Skip to main content

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSNONLINCIRC))

  • 286 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. L.O. Chua, L. Yang, Cellular neural networks: theory and applications. IEEE Trans. Circuits Syst. I 35(10), 1257–1290 (1988)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 831–834

    Google Scholar 

  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. 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. 1–6

    Google Scholar 

  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. 1–6

    Google Scholar 

  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 2010

    Google Scholar 

  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. 625–628

    Google Scholar 

  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. 177–180

    Google Scholar 

  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. 1–5

    Google Scholar 

  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)

    Article  Google Scholar 

  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 2015

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Müştak E. Yalçın .

Rights and permissions

Reprints and permissions

Copyright information

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

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Yalçın, M.E., Ayhan, T., Yeniçeri, R. (2020). Introduction. In: Reconfigurable Cellular Neural Networks and Their Applications. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-030-17840-6_1

Download citation

Publish with us

Policies and ethics