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Institution

Nagoya Institute of Technology

EducationNagoya, Japan
About: Nagoya Institute of Technology is a education organization based out in Nagoya, Japan. It is known for research contribution in the topics: Thin film & Catalysis. The organization has 10766 authors who have published 19140 publications receiving 255696 citations. The organization is also known as: Nagoya Kōgyō Daigaku & Nitech.


Papers
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Journal ArticleDOI
TL;DR: In this article, a deep learning-aided iterative detection algorithm for massive overloaded multiple-input multiple-output (MIMO) systems where the number of transmit antennas $n$ is larger than that of receive antennas $m$ is presented.
Abstract: This paper presents a deep learning-aided iterative detection algorithm for massive overloaded multiple-input multiple-output (MIMO) systems where the number of transmit antennas $n$ is larger than that of receive antennas $m$ . Since the proposed algorithm is based on the projected gradient descent method with trainable parameters, it is named the trainable projected gradient-detector (TPG-detector). The trainable internal parameters, such as the step-size parameter, can be optimized with standard deep learning techniques, i.e., the back propagation and stochastic gradient descent algorithms. This approach is referred to as data-driven tuning and ensures fast convergence during parameter estimation in the proposed scheme. The TPG-detector mainly consists of matrix-vector product operations whose computational cost is proportional to $m n$ for each iteration. In addition, the number of trainable parameters in the TPG-detector is independent of the number of antennas. These features of the TPG-detector result in a fast and stable training process and reasonable scalability for large systems. The numerical simulations show that the proposed detector achieves a comparable detection performance to those of the existing algorithms for the massively overloaded MIMO channels, e.g., the state-of-the-art IW-SOAV detector, with a lower computation cost.

59 citations

Journal ArticleDOI
TL;DR: In this article, the trifluoromethylation reaction has been performed by treatment of Me3SiCF3 under initiation by TBAF, TBAT, TMAF as well as CsF.

59 citations

Journal ArticleDOI
TL;DR: 25 issues needing attention, fitting into three general categories: induction models; electrostimulation models; and human exposure limits, are discussed, presented as a research agenda for refinements in numerical modeling with applications to human Exposure limits.
Abstract: This article treats unsettled issues in the use of numerical models of electrical dosimetry as applied to international limits on human exposure to low-frequency (typically < 100 kHz) electromagnetic fields and contact current. The perspective in this publication is that of Subcommittee 6 of IEEE-ICES (International Committee on Electromagnetic Safety) Technical Committee 95. The paper discusses 25 issues needing attention, fitting into three general categories: induction models; electrostimulation models; and human exposure limits. Of these, 9 were voted as 'high priority' by members of Subcommittee 6. The list is presented as a research agenda for refinements in numerical modeling with applications to human exposure limits. It is likely that such issues are also important in medical and electrical product safety design applications.

59 citations

Journal ArticleDOI
TL;DR: In this paper, the relationship of materials design and thermal stabilization of catalysts is discussed by focusing on a series of useful rare earth elements used in automotive catalysts, where the phase transition and surface modification are closely related to realize better heat-stable materials with nanometer-order particles in size.

59 citations

Journal ArticleDOI
TL;DR: In this article, the authors demonstrate that two residues in Sensory Rhodopsin II (SRII) near the membrane-embedded interface (Tyr174 on helix F and Thr204 onhelix G) are essential for signaling by the SRII-HtrII complex.

59 citations


Authors

Showing all 10804 results

NameH-indexPapersCitations
Luis M. Liz-Marzán13261661684
Hideo Hosono1281549100279
Shunichi Fukuzumi111125652764
Andrzej Cichocki9795241471
Kwok-Hung Chan9140644315
Kimoon Kim9041235394
Alex Martin8840636063
Manijeh Razeghi82104025574
Yuichi Ikuhara7597424224
Richard J. Cogdell7348023866
Masaaki Tanaka7186022443
Kiyotomi Kaneda6537813337
Yulin Deng6464116148
Motoo Shiro6472017786
Norio Shibata6357414469
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202316
202272
2021631
2020718
2019701
2018764