Cite

1. A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. ˇ Z´ıdek, A. W. R. Nelson, A. Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, P. Kohli, D. T. Jones, D. Silver, K. Kavukcuoglu, and D. Hassabis, Improved protein structure prediction using potentials from deep learning, Nature, vol. 577, pp. 706–710, Jan 2020.10.1038/s41586-019-1923-731942072 Search in Google Scholar

2. G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborová, Machine learning and the physical sciences, Rev. Mod. Phys., vol. 91, p. 045002, Dec 2019.10.1103/RevModPhys.91.045002 Search in Google Scholar

3. Darmatasia and M. I. Fanany, Handwriting recognition on form document using convolutional neural network and support vector machines (cnn-svm), 2017 5th International Conference on Information and Communication Technology (ICoIC7), pp. 1–6, 2017.10.1109/ICoICT.2017.8074699 Search in Google Scholar

4. N. H. Tandel, H. B. Prajapati, and V. K. Dabhi, Voice recognition and voice comparison using machine learning techniques: A survey, 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 459–465, 2020.10.1109/ICACCS48705.2020.9074184 Search in Google Scholar

5. S. Ahlawat, A. Choudhary, A. Nayyar, S. Singh, and B. Yoon, Improved handwritten digit recognition using convolutional neural networks (cnn), Sensors, vol. 20, no. 12, 2020.10.3390/s20123344734960332545702 Search in Google Scholar

6. K. Han, D. Yu, and I. Tashev, Speech emotion recognition using deep neural network and extreme learning machine, in Interspeech 2014, September 2014.10.21437/Interspeech.2014-57 Search in Google Scholar

7. P. Hadikhani, N. Borhani, S. Hashemi, and D. Psaltis, Learning from droplet flows in microfluidic channels using deep neural networks, Scientific Reports, vol. 9, p. 8114, 2019.10.1038/s41598-019-44556-x654461131148559 Search in Google Scholar

8. Y. Mahdi and K. Daoud, Microdroplet size prediction in microfluidic systems via artificial neural network modeling for water-in-oil emulsion formulation, Journal of Dispersion Science and Technology, vol. 38, no. 10, pp. 1501–1508, 2017.10.1080/01932691.2016.1257391 Search in Google Scholar

9. J. W. Khor, N. Jean, E. S. Luxenberg, S. Ermon, and S. K. Y. Tang, Using machine learning to discover shape descriptors for predicting emulsion stability in a microfluidic channel, Soft Matter, vol. 15, pp. 1361–1372, 2019.10.1039/C8SM02054J Search in Google Scholar

10. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org. Search in Google Scholar

11. T. Osman, S. S. Psyche, J. M. Shafi Ferdous, and H. U. Zaman, Intelligent traffic management system for cross section of roads using computer vision, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), pp. 1–7, 2017.10.1109/CCWC.2017.7868350 Search in Google Scholar

12. A. Montessori, A. Tiribocchi, M. Bogdan, F. Bonaccorso, M. Lauricella, J. Guzowski, and S. Succi, Translocation dynamics of high-internal phase double emulsions in narrow channels, Langmuir, vol. 37, pp. 9026–9033, Aug 2021.10.1021/acs.langmuir.1c01026 Search in Google Scholar

13. A. Montessori, M. L. Rocca, P. Prestininzi, A. Tiribocchi, and S. Succi, Deformation and breakup dynamics of droplets within a tapered channel, Physics of Fluids, vol. 33, no. 8, p. 082008, 2021.10.1063/5.0057501 Search in Google Scholar

14. M. Bogdan, A. Montessori, A. Tiribocchi, F. Bonaccorso, M. Lauricella, L. Jurkiewicz, S. Succi, and J. Guzowski, Stochastic jetting and dripping in confined soft granular flows, Phys. Rev. Lett., vol. 128, p. 128001, Mar 2022.10.1103/PhysRevLett.128.128001 Search in Google Scholar

15. M. Costantini, C. Colosi, J. Guzowski, A. Barbetta, J. Jaroszewicz, W. Swieszkowski, M. Dentini, and P. Garstecki, Highly ordered and tunable polyhipes by using microfluidics, J. Mater. Chem. B, vol. 2, pp. 2290–2300, 2014.10.1039/c3tb21227k Search in Google Scholar

16. Durve, Mihir, Bonaccorso, Fabio, Montessori, Andrea, Lauricella, Marco, Tiribocchi, Adriano, and Succi, Sauro, Tracking droplets in soft granular flows with deep learning techniques, Eur. Phys. J. Plus, vol. 136, no. 8, p. 864, 2021.10.1140/epjp/s13360-021-01849-3838011734458055 Search in Google Scholar

17. A. S. Utada, E. L. Lorenceau, D. R. Link, P. D. Kaplan, H. A. Stone, and D. A. Weitz, Monodisperse double emulsions generated from a microcapillary device, Science, vol. 308, pp. 537–541, 2005.10.1126/science.110916415845850 Search in Google Scholar

18. A. Montessori, P. Prestininzi, M. La Rocca, and S. Succi, Lattice boltzmann approach for complex nonequilibrium flows, Physical Review E, vol. 92, no. 4, p. 043308, 2015.10.1103/PhysRevE.92.043308 Search in Google Scholar

19. C. Coreixas, B. Chopard, and J. Latt, Comprehensive comparison of collision models in the lattice boltzmann framework: Theoretical investigations, Physical Review E, vol. 100, no. 3, p. 033305, 2019.10.1103/PhysRevE.100.033305 Search in Google Scholar

20. S. Succi, The lattice boltzmann equation: For complex states of flowing matter, Oxford University Press, 2018.10.1093/oso/9780199592357.001.0001 Search in Google Scholar

21. M. Durve, F. Bonaccorso, A. Montessori, M. Lauricella, A. Tiribocchi, and S. Succi, A fast and efficient deep learning procedure for tracking droplet motion in dense microfluidic emulsions, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 379, no. 2208, p. 20200400, 2021.10.1098/rsta.2020.0400 Search in Google Scholar

22. The pascal visual object classes homepage. http://host.robots.ox.ac.uk/pascal/VOC/. Search in Google Scholar

23. Coco dataset homepage. http://cocodataset.org. Search in Google Scholar

24. F. Zhou, H. Zhao, and Z. Nie, Safety helmet detection based on yolov5, in 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), pp. 6–11, 2021.10.1109/ICPECA51329.2021.9362711 Search in Google Scholar

25. L. C. M. Junior and J. Alfredo C. Ulson, Real time weed detection using computer vision and deep learning, in 2021 14th IEEE International Conference on Industry Applications (INDUSCON), pp. 1131–1137, 2021.10.1109/INDUSCON51756.2021.9529761 Search in Google Scholar

26. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: Unified, real-time object detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, 2016.10.1109/CVPR.2016.91 Search in Google Scholar

27. J. Redmon and A. Farhadi, Yolov3: An incremental improvement, ArXiv:1804.02767v1, 2018. Search in Google Scholar

28. N. Wojke, A. Bewley, and D. Paulus, Simple online and realtime tracking with a deep association metric, 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649, 2017.10.1109/ICIP.2017.8296962 Search in Google Scholar

29. A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, Simple online and realtime tracking, in 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468, 2016.10.1109/ICIP.2016.7533003 Search in Google Scholar

30. H. W. Kuhn, The hungarian method for the assignment problem, Naval Research Logistics Quarterly, vol. 2, no. 1-2, pp. 83–97, 1955.10.1002/nav.3800020109 Search in Google Scholar

31. R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol. 82, pp. 35–45, 03 1960.10.1115/1.3662552 Search in Google Scholar

32. A. Cavagna, L. Del Castello, I. Giardina, T. Grigera, A. Jelic, S. Melillo, T. Mora, L. Parisi, E. Silvestri, M. Viale, and A. M. Walczak, Flocking and turning: a new model for self-organized collective motion, Journal of Statistical Physics, vol. 158, pp. 601–627, Feb 2015.10.1007/s10955-014-1119-3 Search in Google Scholar

33. M. Ballerini, N. Cabibbo, R. Candelier, A. Cavagna, E. Cisbani, I. Giardina, V. Lecomte, A. Orlandi, G. Parisi, A. Procaccini, M. Viale, and V. Zdravkovic, Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study, Proceedings of the National Academy of Sciences, vol. 105, no. 4, pp. 1232–1237, 2008.10.1073/pnas.0711437105 Search in Google Scholar

34. T. Vicsek, A. Czir´ok, E. Ben-Jacob, I. Cohen, and O. Shochet, Novel type of phase transition in a system of self-driven particles, Phys. Rev. Lett., vol. 75, pp. 1226–1229, Aug 1995.10.1103/PhysRevLett.75.1226 Search in Google Scholar

35. I. D. COUZIN, J. KRAUSE, R. JAMES, G. D. RUXTON, and N. R. FRANKS, Collective memory and spatial sorting in animal groups, Journal of Theoretical Biology, vol. 218, no. 1, pp. 1–11, 2002.10.1006/jtbi.2002.306512297066 Search in Google Scholar

36. L. Barberis and F. Peruani, Large-scale patterns in a minimal cognitive flocking model: Incidental leaders, nematic patterns, and aggregates, Phys. Rev. Lett., vol. 117, p. 248001, Dec 2016.10.1103/PhysRevLett.117.248001 Search in Google Scholar

37. M. Durve, A. Tiribocchi, F. Bonaccorso, A. Montessori, M. Lauricella, M. Bogdan, J. Guzowski, and S. Succi, Droptrack - automatic droplet tracking with yolov5 and deepsort for microfluidic applications, Physics of Fluids, vol. 34, no. 8, p. 082003, 2022.10.1063/5.0097597 Search in Google Scholar

eISSN:
2038-0909
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Mathematics, Numerical and Computational Mathematics, Applied Mathematics