Context Aware Mobile Application Architecture (CAMAA) for Health Care systems : standardization and abstraction of context aware layers

Context awareness was introduced recently in several fields in quotidian human activities. Among context aware applications, health care systems are the most important ones. Such applications, in order to perceive the context, rely on sensors which maybe physical or virtual. However, these applications lack of standardization in handling the context and the perceived sensors data. In this work, we propose a formal context aware application architecture model to deal with the context taking into account the scalability and interoperability as key features towards an abstraction of the context relatively to end user applications. As a proof of concept, we present also a case study and simulation explaining the operational aspect of this architecture in health care systems

Hawkinsinuria With Direct Hyperbilirubinemia in Egyptian-Lebanese Boy

Tyrosinemia type III is the rarest type of tyrosinemia, because of a mutation in 4-OH-phenylpyruvate dioxygenase (HPD). This causes two different types of diseases with different modes of inheritance: tyrosinemia type III and hawkinsinuria. Hawkinsinuria is an autosomal dominant disease, which presents a failure to thrive and metabolic acidosis; however, the liver is not affected. P.A33T heterozygous mutation was reported by Tomoeda et al. to cause hawkinsinuria. This case report will present the first case of an Egyptian-Lebanese male who developed direct hyperbilirubinemia and was found to have tyrosinemia type III, due to elevated tyrosine levels in the blood and tyrosine derivatives in the urine, but genetic testing revealed a P.A33T heterozygous mutation, a cause of hawkinsinuria

Scalable row-based parallel H.264 decoder on embedded multicore processors

[IF=1.02]International audienceMultimedia applications are present in most mobile hand-held devices, which are still equipped with limited battery resources. The H.264 standard is currently dominating the video compression world. H.264 has high computational requirements in terms of memory, energy, and time. Many techniques emerged that optimize parallel task granularity on multicore systems ranging from groups of pictures until the smallest block of pixels. A scalable parallel technique for the motion compensation phase is proposed in this research that is based on processing of groups of macroblock rows. Moreover, a light dependency detection algorithm is added to the prediction phase that enables parallel execution and minimizes synchronization stall time. Furthermore, a parallel implementation of the deblocking filter is also implemented. The overall result is an efficient and highly scalable parallel H.264 decoder that is evaluated on a real-board platform composed of an ARM Cortex-A9 MPCore with four processors. Various low- and high-definition video sequences are used in experiments. Results show that execution time reaches a speedup of 3.3× for motion compensation stage and an overall speedup of 2.3× on 4 cores including communication and synchronization overhead. Energy consumption decreases up to 63 % for the whole application execution