Composite Strain Encoding (C-SENC) is an MRI acquisition technique for simultaneous acquisition of cardiac tissue viability and contractility images. It combines the use of black-blood delayed-enhancement imaging to identify the infracted (dead) tissue inside the heart wall muscle and the ability to image myocardial deformation (MI) from the strain-encoding (SENC) imaging technique. In this work, we propose an automatic image processing technique to identify the different heart tissues. This provides physicians with a better clinical decision-making tool in patients with myocardial infarction

Software issues related to automotive controls account for an increasingly large percentage of the overall vehicles recalled. To alleviate this problem, vehicle diagnosis and maintenance systems are increasingly being performed remotely, that is while the vehicle is being driven without need for factory recall and there is strong consumer interest in Remote Diagnosis and Maintenance (RD&M) systems. Such systems are developed with different building blocks/elements and various capabilities. This paper presents a novel automotive RD&M system and prognosis architecture. The elements of the

Pervasive healthcare environments provide an effective solution for monitoring the wellbeing of the elderly where the general trend of an increasingly ageing population has placed significant burdens on current healthcare systems. An important pervasive healthcare system functionality is patient motion analysis where gait information can be used to detect walking behavior abnormalities that may indicate the onset of adverse health problems, for quantifying post-operative recovery, and to observe the progression of neurodegenerative diseases. The development of accurate motion analysis models

Cloud computing provides a promising solution to the big data problem associated with next generation sequencing applications. The increasing number of cloud service providers, who compete in terms of performance and price, is a clear indication of a growing market with high demand. However, current cloud computing based applications in bioinformatics do not profit from this progress, because they are still limited to just one cloud service provider. In this paper, we present different use case scenarios using hybrid services and resources from multiple cloud providers for bioinformatics

Image denoising is of paramount importance in image processing. In this paper, we propose a new design technique for the design of Double density Discrete Wavelet Transform (DD DWT) AND DD CWT filter bank structure. These filter banks satisfy the perfect reconstruction as well as alias free properties of the DWT. Next, we utilized this filter bank structure in image denoising. Our denoising scheme is based on utilizing the interscale correlation/interscale dependence between wavelet coefficients of a DD DWT of the noisy image. This is known as the Bivariate Shrinkage scheme. More precisely, we

The contribution of this paper is twofold. First, we provide new theoretical insights into the relationship between a string and its reverse: If the Burrows-Wheeler transform (BWT) of a string has been computed by sorting its suffixes, then the BWT and the longest common prefix array of the reverse string can be derived from it without suffix sorting. Furthermore, we show that the longest common prefix arrays of a string and its reverse are permutations of each other. Second, we provide a parallel algorithm that, given the BWT of a string, computes the BWT of its reverse much faster than all

Numerous commercial tools like Xamarin, React Native and PhoneGap utilize the concept of cross-platform mobile applications development that builds applications once and runs it everywhere opposed to native mobile app development that writes in a specific programming language for every platform. These commercial tools are not very efficient for native developers as mobile applications must be written in specific language and they need the usage of specific frameworks. In this paper, a suggested approach in TCAIOSC tool to convert mobile applications from Android to iOS is used to develop the

Code-Reuse Attacks (CRAs) are solid mechanisms to bypass advanced software and hardware defenses. Due to vulnerabilities found in software which allows attackers to corrupt the memory space of the vulnerable software to modify maliciously the contents of the memory; hence controlling the software to be able to run arbitrary code. The CRAs defenses either prevents the attacker from reading program code, controlling program memory space directly or indirectly through the usage of pointers. This paper provides a thorough evaluation of the current mitigation techniques against CRAs with regards to

In some machine learning applications using soft labels is more useful and informative than crisp labels. Soft labels indicate the degree of membership of the training data to the given classes. Often only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data. In this paper we propose an approach for Fuzzy-Input Fuzzy-Output classification in which the classifier can learn with soft-labeled data and can also produce degree of belongingness to classes as an output for each pattern. Particularly, we investigate the

Cloud computing has become the most convenient environment for startups to run, build and deploy their products. Most startups work on availing platforms as a solution for problems related to education, health, traffic and others. Many of these platforms are mobile applications. With platforms as a service (PaaS), startups can provision their applications and gain access to a suite of IT infrastructure as their business needs. But, startups face many business and technical challenges to adapt rapidly to cloud computing. This paper helps startups to build a migration strategy. It discusses