Skip to main content
SpringerLink
Log in

Amplitude and Frequency Modulations with Cellular Neural Networks

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Amplitude and frequency modulations are still the most popular modulation techniques in data transmission at telecommunication systems such as radio and television broadcasting, gsm etc. However, the architectures of these individual systems are totally different. In this paper, it is shown that a cellular neural network with an opposite—sign template, can behave either as an amplitude or a frequency modulator. Firstly, a brief information about these networks is given and then, the amplitude and frequency surfaces of the generated quasi-sine oscillations are sketched with respect to various values of their cloning templates. Secondly it is proved that any of these types of modulations can be performed by only varying the template components without ever changing their structure. Finally a circuit is designed, simulations are presented and performance of the proposed system is evaluated. The main contribution of this work is to show that both amplitude and frequency modulations can be realized under the same architecture with a simple technique, specifically by treating the input signals as template components.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Chua LO, Yang L (1988) Cellular neural networks: theory. IEEE Trans Circuit Syst 35:1257–1272

    Article  MATH  MathSciNet  Google Scholar 

  2. Costantini G, Casali D, Carota M (2005) Detection of moving objects in 2-D images based on a CNN algorithm and density based spatial clustering. WSEAS Trans Circuits Syst 4:440–447

    Google Scholar 

  3. Gonzales RC, Woods RE (2008) Digital image processing, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  4. Zou F, Nossek JA (1991) Stability of cellular neural networks with opposite-sign templates. IEEE Trans Circuits Syst 38:675–677

    Article  Google Scholar 

  5. Savaci FA, Vanderwalle J (1993) On the stability of cellular neural networks. IEEE Trans CAS- I: Fund Theo Apps 40:213–215

    Article  MATH  Google Scholar 

  6. Pana L, Cao J (2011) Anti-periodic solution for delayed cellular neural networks with impulsive effects. Nonlinear Anal Series B 12(6):3014–3027

    Google Scholar 

  7. Song Q, Cao J (2008) Dynamical behaviors of discrete-time fuzzy cellular neural networks with variable delays and impulses. J Franklin Inst 345(1):39–59

    Article  MATH  MathSciNet  Google Scholar 

  8. Özmen A, Tander B (2006) A numerical method for frequency determination in the astable cellular neural networks with opposte-sign templates. SİU’06 IEEE 14th Signal Processing and Communication Application Symposium, Antalya, Turkey

  9. Tander B, Ün M (2000) Generalized PSPICE and SIMULINK models for the continious-time simulations of cellular neural networks. BCSP’00, 1st IEEE Balkan Conference on Signal Processing, Communications, Circuits and Systems, Istanbul, Turkey

  10. Oppenheim AV, Willsky AS, Nawab H (1997) Signals and systems, 2nd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  11. Tander B, Özmen A (2012) Amplitude and frequency modulation behaviours of cellular neural networks. INISTA’2012: The 6th International Symposium on Innovations in Intelligent Systems and Applications, Trabzon, Turkey

  12. Tander B, Baskan M, Senol C (2002) Experimental analysis of transients in small scaled cellular neural networks. ELECO’02: 4th Electrical, Electronics, Computer Engineering Symposium, Bursa, Turkey

  13. Tander B, Ün M (2000) Diode Bridge Clipper as an activation function circuit at the PSPICE simulations of cellular neural networks. ELECO’00: 2nd Electrical, Electronics, Computer Engineering Symposium, Bursa, Turkey

  14. Tander B, Özmen A, Özcelep Y, Design and implementation of a cellular neural network based oscillator circuit. CSECS’2009 The 8th WSEAS International Conference on Recent Advances in Circuits, Systems, Electronics, Control and Signal Processing, Tenerife, Spain

  15. Tander B, Özmen A, Özcelep Y (2010) Design and implementation of a negative feedback oscillator circuit based on a cellular neural network with an opposite sign template. WSEAS Trans Circuits Syst 9:60–69

    Google Scholar 

  16. FCC: Federal Communications Commission Ofice of Engineering and Technology Policy and Rules Division, FCC Online Table of Frequency Allocations, 47 C.F.R. 2.106, 2010

Download references

Author information

Authors and Affiliations

  1. Kadir Has Vocational School, Kadir Has University, 34590 , Silivri, Istanbul, Turkey

    Baran Tander

  2. Faculty of Engineering and Natural Sciences, Kadir Has University, 34230-01 , Fatih, Istanbul, Turkey

    Atilla Özmen

Authors
  1. Baran Tander
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Atilla Özmen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Atilla Özmen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tander, B., Özmen, A. Amplitude and Frequency Modulations with Cellular Neural Networks. Neural Process Lett 41, 259–272 (2015). https://doi.org/10.1007/s11063-014-9344-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-014-9344-y

Keywords

Search

Navigation