Browse Issues


Call For Paper is Open for Vol.6 No.2 September 2021, Please submit paper Vol. 6 No. 1 - (March 2021)

DOI

Paper Title

10.21058/gjecs.2021.61001

Performance Prediction of SiOC on Insulator based PICs

Author Name

Volume No., Issue No., Year, & Page No.

Ameet Kumar, Abi Waqas, Faisal Memon, Umair Ahmed Korai

Vol. 6, No. 1, March 2021, pp. 1-10

Abstract:

In this emerging world of Photonics Silicon oxycarbide (SiOC) is introduced as a platform that has a wide range of tunable refractive indexes that possess very low absorption coefficients. Its physical properties likewise (Optical) and chemical properties can be altered over a large scale in different applications through its composition. In this manuscript, the results obtained using waveguides and directional couplers by multiple simulations that relied on the SiOC technology. In this paper, most simplified design of the coupling coefficient in a certain defined range of width, gap as well as coupling length is proposed. The directional coupler and the waveguide building block’s mathematical models are parameterized. In our defined model, the passive devices will be exploited in available circuit simulators used commercially for the stochastic and circuit simulations scheme for SiOC based photonic circuits.

Keywords:

Photonics, SiOC, Circuit Simulation, Building Blocks, Stochastic Analysis

Full Text:

References:

  1. Avgerou, M. Smit et al., ‘An introduction to InP-based generic integration technology’, Semicond. Sci. Technol., vol. 29, no. 8, p. 083001, Jun. 2014.
  2. D. Melati et al., ‘Validation of the Building-Block-Based Approach for the Design of Photonic Integrated Circuits’, J. Light. Technol., vol. 30, no. 23, pp. 3610–3616, Dec. 2012.
  3. M. Hochberg and T. Baehr-Jones, ‘Towards fabless silicon photonics’, Nat. Photonics, vol. 4, no. 8, pp. 492–494, Aug. 2010.
  4. T. Korthorst, R. Stoffer, and A. Bakker, ‘Photonic IC design software and process design kits’, Adv. Opt. Technol., vol. 4, no. 2, Jan. 2015.
  5. X. J. M. Leijtens, P. Le Lourec, and M. K. Smit, ‘S-matrix oriented CAD-tool for simulating complex integrated optical circuits’, IEEE J. Sel. Top. Quantum Electron., vol. 2, no. 2, pp. 257–262, Jun. 1996.
  6. L. David, R. Bhandavat, U. Barrera, and G. Singh, ‘Silicon oxycarbide glass-graphene composite paper electrode for long-cycle lithium-ion batteries’, Nat. Commun., vol. 7, no. 1, p. 10998, Apr. 2016.
  7. V. S. Pradeep, M. Graczyk-Zajac, R. Riedel, and G. D. Soraru, ‘New Insights in to the Lithium Storage Mechanism in Polymer Derived SiOC Anode Materials’, Electrochim. Acta, vol. 119, pp. 78–85, Feb. 2014.
  8. A. Grill, ‘Plasma enhanced chemical vapor deposited SiCOH dielectrics: from low- k to extreme low- k
  9. H. J. Kim, Q. Shao, and Y.-H. Kim, ‘Characterization of low-dielectric-constant SiOC thin films deposited by PECVD for interlayer dielectrics of multilevel interconnection’, Surf. Coatings Technol., vol. 171, no. 1–3, pp. 39–45, Jul. 2003.
  10. M. R. Wang, Rusli, J. L. Xie, N. Babu, C. Y. Li, and K. Rakesh, ‘Study of oxygen influences on carbon doped silicon oxide low k thin films deposited by plasma enhanced chemical vapor deposition’, J. Appl. Phys., vol. 96, no. 1, pp. 829–834, Jul. 2004.
  11. V. Nikas et al., ‘The origin of white luminescence from silicon oxycarbide thin films’, Appl. Phys. Lett., vol. 104, no. 6, p. 061906, Feb. 2014.
  12. A. Karakuscu, R. Guider, L. Pavesi, and G. D. SorarÃ1, ‘White Luminescence from Solar Gel-Derived SiOC Thin Films’, J. Am. Ceram. Soc., vol. 92, no. 12, pp. 2969–2974, Dec. 2009.
  13. Y. PENG, J. ZHOU, X. ZHENG, B. ZHAO, and X. TAN, ‘STRUCTURE AND PHOTOLUMINESCENCE PROPERTIES OF SILICON OXYCARBIDE THIN FILMS DEPOSITED BY THE RF REACTIVE SPUTTERING’, Int. J. Mod. Phys. B, vol. 25, no. 22, pp. 2983–2990, Sep. 2011.
  14. Y. Ding, H. Shirai, and D. He, ‘White light emission and electrical properties of silicon oxycarbide-based metal–oxide–semiconductor diode’, Thin Solid Films, vol. 519, no. 8, pp. 2513–2515, Feb. 2011.
  15. G. Bellocchi, G. Franzò, M. Miritello, and F. Iacona, ‘White light emission from Eu-doped SiOC films’.
  16. Waqas, A., Memon, F. A., & Korai, U. A. (2020). Experimental validation of a building block of passive devices and stochastic analysis of PICs based on SiOC technology. Optics Express, 28(15), 21420-21431.
  17. A. Waqas, D. Melati, and A. Melloni, ‘Sensitivity Analysis and Uncertainty Mitigation of Photonic Integrated Circuits’, J. Light. Technol., vol. 35, no. 17, pp. 3713–3721, Sep. 2017.
  18. D. Melati, A. Waqas, and A. Melloni, ‘Stocastic photonics: Tools and approaches for the analysis and optimization of integrated circuits’, in 2017 Opto-Electronics and Communications Conference (OECC) and Photonics Global Conference (PGC), 2017, pp. 1–3.
  19. A. Waqas, D. Melati, and A. Melloni, ‘Stochastic simulation and sensitivity analysis of photonic circuit through Morris and Sobol method’, in Optical Fiber Communication Conference, 2017, p. Th2A.3.
  20. X. Chen, M. Mohamed, Z. Li, L. Shang, and A. R. Mickelson, ‘Process variation in silicon photonic devices’, Appl. Opt., vol. 52, no. 31, p. 7638, Nov. 2013.
  21. A. Waqas, D. Melati, B. S. Chowdhry, and A. Melloni, ‘Efficient Variability Analysis of Photonic Circuits by Stochastic Parametric Building Blocks’, IEEE J. Sel. Top. Quantum Electron., vol. 26, no. 2, pp. 1–8, Mar. 2020.
  22. A. Waqas, D. Melati, P. Manfredi, and A. Melloni, ‘Stochastic process design kits for photonic circuits based on polynomial chaos augmented macro-modelling’, Opt. Express, vol. 26, no. 5, p. 5894, Mar. 2018.

DOI

Paper Title

10.21058/gjecs.2021.61002

Rail Surface Faults Identification from Low Quality Image Data Using Machine Learning Algorithms

Author Name

Volume No., Issue No., Year, & Page No.

Asfar Arain, Tanweer Hussain, Sanaullah Mehran Ujjan, Bhawani Shankar Chowdhry, Tariq Rafique Memon

Vol. 6, No. 1, March 2021, pp. 11-21

Abstract:

Rail surface faults or deformities that form on railhead of the track, owe their existence to various operational and environmental factors. To ensure comfortable and safe operation of railway vehicles, on-time detection of these surface faults is necessary. It is also of paramount importance that fault types are identified because it can lead to the identification of causes. This eventually leads to development of better maintenance strategies. Automation of the rail inspection is highly desirable because it results in accurate, robust, and cost-effective condition monitoring of the railway track. Automated systems of track monitoring currently in use are highly sophisticated instrumentation systems, with high-speed cameras and equipped with state-of-the-art level hardware. In this research, a preliminary work towards developing a low-cost rail condition monitoring system is presented. A suitable action camera EKEN-H9R is used to acquire videos of track surface. This data is preprocessed and later used to train data-driven models for fault identification. A comparative analysis of multiple data-driven classification algorithms is conducted on the acquired data and research is concluded with support vector machine algorithm which was able to achieve about 96% accuracy on the fault classification task.

Keywords:

Rail Surface faults, Fault identification, Condition monitoring, Machine learning, Data-driven models, Classification, Noisy data.

Full Text:

References:

  1. A. Jamshidi et al., “A Big Data Analysis Approach for Rail Failure Risk Assessment,” Risk Anal., vol. 37, no. 8, pp. 1495–1507, 2017.
  2. Q. Y. Li, Z. D. Zhong, M. Liu, and W. W. Fang, Smart Railway Based on the Internet of Things. Elsevier Inc., 2017.
  3. S. Faghih-Roohi, S. Hajizadeh, A. Nunez, R. Babuska, and B. De Schutter, “Deep convolutional neural networks for detection of rail surface defects,” Proc. Int. Jt. Conf. Neural Networks, vol. 2016-Octob, pp. 2584–2589, 2016.
  4. S. Alahakoon, Y. Q. Sun, M. Spiryagin, and C. Cole, “Rail Flaw Detection Technologies for Safer, Reliable Transportation: A Review,” J. Dyn. Syst. Meas. Control. Trans. ASME, vol. 140, no. 2, 2018.
  5. N. Alnaimi and U. Qidwai, “IoT Based on-the-fly Visual Defect Detection in Railway Tracks,” 2020 IEEE Int. Conf. Informatics, IoT, Enabling Technol. ICIoT 2020, pp. 627–631, 2020.
  6. X. Wei, D. Wei, D. Suo, L. Jia, and Y. Li, “Multi-target defect identification for railway track line based on image processing and improved YOLOv3 model,” IEEE Access, vol. 8, pp. 61973–61988, 2020.
  7. M. Niu, K. Song, L. Huang, qi wang, Y. Yan, and Q. Meng, “Unsupervised Saliency Detection of Rail Surface Defects using Stereoscopic Images,” IEEE Trans. Ind. Informatics, vol. 3203, no. c, pp. 1–1, 2020.
  8. J. Gan, J. Wang, H. Yu, Q. Li, and Z. Shi, “Online Rail Surface Inspection Utilizing Spatial Consistency and Continuity,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 50, no. 7, pp. 2741–2751, 2020.
  9. J. Wang, Q. Li, J. Gan, H. Yu, and X. Yang, “Surface defect detection via entity sparsity pursuit with intrinsic priors,” IEEE Trans. Ind. Informatics, vol. 16, no. 1, pp. 141–150, 2020.
  10. A. A. Shah, B. S. Chowdhry, T. D. Memon, I. H. Kalwar, and J. Andrew Ware, “Real time identification of railway track surface faults using canny edge detector and 2D discrete wavelet transform,” Ann. Emerg. Technol. Comput., vol. 4, no. 2, pp. 53–60, 2020.
  11. H. Yu et al., “A Coarse-to-Fine Model for Rail Surface Defect Detection,” IEEE Trans. Instrum. Meas., vol. 68, no. 3, pp. 656–666, 2019.
  12. Y. Min, B. Xiao, J. Dang, B. Yue, and T. Cheng, “Real time detection system for rail surface defects based on machine vision,” Eurasip J. Image Video Process., vol. 2018, no. 1, pp. 1–11, 2018.
  13. L. Zhuang, L. Wang, Z. Zhang, and K. L. Tsui, “Automated vision inspection of rail surface cracks: A double-layer data-driven framework,” Transp. Res. Part C Emerg. Technol., vol. 92, no. May, pp. 258–277, 2018.
  14. Q. Li et al., “A cyber-enabled visual inspection system for rail corrugation,” Futur. Gener. Comput. Syst., vol. 79, pp. 374–382, 2018.
  15. J. Gan, Q. Li, J. Wang, and H. Yu, “A Hierarchical Extractor-Based Visual Rail Surface Inspection System,” IEEE Sens. J., vol. 17, no. 23, pp. 7935–7944, 2017.
  16. K. Ma, T. F. Y. Vicente, D. Samaras, M. Petrucci, and D. L. Magnus, “Texture classification for rail surface condition evaluation,” 2016 IEEE Winter Conf. Appl. Comput. Vision, WACV 2016, 2016.
  17. J. Gan, J. Wang, H. Yu, Q. Li, and Z. Shi, “Online Rail Surface Inspection Utilizing Spatial Consistency and Continuity,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 50, no. 7, pp. 2741–2751, 2020.
  18. Y. W. Lin, C. C. Hsieh, W. H. Huang, S. L. Hsieh, and W. H. Hung, “Railway Track Fasteners Fault Detection using Deep Learning,” 2019 IEEE Eurasia Conf. IOT, Commun. Eng. ECICE 2019, no. October, pp. 187–190, 2019.
  19. K. Shaikh, S. M. Ujjan, I. H. Kalwar, and B. S. Chowdhry, “Indirect identification of varying conicity levels of wheel tread using convolutional neural networks,” Int. J. Adv. Sci. Technol., vol. 29, no. 7, pp. 2537–2547, 2020.
  20. W. He, Y. He, B. Li, and C. Zhang, “A Naive-Bayes-Based Fault Diagnosis Approach for Analog Circuit by Using Image-Oriented Feature Extraction and Selection Technique,” IEEE Access, vol. 8, pp. 5065–5079, 2020.