550 research outputs found

    MULTISPECTRAL IMAGE RESTORATION USING A VECTOR-VALUED REACTION-DIFFUSION BASED MIXED NOISE REMOVAL TECHNIQUE

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    A novel multispectral image filtering technique is proposed in this article. Since the multispectral images are often corrupted by mixed Poisson-Gaussian noise during the sensing and acquisition process, a nonlinear anisotropic diffusion-based restoration approach that deals efficiently with this type of noise mixture is considered here. A second-order vector-valued reaction-diffusion model that leads to a system of well-posed single-valued anisotropic diffusion equations coupled by correlation terms is introduced for this purpose. A finite difference method-based fast-converging approximation algorithm that solves numerically this nonlinear diffusion-based system is then proposed. This iterative numerical approximation scheme is successfully used for removing both the additive Gaussian and quantum noises while preserving the essential features of the multi-valued image. The effectiveness of the described mixed denoising technique is illustrated by the results of the restoration experiments and method comparisons that are also presented here. The proposed restoration approach enhances considerably the spectral image quality, making it well-prepared for the further MSI analysis and computer vision processes, such as the geospatial and remote sensing image analysis

    Random attractors for degenerate stochastic partial differential equations

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    We prove the existence of random attractors for a large class of degenerate stochastic partial differential equations (SPDE) perturbed by joint additive Wiener noise and real, linear multiplicative Brownian noise, assuming only the standard assumptions of the variational approach to SPDE with compact embeddings in the associated Gelfand triple. This allows spatially much rougher noise than in known results. The approach is based on a construction of strictly stationary solutions to related strongly monotone SPDE. Applications include stochastic generalized porous media equations, stochastic generalized degenerate p-Laplace equations and stochastic reaction diffusion equations. For perturbed, degenerate p-Laplace equations we prove that the deterministic, infinite dimensional attractor collapses to a single random point if enough noise is added.Comment: 34 pages; The final publication is available at http://link.springer.com/article/10.1007%2Fs10884-013-9294-

    Dissipative structure formation in cold-rolled Fe and Ni during heavy ion irradiation

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    We report 4-probe resistivity measurements of cold-rolled Ni and Fe during 100 MeV oxygen ion irradiation, at 300K. The resistivity shows increase and saturation, marked by jumps. Employing 200 MeV silver ion irradiation of Fe and Si(100) and topographically identifying strain at an artificial interface in the latter, we assign the resistivity behavior to atomic rearrangements arising from dissipation of incident ion energy at internal interfaces of Ni and Fe, with positive feedback.}Comment: RevTex+ 7 Postscript figures; Fig 2 (topograph) available on demand to [email protected]. To appear in Phys Rev Let

    A Priori Error Estimate of Stochastic Galerkin Method for Optimal Control Problem Governed by Random Parabolic PDE with Constrained Control

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    A stochastic Galerkin approximation scheme is proposed for an optimal control problem governed by a parabolic PDE with random perturbation in its coefficients. The objective functional is to minimize the expectation of a cost functional, and the deterministic control is of the obstacle constrained type. We obtain the necessary and sufficient optimality conditions and establish a scheme to approximate the optimality system through the discretization with respect to both the spatial space and the probability space by Galerkin method and with respect to time by the backward Euler scheme. A priori error estimates are derived for the state, the co-state and the control variables. Numerical examples are presented to illustrate our theoretical results

    South Korea's automotive labour regime, Hyundai Motors’ global production network and trade‐based integration with the European Union

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    This article explores the interrelationship between global production networks(GPNs) and free trade agreements (FTAs) in the South Korean auto industry and its employment relations. It focuses on the production network of the Hyundai Motor Group (HMG) — the third biggest automobile manufacturer in the world — and the FTA between the EU and South Korea. This was the first of the EU’s ‘new generation’ FTAs, which among other things contained provisions designed to protect and promote labour standards. The article’s argument is twofold. First, that HMG’s production network and Korea’s political economy (of which HMG is a crucial part) limited the possibilities for the FTA’s labour provisions to take effect. Second, that the commercial provisions in this same FTA simultaneously eroded HMG’s domestic market and corporate profitability, leading to adverse consequences for auto workers in the more insecure and low-paid jobs. In making this argument, the article advances a multiscalar conceptualization of the labour regime as an analytical intermediary between GPNs and FTAs. It also provides one of the first empirical studies of the EU–South Korea FTA in terms of employment relations, drawing on 105 interviews with trade unions, employer associations, automobile companies and state officials across both parties

    Increasing Detection Performance of Surveillance Sensor Networks

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    We study a surveillance wireless sensor network (SWSN) comprised of small and low-cost sensors deployed in a region in order to detect objects crossing the field of interest. In the present paper, we address two problems concerning the design and performance of an SWSN: optimal sensor placement and algorithms for object detection in the presence of false alarms. For both problems, we propose explicit decision rules and efficient algorithmic solutions. Further, we provide several numerical examples and present a simulation model that combines our placement and detection methods

    The structure of optimal parameters for image restoration problems

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    We study the qualitative properties of optimal regularisation parameters in variational models for image restoration. The parameters are solutions of bilevel optimisation problems with the image restoration problem as constraint. A general type of regulariser is considered, which encompasses total variation (TV), total generalized variation (TGV) and infimal-convolution total variation (ICTV). We prove that under certain conditions on the given data optimal parameters derived by bilevel optimisation problems exist. A crucial point in the existence proof turns out to be the boundedness of the optimal parameters away from 0 which we prove in this paper. The analysis is done on the original -- in image restoration typically non-smooth variational problem -- as well as on a smoothed approximation set in Hilbert space which is the one considered in numerical computations. For the smoothed bilevel problem we also prove that it Γ converges to the original problem as the smoothing vanishes. All analysis is done in function spaces rather than on the discretised learning problem.In Cambridge, this project has been supported by King Abdullah University of Science and Technology (KAUST) Award No. KUK-I1-007-43, EPSRC grants Nr. EP/J009539/1 “Sparse & Higher-order Image Restoration”, and Nr. EP/M00483X/1 “Efficient computational tools for inverse imaging problems”. In Quito, the project has been supported by the Escuela PolitĂ©cnica Nacional de Quito under award PIS 12-14 and the MATHAmSud project SOCDE “Sparse Optimal Control of Differential Equations”. When in Quito, T. Valkonen was moreover supported by a Prometeo scholarship of the Senescyt (Ecuadorian Ministry of Science, Technology, Education, and Innovation).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.jmaa.2015.09.02
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