Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranging and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

Fine particulate matter measurements in Swiss restaurants, cafés and bars: what is the effect of spatial separation between smoking and non-smoking areas?

We performed 124 measurements of particulate matter (PM(2.5)) in 95 hospitality venues such as restaurants, bars, cafés, and a disco, which had differing smoking regulations. We evaluated the impact of spatial separation between smoking and non-smoking areas on mean PM(2.5) concentration, taking relevant characteristics of the venue, such as the type of ventilation or the presence of additional PM(2.5) sources, into account. We differentiated five smoking environments: (i) completely smoke-free location, (ii) non-smoking room spatially separated from a smoking room, (iii) non-smoking area with a smoking area located in the same room, (iv) smoking area with a non-smoking area located in the same room, and (v) smoking location which could be either a room where smoking was allowed that was spatially separated from non-smoking room or a hospitality venue without smoking restriction. In these five groups, the geometric mean PM(2.5) levels were (i) 20.4, (ii) 43.9, (iii) 71.9, (iv) 110.4, and (v) 110.3 microg/m(3), respectively. This study showed that even if non-smoking and smoking areas were spatially separated into two rooms, geometric mean PM(2.5) levels in non-smoking rooms were considerably higher than in completely smoke-free hospitality venues. PRACTICAL IMPLICATIONS: PM(2.5) levels are considerably increased in the non-smoking area if smoking is allowed anywhere in the same location. Even locating the smoking area in another room resulted in a more than doubling of the PM(2.5) levels in the non-smoking room compared with venues where smoking was not allowed at all. In practice, spatial separation of rooms where smoking is allowed does not prevent exposure to environmental tobacco smoke in nearby non-smoking areas.

The multiscenario multienvironment BioSecure Multimodal Database (BMDB)

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008.

Exposure to moderate air pollution during late pregnancy and cord blood cytokine secretion in healthy neonates

Background/Objectives Ambient air pollution can alter cytokine concentrations as shown in vitro and following short-term exposure to high air pollution levels in vivo. Exposure to pollution during late pregnancy has been shown to affect fetal lymphocytic immunophenotypes. However, effects of prenatal exposure to moderate levels of air pollutants on cytokine regulation in cord blood of healthy infants are unknown. Methods In a birth cohort of 265 healthy term-born neonates, we assessed maternal exposure to particles with an aerodynamic diameter of 10 µm or less (PM10), as well as to indoor air pollution during the last trimester, specifically the last 21, 14, 7, 3 and 1 days of pregnancy. As a proxy for traffic-related air pollution, we determined the distance of mothers' homes to major roads. We measured cytokine and chemokine levels (MCP-1, IL-6, IL-10, IL-1ß, TNF-α and GM-CSF) in cord blood serum using LUMINEX technology. Their association with pollution levels was assessed using regression analysis, adjusted for possible confounders. Results Mean (95%-CI) PM10 exposure for the last 7 days of pregnancy was 18.3 (10.3–38.4 µg/m3). PM10 exposure during the last 3 days of pregnancy was significantly associated with reduced IL-10 and during the last 3 months of pregnancy with increased IL-1ß levels in cord blood after adjustment for relevant confounders. Maternal smoking was associated with reduced IL-6 levels. For the other cytokines no association was found. Conclusions Our results suggest that even naturally occurring prenatal exposure to moderate amounts of indoor and outdoor air pollution may lead to changes in cord blood cytokine levels in a population based cohort.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

A prediction model for assessing residential radon concentration in Switzerland

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.

[Allergies to animals and fungi]

Allergies to animals are behind the house-dust mite allergy the most frequent cause for indoor allergic respiratory symptoms. In case of persistent allergen exposure symptoms like rhinitis, itch of the skin or asthma are usually not perceived intensively and, thus, can not assigned to an animal or an animal source. In many cases animal allergies are based on a perennial allergen exposure. Although most likely all animals may be the cause of a respiratory allergy, cats, dogs, and horses are the most frequent elicitors. The diagnosis of an allergy to an animal needs to be set with due care, since it often causes emotional reactions, diverse conflicts, but also lack of understanding. Rarer are allergies to fungi even though fungi as allergen sources since decades belong to the differential diagnosis in respiratory allergies particularly in case of late summer asthma. Fungi are ubiquitous and present indoors as well as outdoors. Unfortunately the field of fungal allergy is not well explored and diagnostic possibilities are limited. The most promising therapy in both allergy to animals and fungi would be complete avoiding of contact with the respective allergen source. Indeed many preventive recommendations are given; however, realization is often not successful. In selected cases specific immunotherapy for both animal and fungal allergies is a potential therapeutic option.

[House dust mite allergy]

House dust mites can be found all over the world where human beings live independent from the climate. Proteins from the gastrointestinal tract- almost all known as enzymes - are the allergens which induce chronic allergic diseases. The inhalation of small amounts of allergens on a regular base all night leads to a slow beginning of the disease with chronically stuffed nose and an exercise induced asthma which later on persists. House dust mites grow well in a humid climate - this can be in well isolated dwellings or in the tropical climate - and nourish from human skin dander. Scales are found in mattresses, upholstered furniture and carpets. The clinical picture with slowly aggravating complaints leads quite often to a delayed diagnosis, which is accidently done on the occasion of a wider spectrum of allergy skin testing. The beginning of a medical therapy with topical steroids as nasal spray or inhalation leads to a fast relief of the complaints. Although discussed in extensive controversies in the literature - at least in Switzerland with the cold winter and dry climate - the recommendation of house dust mite avoidance measures is given to patients with good clinical results. The frequent ventilation of the dwelling with cold air in winter time cause a lower indoor humidity. Covering encasings on mattresses, pillow, and duvets reduces the possibility of chronic contact with mite allergens as well as the weekly changing the bed linen. Another option of therapy is the specific immunotherapy with extracts of house dust mites showing good results in children and adults. Using recombinant allergens will show a better quality in diagnostic as well as in therapeutic specific immunotherapy.

Towards Self-Learning Radio-Based Localization Systems

Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.

Multiple meningiomas: clinical, radiological, surgical, and pathological findings with outcome in four cats

The present report describes the clinical signs, magnetic resonance imaging (MRI) findings, surgical procedure, pathological findings and follow-up in four cats with multiple meningiomas; three castrated male and one spayed female domestic shorthair indoor cats, ranging in age from 11 to 14 years. In three of four cats, clinical signs at presentation were suggestive of a focal lesion. Three cats had two meningiomas and one had four meningiomas. Most of the tumours were supratentorial, one arose from the tentorium and one was infratentorial. The duration of presenting signs before surgery ranged from 10 days to 11 months. Postoperative MRI revealed complete gross tumour removal in three cases. In one cat with two cranial fossa meningiomas, subtotal excision with a small basal remnant (2 x 2 mm) of the ventral part of one meningioma lying on the floor of the skull, was observed. Based on histopathological architecture, six tumours revealed features of a transitional subtype meningioma, and four of a meningotheliomatous meningioma. In each cat, the multiple meningiomas were all assigned to the same histopathological group. The preoperative presenting signs had resolved by the follow-up examinations 4 weeks after surgery in two cats. Long-term follow-up evaluation revealed that surgically-induced or exacerbated neurological deficits in two cats had completely or almost completely resolved within 8 weeks of surgery. All patients are still alive 12 to 21 months after surgery and no clinical signs of recurrence could be detected at that time.

How can we measure the impact of pollutants on respiratory function in very young children? Methodological aspects

There is increasing evidence that air pollution particularly affects infants and small preschool children. However, detecting air pollution effects on lung function in small children is technically difficult and requires non-invasive methods that can assess lung function and inflammatory markers in larger cohorts. This review discusses the principles, usefulness and shortcomings of various lung function techniques used to detect pollution effects in small children. The majority of these techniques have been used to detect effects of the dominant indoor pollutant, tobacco exposure. However there is increasing evidence that non-invasive lung function techniques can also detect the effects of outdoor air pollution.

Exposure modeling of high-frequency electromagnetic fields

We developed a geospatial model that calculates ambient high-frequency electromagnetic field (HF-EMF) strengths of stationary transmission installations such as mobile phone base stations and broadcast transmitters with high spatial resolution in the order of 1 m. The model considers the location and transmission patterns of the transmitters, the three-dimensional topography, and shielding effects by buildings. The aim of the present study was to assess the suitability of the model for exposure monitoring and for epidemiological research. We modeled time-averaged HF-EMF strengths for an urban area in the city of Basel as well as for a rural area (Bubendorf). To compare modeling with measurements, we selected 20 outdoor measurement sites in Basel and 18 sites in Bubendorf. We calculated Pearson's correlation coefficients between modeling and measurements. Chance-corrected agreement was evaluated by weighted Cohen's kappa statistics for three exposure categories. Correlation between measurements and modeling of the total HF-EMF strength was 0.67 (95% confidence interval (CI): 0.33-0.86) in the city of Basel and 0.77 (95% CI: 0.46-0.91) in the rural area. In both regions, kappa coefficients between measurements and modeling were 0.63 and 0.77 for the total HF-EMF strengths and for all mobile phone frequency bands. First evaluation of our geospatial model yielded substantial agreement between modeling and measurements. However, before the model can be applied for future epidemiologic research, additional validation studies focusing on indoor values are needed to improve model validity.Journal of Exposure Science and Environmental Epidemiology (2008) 18, 183-191; doi:10.1038/sj.jes.7500575; published online 4 April 2007.

Stability of coagulation assays performed in plasma from citrated whole blood transported at ambient temperature

Many preanalytical variables affect the results of coagulation assays. A possible way to control some of them would be to accept blood specimens shipped in the original collection tube. The aim of our study was to investigate the stability of coagulation assays in citrated whole blood transported at ambient temperature for up to two days after specimen collection. Blood samples from 59 patients who attended our haematology outpatient ward for thrombophilia screening were transported at ambient temperature (outdoor during the day, indoor overnight) for following periods of time: <1 hour, 4-6, 8-12, 24-28 and 48-52 hours prior to centrifugation and plasma-freezing. The following coagulation tests were performed: PT, aPTT, fibrinogen, FII:C, FV:C, FVII:C, FVIII:C, FIX:C, FX:C, FXI:C, VWF:RCo, VWF:Ag, AT, PC activity, total and free PS antigen, modified APC-sensitivity-ratio, thrombin-antithrombin-complex and D-dimer. Clinically significant changes, defined as a percentage change of more than 10% from the initial value, were observed for FV:C, FVIII:C and total PS antigen starting at 24-28 hours, and for PT, aPTT and FVII:C at 48-52 hours. No statistically significant differences were seen for fibrinogen, antithrombin, or thrombin-antithrombin complexes (Friedman repeated measures analysis of variance). The present data suggest that the use of whole blood samples transported at ambient temperature may be an acceptable means of delivering specimens for coagulation analysis. With the exception of factor V and VIII coagulant activity, and total PS antigen all investigated parameters can be measured 24-28 hours after specimen collection without observing clinically relevant changes.

A prediction model for personal radio frequency electromagnetic field exposure

Radio frequency electromagnetic fields (RF-EMF) in our daily life are caused by numerous sources such as fixed site transmitters (e.g. mobile phone base stations) or indoor devices (e.g. cordless phones). The objective of this study was to develop a prediction model which can be used to predict mean RF-EMF exposure from different sources for a large study population in epidemiological research. We collected personal RF-EMF exposure measurements of 166 volunteers from Basel, Switzerland, by means of portable exposure meters, which were carried during one week. For a validation study we repeated exposure measurements of 31 study participants 21 weeks after the measurements of the first week on average. These second measurements were not used for the model development. We used two data sources as exposure predictors: 1) a questionnaire on potentially exposure relevant characteristics and behaviors and 2) modeled RF-EMF from fixed site transmitters (mobile phone base stations, broadcast transmitters) at the participants' place of residence using a geospatial propagation model. Relevant exposure predictors, which were identified by means of multiple regression analysis, were the modeled RF-EMF at the participants' home from the propagation model, housing characteristics, ownership of communication devices (wireless LAN, mobile and cordless phones) and behavioral aspects such as amount of time spent in public transports. The proportion of variance explained (R2) by the final model was 0.52. The analysis of the agreement between calculated and measured RF-EMF showed a sensitivity of 0.56 and a specificity of 0.95 (cut-off: 90th percentile). In the validation study, the sensitivity and specificity of the model were 0.67 and 0.96, respectively. We could demonstrate that it is feasible to model personal RF-EMF exposure. Most importantly, our validation study suggests that the model can be used to assess average exposure over several months.