Rajaas Engineering College

Engineering, India

Rajaas Engineering College

Engineering, India

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Mariarputham E.J.,Rajaas Engineering College
Computational and Mathematical Methods in Medicine | Year: 2015

Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of the seven classes. This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features) are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern histogram, tamura features, and edge orientation histogram. Few types of support vector machine (SVM) and neural network (NN) classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark, with 917 Pap smear images. The output of SVM is found to be best for the most of the classes and better results for the remaining classes. © 2015 Edwin Jayasingh Mariarputham and Allwin Stephen.


Kumar P.S.,Rajaas Engineering College
European Journal of Scientific Research | Year: 2012

Now a day, Copper-based sintered composites produced by powder metallurgy processes are widely used in bearings and bushes. Also composites based on copper-tin alloys containing a solid lubricant have been developed as self-lubricating materials under onerous conditions of load, atmosphere and temperature. It is well known that the addition of graphite serves to reduce friction and wear in copper-tin alloys. However it should be noted that the addition of graphite or molybdenum disulfide (MoS2) has an adverse effect on the composites mechanical properties. In this paper, the lubricant MoS2 powders were coated with Cu to reinforce their bonding to the Cu particles in the composites during sintering. The hardness, microstructure and compression strength of the sintered specimens were examined. The friction and wear properties of the materials were estimated by a pinon- disc wear testing machine under multi-pass dry conditions at room temperature in air. Although mechanical properties of the composites decreased with increasing amount of added MoS2, the use of Cu-coated lubricant powders improved the compressive strength, impact strength. MoS2 was effective in reducing the wear and friction of the composites. Results showed that the wear rate of the composites decreases at room temperature with MoS2 addition. Particularly, the 5% MoS2 composites showed a very low coefficient of friction of 0.4. Wear Morphology was also studied by using SEM. It is also observed that the wear rates of the MoS2 composites increased considerably with the amount of MoS2 addition. This behavior is thought to be due to the absence of MoS2 and the presence of brittle CuMo2S3 compounds in the sintered composites. © 2012 EuroJournals Publishing, Inc.


PubMed | Rajaas Engineering College
Type: | Journal: Computational and mathematical methods in medicine | Year: 2015

Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of the seven classes. This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features) are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern histogram, tamura features, and edge orientation histogram. Few types of support vector machine (SVM) and neural network (NN) classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark, with 917 Pap smear images. The output of SVM is found to be best for the most of the classes and better results for the remaining classes.


Senthil Kumar P.,Rajaas Engineering College | Manisekar K.,Center for Manufacturing science
International Journal of Engineering, Transactions A: Basics | Year: 2015

Pure coppermaterials are not used as journal bearing material due to their low mechanical and hardness properties.With technological improvements, self-lubricated sintered bearings and plastic materials are used where continuous lubricating is impossible. Cu- composites were prepared by powder metallurgy method from the copper,tin and solid lubricant MoS2 powders in the range of 0-7wt % of MoS2.The wear test with an experimental plan of six loads (5-30 N) and five sliding speeds (0.5-2.5 m/s) was conducted on Pin-On-Disc machine to record a loss in mass due to wear for a six total sliding distance of 500 to3000m.It was confirmed that with increasing concentration of MoS2, the coefficient of friction of composites decreases.


Kumar S.J.J.,Rajaas Engineering College | Madheswaran M.,Center for Advanced Research
Journal of Medical Systems | Year: 2012

An improved Computer Aided Clinical Decision Support System has been developed to classify the retinal images using Neural Network and presented in this paper. The Optic Disc Parameters, thickness of the blood vessels, main vessel, and branch vessel and vein diameter have been extracted. Various types of Neural Network have been used for classification. The percentage of False Acceptance Rate and False Rejection Rate of the SVM classifier is found less than other classifiers. The accuracy of the proposed system has been verified and found to be 97.47%. © 2012 Springer Science+Business Media, LLC.


Sundresan M.,Rajaas engineering college | Joseph D.R.,Rajaas engineering college | Karthick R.,Rajaas engineering college | Shibi Joea J.C.S.,Rajaas engineering college
Procedia Engineering | Year: 2012

Aeroelasticity is the study of the mutual interaction that takes place among the inertial, elastic and aerodynamic forces acting on the structural members exposed to an airstream and the influence of this study on the design. This review paper deals with the investigation of the aeroelasticity phenomena. The effect of the aeroelasticity phenomena occurring while designing the wing of the aircraft are stated in detail. Flutter suppression and its techniques are investigated in this paper. The aeroelastic testing techniques available in this field and the efficient methods to solve these problems are discussed. The aeroelastic optimization techniques processes are reported. The fluid and structure interaction of the non-linear flexible wing structure results have been discussed for the various methods. The application of the MSC software and finite element methods are discussed. The aeroelastic applications are also summarized. © 2012 Published by Elsevier Ltd.


Bibin C.,Rajaas Engineering College | Selvaraj M.J.,Rajaas Engineering College | Sanju S.,Rajaas Engineering College
Procedia Engineering | Year: 2012

Every object has its own natural frequency when the frequency of a source is equal to the objects natural frequency the object will be tends to vibrate this may result in flutter for an aircraft during its cruise speed. Flutter, an unstable oscillatory aerodynamic condition with high frequency and large amplitude ensuing from fluid structure interaction is of precise interest for many aero elastic researchers. This demon results in a catastrophic failure of structure rapidly. Therefore, there is an immense requirement of predicting the flutter speed accurately considering the various uncertain conditions obviously. The general theory of aero elastic instability and the crux of elementary mechanism of flutter have been explained profoundly by many earlier researchers. Flutter problem has been of great interest since early years of flight these will lead to aerodynamic instability and life reduction of an aircraft wing and its components. So while designing an aircraft these flutter has to be consider. We had analyzed a subsonic passenger aircraft in its cruise speed using optimization tools CFD and FEA tools. In our paper we would like to exposure the results computationally including both fluid and structural interaction problem. By this way we can able to predict accurately the nature of an aircraft during its flutter. The structural deformation and stress distribution will be calculated at various conditions. © 2012 Published by Elsevier Ltd.


Sheejakumari V.,Rajaas Engineering College | Sankara Gomathi B.,National Engineering College
Computational and Mathematical Methods in Medicine | Year: 2015

The advantages of magnetic resonance imaging (MRI) over other diagnostic imaging modalities are its higher spatial resolution and its better discrimination of soft tissue. In the previous tissues classification method, the healthy and pathological tissues are classified from the MRI brain images using HGANN. But the method lacks sensitivity and accuracy measures. The classification method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new classification method is proposed in this paper. Here, new tissues classification method is proposed with improved particle swarm optimization (IPSO) technique to classify the healthy and pathological tissues from the given MRI images. Our proposed classification method includes the same four stages, namely, tissue segmentation, feature extraction, heuristic feature selection, and tissue classification. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of the proposed classification method in classifying the tissues and the achieved improvement in sensitivity and accuracy measures. Furthermore, the performance of the proposed technique is evaluated by comparing it with the other segmentation methods. © 2015 V. Sheejakumari and B. Sankara Gomathi.


Palagan C.A.,Rajaas Engineering College | Leena T.,Rajaas Engineering College
ICECT 2011 - 2011 3rd International Conference on Electronics Computer Technology | Year: 2011

Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or correction, semiautomatic methods have become the preferred type of medical image segmentation. As Magnetic Resonance Imaging (MRI) is an important technology of radiological evaluation and computer-aided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing. The paper concerns medical image segmentation based on t-mixture model because of merits of the model. By analyzing the features of MR images, the main procedure of white matter segmentation of brain MR Images based on t-mixture model is outlined follows. The parameters of t-mixture model for the image are firstly estimated. Then the posterior probabilities of the pixels of the image are computed. At last, the image is segmented according to the Bayes decision rule for minimum error. Experimental results show that t-mixture model fits for medical image segmentation up to 400 iterations. © 2011 IEEE.


Bhagavathi Perumal S.,Rajaas Engineering College | Thamarai P.,Salem College | Elango L.,Anna University
Indian Journal of Environmental Protection | Year: 2010

A three - dimensional mathematical model to simulate regional groundwater flow was used in the coastal area of Kanyakumari district in South Tamil Nadu. The study area is characterized by heavy rainfall, less extraction of groundwater for agricultural, industrial and drinking water supplies. The types of soil in the study area aquifer are sandy clay. There are 75 well stations in the study area are located used to design the model as major pumping stations. A regional groundwater model using MODFLOW was developed for the Kanyakumari aquifers in order to simulate the groundwater head. The MODFLOW model simulates groundwater flow over an area of about 860 km 2, 70km long and 18km width with a grid size of 500m X 940m, 20 rows and 140 columns with a total of 1828 numbers of grids and one layer. The model simulated a transient- state condition for the period 2000 - 2010. The MODFLOW model was calibrated for steady and transient state conditions. There was a reasonable match between the computed and observed heads. The transient MODFLOW model was run until the year 2010 to forecast groundwater flow under various scenarios of pumping and recharge. Based on the modeling results, it is shown that the aquifer system is stable at the present rate of pumping along the coastal area of Kanyakumari. © 2010 - Kalpana Corporation.

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