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Computational Intelligence in Embedded System Design: A Review

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Information and Communication Technology for Intelligent Systems

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 106))

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Abstract

A system whose elementary function is not computation but is controlled by a computational system (microprocessor, microcontroller, digital system processors, custom-made hardware with a dedicated software) embedded within it is referred to as an Embedded System (ES). These systems are used in many applications like consumer electronics, business and office equipment, communication systems, automobiles, industrial control, medical systems, etc. Over the years, ES has gone through a radical renovation from traditional single-functioned system to a novel class of Intelligent Embedded Systems (IES), which are flexible and offer an improved consumer experience. The resurgence of Computational Intelligence (CI) paradigms has led to the design of IES which use adaptive mechanisms to exhibit intelligent behavior in multifaceted and dynamic real-world environments. CI offers flexibility, independent behavior, and robustness against changing real-world environment and communication failures. However, ES designers are generally unaware of the prospective CI paradigms, challenges, and opportunities available in the literature. This gap makes association and expansion of the use of CI paradigms in ES design difficult. This paper aims to fulfill this gap and nurtures collaboration by proposing a detailed introduction to ES and their characteristics. An extensive survey of CI applications as well as enabling technologies for IES design is presented and will serve as a guide for using CI paradigms for the design of IES.

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References

  1. Altran: Intelligent Systems Pave the Way to a Smart World Sponsored. France (2013)

    Google Scholar 

  2. Elmenreich, W.: Intelligent methods for embedded systems. In: WISES (2013)

    Google Scholar 

  3. Engelbrecht, A.: Computational Intelligence: An Introduction, 2nd ed. Wiley (2007)

    Google Scholar 

  4. Eisenring, M., Thiele, L., Zitzler, E.: Conflicting criteria in embedded system design. IEEE Design Test Comput. 51–59 (2000)

    Google Scholar 

  5. Wolf, W.: Computers as Components. Elsevier Publications (2005)

    Google Scholar 

  6. Santambrogio, M.D., et al.: Enabling technologies for self-aware adaptive systems. In: IEEE Adaptive Hardware and Systems (AHS) Conference (2010)

    Google Scholar 

  7. Elmenreich, W.: Sensor fusion in time-triggered systems (2002)

    Google Scholar 

  8. Oh, S., Pedrycz, W.: Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems. Fuzzy sets Syst. (2000)

    Google Scholar 

  9. Hu, W., Liu, L., Feng, G.: Output consensus of heterogeneous linear multi-agent systems by distributed event-triggered/self-triggered strategy. IEEE Trans. Cybern. (2017)

    Google Scholar 

  10. Hallmans, D., et al.: Challenges and opportunities when introducing cloud computing into embedded systems. In: IEEE 13th International Conference on Industrial Informatics (INDIN) (2015)

    Google Scholar 

  11. Cai, H., et al.: IoT-based big data storage systems in cloud computing: Perspectives and challenges. IEEE Internet Things J. 4(1), 75–87 (2017)

    Google Scholar 

  12. Jakovljevic, M., Insaurralde, C.C., Ademaj, A.: Embedded cloud computing for critical systems. In: IEEE 33rd. Digital Avionics Systems Conference (DASC) (2014)

    Google Scholar 

  13. Li, Y., et al.: Hardware-software co-design of embedded reconfigurable architectures. In: Proceedings of the 37th Annual Design Automation Conference. ACM (2000)

    Google Scholar 

  14. Liu, T., Wen, W.: A fast and ultra-low power time-based spiking neuromorphic architecture for embedded applications. In: 18th International Symposium on Quality Electronic Design (ISQED). IEEE (2017)

    Google Scholar 

  15. Cordeiro, L.: Automated Verification and Synthesis of Embedded Systems using Machine Learning. arXiv:1702.07847 (2017)

  16. Venayagamoorthy, G.K.: A successful interdisciplinary course on computational intelligence. IEEE Comput. Intell. Mag. 4(1), 14–23 (2009)

    Article  Google Scholar 

  17. Zadeh, L.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 3(1), 28–44 (1973)

    Article  MathSciNet  Google Scholar 

  18. Yong, K., et al.: Computational complexity of general fuzzy logic control and its simplication for a loop controller. Fuzzy Sets Syst. 11(2), 215–224 (2005)

    Google Scholar 

  19. Ferrari, S., Stengel, R.F.: Smooth function approximation using neural networks. IEEE Trans. Neural Netw. 16(1), 24–38 (2005)

    Article  Google Scholar 

  20. Talukdar, J., Mehta, B.: Human action recognition system using good features and multilayer perceptron Network. In: IEEE 6th International Conference on Communication and Signal Processing, pp. 1–6 (2017)

    Google Scholar 

  21. Blickle, T., Jurgen, T., Thiel, L.: System-level synthesis using evolutionary algorithms. Des. Autom. Embed. Syst. 23–58 (1998)

    Google Scholar 

  22. Jiang, C., Zhang, H., Ren, Y., Han, Z.: Machine learning paradigms for next generation wireless communication. IEEE Wirel. Comm. 24(2), 98–105 (2017)

    Article  Google Scholar 

  23. Farahnakian, F., Daneshtalab, M., Polsila, J., Ebrahimi, M.: Q-learning based congestion-aware routing algorithm for on-chip network. In: IEEE 2nd International Conference on Networked Embedded Systems for Enterprise Applications (NESEA) (2011)

    Google Scholar 

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Correspondence to Jonti Talukdar .

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Talukdar, J., Mehta, B., Gajjar, S. (2019). Computational Intelligence in Embedded System Design: A Review. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-13-1742-2_47

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