The Experts below are selected from a list of 15804 Experts worldwide ranked by ideXlab platform
Bulend Ortac - One of the best experts on this subject based on the ideXlab platform.
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Spatiotemporal evolutions of similariton pulses in Multimode Fibers with Raman amplification.
arXiv: Optics, 2020Co-Authors: Leila Graini, Bulend OrtacAbstract:This letter is to pave the way towards the demonstration of spatiotemporal similariton pulses evolution in passive Multimode Fibers with Raman amplification. We present numerically these issues in a graded-index and step-index Multimode Fibers and provide a first look at the complex spatiotemporal dynamics of similariton pulses. The results showed that the similariton pulses can be generated in both Multimode Fibers. The temporal and the spectral evolution of the pulses can be characterized as parabolic shapes with linear chirp and kW peak power. By compressed these, high energy femtoseconds pulses can be obtained starting initial picoseconds pulses. Spatial beam profile could be preserved in both Multimode Fibers under the predominantly excitation of the fundamental mode. Specifically, Raman amplification and similariton pulses generation in graded-index Multimode fiber improves the spatial beam cleaning process under the different initial condition.
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cascaded raman scattering based high power octave spanning supercontinuum generation in graded index Multimode Fibers
Scientific Reports, 2018Co-Authors: Uğur Teğin, Bulend OrtacAbstract:A new method to generate multi-watt-level, octave-spanning, spectrally flat supercontinua stemmed from cascaded Raman scattering in graded-index Multimode Fibers is reported. Formation dynamics of supercontinua are investigated by studying the effect of fiber length and core size. High power handling capacity of the graded-index Multimode Fibers is demonstrated by power scaling experiments. Pump pulse repetition rate is scaled from kHz to MHz while pump pulse peak power remains same and ~4 W supercontinuum is achieved with 2 MHz pump repetition rate. To the best of our knowledge, this is the highest average power and repetition supercontinuum source ever reported based on a graded-index Multimode silica fiber. Spatial properties of the generated supercontinua are measured and Gaussian-like beam profiles obtained for different wavelength ranges. Numerical simulations are performed to investigate underlying nonlinear dynamics in details and well-aligned with experimental observations.
Dapeng Zhou - One of the best experts on this subject based on the ideXlab platform.
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simultaneous strain and temperature measurement with fiber bragg grating and Multimode Fibers using an intensity based interrogation method
IEEE Photonics Technology Letters, 2009Co-Authors: Dapeng ZhouAbstract:A new all-fiber sensor capable of simultaneous measurement of strain and temperature is presented. The sensor system is formed by a fiber Bragg grating and two sections of Multimode Fibers (MMFs). One section of the MMF is isolated from strain and acts as a temperature-dependent edge filter, while the other is isolated from both strain and temperature changes. By monitoring the optical power changes, it is feasible to obtain information that permits simultaneous measurement of strain and temperature with a low-cost and simple structure.
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simultaneous measurement for strain and temperature using fiber bragg gratings and Multimode Fibers
Applied Optics, 2008Co-Authors: Dapeng Zhou, Yu Liu, Li Wei, Wingki Liu, John W Y LitAbstract:An all-fiber sensor capable of simultaneous measurement of temperature and strain is newly presented. The sensing head is formed by a fiber Bragg grating combined with a section of Multimode fiber that acts as a Mach-Zehnder interferometer for temperature and strain discrimination. The strain and temperature coefficients of Multimode Fibers vary with the core sizes and materials. This feature can be used to improve the strain and temperature resolution by suitably choosing the Multimode fiber. For a 10 pm wavelength resolution, a resolution of 9.21 μe in strain and 0.26 °C in temperature can be achieved.
M E Fermann - One of the best experts on this subject based on the ideXlab platform.
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single mode excitation of Multimode Fibers with ultrashort pulses
Optics Letters, 1998Co-Authors: M E FermannAbstract:Single-mode excitation of step-index Multimode Fibers with light sources with short temporal coherence lengths is demonstrated. Multimode fiber designs with reduced microbending-induced mode coupling are described that allow the propagation of the fundamental mode over long lengths with negligible mode coupling even in the presence of tight fiber bends. At a wavelength of 1.56 µm a fiber with a core diameter of 45 µm can preserve the fundamental mode for a propagation length of ?20 m.?Such Fibers allow coiling with a coil diameter as small as 7??cm.
Uğur Teğin - One of the best experts on this subject based on the ideXlab platform.
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Spectral and Spatial Shaping of Spatiotemporal Nonlinearities in Multimode Fibers
2020 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 2020Co-Authors: Uğur Teğin, Eirini Kakkava, Navid Borhani, Babak Rahmani, Christophe Moser, Demetri PsaltisAbstract:Single-mode optical Fibers have been used for various applications such as communications, fiber lasers, optical imaging and nonlinear optics. Recently Multimode Fibers started to attract attention for the aforementioned applications by including the spatial degree of freedom. Specifically, in graded-index Multimode Fibers (GIMFs), ultrashort pulse propagation with complex modal interactions revealed new nonlinear dynamics for beam shaping, frequency conversion and ultrashort pulse generation [1 , 2] . Here, we present methods to control the spectral and spatial properties of propagating light in Multimode Fibers and laser cavities. The Multimode fiber nonlinearities are learned and shaped with a machine learning approach for single-pass pulse propagation. In spatiotemporal mode-locked fiber lasers, complex Multimode laser structures, the generation of high-quality beam shapes are reported by changing temporal dynamics of mode-locked pulses and beam profile improvement with spatiotemporal mode-locking is achieved.
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imaging through Multimode Fibers using deep learning the effects of intensity versus holographic recording of the speckle pattern
Optical Fiber Technology, 2019Co-Authors: Eirini Kakkava, Navid Borhani, Babak Rahmani, Uğur Teğin, Christophe Moser, Damien Loterie, Georgia Konstantinou, Demetri PsaltisAbstract:Abstract Information transmission through Multimode Fibers (MMFs) has been a topic of great interest for many years. Deep learning algorithms have been applied successfully to MMFs in particular to fiber endoscopy. In this work, we show how Deep Neural Networks (DNNs) can be a versatile technique for classification and recovery of input images that have been significantly distorted while propagating along the MMF forming a speckle pattern. A comparison between holographic and intensity-only recording of the speckle output, which is used as an input to the DNNs, shows that high performance can be achieved without having the full field information (amplitude and phase). Impressive reconstruction fidelity and classification accuracy of the fiber inputs from the intensity-only images of the speckle patterns is reported.
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Controlling spatiotemporal nonlinearities in Multimode Fibers with deep neural networks
arXiv: Optics, 2019Co-Authors: Uğur Teğin, Eirini Kakkava, Navid Borhani, Babak Rahmani, Christophe Moser, Demetri PsaltisAbstract:Spatiotemporal nonlinear interactions in Multimode Fibers are of interest for beam shaping and frequency conversion by exploiting the nonlinear propagation of different pump regimes from quasi-continuous wave to ultrashort pulses centered around visible to infrared pump wavelengths. The nonlinear effects in multi-mode Fibers depend strongly on the excitation condition, however relatively little work has been reported on this subject. Here, we present the first machine learning approach to learn and control the nonlinear frequency conversion inside Multimode Fibers by tailoring the excitation condition via deep neural networks. Trained with experimental data, deep neural networks are adapted to learn the relation between the spatial beam profile of the pump pulse and the spectrum generation. For different user-defined target spectra, network-suggested beam shapes are applied and control over the cascaded Raman scattering and supercontinuum generation processes are achieved. Our results present a novel method to tune the spectra of a broadband source.
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cascaded raman scattering based high power octave spanning supercontinuum generation in graded index Multimode Fibers
Scientific Reports, 2018Co-Authors: Uğur Teğin, Bulend OrtacAbstract:A new method to generate multi-watt-level, octave-spanning, spectrally flat supercontinua stemmed from cascaded Raman scattering in graded-index Multimode Fibers is reported. Formation dynamics of supercontinua are investigated by studying the effect of fiber length and core size. High power handling capacity of the graded-index Multimode Fibers is demonstrated by power scaling experiments. Pump pulse repetition rate is scaled from kHz to MHz while pump pulse peak power remains same and ~4 W supercontinuum is achieved with 2 MHz pump repetition rate. To the best of our knowledge, this is the highest average power and repetition supercontinuum source ever reported based on a graded-index Multimode silica fiber. Spatial properties of the generated supercontinua are measured and Gaussian-like beam profiles obtained for different wavelength ranges. Numerical simulations are performed to investigate underlying nonlinear dynamics in details and well-aligned with experimental observations.
Demetri Psaltis - One of the best experts on this subject based on the ideXlab platform.
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Spectral and Spatial Shaping of Spatiotemporal Nonlinearities in Multimode Fibers
2020 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 2020Co-Authors: Uğur Teğin, Eirini Kakkava, Navid Borhani, Babak Rahmani, Christophe Moser, Demetri PsaltisAbstract:Single-mode optical Fibers have been used for various applications such as communications, fiber lasers, optical imaging and nonlinear optics. Recently Multimode Fibers started to attract attention for the aforementioned applications by including the spatial degree of freedom. Specifically, in graded-index Multimode Fibers (GIMFs), ultrashort pulse propagation with complex modal interactions revealed new nonlinear dynamics for beam shaping, frequency conversion and ultrashort pulse generation [1 , 2] . Here, we present methods to control the spectral and spatial properties of propagating light in Multimode Fibers and laser cavities. The Multimode fiber nonlinearities are learned and shaped with a machine learning approach for single-pass pulse propagation. In spatiotemporal mode-locked fiber lasers, complex Multimode laser structures, the generation of high-quality beam shapes are reported by changing temporal dynamics of mode-locked pulses and beam profile improvement with spatiotemporal mode-locking is achieved.
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imaging through Multimode Fibers using deep learning the effects of intensity versus holographic recording of the speckle pattern
Optical Fiber Technology, 2019Co-Authors: Eirini Kakkava, Navid Borhani, Babak Rahmani, Uğur Teğin, Christophe Moser, Damien Loterie, Georgia Konstantinou, Demetri PsaltisAbstract:Abstract Information transmission through Multimode Fibers (MMFs) has been a topic of great interest for many years. Deep learning algorithms have been applied successfully to MMFs in particular to fiber endoscopy. In this work, we show how Deep Neural Networks (DNNs) can be a versatile technique for classification and recovery of input images that have been significantly distorted while propagating along the MMF forming a speckle pattern. A comparison between holographic and intensity-only recording of the speckle output, which is used as an input to the DNNs, shows that high performance can be achieved without having the full field information (amplitude and phase). Impressive reconstruction fidelity and classification accuracy of the fiber inputs from the intensity-only images of the speckle patterns is reported.
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Controlling spatiotemporal nonlinearities in Multimode Fibers with deep neural networks
arXiv: Optics, 2019Co-Authors: Uğur Teğin, Eirini Kakkava, Navid Borhani, Babak Rahmani, Christophe Moser, Demetri PsaltisAbstract:Spatiotemporal nonlinear interactions in Multimode Fibers are of interest for beam shaping and frequency conversion by exploiting the nonlinear propagation of different pump regimes from quasi-continuous wave to ultrashort pulses centered around visible to infrared pump wavelengths. The nonlinear effects in multi-mode Fibers depend strongly on the excitation condition, however relatively little work has been reported on this subject. Here, we present the first machine learning approach to learn and control the nonlinear frequency conversion inside Multimode Fibers by tailoring the excitation condition via deep neural networks. Trained with experimental data, deep neural networks are adapted to learn the relation between the spatial beam profile of the pump pulse and the spectrum generation. For different user-defined target spectra, network-suggested beam shapes are applied and control over the cascaded Raman scattering and supercontinuum generation processes are achieved. Our results present a novel method to tune the spectra of a broadband source.
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Transmission in Multimode Fiber with Deep Learning
2018 International Conference on Optical MEMS and Nanophotonics (OMN), 2018Co-Authors: Babak Rahmani, Demetri Psaltis, Damien Loterie, Georgia Konstantinou, Christophe MoserAbstract:Spatio-temporal control of femtosecond pulse-delivery through Multimode Fibers (MMF) can be used to achieve two photon photo-polymerization. Transmission-matrix method is used in linear domain to solve the scrambling effect at the fiber output and generate focused spots. However, Multimode Fibers suffer from non-linear effects at high peak intensities. The method is not effective to describe light propagation in the nonlinear regime. Here we propose a deep learning network which can learn the relationship between inputs and outputs of the MMF. We show that once the network is properly trained, it can directly calculate the inverse of the transmission matrix.
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learning to see through Multimode Fibers
arXiv: Optics, 2018Co-Authors: Navid Borhani, Eirini Kakkava, Christophe Moser, Demetri PsaltisAbstract:We use Deep Neural Networks (DNNs) to classify and reconstruct a large database of handwritten digits from the intensity of the speckle patterns that result after the images propagated through Multimode Fibers (MMF). Images transmitted through Fibers with up to 1km length were recovered. The ability of the network to recognize the input degraded with fiber length but the performance could be enhanced if the neural networks were trained to first reconstruct the image and then classify it rather than classify it directly from the speckle intensity.