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CONet: A Cognitive Ocean Network

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
abstract

The scientific and technological revolution of the Internet of Things has begun in the area of oceanography. Historically, humans have observed the ocean from an external viewpoint in order to study it. In recent years, however, changes have occurred in the ocean, and laboratories have been built on the seafloor. Approximately 70.8% of the Earth's surface is covered by oceans and rivers. The Ocean of Things is expected to be important for disaster prevention, ocean-resource exploration, and underwater environmental monitoring. Unlike traditional wireless sensor networks, the Ocean Network has its own unique features, such as low reliability and narrow bandwidth. These features will be great challenges for the Ocean Network. Furthermore, the integration of the Ocean Network with artificial intelligence has become a topic of increasing interest for oceanology researchers. The Cognitive Ocean Network (CONet) will become the mainstream of future ocean science and engineering developments. In this article, we define the CONet. The contributions of the paper are as follows: (1) a CONet architecture is proposed and described in detail; (2) important and useful demonstration applications of the CONet are proposed; and (3) future trends in CONet research are presented.

fields

cs.CV 1

years

2019 1

verdicts

UNVERDICTED 1

representative citing papers

Facial Makeup Transfer Combining Illumination Transfer

cs.CV · 2019-07-08 · unverdicted · novelty 3.0

A layered image-processing pipeline with illumination transfer enables real-time facial makeup application from a single reference image while handling dark makeup and air-bangs.

citing papers explorer

Showing 1 of 1 citing paper.

  • Facial Makeup Transfer Combining Illumination Transfer cs.CV · 2019-07-08 · unverdicted · none · ref 32 · internal anchor

    A layered image-processing pipeline with illumination transfer enables real-time facial makeup application from a single reference image while handling dark makeup and air-bangs.