As a newly emerging technology, deep learning (DL) is a very promising field in big data applications. Remote sensing often involves huge data volumes obtained daily by numerous in-orbit satellites. This makes it a pe...
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As a newly emerging technology, deep learning (DL) is a very promising field in big data applications. Remote sensing often involves huge data volumes obtained daily by numerous in-orbit satellites. This makes it a perfect target area for data-driven applications. Nowadays, technological advances in terms of software and hardware have a noticeable impact on Earth observation applications, more specifically in remote sensing techniques and procedures, allowing for the acquisition of data sets with greater quality at higher acquisition ratios. This results in the collection of huge amounts of remotely sensed data, characterized by their large spatial resolution (in terms of the number of pixels per scene), and very high spectral dimensionality, with hundreds or even thousands of spectral bands. As a result, remote sensing instruments on spaceborne and airborne platforms are now generating data cubes with extremely high dimensionality, imposing several restrictions in terms of both processing runtimes and storage capacity. In this article, we provide a comprehensive review of the state of the art in DL for remote sensing data interpretation, analyzing the strengths and weaknesses of the most widely used techniques in the literature, as well as an exhaustive description of their parallel and distributed implementations (with a particular focus on those conducted using cloud computing systems). We also provide quantitative results, offering an assessment of a DL technique in a specific case study (source code available: https://***/mhaut/cloud-dnn-HSI). This article concludes with some remarks and hints about future challenges in the application of DL techniques to distributed remote sensing data interpretation problems. We emphasize the role of the cloud in providing a powerful architecture that is now able to manage vast amounts of remotely sensed data due to its implementation simplicity, low cost, and high efficiency compared to other parallel and distributed
The simulation and optimization of complex engineering designs in automotive or aerospace involves multiple mathematical tools, long-running workflows and resource-intensive computations on distributed infrastructures...
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The simulation and optimization of complex engineering designs in automotive or aerospace involves multiple mathematical tools, long-running workflows and resource-intensive computations on distributed infrastructures. Finding the optimal deployment in terms of task distribution, parallelization, collocation and resource assignment for each execution is a step-wise process involving both human input with domain-specific knowledge about the tools as well as the acquisition of new knowledge based on the actual execution history. In this paper, we present a policy-driven adaptive and reflective middleware that supports smart cloud-based deployment and execution of engineering workflows. This middleware supports deep inspection of the workflow task structure and execution, as well as of the very specific mathematical tools, their executions and used parameters. The reflective capabilities are based on multiple meta-models to reflect workflow structure, deployment, execution and resources. Adaptive deployment is driven by both human input as meta-data annotations as well as adaptation policies that reason over the actual execution history of the workflows. We validate and evaluate this middleware in real-life application cases and scenarios in the domain of aeronautics.
A method of description and optimization of the continuous structure of hierarchical processing system is presented. The structure of the system is defined as a finite sequence of density functions of distributions. E...
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ISBN:
(纸本)0889865248
A method of description and optimization of the continuous structure of hierarchical processing system is presented. The structure of the system is defined as a finite sequence of density functions of distributions. Each distribution will correspond to the connections between this and previous level and shows how the size of previous level is distributed between the sizes of this level. Corresponding optimization problem is a calculus of variations problem. Some reduced variants of this problem have good mathematical properties and is solved analytically. The approach is given in terms of convex analysis, integer programming and calculus of variations.
In this paper we present a scalable, distributed architecture that allocates idle CPUs for task execution, where any node may request the execution of a group of tasks by other ones. A fast, scalable discovery protoco...
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ISBN:
(纸本)3540393684
In this paper we present a scalable, distributed architecture that allocates idle CPUs for task execution, where any node may request the execution of a group of tasks by other ones. A fast, scalable discovery protocol is an essential component. Also, up to date information about free nodes is efficiently managed in each node by an availability protocol. Both protocols exploit a tree-based peer-to-peer network that adds fault-tolerant capabilities. Results from experiments and simulation tests, using a simple allocation method, show discovery and allocation costs scaling logarithmically with the number of nodes, even with low communication overhead and little, bounded state in each node.
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