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论文题目: A Survey on Edge Computing Systems and Tools

论文摘要: 在物联网和5G通信的愿景驱动下,边缘计算系统在网络边缘集成了计算,存储和网络资源,以提供计算基础架构,从而使开发人员能够快速开发和部署边缘应用程序。 如今,边缘计算系统已在业界和学术界引起了广泛关注。 为了探索新的研究机会并帮助用户选择适合特定应用的边缘计算系统,本调查报告对现有边缘计算系统进行了全面概述,并介绍了代表性的项目。 根据开放源代码工具的适用性进行了比较。 最后,我们重点介绍了边缘计算系统的能源效率和深度学习优化。 本次调查还研究了用于分析和设计边缘计算系统的未解决问题。

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Remote sensing (RS) images are usually stored in compressed format to reduce the storage size of the archives. Thus, existing content-based image retrieval (CBIR) systems in RS require decoding images before applying CBIR (which is computationally demanding in the case of large-scale CBIR problems). To address this problem, in this paper, we present a joint framework that simultaneously learns RS image compression and indexing, eliminating the need for decoding RS images before applying CBIR. The proposed framework is made up of two modules. The first module aims at effectively compressing RS images. It is achieved based on an auto-encoder architecture. The second module aims at producing hash codes with a high discrimination capability. It is achieved based on a deep hashing method that exploits soft pairwise, bit-balancing and classification loss functions. We also propose a two stage learning strategy with gradient manipulation techniques to obtain image representations that are compatible with both RS image indexing and compression. Experimental results show the compression and CBIR efficacy of the proposed framework when compared to widely used approaches in RS. The code of the proposed framework is available at https://git.tu-berlin.de/rsim/RS-JCIF.

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