iosr-JCE   Volume-16 ~ Issue-4 ~ July-August 2014

Paper Type	:	Research Paper
Title	:	Six Sigma and CMMI
Country	:	India
Authors	:	Neema Merlin Rodrigues, Anasuya K Lingappa
	:	10.9790/0661-16410105

Abstract: Quality is essential in every walks of life and in all business for their success and profitability. There are many quality improvement initiatives and they are doing their part well. The startups and initial organizations have to decide on which quality model to choose for their improvement and sustainability. CMMI has proved itself good in providing a proper infrastructure for the organizations. But it can be implemented in long term and is not useful in short term projects. Six Sigma can be applied in any field and has a statistical background. Integrating these two according to the need of the organization would give fast and sustainable success.
Keywords: Capability Maturity Model Integrated (CMMI), Six Sigma

[1] Stefan Wagner, Klaus Lochmann, Sebastian Vinter, Andrew Goeb, Michael Klaes, "Quality Models in Practice : A Preliminary Analysis"
[2] Rodney McAdam, Frances Fulton, "The impact of the ISO 9000:2000 quality standards in small software firms", Emerald Insight, 2002
[3] Zhedan Pan, et al., "A six sigma framework for software process improvements and its implementation", 14th Asia-Pacific Software Engineering Conference
[4] Tommy Y Lo, "Quality culture : A product of motivation within organization", Emerald - Managerial Auditing Journal, 2002
[5] Dr. Santwana Chaudhuri, "Knowledge Management in Indian IT Industries", 3rd international conference on information and financial engineering, 2011

Paper Type	:	Research Paper
Title	:	Neighbor-based Data Weight Collaborative Filtering
Country	:	China
Authors	:	Xuesong Yin, Jie Yu, Rongrong Jiang
	:	10.9790/0661-16410610

Abstract : Existing Collaborative Filtering (CF) based recommendation approaches suffer from the following issues: (1) the number of resources accessed and evaluated by each user is only such a very small part that leads to sparse rating matrix; (2) dynamic change of user interest makes recommended resources largely deviate from the need of the user. To address these problems, we develop a novel algorithm titled as neighborbased data weight CF recommendation of learning resources (NARR). Firstly, the neighbor of the user or the neighbor of the resource is selected in terms of the rating matrix; secondly, we compute data weight for representing dynamic change of user interest; finally, we use neighbor relationship and data weight in the objective of CF-based algorithm to choose learning resources. Experiments results show the feasibility and effectiveness of the proposed method.
Keywords: Collaborative Filtering, Data Weight, Similarity, Data Sparseness

[1] J. Bobadilla, F. Serradilla, A. Hernando, MovieLens, "Collaborative filtering adapted to recommender systems of e-learning", Knowledge-Based Systems, vol. 22, pp. 261-265, March 2009.
[2] J. Bobadilla, A. Hernando, F. Ortega, J. Bernal, "A framework for collaborative filtering recommender systems", Expert Systems with Applications, vol. 38, pp. 14609-14623, 2011.
[3] M. Wu, C. Chang, R. Liu, "Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices", Expert Systems with Applications, vol. 41, no. 6, pp. 2754-2761, 2014.
[4] A. Bilge, H. Polat, "A comparison of clustering-based privacy-preserving collaborative filtering schemes", Applied Soft Computing, vol. 13, no. 5, pp. 2478-2489, 2013.
[5] C. Birtolo, D. Ronca, "Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust", Expert Systems with Applications, vol. 40, no. 17, pp. 6997-7009, 2013.

Paper Type	:	Research Paper
Title	:	Removing Atmospheric Turbulence Using Restoration Algorithm
Country	:	India
Authors	:	Vani.M, Shruthi.M.K
	:	10.9790/0661-16411117

Abstract: It remains a challenge to simultaneously remove geometric distortion and space-time-varying blur in frames captured through a turbulent atmospheric medium. To solve, or to reduce these effects, we proposed a new approach in this paper for restoring a single high-quality image from a given image sequence distorted by atmospheric turbulence. This approach is used to reduce the space and time-varying deblurring problem to a shift invariant one. First we use a B-spline based non-rigid image registration algorithm to register each observed frame with respect to a reference image and it also suppresses geometric deformation. Next approach is image stitching algorithm in that the frames are stitched together using an edge-correlation algorithm. Finally, a sharpening high-pass filter is applied to the final image to generate a final high-quality output. This approach can effectively alleviate blur and distortions, recover details of the scene, and significantly improve visual quality

[1] W.E.K. Middleton, Vision through the Atmosphere. Univ.of Toronto Press 1958.
[2] M.C. Roggemann and B.M. Welsh, Imaging through Turbulence. CRC Press, 1996.
[3] D.Li, R.M. Mersereau, and S. Simske, "Atmospheric Turbulence- Degraded Image Restoration Using Principal Components
Analysis," IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 3, pp. 340-344, July 2007.
[4] R.N. Tubbs, "Lucky Exposures: Diffraction Limited Astronomical Imaging through the Atmosphere," PhD thesis, Cambridge Univ,
Sept. 2003.
[5] M.A. Vorontsov and G.W. Carhart, "Anisoplanatic Imaging through Turbulent Media: Image Recovery by Local Information
Fusion from a Set of Short-Exposure Images," J. Optical Soc. Am. A, vol. 18, no. 6, pp. 1312-1324, June 2001.
[6] X. Zhu and P. Milanfar, "Image Reconstruction from Videos Distorted by Atmospheric Turbulence," Proc. SPIE Electronic
Imaging Conf. Visual Information Processing and Comm., Jan. 2010.