Efficient Mobile Data Collection with Mobile Collect

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Do Sensor Networks need Mobile MAC Protocols?

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Managed Competition Reform in the Netherlandsand its Lessons for Canada

This article provides an economic and legal perspective on the managed competition reforms within the Netherlands. After an examination of the rationale and the main features of the reforms, a number of problems and dilemmas that were encountered during the implementation process will be highlighted. The authors conclude that although the logic of the managed competition model is appealing, its implementation is quite complicated and requires a strong government with a continued commitment to set and enforce the rules of competition. If these preconditions are not met, the prospects of a successful introduction of managed competition are bleak. Despite its different health care system, Canada may benefit from the Dutch reform experience, especially if the trend towards decentralization of health planning and funding continues. In particular, the need for an adequate definition of entitlement to health care will become more pronounced

Effectiveness of a cervical pessary for women who did not deliver 48 h after threatened preterm labor (Assessment of perinatal outcome after specific treatment in early labor: Apostel VI trial)

Background: Preterm birth is a major cause of neonatal mortality and morbidity. As preventive strategies are largely ineffective, threatened preterm labor is a frequent problem that affects approximately 10 % of pregnancies. In recent years, risk assessment in these women has incorporated cervical length measurement and fetal fibronectin testing, and this has improved the capacity to identify women at increased risk for delivery within 14 days. Despite these improvements, risk for preterm birth continues to be increased in women who did not deliver after an episode of threatened preterm labor, as indicated by a preterm birth rate between 30 to 60 % in this group of women. Currently no effective treatment is available. Studies on maintenance tocolysis and progesterone have shown ambiguous results. The pessary has not been evaluated in women with threatened preterm labor, however studies in asymptomatic women with a short cervix show reduced rates of preterm birth rates as well as perinatal complications. The APOSTEL VI trial aims to assess the effectiveness of a cervical pessary in women who did not deliver within 48 h after an episode of threatened preterm labor. Methods/Design: This is a nationwide multicenter open-label randomized clinical trial. Women with a singleton or twin gestation with intact membranes, who were admitted for threatened preterm labor, at a gestational age between 24 and 34 weeks, a cervical length between 15 and 30 mm and a positive fibronectin test or a cervical length below 15 mm, who did not deliver after 48 h will be eligible for inclusion. Women will be allocated to a pessary or no intervention (usual care). Primary outcome is preterm delivery <37 weeks. Secondary outcomes are amongst others a composite of perinatal morbidity and mortality. Sample size is based on an expected 50 % reduction of preterm birth before 37 weeks (two-sided test, a 0.05 and beta 0.2). Two hundred women with a singleton pregnancy need to be randomized. Analysis will be done by intention to treat. Discussion: The APOSTEL VI trial will provide evidence whether a pessary is effective in preventing preterm birth in women who did not deliver 48 h after admission for threatened pretermlabor and who remain at high risk for preterm birth

Sensor Networks and Their Radio Environment : On Testbeds, Interference, and Broken Packets

Sensor networks consist of small sensing devices that collaboratively fulfill a sensing task, such as monitoring the soil in an agricultural field or measuring vital signs in a marathon runner. To avoid cumbersome and expensive cabling, nodes in a sensor network are powered by batteries and communicate wirelessly. As a consequence of the latter, a sensor network's communication is affected by its radio environment, i.e., the environment's propagation characteristics and the presence of other radio devices. This thesis addresses three issues related to the impact of the radio environment on sensor networks. Firstly, in order to draw conclusions from experimental results, it is necessary to assess how the environment and the experiment infrastructure affect the results. We design a sensor network testbed, dubbed Sensei-UU, to be easily relocatable. By performing an experiment in different environments, a researcher can asses the environments’ impact on results. We further augment Sensei-UU with support for mobile nodes. The implemented mobility approach adds only little variance to results, and therefore enables repeatable experiments with mobility. The repeatability of experiments increases the confidence in conclusions drawn from them. Secondly, sensor networks may experience poor communication performance due to cross-technology radio interference, especially in office and residential environments. We consider the problem of detecting and classifying the type of interference a sensor network is exposed to. We find that different sources of interference each leave a characteristic "fingerprint" on individual, corrupt 802.15.4 packets. We design and implement the SoNIC system that enables sensor nodes to classify interference using these fingerprints. SoNIC supports accurate classification in both a controlled and an uncontrolled environment. Finally, we consider transmission errors in an outdoor sensor network. In such an environment, errors occur despite the absence of interference if the signal-to-noise ratio at a receiver is too low. We study the characteristics of corrupt packets collected from an outdoor sensor network deployment. We find that content transformation in corrupt packets follows a specific pattern, and that most corrupt packets contain only few errors. We propose that the pattern may be useful for applications that can operate on inexact data, because it reduces the uncertainty associated with a corrupt packet.WISENE

A 3D+time spatio-temporal model for joint segmentation and registration of sparse cardiac cine MR image stacks

We previously developed a hybrid spatio-temporal method for the segmentation of the left ventricle in 2D+time magnetic resonance (MR) image sequences and here extend this model-based approach towards 3D+time sparse stacks of cine MR images with random orientation. The presented method combines an explicit landmark based statistical geometric model of the inter-subject variability at the end-diastolic and end-systolic time frames with an implicit geometric model that constraints the intra-subject frame-to-frame temporal deformations through deterministic non-rigid image registration of adjacent frames. This hybrid model is driven by both local and global intensity similarity, resulting in a combined spatio-temporal segmentation and registration approach. The advantage of our hybrid model is that the segmentation of all image slices and of the whole sequence can be performed at once, guided by shape and intensity information of all time frames. In addition, prior shape and intensity knowledge are incorporated in order to cope with ambiguity in the images, while keeping training requirements limited. © 2012 Springer-Verlag.Elen A., Hermans J., Hermans H., Maes F., Suetens P., ''A 3D+time spatio-temporal model for joint segmentation and registration of sparse cardiac cine MR image stacks'', Lecture notes in computer science, vol. 7085, pp. 198-206, 2011 (Workshop on statistical atlases and computational models of the heart: imaging and modelling challenges - STACOM, in conjunction with MICCAI 2011, September 22, 2011, Toronto, Canada).status: publishe

Detecting and Avoiding Multiple Sources of Interference in the 2.4 GHz Spectrum

Sensor networks operating in the 2.4 GHz band often face cross-technology interference from co-located WiFi and Bluetooth devices. To enable effective interference mitigation, a sensor network needs to know the type of interference it is exposed to. However, existing approaches to interference detection are not able to handle multiple concurrent sources of interference. In this paper, we address the problem of identifying multiple channel activities impairing a sensor network’s communication, such as simultaneous WiFi traffic and Bluetooth data transfers. We present SpeckSense, an interference detector that distinguishes between different types of interference using a unsupervised learning technique. Additionally, SpeckSense features a classifier that distinguishes between moderate and heavy channel traffic, and also identifies WiFi beacons. In doing so, it facilitates interference avoidance through channel blacklisting. We evaluate SpeckSense on common mote hardware and show how it classifies concurrent interference under real-world settings. We also show how SpeckSense improves the performance of an existing multichannel data collection protocol by 30%.RELYonITSeCThing