Optimization of four-wave mixing wavelength conversion in a quantum-dot semiconductor optical amplifier based on the genetic algorithm

Hakimian, Farideh; Shayesteh, Mohammad Reza; Moslemi, Mohammad Reza

doi:10.1007/s11082-021-02763-9

Cited by 4 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extraordinarily short pulses are troublesome to explain due to the massive variety of the parameters concerned [ 180 ]. With such systems, small changes in state variables will cause changes in momentum dynamics, which is particularly necessary with ML-based algorithms [ 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 ] (Roadmap).…”

Section: Nlo Processes Analyzed With MLmentioning

confidence: 99%

See 1 more Smart Citation

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

et al. 2022

View full text Add to dashboard Cite

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.

show abstract