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Exploring Innovation| ISSN:2319-9598(Online)| Reg. No.:68563/BPL/CE/12| Published by BEIESP| Impact Factor:3.47
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Volume-4 Issue-2: Published on June 20, 2016
05
Volume-4 Issue-2: Published on June 20, 2016

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Volume-4 Issue-2, June 2016, ISSN: 2319-9598 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

 

1.

Authors:

Nithyananda C R, Ramachandra A C, Prashanth C R

Paper Title:

Classification of Images into Clusters by Its Properties (CICP)

Abstract:  The Quality of the given image is identified by its Features and properties. In this paper Image Classification of Images into Clusters by its Properties (CICP) we analyze the different Features and Properties of various types of images. The images are of good visible, moderate visible and blur for visibility. The basic properties such as Entropy, Contrast, Skewness, Brightness, Kurtosis, Visibility and Spatial Frequencies are calculated for the given images. These property values are extracted for Weibull, Contrast, Intensity and Fractal images. Image Classification is made based on the properties which are unique for particular type of images.

Keywords:
 Brightness, Moments, Standard Deviation, Spatial Frequency, Visibility.

References:

1.       Emilce Moler, Virginia Ballarin, Franco Pessana, Sebastian Torres and Dario Olmo, “Fingerprint Identification using Image Enhancement Techniques,” International Journal of Forensic Sciences, pp. 689-692, 1998.
2.       Andreas Markus Loening and Sanjiv Sam Gambhir, “AMIDE: A Free  Software Tool for Multimodality Medical Image Analysis,” International Journal on Molecular Imaging,. vol. 2, pp. 131 – 137, 2003.

3.       Eun Kyung Yun and Sung-Bae Cho, “Adaptive fingerprint image enhancement with fingerprint Image Quality Analysis,” Elsevier Journal on Image and Vision Computing, vol. 24, pp.101–110, 2006.

4.       Lukasz Kobylinski and Krzysztof Walczak , “Image Classification with Customized Associative Classifiers,” International Journal of Multiconference on Computer Science and Information Technology, pp. 85–91,  2006.

5.       Vincent de Gardelle and Sid Kouider, “How Spatial Frequencies and Visual Awareness Interact During Face Processing,” International Journal of  Psychological Science, vol.21,  pp. 58-66, 2010.

6.       Haidi Ibrahim, “Histogram Equalization with Range Offset for Brightness Preserved Image Enhancement,” International Journal of Image Processing, vol. 5, pp. 599-609, 2011.

7.       Kother Mohideen, Arumuga Perumal, Krishnan and Mohamed Sathik, “Image Denoising And Enhancement Using Multiwavelet With Hard Threshold In Digital Mammographic Images,” International Arab Journal of e-Technology, vol. 2, pp. 49-55,  2011.

8.       J Quintanilla Dominguez, B Ojeda Magana, M G Cortina-Januchs, R Ruelas, A Vega Corona and D Andina, “Image Segmentation by Fuzzy and Possibilistic Clustering Algorithms for the Identification of Microcalcifications,” Elsevier Scientia Iranica, vol. 8, pp. 580–589, 2011.

9.       Muna F Hanoon, “Contrast Fingerprint Enhancement Based on Histogram Equalization Followed By Bit Reduction of Vector Quantization,” International Journal of Computer Science and Network Security, vol.11, pp. 116-123, 2011.

10.    He Xiaolan and Wu Yili, “Texture Feature Extraction Method Combining Nonsubsampled Contour Transformation with Gray Level Co-occurrence Matrix,” International Journal of Multimedia, vol. 8, pp. 675 - 684 , 2013.

11.    A Sanchez Romero1, J A González1, J Calbo1, and A Sanchez-Lorenzo,  “Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance,” International Journal of Atmospheric Measurement Techniques, vol. 8, 183–194, 2015.

12.    Brian Johnson, “Remote Sensing Image Fusion at the Segment Level Using a Spatially-Weighted Approach: Applications for Land Cover Spectral Analysis and Mapping,” International Journal of Geo-Information, vol. 4, pp. 172-184, 2015.

13.    Najva Izadpanah, “A Divisive  Hierarchical Clustering based Method for Indexing Image Information,” Signal & Image Processing : An International Journal, vol.6, pp. 13-32,  2015.


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2.

Authors:

Amit Banerjee, Rohan Mandloi

Paper Title:

Online Fee Payment Application

Abstract:   We propose an online fee payment  application using which we can pay the college fees from any desirable location. The application will run on the web which makes it portable and accessible from any environment.It acts as an interface between the user and third party e-commerce applications such as Paypal,instamojo, other net banking sites through which the transaction process takes place.The application will also keep track of payment dues and  previous payment records for reference.The application provides the students an option to buy any necessary supplements such as files, journals, paper supplements in  addition to the fees payment.

Keywords:
  application, fee payment, gateway, student.


References:

1.    The Electronic Mall". GS Brown. 30 April 2010. 17 June 2013.
2.    Burgess, S; Sellitto, C; Karanasios, S (2009), Effective Web Presence Solutions for Small Businesses: Strategies and Successful Implementation, IGI Global.

3.    nma.com/papers/FirstVirtual.pdf.

4.    https://en.wikipedia.org/wiki/E-commerce.

5.    https://www.shopify.in/tour/ecommerce-website.

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3.

Authors:

Yoenus Osman, Ramli Rahim, Saleh Pallu, Sumbangan Baja

Paper Title:

GIS-3D Susceptibility Landslide Disaster at Upper Area of Jeneberang River Watershed, South Sulawesi, Indonesia

Abstract: The assessment of landslide hazard and risk has become a topic of major interest for both geoscientists and engineering professionals as well as for local communities and administrations in many parts of the world. Recently, Geographic Information Systems (GIS), with their excellent spatial data processing capacity, have attracted great attention in natural disaster assessment. In this paper, an assessment of landslide hazard at Upper Area of Jeneberang Watershed has been studied using GIS technology. By simulating the potential landslide according the minimum safety factor value using GIS, it can be expected that great contribution as a basic decision making for many prevention works before future landslide occurs at Jeneberang River Watershead, South Sulawesi, Indonesia

Keywords:
Geographic Information Systems (GIS), three-dimensional slope stability, landslide hazards, deterministic model, slope unit, Jeneberang River Watershed


References:

1.          Dai, F.C. and Lee, C.F., Terrain-based Mapping of Landslide Susceptibility Using a Geographical Information System: a case study, Can. Geotech. J., 38, 2001, pp. 911-923.
2.          Tien H., Wu, Mohamed A.A., Prediction and Mapping of Landslide Hazard. Can. Geotech. J. 37, 2000, pp.781-795.

3.          Esaki, T., Xie, M., Zhou, G., Mitani, Y., 3D Critical Slope Stability Analysis Based on GIS and Monte Carlo Simulation, the 38th U.S. Rock Mechanics Symposium, Washington D.C.,USA, pp.1137-1143.

4.          Rosidi D., Seismic Risk Assessment of Levees, Journal of Civil Engineering Dimension, 9(2), 2007, pp.57–63.

5.          Mankelow, Joseph M., Mupphy, Willian, Using GIS in Probabilistic Assessment of Earthquake Triggered Landslide Hazard. Journal of Earthquake Engineering. 2(4), 1998, pp.593-623.

6.          ESRI, MapObjects Programmer’s Reference, GIS and Mapping Components, ESRI, USA, 1999.

7.          Hutchinson, J.N., Landslide Hazard Assessment, Proc. VI Int. Symp. On Landslide, Christchurch, 1, pp.1805-1842, 1995.

8.          Leshchisky, D. and Huang Ching-Chang, Generalized Three Dimensional Slope Stability Analysis, Journal of Geotechnical Engineering, 118(11), 1992.  pp. 1748-1763.

9.          LamL. and Fredland, D.G., A General Limit Equilibrium Model for Three-Dimensional Slope Stability Analysis. Canadian Geotechnical Journal. 30, 1993, pp.905-919.

10.       Hovland, H.J., Three-dimensional Slope Stability Analysis Method. Journal of the Geotechnical Engineering, Division Proceedings of the American Society of Civil Engineers, 103(GT9), 1977, pp.971-986.

11.       Terlien, M.T.J., Van Asch, T.W.J., Van Westen, C.J., Deterministic Modelling in GIS-Based Landslide Hazard Assessment. Geographical Information Systems in Assessing Natural Hazards, Dordrecht, The Netherlands, pp.57–77, 1995.

12.       Van Westen, C.J., GIS in Landslide Hazard Assessment, A View, With Example From the Colombia Andes, Mountain Environment & GIS, Taylor & Francis, 1998.

13.       Van Westen, C.J., Application of Geographic Information Systems to Landslide Hazard Zonation. International Institute for Aerospace Survey and Earth Sciences, Publication, p.15, 1993.

14.       Van Westen, C.J., Rengers, N., Terlien, M.T.J., Soeters, R., Prediction of the Occurrence of Slope Instability Phenomena Through GIS-based Hazard Zonation. Geologische Rundschau, 86, 1997, pp.404–414.

15.       Janbu, N., Slope Stability Computers, The Embankment Dam Engineering, John Wiley & Sons, New York, pp. 47-86, 1973.

16.       Evert H., Development of Rock Engineering. Course of Notes by Evert Hoek, 2001.


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4.

Authors:

Siddhartha Ray, D. M. Joshi, Rahul Chandrashekar

Paper Title:

Analysis of Stress Ribbon Bridge

Abstract:  A stressed ribbon bridge (also known as stress-ribbon bridge or catenary bridge) is a tension structure which is very similar to suspension bridges. The suspension cables form the part of the deck which follows a inverted catenary between supports. The ribbon is stressed such that it is in compression, which increases the stiffness of the structure where as a suspension spans tend to sway and bounce. The supports in turn support upward thrusting arcs that allow the grade to be changed between spans where multiple spans are used. Such bridges are typically made RCC structures with tension cables to support them. Such bridges are generally not designed for vehicular traffic but where it is essential, a certain degree of additional stiffness is required to prevent excessive flexure of the structure. A stress ribbon bridge of 45 meter span is modelled and analyzed using ANSYS version 12. For simplicity in importing civil materials and civil cross sections, CivilFEM version 12 add-on of ANSYS was used. A 3D model of the whole structure was developed and analyzed and according to the analysis results, the design was performed manually.

Keywords:
 Stress Ribbon, Precast segments, Prestressing, Dynamic Analysis, Pedestrian Excitation

References:

1.    D. E. Newland, 2004. “Pedestrian excitation of bridges”, Institution of Mechanical Engineers (IMechE), Volume 218, Part C, 477-492.
2.    Jiri Strasky, et al., 2005. “Guidelines for the design of footbridges”, federation internationale du béton (fib), Bulletin 32.

3.    Jiri Strasky, 2006. “Stress Ribbon and Cable Supported Pedestrian Bridges”, Academy of Sciences of the Czech Republic, Brno, Czech Republic.

4.    Jiri Strasky, 2008. “Stress Ribbon Pedestrian Bridges Supported or Suspended on Arches”, Chinese-Croatian joint colloquium, Long Arch Bridges, Brijuni Islands, Croatia, 135-147.

5.    Jiri Strasky, 2010. “Stress Ribbon and Arch Pedestrian Bridges”, 6th International Conference on Arch Bridges, Fuzhou, China, 38-45.

6.    Dr. Chung C. Fu, “Dynamic Response of Pedestrian Bridges”, Research Professor, University of Maryland.


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