Skip to main content
Book cover

Computer Science On-line Conference

CSOC 2022: Artificial Intelligence Trends in Systems pp 434–448Cite as

Analyzing Embedded AIoT Devices for Deep Learning Purposes

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 502)

Abstract

In this article, we focused on the artificial intelligence of things (AIoT) embedded devices that are characterized by higher computing power. These devices are designed directly to work with the implementation of artificial intelligence - deep learning. We mentioned the importance of deep learning applications and their current use in various industry applications. They also meet their role as IoT devices. Therefore, they can be included in a separate AIoT category. In the article, we described the role of IoT in Industry 4.0 and we also focused on AIoT devices and their use in industry. The aim of the article was to analyze and verify Jetson equipment from the point of view of heat dissipation during operation time and to visually inspect printed circuit boards for the installation of these equipment, taking into account the thermal requirements for the design of electrical equipment where Jetson embedded devices will be physically installed.

Keywords

  • Deep learning
  • Resource-limited devices
  • AIoT
  • Embedded devices

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-031-09076-9_39
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-031-09076-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

References

  1. Menon, S., Shah, S.: Are SMEs Ready for industry 4.0 technologies: an exploratory study of I 4.0 technological impacts. In: 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM), pp. 203–208 (2020)

    Google Scholar 

  2. Di Vaio, A., Hassan, R., Alavoine, C.: Data intelligence and analytics: a bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness. Technol. Forecast. Soc. Chang. 174, 121201 (2022). https://doi.org/10.1016/j.techfore.2021.121201

    CrossRef  Google Scholar 

  3. Aoun, A., Ilinca, A., Ghandour, M., Ibrahim, H.: A review of Industry 4.0 characteristics and challenges, with potential improvements using blockchain technology. Comput. Ind. Eng. 162, 107746 (2021). https://doi.org/10.1016/j.cie.2021.107746

    CrossRef  Google Scholar 

  4. Karnik, N., Bora, U., Bhadri, K., et al.: A comprehensive study on current and future trends towards the characteristics and enablers of industry 4.0. J. Ind. Inf. Integr. 100294 (2021). https://doi.org/10.1016/j.jii.2021.100294

  5. Calderón, R.R., Izquierdo, R.B.: Machines for industry 4.0 in higher education. In: 2020 IEEE World Conference on Engineering Education (EDUNINE), pp. 1–4 (2020)

    Google Scholar 

  6. Powell, D., Morgan, R., Howe, G.: lean first … then digitalize: a standard approach for industry 4.0 implementation in SMEs. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds.) APMS 2021. IAICT, vol. 631, pp. 31–39. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85902-2_4

    CrossRef  Google Scholar 

  7. Dudak, J., Gaspar, G., Michalconok, G.: Extension of 1-wire measuring system SenSys. In: Proceedings of 15th International Conference MECHATRONIKA, pp. 1–4 (2012)

    Google Scholar 

  8. Dudak, J., Sladek, I., Gaspar, G.: Proposal and implementation of universal and non-volatile 1-wire module. In: 2016 17th International Conference on Mechatronics - Mechatronika (ME), pp. 1–5 (2016)

    Google Scholar 

  9. Unipi. https://www.unipi.technology/. Accessed 24 Feb 2022

  10. AIoT.concepts.e.png (Obrázok PNG, 1385×1080 bodov). https://upload.wikimedia.org/wikipedia/commons/2/23/AIoT.concepts.e.png. Accessed 23 Feb 2022

  11. Xia, M., Li, T., Xu, L., et al.: Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Trans. Mechatron. 23, 101–110 (2018). https://doi.org/10.1109/TMECH.2017.2728371

    CrossRef  Google Scholar 

  12. Yunhui, Y., Kechen, S., Zhitao, X., Xuehui, F.: The strip steel surface defects classification method based on weak classifier adaptive enhancement. In: 2011 Third International Conference on Measuring Technology and Mechatronics Automation, pp. 958–961 (2011)

    Google Scholar 

  13. Bao, Y., Qibing, Z., Min, H.: Image identification of glass defects based on non-negative matrix factorization and sparse representation classification. In: 2012 24th Chinese Control and Decision Conference (CCDC), pp. 3225–3229 (2012)

    Google Scholar 

  14. Kan, Y.C., Kalkan, H.: Automatic detection and classification of laser welding defects. In: 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–5 (2021)

    Google Scholar 

  15. Deng, Y.-S., Luo, A.-C., Dai, M.-J.: Building an automatic defect verification system using deep neural network for PCB defect classification. In: 2018 4th International Conference on Frontiers of Signal Processing (ICFSP), pp. 145–149 (2018)

    Google Scholar 

  16. Lin, B.-S., Cheng, J.-S., Liao, H.-C., et al.: Improvement of multi-lines bridge defect classification by hierarchical architecture in artificial intelligence automatic defect classification. In: 2020 International Symposium on Semiconductor Manufacturing (ISSM), pp. 1–4 (2020)

    Google Scholar 

  17. Menegazzo, J., von Wangenheim, A.: Multi-contextual and multi-aspect analysis for road surface type classification through inertial sensors and deep learning. In: 2020 X Brazilian Symposium on Computing Systems Engineering (SBESC), pp. 1–8 (2020)

    Google Scholar 

  18. Mandal, V., Uong, L., Adu-Gyamfi, Y.: Automated road crack detection using deep convolutional neural networks. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5212–5215 (2018)

    Google Scholar 

  19. Yang, Y., Luo, H., Xu, H., Wu, F.: Towards real-time traffic sign detection and classification. IEEE Trans. Intell. Transp. Syst. 17, 2022–2031 (2016). https://doi.org/10.1109/TITS.2015.2482461

    CrossRef  Google Scholar 

  20. Liu, J., Xu, L., Cao, X., et al.: Review on the architectures and applications of deep learning in agriculture. In: 2020 7th International Conference on Information Science and Control Engineering (ICISCE), pp. 1234–1240 (2020)

    Google Scholar 

  21. Mohapatra, D., Choudhury, B., Sabat, B.: An automated system for fruit gradation and aberration localisation using deep learning. In: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 6–10 (2021)

    Google Scholar 

  22. Albanese, A., Nardello, M., Brunelli, D.: Automated pest detection with DNN on the edge for precision agriculture. IEEE J. Emerg. Sel. Top. Circ. Syst. 11, 458–467 (2021). https://doi.org/10.1109/JETCAS.2021.3101740

    CrossRef  Google Scholar 

  23. NVIDIA Xavier - Description. https://developer.ridgerun.com/wiki/index.php?title=Xavier/Processors/GPU/Description. Accessed 24 Feb 2022

  24. Cortex-A57. https://developer.arm.com/Processors/Cortex-A57. Accessed 24 Feb 2022

  25. NVIDIA GM20B GPU Specs. In: TechPowerUp. https://www.techpowerup.com/gpu-specs/nvidia-gm20b.g819. Accessed 24 Feb 2022

  26. Jetson Linux. In: NVIDIA Developer (2015). https://developer.nvidia.com/embedded/linux-tegra. Accessed 24 Feb 2022

  27. NVIDIA Jetson NANO Developer Kit

    Google Scholar 

  28. Quark Carrier for NVIDIA® Jetson NanoTM. In: Connect Tech Inc. https://connecttech.com/product/quark-carrier-nvidia-jetson-nano/. Accessed 24 Feb 2022

  29. Putera, S.H.I., Dzafaruddin, S.F., Mohamad, M.: MATLAB based defect detection and classification of printed circuit board. In: 2012 Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), pp. 115–119 (2012)

    Google Scholar 

  30. Anitha, D.B., Rao, M.: A survey on defect detection in bare PCB and assembled PCB using image processing techniques. In: 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 39–43 (2017)

    Google Scholar 

  31. Doi, H., Suzuki, Y., Hara, Y., et al.: Real-time X-ray inspection of 3-D defects in circuit board patterns. In: Proceedings of IEEE International Conference on Computer Vision, pp. 575–582 (1995)

    Google Scholar 

  32. Huang, X., Zhu, S., Huang, X., et al.: Detection of plated through hole defects in printed circuit board with X-ray. In: 2015 16th International Conference on Electronic Packaging Technology (ICEPT), pp. 1296–1301 (2015)

    Google Scholar 

  33. Ma, J.-Q., Kong, F.-H., Ma, P.-J., Su, X.-H.: Detection of defects at BGA solder joints by using X-ray imaging. In: 2005 International Conference on Machine Learning and Cybernetics, vol. 8, pp. 5139–5143 (2005)

    Google Scholar 

  34. Chen, Y.: On the four types of weight functions for spatial contiguity matrix. Lett. Spat. Resour. Sci. 5, 65–72 (2012). https://doi.org/10.1007/s12076-011-0076-6

    CrossRef  Google Scholar 

Download references

Acknowledgements

This publication was supported by the project of the project Young Scientist 1360 (2021–2022): “Applying machine learning at industry environment” supported by internal grant Young Scientist at Slovak University of Technology in Bratislava.

This paper was supported under the project of Operational Programme Integrated Infrastructure: Independent research and development of technological kits based on wearable electronics products, as tools for raising hygienic standards in a society ex-posed to the virus causing the COVID-19 disease, ITMS2014+ code 313011ASK8. The project is co-funding by European Regional Development Fund.

Author information

Authors and Affiliations

  1. Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Slovak University of Technology in Bratislava, Bratislava, Slovakia

    Roman Budjac, Martin Barton & Peter Schreiber

  2. Research Centre, University of Zilina, Univerzitna 8215/1, 010 26, Zilina, Slovakia

    Martin Skovajsa

Authors
  1. Roman Budjac
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Barton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Schreiber
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martin Skovajsa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roman Budjac .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Prof. Radek Silhavy

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Budjac, R., Barton, M., Schreiber, P., Skovajsa, M. (2022). Analyzing Embedded AIoT Devices for Deep Learning Purposes. In: Silhavy, R. (eds) Artificial Intelligence Trends in Systems. CSOC 2022. Lecture Notes in Networks and Systems, vol 502. Springer, Cham. https://doi.org/10.1007/978-3-031-09076-9_39

Download citation