Efficient multimedia data storage in cloud environment

With the rapid adoption of social media, people are more habituated to utilize the images and video for expressing themselves. Future communication will replace the conventional means of social interaction with the video or images. This, in turn, requires huge data storage and processing power. This paper reports a compression/decompression module for image and video sequences for the cloud computing environment. The reported mechanism acts as a submodule of IaaS layer of the cloud. The compression of the images is achieved using redundancy removal using block matching algorithm. The proposed module had been evaluated with three different video compression algorithms and variable macroblock size. The experimentations has been carried out on a cloud host environment by using VMWarework station platform. Apart from being simple in execution, the proposed module does not incur an additional monetary burden, hardware or manpower to achieve the desired compression of the image data. Experimental analysis has shown a considerable reduction in data storage requirement as well as the processing time.Web of Science39444243

Security and service assurance issues in Cloud environment

Cloud security and service assurance is a wide research area with an unrestrained amount of apprehensions, ensuring equipment and stage innovations, to secure information and asset access. In spite of the colossal advantages of Cloud computing paradigm, the security and service concerns have consistently been the center of various Cloud clients and obstruction to its extensive acceptance. The paper reports a meticulous review in the field of Cloud computing with a focus on the security risk assessment and service assurance. This effort will serve as a ready reckoner to the research aspirants to encompass a general thought of the risk factors in security and the service assurance in a Cloud environment.Web of Science9120719

Performance Evaluation of ParalleX Execution model on Arm-based Platforms

The HPC community shows a keen interest in creating diversity in the CPU ecosystem. The advent of Arm-based processors provides an alternative to the existing HPC ecosystem, which is primarily dominated by x86 processors. In this paper, we port an Asynchronous Many-Task runtime system based on the ParalleX model, i.e., High Performance ParalleX (HPX), and evaluate it on the Arm ecosystem with a suite of benchmarks. We wrote these benchmarks with an emphasis on vectorization and distributed scaling. We present the performance results on a variety of Arm processors and compare it with their x86 brethren from Intel. We show that the results obtained are equally good or better than their x86 brethren. Finally, we also discuss a few drawbacks of the present Arm ecosystem

Efficient Screening of Diseased Eyes based on Fundus Autofluorescence Images using Support Vector Machine

A variety of vision ailments are associated with the foveal region of the eye. In current clinical practice, the ophthalmologist manually detects potential presence of such ailments based on fundus autofluorescence (FAF) images, and hence diagnoses the disease, when relevant. However, in view of the general scarcity of ophthalmologists relative to the large number of subjects seeking eyecare, especially in remote regions, it becomes imperative to develop methods to direct expert time and effort to medically significant cases. To serve the interest of both the ophthalmologist and the potential patient, we plan a screening step, where healthy and diseased eyes are algorithmically differentiated with limited input from only optometrists who are relatively more abundant in number. Specifically, an early treatment diabetic retinopathy study (ETDRS) grid is placed by an optometrist on each FAF image, based on which sectoral statistics are automatically collected. Using such statistics as features, healthy and diseased eyes are proposed to be classified by training an algorithm using available medical records. In this connection, we consider support vector machine (SVM) with linear as well as radial basis function (RBF) kernel, and observe satisfactory performance of both variants. Among those, we recommend the latter in view of its slight superiority in terms of classification accuracy (90.55% at a standard training-to-test ratio of 80:20), and practical class-conditional costs. © 2021 IEE