Estimating and reducing the memory requirements of signal processing codes for embedded systems

Most embedded systems have limited amount of memory. In contrast, the memory requirements of the digital signal processing (DSP) and video processing codes (in nested loops, in particular) running on embedded systems is significant. This paper addresses the problem of estimating and reducing the amount of memory needed for transfers of data in embedded systems. First, the problem of estimating the region associated with a statement or the set of elements referenced by a statement during the execution of nested loops is analyzed. For a fixed execution ordering, a quantitative analysis of the number of elements referenced is presented; exact expressions for uniformly generated references and a close upper and lower bound for nonuniformly generated references are derived. Second, in addition to presenting an algorithm that computes the total memory required, this paper also discusses the effect of transformations (that change the execution ordering) on the lifetimes of array variables, i.e., the time between the first and last accesses to a given array location. The term maximum window size is introduced, and quantitative expressions are derived to compute the maximum window size. A detailed analysis of the effect of unimodular transformations on data locality, including the calculation of the maximum window size, is presented.     

Reliability models for computer systems: An overview including dataflow graphs

The reliability of a system is the probability that the system will perform its intended mission under given conditions. This paper provides an overview of the approaches to reliability modelling and identifies their strengths and weaknesses. The models discussed include structure models, simple stochastic models and decomposable stochastic models. Ignoring time-dependence, structure models give reliability as a function of the topological structure of the system. Simple stochastic models make direct use of the properties of underlying stochastic processes, while decomposable models consider more complex systems and analyse them through subsystems. Petri nets and dataflow graphs facilitate the analysis of complex systems by providing a convenient framework for reliability analysis.     

Swoop: A Web Ontology Editing Browser

In this paper, we describe Swoop, a hypermedia inspired Ontology Browser and Editor based on OWL, the recently standardized Web-oriented ontology language. After discussing the design rationale and architecture of Swoop, we focus mainly on its features, using illustrative examples to highlight its use. We demonstrate that with its Web-metaphor, adherence to OWL recommendations and key unique features, such as Collaborative Annotation using Annotea, Swoop acts as a useful and efficient Web Ontology development tool. We conclude with a list of future plans for Swoop, that should further increase its overall appeal and accessibility.     

Adenosine A1 receptor stimulation antagonizes the negative inotropic effects of the PKC activator dioctanoylglycerol

It has been suggested that adenosine cardioprotection occurs via adenosine A1 receptor-mediated activation of protein kinase C (PKC). However, adenosine has well-known vasodilatory effects in the myocardium, whereas PKC is a vasoconstrictor. This study examined whether adenosine A1 receptor activation alters the effects of the PKC activator. 1,2-dioctanoyl-s,n-glycerol (DOG) in isolated perfused rat hearts (left-ventricular developed pressure) and rat ventricular myocytes ([Ca2+]i and cell shortening). Exposure to DOG decreased left-ventricular developed pressure by 30%, an effect that was completely reversible. Pretreatment of isolated hearts with either the PKC inhibitor chelerythrine or the adenosine A1 agonist 2-chloro-N6-cyclo-cyclo-isolated pentlyadenosine (CCPA) attenuated the negative inotropic effects of DOG. In the isolated myocytes, DOG decreased [Ca2+]i and cell shortening by 25 and 28%, respectively, effects that were attenuated by both chelerythrine and CCPA. The CCPA attenuation of the DOG-induced decrease in [Ca2+]i and cell shortening was blocked by pretreating the myocytes with the adenosine A1 antagonist, 8-cyclopentyl-1,3-dipropylxanthine (DPCPX). These results indicate that in rat ventricular myocardium, adenosine A1 receptor activation attenuates the apparent PKC-dependent negative inotropic effects of DOG via preservation of [Ca2+]i levels.     

The effects of stress on memory and the hippocampus throughout the life cycle: implications for childhood development and aging

Studies in animals showing hippocampal atrophy and associated memory deficits in stress and aging have implications for stress and aging in humans. Clinical studies in traumatized human populations with posttraumatic stress disorder (PTSD) have replicated studies in animals, showing reduction in volume of the hippocampus measured with magnetic resonance imaging and associated memory deficits. Trauma at different stages of development (early childhood abuse versus trauma in later life due to combat) may influence the nature of memory deficits and hippocampal atrophy. Studies in aging human subjects are consistent with animal studies, although future research is needed in this area. The similarities between biological findings related to cortisol and the hippocampus in stress and aging in both animal and human studies raises the question of whether PTSD can be seen as a form of accelerated aging. Evidence that stress affects the hippocampus and the capacity for learning has broad implications for public health policy, underlying the need for additional resources in this important area and a reexamination of our understanding of factors influencing academic achievement.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

Investigation of long term drift of an intraocular pressure sensor

Microsystem Technologies - “Zero drift” behavior of an optical intraocular pressure sensor is studied using an analytical model based on the deflection of a circular membrane. Results...