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Analyzing Uncertainties of Rectangular Periodic Defected Ground Structure Characteristics

by Prakash K Kuravatti, T.s. Rukmini
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 22
Year of Publication: 2012
Authors: Prakash K Kuravatti, T.s. Rukmini
10.5120/7514-0571

Prakash K Kuravatti, T.s. Rukmini . Analyzing Uncertainties of Rectangular Periodic Defected Ground Structure Characteristics. International Journal of Computer Applications. 48, 22 ( June 2012), 38-44. DOI=10.5120/7514-0571

@article{ 10.5120/7514-0571,
author = { Prakash K Kuravatti, T.s. Rukmini },
title = { Analyzing Uncertainties of Rectangular Periodic Defected Ground Structure Characteristics },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 22 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number22/7514-0571/ },
doi = { 10.5120/7514-0571 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:44:47.154615+05:30
%A Prakash K Kuravatti
%A T.s. Rukmini
%T Analyzing Uncertainties of Rectangular Periodic Defected Ground Structure Characteristics
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 22
%P 38-44
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The defected ground structure (DGS) is one such technique which where intentionally modified to enhance the performance of the ground plane metal of a microstrip circuit. Importantly, in microwave application, the DGS plays an important role in analyzing the effect of surface and leaky waves. During the time of experimental analysis, the surface and leaky waves are affected by the propagation uncertainties. Hence, the performance of microstrip circuit is also affected. So, proper mathematical model is needed for analyzing the propagation uncertainty of DGS and to improve the performance. In this paper, the curve fitting mathematical model is proposed for analyzing the propagation uncertainty. In the proposed model, the bi-segmentation process is applied to the experimental characteristics. The proposed curve fitting model is implemented and the Rectangular Periodic defected ground structure propagation uncertainties are analyzed.

References
  1. Jadhav, A. S. , Banerjee, S. , Dutta, P. K. , Paul, R. R. , and Pal, M. 2006. Quantitative analysis of histopathological features of precancerous lesion and condition using image processing technique. 19th IEEE Int. Symposium on Computer-Based Medical Systems, 231-236.
  2. Mukherjee, A. , Paul, R. R. , Chaudhri, K. , Chatterjee, J. , Pal, M. , and Banerjee, P. 2006. Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition. Oral Oncology, 42, 914-928.
  3. Nanci, A. 2008. Oral Mucosa, Ten Cate's Oral Histology. Indian: Mosby- Elsevier.
  4. Pindborg J. J. , and Sirsat, S. M. 1966. Oral submucous fibrosis, Oral Surg. Oral Med. Oral Pathol. 22, pp. 764–779
  5. Cawson, R. A. , and Odell, E. 2002. Cawson's Essentials of Oral Pathology and Oral Medicine. Churchill Livingstone.
  6. Landini, G. , and Rippin, J. W. 1996. How important is tumour shape? Quantification of the epithelial-connective tissue interface in oral lesions using local connected fractal dimension analysis. The Journal of Pathology, 210-217.
  7. Landini, G. , and Othman, I. E. 2004. Architectural analysis of oral cancer, dysplastic and normal epitheliam. Cytometry, 45-55.
  8. Abu-Eid, R. , and Landini, G. 2006. Oral Epithelial Dysplasia: Can quantifiable morphological features help in the grading dilemma? First Image User and Developer Conference. Luxembourg.
  9. Kayser, K. , Sandau, K. , Bohm, G. , Kunze, K. , and Paul J, J. 1991. Analysis of soft tissue tumors by an attributed minimum spanning tree. Analytical & Quantitative Cytology & Histology, 329-334.
  10. Marcelpoil, R. , Davoine, F. , and Robert-Nicaud, M. 1994. Cellular sociology: parametrization of spatial relationships based on Voronoi diagram and Ulam trees. Fractals in biology and medicine, 201-209.
  11. Paul, R. R. , Mukherjee, A. , Dutta, P. K. , Banerjee, S. , Pal, M. , and Chatterjee, J. 2005. A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition. Journal of Clinical Pathology, 58, 932-938.
  12. Mukherjee, A. , Paul, R. R. , Chaudhri, K. , Chatterjee, J. , Pal, M. , and Banerjee, P. 2006. Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition. Oral Oncology, 42, 914 – 928.
  13. M. M. R. K. , Pal, M. , Bomminayuni, S. K. , Chakraborty, C. , Paul, R. R. , Chatterjee, J. , Ray, A. K. 2009. Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis—An SVM based approach, Computers in Biology and Medicine, 39(12), 1096-1104.
  14. Muthu Rama Krishnan, M. , Shah, P. , Pal, M. , Chakraborty, C. , Paul, R. R. , Chatterjee, J. , Ray, A. K. 2010. Structural markers for normal oral mucosa and oral sub-mucous fibrosis. Micron 41 (4), 312–320.
  15. Perona, P. , and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence, 12 (7).
  16. Montagna W, Parakkal PF. 1974. The Structure and Function of Skin, 3rd edn. New York: Academic Press.
  17. Mallat, S. 1989. A theory for multiresolution signal decomposition: the wavelet representation, IEEE transaction on Pattern Analysis and machine intelligence. 11(7), pp 674-693.
  18. Gun, A. M. , Gupta, M. K. , Dasgupta, B. 2008. Fundamentals of Statistics. Vol. 2. World Press.
  19. Jarque, C. M. , Bera, A. K. 1987. A test for normality of observations and regression residuals. International Statistical Review, 55(2), 163-172.
  20. Duda, R. , Hart, P. , & Stork, D. 2007. Pattern classification. Wiley India.
  21. Vapnik, V. 1998. Statistical learning theory. New york: Wiley.
  22. Gunn, S. R. 1998. Support Vector Machines for Classification and Regression.
Index Terms

Computer Science
Information Sciences

Keywords

Dgs Propagation Characteristics Experimental Model Mathematical Model Curve Fitting Uncertainty.

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