Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.

Inverse kinematics of a humanoid robot with non-spherical hip: a hybrid algorithm approach

This paper describes an approach to solve the inverse kinematics problem of humanoid robots whose construction shows a small but non negligible offset at the hip which prevents any purely analytical solution to be developed. Knowing that a purely numerical solution is not feasible due to variable efficiency problems, the proposed one first neglects the offset presence in order to obtain an approximate “solution” by means of an analytical algorithm based on screw theory, and then uses it as the initial condition of a numerical refining procedure based on the Levenberg‐Marquardt algorithm. In this way, few iterations are needed for any specified attitude, making it possible to implement the algorithm for real‐time applications. As a way to show the algorithm’s implementation, one case of study is considered throughout the paper, represented by the SILO2 humanoid robot.

Localización personal en entornos interiores con tecnología RFID

Este trabajo presenta un sistema de posicionamiento local (LPS) para personas en entornos interiores basado en la combinación de tecnología RFID activa y una metodología bayesiana de estimación de la posición a partir de la fuerza de las señales de RF recibidas. La complejidad inherente a la propagación de las ondas de RF en entornos interiores causa grandes fluctuaciones en el nivel de la fuerza de la señal, por lo que las técnicas bayesianas, de naturaleza estadística, tienen ventajas significativas frente a métodos de posicionamiento más comunes, como multilateración, minimización cuadrática o localización por fingerprinting. En la validación experimental del sistema RFID-LPS se consigue un error de posicionamiento medio de 2.10 m (mediana de 1.84 m y 3.89 m en el 90% de los casos), en un área abarcada de 475 m2 con 29 tags RFID, y con velocidades de desplazamiento de hasta 0.5 m/s, prestaciones iguales o superiores a otros sistemas del estado del arte. Aunque existen precedentes en Robótica móvil, la combinación de métodos bayesianos y tecnología RFID activa usada en este trabajo es original en el marco de los sistemas de localización de personas, cuyos desplazamientos son generalmente más impredecibles que los de los robots. Otros aspectos novedosos investigados son la posibilidad de alcanzar una estimación conjunta de posición y orientación de un usuario con dos métodos distintos (uso de antenas directivas y aprovechamiento de la atenuación de la señal de RF por el cuerpo humano), la escalabilidad del sistema RFID-LPS, y la estimación de la posición por técnicas bayesianas en sistemas simples que pueden detectar los marcadores RFID, pero no medir su fuerza de señal.

Effect of composition, sonication and pressure on the rate capability of 5 V-LiNi0.5Mn1.5O4 composite cathodes

Positive composite electrodes having LiNi0.5Mn1.5O4 spinel as active material, a blend of graphite and carbon black for increasing the electrode electrical conductivity and either polyvinyldenefluoride (PVDF) or a blend of PVDF with a small amount of Teflon® (1 wt%) for building up the electrode. They have been processed by tape casting on an aluminum foil as current collector using the doctor blade technique. Additionally, the component blends were either sonicated or not, and the processed electrodes were compacted or not under subsequent cold pressing. Composites electrodes with high weight, up to 17 mg/cm2, were prepared and studied as positive electrodes for lithium-ion batteries. The addition of Teflon® and the application of the sonication treatment lead to uniform electrodes that are well-adhered to the aluminum foil. Both parameters contribute to improve the capacity drained at high rates (5C). Additional compaction of the electrode/aluminum assemblies remarkably enhances the electrode rate capabilities. At 5C rate, remarkable capacity retentions between 80% and 90% are found for electrodes with weights in the range 3–17 mg/cm2, having Teflon® in their formulation, prepared after sonication of their component blends and compacted under 2 tonnes/cm2.

Automatic expert system based on images for accuracy crop row detection in maize fields

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient.

Speed-independent gait identification for mobile devices

Due to the intensive use of mobile phones for diferent purposes, these devices usually contain condential information which must not be accessed by another person apart from the owner of the device. Furthermore, the new generation phones commonly incorporate an accelerometer which may be used to capture the acceleration signals produced as a result of owner s gait. Nowadays, gait identication in basis of acceleration signals is being considered as a new biometric technique which allows blocking the device when another person is carrying it. Although distance based approaches as Euclidean distance or dynamic time warping have been applied to solve this identication problem, they show di±culties when dealing with gaits at diferent speeds. For this reason, in this paper, a method to extract an average template from instances of the gait at diferent velocities is presented. This method has been tested with the gait signals of 34 subjects while walking at diferent motion speeds (slow, normal and fast) and it has shown to improve the performance of Euclidean distance and classical dynamic time warping.

Modelling and Control of Stepper Motors for High Accuracy Positioning Systems Used in Radioactive Environments

Hybrid Stepper Motors are widely used in open-loop position applications. They are the choice of actuation for the collimators in the Large Hadron Collider, the largest particle accelerator at CERN. In this case the positioning requirements and the highly radioactive operating environment are unique. The latter forces both the use of long cables to connect the motors to the drives which act as transmission lines and also prevents the use of standard position sensors. However, reliable and precise operation of the collimators is critical for the machine, requiring the prevention of step loss in the motors and maintenance to be foreseen in case of mechanical degradation. In order to make the above possible, an approach is proposed for the application of an Extended Kalman Filter to a sensorless stepper motor drive, when the motor is separated from its drive by long cables. When the long cables and high frequency pulse width modulated control voltage signals are used together, the electrical signals difer greatly between the motor and drive-side of the cable. Since in the considered case only drive-side data is available, it is therefore necessary to estimate the motor-side signals. Modelling the entire cable and motor system in an Extended Kalman Filter is too computationally intensive for standard embedded real-time platforms. It is, in consequence, proposed to divide the problem into an Extended Kalman Filter, based only on the motor model, and separated motor-side signal estimators, the combination of which is less demanding computationally. The efectiveness of this approach is shown in simulation. Then its validity is experimentally demonstrated via implementation in a DSP based drive. A testbench to test its performance when driving an axis of a Large Hadron Collider collimator is presented along with the results achieved. It is shown that the proposed method is capable of achieving position and load torque estimates which allow step loss to be detected and mechanical degradation to be evaluated without the need for physical sensors. These estimation algorithms often require a precise model of the motor, but the standard electrical model used for hybrid stepper motors is limited when currents, which are high enough to produce saturation of the magnetic circuit, are present. New model extensions are proposed in order to have a more precise model of the motor independently of the current level, whilst maintaining a low computational cost. It is shown that a significant improvement in the model It is achieved with these extensions, and their computational performance is compared to study the cost of model improvement versus computation cost. The applicability of the proposed model extensions is demonstrated via their use in an Extended Kalman Filter running in real-time for closed-loop current control and mechanical state estimation. An additional problem arises from the use of stepper motors. The mechanics of the collimators can wear due to the abrupt motion and torque profiles that are applied by them when used in the standard way, i.e. stepping in open-loop. Closed-loop position control, more specifically Field Oriented Control, would allow smoother profiles, more respectful to the mechanics, to be applied but requires position feedback. As mentioned already, the use of sensors in radioactive environments is very limited for reliability reasons. Sensorless control is a known option but when the speed is very low or zero, as is the case most of the time for the motors used in the LHC collimator, the loss of observability prevents its use. In order to allow the use of position sensors without reducing the long term reliability of the whole system, the possibility to switch from closed to open loop is proposed and validated, allowing the use of closed-loop control when the position sensors function correctly and open-loop when there is a sensor failure. A different approach to deal with the switched drive working with long cables is also presented. Switched mode stepper motor drives tend to have poor performance or even fail completely when the motor is fed through a long cable due to the high oscillations in the drive-side current. The design of a stepper motor output fillter which solves this problem is thus proposed. A two stage filter, one devoted to dealing with the diferential mode and the other with the common mode, is designed and validated experimentally. With this ?lter the drive performance is greatly improved, achieving a positioning repeatability even better than with the drive working without a long cable, the radiated emissions are reduced and the overvoltages at the motor terminals are eliminated.

Aplicación web para la realización de experimentos astronómicos del proyecto GLORIA en la plataforma Facebook

La Ciencia Ciudadana nace del resultado de involucrar en las investigaciones científicas a todo tipo de personas, las cuales pueden participar en un determinado experimento analizando o recopilando datos. No hace falta que tengan una formación científica para poder participar, es decir cualquiera puede contribuir con su granito de arena. La ciencia ciudadana se ha convertido en un elemento a tener en cuenta a la hora de realizar tareas científicas que requieren mucha dedicación, o que simplemente por el volumen de trabajo que estas implican, resulta casi imposible que puedan ser realizadas por una sola persona o un pequeño grupo de trabajo. El proyecto GLORIA (GLObal Robotic-telescopes Intelligent Array) es la primera red de telescopios robóticos del mundo de acceso libre que permite a los usuarios participar en la investigación astronómica mediante la observación con telescopios robóticos, y/o analizando los datos que otros usuarios han adquirido con GLORIA, o desde otras bases de datos de libre acceso. Con el objetivo de contribuir a esta iniciativa se ha propuesto crear una plataforma web que pasará a formar parte del Proyecto GLORIA, en la que se puedan realizar experimentos astronómicos. Con el objetivo de fomentar la ciencia y el aprendizaje colaborativo se propone construir una aplicación web que se ejecute en la plataforma Facebook. Los experimentos los proporciona la red de telescopios del proyecto GLORIA mediante servicios web y están definidos mediante XML. La aplicación web recibe el XML con la descripción del experimento, lo interpreta y lo representa en la plataforma Facebook para que los usuarios potenciales puedan realizar los experimentos. Los resultados de los experimentos realizados se envían a una base de datos de libre acceso que será gestionada por el proyecto GLORIA, para su posterior análisis por parte de expertos. ---ABSTRACT---The citizen’s science is born out of the result of involving all type of people in scientific investigations, in which, they can participate in a determined experiment analyzing or compiling data. There is no need to have a scientific training in order to participate, but, anyone could contribute doing one’s bit. The citizen’s science has become an element to take into account when carrying out scientific tasks that require a lot dedication, or that, for the volume of work that these involve, are nearly impossible to be carried out by one person or a small working group. The GLORIA Project (Global Robotic-Telescopes Intelligent Array) is the first network of free access robotic telescopes in the world that permits the users to participate in the astronomic investigation by means of observation with robotic telescopes, and/or analyzing data from other users that have obtained through GLORIA, or from other free-access databases. With the aim of contributing to this initiative, a web platform has been created and will be part of the GLORIA Project, in which astronomic experiments can be carried out. With the objective of promoting science and collaborative apprenticeship, a web application carried out in the FACEBOOK platform is to be built. The experiments are founded by the telescopes network of the GLORIA project by means of web services and are defined through XML. The web application receives the XML with the description of the experiment, interprets it and represents it in the FACEBOOK platform in order for potential users may perform the experiments. The results of the experiments carried out are sent to a free-access database that will be managed by the GLORIA Project for its analysis on the part of experts.

Sea Bottom Classification by Means of Bathymetric LIDAR Data

Light Detection and Ranging (LIDAR) provides high horizontal and vertical resolution of spatial data located in point cloud images, and is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the data using mainly its three dimensional nature. Bathymetric LIDAR systems and data are mainly focused to map depths in shallow and clear waters with a high degree of accuracy. Additionally, the backscattering produced by the different materials distributed over the bottom surface causes that the returned intensity signal contains important information about the reflection properties of these materials. Processing conveniently these values using a Simplified Radiative Transfer Model, allows the identification of different sea bottom types. This paper presents an original method for the classification of sea bottom by means of information processing extracted from the images generated through LIDAR data. The results are validated using a vector database containing benthic information derived by marine surveys.

Linear latent force models using Gaussian Processes

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.

A new label fusion method using graph cuts: application to hippocampus segmentation

The aim of this paper is to develop a probabilistic modeling framework for the segmentation of structures of interest from a collection of atlases. Given a subset of registered atlases into the target image for a particular Region of Interest (ROI), a statistical model of appearance and shape is computed for fusing the labels. Segmentations are obtained by minimizing an energy function associated with the proposed model, using a graph-cut technique. We test different label fusion methods on publicly available MR images of human brains.

Offline GA-Based optimization for heterogeneous modular multi-configurable chained microrobots

This paper presents a GA-based optimization procedure for bioinspired heterogeneous modular multiconfigurable chained microrobots. When constructing heterogeneous chained modular robots that are composed of several different drive modules, one must select the type and position of the modules that form the chain. One must also develop new locomotion gaits that combine the different drive modules. These are two new features of heterogeneous modular robots that they do not share with homogeneous modular robots. This paper presents an offline control system that allows the development of new configuration schemes and locomotion gaits for these heterogeneous modular multiconfigurable chained microrobots. The offline control system is based on a simulator that is specifically designed for chained modular robots and allows them to develop and learn new locomotion patterns.

White matter damage disorganizes brain functional networks in amnestic mild cognitive impairment

Although progressive functional brain network disruption has been one of the hallmarks of Alzheimer?s Dis- ease, little is known about the origin of this functional impairment that underlies cognitive symptoms. We in- vestigated how the loss of white matter (WM) integrity disrupts the organization of the functional networks at different frequency bands. The analyses were performed in a sample of healthy elders and mild cognitive im- pairment (MCI) subjects. Spontaneous brain magnetic activity (measured with magnetoencephalography) was characterized with phase synchronization analysis, and graph theory was applied to the functional networks. We identified WM areas (using diffusion weighted magnetic resonance imaging) that showed a statistical de- pendence between the fractional anisotropy and the graph metrics. These regions are part of an episodic mem- ory network and were also related to cognitive functions. Our data support the hypothesis that disruption of the anatomical networks influences the organization at the functional level resulting in the prodromal dementia syndrome of MCI.

Disparate connectivity for structural and functional networks is revealed when physical location of the connected nodes is considered

Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical con- nections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 dif- ferent brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from very- large-scale integration circuits analyses, shows that func- tional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrange- ments for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal?ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organi- zations that can only be identified when the physical locations of the nodes are included in the analysis.

The default mode network is functionally and structurally disrupted in amnestic mild cognitive impairment — a bimodal MEG–DTI study

Over the past years, several studies on Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) have reported Default Mode Network (DMN) deficits. This network is attracting increasing interest in the AD community, as it seems to play an important role in cognitive functioning and in beta amyloid deposition. Attention has been particularly drawn to how different DMN regions are connected using functional or structural connectivity. To this end, most studies have used functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET) or Diffusion Tensor Imaging (DTI). In this study we evaluated (1) functional connectivity from resting state magnetoencephalography (MEG) and (2) structural connectivity from DTI in 26 MCI patients and 31 age-matched controls. Compared to controls, the DMN in the MCI group was functionally disrupted in the alpha band, while no differences were found for delta, theta, beta and gamma frequency bands. In addition, structural disconnection could be assessed through a decreased fractional anisotropy along tracts connecting different DMN regions. This suggests that the DMN functional and anatomical disconnection could represent a core feature of MCI.