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Abstract:

Major issue in cluster analysis is determining the number of clusters present in a data set. The automated identification of the number of clusters can be satisfactorily solved with very few techniques. Recent developments have resulted in a very popular visual mechanism for clustering trend determination (VAT, Visual Assessment of Clustering Tendency) in data sets. The techniques used for image processing depend on the structure of the VAT image, without using any cluster validity concept. High speed solutions can be found in conjunction with GAs from VAT approaches. This approach however depends on the ability of the index concerned to identify overlapping clusters.We will explain how VAT algorithms can be very quickly used to correctly determine the number of clusters. The implementation of the approaches proposed by taking cluster accuracy, cluster error and computational time as metrics.

Keywords:

Clustering Analysis,Cluster Accuracy,visual assessment,CCE,DBE,VAT,

Refference:

I. Ahmad A, Dey L (2007), K-Mean clustering algorithm for mixed numeric and categorical data. Data & Knowledge Engineering, 63(2), 503-527.2007.
II. Bandyopadhyay S, Saha S (2008), A point symmetry-based clustering technique for automatic evolution of clusters. Knowledge and Data Engineering, IEEE Transactions, 20(11), 1441-1457.2008.
III. Caliński T, Harabasz J (2012), A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 41(12),1-27.2012.
IV. Cattell R (1944) A note on correlation clusters and cluster search methods. Psychometrika, 9(3) (1944) 169-184.1994.11
V. G.Ravi Kumar, S.Rahamat Basha, Surya Bhupal Rao, “A Summarization on Text Mining Techniques for Information Extracting from Applications and Issues”, Journal of Mechanics of Continua and Mathematical Sciences,Special Issue, No.-5, 2020.
VI. I. J. Sledge and T. C. Havens and J. M. Huband and J. C. Bezdek and J. M. Keller, “Finding the number of clusters in ordered dissimilarities,” in Soft Computing, vol. 13, 2009, pp. 1125-1142.
VII. Liang Wang, Christopher Leckie, KotagiriRamamohanarao, and James Bezdek(2009), “Automatically Determining the Number of Clusters in Unlabeled Data Sets”, Fellow, IEEE, 21(3), 335-350.2009.
VIII. L. Wang and C. Leckie and R. Kotagiri and J. C. Bezdek, “Automatically Determining the Number of Clusters in Unlabeled Data Sets,” in IEEE Transaction on Knowledge and Data Syetems, vol. 21, 2009, pp. 335-350.
IX. Maimon O, Rokach L (2005), Decomposition methodology for knowledge discovery and data mining: Springer, pp 981-1003,2005.
X. S.Rahamat Basha, J. Keziya Rani “A Comparative Approach of Dimensionality Reduction Techniques in Text Classification” Engineering, Technology & Applied Science Research, Vol. 9, No. 6, Dec 2019, PP:4974-4979.
XI. S.Rahamat Basha, J. Keziya Rani,JJC Prasad Yadav, “A Novel Summarization-based Approach for Feature Reduction, Enhancing Text Classification Accuracy” Engineering, Technology &Applied Science Research, Vol. 9, No. 6, Dec 2019, PP 5001-5005.
XII. S.Rahamat Basha, J. Keziya Rani, JJC Prasad Yadav, G.Ravi Kumar, “Impact of feature selection techniques in Text Classification:An Experimental study”, Journal of Mechanics of Continua and Mathematical Sciences, Special Issue, No.-3, September (2019) PP 39-51.
XIII. Surya Bhupal Rao, S.Rahamat Basha, “Chaotic Algorithm for Standard Image Encryption”, Journal of Mechanics of Continua and Mathematical Sciences, Special Issue, No.-3, September (2019).
XIV. Surya Bhupal Rao, S.Rahamat Basha, G.Ravi Kumar, “A Comparative approach of Text Mining: Classification, Clustering and Extraction Techniques”, Journal of Mechanics of Continua and Mathematical Sciences, Special Issue, No.-5,2020.
XV. T. Havens. J. C. Bezdek, J. M. Keller and M. Popescu, “Dunn’s cluster validity index as a contrast measure of VAT images,” in Proc ICPR, Tampa, FL, 2008.
XVI. Timothy C. Havens1, James C. Bezdek1, and James M. Keller1(2012), “A New Implementation of the co-VAT Algorithm for Visual Assessment of Clusters in Rectangular Relational Data”, Fellow, IEEE, 21(3), 335-350.2012.
XVII. Timothy C. Havens, Senior Member (2012), IEEE, and James C. Bezdek, “An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm”, Fellow, IEEE, 21(3), 335-350.2012.
XVIII. Zhang Z, Zhang J, Xue H (2008), Improved K-means clustering algorithm. In Image and Signal Processing, 2008. CISP’08. Congress on, Vol. 5, pp 169-172,2008.

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Abstract:

In this work, a new feature descriptor has been proposed for efficient CT Medical Image Retrieval (MIR). Non Subsampled Pyramid (NSP) of Non Sub-sampled Shearlet Transform (NSST) has been carried out for multiscale and multidirectional image decomposition into low and high frequency sub bands.A newfeature descriptor “Local Multiscale and Multidirectional Frequency Encoded Binary Pattern (LMSMDFEBP)” has been proposed to obtain the local directional information in each sub-bands of images.Feature vectors of database and query images have been obtained from the histogram of LMSMDFEBP. The Euclidean distance has been evaluated to analyse the similarity between query and database feature vectors. Two tests have been carried out on publicly available EXACT-09 and TCIA CT databases to assess the performance of proposed method. The proposed approach shows an improvement of ARP values 3.36% and 8.98% for the EXACT-09 and TCIA-CT respectively, compared with the existing Local Wavelet Pattern (LWP).

Keywords:

Medical Image Retrieval,Non Sub-sampled Shearlet Transform,Local Multiscale and Directional Frequency Encoded Binary Pattern,Local Wavelet Pattern,Euclidean distance,

Refference:

I. Bamberger, R.H.; Smith, M.J.T. A filter bank for the directional decomposition of images: Theory and design. IEEE Trans. Signal Process. 1992, 4, 882–893.
II. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, and Prior F (2013). “The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository,” Journal of Digital Imaging, vol. 26, no. 6, pp. 1045-1057.
III. Da Cunha, A.L.; Zhou, J.; Do, M.N. The nonsubsampled contourlet transform: Theory, design, and applications. IEEE Trans. Image Process. 2006, 10, 3089–3101.
IV. Deep G, Kaur L, Gupta S (2016) Directional local ternary quantized extrema pattern: a new descriptor for biomedical image indexing and retrieval. Eng Sci Technol Int J 19:1895–1909
V. Dubey SR, Singh SK, SinghRK(2015) Local diagonal extrema pattern: a new and efficient feature descriptor for CT image retrieval. IEEE Signal Process Lett 22(9):1215–1219
VI. Dubey SR, Singh SK, Singh RK (2016) Local bit-plane decoded pattern: a novel feature descriptor for biomedical image retrieval. IEEE J Biomed Health Inform 20(4):1139–1147
VII. G. Easley, D. Labate, and W. Q. Lim (2008). “Sparse directional image representation using the discrete shearlet transforms,” Appl. Comput. Harmon. Anal. 25(1), 25–46.
VIII. Kellokumpu, V., Zhao, G. and Pietikäinen, M. (2011), Recognition of Human Actions Using Texture Descriptors. Machine Vision and Applications 22(5):767-780.
IX. Kong, Weiwei & Liu, Jianping. (2013). Technique for image fusion based on nonsubsampled shearlet transform and improved pulse-coupled neural network. Optical Engineering. 52. 7001-10.1117/1.OE.52.1.017001.
X. Li, L.; Si, Y. A Novel Remote Sensing Image Enhancement Method Using Unsharp Masking in NSST Domain. J. Indian Soc. Remote Sens. 2016, 44, 1–11.
XI. L. Li, Y. Si, and Z. Jia, “Medical image enhancement based on CLAHE and unsharp masking in NSCT domain,” Journal of Medical Imaging and Health Informatics, vol. 8, no. 3, pp. 431– 438, 2018.
XII. Lo P, Van Ginneken B, Reinhardt J M, Yavarna T, De Jong P A, Irving B, and De Bruijne M (2012). “Extraction of airways from CT (EXACT’09),” IEEE Transactions on Medical Imaging, vol. 31, no. 11, pp. 2093-2107.
XIII. Miranda, Eka et al. “A survey of medical image classification techniques.” 2016 International Conference on Information Management and Technology (ICIMTech) (2016): 56-61.
XIV. Murala S, and Wu Q J (2013). “Local ternary co-occurrence patterns: a new feature descriptor for MRI and CT image retrieval,” Neurocomputing, vol. 119, pp. 399–412.
XV. Murala S, and Wu Q (2014). “Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 3, pp. 929–938.
XVI. Murala S, Maheshwari R, and Balasubramanian R (2012) “Local tetra patterns: a new feature descriptor for content-based image retrieval,” IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2874–2886.
XVII. Murala, Subrahmanyam & Maheshwari, R.P. & Raman, Balasubramanian. (2011). Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval. Journal of medical systems. 36. 2865-79. 10.1007/s10916-011-9764-4.
XVIII. N. Zhang, P.Wang, and X. Zong, “A novel peripheral enhancement framework for CT and MRI image fusion in NSST domain,” Journal of Medical Imaging and Health Informatics, vol.8, no. 5, pp. 891–899, 2018.
XIX. Quellec G, Lamard M, Cazuguel G, Cochener B, Roux C (2010) Wavelet optimization for content-based image retrieval in medical databases. Med Image Anal 14(2):227–241
XX. Quellec G, Lamard M, Cazuguel G, Cochener B, Roux C (2012) Fast wavelet-based image characterization for highly adaptive image retrieval. IEEE Trans Image Process 21(4):1613–1623

XXI. Raj, S.; Nair, M.; Subrahmanyam, G (2017) Satellite Image Resolution Enhancement Using Nonsubsampled Contourlet Transform and Clustering on Subbands. J. Indian Soc. Remote Sens. 45, 979–991.Ojala, Timo et al. “A comparative study of texture measures with classification based on featured distributions.” Pattern Recognit. 29 (1996): 51-59.
XXII. S. Murala, R. P. Maheshwari and R. Balasubramanian, “Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval,” in IEEE Transactions on Image Processing, vol. 21(5), pp. 2874-2886, May 2012.doi: 10.1109/TIP.2012.2188809
XXIII. S. R. Dubey, S. K. Singh and R. K. Singh, “Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases,” in IEEE Transactions on Image Processing, vol. 24(12), pp. 5892-5903,December2015.doi: 10.1109/TIP.2015.2493446.
XXIV. T. Ahonen, A. Hadid and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition,” in IEEE Transactions on Pattern Analysis and Machine Intelligence,28(12), pp. 2037-2041, Dec. 2006.
doi: 10.1109/TPAMI.2006.244
XXV. Tan X, and Triggs B (2010). “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1635–1650.
XXVI. Wang, Z.; Yang, F.; Peng, Z.; Chen, L.; Ji, L. Multi-sensor image enhanced fusion algorithm based on NSST and top-hat transformation. Opt.-Int. J. Light Electron. Opt. 2015, 126, 4184–4190.
XXVII. Wu, Y.; Yin, J.; Dai, Y. Image Enhancement in NSCT Domain Based on Fuzzy Sets and Artificial Bee Colony Optimization. J. S. China Univ. Technol. (Nat. Sci. Ed.) 2015, 43, 59–65.
XXVIII. Zhang B, Gao Y, Zhao S, and Liu J (2010). “Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor,” IEEE Transactions on Image Processing, vol. 19, no. 2, pp. 533–544.

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Abstract:

Commodity derivatives are risk management tools and are contracts built on commodities which will have transaction on the same day and settlement in the future. Futures are one among the derivative products which does the role of hedging and helps in price discovery of the underlying commodity. If the futures price of a commodity has to converge to the spot price in order to discover the price, information that affects the demand and supply factors leading to convergence need to be focused. Past research posted the establishment of organized futures exchanges, their role in price discovery with respect to some commodities as well as commodity indices.  But the evidences are neither comprehensive nor conclusive in any manner. Empirical research on the subject over the last decade showed that the introduction of derivatives contracts improved the liquidity and reduced informational asymmetries in the market to some extent. Researchers attempted to find the impact of price information dissemination on price discovery and hence the benefits to market participants, both producers and consumers. In this study authors attempted to evaluate the problem of information dissemination across market players in Karnataka, Andhra Pradesh and Telangana states with special reference to maize. Maize the ‘queen of cereals’, is the hope of India as a substitute to rice and wheat, to mitigate the shortage of million tonnes of food material that Indians will be facing very soon. It is known that maize futures which were given by both NCDEX and MCX platforms were serving the roles and in the recent times no new contracts were announced. This research tries to check whether the Information Dissemination Project implemented by Central Government of India, with help of commodity derivative exchanges was successful in disseminating the right information, at right time with right approach/tools, whether the stake holders got benefitted with information and maize futures and finally it tries to study whether the maize futures have helped in price discovery?  The study is done with the help of primary data collected through questionnaires and secondary data collected through NCDEX and other spot markets. Statistical Package for the Social Sciences (SPSS) and E-Views are used to test the hypotheses framed hence, to prove the role of maize futures as a risk management and price discovery tool. Authors came out with the conclusion that maize futures do the role and future contracts can be rolled on for benefit of all the stakeholders

Keywords:

Commodity Futures,Price Discovery,Risk Management ,Price information Dissemination,

Refference:

I. Ali, Jabir and Gupta, KritiBardhan. “Agricultural Price Volatility and Effectiveness of Commodity Futures Markets in India”, Indian Journal of Agricultural Economics. Vol. 62, No.3, pp. 537, 2011
II. Angad Singh Maravi&HarisinghGour. “Performance Analysis of Indian Agricultural Commodity Market”, International Journal of Commerce, Business and Management (IJCBM), Vol. 4, No.2, pp. 1125-1135, 2015.
III. Amrutha C.P. “Market information system and its application for Agricultural commodities in Karnataka state – A case of onion”. (Doctoral dissertation). Retrieved from http://krishikosh.egranth.ac.in/bitstream/1/80770/1/th9838.pdf, 2009.
IV. AthmaPrashanta& K. P. VenugopalaRao. “Agricultural commodity derivatives in India: A study of mentha oil futures”, Asian Journal of Research in Business Economics and Management, Vol:3, Issue: 8, pp. 197- 214. 2013.
V. Dr. Sunitha Ravi. “Price Discovery and Volatility Spillover in Indian Commodity Futures Markets Using Selected Commodities”, PARIPEX Indian Journal of Research, Vol: 2, Issue: 12, pp. 128-130, 2013.
VI. G. Ranganath, P.K. Mandanna& S. Kumar, “Structure and Competitiveness of the Maize market in Davanagere”, International Journal of Commerce and Business Management,Vol:6, Issue 1, April, 2013, pp.111-113, 2013.
VII. Jackline, S. and Deo, M. “Lead Lag Relationship between Futures and Spot Prices”, Journal of Economics and International Finance, Vol. 3 No 7, pp. 424-427, 2011.
VIII. K. Singha& A. Chakravorty, “Crop diversification in India: a study of maize cultivation in Karnataka”, Scientific Journal of Review Vol:2, Issue 1,pp. 1-10, 2013.
IX. MudigondaRaju. “Agricultural Marketing system in Telangana State –A Study”, Scholarly Research Journal for Interdisciplinary Studies (SRJIS), Vol:4 No 26, pp.3050-3057, 2016.
X. Mohan Paramkusam&Sivaramane. “A Socio-Economic Status of Maize Farmers of Telangana and Andhra Pradesh, India”, Indian Journal of Economics and Development, Vol: 4 No6, pp. 01-06, 2016.
XI. Narendra Singh Manohar, A. K. Dikshit& B. S. Reddy, “Marketing Pattern of Maize’ An Insight Household Survey Results in India: A Case Study”,International Journal of Retailing & Rural Business Perspectives, Vol: 2, Number 1, pp. 319-324, 2013.

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Abstract:

FMCG have attracted Indian villagers. When the urban demands for the FMCG goods are getting saturated, the FMCG companies looks at this development as an opportunity. The untapped rural market is fast becoming a major attraction to many domestic and foreign organizations.  But they lack in getting required support from the concerned Government Departments, Banks and other financial institutions and corporates, which is handicap in becoming more competitive in the national and international markets. The rural market, thus are the growth engines of Indian economy, a number of international brands are entering in to India which is one of the fastest growing and highly competitive markets in the world. Though, most of the global firms failed to understand the needs of Indian consumers as well as the market characteristics but there are a few of them who have been successful in positioning their brands into the Indian market because they attempt to understand well the needs of target group before introducing a brand into the market. Even some of the most successful brands in today’s time had committed several blunders or mistake while initially entering into Indian market.

Keywords:

FMCG,India,Rural market,Consumers,Global firms,

Refference:

I. Anandam. C, Prasanna. M & Madhu. S. “A study of brand preferences of washing soaps in rural areas”. Indian Journal of Marketing, Vol: (3), March, Pp. 30-37, 2007
II. Agres, S.J. &Dubitsky, T.M.“Changing Needs for Brands”.Journal of Advertising Research, Vol. 36, No 1, P.pp. 21-30, 1996.
III. Bressoud, E.: “Innovative Research Methodologies”, Journal of Product & Brand Management, Vol: 22 No4, pp. 286-292, 2013.
IV. Behura C. K., & Panda, K. J. “Rural Marketing of FMCG Companies in India”. International Journal of Biological and Medical Research, vol:2 no2, pp. 65-74. 2012.
V. Carson, D. and CromieS.“Marketing Planning in Small Enterprises: A Model and Some Empirical Evidence”. The Journal of Consumer Marketing. Vol7 No 3, pp. 5- 18, 1990.
VI. Gordon, A., Finlay, K., and T. Watts.“The Psychological Effects of Color in Consumer Product Packaging”. Canadian Journal of Marketing Research, Vol: 13,pp.3-11, 1994.
VII. Keller, K. L.“Conceptualizing, Measuring, and Managing Customer Based Brand Equity”. Journal of Marketing. Vol57 No1, pp. 1-22, 1993.
VIII. Kotler, P. et al. Principles of marketing. 2th edition. New Jersey: Prentice Hall Europe, 1999.
IX. Prajapati, S.AndThakor, M. “Predictors of Consumer Behaviour”, Journal of Arts, Science &Commerce,Vol: 3 no2, pp. 82-86, 2012.
X. Srinivasu, R.: “The role of sustainability in reverse logistics for returns and recycling”, International Journal of Innovative Research in Science, Engineering and Technology.Vol:3 No 1, pp. 8422-8430, 2014.
XI. Wheeler Thomos, “Concept in strategic Management & Business Policy”,Pearson Pub. 12th edition 2010.

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Abstract:

In India, numerous tribal people are living. From generations onwards,there are socio economic disparities and problems among the tribal people. This paper discusses about trivials and tribulations of sugali people, who secluded in Jilleamanda Pedda Thanda, in Chittoor District. Sugalis are migratory one. Culture,traditions, pastoral life are part of their life. Aim: To evaluate the social status of sugalis and rehabilitation in Chittoor District, Andhra Pradesh. Materials and Methods: We spent 15 days in the Thanda, and surveyed about the life style of living, interaction with tribe’s. We garnered some information from secondary sources. Results: Status of marriage system, living style, cultivation, political empowerment, cattle rearing, and alcoholism impact on their economic status are discussed. Conclusion: Tribal people must take care about their self-development. It leads to familial, society development

Keywords:

Income,marriage system,schemes,political upliftment,

Refference:

I. Ayappan, A., 1948, “Report on the Socio-economic conditions of the Aboriginal Tribes of the Province of Madras”. Government Press, Madras, pp.164-166.
II. Briggs, T.“An account of the origin, history and manners of Banjaras and transactions of the literary society of Bombay”, Vol 08, pp. 172-191, 1877.
III. Crook, W, “The tribes and castes of the North western India”, Delhi, pp. 149-173, 1974.
IV. Cumberlege, 1882, “Same account of the Banjara class”. Bombay, pp. 149-173.
V. Elliot, H. M. “Banjara’ the races of N.W. province of India. London, Volume 01, pp. 55-56, 1869.
VI. Government of Andhra Pradesh Panchayat Raj Engineering Departemnt, Andhra Pradesh Rural Roads Connectivity Project The Asian Infrastructure Investment Bank assisted, Tribal Peoples Planning Framework (TPPF), Financial Report, July 2018.
VII. Government of India, Ministry of Tribal Affairs, Lok Sabha, Unstarred Question No. 221, to be answered on 17.07.2017, Tribal Population,
VIII. http://www. indiaenvironmentportal.org.in /files /file/ tribal % 20population_1.pdf
IX. Jost, C. “of Caravans and Wanderlust: the Banjarans”. The India Magazine of her people and culture, Vol 02, pp41-47,1982.
X. Malhotra, S.P., and Bose, A.P. “Problems of Rehabilitation of Nomadic Banjaras”. Annals of the Arid Zone, Volume 02, no 3, pp. 74-76,1963.
XI. Nanjundaiah, H.V. and Ananta Krishan Iyer, L.K.“The Mysore tribes and castes”. Mysore University, Mysore, Volume 02, pp.39-142,1928.
XII. RamaswamyAiyer, C.P.andChman Lal’s “Gipsics-Forgotten children in India”, Ministry of Publication Division of Information and Broadcasting, Government of India, Volume 07, issue 09, 1962.
XIII. RanjitaSingh.“Social Conditions of Elders and Problems”, Quest Journals Journal of Research in Humanities and Social Science, Vol 3, Issue 3, pp. 52-54, 2015.
XIV. Rao, A .V. “Problems of the Aged Seeking Psychiatric Help”, New Delhi: ICMR,1985.
XV. Robertson B. “Banjara’ Census of growth”. Journal of Biosocial Science, Volume01,pp. 43-67,1892.
XVI. Russel, R.V. and Hiralal, R.B. “Tribes and Castes of the Central province in India”.Rajadhani book Centre, Delhi: Vol 02, pp162-191, 1975.
XVII. Roma Banjara. “Shampan India – the Banjara People of India’.Jyoti Industrial estate,Vol 03,issue 03, 1983.
XVIII. Singh. R. “Social conditions of elderly and problems”, Journal of Research in Humanities and Social Science. Volume 03, issue03, pp. 20-25 2015.
XIX. Sira-j-ul-Hassan Syed., “The castes and tribes of H.E.H”. TheNizam’s Dominions, Volume01, pp. 15-25, 1920.
XX. Tanuja M. “Care and support for the elderly: a comparative study in rural and urban setups in Odisha”. International Journal Social Economics,pp. 52–64, 2012.
XXI. Websites: http:// aptribes. gov.in /pdfs/table2.pdf.

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Abstract:

Tensor Completion from a limited number of non-distorted observations, has enticed researchers interest. The color image has been considered as the three dimensional tensor. Low rank property in Optimization has been used to recover the tensors in the image. The Low rank prior alone not enough to tensor completion. The traditional tensor truncated nuclear norm approaches have been able to approximate the real rank of the tensor, but these are low rank prior approaches. Here a transformation-based optimization method has been proposed to complete the tensors of the image. The Discrete Cosine Transformation (DCT) has been used as transformation method. The tensor singular value decomposition (t-SVD) and accelerated proximal gradient line (APGL) approaches have been considered. The Full Reference metrics i.e., peak signal to noise ratio (PSNR) and structural similarity (SSIM) have been used to evaluate the proposed approach. The obtained results are superior to the existing algorithms. The PSNR and SSIM have been recorded as 27.30 dB and 0.8845 respectively

Keywords:

Tensor Completion,Tensor Singular Value Decomposition,Discrete Cosine Transform,Convex Optimization,

Refference:

I. Emmanuel J Candès and Benjamin Recht. Exact matrix completion via convex optimization. Foundations of Computational mathematics, 9(6):717, 2009.

II. Ji Liu, Przemyslaw Musialski, Peter Wonka, and Jieping Ye. Tensor completion for estimating missing values in visual data. IEEE transactions on pattern analysis and machine intelligence, 35(1):208–220, 2012.

III. Jing Dong, Zhichao Xue, Jian Guan, Zi-Fa Han, and Wenwu Wang. Low rank matrix completion using truncated nuclear norm and sparse regularizer. Signal Processing: Image Communication, 68:76–87, 2018.

IV. Misha E Kilmer, Karen Braman, Ning Hao, and Randy C Hoover. Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications, 34(1):148–172, 2013.

V. Ping-Ping Wang, Liang Li, and Guang-Hui Cheng. Low rank tensor completion with sparse regularization in a transformed domain. arXiv preprint arXiv:1911.08082, 2019.

VI. Shengke Xue, Wenyuan Qiu, Fan Liu, and Xinyu Jin. Low-rank tensor completion by truncated nuclear norm regularization. In 2018 24th International Conference on Pattern Recognition (ICPR), pages 2600–2605. IEEE, 2018.

VII. Yao Hu, Debing Zhang, Jieping Ye, Xuelong Li, and Xiaofei He. Fast and accurate matrix completion via truncated nuclear norm regularization. IEEE transactions on pattern analysis and machine intelligence, 35(9):2117–2130, 2012.

VIII. Yaru Su, Xiaohui Wu, and Wenxi Liu. Low-rank tensor completion by sum of tensor nuclear norm minimization. IEEE Access, 7:134943–134953, 2019.

IX. Yunhe Wang, Chang Xu, Shan You, Chao Xu, and Dacheng Tao. Dct regularized extreme visual recovery. IEEE Transactions on Image Processing, 26(7):3360–3371, 2017.

X. Zemin Zhang, Gregory Ely, Shuchin Aeron, Ning Hao, and Misha Kilmer. Novel methods for multilinear data completion and de-noising based on tensor-svd. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3842–3849, 2014.

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Abstract:

This power converter is a device thattransforms single-line power to three-phase power. The proposed single line to three-line ((1φ or DC)/3φ) power-conversion system contains a power converter; zero-sequence transformer set, and filter capacitors and inductors. Generally, converters are utilized wherever the supply is single-phase to convert it into three-phase we use this type of converters. These converters are mostly used in secluded location and surcharges because of the electric utilities don't install due to cost is too high to install. Three-phase services usually require a high price due to the installation of extra equipment and meters at the transformer and also extra electric wire for transmission is required. In this paper, the single-line to three-phase converter is designed by using SIMULINK toolbox in MATLAB software.

Keywords:

MOSFET,Single Line,Three Phase ,Fly back Converter,MPPT,

Refference:

I. Ashraf A. Mohammed and Samah M. Nafie; fly back Converter Design forLow Power Application.International conference on computing control, networking,electronicsandEmbedded systems.

II. EuzeliCipriano, CursinoBrandãoJacobina, Edison Roberto Cabral da Silva, Nady Rocha ” Single-Phase to Three-Phase Power Converters: State of the Art”, IEEE – Institute of Electrical and Electronics Engineering, Vol. 27, Issue. 5, (2012) PP- 2437 – 2452.

III. Mohan Reddy K.; Naveen Reddy A.; “solar PV Array fed four switch buck-boost converter for LHB Coach” ijcta, 9 (29), 2016, pp.249-255.

IV. M.SubbaRao, Dr.Ch.SaiBabu, Dr.S.Satyanarayana, “Digital Fuzzy Current Mode Controlled Integrated PFC Converter with External Ramp Compensation”, Journal of Circuits, Systems, and Computers (JCSC), Vol. 27, No. 9 (2018), p.p.1850147-1-23.

V. Sanmesh S Khandolkar, Noah Dias “Design and Fabrication of Single Phase to Three Phase Variable Voltage Power Converter” GRD Journals- Global Research and Development Journal for Engineering, Volume 2, Issue 5, (2017) PP-31-37.
VI. SmithaPaulose , Charles K J, Xaviour K, Niju Raphael “Single Phase to Three Phase Converter” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 7, Issue 3, (2018) PP- 1519 – 1525.

VII. Subbarao M, Ch. SaiBabu, S. Satyanarayana ,” Design and analysis of variable switching frequency controlled integrated switched mode power converter for class C & class D appliances”, Ain Shams EngJol.,vol. 9 (2018),pp 2849–2858.

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