Synthesis of coherent optical pulses using a field-programmable gate array (FPGA)-based gradient descent phase-locking algorithm with three semiconductor lasers

Underwood, Kenneth J.; Jones, Andrew M.; Gopinath, Juliet T.

doi:10.1364/cleo_si.2015.sm3f.3

Cited by 1 publication

(1 citation statement)

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“…ML for studying chaotic nonlinear dynamics [ 162 ], self-tuning for mode-locked lasers [ 163 ], laser optimization [ 164 ], and the measurement of extremely short pulses; it should be noted that their duration is much shorter than the response times of most photodetectors [ 165 ]. Ultrashort pulses are widely used to monitor chemical reactions, control THz radiation, cipher pulses for communication, and form optical pulses [ 166 ]. ML has been used to measure time unit pulse duration using time unit detectors [ 167 ].…”

Section: Nlo Processes Analyzed With MLmentioning

confidence: 99%

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

et al. 2022

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The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

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