Yuri Lu,∗ Menghan Hu,∗,∗∗ Guangtao Zhai,∗∗ and Simon X. Yang∗∗∗


  1. [1] K.S. Younis, W. Ayyad, and A. Al-Ajlony, Embedded system implementation for material recognition using deep learning, 2017 IEEE Jordan Conf. on Applied Electrical Engineering and Computing Technologies (AEECT), Aqaba, Jordan, 2017, 1–6.
  2. [2] S. Baglio, L. Cantelli, F. Giusa, and G. Muscato, Intelligent prodder: Implementation of measurement methodologies for material recognition and classification with humanitarian demining applications, IEEE Transactions on Instrumentation and Measurement, 4(8), 2015, 2217–2226.
  3. [3] M. Brandao, Y.M. Shiguematsu, K. Hashimoto, and A. Takanishi, Material recognition CNNS and hierarchical planning for biped robot locomotion on slippery terrain, 2016 IEEE-RAS 16th Int. Conf. on Humanoid Robots (Humanoids), Cancun, Mexico, 2016, 81–88.
  4. [4] J. DeGol, M. Golparvar-Fard, and D. Hoiem, Geometryinformed material recognition, Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, Nevada, 2016, 1554–1562.
  5. [5] C. Zhao, L. Sun, and R. Stolkin, A fully end-to-end deep learning approach for real-time simultaneous 3d reconstruction and material recognition, 2017 18th Int. Conf. on Advanced Robotics (ICAR), Hong Kong, China, 2017, 75–82.
  6. [6] Z. Xu, Q. Wang, D. Li, M. Hu, N. Yao, and G. Zhai, Estimating departure time using thermal camera and heat traces tracking technique, Sensors, 20(3), 2020, 782.
  7. [7] P.S. Vemulapalli, A.K. Rachuri, H. Patel, and K.P. Upla, Multiobject detection in night time, Asian Journal For Convergence in Technology, 5(3), 2020, 1–7.
  8. [8] M. Hu and Q. Li, An efficient model transfer approach to suppress biological variation in elastic modulus and firmness regression models using hyperspectral data, Infrared Physics & Technology, 99, 2019, 140–151.
  9. [9] L. Yan, L. Pang, H. Wang, and J. Xiao, Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics, Journal of Food Process Engineering, 43, 2020, e13378.
  10. [10] E. Bonah, X. Huang, R. Yi, J. H. Aheto, and S. Yu, VisNIR hyperspectral imaging for the classification of bacterial foodborne pathogens based on pixel-wise analysis and a novel cars-pso-svm model, Infrared Physics & Technology, 105, 2020, 103220.
  11. [11] A. Zdunek, A. Adamiak, P.M. Pieczywek, and A. Kurenda, The biospeckle method for the investigation of agricultural crops: A review, Optics and Lasers in Engineering, 52, 2014, 276–285.
  12. [12] N. Ozana, I. Margalith, Y. Beiderman, et al., Demonstration of a remote optical measurement configuration that correlates with breathing, heart rate, pulse pressure, blood coagulation, and blood oxygenation, Proceedings of the IEEE, 103(2), 2015, 248–262.
  13. [13] B. Mandracchia, J. Palpacuer, F. Nazzaro, et al., Biospeckle decorrelation quantifies the performance of alginateencapsulated probiotic bacteria, IEEE Journal of Selected Topics in Quantum Electronics, 25(1), 2018, 1–6.
  14. [14] Y. Zhang, H.C. Koydemir, M.M. Shimogawa, et al., Motilitybased label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning, Light: Science & Applications, 7(1), 2018, 1–18.
  15. [15] R. Pandiselvam, V. Mayookha, A. Kothakota, S. Ramesh, R. Thirumdas, and P. Juvvi, Biospeckle laser technique–a novel non-destructive approach for food quality and safety detection, Trends in Food Science & Technology, 97, 2020, 1–13.
  16. [16] M. Kim, D.-G. Nam, P. Im, J.-S. Choe, and A.-J. Choi, Optimal conditions for the encapsulation of weissella cibaria jw15 using alginate and chicory root and evaluation of capsule stability in a simulated gastrointestinal system, Journal of Food Science, 85(2), 2020, 394–403.
  17. [17] S. Muench, M. Roellig, U. Cikalova, B. Bendjus, L. Chen, and S. Sudip, A laser speckle photometry based non-destructive method for measuring stress conditions in direct-copper-bonded ceramics for power electronic application, 2017 18th Int. Conf. on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), Dresden, Germany, 2017, 1–8.
  18. [18] L. Chen, U. Cikalova, S. Muench, M. Roellig, and B. Bendjus, Stress characterization of ceramic substrates by laser speckle photometry, 2019 42nd Int. Spring Seminar on Electronics Technology (ISSE), Wroclaw, Poland, 2019, 1–6.
  19. [19] A.L. Dai Pra, L.I. Passoni, and H. Rabal, Evaluation of laser dynamic speckle signals applying granular computing, Signal Processing, 89(3), 2009, 266–274.
  20. [20] P.D. Minz and A. Nirala, Intensity based algorithms for biospeckle analysis, Optik, 125(14), 2014, 3633–3636.
  21. [21] L. Mart´ı-L´opez, H. Cabrera, R. Mart´ınez-Celorio, and R. González-Peña, Temporal difference method for processing dynamic speckle patterns, Optics Communications, 283(24), 2010, 4972–4977.
  22. [22] N. Budini, C. Mulone, F. Vincitorio, C. Freyre, A. L´opez, and A. Ramil, Two simple methods for overall determination of mobility in dynamic speckle patterns, Optik, 124(24), 2013, 6565–6569.
  23. [23] Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature, 521(7553), 2015, 436–444.
  24. [24] L. Deng and D. Yu, Deep learning: methods and applications, Foundations and Trends r in Signal Processing, 7(3–4), 2014, 197–387.
  25. [25] A. Singla, L. Yuan, and T. Ebrahimi, Food/non-food image classification and food categorization using pre-trained GoogLeNet model, Proc. of the 2nd Int. Workshop on Multimedia Assisted Dietary Management, Amsterdam, Netherlands, 2016, 3–11.
  26. [26] M. Al-Qizwini, I. Barjasteh, H. Al-Qassab, and H. Radha, Deep learning algorithm for autonomous driving using GoogLeNet, 2017 IEEE Intelligent Vehicles Sympos. (IV), Los Angeles, California USA, 2017, 89–96.
  27. [27] R. Lan, L. Sun, Z. Liu, et al., Cascading and enhanced residual networks for accurate single-image super-resolution, IEEE Transactions on Cybernetics, 2020, 1–11. DOI: 10.1109/TCYB.2019.2952710.
  28. [28] L. Liu, W. Ouyang, X. Wang, et al., Deep learning for generic object detection: A survey, International Journal of Computer Vision, 128(2), 2020, 261–318.
  29. [29] J.D. Briers and S. Webster, Laser speckle contrast analysis (lasca): a nonscanning, full-field technique for monitoring capillary blood flow, Journal of Biomedical Optics, 1(2), 1996, 174–180.
  30. [30] T.S. Lee, Image representation using 2d Gabor wavelets, IEEE Transactions on pattern analysis and machine intelligence, 18(10), 1996, 959–971. 262
  31. [31] C. Szegedy, W. Liu, Y. Jia, et al., Going deeper with convolutions, Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Boston, Massachusetts USA, 2015, 1–9.
  32. [32] M. Lin, Q. Chen, and S. Yan, Network in network, arXiv:1312.4400, 2013.
  33. [33] S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv:1502.03167, 2015.
  34. [34] C. Szegedy, S. Ioffe, V. Vanhoucke, and A.A. Alemi, Inceptionv4, inception-resnet and the impact of residual connections on learning, Thirty-first AAAI Conf. on Artificial Intelligence, San Francisco, California USA, 2017.
  35. [35] Z. Li, H. Yan, H. Zhang, X. Zhan, and C. Huang, Improved inequality-based functions approach for stability analysis of time delay system, Automatica, 108, 2019, 108416.
  36. [36] Z. Li, H. Yan, H. Zhang, J. Sun, and H.-K. Lam, Stability and stabilization with additive freedom for delayed Takagi– Sugeno fuzzy systems by intermediary-polynomial-based functions, IEEE Transactions on Fuzzy Systems, 28(4), 2019, 692–705.
  37. [37] M. Hu, Y. Chen, G. Zhai, Z. Gao, and L. Fan, An overview of assistive devices for blind and visually impaired people, International Journal of Robotics and Automation, 34(5), 2019, 580–598.

Important Links:

Go Back