This work is licensed under the Creative Commons Attribution 4.0 International License.
Lixin, L. (2022). Application of adaptive optics in ophthalmology. Photonics, 9(5), 1-11.Search in Google Scholar
Riesgo, F., Gomez, S., Alonso, E., Gutierrez, C., & Santos, J. (2021). Fully convolutional approaches for numerical approximation of turbulent phases in solar adaptive optics. Mathematics, 9(14), 1-19.Search in Google Scholar
Liu, H., Wang, H., & Ji, Y. (2019). Simultaneous all-optical channel aggregation and de-aggregation for 8QAM signal in elastic optical networking. IEEE Photonics Journal, 11(1), 1–9.Search in Google Scholar
Angelou, M., & Azodolmolky, S. (2012). Benefits of implementing a dynamic impairment-aware optical network: results of EU project DICONET. IEEE Communications Magazine, 50(8), 79–88.Search in Google Scholar
Patel, D., & Pancholi, P. (2012). Security issues and attack management in AON-a review. IEEE International Conference on Emerging Technology Trends in Electronics, Communication and Networking, pp. 1–6.Search in Google Scholar
Ebrahimzadeh, A., Rahbar, A., & Alizadeh, B. (2015). Dynamic impairment-aware provisioning based on a quadratic model in all-optical networks. IEEE Iranian Conference on Electrical Engineering, pp. 193–197.Search in Google Scholar
Shadab, M., & Piruthiviraj, P. (2014). Simulation and analysis of blocking probability using RWA algorithm for all-optical networks. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4311–4317.Search in Google Scholar
Bonani, L., & Forghani, M. (2016). An improved least cost routing approach for WDM optical network without wavelength converters. Optical Fiber Technology, 16(32), 30–35.Search in Google Scholar
Al-Tarawneh, L., Alqatawneh, A., Tahat, A., & Saraereh, O. (2020). Evolution of optical networks: from legacy networks to next-generation networks. Journal of Optical Communications.Search in Google Scholar
Tyagi, D., Chaubey, V., & Khandelwal, P. (2017). Routing and wavelength assignment in WDM network using IWD based algorithm. IEEE International Conference on Computing, Communication and Automation, pp. 1424–1429.Search in Google Scholar
Abdo, A., & Amours, C. (2020). Adaptive pre/post-compensation of cascade filters in coherent optical transponders. Future Internet, 12(2), 1–19.Search in Google Scholar
Marsden, A., Maruta, A., & Kitayama, K. (2009). Reducing the lightpath establishing time of FWM-aware dynamic RWA for wavelength-routed optical networks. Photonic Network Communications, 18(2), 183–190.Search in Google Scholar
Wang, Y., Li, C., Hu, Q., Flor, J., & Jalalitabar, M. (2021). Routing and spectrum allocation in spectrum-sliced elastic optical networks: a primal-dual framework. Electronics, 10(22), 1–22.Search in Google Scholar
Velasco, L., Jirattigalachote, A., & Monti, P. (2010). Probabilistic-based approach for fast impairment-aware RWA in all-optical networks. IEEE International Conference on Optical Fiber Communications, pp. 1–3.Search in Google Scholar
Yu, X., Ning, X., Zhu, Q., Lv, J., Zhao, Y., et al. (2021). Multi-dimensional routing, wavelength, and timeslot allocation (RWTA) in quantum key distribution optical networks (QKD-ON). Applied Sciences, 11(1), 1–18.Search in Google Scholar
Jaumard, B., & Daryalal, M. (2016). Optimizing spectrum utilization in dynamic RWA. IEEE International Conference on Optical Network Design and Modeling, Cartagena, Spain, pp. 1–6.Search in Google Scholar
Virgillito, E., Ferrari, A., Damico, A., & Curri, V. (2019). Statistical assessment of open optical networks. Photonics, 6(2), 1–16.Search in Google Scholar
Ricciardi, S., Sembroiz, D., & Palimieri, F. (2015). A hybrid load-balancing and energy-aware RWA algorithm for telecommunication networks. Computer Communications, 77(3), 85–99.Search in Google Scholar
Pavarangkoon, P., & Oki, E. (2018). A routing and wavelength assignment scheme considering full optical carrier replication in multi-carrier-distributed optical mesh networks with wavelength reuse. Optical Switching & Networking, 18(28), 23–35.Search in Google Scholar
Hsu, H., Cho, H., & Fang, S. (2017). Solving routing and wavelength assignment problem with maximum edge-disjoint paths. Journal of Industrial & Management Optimization, 13(2), 62–68.Search in Google Scholar
Muro, F., Garrich, M., Castreno, I., Zahir, S., & Marino, P. (2021). Emulating software-defined disaggregated optical networks in a containerized framework. Applied Sciences, 11(5), 1–17.Search in Google Scholar
Zhao, Y., Ji, Y., Zhang, J., Li, H., Xiong, Q., et al. (2014). Software-defined networking (SDN) controlled all optical switching networks with multi-dimensional switching architecture. Optical Fiber Technology, 20(2), 353–357.Search in Google Scholar
Zhang, S., Xue, X., Tangdiongga, E., & Calabretta, N. (2022). Low-latency optical wireless data-center networks using nanoseconds semiconductor-based wavelength selectors and arrayed waveguide grating router. Photonics, 9(3), 1–17.Search in Google Scholar
Wang, Y., Li, C., Hu, Q., Flor, J., & Jalalitabar, M. (2021). Routing and spectrum allocation in spectrum-sliced elastic optical path networks: a primal-dual framework. Electronics Journal, 10(22), 1–24.Search in Google Scholar
Yu, X., Wang, J., Zhang, K., Lv, J., Zhao, Y., et al. (2022). Brown-field migration aware routing and spectrum assignment in backbone optical networks. Applied Sciences, 12(1), 1–19.Search in Google Scholar
Chatterjee, B., Ba, S., & Oki, E. (2018). Fragmentation problems and management approach in elastic optical networks: a survey. IEEE Communications Surveys & Tutorials, 20(1), 183–210.Search in Google Scholar
Ruiz, L., Barroso, R., Miguel, I., Merayo, N., & Carlos, J. (2021). Routing, modulation and spectrum assignment algorithm using multi-path routing and best-fit. IEEE Access, 9, 1116333–111650.Search in Google Scholar
Luo, S., Dong, M., Ota, K., Wu, J., & Li, J. (2015). A security assessment mechanism for software-defined networking-based mobile networks. Sensors Journal, 15(12), 1–19.Search in Google Scholar
Chatterjee, B., Sato, T., & Oki, E. (2018). Recent research progress on spectrum management approaches in software-defined elastic optical networks. Optical Switching and Networking, 30(4), 93–104.Search in Google Scholar
Yousefi, F., Rahbar, A., & Namaad, M. (2019). Fragmentation-aware algorithms for multi-path routing and spectrum assignment in elastic optical networks. Optical Fiber Technology, 53(2), 1029–1036.Search in Google Scholar
Altarawneh, L., & Taebi, S. (2017). Minimizing Blocking Probability in Elastic Optical Networks by Varying the Bandwidth Granularity Based on Optical Path Fragmentation. Photonics, 4(20).Search in Google Scholar
Altarawneh, L., & Taebi, S. (2015). Dynamic adaptation of bandwidth granularity for multipath routing in elastic optical OFDM networks. IEEE Conference on Lasers and Electro-Optics (CLEO), San Jose, USA, pp. 1-2.Search in Google Scholar
Al-Tarawneh, L., & Taebi, S. (2015). Linear dynamic adaptation of the BW granularity allocation for elastic optical OFDM networks. International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), Chicago, USA, pp. 1-7.Search in Google Scholar
Altarawneh, L., & Taebi, S. (2015). Bandwidth granularity adaptation for multipath provisioning in elastic optical OFDM-based networks. 2015 IEEE International Conference on Electro/Information Technology (EIT), DeKalb, USA, pp. 236-240.Search in Google Scholar
Yang, F., Wang, L., & Wang, Q. (2018). Holding-time-aware spectrum allocation algorithm for elastic optical networks. Optical Fiber Technology, 41(3), 155–162.Search in Google Scholar
He, S., Qiu, Y., & Xu, J. (2020). Invalid-resource-aware spectrum assignment for advanced-reservation traffic in elastic optical network. Sensors Journal, 20(15), 1–19.Search in Google Scholar
Zhang, S., Yeung, K., & Jin, A. (2021). LBFA: a load-balanced and fragmentation-aware resource allocation algorithm in space-division multiplexing elastic optical networks. Photonics, 8(10), 1–18.Search in Google Scholar
Cao, Y., Zhang, Z., Peng, X., Wang, Y., & Qin, H. (2022). Research on orbital angular momentum multiplexing communication system based on neural network inversion phase. Electronics, 11(10), 1-18.Search in Google Scholar
Zhang, W., Man, T., Zhang, M., Zhang, L., & Wan, Y. (2022). Computational adaptive holographic fluorescence microscopy based on stochastic parallel gradient descent algorithm. Biomedical Optics Express, 13(12), 6431-6442.Search in Google Scholar
Iyer, R., Sorrells, J., Yang, L., Chaney, E., et al. (2022). Label-free metabolic and structural profiling of dynamic biological samples using multimodal optical microscopy with sensorless adaptive optics. Scientific Reports, 12(3438), 1-18.Search in Google Scholar
Kim, J., Lee, K., Lee, S., Bae, J., Kim, D., et al. (2022). Model predictive control of time-varying aberrations for sensorless adaptive optics. IEEE Transactions on Control Systems Technology, 8(1), 1-11.Search in Google Scholar
Chen, Y., Zhao, T., Peng, J., & Mao, Y. (2022). Fuzzy fraction-order stochastic parallel gradient descent approach for efficient fiber coupling. Optical Engineering, 61(1)Search in Google Scholar
Zommer, S., Ribak, E. N., Lipson, S. G., et al. (2006). Simulated annealing in ocular adaptive optics. Optics Letters, 31(7), 939-941.Search in Google Scholar
Peng, J., Qi, B., Li, H., Mao, Y. (2022). AS-SPGD algorithm to improve convergence performance for fiber coupling in free space optical communication. Optics Communications, 519(3), 1283-1297.Search in Google Scholar
Hu, S., Hu, L., Gong, W., Li, Z., Si, K. (2021). Deep learning based wavefront sensor for complex wavefront detection in adaptive optical microscopes. Frontiers of Information Technology & Electronic Engineering, 22(4), 1277-1288.Search in Google Scholar
Zhou, Z., Huang, J., Li, X., Guo, X., Chen, Z., et al. (2022). Adaptive optical microscopy via virtual-imaging-assisted wavefront sensing for high-resolution tissue imaging. PhotoniX, 3(2), 1-20.Search in Google Scholar
Pozzi, P., Soloviev, O., Wilding, D., Vdovin, G., Verhaegen, M. (2018). Optimal model-based sensorless adaptive optics for epifluorescence microscopy. Plos One, 13(3), e0194523.Search in Google Scholar
Lianghua, W., Ping, Y., Shuai, W., et al. (2018). A high-speed model-based approach for wavefront sensorless adaptive optics systems. Optics & Laser Technology, 99, 124-132.Search in Google Scholar
Mills, B., Jacob, J., Praeger, M., Eason, R., Nilsson, J., Zervas, M. (2022). Single step phase optimization for coherent beam combination using deep learning. Scientific Reports, 12(5188), 1-17.Search in Google Scholar
Lianghua, W., Ping, Y., Kangjian, Y., et al. (2017). Synchronous model-based approach for wavefront sensorless adaptive optics system. Optics Express, 25(17), 20584-20597.Search in Google Scholar
Tian, Q., Lu, C., Liu, B., Zhu, L., et al. (2019). DNN-based aberration correction in a wavefront sensorless adaptive optics system. Optics Express, 27(8), 10765-10776.Search in Google Scholar
Gutierrez, J., Santos, M., Zarzuela, M., Basden, A., Osborn, J., et al. (2017). Comparative study of neural network frameworks for the next generation of adaptive optics systems. Sensors, 17(6), 1-18.Search in Google Scholar
Gomez, S., Gutierrez, C., Riesgo, F., Rodriguez, M., et al. (2019). Convolutional neural networks approach for solar reconstruction in SCAO configurations. Sensors, 19(10), 1-23.Search in Google Scholar
Liu, Z., Peng, Q., Xu, Y., Ren, G., Ma, H. (2020). Misalignment calculation on off-axis telescope system via fully connected neural network. IEEE Photonics Journal, 12(4), 1-13.Search in Google Scholar
Paine, S., Fienup, J. (2018). Machine learning for improved image-based wavefront sensing. Optics Letters, 43(6), 1235-1238.Search in Google Scholar
Szegedy, C., Vanhoucke, V., Ioffe, S., et al. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818-2826.Search in Google Scholar
Kimiaei, M., Neumaier, A., Azmi, B. (2022). LMBOPT; a limited memory method for bound-constrained optimization. Mathematical Programming Computation, 14(1), 271-318.Search in Google Scholar
Nishizaki, Y., Valdivia, M., Horisaki, R., et al. (2019). Deep learning wavefront sensing. Optics Express, 27(1), 240-251.Search in Google Scholar
Chollet. (2017). Xception: Deep learning with depth wise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251-1258.Search in Google Scholar
Kingma, D., Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint, arXiv: 1412.6980.Search in Google Scholar
Tian, Q., Lu, C., Liu, B., et al. (2019). DNN-based aberration correction in a wavefront sensorless adaptive optics system. Optics Express, 27(8), 10765-10776.Search in Google Scholar
Siemons, M., Hanemaaijer, N., Kole, M., Kapitein, L. (2021). Robust adaptive optics for localization microscopy deep in complex tissue. Nature Communications, 12(3407), 1-13.Search in Google Scholar
Lv, Y., Yin, Z., Yu, Z. (2021). A target detection algorithm for remote sensing images based on deep learning. Contrast Media & Molecular Imaging, 3474921, 1-5.Search in Google Scholar
Huimin, M., Haiqiu, L., Yan, Q., et al. (2019). Numerical study of adaptive optics compensation based on convolutional neural networks. Optics Communications, 433, 283-289.Search in Google Scholar
Qin, S., Zhang, Y., Wang, H., Chan, W. (2020). Simple accurate model-based phase diversity phase retrieval algorithm for wavefront sensing in high-resolution optical imaging systems. IET Image Processing, 14(17), 4513-4519.Search in Google Scholar
Lin, H., He, X., Wang, S., Yang, P. (2021). Wavefront restoration technology of dynamic non-uniform intensity distribution based on extreme learning machine. Sensors, 21(11), 1-15.Search in Google Scholar
Varela, S., Zheng, X., Njuguna, J., Sacks, E., Allen, D., et al. (2022). Deep convolutional neural networks exploit high-spatial-and-temporal-resolution aerial imagery to phenotype key traits in miscanthus. Remote Sensing, 14(21), 1-18.Search in Google Scholar
Hongyang, Y., Xu, Q., Li, Q., et al. (2019). Improved machine learning approach for wavefront sensing. Sensors, 19(16), 1-19.Search in Google Scholar
Wu Yu, Guo Youming, Bao Hua, et al. (2020). Sub-millisecond phase retrieval for phase-diversity wavefront sensor. Sensors, 20(17), 1-18.Search in Google Scholar
Shin, D., Kim, J. (2022). A deep learning framework performance evaluation to use YOLO in Nvidia Jetson platform. Applied Sciences, 12(8), 1-17.Search in Google Scholar
Vera, E., Guzmán, F., Weinberger, C. (2021). Boosting the deep learning wavefront sensor for real-time applications. Applied Optics, 60(10), 119-124.Search in Google Scholar
Weinberger, C., Guzmán, F., Vera, E. (2020). Improved training for the deep learning wavefront sensor. Proceedings of Adaptive Optics Systems VII, 114484G.Search in Google Scholar
Wang, K., Zhang, M., Tang, J., et al. (2021). Deep learning wavefront sensing and aberration correction in atmospheric turbulence. PhotoniX, 2(8), 2021.Search in Google Scholar
He, K., Zhang, X., Ren, S., et al. (2016). Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition, Valencia, 770-778.Search in Google Scholar
Ming, X., Chen, X., Fang, L., Zhang, L., Li, C., et al. (2022). Ultralow complexity long short-term memory network for fiber nonlinearity mitigation in coherent optical communication systems. Journal of Lightwave Technology, 40(8), 2427-2434Search in Google Scholar
Qi, X., Guohao, J., Chunyue, Z., et al. (2019). Object-independent image-based wavefront sensing approach using phase diversity images and deep learning. Optics Express, 27(18), 26102-26119.Search in Google Scholar
Liu, X., Morris, T., Saunter, C. (2019). Using long short-term memory for wavefront prediction in adaptive optics. Proceeding of the 28th International Conference on Artificial Neural Networks, Munich, 537-542.Search in Google Scholar
Chen, Y. (2020). LSTM recurrent neural network prediction algorithm based on Zernike modal coefficients. Optik, 203, 163796.Search in Google Scholar
Swanson, R., Lamb, M., Correia, C., et al. (2018). Wavefront reconstruction and prediction with convolutional neural networks. Proceedings of Adaptive Optics Systems VI, 10703, 107031F.Search in Google Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.Search in Google Scholar
Hu, K., Xu, B., Xu, Z., et al. (2019). Self-learning control for wavefront sensorless adaptive optics system through deep reinforcement learning. Optik, 178, 785-793.Search in Google Scholar
Hu, K., Xu, Z., Yang, W., et al. (2018). Build the structure of WFSless AO system through deep reinforcement learning. IEEE Photonics Technology Letters, 30(23), 2033-2036.Search in Google Scholar
Qiu, S., Yang, Z., Ye, J., Wang, Z. (2021). On finite-time convergence of actor-critic algorithm. IEEE Journal on Selected Areas in Information Theory, 2(2), 652-664.Search in Google Scholar
Nousiainen, J., Rajani, C., Kasper, M., et al. (2021). Adaptive optics control using model-based reinforcement learning. Optics Express, 29(10), 15327-15344.Search in Google Scholar
Cantalloube, F., Farley, O., Milli, J., et al. (2020). Wind-driven halo in high-contrast images. I. Analysis of the focal-plane images of SPHERE. Astronomy and Astrophysics, 638(A98), 1-21.Search in Google Scholar
Landman, R., Haffert, S., Radhakrishnan, V., et al. (2020). Self-optimizing adaptive optics control with reinforcement learning. Proceedings of Adaptive Optics Systems VII, 1144849.Search in Google Scholar
Por, E., Haffert, S., Radhakrishnan, V., et al. (2018). High Contrast Imaging for Python (HCIPy): an open-source adaptive optics and coronagraph simulator. Proceedings of Adaptive Optics Systems VI, 1070342.Search in Google Scholar
Zhang, D., Li, Z., Jia, P., Zheng, Y., Liu, S. (2023). Optimization design and trajectory error compensation of a façade-adaptive wall-climbing robot. Symmetry, 15(2), 1-18.Search in Google Scholar
Xing, X., Chang, D. (2021). The adaptive dynamic programming toolbox. Sensors, 21(16), 1-18.Search in Google Scholar
Lasheras, F., Ordonez, C., Pardinas, J., Juez, F. (2018). Real-time tomographic reconstructor based on convolutional neural networks for solar observation. Mathematical Methods in the Applied Sciences, 43(14), 8032-8041.Search in Google Scholar
Basden, N., Bharmal, D., Jenkins, et al. (2018). The Durham adaptive optics simulation platform (DASP): current status. SoftwareX, 7, 63-69.Search in Google Scholar
Zhu, L., Wen, L., Yang, P., et al. (2019). Aberration correction based on wavefront sensorless adaptive optics in membrane diffractive optical telescope. Optics Communications, 451, 220-225.Search in Google Scholar
Marx, V. (2017). Microscopy: hello, adaptive optics. Nature Methods, 14(12), 1133-1136.Search in Google Scholar
Booth, M. (2014). Adaptive optical microscopy: the ongoing quest for a perfect image. Light: Science & Applications, 3, e165.Search in Google Scholar
Hussain, S., Kubo, T., Hall, N., et al. (2020). Wavefront-sensor less adaptive optics with a laser-free spinning disk confocal microscope. Journal of Microscopy.Search in Google Scholar
Sahu, P., Mazumder, N. (2020). Advances in adaptive optics – based two-photon fluorescence microscopy for brain imaging. Lasers in Medical Science, 35(5), 317-328.Search in Google Scholar
Qin, Z., He, S., Yang, C., et al. (2020). Adaptive optics two-photon microscopy enables near-diffraction-limited and functional retinal imaging in vivo. Light: Science & Applications, 9, 79-88.Search in Google Scholar
Jian, Y., Lee, S., Ju, M., et al. (2016). Lens-based wavefront sensorless adaptive optics swept source OCT. Scientific Reports, 6, 27620.Search in Google Scholar
Zhang, S., Wang, R., Wang, Y., Mao, H., et al. (2021). Extending the detection and correction abilities of an adaptive optics system for free-space optical communication. Optics Communications, 482(1), 1276-1289.Search in Google Scholar
Li, R., Lin, B., Liu, Y., Dong, M., Zhao, S. (2022). A survey on laser space network: terminals, links, and architectures. IEEE Access, 10, 34815-34834.Search in Google Scholar
