Reliable link quality estimation in low-power wireless networks and its impact on tree-routing

Radio link quality estimation is essential for protocols and mechanisms such as routing, mobility management and localization, particularly for low-power wireless networks such as wireless sensor networks. Commodity Link Quality Estimators (LQEs), e.g. PRR, RNP, ETX, four-bit and RSSI, can only provide a partial characterization of links as they ignore several link properties such as channel quality and stability. In this paper, we propose F-LQE (Fuzzy Link Quality Estimator, a holistic metric that estimates link quality on the basis of four link quality properties—packet delivery, asymmetry, stability, and channel quality—that are expressed and combined using Fuzzy Logic. We demonstrate through an extensive experimental analysis that F-LQE is more reliable than existing estimators (e.g., PRR, WMEWMA, ETX, RNP, and four-bit) as it provides a finer grain link classification. It is also more stable as it has lower coefficient of variation of link estimates. Importantly, we evaluate the impact of F-LQE on the performance of tree routing, specifically the CTP (Collection Tree Protocol). For this purpose, we adapted F-LQE to build a new routing metric for CTP, which we dubbed as F-LQE/RM. Extensive experimental results obtained with state-of-the-art widely used test-beds show that F-LQE/RM improves significantly CTP routing performance over four-bit (the default LQE of CTP) and ETX (another popular LQE). F-LQE/RM improves the end-to-end packet delivery by up to 16%, reduces the number of packet retransmissions by up to 32%, reduces the Hop count by up to 4%, and improves the topology stability by up to 47%.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Understanding Interactions in Social Networks and Committees

While much of the literature on cross section dependence has focused mainly on estimation of the regression coefficients in the underlying model, estimation and inferences on the magnitude and strength of spill-overs and interactions has been largely ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations and provide useful interpretation and structural explanation for the strength of any interactions. In this paper we propose GMM methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the interval censored regression model. Our methods are applied to a study of committee decision making within the Bank of England’s monetary policy committee.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

Suivi de l'adhésion thérapeutique des patients hypertendus ou dyslipi- démiques par les cercles de qualité médecins-pharmaciens [Monitoring drug adherence for hypertensive and dyslipidemic patients in networks of community-based pharmacists and physicians].

Patient adherence is often poor for hypertension and dyslipidaemia. A monitoring of drug adherence might improve these risk factors control, but little is known in ambulatory care. We conducted a randomised controlled study in networks of community-based pharmacists and physicians in the canton of Fribourg to examine whether monitoring drug adherence with an electronic monitor (MEMS) would improve risk factor control among treated, but uncontrolled hypertensive and dyslipidemic patients. The results indicate that MEMS achieve a better blood pressure control and lipid profile, although its implementation requires considerable resources. The study also shows the value of collaboration between physicians and pharmacists in the field of patient adherence to improve ambulatory care of patients with cardiovascular risk factors.

Aversive Stimuli and Functional Networks of Fear Learning in PTSD : 734

Background: One characteristic of post traumatic stress disorder is an inability to adapt to a safe environment i.e. to change behavior when predictions of adverse outcomes are not met. Recent studies have also indicated that PTSD patients have altered pain processing, with hyperactivation of the putamen and insula to aversive stimuli (Geuze et al, 2007). The present study examined neuronal responses to aversive and predicted aversive events. Methods: Twenty-four trauma exposed non-PTSD controls and nineteen subjects with PTSD underwent fMRI imaging during a partial reinforcement fear conditioning paradigm, with a mild electric shock as the unconditioned stimuli (UCS). Three conditions were analyzed: actual presentations of the UCS, events when a UCS was expected, but omitted (CS+), and events when the UCS was neither expected nor delivered (CS-). Results: The UCS evoked significant alterations in the pain matrix consisting of the brainstem, the midbrain, the thalamus, the insula, the anterior and middle cingulate and the contralateral somatosensory cortex. PTSD subjects displayed bilaterally elevated putamen activity to the electric shock, as compared to controls. In trials when USC was expected, but omitted, significant activations were observed in the brainstem, the midbrain, the anterior insula and the anterior cingulate. PTSD subjects displayed similar activations, but also elevated activations in the amygdala and the posterior insula. Conclusions: These results indicate altered fear and safety learning in PTSD, and neuronal activations are further explored in terms of functional connectivity using psychophysiological interaction analyses.

Exposition au VIH, à l'hépatite B et C au cabinet médical et à l'hôpital. Prévention et prophylaxie postexpositionnelle [Exposure to HIV, hepatitis B and C in office practice and hospital. Prevention and post-exposure prophylaxis]

Suite à un accident exposant à du sang (piqûre; coupure), provenant d'un patient infecté, le risque d'infection par VIH est d'environ 0,3% et par le virus de l'hépatite C (VHC) d'environ 0,5%. Chez les personnes vaccinées avec une réponse immunitaire adéquate (titre d'anticorps HBs >100 mUI/ml), aucune infection professionnelle par hépatite B n'a été reconnue en Suisse. La plupart des infections par VIH et VHB peuvent être prévenues par un traitement d'urgence et une prophylaxie postexpositionnelle (PEP). Il n'y a actuellement aucune prophylaxie postexpositionnelle pour le VHC. En cas de transmission de VHC, un traitement rapide par peginterféron et ribavirine est à envisager. Chaque hôpital et cabinet médical doivent mettre sur pied un système pour assurer une prise en charge optimale et en urgence des blessures par piqûres ou coupures. Lors de blessures accidentelles avec du sang de patients séropositifs pour le VIH et dans des situations complexes, il est recommandé de consulter un médecin du personnel ou un infectiologue expérimenté. The risk of infection after an occupational needle stick injury with blood from an infected source patient is approximately 0.3% for HIV and 0.5% for hepatitis C virus (HCV). In Switzerland no cases of occupational HBV infection have been recorded in fully vaccinated persons with a documented adequate vaccine response (HBsantibody titer >100 mIU/mL). Most occupational HIV und HBV infections can be prevented by appropriate emergency measures and post-exposure prophylaxis (PEP). No HCV-PEP is currently available. Early therapy with peginterferon and ribavirin should be considered in cases of occupational HCV seroconversion. Every hospital and office practice should establish a system for 24 h/24 h emergency management of occupational needle stick injuries. In the setting of an HIV-seropositive source patient and in complex situations, early consultation with a specialist in occupational medicine or infectious diseases should be considered.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Can routine offering of influenza vaccination in office-based settings reduce racial and ethnic disparities in adult influenza vaccination?

BACKGROUND: Influenza vaccination remains below the federally targeted levels outlined in Healthy People 2020. Compared to non-Hispanic whites, racial and ethnic minorities are less likely to be vaccinated for influenza, despite being at increased risk for influenza-related complications and death. Also, vaccinated minorities are more likely to receive influenza vaccinations in office-based settings and less likely to use non-medical vaccination locations compared to non-Hispanic white vaccine users. OBJECTIVE: To assess the number of "missed opportunities" for influenza vaccination in office-based settings by race and ethnicity and the magnitude of potential vaccine uptake and reductions in racial and ethnic disparities in influenza vaccination if these "missed opportunities" were eliminated. DESIGN: National cross-sectional Internet survey administered between March 4 and March 14, 2010 in the United States. PARTICIPANTS: Non-Hispanic black, Hispanic and non-Hispanic white adults living in the United States (N = 3,418). MAIN MEASURES: We collected data on influenza vaccination, frequency and timing of healthcare visits, and self-reported compliance with a potential provider recommendation for vaccination during the 2009-2010 influenza season. "Missed opportunities" for seasonal influenza vaccination in office-based settings were defined as the number of unvaccinated respondents who reported at least one healthcare visit in the Fall and Winter of 2009-2010 and indicated their willingness to get vaccinated if a healthcare provider strongly recommended it. "Potential vaccine uptake" was defined as the sum of actual vaccine uptake and "missed opportunities." KEY RESULTS: The frequency of "missed opportunities" for influenza vaccination in office-based settings was significantly higher among racial and ethnic minorities than non-Hispanic whites. Eliminating these "missed opportunities" could have cut racial and ethnic disparities in influenza vaccination by roughly one half. CONCLUSIONS: Improved office-based practices regarding influenza vaccination could significantly impact Healthy People 2020 goals by increasing influenza vaccine uptake and reducing corresponding racial and ethnic disparities.