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Blind Signal Processing for Medical Sensing Systems with Optical-Fiber Signal Transmission

  • Kim, Namyong (Division of Electronics, Information & Communication Engineering, Kangwon National University) ;
  • Byun, Hyung-Gi (Division of Electronics, Information & Communication Engineering, Kangwon National University)
  • Received : 2013.11.28
  • Accepted : 2014.01.07
  • Published : 2014.01.29

Abstract

In many medical image devices, dc noise often prevents normal diagnosis. In wireless capsule endoscopy systems, multipath fading through indoor wireless links induces inter-symbol interference (ISI) and indoor electric devices generate impulsive noise in the received signal. Moreover, dc noise, ISI, and impulsive noise are also found in optical fiber communication that can be used in remote medical diagnosis. In this paper, a blind signal processing method based on the biased probability density functions of constant modulus error that is robust to those problems that can cause error propagation in decision feedback (DF) methods is presented. Based on this property of robustness to error propagation, a DF version of the method is proposed. In the simulation for the impulse response of optical fiber channels having slowly varying dc noise and impulsive noise, the proposed DF method yields a performance enhancement of approximately 10 dB in mean squared error over its linear counterpart.

Keywords

References

  1. D. Arnold, "A general noise model and its effects on evolution strategy performance", IEEE Trans. Evol. Comput., vol. 10, no. 4, pp. 380-391, 2006. https://doi.org/10.1109/TEVC.2005.859467
  2. E. Kappenman and S. Luck, "The effects of electrode impedance on data quality and statistical significance in ERP recordings", Psychophysiology, vol. 47, pp. 888-904, 2010.
  3. R. Ozawa and K. Iketani, "Electronic endoscope system for reducing random noise of a video signal", U.S. Patent 6900829 B1, May 31, 2005.
  4. P. Chandra, "Measurements of radio impulsive noise from various sources in an indoor environment at 900 MHz and 1800 MHz", The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 2, pp. 639-643, 2002.
  5. K. Blackard, T. Rappaport, and C. Bostian, "Measurements and models of radio frequency impulsive noise for indoor wireless communications", IEEE J. Sel. Areas Commun., vol. 11, pp. 991-1001, 1993. https://doi.org/10.1109/49.233212
  6. S. Miaou, S. Jeng, S. Tsung, C. Hsiao, and T. Lin, "Transmitting capsule endoscope images with wireless LAN and smart antenna systems", Biomed. Eng.-Appl. Basis Commun., vol. 18, issue 5, pp. 246-254, 2006. https://doi.org/10.4015/S1016237206000385
  7. T. Hasan-Al-Mahmud, M. Rahman, and S. Debnath, "Performance analysis of best suited adaptive equalization algorithm for optical communication", Journal of Telecommunications, vol. 1, no. 2, pp. 35-41, 2010.
  8. J. Fickers, A. Ghazisaeidi, M. Salsi, G. Charlet, P. Emplit, and F. Horlin, "Decision-feedback equalization of bandwidth constrained N-WDM coherent optical communication sytems", J. Lightwave Technol., vol. 31, pp. 1529-1537, 2013.
  9. L. Garth, "A dynamic convergence analysis of blind equalization algorithms", IEEE Trans. Commun., vol. 49, pp. 624-634, 2001. https://doi.org/10.1109/26.917769
  10. J. Principe, D. Xu, and J. Fisher, Information theoretic learning in: S. Haykin, Unsupervised adaptive filtering, Wiley, (New York, USA), pp. 265-319, 2000.
  11. I. Santamaria, D. Erdogmus, and J. Principe, "Entropy minimization for supervised digital communications channel equalization", IEEE Trans. Signal Process., vol. 50, pp. 1184-1192, 2002. https://doi.org/10.1109/78.995074
  12. K. Jeong, J. Xu, D. Erdogmus, and J. Principe, "A new classifier based on information theoretic learning with unlabeled data", Neural Netw., vol. 18, pp. 719-726, 2005. https://doi.org/10.1016/j.neunet.2005.06.018
  13. I. Santamaria, P. Pokharel, and J. Principe, "Generalized correlation function: definition, properties, and application to blind equalization", IEEE Trans. Signal Process., vol. 54, pp. 2187-2197, 2006. https://doi.org/10.1109/TSP.2006.872524
  14. N. Kim, H. Byun, and J. Lim, "Blind signal processing algorithms under DC biased Gaussian noise", in Proceedings of SPIE 2013 Nano-bio Sensing, Imaging & Spectroscopy, Jeju, pp. OC3-5-1-OC3-5-2, 2013.
  15. T. Luo, "Digital equalization of fiber-optic transmission system impairments", M.S. thesis, McMaster University, Canada, 2011.
  16. S. Haykin, Adaptive Filter Theory, Prentice Hall, Upper Saddle River, 4th ed. 2001.
  17. E. Parzen, "On the estimation of a probability density function and the mode", Ann. Math. Stat. vol. 33, p. 1065, 1962. https://doi.org/10.1214/aoms/1177704472
  18. K. Surachet, "Modeling and analysis of the effects of impairments in fiber optic links", M.S. thesis, Virginia polytechnic institute and state University, United States, 1999.
  19. J. Proakis, Digital Communications, McGraw-Hill, 2nd ed, 1989.