@ARTICLE{Kumar_Sunil_Adaptive_2022,
 author={Kumar, Sunil and Sengupta, Somnath},
 volume={30},
 number={4},
 journal={Opto-Electronics Review},
 pages={e144227},
 howpublished={online},
 year={2022},
 publisher={Polish Academy of Sciences (under the auspices of the Committee on Electronics and Telecommunication) and Association of Polish Electrical Engineers in cooperation with Military University of Technology},
 abstract={An adaptive and precise peak wavelength detection algorithm for fibre Bragg grating using generative adversarial network is proposed. The algorithm consists of generative model and discriminative model. The generative model generates a synthetic signal and is sampled for training using a deep neural network. The discriminative model predicts the real fibre Bragg grating signal by the calculation of the loss functions. The maxima of loss function of the discriminative signal and the minima of loss function of the generative signal are matched and the desired peak wavelength of fibre Bragg grating is determined. The proposed algorithm is verified theoretically and experimentally for a single fibre Bragg grating peak. The accuracy has been obtained as ±0.2 pm. The proposed algorithm is adaptive in the sense that any random fibre Bragg grating peak can be identified within a short wavelength range.},
 type={Article},
 title={Adaptive and precise peak detection algorithm for fibre Bragg grating using generative adversarial network},
 URL={http://journals.pan.pl/Content/125569/PDF/OPELRE_2022_30_4_S_Kumar.pdf},
 doi={10.24425/opelre.2022.144227},
 keywords={fibre Bragg grating, generative model, discriminative model, loss function},
}