Advertisement

Introduction

  • Müştak E. YalçınEmail author
  • Tuba Ayhan
  • Ramazan Yeniçeri
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

Conventional algorithmic solution for today’s engineering problems is started to digitize the sensory data and then process this raw data on a conventional computer architecture. To obtain real-time response from the algorithms, low latency is required which demands to process huge amount of input data.

References

  1. 1.
    L.O. Chua, L. Yang, Cellular neural networks: theory and applications. IEEE Trans. Circuits Syst. I 35(10), 1257–1290 (1988)MathSciNetCrossRefGoogle Scholar
  2. 2.
    T. Roska, L. Chua, The CNN universal machine—an analogic array computer. IEEE Trans. Circuits Syst. II Analog Digit. Signal Process. 40(3), 163–173 (1993)CrossRefGoogle Scholar
  3. 3.
    S. Espejo, C. Carmona, R. Dominguez-Castro, A. Rodriguez-Vazquez, CNN Universal chip in CMOS technology. Int. J. Circuit Theory Appl. 24, 93–111 (1996)CrossRefGoogle Scholar
  4. 4.
    A. Rodriguez-Vazquez, G. Linan-Cembrano, L. Carranza, E. Roca-Moreno, R. Carmona-Galan, F. Jimenez-Garrido, R. Dominguez-Castro, S. Meana, ACE16k: The third generation of mixed-signal SIMD-CNN ACE chips toward VSoCs. IEEE Trans. Circuits Syst. I Regul Pap. 51(5), 851–863 (2004)CrossRefGoogle Scholar
  5. 5.
    R. Yeniceri, M.E. Yalcin, An emulated digital wave computer core implementation, in European Conference on Circuit Theory and Design, ECCTD 2009 (2009), pp. 831–834Google Scholar
  6. 6.
    R. Yeniceri, M.E. Yalcin, Path planning on cellular nonlinear network using active wave computing technique, in Proceedings of SPIE, Bio-engineered and Bioinspired Systems IV, vol. 7365 (2009)Google Scholar
  7. 7.
    T. Ayhan, K. Muezzinoglu, M.E. Yalcin, Cellular Neural Network Based Artificial Antennal Lobe, in Proceedings of the 12th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA 2010) (2010), pp. 1–6Google Scholar
  8. 8.
    V. Kilic, R. Yeniceri, M.E. Yalcin, A new active wave computing based real time mobile robot navigation algorithm for dynamic environment, in 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA) (2010), pp. 1–6Google Scholar
  9. 9.
    T. Ayhan, Using CNN baased antennal lobe model to accelerate odor classification. ITU, Graduate School Of Science, Engineering and Technology, M.Sc. Thesis, Istanbul, October 2010Google Scholar
  10. 10.
    T. Ayhan, M.E. Yalcin, Randomly reconfigurable cellular neural network, in Proceedings ofthe 20th European Conference on Circuit Theory and Design (ECCTD11) (2011), pp. 625–628Google Scholar
  11. 11.
    T. Ayhan, R. Yeniceri, S. Ergunay, M.E. Yalcin, Hybrid processor population for odor processing, in 2012 IEEE International Symposium on Circuits and Systems (ISCAS) (2012), pp. 177–180Google Scholar
  12. 12.
    R. Yeniceri, M.E. Yalcin, A new CNN based path planning algorithm improved by the Doppler Effect, in 13th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA) (2012), pp. 1–5Google Scholar
  13. 13.
    T. Ayhan, M.E. Yalcin, An application of small-world cellular neural networks on odor classification. Int. J. Bifurc. Chaos 22(1), 1–12 (2012)CrossRefGoogle Scholar
  14. 14.
    R. Yeniceri, Implementations of novel cellular nonlinear and cellular logic networks and their applications. ITU, Graduate School Of Science, Engineering And Technology, Doctorate Thesis, Istanbul, October 2015Google Scholar

Copyright information

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Müştak E. Yalçın
    • 1
    Email author
  • Tuba Ayhan
    • 1
  • Ramazan Yeniçeri
    • 2
  1. 1.Department of Electronics and Telecommunications EngineeringIstanbul Technical UniversityIstanbulTurkey
  2. 2.Aeronautical EngineeringIstanbul Technical UniversityIstanbulTurkey

Personalised recommendations