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Anupam Mitra

Researcher at Birla Institute of Technology and Science

Publications -  13
Citations -  155

Anupam Mitra is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Fiber Bragg grating & Rydberg formula. The author has an hindex of 6, co-authored 10 publications receiving 123 citations. Previous affiliations of Anupam Mitra include University of New Mexico & Indian Statistical Institute.

Papers
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Robust Mølmer-Sørensen gate for neutral atoms using rapid adiabatic Rydberg dressing

TL;DR: In this article, the authors show that the typical error in implementing a two-qubit gate, such as the controlled phase gate, is dominated by errors in the single-atom light shift, and that this can be easily corrected using adiabatic dressing interleaved with a simple spin echo sequence.
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Optical fiber nanoprobe preparation for near-field optical microscopy by chemical etching under surface tension and capillary action

TL;DR: A technique of chemical etching for fabrication of near perfect optical fiber nanoprobe (NNP) that uses photosensitive single mode optical fiber to etch in hydro fluoric (HF) acid solution and creates near perfect symmetric tip at the apex of the fiber.
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e + e - →μ + μ - scattering in the noncommutative standard model

TL;DR: In this article, the authors studied muon pair production in the non-commutative (NC) extension of the standard model using the Seiberg-Witten maps of this to the second order of the non commutative parameter and found that the process can probe the NC scale in the range 0.8-1.0 TeV for typical ILC energy ranges.
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Ultrafine Fiber Tip Etched in Hydrophobic Polymer Coated Tube for Near-Field Scanning Plasmonic Probe

TL;DR: In this article, a hydrophobic polymer coated tube (HP) containing 48% hydrofluoric acid (HF) was used to etch a singlemode fiber tip for optical fiber.
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Automatic seizure detection by modified line length and Mahalanobis distance function

TL;DR: It is shown that line length is one feature that is extractable in linear time from EEG signals and capable of automatic seizure onset detection with highest accuracy among linear time extractable features.