Localization and activity detection Based on the fusion of environment And inertial sensors = Localización y detección de actividad Basadas en integración de sensores y dispositivos inerciales

Los sensores inerciales (acelerómetros y giróscopos) se han ido introduciendo poco a poco en dispositivos que usamos en nuestra vida diaria gracias a su minituarización. Hoy en día todos los smartphones contienen como mínimo un acelerómetro y un magnetómetro, siendo complementados en losmás modernos por giróscopos y barómetros. Esto, unido a la proliferación de los smartphones ha hecho viable el diseño de sistemas basados en las medidas de sensores que el usuario lleva colocados en alguna parte del cuerpo (que en un futuro estarán contenidos en tejidos inteligentes) o los integrados en su móvil. El papel de estos sensores se ha convertido en fundamental para el desarrollo de aplicaciones contextuales y de inteligencia ambiental. Algunos ejemplos son el control de los ejercicios de rehabilitación o la oferta de información referente al sitio turístico que se está visitando. El trabajo de esta tesis contribuye a explorar las posibilidades que ofrecen los sensores inerciales para el apoyo a la detección de actividad y la mejora de la precisión de servicios de localización para peatones. En lo referente al reconocimiento de la actividad que desarrolla un usuario, se ha explorado el uso de los sensores integrados en los dispositivos móviles de última generación (luz y proximidad, acelerómetro, giróscopo y magnetómetro). Las actividades objetivo son conocidas como ‘atómicas’ (andar a distintas velocidades, estar de pie, correr, estar sentado), esto es, actividades que constituyen unidades de actividades más complejas como pueden ser lavar los platos o ir al trabajo. De este modo, se usan algoritmos de clasificación sencillos que puedan ser integrados en un móvil como el Naïve Bayes, Tablas y Árboles de Decisión. Además, se pretende igualmente detectar la posición en la que el usuario lleva el móvil, no sólo con el objetivo de utilizar esa información para elegir un clasificador entrenado sólo con datos recogidos en la posición correspondiente (estrategia que mejora los resultados de estimación de la actividad), sino también para la generación de un evento que puede producir la ejecución de una acción. Finalmente, el trabajo incluye un análisis de las prestaciones de la clasificación variando el tipo de parámetros y el número de sensores usados y teniendo en cuenta no sólo la precisión de la clasificación sino también la carga computacional. Por otra parte, se ha propuesto un algoritmo basado en la cuenta de pasos utilizando informaiii ción proveniente de un acelerómetro colocado en el pie del usuario. El objetivo final es detectar la actividad que el usuario está haciendo junto con la estimación aproximada de la distancia recorrida. El algoritmo de cuenta pasos se basa en la detección de máximos y mínimos usando ventanas temporales y umbrales sin requerir información específica del usuario. El ámbito de seguimiento de peatones en interiores es interesante por la falta de un estándar de localización en este tipo de entornos. Se ha diseñado un filtro extendido de Kalman centralizado y ligeramente acoplado para fusionar la información medida por un acelerómetro colocado en el pie del usuario con medidas de posición. Se han aplicado también diferentes técnicas de corrección de errores como las de velocidad cero que se basan en la detección de los instantes en los que el pie está apoyado en el suelo. Los resultados han sido obtenidos en entornos interiores usando las posiciones estimadas por un sistema de triangulación basado en la medida de la potencia recibida (RSS) y GPS en exteriores. Finalmente, se han implementado algunas aplicaciones que prueban la utilidad del trabajo desarrollado. En primer lugar se ha considerado una aplicación de monitorización de actividad que proporciona al usuario información sobre el nivel de actividad que realiza durante un período de tiempo. El objetivo final es favorecer el cambio de comportamientos sedentarios, consiguiendo hábitos saludables. Se han desarrollado dos versiones de esta aplicación. En el primer caso se ha integrado el algoritmo de cuenta pasos en una plataforma OSGi móvil adquiriendo los datos de un acelerómetro Bluetooth colocado en el pie. En el segundo caso se ha creado la misma aplicación utilizando las implementaciones de los clasificadores en un dispositivo Android. Por otro lado, se ha planteado el diseño de una aplicación para la creación automática de un diario de viaje a partir de la detección de eventos importantes. Esta aplicación toma como entrada la información procedente de la estimación de actividad y de localización además de información almacenada en bases de datos abiertas (fotos, información sobre sitios) e información sobre sensores reales y virtuales (agenda, cámara, etc.) del móvil. Abstract Inertial sensors (accelerometers and gyroscopes) have been gradually embedded in the devices that people use in their daily lives thanks to their miniaturization. Nowadays all smartphones have at least one embedded magnetometer and accelerometer, containing the most upto- date ones gyroscopes and barometers. This issue, together with the fact that the penetration of smartphones is growing steadily, has made possible the design of systems that rely on the information gathered by wearable sensors (in the future contained in smart textiles) or inertial sensors embedded in a smartphone. The role of these sensors has become key to the development of context-aware and ambient intelligent applications. Some examples are the performance of rehabilitation exercises, the provision of information related to the place that the user is visiting or the interaction with objects by gesture recognition. The work of this thesis contributes to explore to which extent this kind of sensors can be useful to support activity recognition and pedestrian tracking, which have been proven to be essential for these applications. Regarding the recognition of the activity that a user performs, the use of sensors embedded in a smartphone (proximity and light sensors, gyroscopes, magnetometers and accelerometers) has been explored. The activities that are detected belong to the group of the ones known as ‘atomic’ activities (e.g. walking at different paces, running, standing), that is, activities or movements that are part of more complex activities such as doing the dishes or commuting. Simple, wellknown classifiers that can run embedded in a smartphone have been tested, such as Naïve Bayes, Decision Tables and Trees. In addition to this, another aim is to estimate the on-body position in which the user is carrying the mobile phone. The objective is not only to choose a classifier that has been trained with the corresponding data in order to enhance the classification but also to start actions. Finally, the performance of the different classifiers is analysed, taking into consideration different features and number of sensors. The computational and memory load of the classifiers is also measured. On the other hand, an algorithm based on step counting has been proposed. The acceleration information is provided by an accelerometer placed on the foot. The aim is to detect the activity that the user is performing together with the estimation of the distance covered. The step counting strategy is based on detecting minima and its corresponding maxima. Although the counting strategy is not innovative (it includes time windows and amplitude thresholds to prevent under or overestimation) no user-specific information is required. The field of pedestrian tracking is crucial due to the lack of a localization standard for this kind of environments. A loosely-coupled centralized Extended Kalman Filter has been proposed to perform the fusion of inertial and position measurements. Zero velocity updates have been applied whenever the foot is detected to be placed on the ground. The results have been obtained in indoor environments using a triangulation algorithm based on RSS measurements and GPS outdoors. Finally, some applications have been designed to test the usefulness of the work. The first one is called the ‘Activity Monitor’ whose aim is to prevent sedentary behaviours and to modify habits to achieve desired objectives of activity level. Two different versions of the application have been implemented. The first one uses the activity estimation based on the step counting algorithm, which has been integrated in an OSGi mobile framework acquiring the data from a Bluetooth accelerometer placed on the foot of the individual. The second one uses activity classifiers embedded in an Android smartphone. On the other hand, the design of a ‘Travel Logbook’ has been planned. The input of this application is the information provided by the activity and localization modules, external databases (e.g. pictures, points of interest, weather) and mobile embedded and virtual sensors (agenda, camera, etc.). The aim is to detect important events in the journey and gather the information necessary to store it as a journal page.

Comparison of full-field electroretinogram in diabetic and non diabetic dogs with cataracts

Being the commonest ocular disorder, dense cataracts disable fundoscopic examination and the diagnosis of retinal disorders, which dogs may be predisposed. The aim of this study was to compare the electroretinographic responses recorded according to the International Society for Clinical Electrophysiology of Vision human protocol to evaluate retinal function of diabetic and non diabetic dogs, both presenting mature or hypermature cataracts. Full-field electroretinogram was recorded from 66 dogs, with ages varying from 6 to 15 years old allocated into two groups: (1) CG, non diabetic cataractous dogs, and (2) DG, diabetic cataractous dogs. Mean peak-to-peak amplitude (microvolts) and b-wave implicit time (milliseconds) were determined for each of the five standard full-field ERG responses (rod response, maximal response, oscillatory potentials, single-flash cone response and 30 Hz flicker). Comparing CG to DG, ERGs recorded from diabetic dogs presented lower amplitude and prolonged b-wave implicit time in all ERG responses. Prolonged b-wave implicit time was statistically significant (p< 0.05) at 30 Hz flicker (24.0 ms versus 22.4 ms). These data suggests full-field ERG is capable to record sensible alterations, such as flicker's implicit time, being useful to investigate retinal dysfunction in diabetic dogs.

Comparison of the Effects of Electrical Field Stimulation and Low-Level Laser Therapy on Bone Loss in Spinal Cord-Injured Rats

Objective: This study investigated the effects of low-level laser therapy (LLLT) and electrical stimulation (ES) on bone loss in spinal cord-injured rats. Materials and Methods: Thirty-seven male Wistar rats were divided into four groups: standard control group (CG); spinal cord-injured control (SC); spinal cord-injured treated with laser (SCL; GaAlAs, 830 nm, CW, 30mW/cm, 250 J/cm(2)); and spinal cord-injured treated with electrical field stimulation (SCE; 1.5 MHz, 1: 4 duty cycles, 30 mW, 20 min). Biomechanical, densitometric, and morphometric analyses were performed. Results: SC rats showed a significant decrease in bone mass, biomechanical properties, and morphometric parameters (versus CG). SCE rats showed significantly higher values of inner diameter and internal and external areas of tibia diaphyses; and the SCL group showed a trend toward the same result (versus SC). No increase was found in either mechanical or densitometric parameters. Conclusion: We conclude that the mentioned treatments were able to initiate a positive bone-tissue response, maybe through stimulation of osteoblasts, which was able to determine the observed morphometric modifications. However, the evoked tissue response could not determine either biomechanical or densitometric modifications.

The metal content of bulge field stars from FLAMES-GIRAFFE spectra - I. Stellar parameters and iron abundances

Aims. We determine the iron distribution function (IDF) for bulge field stars, in three different fields along the Galactic minor axis and at latitudes b = -4 degrees, b = -6 degrees, and b = -12 degrees. A fourth field including NGC 6553 is also included in the discussion. Methods. About 800 bulge field K giants were observed with the GIRAFFE spectrograph of FLAMES@VLT at spectral resolution R similar to 20 000. Several of them were observed again with UVES at R similar to 45 000 to insure the accuracy of the measurements. The LTE abundance analysis yielded stellar parameters and iron abundances that allowed us to construct an IDF for the bulge that, for the first time, is based on high-resolution spectroscopy for each individual star. Results. The IDF derived here is centered on solar metallicity, and extends from [Fe/H] similar to -1.5 to [Fe/H] similar to + 0.5. The distribution is asymmetric, with a sharper cutoff on the high-metallicity side, and it is narrower than previously measured. A variation in the mean metallicity along the bulge minor axis is clearly between b = -4 degrees and b = -6 degrees ([Fe/H] decreasing similar to by 0.6 dex per kpc). The field at b = -12 degrees. is consistent with the presence of a gradient, but its quantification is complicated by the higher disk/bulge fraction in this field. Conclusions. Our findings support a scenario in which both infall and outflow were important during the bulge formation, and then suggest the presence of a radial gradient, which poses some challenges to the scenario in which the bulge would result solely from the vertical heating of the bar.

Near-Barrier Fusion of the (8)B+(58)Ni Proton-Halo System

Fusion cross sections were measured for the exotic proton-halo nucleus (8)B incident on a (58)Ni target at several energies near the Coulomb barrier. This is the first experiment to report on the fusion of a protonhalo nucleus. The resulting excitation function shows a striking enhancement with respect to expectations for normal projectiles. Evidence is presented that the sum of the fusion and breakup yields saturates the total reaction cross section.

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.

Homogenization of scalar field cosmologies

We summarise recent results about the evolution of linear density perturbations in scalar field cosmologies with an exponential potential. We use covariant and gauge invariant perturbation variables and a dynamical systems' approach. We establish under what conditions do the perturbations decay to the future in agreement with the cosmic no-hair conjecture.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Reptiles of Marshall Field North Arabian desert expeditions, 1927-1928, by Karl P. Schmidt.

v.17:no.6(1930)

Poor value of pulsed-field gel electrophoresis to investigate long-term scale epidemiology of methicillin-resistant Staphylococcus aureus.

Pulsed-field gel electrophoresis (PFGE) is widely used for epidemic investigations of methicillin-resistant Staphylococcus aureus (MRSA). In the present study, we evaluated its use in a long-term epidemiological setting (years to few decades, country to continent level). The clustering obtained from PFGE patterns after SmaI digestion of the DNA of 20 strains was compared to that obtained using a phylogenetic typing method (multiprimer RAPD). The results showed that the analysis of small PFGE bands (10-85kb) correlates better with multiprimer RAPD than the analysis of large PFGE bands (>85-700kb), suggesting that the analysis of small bands would be more suitable for the investigation of long-term epidemiological setting. However, given the technical difficulties to obtain a good resolution of these bands and the putative presence of plasmids among them, PFGE does not appear to be a method of choice for the long-term epidemiology analysis of MRSA.

Lightweight monitoring of label switched paths for bandwidth management

The purpose of resource management is the efficient and effective use of network resources, for instance bandwidth. In this article, a connection oriented network scenario is considered, where a certain amount of bandwidth is reserved for each label switch path (LSP), which is a logical path, in a MPLS or GMPLS environment. Assuming there is also some kind of admission control (explicit or implicit), these environments typically provide quality of service (QoS) guarantees. It could happen that some LSPs become busy, thus rejecting connections, while other LSPs may be under-utilised. We propose a distributed lightweight monitoring technique, based on threshold values, the objective of which is to detect congestion when it occurs in an LSP and activate the corresponding alarm which will trigger a dynamic bandwidth reallocation mechanism