New technologies to reduce pediatric radiation doses

X-ray dose reduction in pediatrics is particularly important because babies and children are very sensitive to radiation exposure. We present new developments to further decrease pediatric patient dose. With the help of an advanced exposure control, a constant image quality can be maintained for all patient sizes, leading to dose savings for babies and children of up to 30%. Because objects of interest are quite small and the speed of motion is high in pediatric patients, short pulse widths down to 4 ms are important to reduce motion blurring artifacts. Further, a new noise-reduction algorithm is presented that detects and processes signal and noise in different frequency bands, generating smooth images without contrast loss. Finally, we introduce a super-resolution technique: two or more medical images, which are shifted against each other in a subpixel region, are combined to resolve structures smaller than the size of a single pixel. Advanced exposure control, short exposure times, noise reduction and super-resolution provide improved image quality, which can also be invested to save radiation exposure. All in all, the tools presented here offer a large potential to minimize the deterministic and stochastic risks of radiation exposure

Asymmetric polarity reversals, bimodal field distribution, and coherence resonance in a spherically symmetric mean-field dynamo model

Using a mean-field dynamo model with a spherically symmetric helical turbulence parameter alpha which is dynamically quenched and disturbed by additional noise, the basic features of geomagnetic polarity reversals are shown to be generic consequences of the dynamo action in the vicinity of exceptional points of the spectrum. This simple paradigmatic model yields long periods of constant polarity which are interrupted by self-accelerating field decays leading to asymmetric polarity reversals. It shows the recently discovered bimodal field distribution, and it gives a natural explanation of the correlation between polarity persistence time and field strength. In addition, we find typical features of coherence resonance in the dependence of the persistence time on the noise.Comment: 5 pages, 7 figure

Stressful life events are associated with low secretion rates of immunoglobulin A in saliva in the middle aged and elderly

Whether chronic stress experience is related to down-regulation of secretory immunoglobulin A (S-IgA) was tested in two substantial cohorts, one middle-aged (N = 640) and one elderly (N = 582), comprising similar numbers of men (N = 556) and women (N = 666) and manual (N = 606) and non-manual (N = 602) workers. Participants indicated from a list of major stressful life events up to six they had experienced in the previous two years. They also rated how disruptive and stressful the events were, at the time and now, as well as their perceived seriousness; the products of these impact values and event frequency were adopted as measures of stress load. From unstimulated 2-minute saliva samples, saliva volume and S-IgA concentration were measured, and S-IgA secretion rate determined as their product. There was a negative association between the stress load measures and S-IgA secretion rate, still evident following adjustment for such variables as smoking and saliva volume. The associations also withstood adjustment for sex, cohort, and household occupational status. Although these associations are small in terms of the amount of variance explained, they nonetheless suggest that chronic stress experience either decreases IgA production by the local plasma cells or reduces the efficiency with which S-IgA is transported from the glandular interstitium into saliva. Given the importance of S-IgA in immune defence at mucosal surfaces and the frequency with which infections are initiated at these surfaces, S-IgA down-regulation could be a means by which chronic stress increases susceptibility to upper respiratory tract infection

The smartphone-based offline indoor location competition at IPIN 2016: analysis and future work

This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors' estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.We would like to thank Tecnalia Research & Innovation Foundation for sponsoring the competition track with an award for the winning team. We are also grateful to Francesco Potortì, Sangjoon Park, Jesús Ureña and Kyle O’Keefe for their invaluable help in promoting the IPIN competition and conference. Parts of this work was carried out with the financial support received from projects and grants: LORIS (TIN2012-38080-C04-04), TARSIUS (TIN2015-71564-C4-2-R (MINECO/FEDER)), SmartLoc (CSIC-PIE Ref.201450E011), “Metodologías avanzadas para el diseño, desarrollo, evaluación e integración de algoritmos de localización en interiores” (TIN2015-70202-P), REPNIN network (TEC2015-71426-REDT) and the José Castillejo mobility grant (CAS16/00072). The HFTS team has been supported in the frame of the German Federal Ministry of Education and Research programme “FHprofUnt2013” under contract 03FH035PB3 (Project SPIRIT). The UMinho team has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT — Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

On Wi-Fi Model Optimizations for Smartphone-Based Indoor Localization

Indoor localization and indoor pedestrian navigation is an active field of research with increasing attention. As of today, many systems will run on commercial smartphones, but most of them still rely on fingerprinting, which demands high setup and maintenance times. Alternatives, such as simple signal strength prediction models, provide fast setup times, but often do not provide the accuracy required for use cases like indoor navigation or location-based services. While more complex models provide an increased accuracy by including architectural knowledge about walls and other obstacles, they often require additional computation during runtime and demand prior knowledge during setup. Within this work, we will thus focus on simple, easy to set up models and evaluate their performance compared to real-world measurements. The evaluation ranges from a fully-empiric, instant setup, given that the transmitter locations are well known, to a highly optimized scenario based on some reference measurements within the building. Furthermore, we will propose a new signal strength prediction model as a combination of several simple ones. This tradeoff increases accuracy with only minor additional computations. All of the optimized models are evaluated within an actual smartphone-based indoor localization system. This system uses the phone’s Wi-Fi, barometer and IMU to infer the pedestrian’s current location via recursive density estimation based on particle filtering. We will show that while a 100% empiric parameter choice for the model already provides enough accuracy for many use cases, a small number of reference measurements is enough to dramatically increase such a system’s performance

Improving Object Recognition by Fusion of Multiple Views

In the past decades most object recognition systems were based on passive approaches. But in the last few years a lot of research was done in the field of active approaches for object recognition. In this context there are several unique problems to be solved. One of them is how to fuse images from several viewpoints

Using Barometer for Floor Assignation within Statistical Indoor Localization

This paper presents methods for floor assignation within an indoor localization system. We integrate the barometer of the phone as an additional sensor to detect floor changes. In contrast to state-of-the-art methods, our statistical model uses a discrete state variable as floor information, instead of a continuous one. Due to the inconsistency of the barometric sensor data, our approach is based on relative pressure readings. All we need beforehand is the ceiling height including the ceiling’s thickness. Further, we discuss several variations of our method depending on the deployment scenario. Since a barometer alone is not able to detect the position of a pedestrian, we additionally incorporate Wi-Fi, iBeacons, Step and Turn Detection statistically in our experiments. This enables a realistic evaluation of our methods for floor assignation. The experimental results show that the usage of a barometer within 3D indoor localization systems can be highly recommended. In nearly all test cases, our approach improves the positioning accuracy while also keeping the update rates low