[1] Richardson, D. J., Fini, J. M. & Nelson, L. E. Space-division multiplexing in optical fibres. Nature Photonics 7, 354-362 (2013). doi: 10.1038/nphoton.2013.94
[2] Yan, S. Y. et al. Archon: a function programmable optical interconnect architecture for transparent intra and inter data center SDM/TDM/WDM networking. Journal of Lightwave Technology 33, 1586-1595 (2015). doi: 10.1109/JLT.2015.2392554
[3] Cao, H. et al. Controlling light propagation in multimode fibers for imaging, spectroscopy, and beyond. Advances in Optics and Photonics 15, 524-612 (2023). doi: 10.1364/AOP.484298
[4] Rothe, S. et al. Intensity-only mode decomposition on multimode fibers using a densely connected convolutional network. Journal of Lightwave Technology 39, 1672-1679 (2021). doi: 10.1109/JLT.2020.3041374
[5] Manuylovich, E. S., Dvoyrin, V. V. & Turitsyn, S. K. Fast mode decomposition in few-mode fibers. Nature Communications 11, 5507 (2020). doi: 10.1038/s41467-020-19323-6
[6] Inan, B. et al. DSP complexity of mode-division multiplexed receivers. Optics Express 20, 10859-10869 (2012).
[7] Zhou, Y. Y. et al. High-fidelity spatial mode transmission through a 1-km-long multimode fiber via vectorial time reversal. Nature Communications 12, 1866 (2021). doi: 10.1038/s41467-021-22071-w
[8] Caravaca-Aguirre, A. M. et al. Real-time resilient focusing through a bending multimode fiber. Optics Express 21, 12881-12887 (2013). doi: 10.1364/OE.21.012881
[9] Popoff, S. M. et al. Measuring the transmission matrix in optics: an approach to the study and control of light propagation in disordered media. Physical Review Letters 104, 100601 (2010). doi: 10.1103/PhysRevLett.104.100601
[10] Lyu, M. et al. Fast modal decomposition for optical fibers using digital holography. Scientific Reports 7, 6556 (2017). doi: 10.1038/s41598-017-06974-7
[11] Rothe, S. et al. Transmission matrix measurement of multimode optical fibers by mode-selective excitation using one spatial light modulator. Applied Sciences 9, 195 (2019). doi: 10.3390/app9010195
[12] Kaiser, T. et al. Complete modal decomposition for optical fibers using CGH-based correlation filters. Optics Express 17, 9347-9356 (2009). doi: 10.1364/OE.17.009347
[13] Flamm, D. et al. Mode analysis with a spatial light modulator as a correlation filter. Optics Letters 37, 2478-2480 (2012). doi: 10.1364/OL.37.002478
[14] Rothe, S. et al. Securing data in multimode fibers by exploiting mode-dependent light propagation effects. Research 6, 0065 (2023). doi: 10.34133/research.0065
[15] An, Y. et al. Learning to decompose the modes in few-mode fibers with deep convolutional neural network. Optics Express 27, 10127-10137 (2019). doi: 10.1364/OE.27.010127
[16] Rothe, S. et al. Deep learning for computational mode decomposition in optical fibers. Applied Sciences 10, 1367 (2020). doi: 10.3390/app10041367
[17] Fan, X. J. et al. Mitigating ambiguity by deep-learning-based modal decomposition method. Optics Communications 471, 125845 (2020). doi: 10.1016/j.optcom.2020.125845
[18] Brüning, R. et al. Comparative analysis of numerical methods for the mode analysis of laser beams. Applied Optics 52, 7769-7777 (2013). doi: 10.1364/AO.52.007769
[19] Choi, K. & Jun, C. Sub-sampled modal decomposition in few-mode fibers. Optics Express 29, 32670-32681 (2021). doi: 10.1364/OE.438533
[20] Zhang, Q. et al. Learning the matrix of few-mode fibers for high-fidelity spatial mode transmission. APL Photonics 7, 066104 (2022). doi: 10.1063/5.0088605
[21] Guo, K. Y. et al. [DL] a survey of FPGA-based neural network inference accelerators. ACM Transactions on Reconfigurable Technology and Systems (TRETS) 12, 2 (2019).
[22] Conkey, D. B., Caravaca-Aguirre, A. M. & Piestun, R. High-speed scattering medium characterization with application to focusing light through turbid media. Optics Express 20, 1733-1740 (2012). doi: 10.1364/OE.20.001733
[23] Radner, H. et al. Field-programmable system-on-chip-based control system for real-time distortion correction in optical imaging. IEEE Transactions on Industrial Electronics 68, 3370-3379 (2021). doi: 10.1109/TIE.2020.2979557
[24] Nauber, R., Bu¨ttner, L. & Czarske, J. Measurement uncertainty analysis of field-programmable gate-array-based, real-time signal processing for ultrasound flow imaging. Journal of Sensors and Sensor Systems 9, 227-238 (2020). doi: 10.5194/jsss-9-227-2020
[25] Snyder, A. W. & Love, J. D. Optical Waveguide Theory. (New York: Springer, 1983).
[26] An, Y. et al. Numerical mode decomposition for multimode fiber: from multi-variable optimization to deep learning. Optical Fiber Technology 52, 101960 (2019). doi: 10.1016/j.yofte.2019.101960
[27] Barbu, T. Variational image denoising approach with diffusion porous media flow. Abstract and Applied Analysis 2013, 856876 (2013).
[28] Boyat AK, Joshi BK. A review paper : noise models in digital image processing. Signal Image Process Int J 6(2): 63–75 (2015). doi: 10.5121/sipij.2015.6206
[29] Dong, X. W., Yu, Z. H. & Su, X. X. High-accuracy mode decomposition for multi-mode fibers using hybrid network with mini-datasets. Optical and Quantum Electronics 56, 1006 (2024). doi: 10.1007/s11082-024-06945-z
[30] Krizhevsky, A. , Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Proceedings of the 26th International Conference on Neural Information Processing Systems. Lake Tahoe: Curran Associates Inc. , 2012, 1097-1105.
[31] Zhang, C. et al. Optimizing FPGA-based accelerator design for deep convolutional neural networks. Proceedings of 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. Monterey: ACM, 2015, 161-170.
[32] He, K. et al. Integrating large circular kernels into CNNs through neural architecture search. (2021). at https://doi.org/10.48550/arXiv.2107.02451.
[33] Sahin, S. , Becerikli, Y. & Yazici, S. Neural network implementation in hardware using FPGAs. Proceedings of the 13th International Conference on Neural Information Processing. Hong Kong, China: Springer, 2006, 1105-1112.
[34] Zhou, Y. M. & Jiang, J. F. An FPGA-based accelerator implementation for deep convolutional neural networks. Proceedings of the 2015 4th International Conference on Computer Science and Network Technology. Harbin: IEEE, 2015, 829-832.
[35] Asuero, A. G., Sayago, A. & González, A. G. The correlation coefficient: an overview. Critical Reviews in Analytical Chemistry 36, 41-59 (2006). doi: 10.1080/10408340500526766
[36] Ma, Y. F. et al. Optimizing the convolution operation to accelerate deep neural networks on FPGA. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 26, 1354-1367 (2018). doi: 10.1109/TVLSI.2018.2815603
[37] Syed Yasir Abbas Zaidi, Muhammad Faisal Aslam, Faisal Mahmood, Bilal Ahmad, Sadia Bint Raza, How Will Artificial Intelligence (AI) Evolve Organizational Leadership? Understanding the Perspectives of Technopreneurs, Global Business and Organizational Excellence, 10.1002/joe. 22275, 44, 3, (66-83), (2024).
[38] Rodríguez-Andina, J. J., Valdés-Peña, M. D. & Moure, M. J. Advanced features and industrial applications of FPGAs—a review. IEEE Transactions on Industrial Informatics 11, 853-864 (2015). doi: 10.1109/TII.2015.2431223
[39] Kim, B., Na, J. & Jeong, Y. Convolutional neural network combined with stochastic parallel gradient descent to decompose fiber modes based on far-field measurements. Journal of Lightwave Technology 41, 5973-5982 (2023). doi: 10.1109/JLT.2023.3276366
[40] Ruan, Z. S. et al. Flexible orbital angular momentum mode switching in multimode fibre using an optical neural network chip. Light: Advanced Manufacturing 5, 296-307 (2024).
[41] Li, Z. W. et al. Self-supervised dynamic learning for long-term high-fidelity image transmission through unstabilized diffusive media. Nature Communications 15, 1498 (2024). doi: 10.1038/s41467-024-45745-7
[42] Turtaev, S. et al. High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging. Light: Science & Applications 7, 92 (2018).
[43] Murray, M. J. et al. Speckle-based strain sensing in multimode fiber. Optics Express 27, 28494-28506 (2019). doi: 10.1364/OE.27.028494
[44] Sun, J. W. et al. Quantitative phase imaging through an ultra-thin lensless fiber endoscope. Light: Science & Applications 11, 204 (2022).
[45] Du, Y. et al. Hybrid multimode-multicore fibre based holographic endoscope for deep-tissue neurophotonics. Light: Advanced Manufacturing 3, 408-416 (2022).
[46] Koukourakis, N. et al. Investigation of human organoid retina with digital holographic transmission matrix measurements. Light: Advanced Manufacturing 3, 211-225 (2022).
[47] Goodfellow, I. , Bengio, Y. & Courville, A. Deep Learning. (Cambridge: MIT Press, 2016).
[48] Qiu, J. T. et al. Going deeper with embedded FPGA platform for convolutional neural network. Proceedings of 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. Monterey: ACM, 2016, 26-35.