Neural Network Based Order Statistic Processing Engines

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DOI: 10.4236/jsip.2012.31004    4,018 Downloads   6,434 Views   Citations

ABSTRACT

Order statistic filters are used often in the applications of science and engineering problems. This paper investigates the design and training of a feed-forward neural network to approximate minimum, median and maximum operations. The design of order statistic neural network filtering (OSNNF) is further refined by converting the input vectors with elements of real numbers to a set of inputs consisting of ones and zeros, and the neural network is trained to yield a rank vector which can be used to obtain the exact ranked values of the input vector. As a case study, the OSNNF is used to improve the visibility of target echoes masked by clutter in ultrasonic nondestructive testing applications.

Cite this paper

M. Unluturk and J. Saniie, "Neural Network Based Order Statistic Processing Engines," Journal of Signal and Information Processing, Vol. 3 No. 1, 2012, pp. 30-34. doi: 10.4236/jsip.2012.31004.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] J. Serra, “Image Analysis and Mathematical Morphology,” Academic Press, New York, 1988.
[2] I. Pitas and A. N. Venetsanopoulos, “Nonlinear Digital Filters, Principles and Applications,” Kluwer Academic Publishers, Boston, 1990.
[3] J. Astola and P. Kuosmanen, “Fundamentals of Nonlinear Digital Filtering,” CRC Press, Boca Raton, 1997.
[4] J. Saniie, K. D. Donohue and N. M. Bilgutay, “Order Statistic Filters as Postdetection Processor,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 38, No. 10, 1990, pp. 1722-1732.
[5] J. Saniie, D. T. Nagle and K. D. Donohue, “Analysis of Order Statistic Filters Applied to Ultrasonic Flaw Detection Using Split-Spectrum Processing,” IEEE Transactions on Ultrasonics, Ferrorelectrics, and Frequency Control, Vol. 38, No. 2, 1999, pp. 133-140. doi:10.1109/58.68470
[6] M. A. Weiss, “Data Structures and Algorithm Analysis in C++,” Addison Wesley, Reading, 2006.
[7] T. Tambouratzis, “A Novel Artificial Neural Network for Sorting,” IEEE Transact?ons on Systems, Man, and Cybernet?cs—Part B: Cybernet?cs, Vol. 29, No. 2, 1999, pp. 271-275. doi:10.1109/3477.752799
[8] P. W. Hollis and J. J. Paulos, “A Neural Network Learning Algorithm Tailored for VLSI Implementation,” IEEE Transactions on Neural Networks, Vol. 5, No. 5, 1994, pp. 784-791. doi:10.1109/72.317729
[9] B. M. Wilamowski and R. C. Jaeger, “Neuro-Fuzzy Architecture for CMOS Implementation,” IEEE Transaction on Industrial Electronics, Vol. 46, No. 6, 1999, pp. 1132-1136. doi:10.1109/41.808001
[10] X. Zhu, L. Yuan, D. Wang and Y. Chen, “FPGA Implementation of a Probabilistic Neural Network for Spike Sorting,” 2010 2nd International Conference on Information Engineering and Computer Science, Wuhan, 25-26 December 2010, pp. 1-4.
[11] J. Misra and I. Saha, “Artificial Neural Networks in Hardware: A Survey of Two Decades of Progress,” Neurocomputing, Vol. 74, No. 1-3, 2010, pp. 239-255.
[12] K. Hornik, “Multilayer Feedforward Networks as Universal Approximators,” Neural Networks, Vol. 2, No. 5, 1989, pp. 359-366. doi:10.1016/0893-6080(89)90020-8
[13] T. Masters, “Practical Neural Network Recipes in C++,” Academic Press Inc., New York, 1993.
[14] F. J. Bremner, S. J. Gotts and D. L. Denham, “Hinton Diagrams: Viewing Connection Strengths in Neural Networks,” Behavior Research Methods, Vol. 26, No. 2, 1994, pp. 215-218.
[15] E. Parzen, “On Estimation of a Probability Density Function and Mode,” Annals of Mathematical Statistics, Vol. 33, No. 3, 1962, pp. 1065-1076. doi:10.1214/aoms/1177704472

  
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