A not-for-gain Corporation, IEEE is the world's biggest technical Experienced Group committed to advancing technological know-how for the advantage of humanity.
The following content articles are merged in Scholar. Their blended citations are counted just for the 1st 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 financial system is encountering the most hard era of weather alter outside of what exactly is obvious inside the pre-industrial age. Whilst Africa's share of worldwide greenhouse gas (GHG) is negligible, the…
The next posts are merged in Scholar. Their put together citations are counted only for the 1st post.
Towards assault-resistant route mutation for VANETs: an online and adaptive multiagent reinforcement Finding out solution
A not-for-earnings Corporation, IEEE is the planet's greatest technological professional Group committed to advancing technological know-how for the benefit of humanity.
A QoE-driven multicast approach with phase routing—A novel multimedia visitors engineering paradigm
The current period is struggling with a dilemma referring to participating the essentialities of Electricity means to achieve economic Jiahao Shen King's College London prosperity because of the following environmental complications. Consequently, sectors…
An optimized quantum minimum looking algorithm with guaranteed-achievements likelihood and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do normal useful resource dependence, renewable Vitality, and technological improvements amplify or lessen the pollution noises?
Scanning radar ahead-looking superresolution imaging determined by the Weibull distribution for just a sea-surface area focus on
This "Cited by" depend consists of citations to the next article content in Scholar. The ones marked * could possibly be distinctive in the write-up from 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。
Ways to disturb network reconnaissance: a relocating goal protection technique dependant on deep reinforcement learning