Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications

Extracellular ATP is a pro-angiogenic factor for pulmonary artery vasa vasorum endothelial cells

Expansion of the vasa vasorum network has been observed in a variety of systemic and pulmonary vascular diseases. We recently reported that a marked expansion of the vasa vasorum network occurs in the pulmonary artery adventitia of chronically hypoxic calves. Since hypoxia has been shown to stimulate ATP release from both vascular resident as well as circulatory blood cells, these studies were undertaken to determine if extracellular ATP exerts angiogenic effects on isolated vasa vasorum endothelial cells (VVEC) and/or if it augments the effects of other angiogenic factors (VEGF and basic FGF) known to be present in the hypoxic microenvironment. We found that extracellular ATP dramatically increases DNA synthesis, migration, and rearrangement into tube-like networks on Matrigel in VVEC, but not in pulmonary artery (MPAEC) or aortic (AOEC) endothelial cells obtained from the same animals. Extracellular ATP potentiated the effects of both VEGF and bFGF to stimulate DNA synthesis in VVEC but not in MPAEC and AOEC. Analysis of purine and pyrimidine nucleotides revealed that ATP, ADP and MeSADP were the most potent in stimulating mitogenic responses in VVEC, indicating the involvement of the family of P2Y1-like purinergic receptors. Using pharmacological inhibitors, Western blot analysis, and Phosphatidylinositol-3 kinase (PI3K) in vitro kinase assays, we found that PI3K/Akt/mTOR and ERK1/2 play a critical role in mediating the extracellular ATP-induced mitogenic and migratory responses in VVEC. However, PI3K/Akt and mTOR/p70S6K do not significantly contribute to extracellular ATP-induced tube formation on Matrigel. Our studies indicate that VVEC, isolated from the sites of active angiogenesis, exhibit distinct functional responses to ATP, compared to endothelial cells derived from large pulmonary or systemic vessels. Collectively, our data support the idea that extracellular ATP participates in the expansion of the vasa vasorum that can be observed in hypoxic conditions

The influence of contextual factors on healthcare quality improvement initiatives:a realist review

Background Recognising the influence of context and the context-sensitive nature of quality improvement (QI) interventions is crucial to implementing effective improvements and successfully replicating them in new settings, yet context is still poorly understood. To address this challenge, it is necessary to capture generalisable knowledge, first to understand which aspects of context are most important to QI and why, and secondly, to explore how these factors can be managed to support healthcare improvement, in terms of implementing successful improvement initiatives, achieving sustainability and scaling interventions. The research question was how and why does context influence quality improvement initiatives in healthcare? Methods A realist review explored the contextual conditions that influence healthcare improvement. Realist methodology integrates theoretical understanding and stakeholder input with empirical research findings. The review aimed to identify and understand the role of context during the improvement cycle, i.e. planning, implementation, sustainability and transferability; and distil new knowledge to inform the design and development of context-sensitive QI initiatives. We developed a preliminary theory of the influence of context to arrive at a conceptual and theoretical framework. Results Thirty-five studies were included in the review, demonstrating the interaction of key contextual factors across healthcare system levels during the improvement cycle. An evidence-based explanatory theoretical model is proposed to illustrate the interaction between contextual factors, system levels (macro, meso, micro) and the stages of the improvement journey. Findings indicate that the consideration of these contextual factors would enhance the design and delivery of improvement initiatives, across a range of improvement settings. Conclusions This is the first realist review of context in QI and contributes to a deeper understanding of how context influences quality improvement initiatives. The distillation of key contextual factors offers the potential to inform the design and development of context-sensitive interventions to enhance improvement initiatives and address the challenge of spread and sustainability. Future research should explore the application of our conceptual model to enhance improvement-planning processes

Microbial Fuel Cells and Microbial Ecology: Applications in Ruminant Health and Production Research

Microbial fuel cell (MFC) systems employ the catalytic activity of microbes to produce electricity from the oxidation of organic, and in some cases inorganic, substrates. MFC systems have been primarily explored for their use in bioremediation and bioenergy applications; however, these systems also offer a unique strategy for the cultivation of synergistic microbial communities. It has been hypothesized that the mechanism(s) of microbial electron transfer that enable electricity production in MFCs may be a cooperative strategy within mixed microbial consortia that is associated with, or is an alternative to, interspecies hydrogen (H2) transfer. Microbial fermentation processes and methanogenesis in ruminant animals are highly dependent on the consumption and production of H2in the rumen. Given the crucial role that H2 plays in ruminant digestion, it is desirable to understand the microbial relationships that control H2 partial pressures within the rumen; MFCs may serve as unique tools for studying this complex ecological system. Further, MFC systems offer a novel approach to studying biofilms that form under different redox conditions and may be applied to achieve a greater understanding of how microbial biofilms impact animal health. Here, we present a brief summary of the efforts made towards understanding rumen microbial ecology, microbial biofilms related to animal health, and how MFCs may be further applied in ruminant research

Hypofibrinolysis in diabetes: a therapeutic target for the reduction of cardiovascular risk

An enhanced thrombotic environment and premature atherosclerosis are key factors for the increased cardiovascular risk in diabetes. The occlusive vascular thrombus, formed secondary to interactions between platelets and coagulation proteins, is composed of a skeleton of fibrin fibres with cellular elements embedded in this network. Diabetes is characterised by quantitative and qualitative changes in coagulation proteins, which collectively increase resistance to fibrinolysis, consequently augmenting thrombosis risk. Current long-term therapies to prevent arterial occlusion in diabetes are focussed on anti-platelet agents, a strategy that fails to address the contribution of coagulation proteins to the enhanced thrombotic milieu. Moreover, antiplatelet treatment is associated with bleeding complications, particularly with newer agents and more aggressive combination therapies, questioning the safety of this approach. Therefore, to safely control thrombosis risk in diabetes, an alternative approach is required with the fibrin network representing a credible therapeutic target. In the current review, we address diabetes-specific mechanistic pathways responsible for hypofibrinolysis including the role of clot structure, defects in the fibrinolytic system and increased incorporation of anti-fibrinolytic proteins into the clot. Future anti-thrombotic therapeutic options are discussed with special emphasis on the potential advantages of modulating incorporation of the anti-fibrinolytic proteins into fibrin networks. This latter approach carries theoretical advantages, including specificity for diabetes, ability to target a particular protein with a possible favourable risk of bleeding. The development of alternative treatment strategies to better control residual thrombosis risk in diabetes will help to reduce vascular events, which remain the main cause of mortality in this condition

Integrating the Projective Transform with Particle Filtering for Visual Tracking

<p/> <p>This paper presents the projective particle filter, a Bayesian filtering technique integrating the projective transform, which describes the distortion of vehicle trajectories on the camera plane. The characteristics inherent to traffic monitoring, and in particular the projective transform, are integrated in the particle filtering framework in order to improve the tracking robustness and accuracy. It is shown that the projective transform can be fully described by three parameters, namely, the angle of view, the height of the camera, and the ground distance to the first point of capture. This information is integrated in the importance density so as to explore the feature space more accurately. By providing a fine distribution of the samples in the feature space, the projective particle filter outperforms the standard particle filter on different tracking measures. First, the resampling frequency is reduced due to a better fit of the importance density for the estimation of the posterior density. Second, the mean squared error between the feature vector estimate and the true state is reduced compared to the estimate provided by the standard particle filter. Third, the tracking rate is improved for the projective particle filter, hence decreasing track loss.</p

Distributed training of 3Dpyranet over intel AI DevCloud platform

Neural network architectures have demonstrated to achieve impressive results across a wide range of different domains. The availability of very large datasets makes possible to overcome the limitation of the training stage thus achieving significant level of performance. On the other hand, even though the advancements in GPU hardware, training a complex neural network model still represents a challenge. Long time is required when the computation is demanded to a single machine. In this work, a distributed training approach for 3DPyraNet model built for a specific domain, that is the emotion recognition from videos, is discussed. The proposed work aims at distributing the training procedures over the nodes of the Intel DevCloud Platform and demonstrating how the training performance are affected in terms of both computational demand and achieved accuracy compared to the use of a single machine. The results obtained in an experimental design suggests the feasibility of the approach for challenging computer vision tasks even in presence of limited computing power based on exclusive use of CPUs