CSE

Enterprise Service Bus (ESB) is responsible for publishing and discovery of services in such environments. Contextaware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems' behavior accordingly. In this paper, we propose a Context Aware ESB (CA-ESB) that will publish and discover services based on location context. The main modules of the framework consist of Context Provider (senses location context), Context Aware Logic Module (decides which regional service to be selected based on location context) and Service Choreographer (choreographs selected services). We propose a graphical model named Context Aware Graph (CA-Graph) that will help us to dynamically choreograph the services. These modules along with other modules of SOA reference architecture will help the ESB to sense the location of users, to select the required services and dynamically choreograph those services. We define a set of metrics based on CA-graph and analyse of performance CA-ESB. An algorithm is proposed that will dynamically choreograph the selected services based on location context. The results of the case study of an Insurance System are used to illustrate our approach.

Service Oriented Architecture caters to the "Separation of Concerns" and incorporates reusability, granularity, modularity, componentization and interoperability. There are many software products that have been developed in the object-oriented paradigm. To incorporate the positive aspects of the service-oriented paradigm, they need to be evolved to the service-oriented domain. In this paper, we define an integrated approach towards consistent evolution of Object Oriented (OO) paradigm to the Service Oriented (SOA) domain. There are some proven Object Oriented Design Tools that can be used for Service Oriented Application design incorporating both the behavioral and structural aspects. The work in this paper involves a set of activities like formalizing the different UML diagrams, formal mapping of object components to service level components and establishing consistency among them. We also establish a traceability model for consistent evolution of Service Oriented Applica...

In Fingerprint based Biometric authentication image of the fingerprint can be scanned and can be used later on for the purpose of authentication. So this process does not provide very high security. This paper proposes another level of security by using the concept of combined key. The key is obtained by crossing over of the Session key generated from the password given by the legitimate user and the Biometric key generated from the fingerprint of the same user. The proposed approach trained the system by Artificial Neural Network in such a way that a small portion of the fingerprint is enough to generate the Biometric key which minimizes the chance of false rejection. So in this approach there is a significant improvement of the traditional authentication techniques.

Electronically stored health information enhances resource sharing, help us to reduce the number of errors, speed up clinical communication and assist doctors in diagnosis and treatment. Medical image watermarking is a special type of watermarking technique where the watermarked medical images should not differ perceptually from the original images, because the diagnosis must not be affected due to the presence of watermark. This paper presents a new blind watermarking technique for medical image using Stationary Wavelet Transformation (SWT). In the proposed method multiple watermarks containing patient's information are embedded and original image is not needed for the extraction of those watermarks.

Image authentication is important in content delivery to preserve originality as well as integrity of data. Using handwritten signature we can authenticate a person accurately. This paper proposes an efficient image authentication technique by hiding handwritten signature image in selected DWT sub-band of the image. At the receiver end signature image is extracted and verified with template signature using Artificial Neural Network and hence image authentication is achieved.

Thinning of character images is a big challenge. Removal of strokes or deformities in thinning is a difficult problem. In this paper, we have proposed a nearest opposite contour pixel based thinning strategy used for performing skeletonization of printed and handwritten character images. In this method, we have used shape characteristics of text to get skeleton of nearly same as the true character shape. This approach helps to preserve the local features and true shape of the character images. The proposed algorithm produces one pixel-width thin skeleton. As a by-product of our thinning approach, the skeleton also gets segmented into strokes in vector form. Hence further stroke segmentation is not required. Experiment is done on printed English and Bengali characters and we obtain less spurious branches comparing with other thinning methods without any post-processing.

Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method...

This paper is a brief survey in the field of Sixth Sense Technology. Sixth Sense is an interface of wearable gestures which upgrade the physical world around us with digital information and let us use the natural gestures to interact with that information. The advent of technology over the last decade has established a new dimension in the field of Human computer Interaction (HCI). The aim of this paper is to review various gestures, components used, methodology adopted in sixth sense technology and its application.

A mosquito borne pathogen called Dengue virus (DENV) has been emerged as one of the most fatal threats in the recent time. Infections can be in two main forms, namely the DF (Dengue Fever), and DHF (Dengue Hemorrhagic Fever). An efficient detection method for both fever types turns out to be a significant task. Thus, in the present work, a novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF. The ANN's input weight vector are optimized using PSO to achieve the expected accuracy and to avoid premature convergence toward the local optima. Therefore, a gene expression data (GDS5093 dataset) available publicly is used. The dataset contains gene expression data for DF, DHF, convalescent and healthy control patients of total 56 subjects. Greedy forward selection method has been applied to select most promising genes to identify the DF, DHF and normal (either convalescent or healthy controlled) patients. The proposed system perfor

Nowadays there is a need to protect our health related information for authentication and privacy purpose. In the proposed work multiple QR codes are watermarked inside a single color medical image using Variable Bit Replacement (VBR) technique. Three different 128 bit session keys are used to watermark QR Codes in three different color components (R, G and B) of a single color medical image. The QR Code is embedded in between moderately significant bit (MdSB) and least significant bit (LSB). The watermarked extraction process determines an invisible watermark in the image by reversing the embedding process. Results show that the process is robust against different attacks and imperceptibility is quite good.

Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN's since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms.

Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN's since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms.

Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu's method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study wasarried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.

Object recognition can be done based on local feature description algorithm or through global feature description algorithm. Both types of these descriptors have the efficiency in recognizing an object quickly and accurately. The proposed work judges their performance in different circumstances such as rotational effect scaling effect, illumination effect and blurring effect. Authors also investigate the speed of each algorithm in different situations. The experimental result shows that each one has some advantages as well as some drawbacks. SIFT (Scale Invariant Feature Transformation) and SURF (Speeded Up Robust Features) performs relatively better under scale and rotation change. MSER (Maximally stable extremal regions) performs better under scale change, MinEigen in affine change and illumination change while FAST (Feature from Accelerated segment test) and SURF consume less time.

Electronically stored health information enhances resource sharing, help us to reduce the number of errors, speed up clinical communication and assist doctors in diagnosis and treatment. Medical image watermarking is a special type of watermarking technique where the watermarked medical images should not differ perceptually from the original images, because the diagnosis must not be affected due to the presence of watermark. This paper presents a new blind watermarking technique for medical image using Stationary Wavelet Transformation (SWT). In the proposed method multiple watermarks containing patient's information are embedded and original image is not needed for the extraction of those watermarks.

Image authentication is important in content delivery to preserve originality as well as integrity of data. Using handwritten signature we can authenticate a person accurately. This paper proposes an efficient image authentication technique by hiding handwritten signature image in selected DWT sub-band of the image. At the receiver end signature image is extracted and verified with template signature using Artificial Neural Network and hence image authentication is achieved.

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

A mosquito borne pathogen called Dengue virus (DENV) has been emerged as one of the most fatal threats in the recent time. Infections can be in two main forms, namely the DF (Dengue Fever), and DHF (Dengue Hemorrhagic Fever). An efficient detection method for both fever types turns out to be a significant task. Thus, in the present work, a novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF. The ANN's input weight vector are optimized using PSO to achieve the expected accuracy and to avoid premature convergence toward the local optima. Therefore, a gene expression data (GDS5093 dataset) available publicly is used. The dataset contains gene expression data for DF, DHF, convalescent and healthy control patients of total 56 subjects. Greedy forward selection method has been applied to select most promising genes to identify the DF, DHF and normal (either convalescent or healthy controlled) patients. The proposed system perfor

In Fingerprint based Biometric authentication image of the fingerprint can be scanned and can be used later on for the purpose of authentication. So this process does not provide very high security. This paper proposes another level of security by using the concept of combined key. The key is obtained by crossing over of the Session key generated from the password given by the legitimate user and the Biometric key generated from the fingerprint of the same user. The proposed approach trained the system by Artificial Neural Network in such a way that a small portion of the fingerprint is enough to generate the Biometric key which minimizes the chance of false rejection. So in this approach there is a significant improvement of the traditional authentication techniques.

This paper is a brief survey in the field of Sixth Sense Technology. Sixth Sense is an interface of wearable gestures which upgrade the physical world around us with digital information and let us use the natural gestures to interact with that information. The advent of technology over the last decade has established a new dimension in the field of Human computer Interaction (HCI). The aim of this paper is to review various gestures, components used, methodology adopted in sixth sense technology and its application.