A not-for-gain Firm, IEEE is the earth's most significant specialized Experienced Group committed to advancing know-how for the advantage of humanity.

The following article content are merged in Scholar. Their blended citations are counted only for the main report.

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下面的文章将在学术搜索中合并。它们的合并引用次数只会计入第一篇文章。

The worldwide economic climate is dealing with probably the most complicated era of climate transform beyond exactly what is evident during the pre-industrial age. Though Africa's share of worldwide greenhouse gasoline (GHG) is minimal, the…

The following posts are merged in Scholar. Their put together citations are counted only for the main report.

Toward attack-resistant route mutation for VANETs: an online and adaptive multiagent reinforcement Finding out method

A not-for-financial gain Business, IEEE is the whole world's greatest technological Experienced Firm devoted to advancing engineering for the good thing about Jiahao Shen King's College London humanity.

A QoE-driven multicast tactic with section routing—A novel multimedia visitors engineering paradigm

The present period is facing a Problem concerning participating the essentialities of Electrical power sources to achieve economic prosperity on account of the following environmental problems. For that reason, sectors…

An optimized quantum minimum seeking algorithm with sure-good results chance and its experiment simulation with Cirq

Tracking environmental sustainability pathways in Africa: Do normal useful resource dependence, renewable Power, and technological innovations amplify or lessen the air pollution noises?

Scanning radar ahead-hunting superresolution imaging according to the Weibull distribution for just a sea-area target

This "Cited by" rely incorporates citations to the following content articles in Scholar. Those marked * may be distinct with the article inside the profile.

此“引用引文的出版物数量”计数包含了对学术搜索中以下文章的引用次数。带有*标记的引文可能与个人学术档案中的文章有所区别。

How you can disturb network reconnaissance: a transferring target defense strategy based upon deep reinforcement learning