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Institution

Tokyo Institute of Technology

EducationTokyo, Tôkyô, Japan
About: Tokyo Institute of Technology is a education organization based out in Tokyo, Tôkyô, Japan. It is known for research contribution in the topics: Thin film & Catalysis. The organization has 46775 authors who have published 101656 publications receiving 2357893 citations. The organization is also known as: Tokyo Tech & Tokodai.


Papers
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Journal ArticleDOI
TL;DR: Systematic trends not only show how the intrinsic high-density transport properties of graphene can be accessed by field effect, but also demonstrate the robustness of ion-gated graphene, which is crucial for possible future applications.
Abstract: We present a comparative study of high carrier density transport in mono-, bi-, and trilayer graphene using electric double-layer transistors to continuously tune the carrier density up to values exceeding 10(14) cm(-2). Whereas in monolayer the conductivity saturates, in bi- and trilayer filling of the higher-energy bands is observed to cause a nonmonotonic behavior of the conductivity and a large increase in the quantum capacitance. These systematic trends not only show how the intrinsic high-density transport properties of graphene can be accessed by field effect, but also demonstrate the robustness of ion-gated graphene, which is crucial for possible future applications.

322 citations

Journal ArticleDOI
TL;DR: In this paper, the authors reevaluate the energy determination method used for AGASA events with respect to the lateral distribution of shower particles, their attenuation with zenith angle, shower front structure, delayed particles observed far from the core and other factors.

322 citations

Journal ArticleDOI
01 Jan 1997
TL;DR: In this article, the convergence of the sequence defined by x0 ∈ C, xn+1 = αnx + (1− αn)Txn, n = 0, 1, 2,..., where T is a nonexpansive mapping from a closed convex subset of a Banach space into itself.
Abstract: In this paper, we study the convergence of the sequence defined by x0 ∈ C, xn+1 = αnx + (1− αn)Txn, n = 0, 1, 2, . . . , where 0 ≤ αn ≤ 1 and T is a nonexpansive mapping from a closed convex subset of a Banach space into itself.

322 citations

Journal ArticleDOI
TL;DR: In this paper, the relative Pearson divergence is used as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation, which can detect abrupt property changes lying behind time-series data.

321 citations

Proceedings ArticleDOI
08 May 1994
TL;DR: This paper discusses the positioning accuracy of the authors' method with error variances for an example with three mobile robots, and proposes a new method called "cooperative positioning with multiple robots", which has inherent landmarks and therefore works in uncharted environments.
Abstract: A number of positioning identification techniques have been used for mobile robots Dead reckoning is a popular method, but is not reliable when a robot travels long distances or over an uneven surface because of variations in wheel diameter and wheel slippage The landmark method, which estimates the current position relative to landmarks, cannot be used in an uncharted environment The authors propose a new method called "cooperative positioning with multiple robots" For cooperative positioning, the authors divide the robots into two groups, A and B One group, say A, remains stationary and acts as a landmark while group B moves The moving group B then stops and acts as a landmark for group A This "dance" is repeated until the target robot position are reached Cooperative positioning has a far lower accumulated positioning error than dead reckoning, and can work in three-dimensions which is not possible with dead reckoning Also, this method has inherent landmarks and therefore works in uncharted environments This paper discusses the positioning accuracy of the authors' method with error variances for an example with three mobile robots >

321 citations


Authors

Showing all 46967 results

NameH-indexPapersCitations
Matthew Meyerson194553243726
Yury Gogotsi171956144520
Masayuki Yamamoto1711576123028
H. Eugene Stanley1541190122321
Takashi Taniguchi1522141110658
Shu-Hong Yu14479970853
Kazunori Kataoka13890870412
Osamu Jinnouchi13588586104
Hector F. DeLuca133130369395
Shlomo Havlin131101383347
Hiroyuki Iwasaki131100982739
Kazunari Domen13090877964
Hideo Hosono1281549100279
Hideyuki Okano128116967148
Andreas Strasser12850966903
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202388
2022358
20213,457
20203,694
20193,783
20183,531