O
Osamu Akashi
Researcher at Harvard University
Publications - 92
Citations - 2358
Osamu Akashi is an academic researcher from Harvard University. The author has contributed to research in topics: Routing protocol & Network packet. The author has an hindex of 16, co-authored 88 publications receiving 1937 citations. Previous affiliations of Osamu Akashi include Nippon Telegraph and Telephone.
Papers
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Journal ArticleDOI
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems
Zubair Md. Fadlullah,Fengxiao Tang,Bomin Mao,Nei Kato,Osamu Akashi,Takeru Inoue,Kimihiro Mizutani +6 more
TL;DR: An overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems, and a new use case, i.e., deep learning based intelligent routing, which is demonstrated to be effective in contrast with the conventional routing strategy.
Journal ArticleDOI
The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective
Nei Kato,Zubair Md. Fadlullah,Bomin Mao,Fengxiao Tang,Osamu Akashi,Takeru Inoue,Kimihiro Mizutani +6 more
TL;DR: Preliminary results are reported that demonstrate the encouraging performance of the proposed deep learning system compared to a benchmark routing strategy (Open Shortest Path First (OSPF)) in terms of significantly better signaling overhead, throughput, and delay.
Journal ArticleDOI
Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning
Bomin Mao,Zubair Md. Fadlullah,Fengxiao Tang,Nei Kato,Osamu Akashi,Takeru Inoue,Kimihiro Mizutani +6 more
TL;DR: Simulation results demonstrate that the proposal outperforms the benchmark method in terms of delay, throughput, and signaling overhead, and it is demonstrated how the uniquely characterized input and output traffic patterns can enhance the route computation of the deep learning based SDRs.
Journal ArticleDOI
On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control
Fengxiao Tang,Bomin Mao,Zubair Md. Fadlullah,Nei Kato,Osamu Akashi,Takeru Inoue,Kimihiro Mizutani +6 more
TL;DR: This article proposes a new, real-time deep learning based intelligent network traffic control method, exploiting deep Convolutional Neural Networks (deep CNNs) with uniquely characterized inputs and outputs to represent the considered Wireless Mesh Network (WMN) backbone.
Patent
On-line information providing scheme featuring function to dynamically account for user's interest
TL;DR: In this article, an on-line information providing scheme capable of dynamically accounting for user's interest with respect to information and providing appropriate information presentation according to the users' interest is presented.