基于连续计算范式的水下地中海图像分析

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-12 DOI:10.1016/j.future.2024.107481
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引用次数: 0

摘要

人类活动的食物、交通、休闲和其他许多用途都依赖于海洋。海洋覆盖了地球表面的 70%,但其中大部分不为人类所知。这就是水下成像成为海洋科学宝贵资源的原因。目前,通过观测系统(如自动水下航行器或水下观测站)获取的图像会将所有原始数据传送到陆地观测站。然而,考虑到这些系统的电力供应和传输带宽有限,传输如此大量的数据可能具有挑战性。在本文中,我们将讨论这些方面,特别是如何将边缘计算和云计算结合起来,根据计算连续性范式有效管理整个处理管道。
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Underwater Mediterranean image analysis based on the compute continuum paradigm

Human activity depends on the oceans for food, transportation, leisure, and many more purposes. Oceans cover 70% of the Earth’s surface, but most of them are unknown to humankind. This is the reason why underwater imaging is a valuable resource asset to Marine Science. Images are acquired with observing systems, e.g. autonomous underwater vehicles or underwater observatories, that presently transmit all the raw data to land stations. However, the transfer of such an amount of data could be challenging, considering the limited power supply and transmission bandwidth of these systems. In this paper, we discuss these aspects, and in particular how it is possible to couple Edge and Cloud computing for effective management of the full processing pipeline according to the Compute Continuum paradigm.

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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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