scispace - formally typeset
J

James Taylor

Researcher at Newcastle University

Publications -  1190
Citations -  43346

James Taylor is an academic researcher from Newcastle University. The author has contributed to research in topics: Laser & Fiber laser. The author has an hindex of 95, co-authored 1161 publications receiving 39945 citations. Previous affiliations of James Taylor include Institut national de la recherche agronomique & European Spallation Source.

Papers
More filters
Journal ArticleDOI

Femtosecond figure of eight Yb:Er fibre laser incorporating a dispersion decreasing fibre

TL;DR: In this paper, a femtosecond figure of eight fiber laser was constructed incorporating a dispersion decreasing (tapered) fiber within the nonlinear loop to give rise to intensity (phase) dependent switching and adiabatic compression.
Journal ArticleDOI

All-fibre periodic spectral filter for simultaneous generation of multiple WDM channels from broad bandwidth pulsed sources

TL;DR: In this article, an all-fibre spectral filter is demonstrated for the simultaneous generation of several wavelength division multiplexed (WDM) pulsed sources using the spectral slicing technique.
Journal ArticleDOI

Fibre-grating pair pulse compression at 1.32μm

TL;DR: In this article, the authors describe the use of a laser YAG with a generation of 1.4 ps and an impulsive force of 85 ps in the presence of 60 ps compression.
Journal ArticleDOI

Second harmonic generation around 0.53 /spl mu/m of seeded Yb fibre system in periodically-poled lithium niobate

TL;DR: Fibre-integrated, seeded, high-power Yb-doped fiber amplifiers have been used in combination with periodically-poled lithium niobate crystals in simple experimental configurations to give second harmonic generation to the visible both in CW and pulsed formats with conversion efficiencies as high as 41% in compact configurations as discussed by the authors.
Journal ArticleDOI

Anddr with novel gross error detection and smart tracking system

TL;DR: An adaptive nonlinear dynamic data reconciliation (ANDDR) method is proposed that includes the application to processes with an unknown statistical model and enables gross error detection (GED) as well.