Journal Vol – 14 No -2, April 2019

Abstract:

In multimedia community, cross modal similarity search based hashing received extensive attention because of the effectiveness and efficiency of query. This research work contributes large scale dataset for weakly managed cross-media recovery, named Twitter100k. Current datasets namely Wikipedia, NUS Wide and Flickr30k, have two main restrictions. First, these datasets are deficient in content diversity, i.e., only some pre-characterized classes are secured. Second, texts in these datasets are written informal dialect, that leads to irregularity with practical applications. To overcome these disadvantages, the proposed method used Twitter100k dataset because of two major points, first, it has 100,000 content-image pairs that are randomly crawled from Twitter and it has no importance in the image classifications. Second, text in Twitter100k is written in informal language by the clients. Since strongly supervised strategies use the class labels that might be missing in practice, this paper mainly concentrates on weakly managed learning for cross-media recovery, in which only text-image sets misused during training. This paper proposed a Random Multimodal Deep Learning (RMDL) based Recurrent Neural Network (RNN) for cross-media retrieval. The variety of input data such as video, text, images etc. are used for cross-media recovery which can be accept by proposed RMDL in weakly dataset. In RMDL, the various input data can be classified by using RNN architecture. to improve the accuracy and robustness of the proposed method, RMDL uses the specific RNN structure i.e. Long Short-Term Memory (LSTM). In the experimental analysis, the results demonstrated that the proposed RMDL-based strategy achieved 78% of Cumulative Match Characteristic (CMC) compared to other datasets.

Keywords:

Cross modal similarity search,witter dataset,class labels,strong supervised methods, NUS Wide,Random Multimodal Deep Learning,

Refference:

I.Ahmad, Khaleel, Monika Sahu, Madhup Shrivastava, Murtaza Abbas Rizvi, and Vishal Jain., “An efficient image retrieval tool: query based image management system,” International Journal of Information Technology, pp. 1-9, 2018.

II.Ballan Lamberto, Tiberio Uricchio, Lorenzo Seidenari, and Alberto Del Bimbo,“A cross-media model for automatic image annotation”, In Proceedings of International Conference on Multimedia Retrieval, pp. 73, 2014.

III.Ding Guiguang, Yuchen Guo, and Jile Zhou,“Collective matrix factorizationhashing for multimodal data,” Proceedings of the IEEE conference on computer vision and pattern recognition, 2014.

IV.Deng Cheng, Xu Tang, Junchi Yan, Wei Liu, and Xinbo Gao, “Discriminative dictionary learning with common label alignment for cross-modal retrieval,” IEEE Transactions on Multimedia, vol. 18, 2, pp. 208-218, 2016.

V.Ding, Kun, Bin Fan, Chunlei Huo, Shiming Xiang, and Chunhong Pan, “Cross-modal hashing via rank-order preserving,” IEEE Transactions on Multimedia, vol. 19, no. 3, pp. 571-585, 2017.

VI.Hauptmann, A. G., Yi Yang, and L. Zheng,“Person Re-identification: Past, Present and Future,” 2016.

VII.Hwang Sung Ju, and Kristen Grauman,“Reading between the lines: Object localization using implicit cues from image tags,” IEEE transactions on pattern analysis and machine intelligence vol. 34, no.6,pp. 1145-1158, 2012.

VIII.Jiang Bin, Jiachen Yang, Zhihan Lv, Kun Tian, Qinggang Meng, and Yan Yan, “Internet cross-media retrieval based on deep learning”, Journal of Visual Communication and Image Representation, vol.48, pp. 356-366, 2017.

IX.Kang Cuicui, Shiming Xiang, Shengcai Liao, Changsheng Xu, and Chunhong Pan, “Learning consistent feature representation for cross-modal multimedia retrieval,” IEEE Transactions on Multimedia, vol. 17, no. 3, pp. 370-381, 2015.

X.L. Malliga, and K. Bommanna Raja, “A Novel Content Based Medical Image Retrieval Technique with Aid of Modified Fuzzy C-Means Clustering (CBMIR-MFCM),” Journal of Medical Imaging and Health Informatics vol. 6, no. 3, pp. 700-709, 2016

XI.Pennington Jeffrey, Richard Socher, and Christopher Manning,“Glove: Global vectors for word representation,” Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014.

XII.Pascanu Razvan, Tomas Mikolov, and Yoshua Bengio,“On the difficulty oftraining recurrent neural networks,” International Conference on Machine Learning. 2013.

XIII.Rasiwasia Nikhil, Jose Costa Pereira, Emanuele Coviello, Gabriel Doyle, Gert RG Lanckriet, Roger Levy, and Nuno Vasconcelos,“A new approach to cross-modal multimedia retrieval,” In Proceedings of the 18th ACM international conference on Multimedia, pp. 251-260, ACM.

XIV.Rehman Sadaqat Ur, Shanshan Tu, Yongfeng Huang, and Obaid Ur Rehman, “A Benchmark Dataset and Learning High-Level Semantic Embeddings of Multimedia for Cross-Media Retrieval,” IEEE Access, vol. 6, pp. 67176-67188, 2018.

XV.SharmaAbhishek, Abhishek Kumar, Hal Daume, and David W. Jacobs,“Generalized multiview analysis: A discriminative latent space”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2160-2167, 2012.

XVI.Shen Fumin, Chunhua Shen, Qinfeng Shi, Anton Van Den Hengel, and Zhenmin Tang,“Inductive hashing on manifolds,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1562-1569, 2013.

XVII.Song Jingkuan, Yi Yang, Zi Huang, Heng Tao Shen, and Jiebo Luo,“Effective multiple feature hashing for large-scale near-duplicate video retrieval,” IEEE Transactions on Multimedia, vol. 15, no. 8, pp. 1997-2008, 2013.

XVIII.Song Jingkuan, Yang Yang, Yi Yang, Zi Huang, and Heng Tao Shen, “Inter-media hashing for large-scale retrieval from heterogeneous data sources,” In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 785-796, 2015.

XIX.Wu Fei, Zhou Yu, Yi Yang, Siliang Tang, Yin Zhang, and Yueting Zhuang,“Sparse Multi-Modal Hashing,” IEEE Trans. Multimedia, vol. 16, no. 2, pp. 427-439, 2014.

XX.Xu Xing, Yang Yang, Atsushi Shimada, Rin-ichiro Taniguchi, and Li He,“Semi-supervised coupled dictionary learning for cross-modal retrieval in internet images and texts”, In Proceedings of the 23rd ACM international conference on Multimedia, pp. 847-850, 2015.

XXI.Yang Yi, Feiping Nie, Dong Xu, Jiebo Luo, Yueting Zhuang, and Yunhe Pan,“A multimedia retrieval framework based on semi-supervised ranking and relevance feedback,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 723-742, 2012.

XXII.Yang Yang, Zheng-Jun Zha, Yue Gao, Xiaofeng Zhu, and Tat-Seng Chua, “Exploiting web images for semantic video indexing via robust sample-specific loss,” IEEE Transactions on Multimedia, vol. 16, no. 6, pp. 1677-1689, 2014.

XXIII.Yang Yang, Hanwang Zhang, Mingxing Zhang, Fumin Shen, and Xuelong Li,“Visual coding in a semantic hierarchy,” In Proceedings of the 23rd ACM international conference on Multimedia pp. 59-68, 2015.

XXIV.Zhang Hong, Yun Liu, and Zhigang Ma “Fusing inherent and external knowledge with nonlinear learning for cross-media retrieval”, Neurocomputing, vol.119, pp.10-16, 2013.

XXV.Zha Zheng-Jun, Meng Wang, Yan-Tao Zheng, Yi Yang, Richang Hong, and Tat-Seng Chua,“Interactive video indexing with statistical active learning,” IEEE Transactions on Multimedia, vol. 14, no. 1, pp. 17-27, 2014.

XXVI.Zheng Liang, Zhi Bie, Yifan Sun, Jingdong Wang, Chi Su, Shengjin Wang, and Qi Tian,“Mars: A video benchmark for large-scale person re-identification.”In European Conference on Computer Vision, pp. 868-884, Springer, 2016.

XXVII.Zhou Jile, Guiguang Ding, and Yuchen Guo,“Latent semantic sparse hashing for cross-modal similarity search,”In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014.

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Abstract:

In a lot of papers the main focus is given to study finite difference schemes for one dimensional hyperbolic equation. Since this idea is valid for one dimensional hyperbolic equation, one can also consider finite difference schemes for two dimensional hyperbolic equations. It is convenient to apply Fourier transform to check stability analysis. The present paper studies stability analysis of finite difference schemes for two dimensional hyperbolic equations with constant coefficients [IV].

Keywords:

Hyperbolic equation,fourier transform,difference schemes, stability analysis ,

Refference:

I.A.M.Blokhin, R.D. Aloev “Energy integrals and thier applications to investigation of stability of difference schemes”. Novosibirsk, 1993. 224 p.

II.A.G.Kulikovskii, N.V.Pogorelov,A.Yu.Semenov “Mathematical problems of numerical solution of hyperbolic systems”. M.:Physics and mathematics publishers, 2001, 608 p.

III.A.M.Blokhin,I.G.Sokovikov “About one approach to formulation of difference schemes for quasi-linear equations of gas dynamics”. Siberian Mathematical Journal, 1999, V.40, No 6, Pp.1236-1243.

IV.A.Harten “On the symmetric form of systems of conservation laws with enthropy”. J. Comput. Phys. 1983. V. 49, No 1, p.151-164.

V.A. M. Blokhin and R. D. Aloev, “Energy Integrals and Their Applications to Investigation of Stability of Difference Schemes”, Novosibirsk, 1993. 224 p(in Russian).

VI.A. I. Vol’pert and S. I. Khudyaev, “On the Cauchy problem for composite systems of nonlinear differential equations”,Math. USSR Sb.,16 (1972), 517–544.

VII.R. D. Aloev, Z. K. Eshkuvatov, Sh. O. Davlatov and N. M. A. Nik Long, “Sufficient condition of stability of finite element method for symmetric t-hyperbolic systems with constant coefficients”,Computersand Mathematics with Applications,68 (2014), 1194–1204.

VIII.R. D. Aloev,A. M. Blokhin and M. U. Hudayberganov, “One class of stable difference schemes for hyperbolic system”, American Journal of Numerical Analysis,2(2014), 85–89.

IX.S. K. Godunov, “Equations of Mathematical Physics”, Nauka, Moscow, 1979 (in Russian).

X.S. K. Godunov, “An interesting class of quasi-linear systems”, Dokl. Akad. Nauk SSSR,139 (1961), 521–523. (in Russian).

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Abstract:

This study investigates the dynamic response | E*|, rutting susceptibility and fatigue resistance of the virgin HMA and HMA blended with RAP and further RAP with hydrated lime mixtures. Optimum binder contents were obtained using Marshal Mix design method and the samples for performance testing were prepared. The Superpave Gyratory Compactor (SGC) was used. The samples were then cored and trimmed to the specified dimensions. Using Asphalt mixture performance tester (AMPT), test was conducted at four different temperatures (4. 4, 21. 1, 37. 7 and 54. 4) and six different frequencies (0.1, 0.5,1,5,10 and 25). And flow tests were conducted at only one temperature of 54.4 C. The viscous properties of the mixture and the dynamic response indicators were brought into account to obtain the fatigue parameters to evaluate the fatigue resistance and from flow tests the rutting susceptibility was evaluated and the results showed that RAP and lime has weak resistance to fatigue but are less susceptible to permanent deformation. Master curves for all the mixtures were developed. HMA blended with RAP are very cost effective and environmentally friendly. The flow number results revealed that the virgin HMA accumulated more strains at less loading cycles as compared to the other mixes.

Keywords:

Dynamic Modulus,Superpave,Flow numbe, RAP, Lime, HMA,

Refference:

I.AASHTO, T.J.S.S.f.T.M., M.o. Sampling, and Testing, Standard method of test for determining dynamic modulus of hot-mix asphalt concrete mixtures. 2005.

II.Bari, J., Development of a new revised version of the Witczak E* predictive models for hot mix asphalt mixtures. 2005, Arizona State University Tempe, AZ.

III.Bayane, B.M., et al., Dynamic Modulus Master Curve Construction Using Christensen-Anderson-Marasteanu (CAM) model. 2017. 7(1): p. 53-63.

IV.D, A. Standard practice for preparation of bituminous specimens using Marshall apparatus. 2010. American Society for Testing and Materials USA.

V.Dougan, C.E., et al., E*-dynamic modulus: test protocol-problems and solutions. 2003.

VI.Ekwulo, E.O., D.B.J.A.J.o.E.S. Eme, and Technology, Fatigue and rutting strain analysis of flexible pavements designed using CBR methods. 2009. 3(12).

VII.Flintsch, G.W., et al., Asphalt materials characterization in support of implementation of the proposed mechanistic-empirical pavement design guide. 2007, Virginia Center for Transportation Innovation and Research.

VIII.Gul, M.A., Laboratory characterization of HMA mixes subjected to indirect tensile fatigue test. 2015, NUST.

IX.Ma, T., et al., Using RAP material in high modulus asphalt mixture. 2015. 44(2): p. 781-787.

X.Olidis, C. and D. Hein. Guide for the Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures Materials Characterization: Is Your Agency Ready. in 2004 Annual Conference of the Transportation Association of Canada. 2004.

XI.Pellinen, T.K. and M.W.J.J.o.t.A.o.A.P.T. Witczak, Stress dependent master curve construction for dynamic (complex) modulus (with discussion). 2002. 71

XII.Seo, J., et al., Estimation of in situ dynamic modulus by using MEPDG dynamic modulus and FWD data at different temperatures. 2013. 14(4): p. 343-353

XIII.Specifications, N.G.J.L., Pakistan. National Highway Authority, prepared by SAMPAK International (Pvt.) Ltd. 1998.

XIV.Witczak, M. and O.J.T.R.R.J.o.t.T.R.B. Fonseca, Revised predictive model for dynamic (complex) modulus of asphalt mixtures. 1996(1540): p. 15-23.

XV.Witczak, M. and J.J.A.S.U.R.R. Bari, Tempe : Arizona State University, Development of a master curve (E*) database for lime modified asphaltic mixtures. 2004.

XVI.Yu, H. and S.J.R.N.T. Shen, An investigation of dynamic modulus and flow number properties of asphalt mixtures in Washington State. 2012. 2.

XVII.Ye, Q., S. Wu, and N.J.I.J.o.F. Li, Investigation of the dynamic and fatigue properties of fiber-modified asphalt mixtures. 2009. 31(10): p. 1598-1602.

XVIII.Zhang, Y., R. Luo, and R.L.J.J.o.M.i.C.E. Lytton, Characterizing permanent deformation and fracture of asphalt mixtures by using compressive dynamic modulus tests. 2011. 24(7): p. 898-906.

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Abstract:

Soil pH is one of the major factors to be considered before doing any cultivation. Farmers always tested their soil pH either in soil pH laboratory, soil pH color chart or sometimes with the help of an expert. But these methods need time, labor and expertness. In this paper, a digital Smartphone image-based method is presented which predicts the soil pH in a simple and accurate way. Soil images are captured with the help of Redmi 3S prime Smartphone and store all the images as soil dataset. Soil images are processed through the different steps of digital image processing including soil image enhancement, soil image segmentation, and soil image feature extraction. During the feature extraction, Hue, Saturation and Value of the soil image are calculated and store Saturation and Hue plus Saturation as an index for the feature vector of the soil images. Prediction of soil pH is done with the help of Linear Regression, Artificial Neural Network, and KNN Regression. The coefficient of the linear regression is 0.859 for the Saturation feature of the soil image. Again, the coefficient of linear regression is 0.823 for Hue plus Saturation. The regression coefficient for ANN is 0.94064 with Levenberg-Marquardt algorithm and 0.92932 with Scaled Conjugate Gradient Backpropagation Algorithm. The regression coefficient of KNN is 0.89326 for K=5 with an RMSE value 0.1311. It is found that ANN always gives a better result as compare to another one.

Keywords:

Soil pH, K Mean,HSV,Linear Regression, KNN,ANN,

Refference:

I.Aziz, M.M, Ahmed, D.R., Abraham, B.F, 2016. “Determine the pH of Soil by using Neural Network Based on Soil’s Colour”. International Journal of Advanced Research in Computer science and Software Engineering, Vol.: 6, Issue: 11, pp: 51-54, 2018.

II.Abu, M.A., Nasir, E.M.M. and Bala, C.R, “Simulation of Soil PH Control system using Fuzzy Logic Method”,International Journal of Emerging Trends in Computer Image & Processing. Vol.: 3, Issue: 1,pp: 15-19, 2014.

III.Aditya, A., Chatterjee, N., Pradhan, C., “Computation and Storage of Possible Pouvoir Hydrogen Level of Soil using Digital Image processing”, International Conference on Communication and Signal Processing, India. pp: 205-209, 2017.

IV.Ayoubi, S., ShahriA, P., Karchegani, P.M., Sahrawat, K.L., “Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems”. In: I. Atazadeh (Ed), Biomass and Remote Sensing Biomass. ISBN: 978-953-307. In Tech Publication. 2011.

V.Babu, C.S.M. and Pandian, M.A, “Determination of Chemical and Physical Characteristics of Soil using Digital Image processing”,International Journal of Emerging Technology in Computer Science & Electronics, Vol.: 20, Issue: 2,pp: 331-335, 2016.

VI.Barman, U., Choudhury, R., Talukdar, N., Deka, P., Kalita, I., & Rahman, N, “Prediction of soil pH using HSI colour image processing and regression over Guwahati, Assam”, India.Journal of Applied and Natural Science,Vo.: 10, Issue: 2,pp: 805-809,2018.

VII.Barman, U, Choudhury, R. D., Saud, A., Dey, S., Dey, B. K., Medhi, B.P., Barman, G.G., “Estimation of Chlorophyll Using Image Processing”, Int J Recent Sci Res, Vol.: 9, Issue: 3, pp: 24850-24853, 2018

VIII.Bodaghabadi, M.B., Martínez-Casasnovas, J.A., Salehi, M.H., Mohammadi, J., Borujeni, I.E., Toomanian, N., Gandomkar, A., “Digital Soil Mapping Using Artificial Neural Networks and Terrain-Related Attributes”, Pedosphere, Vol.: 25, Issue: 4, pp: 580-591, 2015.

IX.Dhanachandra, N., Manglem, k., Chanu y.J., “Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm”,Procedia Computer Science. Vol.: 54, pp: 764-771, 2015.

X.Ebrahimi, M., Sinegani, A.K.S., Sarikhani, M.R.,Mohammadi, S.A., “Comparison of artificial neural network and multivariate regression models for prediction of Azotobacteria population in soil under different land uses”. Computers and Electronics in Agriculture.Vol.: 140, pp: 409-421, 2017.

XI.Gurubasava, Mahantesh S.D., “Analysis of Agricultural soil pH using Digital Image Processing” , International Journal of Research in Advent Technology, Vol.: 6, Issue: 8, pp: 1812-1816, 2018.

XII.Guwahati.Assam.Link:https://www.google.co.in/maps/place/Guwahati,+Assam/data=!4m2!3m1!1s0x375a5a287f9133ff:0x2bbd1332436bde32?sa=X&ved=2ahUKEwj4jYCo1_rcAhVFKo8KHWMoB3AQ8gEwAHoECAQQAQ

XIII.Kumar, V., Vimal, B., Kumar, R., Kumar, R., & Kumar, M, “Determination ofsoil pH by using digital image processing technique”.Journal of Applied and Natural Science, Vol.: 6, Issue: 1, pp: 14-18, 2014.

XIV.Matei, O., Rusu, T., Petrovan, A., Mihuţ G.,“A Data Mining System for Real Time Soil Moisture Prediction”, Procedia Engineering,Vol.: 181, pp: 837-844, 2017

XV.Mohan, R.R., Mridula S., Mohanan P., “Artificial Neural Network Model for Soil Moisture Estimation At Microwave Frequency”, Progress In Electromagnetics Research M, Vol.:43, pp: 175–181, 2015.

XVI.Pandey, A., Jha, S.K., Srivastava, J.K., Prasad R.,“Artificial neural network for the estimation of soil moisture and surface roughness”,Russ. Agricult. Sci. Vol.: 36, pp: 428-432, 2010.

XVII.Riccardi, M., Mele, G., Pulvento, C., lavini A., D‟AndriaS R.,Jacobsen E., “Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components”. Photosynth Res. Vol.: 120, pp: 263–72, 2014.

XVIII.Rigon, J.P.G., Capuani, S., Fernandes, D.M., Guimarães, T. M., 2016. “A novel method for the estimation of soybean chlorophyll content using asmartphone and image analysis”, Photosynthetica, Vol.:54, pp:559–566, 2016.

XIX.Ruiz, N. L., Curto, V.F., Erenas, M. M., Lopez, F. B., Diamond, D., Lopez, A. J. P, Valley, A.F.C., “Smartphone-Based Simultaneous pH and Nitrite Colorimetric Determination for Paper Microfluidic Devices”. Analytical Chemistry. Vol: 86, Issue: 19, pp:1-23, 2014.

XX.Sagar, S, Debjeet, B, Advait, L,Mishra, N., “Moisture And pH Detection Using Sensors And Automatic Irrigation System Using Raspberry Pi Based Image Processing”, International Journal of Engineering Technologies and Management Research, Vol.: 5, issue: 2, pp: 153-157, 2018.

XXI.Soil pH. Link: www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_051574.pdfXXII.Swapna, U.C., Prapulla, Kumar. “Measurement of Soil PH Value Using HSV Color Space Value of Image”. International Journal of Innovative Research and Advanced Studies, Vol.: 3, Issue: 6, pp: 1-4, 2016.

XXIII.Taheri-Garavand, A., Meda, V., Naderloo, L., “Artificial neural Network−Genetic algorithm modeling for moisture content prediction of savory leaves drying process in different drying conditions”. Engineering in Agriculture, Environment and Food. Vol.: 11, Issue: 4, pp: 232-238.2018.

XXIV.Tenpe, A., Kaur,S., “Artificial neural network modeling for predicting compaction parameters based on index properties of soil”, Int J Sci Res (IJSR),Vol.: 4, issue: 7,pp: 1198–1202, 2015.

XXV.Utai, K., Nagle, M., Hämmerle, S., Spreer, W., Mahayothee, B., Müller, J., “Mass estimation of mango fruits (Mangifera indica L., cv. „Nam Dokmai‟) by linking image processing and artificial neural network”,Engineering in Agriculture, Environment and Food., Vol.: 12, Issue: 1, pp:103-110, 2019.

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Abstract:

With an increasing demand for electrical energy, it is certain that the production will also increase, especially in rapid developing countries like Pakistan. Rapid industrialization is carving for more electrical energy, investment and suitable space for its infrastructure. But this development has to be sustainable keeping in mind the increasing global temperature due to pollution. Pakistan is the six largest populations in the world and hence produces a lot of waste daily. As of now, most of the waste goes to the landfills and gets burnt there or decomposed, either way releasing greenhouse gases in the process and degrading the environment. The municipal waste management is a challenging process in developing countries because of non-availability of proper infrastructure. There are some methods to manage this waste, such as scientific landfills, Incineration, Biomethanation, Gasification, Pyrolysis and Plasma Arc Gasification. By gasification the solid waste is converted into synthesis gas which can be used for chemical industries, power generation, transportation and industrial heating etc. This process shrinks the solid waste to slag or ash which can either be used to manufacture eco bricks or can be disposed of on landfill. Thus saving a lot of place from land filling and if used for power generation it does not release any considerable harmful gases into the environment making it a sustainable process and partially renewable source of energy. This project will estimate the cost and procedure to setup gasification plant. In the study, the generation, composition, treatment and energy potential of solid waste have been studied. The technologies for waste-to-energy conversion have also been studied and the feasibility comparison of two leading technologies has been done.

Keywords:

Municipal Solid Waste,Gasification,aste-to-Energy,

Refference:

I.Abbe, O.O., Harvey, C.M., Ikuma, L.H. and Aghazadeh, F., 2011. Modelling the relationship between occupational stressors, psychosocial/physical symptoms and injuries in the construction industry. International Jou

II.Altmann, E., Kellett, P., 1999. Thermal Municipal Solid Waste Gasification. Renewable Energy Information Office, Irish Energy Centre.

III.Aznar, M.P., Gracia-Gorria, F.A., Corella, J., 1998. La velocidad minima defluizacion y de completafluidizacion de mezclas de residuosagrarios y forestales con secundosolidofluidizante. Anales de Quimica 84 (3), 385–394.

IV.Barducci G., 1992. The RDF gasifier of Florentine area (Gre`ve in Chianti Italy). The first Italian-Brazilian symposiumon Sanitary and Environmental Engineering.

V.Barducci P., Neri G., 1997. An IGCC plant in Italy for power generation from biomass. Bioelettrica Internal Report.

VI.Barducci, P., Neri, G., Trebbi, G., 1997. The Energy Farm Project. World Gas Conference, Copenhagen.

VII.Baykara, S.Z., Bilgen, E., 1981. A Feasibility Study on Solar Gasification of Albertan Coal. Alternative Energy Sources IV, vol. VI. Ann Arbor Science, New York.

VIII.Becker, B., Schetter, B., 1992. Gas turbine above 150 MW for integrated coal gasification combined cycles (IGCC). Journal of Engineering for Gas Turbines and Power 114, 660–664. Bingyan, X., Zengfan, L., Chungzhi, W., Haitao, H., Xiguang, Z., 1994. Circulating Fluidized Bed Gasifier for Biomass. Integrated Energy Systems in China. The cold Northeastern Region Experience FAO. FAO.

IX.De Lange, H.J., Barducci, P., 2000. The realization of a biomass-fuelled IGCC plant in Italy. In: European Congress on Biomass TEF.

X.Delgado, J., Aznar, M.P., Corella, J., 1996. Calcined dolomite, magnesite, and calcite for cleaning hot gas from a fluidized bed biomass gasifier with steam: life and usefulness. Industrial & Engineering Chemistry Research 35 (10), 3637–3643.

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Abstract:

The main objective of this work is to detect and classify the chronic kidney diseases (CKDs) particularly kidney stone, cystic kidney and suspected renal carcinoma. CKDs make a ground for developing several numbers of diseases other than urinal system. It will cause the pervasiveness of Coronary heart diseases, stroke, cardiomyopathy, pulmonary hypertension, and heart valves diseases, Early prediction of chronic kidney disease will save life from worse diseases, Ultrasound imaging is widely used diagnostic method for abdominal studies. In this proposed system chronic kidney diseases have detected using a framework containing Histogram of oriented gradient feature and Adaboost Algorithm. Convolution Neural Network (CNN) multi layered architecture has trained for kidney diseases classification, Batch prediction method is evaluated for prediction of chronic kidney diseases. The performance accuracy for detection of kidney disease is given as 96.67% The accuracy for the classification of CKD ultrasound using CNN is given by 85.2 %..

Keywords:

Adaboost,Chronic Kidney Diseases, HOG,Convolutional Neural Network,Ultrasound image,

Refference:

I.Atsushi Takemura, Akinobu Shimizu, and Kazuhiko Hamamoto, “Discrimination of Breast Tumors in Ultrasonic Images Using an Ensemble Classifier Based on the AdaBoost Algorithm With Feature Selection”, IEEE transactions on Medical Imaging, Vol. 29, no. 3, pp 598-609, March 2010.

II.Chensi Cao, Feng Liu, Hai Tan, Deshou Song, Wenjie Shu, WeizhongLi, Yiming Zhou, Xiaochen Bo, ZhiXie “Deep Learning and Its Applications in Biomedicine”, Elsevier Transaction on Genomics Proteomics Bioinformatics, vol. 16, pp. 17-32, Mar 2018.

III.Fangwang, KevinHe, JinweiWang, MingHuiZhao, YiLiLuxiaZhang, RajivSaran, Jennifer L.Bragg Gresham, “Prevalence and Risk Factors for CKD: A Comparison Between the Adult Populations in China and the United States” Elsevier transaction on Kidney International Reports, vol. 3, No. 5, pp. 1135-1143 Sep 2018.

IV.Hidenori Ide Takio Kurita, “Improvement of Learning for CNN with ReLU Activation by Sparse Regularization”, In Proc. International Joint Conference on Neural Networks (IJCNN) pp. 2684-2691. Jul 2017.

V.Hyunho Choi, JechangJeong ” Speckle Noise Reduction in Ultrasound Images using SRADand Guided Filter”, In proc IEEE International Workshop on Advanced Image Technology (IWAIT) , pp no 1-4, Jan 2018

VI.Kemal Adem, SerhatKiliçarslan, OnurCömert, “Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder” Elsevier transaction on Expert Systems With Applications Vol 115, pp 557-564, Jan 2019.

VII.Ling Zhang , Le Lu, Isabella Nogues, Ronald M. Summers, Shaoxiong Liu, and Jianhua Yao.” DeepPap: Deep Convolutional Networks for Cervical Cell Classification”, IEEE transaction on Journal of Biomedical and Health Informatics, vol. 21, no. 6, Nov 2017.

VIII.M. Balasubramanian, S. Palanivel, V. Ramalingam, “Video-based person recognition using fovea intensity comparison code”, Behaviour & Information Technology, November Vol.30, No. 6, pp. 747-760. 2011.,

IX.Matthieu de La Roche Saint Andre, Laura Rieger, Morten Hannemose, and Junmo Kim, “Tunnel Effect in CNNs: Image Reconstruction from Max Switch Locations”, IEEE Signal Processing Letters, Vol. 24, No. 3, pp. Mar 2017.

X.Mr.B.Perumal, 2 Dr.M.PallikondaRajasekaran, “A Hybrid Discrete Wavelet Transform with Neural Network Back Propagation Approach for Efficient medical Image Compression”, In Proc International Conference on Emerging Trends in Engineering, Technology and Science(ICETETS), pp 1-5, Feb 2016.

XI.Pietro Perona, Jitendra Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion”, IEEE Transactions ON Pattern Analysis and Machine Intelligence, vol. 12. no. 7, Pp 629-639, July 1990

XII.R. Vasanthselvakumar, M. Balasubramanian, S.Palanivel, “Pattern Analysis of Kidney Diseases For Detection And Classification Using Ultrasound B -Mode Images”, International Journal of Pure and Applied Mathematics, Volume 117 No. 15, pp. 635-653, 2017.

XIII.Yanwei Pang, Manli Sun, XiaohengJiang, and Xuelong Li, “Convolution in Convolution for Network in Network” IEEE Transaction on Neural Networks and Learning Systems, Vol. 29, no. 5, pp. 1587-1597, May 2018.

XIV.Yongjin Zhou, Jingxu Xu, Qiegen Liu, Cheng Li, Zaiyi Liu, Meiyun Wang, Hairong Zheng, and Shanshan Wang, “A Radiomics Approach With CNN forShear-Wave Elastography Breast Tumor Classification”, IEEE Transactions on Biomedical Engineering, vol. 65, no. 9, pp. 1935-1942, Sep 2018.

XV.YujiIwahoria, AkiraHattoria, YoshinoriAdachia, M.K.Bhuyanb, Robert J. Woodhamc, KunioKasugaid, “Automatic Detection of Polyp Using Hessian Filter and HOG Features”, Elsevier transaction on Procedia of Computer Science, Volume 60, pp. 730-739, 2015

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Abstract:

This paper presents a comparative study of the performances of a doubly fed induction generator (DFIG) based grid connected wind turbine (WT) system using back-to-back converter (B2BC) and nine-switch converter (NSC). The time domain simulink results of the system variables, under varying wind velocity, are presented and analyzed all the results in details. Results show that the B2BC- used with DFIG-WT system can be replaced by NSC under any wind speed.

Keywords:

WECS,DFIG,B2BCand NSC,

Refference:

I.A. M. Hava, R. Kerkman, and T. A. Lipo, “Carrier-based PWM-VSI over modulation strategies: analysis, comparison, design,” IEEE Transactions on Power Electronics, Vol. 13, no.4, July 1998, pp. 674–689.

II.B. H. Chowdhury, S. Chellapilla, “Double Fed Induction Generator Control for VariableSpeed Wind Power Generation,” Elsevier Power System Research, Vol.76, pp: 786-800, 2006.

III.C. Liu, B. Wu, N. R. Zargari, D. Xu, and J. Wang, “A novel three phase three-leg ac/ac converter using nine IGBTs,” IEEE Trans. Power Electron., vol. 24, pp. 1151–1160, 2009.

IV.C. Liu , B.Wu, N.Zargari and D.Xu, “A novel nine-switch PWM rectifier-inverter topology for three-phase UPS applications, ” Journal of European Power Electronics (EPE), vol. 19, pp. 1 -10, 2009.

V.C. Klumpner and F. Blaabjerg, “Experimental evaluation of ride-through capabilities for a matrix converter under short power interruptions,” IEEE Trans. Industrial Electronics, Vol. 49, pp. 315-324, 2002.

VI.F. Blaabjerg, S. Freysson, H. H. Hansen, and S. Hansen, “A new optimized space-vector modulation strategy for a component-minimized voltage source inverter,” IEEE Trans. Power Electron., vol. 12, pp. 704–714, 1997.

VII.F. Gao, L. Zhang, D. Li, P.C. Loh,Yi Tang and H. Gao, “Optimal pulse-width modulation of nine-switch converter, ” IEEE Transaction on PowerElectronics, vol. 25, pp. 2331-2343, 2010.

VIII.G. Li, M. Yin, M. Zhou and C. Zhao, “Decoupling control for multi terminal VSC HVDC based wind farm interconnection,” IEEE Power Engineering Society General Meeting, pp.1-6, 2007.

IX.Gui-Jia Su Hsu, J.S., “A five-leg inverter for driving a traction motor and a compressor motor,” IEEE Transactions on Power Electronics, vol. 21, pp. 687 -692, 2006.

X.H. Nikkhajoei, R. H. Lasseter, “Power quality enhancement of a wind-turbine generator under variable wind speeds usingmatrix converter,” Power Electronic Specialists Conference, pp. 1755-1761, 2008.

XI.Lie Xu, “Coordinated Control of DFIG’s Rotor and Grid Side Converters during Network Unbalance,” IEEE Trans. on Power Electronics, Vol. 23, pp.1041-1049, 2008.

XII.L. Holdsworth, X. G. Wu, J. B. Ekanayake and N. Jenkins, “Comparison of fixed speed and doubly-fed induction wind turbines during power system disturbances,” IEE Proc. Gener. Transm. Distrib., Vol. 150, pp. 343-352, 2003.

XIII.M.V.A. Nunes, H.H. Zurn, U.H. Bezerra, J.A. Pecas Lopes, R.G. Almeida, “Influence of the variable Speed wind Generators in Transient Stability Margin of the Conventional Generators Integrated in Electrical Grids,” IEEE Transactions on Energy Conversion, Vol. 21, pp257-264, 2006.

XIV.M. Jones, S. N. Vukosavic, D. Dujic, E. Levi, and P. Wright, “Five-leg inverter PWM technique for reduced switch count two-motor constant power applications,” IET Proc. Electric Power Application, vol. 2, pp. 275–287, 2008.

XV.O. Ojo, “The generalized discontinuous PWM scheme for three phase voltage source inverters,” IEEE Trans. Ind. Electron., vol.51, pp.1280-1289, 2004.

XVI.P. C. Loh, F. Blaabjerg, F. Gao, A. Baby, and D. A. C. Tan, “Pulse width modulation of neutral-point-clamped indirect matrix converter,” IEEE Trans. Ind. Application, vol. 44, pp. 1805–1814, 2008.

XVII.R.G. Almeida, E.D. Castronuovo, J.A. Pacas Lopes, “Optimum Control in Wind Parks when Carrying out system Operator Requests,” IEEE Transactions Power System. Vol.19, pp 1942-1950, 2006.

XVIII.R. Cardenas, R. Pena, G. Tobar, J. Clare, P. Wheeler, G. Asher, “Stability Analysis of a Wind Energy Conversion System Based Doubly Fed Induction Generator Fed By a Matrix Converter,” IEEE Trans. on Industrial Electronics, Vol. 56, pp.4194-4206, 2009.

XIX.S. Datta, J. P. Mishra and A. K. Roy, “Modified Speed Sensor-less Grid Connected DFIG based WECS”, Indian Journal of Science of Technology, Vol. 8, Issue No. 16, pp.1-12, 2015.

XX.S. Datta, J. P. Mishra and A. K. Roy, “Operation and control of a DFIG-based grid-connected WECS using NSC during grid fault and with unbalanced non-linear load”, International Journal of Ambient Energy, Vol.39, No.7, pp.732-742, 2018.

XXI.T. Kominami and Y. Fujimoto, “A novel nine-switch inverter for independent control of two three-phase loads,” IEEE Industry Applications Society Annual Conference (IAS), pp. 2346-2350, 2007.

XXII.Y. Lei, A. Mullane, G. Lightbody, R. Yacamini, “ Modeling of the wind turbine with a Doubly fed Induction Generator for Grid Integration Studies,” IEEE Transactions on Energy Conversion, Vol. 21, pp.257-264, 2006.

XXIII.Z. Lei, P. C. Loh and F. Gao, “An integrated nine-switch power conditioner for power quality enhancement and voltage sag mitigation,” IEEE Transaction on Power Electronics, vol. 27, pp. 1177-1190, 2012.

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Abstract:

This paper deals with an inventory model for deteriorating items with linear price and frequency of advertisement dependent demand and exponentially backlogged shortages. The deterioration rate follows three-parameter Weibull distribution. The corresponding non-linear problem have been formulated and solved. Numerical example has been considered to illustrate the model and the significant features of the result are discussed. Finally, we have performed the sensitivity analysis taking one or more parameters at a time.

Keywords:

Inventory,Weibul distributiondeterioration,linear price dependent demand,Partially backlogged shortage,

Refference:

I.Abad, P.L.: Optimal pricing and lot-sizing under of conditions of perishability and partial backordering, Management Science, 42,1093-1104(1996).

II.Bhunia, A.K., Maiti, M.: An inventory model of deteriorating items with lot-size dependent replenishment cost and a linear trend in demand, Applied Mathematical Modelling,23, 301-308(1998).

III.Bhunia,A.K., Maiti, M.: Deterministic inventory model for deteriorating items with finite rate of replenishment dependent on inventory level,Computers and Operations Research, 25,997-1006(1998).

IV.Bhunia, A.K., Shaikh, A.A, Dhaka, V., Pareek, S. An application of Genetic Algorithm and PSO in an inventory model for single deteriorating item with variable demand dependent on marketing strategy and displayed stock level, Scientica Iranica (in press) (2017)

V.Bhunia, A.K., Shaikh, A.A. A two-warehouse inventory model for deteriorating items with time dependent partial backlogging and variable demand dependent on marketing strategy and time, International Journal of Inventory Control and Management, 1(2), 95-110 (2011).

VI.Bhunia, A.K., Shaikh, A.A., A deterministic inventory model for deteriorating items with selling price dependent demand and three-parameter Weibull distributed deterioration, International Journal of Industrial Engineering Computations (Growing Science), 5(3), 497–510(2014).

VII.Bhunia, A.K., Shaikh, A.A., A deterministic model for deteriorating items with displayed inventory level dependent demand rate incorporating marketing decision with transportationcost, International Journal of Industrial Engineering Computations, 2(3),547-562 (2011).

VIII.Bhunia, A.K., Shaikh, A.A., Cárdenas-Barrón, L.E., A partially integrated production inventory model with interval valued inventory costs, variable demand and flexible reliability, Applied Soft Computing, 55, 491-502 (2017).

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Abstract:

To determine the parameters of Stochastic Volatility Model (SVM), a modification to the Quasi Maximum Likelihood (QML) scheme has been proposed by employing (modified) Adaptive Kalman Filter (AKF). AKF allows optimization over lesser number of parameters as the variance ( 2 v  ) of the noise in the volatility state equation is determined by the AKF. The adaptive method, instead of a constant 2 v  , allows it to be time varying. Before applying the methodology on market data, the proposed method is characterized here by synthetic data through simulation investigations. Numerical experiments show that the performance of SVM based QMLKF and novel QML-AKF are comparable to that of more popular GARCH family based techniques

Keywords:

Adaptive Estimation, Noise Covariance Adaptation, Modified AKF,Stochastic Volatility Model,Quasi-Maximum Likelihood,

Refference:

I.A. C. Harvey, E. Ruiz and N. Shephard, Multivariate stochastic variance models, Review of Economic Studies, vol. 61, pp. 247-264, 1994.

II.A. Das, Estimation and Prediction in Finance–A Review, in Ed. Vol. Dynamics of Commerce and Management in the New Millennium, edited by N. Chaudhary, pp. 267-306, International Research Publication House, 2014.

III.A. Das, Higher Order Adaptive Kalman Filter for Time Varying Alpha and Cross Market Beta Estimation in Indian Market, Economic Computation and Economic Cybernetics Studies and Research, vol.-50, no-3, pp. 211-228, 2016.

IV.A. Das, T. K. Ghoshal, Market Risk Beta Estimation using Adaptive Kalman Filter, International Journal of Engineering Science and Technology, vol. 2, no. 6, pp. 1923-1934, 2010.

V.A. Das, T. K. Ghoshal, P. N. Basu, “A Review on Recent Trends of Stochastic Volatility Models”, International Review of Applied Financial Issues and Economics, vol. 1, no. 1, 2009.

VI.A. Javaheri, Inside volatility arbitrage: the secrets of skewness, John Wiley & Sons, 2005.

VII.B. Liu and I. Hoteit, Nonlinear Baysian Mode Filtering, International Journal of Innovative Computing Information and Control, vol. 11, no. 1, pp. 231-245, 2015.

VIII.C. Broto and E. Ruiz, Estimation Methods For Stochastic Volatility Models:A Survey, Working Paper, available at http://docubib.uc3m.es/WORKINGPAPERS /WS/ ws025414.pdf, 2004.

IX.C. I. Mota-Hernández, T. I. Contreras-Troya and R. Alvarado-Corona, A Systems Methodology to Solve Economical-Financial Problems (SMEFP), International Journal of Innovative Computing Information and Control, vol. 11, no. 1, pp. 173-188, 2015.

X.E. Jacquier, N. G. Polson, and P. E. Rossi, Bayesian Analysis of Stochastic Volatility Models, Journal of Business and Economic Statistics, vol. 12, pp. 371-389, 1994.

XI.E. Ruiz, Quasi-Maximum Likelihood Estimation of Stochastic Volatility Models, Journal of Econometrics, vol. 63, pp. 284–306, 1994.

XII.F. J. Breidt and Alicia L Carriquiry, Improved Quasi-Maximum Likelihood Estimation for Stochastic Volatility Models, Iowa State University, available at http://www.public.iastate.edu/~alicia/ Papers/General%20Applications/ improved.pdf, 1996.

XIII.H. Kawakatsu, Numerical Integration Filters for Maximum Likelihood Estimation of Asymmetric Stochastic Volatility Models, School of Management and Economics, 25 University Square, Queen’s University, Belfast, January 28, 2005.

XIV.H. Kawakatsu, Numerical integration-based Gaussian mixture filters for maximum likelihood estimation of asymmetric stochastic volatility models, The Journal of Econometrics, vol. 10, Issue 2, 2007.

XV.N. Shephard, Stochastic Volatility: Selected Readings, Oxford University Press, 2005.

XVI.Nelson, D. B., The Time Series Behaviour of Stock Market Volatility and Returns, Ph.D. Thesis, Massachusetts Institute of Technology, 1988.

XVII.S. Alizadeh, , M. Brandt, and F. Diebold, Range based estimation of stochastic volatility models, The Journal of Finance, vol. 57, pp. 1047-1091, 2002.

XVIII.S. H. Poon, A Practical Guide to Forecasting Financial Market Volatility, John Wiley & Sons, 2005.

XIX.S. J. Taylor, Financial returns modelled by the product of two stochastic processes-a study of daily sugar prices 1961–79, in Anderson, O. D. (ed.), Time Series Analysis: Theory and Practice, pp. 203–226, Amsterdam, North-Holland, 1982.

XX.S. Tong, D. Qian and J. Fang, Joint Estimation of Parameters, States and Time Delay Based on Singular Pencil Model, International Journal of Innovative Computing Information and Control, vol. 12, no. 1, pp. 173-188, 2016.

XXI.T. Bollerslev, R. Y. Chou, and K. F. Kroner, ARCH modeling in finance, Journal of Econometrics, vol. 52, pp. 5–59, 1992.

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Abstract:

During the studying and job author noticed that in construction industry, the counterfeit items, are many and becoming a high reason of concern for the population. Further digging out the subject, exploring the other parallel industries of neighboring economies shows that the counterfeit items are produce in such manner that it become an industry itself. And it has penetrated the other national and international trades of all sorts, where civil work industry is also not speared keeping that its growing day by day and profit margin is higher for the opportunist of the counterfeit material manufactures and distributors. China, Turkey, Taiwan are the main lands of these manufacturer to produce the counterfeit items where Honking, Malaysia, UAE are the main distributing hubs for these counterfeit products due to weak law enforcement or flexible business rules. The impact and presence of counterfeit material in civil industry Peshawar region, 150 participants were selected in three subgroups such as Contractors, client and consultants to collect data through open and closed ended questionnaires, interviews, discussion, physical inspection visits of manufacture, warehouses and deliveries regarding the availability, use and volume of the counterfeit products in the Peshawar market. This data was further analyzed and evaluated with SPSS. The outcome of the data evaluation on the subject exposes the enormous increase of counterfeit material in the industry special in wood work, water sanitation, electric items and civil works as these items were the target of this research. Most factors are the unawareness, low price, scarcity of original product in market that these items exist in substitute product.

Keywords:

Refference:

I.Box Po. Counterfeit Construction Products From Low-Cost Sourcing Countries. 2011;(June):1–12.

II.Buxbaum P. Aafa ’ S Top Counterfeiting Countries. 2018;2017–9.

III.Cademan A, Henriksson R, Nyqvist V. The Affect Of Counterfeit Products On Luxury Brands. Linnaeus Univ [Internet]. 2012;3.

IV.Favourites Addto. Fake Building Materials Are Endangering Lives 0 05. 2018;1–4.

V.Government Of The United States. Strtegy To Combat Transnational Organized Crime. 2011;28.

VI.Lewis K, Lewis B. The Fake And The Fatal : The Consequences Of Counterfeits. 2007;Xvii:47–58.

VII.Luborsky Fe, Barber Wd. Nondestructive Readout In Plated Wires. Ieee Trans Magn. 1971;7(3):490–3.

VIII.Rutter J, Bryce J. The Consumption Of Counterfeit Goods: “Here Be Pirates” Sociology. 2008.

IX.Tom G, Garibaldi B, Zeng Y, Pilcher J. Consumer Demand For Counterfeit Goods. 15(August 1998):405–21.

X.Understanding Counterfeit Supply. 2006.

XI.Wilcox K, Kim Hm, Sen S. Why Do Consumers Buy Counterfeit Luxury BrandsJ Mark Res [Internet]. 2009;46(2):247–59.

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Abstract:

The objective of this research paper is to explain the process of PLC based sorting of different parcels in companies. Automatic parcel sorting systems are essential for courier companies with a high distribution capacity and short time-to-deliver and courier companies need to increase the quality and reliability of their services as the Customers demand quicker deliveries of goods. In many courier companies parcel sorting and placing on their particular location is done manually which seems complex and takes time so we have decide to provide ease to courier companies by implementing a system which does all these work without the interference of human being. Our proposed project automatic parcel sorting system based on PLC is one of the useful projects for couriers companies; we used the technique of RFID for the identification of different parcel the solution that we are providing to the courier companies

Keywords:

RFID,PLC,reliability,short time delivery,

Refference:

I.Automatic Sorting Machine Using Delta PLC”, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163 Volume 1 Issue 7 (August 2014)

II.Automatic letter sorting system for Indian postal address recognition system based on PIN codes”, Georgian Electronic Scientific Journal: Computer science and Telecommunications 2010

III.Automatic Box Sorting Machine Shreeya V. Kulkarni1 Swati R. Bhosale2 Priyanka P. Bandewar3 Prof. G.B.Firame4 IJSRD -International Journal for Scientific Research & Development| Vol. 4, Issue 04, 2016 | ISSN (online): 2321-0613.

IV.Adeoye, A. O. M., A. A. Aderoba, and B. I. Oladapo. “Simulated designof a flow control valve for stroke speed adjustment of hydraulic power of robotic lifting device.” Procedia engineering 173 (2017): 1499-1506.

V.Berger I, Chevion D, Heilper A, Navon Y, Tzadok A, Tross M, Wallach E, inventors; International Business Machines Corp, assignee. Automatic location of address information on parcels sent by mass mailers. United States patent US 6,360,001. 2002 Mar 19.

VI.Bargal, Nilima, et al. “PLC based object sorting automation.” International Research Journal of Engineering and Technology (IRJET) 3.7.

VII.Oladapo, Bankole I., et al. “Experimental analytical design of CNC machine tool SCFC based on -pneumatic system simulation.” Engineering Science and Technology, an International Journal 19.4 (2016): 1958-1965.

VIII.Sowmiya D (2013). Monitoring and control of a PLC based VFD fed three phase induction motor for powder compacting press machine. Intelligent Systems and Control (ISCO), 7th International Conference on Digital Object Identifier: 10.1109/ISCO.2013.6481128. 90 –92.

IX.Thirumurugan, P., et al. “Automatic sorting in process industries using PLC.” Global Research and Development Journal for Engineering 3.3 (2018

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Abstract:

P. Vasudeva ReddyThe most important contribution of modern cryptography is the invention of digital signatures. Digital signature schemes have been extended to meet the specific requirements for real world applications. A directed signature scheme is a kind of signature scheme intended to protect the privacy of the signature verifier. In directed signature schemes, a signer signs the document/message for a designated verifier so that only the designated verifier can verify the validity of the signature and others cannot do. Thus the restriction of verification is controlled by the signer. Such directed signature schemes are applicable in many situations where the signed message is sensitive to the receiver such as signature on medical records, tax information etc. However all the existing directed signature schemes in ID based setting uses bilinear pairings over elliptic curves. Due to the heavy computational cost of pairing operations, these existing ID based directed signature schemes are not much efficient in practice. In order to improve the efficiency, in this paper, we present an efficient Identity-based directed signature scheme without pairings. The proposed scheme is proven secure under the assumption of elliptic curve discrete logarithm problem is hard. In addition, this scheme improves the efficiency than the existing directed signature schemes in terms of computational cost.

Keywords:

Digital signature,Directed Signature,Elliptic Curve Discrete Logarithm Problem,Identity-based Framework,Random Oracle Model,

Refference:

I.A. Shamir; “Identity-based Cryptosystems and Signature Schemes”, Advances in Cryptology, Crypto-84, Lecture Notes in Computer Science, Springer, vol. 196, pp.47-53, 1984

II.B. Uma Prasada Rao; P. Vasudeva Reddy; T. Gowri; “An efficient ID-Based Directed Signature Scheme from Bilinear Pairings”, Available at https://eprint.iacr.org/2009/617.pdf.

III.C. H. Lim; P. J. Lee; “Directed Signatures and Applications to Threshold Cryptosystem”, Workshop on Security Protocol, Cambridge, pp. 131-138, 1996

IV.C. P. Schnorr; “Efficient Identification and Signatures for Smart Cards”,Advances in Cryptology-Crypto’89, Lecture Notes in Computer Science, Springer, vol. 435, pp. 239-252, 1989

V.D. Pointcheval; J. Stern; “Security Arguments for Digital Signatures and Blind Signatures”, Journal of Cryptology, vol. 13, No.3, pp.361-369, 2000

VI.E. S. Ismail; Y. Abu-Hassan; “A Directed Signature Scheme Based on Discrete Logarithm Problems”, Jurnal Teknologi, vol. 47(C), pp. 37-44, 2007

VII.F. Laguillaumie; P. Paillier; D. Vergnaud; “Universally Convertible Directed Signatures”, Advances in Cryptology -ASIACRYPT’05, Lecture Notes in Computer Science, Springer, vol. 3788, pp. 682–701, 2005

VIII.J. Ku; D. Yun; B. Zheng; S. Wei; “An Efficient ID-Based Directed Signature Scheme from Optimal Eta Pairing”, Computational Intelligence and Intelligent Systems, vol. 316, pp. 440-448, 2012

IX.J. Zhang; Y. Yang; X. Niu; “Efficient Provable Secure ID-Based Directed Signature Scheme without Random Oracle”, 6th International Symposium on Neural Networks: Advances in Neural Networks-ISNN 2009, Lecture Notes in Computer Science, Springer, vol. 5553, pp.318-327, 2009

X.L. C. Guillou; J. J. Quisquater; “A “Paradoxical” Indentity-BasedSignature Scheme Resulting from Zero-Knowledge”, Advances in Cryptology-Crypto’88, Lecture Notes in Computer Science, Springer, vol. 403, pp. 216-231, 1988

XI.N. B. Gayathri; T. Gowri; R. R. V. Krishna Rao; P. Vasudeva Reddy; “Efficient and Secure Pairing-free Certificateless Directed Signature Scheme”, Journal of King Saud University-Computer and Information Sciences, Article in press, 2018

XII.N. Koblitz; “Elliptic Curve Cryptosystems”, Mathematics of Computation, vol. 48, no. 177, pp. 203-209, 1987

XIII.N. N. Ramlee; E. S. Ismail; “A New Directed Signature Scheme with Hybrid Problems”, Applied Mathematical Sciences, vol. 7, No. 125, pp. 6217-6225, 2013

XIV.N. Tiwari; S. Padhye; “Provable Secure Multi-proxy Signature Scheme without Bilinear Maps”, International Journal of Network Security,vol.17, no.6, pp.736-742, 2015XV.P.S.L.M. Barreto; B. Libert; N. McCullagh; J.J. Quisquater; “Efficient and Provably Secure Identity-based Signatures and Signcryption from Bilinear Maps”, Advances in Cryptology-ASIACRYPT’05, Lecture Notes in Computer Science, Springer, vol. 3788, pp. 515-532, 2005

XVI.Q. Wei; J. He; H. Shao; “Directed Signature Scheme and its Application to Group Key Initial Distribution”, 2ndInternational Conference on Interaction Sciences: Information Technology, Culture and Human (ICIS-2009), ACM, 2009, pp. 24-26, 2009

XVII.R. Lu; Z. Cao; “A Directed Signature Scheme Based on RSA Assumption”, International Journal of Network Security, vol. 2, No. 3, pp.182–421, 2006

XVIII.S. Lal; M. Kumar; “A Directed Signature Scheme and its Applications”, 2004. Available at http://arxiv.org/abs/cs/0409035.

XIX.S. Y. Tan; S. H. Heng; B. M. Goi; “Java Implementation for Pairing-Based Cryptosystems”, Computational Science and Its Applications (ICCSA’10), Lecture Notes in Computer Science, Springer, vol. 6019, pp. 188-198, 2010

XX.Shamus Software Ltd. Miracl Library. Available: http://certivox.org/display /EXT/MIRACL.

XXI.V. Miller; “Uses of Elliptic Curves in Cryptography”, Advances in Cryptology-Crypto 85, pp. 417-426, 1985

XXII.W. Diffie; M.E. Hellman; “New Directions in Cryptography”, IEEE Transactions in Information Theory, vol. 22, pp.644-654, 1976

XXIII.X. Cao; W. Kou; X. Du; “A Pairing-free Identity-based Authenticated Key Agreement Protocol with MinimalMessage Exchanges”, Information Sciences, vol. 180, No. 15, pp. 2895-2903, 2010

XXIV.X. Sun; J. Li; G. Chen; S. Yung; “Identity-Based Directed Signature Scheme from Bilinear Pairings”, Available at https:// eprint.iacr.org/2008/305.pdf.

XXV.Y. Wang; “Directed Signature Based on Identity”, Journal of Yulin College, vol. 15, No. 5, pp. 1–3, 2005

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Abstract:

In this work we try to introduce the concept of codes of polynomial type and polynomial codes that are built over the ring A[X]/A[X]f(X).It should be noted that for particular cases of f we will find some classic codes for example cyclic codes, constacyclic codes, So the study of these codes is a generalization of linear codes.

Keywords:

Cyclic codes,dual code,Polynomial code, principal polynomial code,codes of polynomial type,

Refference:

I.Adamek, J. (1991). Foundations of coding. Interscience, Prague.

II.Greferath, M. (1997). Cyclic codes over finite rings. Discrete Mathematics 177, University of Duisburg.

III.Klein, P. N. (2013). Coding the Matrix: Linear Algebra through Computer Science Applications. Newtonian Press, Brown, first edition.

IV.Neubauer, A., Freudenberger, J., and Kuhn,

V. (2007). Coding Theory -Algorithms, Architectures, and Applications.Wiley-Interscience, Germany.

V.Springer, Eindhoven University, third edition.

VI.van Lint, J. (1973). Coding Theory. Springer-Verlag Berlin Heidelberg, London, 2nd edition.

VII.van Lint, J. (1999). Introduction to Coding Theory.VIII.Williams, F. M. and Sloane, N. J. A. (1981). The theory of error-corecting codes. Mathematical Library, North-Holland, third edition

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Abstract:

In this paper, multilevel image thresholding for image compression is proposed for the first time using Shannon entropy and Fuzzy entropy, which are maximized by the nature-inspired hybrid Bat algorithm and Pattern Search (hBA-PS).The ordinary thresholding method gives high computational complexity, but while extending for multilevel image thresholding, the optimization techniques are needed in order to reduce the computational time. Particle Swarm Optimization (PSO) and FA (Firefly Algorithm) undergo instability when the particle velocity is maximum. It is evident that Bat Algorithm (BA) is good in exploitation whereas Pattern Search (PS) is good in exploration. We hybridized the BA and PS based on their strengths and weaknesses. The proposed technique (hBA-PS) is compared with Differential Evolution (DE), PSO and BA for which the experimental results are compared in terms of Standard deviation, Computational time, Peak Signal to Noise Ratio (PSNR), Weighted PSNR and Reconstructed image quality. The performance of the proposed algorithm is found to be better with Fuzzy entropy compared to Shannon.

Keywords:

Bat algorithm,Pattern Search,Image compression,Thresholding,Shannon entropy,Fuzzy entropy,

Refference:

I.Chandra Sekhar. G.T, Sahu. R. K, Baliarsingh. A.K, and Panda.S,“Load frequency control of power system under deregulated environment using optimal firefly algorithm”, Electrical Power and Energy Systems, Vol.74 pp. 195–211, 2016

II.Chen-Kuei.Y and Wen-Hsiang. T, “Color image compression using quantization, thresholding, and edge detection techniques all based on the moment-preserving principle”, Pattern Recognition Letters,Vol. 19, pp. 205-215, 1998

III.Hooke. R and Jeeves. T.A, “Direct search” solution of numerical and statistical problems. Journal of the Association for Computing Machinery (ACM) 8 (2): 212–229, 1960

IV.Kapur. J. N, P.K.Sahoo, A.K.C Wong, “A new method for gray-level picture thresholding using the entropy of the histogram”, Computer Vision Graphics Image Process, Vol. 29, pp. 273-285, 1985

V.Kaur. L, S. Gupta, R.C. Chauhan, S.C. Saxenac, “Medical ultrasound image compression using joint optimization of thresholding quantization and best-basis selection of wavelet packets”, Digital Signal Processing,Vol.17, pp.189–198, 2007

VI.Kaveh Ahmadi, Ahmad Y. Javaid, Ezzatollah Salari, “An efficient compression scheme based on adaptive thresholding in wavelet domain using particle swarm optimization”Signal Processing:Image Communication,Vol. 32, pp. 33–39,2015

VII.Kiruba M, Sumathy V (2018) Register Pre-allocation based Folded Discrete Tchebichef Transform Architecture for Image compression. InternationaltheVLSI Journal, volume 60, pp. 13-24. https://doi.org / 10.1016/j.vlsi.2017.07.003

VIII.Luca. A, S. Termini, “A definition of a non-probabilistic entropy in the setting of fuzzy sets theory”, Information Control,Volume 20, pp. 301-312, 1972

IX.Navas. K. A, Gayathri Devi K. G, Athulya M. S, Anjali Vasudev, “MWPSNR: A new image fidelity metric”, IEEE Recent Advances in Intelligent Computational Systems (RAICS),pp. 627-632, 2011

X.Otsu. N, “A threshold selection from gray level histograms” IEEE Transactions on System, Man and Cybernetics,Vol. 66, 1979

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Abstract:

In Pakistan, lack of adoption of modern automated designs tools have kept the drafting, designing and construction industry, unintegrated. Almost all draftsman provide their architecture design in AutoCAD with a lot of limitation. These limitation tends to create hurdles for structural engineer while designing. After design detailing in AutoCAD and preparation of BOQ and cost estimation in a non-interoperable software is a tedious work and require time. The Architecture Engineer and Construction (AEC) trades needs such techniques to drop project rate, delivery time and increase quality, efficiency and productivity. Building Information Modeling technology can be used as a choice to get above mention parameters in which an accurate BIM model is constructed in software which is used for planning, designing and construction of the facility. In this paper BIM tools Revit and Robot structural analysis professional software are used for design and analysis of structure and in ETABs software for cross check. Detailing, BOQ and cost estimation reports are prepared at the end.

Keywords:

Building Information Modeling,,BIM model,Robot structural analysis,cost estimation,

Refference:

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