Web Service selection is a key component in service-oriented computing. Service-oriented Architectures follow the find-bindexecute paradigm in which service providers register their services in public or private registries, which clients use to locate web services. The QoS based web service selection mechanisms plays an essential role in service-oriented architectures, because most of the applications want to use services that accurately meet their requirements. Currently, the UDDI catalogue supports only primitive matching mechanisms and provides no control over the quality of registered services. We propose a QoS broker based architecture for dynamic web service selection which facilitates the clients to specify the non-functional requirements like QoS along with functional requirements. The paper presents an efficient mechanism for finding the most suitable web service according to the consumer's requirements.

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The applications of digital images are increasing exponentially in the field of image processing. Many image editing tools and computer applications are available to manipulate the images. Hence image tampering has been increasingly easy to perform. It is very difficult to say whether an image is original or a manipulated version by just looking it. As a result of such modifications digital images have almost lost their reliability. Watermarking can be used to identify such modifications. Watermark can be hash values of the image, compressed content of the image etc. This paper discusses about various image tampering detection and recovery techniques.

Blurring is a common artifact that produces distorted images with unavoidable information loss. The Blind image deconvolution is to recover the sharp estimate of a given blurry image when the blur kernel is unknown. Despite the availability of deconvolution methods, it is still uncertain how to regularize the blur kernel in an effectual fashion which could substantially improve the results even when the image is blurred to its extend. This paper presents a novel deconvolution method that describes an efficient optimization scheme that alternates between estimation of blur kernel and restoration of sharp image until convergence. The system engenders a more efficient regularizer for the blur kernel that can generally and considerably benefit the solution for the problem of blind deconvolution. Also the blur metric concept in the system provides an automated environment for the selection of deconvolutoin parameters. The outlier handling model used in this work detects and eliminates the major causes of visual artifacts. As a result the system produces high quality deblurred results that preserves fine edge details of an image and complex image structures, while avoiding visual artifacts. The experiments on realistic images show that the proposed deconvolution method can produce high quality deblurred images with very little ringing artifacts even when the image is severely blurred, and the ability of system in choosing the appropriate input parameters for deconvolution.