S
Shingo Ono
Researcher at Nagoya Institute of Technology
Publications - 236
Citations - 1500
Shingo Ono is an academic researcher from Nagoya Institute of Technology. The author has contributed to research in topics: Laser & Terahertz radiation. The author has an hindex of 20, co-authored 227 publications receiving 1365 citations. Previous affiliations of Shingo Ono include Graduate University for Advanced Studies & University of Tokyo.
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Journal ArticleDOI
Teflon Photonic Crystal Fiber as Terahertz Waveguide
TL;DR: In this paper, the authors demonstrate the construction of reasonably long and non-polarization changing photonic fiber waveguide using Teflon which is a readily available and highly flexible material.
Proceedings ArticleDOI
Person name disambiguation by bootstrapping
TL;DR: This paper proposes to use a two-stage clustering algorithm by bootstrapping to improve the low recall values, in which clustering results of the first stage are used to extract features used in the second stage clustering.
Journal ArticleDOI
Saturation of THz-radiation power from femtosecond-laser-irradiated InAs in a high magnetic field
TL;DR: In this article, the magnetic field of femtosecond-pulse-irradiated InAs is found to be saturated at the magnetic fields around 3 T. The magnetic field strongly depends on geometrical layout.
Journal ArticleDOI
Proposed design principle of fluoride-based materials for deep ultraviolet light emitting devices
Nobuhiko Sarukura,Hidetoshi Murakami,Elmer Estacio,Shingo Ono,Riadh El Ouenzerfi,Marilou Cadatal,Takeshi Nishimatsu,Noriaki Terakubo,Hiroshi Mizuseki,Yoshiyuki Kawazoe,Akira Yoshikawa,Tsuguo Fukuda +11 more
TL;DR: In this article, the design principle of fluoride-based devices for deep ultraviolet emission was presented using ab initio calculations based on local density approximation, and lattice-matched double-heterostructures of direct-band-gap compounds LiBaxCaySr(1−x−y)F3 on LiSrF3 and Li( 1−x)KxBa(1 −y)MgyF3 were shown to be feasible to fabricate.
Person Name Disambiguation on the Web by Two-Stage Clustering
TL;DR: This paper proposes a two-stage clustering algorithm to improve the low recall values, in which the clustering results of the first stage are used to extract features used in the second stage clustering.