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Regression Model of Frame Rate Processing Performance for Embedded Systems Devices

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Applications of Machine Learning

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

In this paper, regression model for estimation of the multimedia device performance has been established. The model takes into consideration peculiarities of the processing pipeline organized on the device. In this case, system works in the single-threaded mode, and therefore, all stages of processing are performed sequentially. We define all the stages for data processing within embedded system based on the system-on-chip circuit. According to the analysis of the process, we identified that the conventional unit of display area can be selected as independent variable in the model. The attained experimental results show that the proposed model is feasible for performance assessment and can be used in practical implementation to observe the frame rate parameter of the system and thus develop programming convention to follow during functionality implementation. The main peculiarity of the current investigation is that device processes multimedia data using its own resources without external assist from other devices.

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References

  1. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660. https://doi.org/10.1016/j.future.2013.01.010

    Article  Google Scholar 

  2. Miraz MH, Ali M, Excell PS, Picking R (2015) A review on internet of things (IoT), internet of everything (IoE) and internet of nano things (IoNT). In: 2015 internet technologies and applications (ITA), IEEE, pp 219–224. https://doi.org/10.1109/itecha.2015.7317398

  3. Al-Turijan F, Alturijan S (2018) 5G/IoT-enabled UAVs for multimedia delivery in industry-oriented applications. Multimedia Tools Appl 1–22. https://doi.org/10.1007/s11042-018-6288-7

  4. Alvi SA, Aflaz B, Shah GA, Atzori L, Mahmood W (2015) Internet of multimedia things: vision and challenges. Ad Hoc Netw 33:87–111. https://doi.org/10.1016/j.adhoc.2015.04.006

    Article  Google Scholar 

  5. Conti F, Schilling R, Schiavone PD, Pullini A, Rossi D, Gurkaynak FK, Muehlberghuber M, Gautschi M, Loi I, Hauhou G, Mangard S, Benini L (2017) An IoT endpoint system-on-chip for secure and energy-efficient near-sensor analytics. IEEE Trans Circ Syst I Regul Pap 64(9):2481–2494. https://doi.org/10.1109/TCSI.2017.2698019

    Article  Google Scholar 

  6. Chianese A, Piccialli F (2014) Designing a smart museum: when cultural heritage joins IoT. In: 2014 eighth international conference on next generation mobile apps, services and technologies, IEEE, pp 300–306. https://doi.org/10.1109/ngmast.2014.21

  7. Sivanathan A, Sherratt D, Gharakheili HH, Radford A, Wijenayke C, Vishwanath A, Sivarman V (2017) Characterizing and classifying IoT traffic in smart cities and campuses. In: 2017 IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, pp 559–564. https://doi.org/10.1109/infcomw.2017.8116438

  8. Wan J, Al-awlaqi M, Li M, O’Grady M, Gu X, Wang J, Cao N (2018) Wearable IoT enabled real-time health monitoring system. EURASIP J Wirel Commun Netw. https://doi.org/10.1186/s13638-018-1308-x

    Article  Google Scholar 

  9. Patel S, Park H, Bonato P, Chan L, Rodgers M (2012) A review of wearable sensors and systems with application in rehabilitation. J NeuroEng Rehabil 9:21. https://doi.org/10.1186/1743-0003-9-21

    Article  Google Scholar 

  10. Albagory Y, Nofal M, Al Raddadym F (2018) IoT-RTP and IoT-RTCP: adaptive protocols for multimedia transmission over internet of things environments. IEEE Access 5:16757–16773. https://doi.org/10.1109/ACCESS.2017.2726902

    Article  Google Scholar 

  11. NanoPi Neo Air. https://www.friendlyarm.com/index.php?route=product/product&product_id=151. Last accessed 2019/08/03

  12. Yang Y, Sun L, Guo C (2018) Aero-material consumption prediction based on linear regression model. Procedia Comput Sci 131:825–831. https://doi.org/10.1016/j.procs.2018.04.271

    Article  Google Scholar 

  13. Permai SD, Tanty H (2018) Linear regression model using bayesian approach for energy performance of residential building. Procedia Comput Sci 135:671–677. https://doi.org/10.1016/j.procs.2018.08.219

    Article  Google Scholar 

  14. Austin PC, Steyerberg EW (2015) The number of subjects per variable required in linear regression analyses. J Clin Epidemiol 68(6):627–636. https://doi.org/10.1016/j.jclinepi.2014.12.014

    Article  Google Scholar 

  15. Ibrahim S, Daut I, Irwan YM, Irwanto M, Gomesh N, Farhana Z (2012) Linear regression model in estimating solar radiation in Perlis. Energy Procedia 18:1402–1412. https://doi.org/10.1016/j.egypro.2012.05.156

    Article  Google Scholar 

  16. Shreiner D, Sellers G, Kessenich J, Licea-Kane B (2013) OpenGL programming guide: the official guide to learning OpenGL, Version 4.3. Addison-Wesley

    Google Scholar 

  17. LWJGL—Lightweight Java Game Library. https://www.lwjgl.org/. Last accessed 2019/08/05

  18. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Petro Mohyla Black Sea National University, 10. 68 Desantnykiv str., Mykolaiv, Ukraine

    Yaroslav Krainyk

Authors
  1. Yaroslav Krainyk
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Correspondence to Yaroslav Krainyk .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

    Prashant Johri

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Haryana, Gurugram (Manesar), Haryana, India

    Jitendra Kumar Verma

  3. Department of Biomedical Engineering, North-Eastern Hill University, Shillong, Meghalaya, India

    Sudip Paul

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Krainyk, Y. (2020). Regression Model of Frame Rate Processing Performance for Embedded Systems Devices. In: Johri, P., Verma, J., Paul, S. (eds) Applications of Machine Learning. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3357-0_17

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  • DOI: https://doi.org/10.1007/978-981-15-3357-0_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3356-3

  • Online ISBN: 978-981-15-3357-0

  • eBook Packages: EngineeringEngineering (R0)

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