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i-manager's Journal on Future Engineering and Technology (JFET)

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Biomaterial Strategies for Immune System Enhancement and Tissue Healing
Qualitative and Quantitative Performance Optimization of Simple Gas Turbine Power Plant using Three Different Types of Fuel
Efficient Shopping: RFID-Powered Cart with Automated Billing System
Medical Drone System for Automated External Defibrillator Shock Delivery for Cardiac Arrest Patients
A Critical Review on Biodiesel Production, Process Parameters, Properties, Comparison and Challenges
Review on Deep Learning Based Image Segmentation for Brain Tumor Detection

Most Cited

Volume 6 Issue 2 November - January 2011

Research Paper

Object-based classification of remotely sensed olive tree fields, using mathematical morphology

Mounir Salhi*

Advanced Technologies for Medical and Signals, Ecole Nationale d'Ingénieurs de Sfax Tunisia.

Salhi , M. (2011). Object-Based Classification Of Remotely Sensed Olive Tree Fields, Using Mathematical Morphology. i-manager’s Journal on Future Engineering and Technology, 6(2), 1-9. https://doi.org/10.26634/jfet.6.2.1320

Abstract

In this paper, we present an object-based approach for rural land cover classification from high-resolution multispectral image data that builds upon a pixel-based segmentation.A mathematical morphology algorithm is then implemented to build objects by a generalization technique to facilitate further object-based classification. The imagery used for this study was acquired by the IKONOS commercial remote sensing satellite and consists of four multi- spectral bands. The object-based classifier uses shape and spectral features to determine the final classification of a segmented image. Using these techniques, the object-based classifier is able to identify olive tree fields from farms and impervious surface.

References

[1]. Acqua, F., P. Gamba, Ferrari, A., Palmason, J.A., Benediktsson, J.A, and Arnason, K. (2004). Exploiting spectral and spatial information in hyperspectral urban data with high resolution. IEEE Geoscience and Remote Sensing Letters, 1, 322–326.

[2]. Barata, T., and Pina, P. (2006). A morphological approach for feature space partitioning. IEEE Geoscience and Remote Sensing Letters, 3, 173–177.

[3]. Benediktsson, J.A., Palmason, J.A., and Sveinsson, J.R. (2005). Classification of Hyperspectral Data From Urban Areas Based on Extended Morphological Profiles. IEEE Transactions on Geoscience and Remote Sensing, 43, 480–491.

[4]. Benediktsson, J.A., Pesaresi, M., and Arnason, K. (2003). Classification and Feature Extraction for Remote Sensing Images From Urban Areas Based on Morphological Transformations. IEEE Transactions on Geoscience and Remote Sensing, 41, 1940–1949.

[5]. Brown, K. M., Foody, G. M., and Atkinson, P. M. (2009). Estimating per-pixel thematic uncertainty in remote sensing classifications. International Journal of Remote Sensing, 30(1), 209-229.

[6]. Ferrier, G., White, K., Griffiths, G., Brayant, R., and Steffouli, M. (2002). The mapping of hydrothermal alteration zones on the island of Lesvos, Greece using an integrated remote sensing dataset. International Journal of Remote Sensing, 23, 341–356.

[7]. Gonzales, R., and Wintz, P. (1987). Digital Image nd Processing, 2 edition Addison Wesley Publishing.

[8]. Ingram, R., Lewis, A., and Tutwiller, L. (2004). An automatic nonlinear correlation approach for processing of hyperspectral images. International Journal of Remote Sensing, 25, 4981– 4998.

[9]. J. Holmgren, and Persson, U. Söderman, (2008). Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images. International Journal of Remote Sensing, 29, 1537 – 1552.

[10]. Krishnamurthy, S., Iyenga, S.S., Holyer, R.J., and Lybanon, M. (1994). Histogram-Based Morphological Edge Detector. IEEE Transactions on Geoscience and Remote Sensing, 32, 759–761.

[11]. Meer, F.V.D. (2000). Spectral curve shape matching with a continuum removed CCSM algorithm. International Journal of Remote Sensing, 21, 3178–3185.

[12]. Meer, F.V.D., and Bakker, W. (1997a). CCSM: Cross Corelogram Spectral Matching. International Journal of Remote Sensing, 18, 1197–1201.

[13]. Meer, F.V.D., and Bakker, W. (1997b). CCSM: Cross Corelogram Spectral Matching. Remote Sensing of Environment, 61, 371–382.

[14]. M.J. Chopping, L.H. Su, A. Rango and C. MaxWell, (2004). Modeling the reflectance anistropy of chihuahuan desert grass-shrub transition canopy-soil complexes. International Journal of Remote Sensing, 25, 2725–2745.

[15]. Moskal, L., and Franklin, S. (2004). Relationship between airborne multispectral image texture and aspen defoliation. International Journal of Remote Sensing, 25, 2701–2711.

[16]. Pesaresi, M., and Benediktsson, J.A. (2001). A New Approach for the Morphological Segmentation of High- Resolution Satellite Imagery. IEEE Transactions on Geoscience And Remote Sensing, 39, 309–320.

[17]. Peter Bunting, Richard M. Lucas, Kerstin Jones, and Anthony R. Bean, (2010). Characterization and mapping of forest communities by clustering individual tree crowns. Remote Sensing of Environment 114, Issue 11, Pages 2536-2547.

[18]. Plaza, A., Martinez, P., Perez, R., and Plaza, J. (2002). Spatial/Spectral Endmember Extraction by Multidimensional Morphological Operations. IEEE Transactions on Geoscience and Remote Sensing, 40, 2025–2041.

[19]. Plaza, A., Martinez, P., Plaza, J., and Perez, R. (2005). Dimensionality Reduction and Classification of Hyperspectral Image Data Using Sequences of Extended Morphological Transformations. IEEE Transactions on Geoscience And Remote Sensing, 43, 466–479.

[20]. Q. Xiao, S.L.Ustin, and Pherson, E. (2004). Using AVIRIS data and multiple-Masking techniques to map urban forest tree species. International Journal of Remote Sensing, 25, 5637–5654.

[21]. Salhi, M., Kallel, A., Masmoudi, D.S., and Derbel, N. (2006). Remotely sensed image segmentation by nonlinear correlation. Information and Communication Technologies, ICTTA '06. 2nd Issue Damascus, Pages 424 - 429.

[22]. Soille, P., and Pesaresi, M. (2002). Advances in Mathematical Morphology Applied to Geoscience and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing,40, 2042–2055.

[23]. Su, B., Li, Z., GrahamLodwick, and Muller, J.C. (1997). Algebraic models for the aggregation of area features based upon morphological operators. International Journal of Geographical Information Science, 11, 233–246.

[24]. Tripathi, N.K., Gokhale, K.V.G.K., and Siddiqui, M.U. (2000). Directional morphological image transforms for lineament extraction from remotely sensed images. International Journal of Remote Sensing, 21, 3281–2392.

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Research Paper

A New Design of Blood Flow Through Hollow Fibers For Prevention/Minimization Of Hemolysis For Possible Use In Blood Oxygenators And Dialyzers

Karim Salehpoor* , Joseph Genin, James A. Smoake*

* Mechanical Engineering Department, New Mexico Tech, Socorro.

Mechanical Engineering Department, New Mexico State University, Las Cruces.

* Professor Emeritus, Biology Department, New Mexico Tech, Socorro.

Salehpoor, K., Genin , J., and Smoake , J. A. (2011). A New Design Of Blood Flow Through Hollow Fibers For Prevention/Minimization Of Hemolysis For Possible Use In Blood Oxygenators And Dialyzers. i-manager’s Journal on Future Engineering and Technology, 6(2), 10-19. https://doi.org/10.26634/jfet.6.2.1321

Abstract

Flow-induced hemolysis is the rupture of red blood cells which occurs in blood flow devices. Examples of such devices include blood oxygenators or artificial lungs and dialyzers. In addition to hemolysis, blood coagulation is also associated with the use of blood oxygenators and dialyzers. Blood coagulation increases the risk of heart attack and stroke while flow-induced hemolysis imposes a limitation on how long a blood oxygenator or a dialyzer can be used by a patient.The objective of this study was to examine if a solid-like flow through the entrance length of a tube could minimize shear stress acting on the red blood cells, thus minimize hemolysis in the tube. Blood circulation experiments were conducted to determine the effect of the ratio of tube length to entrance length on flow-induced hemolysis in the tube. Blood samples were taken at different time intervals and tested for hematocrits and concentration of hemoglobin in plasma. Comparison of the results indicated a decrease in flow-induced hemolysis in the tube with a decrease in the ratio of the tube length to entrance length.

References

[1]. Kawahito, S., Maeda, T., Motomura, T., Ishitoya, H., Takano, T., Nonaka, K., Linneweber, J., Ichikawa, S., Kawamura, M., Hanazaki, K., Glueck, J., and Nose, Y. (2002). Hemolytic Characteristics of Oxygenators During Clinical Extracorporeal Membrane Oxygenation. ASAIO Journal, 48, 636-639.

[2]. Van Meurs, K. P., Mikesell, G. T., Seale, W. R., Short, B. L., and Rivera, O. (1990). Maximum blood flow rates for arterial cannulae used in neonatal ECMO. ASAIO Trans., 36, M679-M681.

[3]. Yang, M., and Lin, C. (2000). In Vitro Characterization of the Occurrence of Hemolysis During Extracorporeal Blood Circulation Using a Mini Hemodialyzer. ASAIO Journal, 46(3), 293-297.

[4]. Sharp, M. K., and Fazal Mohammad, S. (1998). Scaling of Hemolysis in Needles and Catheters. Annals of Biomedical Engineering, 26, 788-797.

[5]. Kennedy, C., Angermuller, S., King, R., Noviello, S., Walker, J., Warden, J., and Vang, S. (1996). A comparison of hemolysis rates using intravenous catheters versus venipuncture tubes for obtaining blood samples. Journal Of Emergency Nursing, 22(6), 566-569.

[6]. Kameneva, M. V., Burgreen, G. W., Kono, K., Perko, B., Antaki, J. F., and Umezu, M. (2004). Effects of Turbulent Stresses upon Mechanical Hemolysis: Experimental and Computational Analysis. ASAIO Journal, 50, 418-423.

[7]. Middleman, S. (1972). Transport Phenomena in the Cardiovascular System, (pp. 76-86). Wiley-Interscience, New York.

[8]. Fung, Y. C. (2004). Biomechanics: Mechanical Properties of Living Tissues, (pp. 99-100). Springer, New York.

[9]. Arora, D., Behr, M., and Pasquali, M. (2004). A Tensorbased Measure for Estimating Blood Damage. Artificial Organs, 28(11), 1002-1015.

[10]. Sugii, Y., Okuda, R., Okamoto, K., and Madarame, H. (2005). Velocity measurement of both red blood cells and plasma of in vitro blood flow using high-speed micro PIV technique. Meas. Sci. Technol., 16, 1126-1130.

[11]. Pinotti, M. (2000). Is there Correlation between the Turbulent Eddies Size and Mechanical Hemolysis? J. Braz. Soc. Mech. Sci., 22(4), 565-569.

[12]. Rand, R.P. (1964). Mechanical Properties of the Red Cell Membrane, II: Viscoelastic Breakdown of the Membrane. Biophysical Journal, 4, 303-316.

[13]. Blackshear P.L. and Blackshear, G. L. (1987). Mechanical Hemolysis. In Skalak R. and Chien S. (Eds.), Handbook of Bioengineering (pp. 15.1-15.19). McGraw- Hill, New York.

[14]. Leverett, L.B., Hellums, J.D., Alfrey, C.P., and Lynch, E.C. (1972). Red blood cell damage by shear stress. Biophys J., 12(3), 257-273.

[15]. Gu, L., and Smith, W.A. (2005). Evaluation of Computational Models for Hemolysis Estimation. ASAIO Journal, 51(3), 202-207.

[16]. Yeleswarapu, K.K., Antaki, J.F., Kameneva, M.V., and Rajagopal, K.R. (2004). A mathematical model for shearinduced hemolysis. Artificial Organs, 19(7), 576-582.

[17]. Laugel, J.F. and Beissinger, R.L. (1983). Low stress shear-induced hemolysis in capillary flow. Trans Am Soc Artif Intern Organs., 29, 158-162.

[18]. Reinhard, P., Jorn, A., Klaus, S., Schugner, F., Schwindke, P., and Helmut, R. (2003). Shear stress related blood damage in laminar couette flow. Artificial Organs, 27(6), 517-529.

[19]. Hua, J., Erickson, L.E., Yiin, T.Y., and Glasgow, L.A. (1993). A review of the effects of shear and interfacial phenomena on cell viability. Crit Rev Biotechnol, 13(4), 305-328.

[20]. Salehpoor, K. (2007). A New Design Of Blood Flow Through Hollow Fibers For Prevention/minimization Of Hemolysis For Possible Use In Blood Oxygenators And Dialyzers. Ph.D. thesis, New Mexico State University, Las Cruces, NM.

[21]. White, F. M. (1999). Fluid Mechanics, (pp. 330-331). McGraw-Hill Companies, Inc., Boston.

[22]. Welty, J.R., Wicks, C.E., Wilson, R.E., and Rorrer, G. (2001). Fundamentals of Momentum, Heat, and Mass Transfer, (pp. 195-198). John Wiley & Sons, Inc., New York.

[23]. Ku, D.N. (1997). Blood Flow in Arteries. Annual Review of Fluid Mechanics, 29, 399-434.

[24]. Zamir, M. (2000). The Physics of Pulsatile Flow, (pp.57- 59). Springer-Verlag, New York.

[25]. Nichols, W.W. and O'Rourke, M.F. (2005). McDonald's Blood Flow in Arteries: Theoretical, experimental and clinical principles, (pp. 38-39). Hodder Arnold, London.

[26]. Mazumdar, J.N. (1992). Biofluid Mechanics, (pp.42- 44). World Scientific Publishing Co., New Jersey.

[27]. Lighthill, S.J. (1975). Mathematical Biofluiddynamics, (pp.202-203). Society for Industrial and Applied Mathematics, Philadelphia.

[28]. Fung, Y.C. (1997). Biomechanics: Circulation, (pp. 182-189). Springer-Verlag, New York.

[29]. Umezu, M., Yamada, T., Fujimasu, H., Fujimoto, T., Ranawake, M., Nogawa, A., and Kijima, T. (1996). Effects of surface roughness on mechanical hemolysis. Artificial Organs, 20(6), 575-578.

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Research Paper

Structural analysis of tissue in contiguous micro calcifications inmammograms for breast cancer identification

Bhagwati Charan Patel* , G. R. Sinha**

* Associate Professor (IT), Shri Shankaracharya College of Engg. & Tech, Bhilai, India.

** Professor & Head (IT), Shri Shankaracharya College of Engg. & Tech, Bhilai, India.

Patel , B. C., and Sinha, G. R. (2011). Structural Analysis Of Tissue In Contiguous Micro Calcifications In Mammograms For Breast Cancer Identification. i-manager’s Journal on Future Engineering and Technology, 6(2), 20-27. https://doi.org/10.26634/jfet.6.2.1322

Abstract

Identification of micro-calcifications (MCs) is challenged by the presence of dense breast tissue, resulting in low specificity values and thus in unnecessary biopsies. The current study investigates whether structural properties of the tissue in contiguous MCs can contribute to breast cancer identification. A sample of 75 dense mammographic images affected with malignant and benign were collected from BSR APPOLO for cancer research and diagnosis and included in the digital Database. Regions of interest (ROIs) containing the MCs were pre-processed using a wavelet based contrast enhancement method, followed by local thresholding to segment MCs; the segmented MCs were excluded from original image ROIs, and the remaining area (in contiguous tissue) was subjected to structural analysis. Four categories of structural features (first order statistics, co-occurrence matrices features, run length matrices features and Laws’ structural energy measures) were extracted from the contiguous tissue. The ability of each feature category in discriminating malignant from benign tissue was investigated using a k-closest neighbor (kCN) classifier. Receiver Structural Characteristic (RSC) analysis was conducted for classifier performance evaluation of the individual structural feature categories and of the combined classification scheme. The best performance was achieved by the combined classification scheme yielding an area under the RSC curve of 0.96 (sensitivity 94.4%, specificity 80.0%). Structural analysis of tissue in contiguous MCs shows promising results in computer-aided diagnosis of breast cancer and may contribute to the reduction of unnecessary biopsies.

References

[1]. Majid, A.S., de Paredes, E.S., Doherty, R.D., and Sharma, N.R. (2003). “Missed breast carcinoma: pitfalls and pearls”, Radiographics, 23: 881-95.

[2]. Kopans, D.B. (2002). “The positive predictive value of mammography”, AJR Am J Roentgenol, 158: 521-6.

[3]. Kolb, T.M., Lichy J., and Newhouse, J.H. (2002). “Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations”, Radiology, 225:165-75.

[4]. Bird, R.E., Wallace, T.W., and Yankaskas, B.C. (1992). “Analysis of cancers missed at screening mammography”, Radiology, 184: 613-17.

[5]. Van Gils CH, Otten J.D., Verbeek A.L., Hendriks, J.H., and Holland, R. (1998). “Effect of mammographic breast density on breast cancer screening performance: a study in Nijmegen, the Netherlands”, J Epidemiol Commun Health, 52:267-71.

[6]. Cole, E.B., Pisano, E.D., Kistner, E.O., Muller, K.E., Brown, M.E., Feig, S.A., et.al (2003). “Diagnostic accuracy of digital mammography in patients with dense breasts who underwent problem solving mammography: effects of image processing and lesion type”. Radiology, 226:153-60.

[7]. American Cancer Society. (1998). ”Cancer facts and figures“, Atlanta, GA: American Cancer Society.

[8]. Sampat, P.M., Markey, M.K., and Bovik, A.C. (2005). “Computer-aided detection and diagnosis in mammography”, In: Bovik AC, editor. Academic Press, 1195-217.

[9]. Markey, M.K., Lo, J.Y., and Floyd, C.E., Jr. (2002). “Differences between computer-aided diagnosis of breast masses and that of calcifications”. Radiology 223:469-93.

[10]. Cheng, H.D., Cai, X., Chen, X., Hu, L., and Lou, X. (2003). “Computer-aided detection and classification of mic RS Calcifications in mammograms: a survey”. Pattern Recognition 36:2967-91.

[11]. Wu, Y., Giger, M.L., Doi, K., Vyborny, C.J., Schmidt, R.A., and Metz, C.E. (1993). “Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer”. Radiology 187:81-7.

[12]. Baker, J.A., Kornguth, P.J., Lo, J.Y., and Floyd, C.E. (1996). “Artificial neural network: improving the quality of breast biopsy recommendations”. Radiology 198:131-5.

[13]. Lo, J.Y., Baker, J.A., Kornguth, P.J., Iglehart, J.D., Floyd, C.E. (1997). “Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features”, Radiology 203:159-63.

[14]. Shen, L., Rangayyan, R.M., Desautels, J.E.L. (1994). “Application of shape analysis to mammographic calcifications”, IEEE Trans Med Imaging, 13:263-74.

[15]. Jiang, Y., Nishikawa, R.M., Wolverton, D.E., Metz, C.E., Giger, M.L., Schmidt, R.A., et.al (1996). “Malignant and benign clustered micRSCalcifications: automated feature analysis and classification”, 198:671-8.

[16]. Chan, H.P., Sahiner, B., Lam, K.L., Helvie, M.A., and Goodsitt (1998). 'Computerized analysis of mammographic micRSCalcifications in morphological and structural feature spaces'. Med Phys 25:2007–19.

[17]. Tsujii, O., Freedman, M.T., and Mun, S.K. (1999). “ Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network”, Pattern Recognition 32:891-903.

[18]. Veldkamp, W.J.H., Karssemeijer, N., Otten, J.D.M., and Hendriks, J.H.C.L. (2000). “Automated classification of clustered micRSCalcifications into malignant and benign types”. Med Phys 27:2600-8.

[19]. Markopoulos, C., Kouskos, E., Koufopoulos, K., Kyriakou, V., and Gogas, J. (2001). “Use of artificial neural networks (computer analysis) in the diagnosis of micRSCalcifications on mammography”, 37:60-5.

[20]. Verma, B., and Zakos, J.A. (2001). “Computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques”. IEEE Trans Inform Technol Biomed 5:44-54.

[21]. De Santo, M., Molinara, M., Tortorella, F., and Vento, M. (2003). “Automatic classification of clustered micRSCalcifications by a multiple expert system”. Pattern Recognition 36:1445-77.

[22]. Lee, S.K., Chung, P., Chang, C.I., Lo, C.S., Lee, T., and Hsu, G.C. (2003). “ Classification of clustered micRSCalcifications using a shape cognition neural network”, Neural Netw 16:121-32.

[23]. Kallergi, M. (2004). “Computer-aided diagnosis of mammographic microcalcification clusters”. Med Phys 31:314-26.

[24]. Wei, L., Yang, Y., Nishikawa, R.M., and Jiang, Y. (2005). “A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications”. IEEE Trans Med Imaging, 24:349-80.

[25]. Papadopoulos, A., Fotiadis, D.I., and Likas, A. (2005). “Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines”. Artif Intell Med, 34:139-50.

[26]. Dhawan, A.P., Chitre, Y., and Kaiser-Bonasso, C. (1996). “Analysis of mammographic micRSCalcifications using gray-level image structure features”. IEEE Trans Med Imaging, 15:244-59.

[27]. Soltanian, H., Rafee, F., and Pourabdollah, D. (2004). ”Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms”. Pattern Recognition, 35:1973-86.

[28]. Kramer, D., and Aghdasi, F. (1999). “Structural analysis techniques for the classification of microcalcifications in digitized mammograms”.

[29]. Sakka, E., Prentza, A., and Koutsouris, D. (2006). ”Classification algorithms for microcalcifications in mammograms“, (review). Oncol Rep1049-56.

[30]. Veldkamp, W.J.H., and Karssemeijer, N. (1996). ”Influence of segmentation on classification of microcalcifications in digital mammography ”. In: th Proceedings of the 18 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 1996 October 31 – November 3. Amsterdam, Netherlands: Institute of Electrical and Electronic Engineers, Inc.

[31]. Paquerault, S., Yarusso, L.M., Papaioannou, J., Jiang, Y., and Nishikawa, R.M. (2004). “Radial gradient b Ased segmentation of mammographic microcalcifications” Observer evaluation and effect on CAD performance. Med Phys 31:2644-57.

[32]. Thiele, D.L., Kimme-Smith, C., Johnson, T.D., McCombs, M., and Bassett, L.W. (1996). “Using tissue structural in contiguous calcification clusters to predict benign vs malignant outcomes”. Med Phys 23:549-55.

[33]. Sakellaropoulos, P., Costaridou, L., and Panayiotakis, G. (1999). “An image visualization tool in mammography”. Med Inform 24: 53-73.

[34]. Sakellaropoulos, P., Costaridou, L., and Panayiotakis, G. (2000). “Using component technologies for web based wavelet enhanced mammographic image visualization”. Med Inform 25:171-81.

[35]. Sakellaropoulos, P., Costaridou, L., and Panayiotakis, G. (2003). A” wavelet- based spatially adaptive method for mammographic contrast enhancement”. Phys Med Biol 44:787-803.

[36]. Costaridou, L., Sakellaropoulos, P., Skiadopoulos, S., and Panayiotakis, G. (2005). “Locally adaptive wavelet contrast enhancement”. In: Costaridou L, editor. Medical image analysis methods. Boca Raton, FL: Taylor & Francis Group LCC,MCRC Press, 225-70.

[37]. Costaridou, L., Skiadopoulos, S., Karahaliou, A., Sakellaropoulos, P, and Panayiotakis G. (2005). ”On the lesion specific enhancement hypothesis in mammography”. In: Proceedings of 14th International Conference of Medical Physics, ICMP; 2005 September 14-17; Nuremberg, Germany. Berlin, Germany: Fachverlag Schiele & Schon GmbH.

[38]. Gonzalez, R.C., and Woods, R.E. editors. (2002). “Digital image Processing”. Upper Saddle River, NJ: Prentice-Hall, Inc.

[39]. Dudani, S.A (1976). The distance weighted nearest neighbour rule. IEEE Trans Systems Man Cybern SMC- 6:325-7.

[40]. Ursin, G., Hovanessian-Larsen L., Parisky, Y.R., Pike, M.C., Wu, A.H. (2005). Greatly increased occurrence of breast cancers in areas of mammographically dense tissue. Breast Cancer Res 2005; 7: R605-8.

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Research Paper

Implementation Of Cloud Based File Management System [CFMS] For Affording Distributed Warehouse Service

D. Kesavaraja* , A. Suthan, D. Sasireka*, D. Jeyabharathi****

* Lecturer, Department of Computer Science and Engineering, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur.

HOD, Department of Computer Applications, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur.

* Lecturer, Department of Information Technology, PSN College of Engineering and Technology, Tirunelveli.

**** Lecturer, Department of Computer Science and Engineering, Einstein College of Engineering, Tirunelveli.

Kesavaraja, D., Suthan, A., Sasireka , D., and Jeyabharathi, D. (2011). Implementation Of Cloud Based File Management System [CFMS] For Affording Distributed Warehouse Service. i-manager’s Journal on Future Engineering and Technology, 6(2), 28-35. https://doi.org/10.26634/jfet.6.2.1323

Abstract

Remote web based services provide new scenarios in the emerging computing world. File access has been easy after the distributed computing scenario came into limelight. But security for the Distributed file system has been a very crucial problem to take into account. We propose a Cloud based File Management System[CFMS], so as to provide Data services in the cloud along with required security in the form of Intrusion Tolerance. We use the Model View Controller architecture, separating the business layer from the other layers thereby increasing the security of the system. Intrusion Tolerance is maintained by providing hash values and storing them in the controller, which is located separately from the cluster. These hash codes provide the required information on the attacks performed on a file, which when increases is introduced to the manual administrator of the system, thereby permitting him to increase the security still further. Load balancing and replication transparency are maintained in the system for efficient access to the files contained in the system.

References

[1]. Gruschka, N., and Luttenberger, N. (2006). "Protecting Web Services From DoS Attacks by SOAP Message Validation", IFIP International Federation for Information Processing, Springer Boston, Vol. 201, 171-182.

[2]. J.E. Just, J.C. Reynolds, and K. Levitt, (2002). “Intrusion tolerance through forensics-based attack learning,” in Intrusion Tolerant System Workshop, Supplemental Volume on 2002 International Conference on Dependable System and Networks, pp. C–4–1.

[3]. Organically assured and survivable information system (OASIS). http://www.tolerantsystems.org.

[4]. H. Kopetz and P. Ver´issimo, (1993). Real Time and Dependability Concepts, ch. 16, pp. 411–446. Addison- Wesley.

[5]. Reynolds, J. et al. (2001). "The Design and Implementation of an Intrusion Tolerant System”, Proceedings of the 2002 International Conference on Dependable Systems and Networks (2001) 285-290.

[6]. Stavridou, V. et al. (2002). "Intrusion Tolerant Software Architectures", DARPA Information Sur vivability Conference and Exposition (DISCEX II'01), Vol. II, pp.230- 241.

[7]. Veríssimo, P., Neves, N., and Pupo Correia, M. (2003). "Intrusion-Tolerant Architectures: Concepts and Design", Lecture Notes in Computer Science, Vol. 2677, pp.3-36.

[8]. D. O'Brien, R. Smith, T. Kappel, and C. Bitzer, (2003). “Intrusion tolerant via network layer controls,” in Proceedings of the DARPA Information Survivability Conference and Exposition (DISCEX'03), pp. 90-96.

[9]. http://en.wikipedia.org/wiki/HMAC.

[10]. http://csrc.nist.gov/publications/fips/fips198/fips- 198a.pdf.

[11]. M. Nagaratna, Dr. V. Kamakshi Prasad, S. Tanuz Kumar (n.d). ”Detecting and Preventing IP-spoofed DDoS Attacks by Encrypted Marking based Detection and Filtering (EMDAF)”, Dept. of CSE, Jawaharlal Nehru Technological University.

[12]. K. Kar, S. Sarkar, and L. Tassiulas, (2001). “Optimization based rate control for multirate multicast sessions,” in Proc. IEEE INFOCOM, pp.123-132.

[13]. S. Deb, and R. Srikant, (2001). “Congestion control for fair resource allocation in networks with multicast flows,” in Proc. IEEE Conf. Decision and Control, Dec., pp.1911-1916.

[14]. D. Kesavaraja, R. Balasubramanian and D. Sasireka (n.d). Implementation of a Cloud Data Server (CDS) for Providing Secure Service in E-Business, International Journal of Database Management Systems ( IJDMS ), (ISSN: 0975-5705).

[15]. E. Graves, R. Srikant, and D. Towsley, (2001). “Decentralized computation of weighted max-min fair bandwidth allocation in networks with multicast flows,” in Proc. Tyrrhenian Int. Workshop Digital Communications (IWDC'01), Taormina, Italy, Sept., pp.326-342.

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Research Paper

Treatment Of Distillery Wastewater Using Catalytic Wet Air Oxidation

S. Suresh* , Ravi Shankar, Shri Chand*

* Department of Chemical Engineering, Maulana Azad National Institute of Technology (MANIT) Bhopal, M.P., India.

-* Department of Chemical Engineering, Indian Institute of Technology Roorkee, India.

Suresh, S., Shankar, R., and Chand, S. (2011). Treatment Of Distillery Wastewater Using Catalytic Wet Air Oxidation. i-manager’s Journal on Future Engineering and Technology, 6(2), 36-44. https://doi.org/10.26634/jfet.6.2.1324

Abstract

In the present paper, treatment of typical high strength distillery wastewater (DWW) by catalytic wet-air oxidation (CWAO) at atmospheric pressure and 343-373 K. Experiments were conducted to investigate the effects of temperature (T) and catalyst dosage (m) on the chemical oxygen demand (COD) and colour removal. In this study, five catalysts were used namely, CuSO₄, ZnCl₂, FeCl₃, Al(OH)₃ and Cu (II)-exchanged NaY zeolite (SZ). The treatment efficiencies were 58.2%, 56.6%, 21.9%, 17.8% of the COD and 88.4%, 80.2%, 13.3%, 12.8% of colour for CuSO₄, ZnCl₂, FeCl₃, and Al(OH)₃ in 8h at 373 K and atmospheric pressure, respectively, where else, 13% and 13.3% removal efficiencies of COD, colour at same temperature and pressure, when SZ as a catalyst. The CuSO₄ catalyst was found to be best amongst all the catalysts for the treatment of DWW. The experimental data on the DWW exhibited two clearly distinct COD reduction phases: the first phase having fast rate followed by the second phase having slower rate. Both the phases of the CWAO were found to be first order reduction kinetics. The activation energy evaluated for CuSO4 was found to be 2.86 kcal/gmol for the first step and 3.65 kcal/gmol for the second step. The residue can be used as a fuel in the combustion furnaces and the ash obtained can be blended with organic manure and used in agriculture/horticulture.

References

[1]. Anon. (2001). 'Pollution control acts, rules and notifications issued thereunder', Central Pollution Control Board, Delhi, India.

[2]. APHA. (1995). (American Public Health Association), AWWA (American Water Works Association), WPCF (Water Pollution Control Federation), 'Standard Methods for the Examination of Water and Wastewater,' Washington, DC, USA.

[3]. Belkacemi, K., Larachi, F., Hamoudi, S., and Sayari, A. (2000). 'Catalytic wet oxidation of high strength alcoholdistillery liquors', Appl. Catal. A: Gen., Vol. 199, pp. 199- 209.

[4]. Casey, J.P. (1960). 'Pulp and Paper Chemistry and Chemical Technology', Vol. 1, 2nd ed., Pulping and Bleaching, Interscience, New York.

[5]. Chaudhari, P. K., Mishra, I.M., and Chand, S. (2008). 'Effluent treatment for alcohol distillery: Catalytic thermal pretreatment (catalytic thermolysis) with energy recovery', Chem. Eng. J., Vol. 136, pp. 14–24.

[6]. Chaudhari, P.K., Majumdar, B., Singh, R., Mishra, I.M. and Chand, S. (2010). 'Catalytic Thermal Pretreatment (Catalytic Thermolysis) of Distillery, Wastewater and Bio- Digester Effluent of Alcohol Production Plant at Atmospheric Pressure', Int. J. Chem. React. Eng., pp.1-32.

[7]. Clesceri, L.A., Greenberg, A.E., and Trussel, R.R. (1989). 'Standard methods for the examination of water and wastewater', 17th edn, APHA-AWWA-WPCF, Washington.

[8]. Daga, N.S., Prasad, C.V.S., and Joshi J.B. (1986). 'Kinetics of hydrolysis and wet air oxidation of alcohol distillery waster', Indian Chem. Eng., Vol. 28, pp.22-31.

[9]. Garg, A., Mishra, I.M., and Chand, S. (2010). 'Oxidative Phenol Degradation Using Non-Noble Metal Based Catalysts', Clean, Vol. 38, pp. 27–34.

[10]. Imamura, S. (1999). 'Catalytic and Noncatalytic Wet Oxidation', Ind. Eng. Chem. Res., Vol. 38, pp.1743.

[11]. Kirk-Othmer. (1993). 'Encyclopedia of Chemical Technology', 4th edn, Vol. 4, New York: John Wiley.

[12]. Lele, S.S, Rajadhyaksha, P.J., and Joshi, J.B. (1989). 'Effluent treatment for alcohol distiller y: thermal pretreatment with energy recovery', Environ. Prog., Vol. 8, pp. 245-252.

[13]. Matatov-Meytel, Y.I., and Sheintuch, M. (1998). 'Catalytic Abatement of Water Pollutants', Ind. Eng. Chem. Res., Vol. 37, pp.309.

[14]. Mishra, V.S., Mahajani, V.V., and Joshi, J.B. (1995). 'Wet Air Oxidation', Ind. Eng. Chem. Res., Vol. 34, pp. 2-48.

[15]. Suneeth Kumar S.M., and I.M. Mishra. (2004). Treatment of distillery wastewater in an UASB reactor, in: Anaerobic Digestion, Montreal, Canada.

[16]. Suresh S., and D. Rameshraja. (2011). Treatment of tannery wastewater by various oxidation and combined processes: review. Int. J. Environment Res. Vol. 5(2), pp.349-360.

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Research Paper

MHD Thermal Diffusion Natural Convection Flow Between Heated Inclined Plates In Porous Medium

M. C. Raju* , S.V.K. Varma, N. Anandareddy*

* Principal, Annamacharya PG College of Computer Studies, Rajampet A.P., India.

Department of Mathematics, S.V. University, Tirupati, A.P., India.

* Department of Mathematics, A.I.T.S., Rajampet, A.P., India.

Raju, M. C., Varma , S. V. K., and Anandareddy, N. (2011). MHD Thermal Diffusion Natural Convection Flow Between Heated Inclined Plates In Porous Medium. i-manager’s Journal on Future Engineering and Technology, 6(2), 45-59. https://doi.org/10.26634/jfet.6.2.1325

Abstract

In this paper, the effect of small uniform magnetic field on separation of a binary mixture in presence of thermo diffusion for the case of fully developed natural convection of a fluid between two heated inclined plates in porous medium is investigated. Neglecting the induced electric field the equations governing the motion, temperature and concentration are solved by simple perturbation technique, in terms of dimensionless parameter measuring buoyancy force. The expressions for velocity, temperature and concentration are obtained. The effects of Hartmann number M, thermal diffusion number t_d , the constant N which measures the buoyancy force and the angle ? that the plates make with the horizontal are studied on the flow quantities and the results are discussed through graphs.

References

[1]. Fletcher Osterle, J., and Frederick J. Young (1961). Natural convection between heated vertical plates in a horizontal magnetic field. Journal of Fluid Mechanics, 11, pp.512-518.

[2]. De Groot, S.R., and Mazur, P. (1962). Non-Equilibrium Thermodynamics, North-Holland, Amsterdam.

[3]. Hurle, D.T.J., and E. Jakeman (1971). Soret-driven thermosolutal convection. Journal of Fluid Mechanics, 47, pp.667-687.

[4]. Sarma, G.S.R. (1973). “Barodiffusion in a binary mixture at a rotating disk", Z. Anzew Math. Phys., 24, pp.789-800 (1973).

[5]. Takhar. H.S., and Pop, I. (1987). Free convection from a vertical flat plate to a thermally stratified Darcian flow. Mechanics Research Communications. 14(2), pp.81-86.

[6]. Tewari, K., and Singh, P. (1992). “Natural convection in a thermally stratified fluid saturated porous medium”, Int. J. Eng. Sci., 30 (8), 1003-1007.

[7]. Rees, O.A.S., and Lage, J.L. (1997). “The effect of thermal stratification on natural convection in a vertical porous insulation layer”, Int. J. Heat Mass Transfer, 40(1), p.111-121.

[8]. Angirasa, D., and Peterson, G.P. (1997). “Natural convection heat transform from an isothermal vertical surface to a fluid saturated thermally stratified porous medium”, Int. J. Heat Mass Transfer,14(8), 4329-4335.

[9]. Nield, D.A., and Bejan, A. (1999). Convection in nd porous media, II ed., Springer, New York.

[10]. Rathis Kumar, B.V., and Singh, P. (2003). “Effect of thermal stratification on free convection in a fluid saturated porous enclosure”, Numer. Heat Transfer-A34, 343-356.

[11]. Bejan, A., Kraus, A.D. (Eds) (2003). Heat Transfer hand book, Wiley, New York.

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Research Paper

Finite Element Analysis of Hybrid Composite Joints

Mohamed bak* , K. Kalaichelvan**

* Assistant Professor, Department of Aeronautical Engineering, Tagore Engineering College, Tamil Nadu, India.

** Associate Professor, Department of Production Technology, Anna University, Chennai.

Bak , K. M., and Chelvan, K. K. (2011). Finite Element Analysis Of Hybrid Composite Joints. i-manager’s Journal on Future Engineering and Technology, 6(2), 50-54. https://doi.org/10.26634/jfet.6.2.1326

Abstract

The composite structural members are highly used in the following applications such as aerospace, automobiles, robotic arms, architecture etc., has attracted extensive attention in the past decades. One of the important issues in the composite technology is the repairing of aging aircraft structures. In such applications and also for joining various composite parts together, they are fastened together either using adhesives or mechanical fasteners. Modeling and static analysis of 3-D models of the joints (bonded, riveted and hybrid) were carried out using ANSYS 11 FEA software. The results were interpreted in terms of Von Mises stress. A parametric study was also conducted to compare the performance of the hybrid joint with varying adherent thickness, adhesive thickness and overlap length. To utilize the full potential of composite materials as structural elements, the strength and stress distribution of these joints must be understood. ANSYS FEA tool has been performed to investigate the stress distribution characteristics of various configurations of single lap joint. This study was focused on the analysis of stress distribution in three prominent joining methods namely, bonded, riveted and hybrid joints. FEA is used to study the stress distribution in the members involved under various design conditions and various joints failure criteria.

References

[1]. Hart-Smith, L.J. (1985). Bonded-Bolted composite Joints, J. Aircraft 1985:22:993-1000.

[2]. Chan, W.S., and Vedhagiri, S. (2001). Analysis of composite bonded/bolted joints used in repairing. J. Composite Materials: 35(12):1045-61.

[3]. Gordon, Kelly. (2005). Load Transfer in hybrid (bonded/bolted) composite single lap joints. Composite Structures, 6935-43.

[4]. Jin-Hwe, Kweon, Jae-Woo, Jung, Tae-Hwan, Kim, Jin- Ho, Choi, and Dong-Hyun, Kim. (2006). Failure of carbon composite-to-aluminum joints with combined mechanical fastening and adhesive bonding. Composite Structures, 75192-198.

[5]. Mathews, F.L., and Rawlings, R.D. (1999). Composite Materials: Engineering and Science. Boca Raton: CRC Press. ISBN 0- 8493-0621-7.

[6]. Fu, M., and Mallick, P.K. (2002). Static and fatigue strength of hybrid joints in a Structural Reaction Injection Moulded (SRIM) composite materials.

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Research Paper

Nanowire Sensors Characterization and Validation using Matlab Models for Disease Detection

Ushaa Eswaran* , M. Madhavi Latha, G. Madhusudhan Rao*

* Head, Department of ECE, Siddharth Institute of Engineering & Technology, Puttur, Affiliated to JNTU, Anantapur

Professor and Head, Department of ECE, JNTU, Hyderabad.

* Principal, Al Aman College of Engineering, Vishakapatnam, Affiliated to JNTU-Kakinada.

Ushaa, S. M., Madhavilatha, M., and Rao, G. M. (2011). Nanowire Sensors Characterization And Validation Using Matlab Models For Disease Detection. i-manager’s Journal on Future Engineering and Technology, 6(2), 55-62. https://doi.org/10.26634/jfet.6.2.1327

Abstract

In this paper, the mathematical models required to describe the functionality of nanodevices have been reviewed. Based on these mathematical models sensor equivalent circuits have been developed. An experimental setup is developed to analyze the characteristics of IS Field Effect Transistor (ISFET), nanowire and nanosphere devices. The impact of geometrical properties on device performance is estimated based on the experimental setup. Settling time and surface analyte concentration graphs obtained using the experimental setup is used in designing a nanobio sensor for disease detection. Based on the test results, a mathematical model has been developed in Matlab to model nanodevices. The sensors modeled can be used for automated drug detection and delivery unit.

References

[1]. Ludwig, J., and Weinstein, J. (2005). Biomarkers in cancer staging, prognosis and treatment selection. Nature Rev. Cancer, 5, 845-856.

[2]. Catalona, W. (1996). Clinical utility of measurements of free and total prostate-specific antigen (PSA): a review. Prostate, 7, 64-69.

[3]. Ushaa Eswaran, M. Madhavilatha, and Madhusudhana Rao Ganji. (2006). A Proposal for Design of Hybrid Multi-Functional Implantable Biochip Using Bio- Intelligent Expert System' The 2006 International Conference on Artificial Intelligence (ICAI'06) June 26-29, Las Vegas, USA.

[4]. Ushaa Eswaran, Madhusudhan Rao Ganji, Jayashankar, T.S., and Lekha Suresh, (2006). 'Design of implantable VLSI bio-chip', National conference on Advanced Communication Technologies [ACT-06], 8-9 December.

[5]. Ushaa Eswaran, and M. Madhavilatha, (2009). “ Disease Detection Using Pattern Recognition Techniques” National Conference on 'Emerging Trends in Information Communication Technology' (ETICT-08) held th th in GITAM University, India during 19 & 20 December and is published in GITAM Journal of Information Communication Technology, Vol. 2, January - July 2009 Number - 1 (ISSN 0974-4622) pp.34-37.

[6]. Ushaa Eswaran, Madhusudhana Rao, and M.S. Thakur (2004). 'Microprocessor Based Biosensors for Determination of Toxins and Pathogens in Restricted Areas of Human Intervention' IC AI'04 IEEE sponsored International Conference on Artificial Intelligence held in Las Vegas, Nevada, USA during 21–24 June.

[7]. Beckett, M., Cazares, L., Vlahou, A., Schellhammer, P., and Wright, G. (1995). Prostate-specific membrane antigen levels in sera from healthy men and patients with benign prostate hyperplasia or prostate cancer. Clin. Cancer Res., 5, 4034-4040.

[8]. Henderson, C., and Patek, A. (1998). The relationship between prognostic and predictive factors in the management of breast cancer. Breast Cancer Res. Treat., 52, 261-288.

[9]. Dandachi, N., Dietze, O., and Hauser-Kronberger, C. (2002). Chromogenic in situ hybridization: a novel approach to a practical and sensitive method for the detection of HER2 oncogene in archival human breast carcinoma. Lab. Invest., 82, 1007–1014.

[10]. Molina, R., Auge, J., Escudero, J., Marrades, R., Vinolas, N., Carcereny, E., Ramirez, J., and Filella, X. Mucins. (2008). CA 125, CA 19.9, CA 15.3 and TAG-72.3 as tumor markers in patients with lung cancer: comparison with CYFRA 21-1, CEA, SCC and NSE. Tumor Biol., 29, 371-380.

[11]. Landman, J., Chang, Y., Kavaler, E., Droller, M., and Liu, B. (1998). Sensitivity and specificity of NMP-22, telomerase, and BTA in the detection of human bladder cancer. Urology 1998, 52, 398-402.

[12]. Prow, T.W., Rose, W.A., Wang, N., Reece, L.M., Lvov, Y., and Leary, J.F. (2005). "Biosensor-Controlled Gene Therapy/Drug Delivery with Nanoparticles for Nanomedicine" Proc. of SPIE 5692: 199-208.

[13]. Prow, T.W., Smith, J.N., Grebe, R., Salazar, J.H., Wang, N., Kotov, N., Lutty, G., and Leary, J.F. (2006). "Construction, Gene Delivery, and Expression of DNA Tethered Nanoparticles" Molecular Vision 12: 606-615.

[14]. Prow, T.W., Grebe, R., Merges, C., Smith, J.N., McLeod, D.S., Leary, J.F., Gerard A., and Lutty, G.A. (2006). "Novel therapeutic gene regulation by genetic biosensor tethered to magnetic nanoparticles for the detection and treatment of retinopathy of prematurity" Molecular Vision 12: 616-625.

[15]. www.nanohub.org

[16]. Seeman, (2005). From genes to machines: DNA Nanomechanical devices. TRENDS in Biochemical Sciences 30.

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Research Paper

Estimating the Quality of the Blurred Images using Standard Error as a Quality Metric

R. Maruthi* , S.P. Faustina Joan, R.Nithya*, R.Sridevi****

* Assistant Professor, Department of MCA, SSN College of Engineering, Chennai.

-** PG Students, Department of MCA, SSN College of Engineering, Chennai.

Maruthi, R , Joan, S.P. F., Nithya , R., and Sridevi , R. (2011). Estimating The Quality Of The Blurred Images Using Standard Error As A Quality Metric. i-manager’s Journal on Future Engineering and Technology, 6(2), 63-67. https://doi.org/10.26634/jfet.6.2.1328

Abstract

The goal of this research paper is to assess the quality of the blurred images using a reference image. This is done using a bi-variate measure and is also demonstrated to show how well this measure is used in estimating the quality of the blurred images. Experiments have been performed on various sets of blurred images and the information content in those images has been estimated using standard error as a quality metric.

References

[1]. Zhou Wang, Alan Conrad Bovik. (2006). “Modern image Quality assessment” Morgan and Claypool publishers.

[2]. Xin Wang; Baofeng Tian; Chao Liang; Dongcheng Shi. (May 2008). “Blind Image Quality Assessment for Measuring Image Blur” Image and Signal Processing, 2008. CISP apos; 08. Congress on, Volume 1, Issue, 27-30 Page(s):467 - 470.

[3]. Izak van zyl Marais, Willem Herman steyn. (November 2007). “Robust defocus blur identification in the context of blind image quality assessment ”Image Communication Volume 22, Issue Pages 833-844, ISSN:0923-5965.

[4]. Li, J.T. Kwok, Y. Wang. (2001). “Combination of images with diverse focuses using the spatial frequency”, Information. Fusion 2, 169–176.

[5]. Anjali Malviya S.G. Bhirud. (2010). “Objective Criterion for Performance Evaluation of Image Fusion Techniques” International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 25.

[6]. Dheeraj Agrawal, Dr.Al-Dahoud Ali, Dr.J.Singhai. (2009). A modified partition fusion technique of multifocus images for improved image quality, UbiCC Journal – Volume 4 No. 3, Special Issue on ICIT 2009 Conference - Bioinformatics and Image.

[7]. Siddiqui, A.B. Jaffar, M.A. Hussain, A. Mirza, A.M. (May 2010). “Block-Based Feature-Level Multi-Focus Image Fusion” Future Information Technology (FutureTech), 2010 5th International Conference on 21-23.

[8]. V.Aslantas, R.Kurban. (2010). “Fusion of multi-focus images using different evolution algorithm” Experts systems with Applications 37, 8861-8870.

[9]. WeiHuang a, ,ZhongliangJin. (2007). “Evaluation of focus measures in multi-focus image fusion” Pattern Recognition Letters28, 493–500.

[10]. Yingjie Zhang, Liling Ge. (2009). “Efficient fusion scheme for multi-focus images by using blurring measure. Digital Signal Processing 19, 186-193.

[11]. William M.K. Trochim. (2007). “Research Methods Knowledge Base” AtomicDog publishing.

i-manager's Journal on Future Engineering and Technology (JFET)

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Volume 6 Issue 2 November - January 2011

Mounir Salhi*

Advanced Technologies for Medical and Signals, Ecole Nationale d'Ingénieurs de Sfax Tunisia.

Salhi , M. (2011). Object-Based Classification Of Remotely Sensed Olive Tree Fields, Using Mathematical Morphology. i-manager’s Journal on Future Engineering and Technology, 6(2), 1-9. https://doi.org/10.26634/jfet.6.2.1320

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Karim Salehpoor* , Joseph Genin**, James A. Smoake***

* Mechanical Engineering Department, New Mexico Tech, Socorro. ** Mechanical Engineering Department, New Mexico State University, Las Cruces. *** Professor Emeritus, Biology Department, New Mexico Tech, Socorro.

Abstract

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Bhagwati Charan Patel* , G. R. Sinha**

* Associate Professor (IT), Shri Shankaracharya College of Engg. & Tech, Bhilai, India. ** Professor & Head (IT), Shri Shankaracharya College of Engg. & Tech, Bhilai, India.

Patel , B. C., and Sinha, G. R. (2011). Structural Analysis Of Tissue In Contiguous Micro Calcifications In Mammograms For Breast Cancer Identification. i-manager’s Journal on Future Engineering and Technology, 6(2), 20-27. https://doi.org/10.26634/jfet.6.2.1322

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D. Kesavaraja* , A. Suthan**, D. Sasireka***, D. Jeyabharathi****

Kesavaraja, D., Suthan, A., Sasireka , D., and Jeyabharathi, D. (2011). Implementation Of Cloud Based File Management System [CFMS] For Affording Distributed Warehouse Service. i-manager’s Journal on Future Engineering and Technology, 6(2), 28-35. https://doi.org/10.26634/jfet.6.2.1323

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S. Suresh* , Ravi Shankar**, Shri Chand***

* Department of Chemical Engineering, Maulana Azad National Institute of Technology (MANIT) Bhopal, M.P., India. **-*** Department of Chemical Engineering, Indian Institute of Technology Roorkee, India.

Suresh, S., Shankar, R., and Chand, S. (2011). Treatment Of Distillery Wastewater Using Catalytic Wet Air Oxidation. i-manager’s Journal on Future Engineering and Technology, 6(2), 36-44. https://doi.org/10.26634/jfet.6.2.1324

Abstract

References

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M. C. Raju* , S.V.K. Varma**, N. Anandareddy***

* Principal, Annamacharya PG College of Computer Studies, Rajampet A.P., India. ** Department of Mathematics, S.V. University, Tirupati, A.P., India. *** Department of Mathematics, A.I.T.S., Rajampet, A.P., India.

Raju, M. C., Varma , S. V. K., and Anandareddy, N. (2011). MHD Thermal Diffusion Natural Convection Flow Between Heated Inclined Plates In Porous Medium. i-manager’s Journal on Future Engineering and Technology, 6(2), 45-59. https://doi.org/10.26634/jfet.6.2.1325

Abstract

References

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Mohamed bak* , K. Kalaichelvan**

* Assistant Professor, Department of Aeronautical Engineering, Tagore Engineering College, Tamil Nadu, India. ** Associate Professor, Department of Production Technology, Anna University, Chennai.

Bak , K. M., and Chelvan, K. K. (2011). Finite Element Analysis Of Hybrid Composite Joints. i-manager’s Journal on Future Engineering and Technology, 6(2), 50-54. https://doi.org/10.26634/jfet.6.2.1326

Abstract

References

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Ushaa Eswaran* , M. Madhavi Latha**, G. Madhusudhan Rao***

* Head, Department of ECE, Siddharth Institute of Engineering & Technology, Puttur, Affiliated to JNTU, Anantapur ** Professor and Head, Department of ECE, JNTU, Hyderabad. *** Principal, Al Aman College of Engineering, Vishakapatnam, Affiliated to JNTU-Kakinada.

Ushaa, S. M., Madhavilatha, M., and Rao, G. M. (2011). Nanowire Sensors Characterization And Validation Using Matlab Models For Disease Detection. i-manager’s Journal on Future Engineering and Technology, 6(2), 55-62. https://doi.org/10.26634/jfet.6.2.1327

Abstract

References

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R. Maruthi* , S.P. Faustina Joan**, R.Nithya***, R.Sridevi****

* Assistant Professor, Department of MCA, SSN College of Engineering, Chennai. **-**** PG Students, Department of MCA, SSN College of Engineering, Chennai.

Maruthi, R , Joan, S.P. F., Nithya , R., and Sridevi , R. (2011). Estimating The Quality Of The Blurred Images Using Standard Error As A Quality Metric. i-manager’s Journal on Future Engineering and Technology, 6(2), 63-67. https://doi.org/10.26634/jfet.6.2.1328

Abstract

References

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Karim Salehpoor* , Joseph Genin, James A. Smoake*

* Mechanical Engineering Department, New Mexico Tech, Socorro.

Mechanical Engineering Department, New Mexico State University, Las Cruces.

* Professor Emeritus, Biology Department, New Mexico Tech, Socorro.

* Associate Professor (IT), Shri Shankaracharya College of Engg. & Tech, Bhilai, India.

** Professor & Head (IT), Shri Shankaracharya College of Engg. & Tech, Bhilai, India.

D. Kesavaraja* , A. Suthan, D. Sasireka*, D. Jeyabharathi****

S. Suresh* , Ravi Shankar, Shri Chand*

* Department of Chemical Engineering, Maulana Azad National Institute of Technology (MANIT) Bhopal, M.P., India.

-* Department of Chemical Engineering, Indian Institute of Technology Roorkee, India.

M. C. Raju* , S.V.K. Varma, N. Anandareddy*

* Principal, Annamacharya PG College of Computer Studies, Rajampet A.P., India.

Department of Mathematics, S.V. University, Tirupati, A.P., India.

* Department of Mathematics, A.I.T.S., Rajampet, A.P., India.

* Assistant Professor, Department of Aeronautical Engineering, Tagore Engineering College, Tamil Nadu, India.

** Associate Professor, Department of Production Technology, Anna University, Chennai.

Ushaa Eswaran* , M. Madhavi Latha, G. Madhusudhan Rao*

* Head, Department of ECE, Siddharth Institute of Engineering & Technology, Puttur, Affiliated to JNTU, Anantapur

Professor and Head, Department of ECE, JNTU, Hyderabad.

* Principal, Al Aman College of Engineering, Vishakapatnam, Affiliated to JNTU-Kakinada.

R. Maruthi* , S.P. Faustina Joan, R.Nithya*, R.Sridevi****

* Assistant Professor, Department of MCA, SSN College of Engineering, Chennai.

-** PG Students, Department of MCA, SSN College of Engineering, Chennai.