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In this paper, the generalized Hankel-Clifford wavelet transformation is developed. Using the developed theory of generalized Hankel-Clifford convolution, the generalized Hankel-Clifford translation is introduced. Properties of the kernel D�,α,β(x,y,z) are developed in the study. Using the properties of kernel the generalized Hankel-Clifford wavelet transformation is defined. The existence of the generalized Hankel-Clifford wavelet transformation is given by a theorem. The boundedness and inversion formula for the generalized Hankel-Clifford wavelet transformation is obtained. A basic wavelet which defines continuous generalized Hankel-Clifford wavelet transformation, its admissibility conditions and the wavelet to the function is proved. Examples have been shown to explain the studied continuous generalized Hankel-Clifford wavelet transformation. MSC: 44A20, 42C40, 46

Continuous Generalized Hankel-Clifford Wavelet Transformation, Generalized Hankel-Clifford Transformation, Generalized Hankel Convolution

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[10] V. R. Lakshmi Gorty, �Continuous Hankel-Clifford wavelet transformation�, Journal of Wavelet Theory and Applications, 7(1), (2013), 45-55.

V.R.L. Gorty, "Continuous generalized Hankel-Clifford wavelet transformation," International Journal of Computer Sciences and Engineering, Vol.1, Issue.4, pp.1-10, 2013.

In the past decade, sufficient powerful Denoising algorithms have been devised - among them the patch-based nonlocal schemes, such as BM3D, have shown outstanding performance The BM3D employs a non-local modeling of images by collecting similar image patches in 3D arrays. The so-called collaborative filtering applied on such a 3D array is realized by transform-domain shrinkage. The block-matching with 3D transform domain collaborative filtering (BM3D) achieves very good performance in image Denoising. However, BM3D becomes ineffective when an image is heavily contaminated by noise. This is because it allows block-matching to search out of the region where a template block is located, resulting in poor matching. To address this, paper proposes an approach that is an extension of BM3D to represent to volumetric data & image reconstruction.

Modified BM3D, Volumetric Data, Image reconstruction

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[2]. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image denoising with block-matching and 3D filltering,� in Proc. SPIE Electronic Imaging: Algorithms and Systems V, vol. 6064A-30, San Jose, CA, USA,January 2006.
[3]. J. V. Manjon, P. Coupe, L. Martı-Bonmat, D. L. Collins, and M. Robles, �Adaptive non-ocal means denoising of MR images with spatially varying noise levels,� Journal of Magnetic Resonance Imaging, vol. 31,pp. 192�203, 2010
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[6]. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, .Image Denoising by sparse 3D transform-domain collaborative filtering,. IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080.2095, August 2007.
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http://www.cs.wisc.edu/~evanswes/cs766.html
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N. Kamalakshi, H. Naganna, M.N. Shanmukhaswamy , "Modification of BM3D Algorithm for Representing Volumetric Data on Medical Images," International Journal of Computer Sciences and Engineering, Vol.1, Issue.4, pp.11-17, 2013.

Along with the development and application of Cloud Computing in recent years, Cloud Storage, as the module which provides data storage service in the Cloud Computing architecture, has become the kernel component of Cloud Computing. This is one of the advantages of cloud computing to create and store data at remote servers. But this advantage implicitly contains drawback of data security and privacy vulnerabilities. Many algorithms and methodologies are there by which data security in cloud computing can be achieved but at the same time it possesses many security risks. In this paper we describe several aspect of data security.

Cloud Security, Security Challenges, Cloud computing

[1]. Peter Mell, and Tim Grance, "The NIST Definition of Cloud Computing, �2009, v15.pdf, Accessed Apri2010.
[2]. Maneesha Sharma, Himani Bansal, Amit KumarSharma ,�CloudComputing: Different Approach & Security Challenge � International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-1, March 2012 421.
[3]. Mohamed Al Morsy, John Grundy and Ingo M�ller. Computer Science & Software Engineering, Faculty of Information & Communication Technologies Swinburne University of Technology, Hawthorn, Victoria, Australia {malmorsy, jgrundy, imueller}@ swin.edu.au � An Analysis of The Cloud Computing Security Problem� In Proceedings of APSEC 2010 Cloud Workshop, Sydney, Australia, 30th Nov 2010.
[4]. Thomas Ristenpart, Eran Tromer, Hovav Shacham, Stefan Savage, "Hey, you, get off of my cloud: exploring information leakage in third-partycompute clouds," presented at the Proceedings of the 16th ACMconference on Computer and communications security, Chicago, Illinois, USA, 2009.
[5]. Microsoft. (2006, October, 2010). Multi-Tenant Data Architecture.

S. Ayyub, D. Roy, "Cloud Computing Characteristics and Security Issues," International Journal of Computer Sciences and Engineering, Vol.1, Issue.4, pp.18-22, 2013.

This paper presents the methods to reduce dynamic power consumption of a digital Finite Impulse Response (FIR) filter these methods include low power serial multiplier and serial adder, combinational booth multiplier, shift/add multipliers, folding transformation in linear phase architecture and applied to fir filters to reduce power consumption due to this glitching is also reduced. The minimum power achieved is 110mw in fir filter based on shift/add multiplier in 100MHZ to 8taps and 8bits inputs and 8bits coefficients. The proposed FIR filters were synthesized implemented using Xilinx ISE Spartan 3E FPGA and power is analyzed using Xilinx XPower analyzer.

Low Power, Booth Multiplier, Folding Transformation

[1]. Jin-Gyun Chung, Keshab K. Parhi �Frequency Spectrum Based Low-Area Low-Power Parallel FIR Filter Design� EURASIP Journal on Applied Signal Processing 2002, vol. 31, pp.944� 953.
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[3]. Shahnam Mirzaei, Anup Hosangadi, Ryan Kastner, �FPGA Implementation of High Speed FIR Filters Using Add and Shift Method�, IEEE, 2006.
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[5]. �Design and Implementation of Low Power Digital FIR Filters relying on Data Transition Power Diminution Technique� DSP Journal, Volume 8, pp. 21-29, 2008.
[6]. A. Senthilkumar, 2A.M. Natarajan, �FPGA Implementation of Power Aware FIR Filter Using Reduced Transition Pipelined Variable Precision Gating,� Journal of Computer Science , pp. 87-94, 2008.
[7]. Uwe Meyer-Baese, �Digital Signal with Field Programmable Gate Arrays�, Springer-Verlag Berlin Heidelberg 2007
[8]. Shibi Thankachan, �64 x 64 Bit Multiplier Using Pass Logic�,2006.
[9]. Ronak Bajaj, Saransh Chhabra, Sreehari Veeramachaneni, M B Srinivas, �A Novel, Low-Power Array Multiplier Architecture
[10]. Yun-NanChang, Janardhan H. Satyana:rayanaKeshabK.Parhi� LOW-POWER DIGIT-SERIAL MULTIPLIERS�, 1997 IEEE. International Symposium on Circuits and Systems, June i3-12,1997

U.V. Sivaiah, P.P.M. Krishnna, Y. Devaraju, "Implementation of Low Power Digital FIR Filter Design Based on low power multipliers and adders," International Journal of Computer Sciences and Engineering, Vol.1, Issue.4, pp.23-28, 2013.