed from the probabilistic and statistical point of view. The proposed model decomposes the input–output characteristics into noise-free part and probabilistic noise part and identifies them simultaneously. The noise-free model recovers the nominal input–output characteristics of the target system and the noise model gives approximation to the probabilistic nature of the added noise. To identify the two submodels simultaneously, we propose the Fuzzification–Maximization (FM). Finally, some simulations are conducted and the effectiveness of the proposed method is demonstrated through the comparison with the previous methods.     

Seismic denoising using curvelet analysis

A curvelet is a new and effective spectral transform, that allows sparse representations of complex data. It has many applications in several fields, including denoising, wave propagation in disordered media and pattern recognition. This spectral technique is based on directional basis functions that represent objects having discontinuities along smooth curves. In this work we apply this technique to the removal of Ground Roll, which is an undesired feature signal present in seismic data obtained by sounding the geological structures of the Earth. In this methodology the original seismic data is decomposed by curvelet transform in scales and angular domains. For each scale the curvelet denoising technique allows a very efficient separation of the Ground Roll in angle sections. The precise identification of the Ground Roll pattern allows an effective erasing of its coefficients. In contrast to conventional denoising techniques we do not use any artificial attenuation factor to decrease the amplitude of the Ground Roll coefficients. We have estimated that, depending on the scale, around 75% of the energy of the strong undesired signal is removed.     

Polynomial fitting of DT-MRI fiber tracts allows accurate estimation of muscle architectural parameters

Fiber curvature is a functionally significant muscle structural property, but its estimation from diffusion-tensor magnetic resonance imaging fiber tracking data may be confounded by noise. The purpose of this study was to investigate the use of polynomial fitting of fiber tracts for improving the accuracy and precision of fiber curvature (κ) measurements. Simulated image data sets were created in order to provide data with known values for κ and pennation angle (θ). Simulations were designed to test the effects of increasing inherent fiber curvature (3.8, 7.9, 11.8 and 15.3 m(-1)), signal-to-noise ratio (50, 75, 100 and 150) and voxel geometry (13.8- and 27.0-mm(3) voxel volume with isotropic resolution; 13.5-mm(3) volume with an aspect ratio of 4.0) on κ and θ measurements. In the originally reconstructed tracts, θ was estimated accurately under most curvature and all imaging conditions studied; however, the estimates of κ were imprecise and inaccurate. Fitting the tracts to second-order polynomial functions provided accurate and precise estimates of κ for all conditions except very high curvature (κ=15.3 m(-1)), while preserving the accuracy of the θ estimates. Similarly, polynomial fitting of in vivo fiber tracking data reduced the κ values of fitted tracts from those of unfitted tracts and did not change the θ values. Polynomial fitting of fiber tracts allows accurate estimation of physiologically reasonable values of κ, while preserving the accuracy of θ estimation.