Abstract In this paper a new family of accumulate repeat accumulate codes are established by a collection of interconnected proto-graphs in a spatially coupled manner. A little modification in the repeater-combiner stage of an accumulate repeat accumulate code emphasizes this development. Spatially coupled low density parity check (SCLDPC) codes appear to approach the capacity universally across the binary-input memoryless (BMS) channels.... In this paper a new family of accumulate repeat accumulate codes are established by a collection of interconnected proto-graphs in a spatially coupled manner. A little modification in the repeater-combiner stage of an accumulate repeat accumulate code emphasizes this development. Spatially coupled low density parity check (SCLDPC) codes appear to approach the capacity universally across the binary-input memoryless (BMS) channels. However, the maximum degree distribution is unbounded and this leads to computational complexity problems at encoders and decoders. Accumulate repeat accumulate (ARA) codes could introduce bounded complexity ensembles that asymptotically achieve capacity on the binary erasure channels (BEC). So, we provide a density evolution (DE) analysis for systematic SC-ARA proto-graphs over the binary erasure channels (BEC). We also discuss the stability conditions for them. Simulation results show that over the BEC spatially coupling of ensembles of ARA codes drives the message-passing belief propagation decoding threshold (BP) to be closed to the maximum a posterior (MAP) threshold of the underlying codes. Read More Read Less

Abstract Automated fundus image analysis plays an important role in the computer aided diagnosis of ophthalmologic disorders. A lot of eye disorders, as well as cardiovascular disorders, are known to be related with retinal vasculature changes. Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process... Automated fundus image analysis plays an important role in the computer aided diagnosis of ophthalmologic disorders. A lot of eye disorders, as well as cardiovascular disorders, are known to be related with retinal vasculature changes. Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this paper focuses on retinal image analysis and their clinical implications. The most prevalent causes of blindness in the industrialized world are age-related macular degeneration, diabetic retinopathy, and glaucoma. Retinal exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Correct and efficient screening of exudates is very expensive in professional time and may cause human error. Nowadays, the digital retinal image is frequently used to follow-up and diagnoses eye diseases. Therefore, the retinal image is crucial and essential for experts to detect exudates. In age related Macular degeneration, the macula is responsible for the sharp central vision needed for detailed activities such as reading, writing, driving, face recognition and ability to see colors. Age related macular degeneration is degeneration of the macula area and the delicate cells of the macula become inactive and stop working. Unfortunately, age-related macular degeneration cannot be completely cured, but if diagnosed at an early stage degeneration laser treatment can help some people to prevent further deterioration of macula. The algorithm locates disease affected pixels on macula and displays their location. After pre-processing particle analysis tool is applied to locate the effected parts on the fundus image. Read More Read Less

Abstract Considering the proliferation in the number of cloud users on an everyday basis, the task of resource provisioning in order to support all these users becomes a challenging problem. When resource allocation is non-optimal, users may facehigh costs or performance issues. So, in order to maximize profit and resource utilization while satisfying all client requests, it is essential for Cloud... Considering the proliferation in the number of cloud users on an everyday basis, the task of resource provisioning in order to support all these users becomes a challenging problem. When resource allocation is non-optimal, users may facehigh costs or performance issues. So, in order to maximize profit and resource utilization while satisfying all client requests, it is essential for Cloud Service Providers to come up with ways to allocate resources adaptively for diverse conditions. This is a constrained optimization problem. Each client that submits a request to the cloud has its own best interests in mind. But each of these clients competes with other clients in the quest to obtain required quantum of resources. Hence, every client is a participant in this competition. So, a preliminary analysis of the problem reveals that it can be modeled as a game between clients. A game theoretic modeling of this problem provides us an ability to find an optimal resource allocation by employing game theoretic concepts. Resource allocation problems are NP-Hard, involving VM allocation and migration within and possibly, among data centers. Owing to the dynamic nature and number of requests, static methods fail to surmount race conditions. Using a Min-Max Game approach, we propose an algorithm that can overcome the problems mentioned. We propose to employ a utility maximization approach to solve the resource provisioning and allocation problem. We implement a new factor into the game called the utility factor which considers the time and budget constraints of every user. Resources are provisioned for tasks having the highest utility for the corresponding resource. Read More Read Less