No abstract

The standalone computer machines are powerful with diverse applications. But the actual performance and utilization of these machines was achieved by interlinking them to form networks which will work in coordination and cooperation. Cloud computing, an application of computer networks, deserves a separate field for research due to its enormous applications and elegant performance. The basic intend to develop this technology was to enhance the efficiency, utilization and performance of the computer resources but despite the enormous potential that is promised by this discipline is still to be fulfilled because of the various security issues. Here, in this paper we address the issue of access control to the critical data on a cloud by the use the biometric iris verification and authentication for a controlled flow of data. In order to achieve the above goal we are using biometric iris verification for the purpose of authentication of users. MATLAB is used in the implementation for the proposed scheme where in iris textural features (gray level co-occurrence matrix) are extracted and later validated on the CASIA iris database.Index Terms-Cloud computing, security and privacy, access control, iris biometric, gray level co-occurrence matrix (GLCM).

With the improvement and development of the high performance computing systems, the need arises to use the resources available at hand efficiently. Rendering is one kind of application which is suitable for high computing. In the modern era of computing we have got the quad processors available, but the processes and data we manipulate on these, is still based on the serial algorithms in many of the cases. As rendering needs more computing and is associated with huge data access, it can be broken into the smaller subtasks of same nature to be executed on the different processors. The parallel approach works on the principle of solving or computing the different similar sub-tasks using the different available resources of computing (processors) in a parallel fashion. Means at the end of one unit of time, we are having the result of as much number of subtasks in hand, as the number of processors available. The only thing we need to keep in mind is how to efficiently share the resources between different subtasks and how to balance the load factor as per different processors are considered. In this paper we are proposing a hierarchical scheduling policy for rendering to improve its performance as compared to the present day available methods. We will evaluate the approach with the existing approaches for rendering to show the improvement in performance.

With the progression of picture archiving and communication systems (PACSs) over the past decade, it has become imperative that such systems be optimised in security, storage, and transmission aspects. The work presented in this Letter shows a framework for medical image compression and secure image transmission for PACSs. The work aims to achieve a lower dimensionality of input medical image signified by a high-compression ratio, a secure image transmission that can withstand adversarial attacks and provide a reversible reconstruction with minimal error. The authors illustrate that sinusoid modulated Gaussian texture maps, multi-level chaotic maps, and high-frequency image maps can be efficiently fused and utilised in a deep learning architecture. The overall analysis depicts promising results with regard to the capability of image compression, security, and transmission. The proposed framework will be a potential candidate for use in PACSs, which effectively is the backbone of the current healthcare paradigm.

No abstract