Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals.

Numerical CFD modelling of non-neutral atmospheric boundary layers for offshore wind resource assessment based on Monin-Obukhov theory

The presented works aim at proposing a methodology for the simulation of offshore wind conditions using CFD. The main objective is the development of a numerical model for the characterization of atmospheric boundary layers of different stability levels, as the most important issue in offshore wind resource assessment. Based on Monin-Obukhov theory, the steady k-ε Standard turbulence model is modified to take into account thermal stratification in the surface layer. The validity of Monin-Obukhov theory in offshore conditions is discussed with an analysis of a three day episode at FINO-1 platform.

Brain structures identification based on feature descriptor

Traumatic Brain Injury -TBI- -1- is defined as an acute event that causes certain damage to areas of the brain. TBI may result in a significant impairment of an individuals physical, cognitive and psychosocial functioning. The main consequence of TBI is a dramatic change in the individuals daily life involving a profound disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges of TBI Neuroimaging is to develop robust automated image analysis methods to detect signatures of TBI, such as: hyper-intensity areas, changes in image contrast and in brain shape. The final goal of this research is to develop a method to identify the altered brain structures by automatically detecting landmarks on the image where signal changes and to provide comprehensive information to the clinician about them. These landmarks identify injured structures by co-registering the patient?s image with an atlas where landmarks have been previously detected. The research work has been initiated by identifying brain structures on healthy subjects to validate the proposed method. Later, this method will be used to identify modified structures on TBI imaging studies.

Multi-Camera very wide baseline feature matching based on view-adaptive junction detection

This paper presents a strategy for solving the feature matching problem in calibrated very wide-baseline camera settings. In this kind of settings, perspective distortion, depth discontinuities and occlusion represent enormous challenges. The proposed strategy addresses them by using geometrical information, specifically by exploiting epipolar-constraints. As a result it provides a sparse number of reliable feature points for which 3D position is accurately recovered. Special features known as junctions are used for robust matching. In particular, a strategy for refinement of junction end-point matching is proposed which enhances usual junction-based approaches. This allows to compute cross-correlation between perfectly aligned plane patches in both images, thus yielding better matching results. Evaluation of experimental results proves the effectiveness of the proposed algorithm in very wide-baseline environments.

Using Knowledge Modelling Tools for Agent-Based Systems: The Experience of KSM

The aim of this chapter is to discuss the applicability of recently proposed knowledge modelling tools to the development of agent-based systems. The discussion is derived from the real world experience of a particular software tool called KSM (Knowledge Structure Manager). The chapter provides details about this tool and then proceeds to show in which forms the software may be used to support the development of agent-based systems. Two multiagent systems, one in the field of telecommunications management and the other one in the field of flood control, are described. Conclusions about these studies are presented, summarizing the main contributions that knowledge modelling tools can bring to the development of agent-based systems.

Análisis y diseño de multiplicadores y mezcladores mediante el método de Monte Carlo en la banda de THz = Analysis and design of multipliers and mixers via Monte Carlo modelling at THz bands

La región del espectro electromagnético comprendida entre 100 GHz y 10 THz alberga una gran variedad de aplicaciones en campos tan dispares como la radioastronomía, espectroscopíamolecular, medicina, seguridad, radar, etc. Los principales inconvenientes en el desarrollo de estas aplicaciones son los altos costes de producción de los sistemas trabajando a estas frecuencias, su costoso mantenimiento, gran volumen y baja fiabilidad. Entre las diferentes tecnologías a frecuencias de THz, la tecnología de los diodos Schottky juega un importante papel debido a su madurez y a la sencillez de estos dispositivos. Además, los diodos Schottky pueden operar tanto a temperatura ambiente como a temperaturas criogénicas, con altas eficiencias cuando se usan como multiplicadores y con moderadas temperaturas de ruido en mezcladores. El principal objetivo de esta tesis doctoral es analizar los fenómenos físicos responsables de las características eléctricas y del ruido en los diodos Schottky, así como analizar y diseñar circuitos multiplicadores y mezcladores en bandas milimétricas y submilimétricas. La primera parte de la tesis presenta un análisis de los fenómenos físicos que limitan el comportamiento de los diodos Schottky de GaAs y GaN y de las características del espectro de ruido de estos dispositivos. Para llevar a cabo este análisis, un modelo del diodo basado en la técnica de Monte Carlo se ha considerado como referencia debido a la elevada precisión y fiabilidad de este modelo. Además, el modelo de Monte Carlo permite calcular directamente el espectro de ruido de los diodos sin necesidad de utilizar ningún modelo analítico o empírico. Se han analizado fenómenos físicos como saturación de la velocidad, inercia de los portadores, dependencia de la movilidad electrónica con la longitud de la epicapa, resonancias del plasma y efectos no locales y no estacionarios. También se ha presentado un completo análisis del espectro de ruido para diodos Schottky de GaAs y GaN operando tanto en condiciones estáticas como variables con el tiempo. Los resultados obtenidos en esta parte de la tesis contribuyen a mejorar la comprensión de la respuesta eléctrica y del ruido de los diodos Schottky en condiciones de altas frecuencias y/o altos campos eléctricos. También, estos resultados han ayudado a determinar las limitaciones de modelos numéricos y analíticos usados en el análisis de la respuesta eléctrica y del ruido electrónico en los diodos Schottky. La segunda parte de la tesis está dedicada al análisis de multiplicadores y mezcladores mediante una herramienta de simulación de circuitos basada en la técnica de balance armónico. Diferentes modelos basados en circuitos equivalentes del dispositivo, en las ecuaciones de arrastre-difusión y en la técnica de Monte Carlo se han considerado en este análisis. El modelo de Monte Carlo acoplado a la técnica de balance armónico se ha usado como referencia para evaluar las limitaciones y el rango de validez de modelos basados en circuitos equivalentes y en las ecuaciones de arrastredifusión para el diseño de circuitos multiplicadores y mezcladores. Una notable característica de esta herramienta de simulación es que permite diseñar circuitos Schottky teniendo en cuenta tanto la respuesta eléctrica como el ruido generado en los dispositivos. Los resultados de las simulaciones presentados en esta parte de la tesis, tanto paramultiplicadores comomezcladores, se han comparado con resultados experimentales publicados en la literatura. El simulador que integra el modelo de Monte Carlo con la técnica de balance armónico permite analizar y diseñar circuitos a frecuencias superiores a 1 THz. ABSTRACT The terahertz region of the electromagnetic spectrum(100 GHz-10 THz) presents a wide range of applications such as radio-astronomy, molecular spectroscopy, medicine, security and radar, among others. The main obstacles for the development of these applications are the high production cost of the systems working at these frequencies, highmaintenance, high volume and low reliability. Among the different THz technologies, Schottky technology plays an important rule due to its maturity and the inherent simplicity of these devices. Besides, Schottky diodes can operate at both room and cryogenic temperatures, with high efficiency in multipliers and moderate noise temperature in mixers. This PhD. thesis is mainly concerned with the analysis of the physical processes responsible for the characteristics of the electrical response and noise of Schottky diodes, as well as the analysis and design of frequency multipliers and mixers at millimeter and submillimeter wavelengths. The first part of the thesis deals with the analysis of the physical phenomena limiting the electrical performance of GaAs and GaN Schottky diodes and their noise performance. To carry out this analysis, a Monte Carlo model of the diode has been used as a reference due to the high accuracy and reliability of this diode model at millimeter and submillimter wavelengths. Besides, the Monte Carlo model provides a direct description of the noise spectra of the devices without the necessity of any additional analytical or empirical model. Physical phenomena like velocity saturation, carrier inertia, dependence of the electron mobility on the epilayer length, plasma resonance and nonlocal effects in time and space have been analysed. Also, a complete analysis of the current noise spectra of GaAs and GaN Schottky diodes operating under static and time varying conditions is presented in this part of the thesis. The obtained results provide a better understanding of the electrical and the noise responses of Schottky diodes under high frequency and/or high electric field conditions. Also these results have helped to determine the limitations of numerical and analytical models used in the analysis of the electrical and the noise responses of these devices. The second part of the thesis is devoted to the analysis of frequency multipliers and mixers by means of an in-house circuit simulation tool based on the harmonic balance technique. Different lumped equivalent circuits, drift-diffusion and Monte Carlo models have been considered in this analysis. The Monte Carlo model coupled to the harmonic balance technique has been used as a reference to evaluate the limitations and range of validity of lumped equivalent circuit and driftdiffusion models for the design of frequency multipliers and mixers. A remarkable feature of this reference simulation tool is that it enables the design of Schottky circuits from both electrical and noise considerations. The simulation results presented in this part of the thesis for both multipliers and mixers have been compared with measured results available in the literature. In addition, the Monte Carlo simulation tool allows the analysis and design of circuits above 1 THz.

Finite element code-based modeling of a multi-feature isolation system and passive alleviation of possible inner pounding

The existing seismic isolation systems are based on well-known and accepted physical principles, but they are still having some functional drawbacks. As an attempt of improvement, the Roll-N-Cage (RNC) isolator has been recently proposed. It is designed to achieve a balance in controlling isolator displacement demands and structural accelerations. It provides in a single unit all the necessary functions of vertical rigid support, horizontal flexibility with enhanced stability, resistance to low service loads and minor vibration, and hysteretic energy dissipation characteristics. It is characterized by two unique features that are a self-braking (buffer) and a self-recentering mechanism. This paper presents an advanced representation of the main and unique features of the RNC isolator using an available finite element code called SAP2000. The validity of the obtained SAP2000 model is then checked using experimental, numerical and analytical results. Then, the paper investigates the merits and demerits of activating the built-in buffer mechanism on both structural pounding mitigation and isolation efficiency. The paper addresses the problem of passive alleviation of possible inner pounding within the RNC isolator, which may arise due to the activation of its self-braking mechanism under sever excitations such as near-fault earthquakes. The results show that the obtained finite element code-based model can closely match and accurately predict the overall behavior of the RNC isolator with effectively small errors. Moreover, the inherent buffer mechanism of the RNC isolator could mitigate or even eliminate direct structure-tostructure pounding under severe excitation considering limited septation gaps between adjacent structures. In addition, the increase of inherent hysteretic damping of the RNC isolator can efficiently limit its peak displacement together with the severity of the possibly developed inner pounding and, therefore, alleviate or even eliminate the possibly arising negative effects of the buffer mechanism on the overall RNC-isolated structural responses.

Multisensor data fusion for accurate modelling of mobile objects

In the last decade, multi-sensor data fusion has become a broadly demanded discipline to achieve advanced solutions that can be applied in many real world situations, either civil or military. In Defence,accurate detection of all target objects is fundamental to maintaining situational awareness, to locating threats in the battlefield and to identifying and protecting strategically own forces. Civil applications, such as traffic monitoring, have similar requirements in terms of object detection and reliable identification of incidents in order to ensure safety of road users. Thanks to the appropriate data fusion technique, we can give these systems the power to exploit automatically all relevant information from multiple sources to face for instance mission needs or assess daily supervision operations. This paper focuses on its application to active vehicle monitoring in a particular area of high density traffic, and how it is redirecting the research activities being carried out in the computer vision, signal processing and machine learning fields for improving the effectiveness of detection and tracking in ground surveillance scenarios in general. Specifically, our system proposes fusion of data at a feature level which is extracted from a video camera and a laser scanner. In addition, a stochastic-based tracking which introduces some particle filters into the model to deal with uncertainty due to occlusions and improve the previous detection output is presented in this paper. It has been shown that this computer vision tracker contributes to detect objects even under poor visual information. Finally, in the same way that humans are able to analyze both temporal and spatial relations among items in the scene to associate them a meaning, once the targets objects have been correctly detected and tracked, it is desired that machines can provide a trustworthy description of what is happening in the scene under surveillance. Accomplishing so ambitious task requires a machine learning-based hierarchic architecture able to extract and analyse behaviours at different abstraction levels. A real experimental testbed has been implemented for the evaluation of the proposed modular system. Such scenario is a closed circuit where real traffic situations can be simulated. First results have shown the strength of the proposed system.

Three-dimensional inverse modelling of magnetic anomaly sources based on a genetic algorithm.

We present a modelling method to estimate the 3-D geometry and location of homogeneously magnetized sources from magnetic anomaly data. As input information, the procedure needs the parameters defining the magnetization vector (intensity, inclination and declination) and the Earth's magnetic field direction. When these two vectors are expected to be different in direction, we propose to estimate the magnetization direction from the magnetic map. Then, using this information, we apply an inversion approach based on a genetic algorithm which finds the geometry of the sources by seeking the optimum solution from an initial population of models in successive iterations through an evolutionary process. The evolution consists of three genetic operators (selection, crossover and mutation), which act on each generation, and a smoothing operator, which looks for the best fit to the observed data and a solution consisting of plausible compact sources. The method allows the use of non-gridded, non-planar and inaccurate anomaly data and non-regular subsurface partitions. In addition, neither constraints for the depth to the top of the sources nor an initial model are necessary, although previous models can be incorporated into the process. We show the results of a test using two complex synthetic anomalies to demonstrate the efficiency of our inversion method. The application to real data is illustrated with aeromagnetic data of the volcanic island of Gran Canaria (Canary Islands).

Exploring automatic feature selection for transition-based dependency parsing

En este artículo se investigan técnicas automáticas para encontrar un modelo óptimo de características en el caso de un analizador de dependencias basado en transiciones. Mostramos un estudio comparativo entre algoritmos de búsqueda, sistemas de validación y reglas de decisión demostrando al mismo tiempo que usando nuestros métodos es posible conseguir modelos complejos que proporcionan mejores resultados que los modelos que siguen configuraciones por defecto.

3D Feature Extraction and Modelling for SLAM

Paper submitted to the 39th International Symposium on Robotics ISR 2008, Seoul, South Korea, October 15-17, 2008.

Forest-based supply chain modelling using the SimPy simulation framework

Proper management of supply chains is fundamental in the overall system performance of forestbased activities. Usually, efficient management techniques rely on a decision support software, which needs to be able to generate fast and effective outputs from the set of possibilities. In order to do this, it is necessary to provide accurate models representative of the dynamic interactions of systems. Due to forest-based supply chains’ nature, event-based models are more suited to describe their behaviours. This work proposes the modelling and simulation of a forestbased supply chain, in particular the biomass supply chain, through the SimPy framework. This Python based tool allows the modelling of discrete-event systems using operations such as events, processes and resources. The developed model was used to access the impact of changes in the daily working plan in three situations. First, as a control case, the deterministic behaviour was simulated. As a second approach, a machine delay was introduced and its implications in the plan accomplishment were analysed. Finally, to better address real operating conditions, stochastic behaviours of processing and driving times were simulated. The obtained results validate the SimPy simulation environment as a framework for modelling supply chains in general and for the biomass problem in particular.

Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling

Atmospheric inverse modelling has the potential to provide observation-based estimates of greenhouse gas emissions at the country scale, thereby allowing for an independent validation of national emission inventories. Here, we present a regional-scale inverse modelling study to quantify the emissions of methane (CH₄) from Switzerland, making use of the newly established CarboCount-CH measurement network and a high-resolution Lagrangian transport model. In our reference inversion, prior emissions were taken from the "bottom-up" Swiss Greenhouse Gas Inventory (SGHGI) as published by the Swiss Federal Office for the Environment in 2014 for the year 2012. Overall we estimate national CH₄ emissions to be 196 ± 18 Gg yr⁻¹ for the year 2013 (1σ uncertainty). This result is in close agreement with the recently revised SGHGI estimate of 206 ± 33 Gg yr⁻¹ as reported in 2015 for the year 2012. Results from sensitivity inversions using alternative prior emissions, uncertainty covariance settings, large-scale background mole fractions, two different inverse algorithms (Bayesian and extended Kalman filter), and two different transport models confirm the robustness and independent character of our estimate. According to the latest SGHGI estimate the main CH₄ source categories in Switzerland are agriculture (78 %), waste handling (15 %) and natural gas distribution and combustion (6 %). The spatial distribution and seasonal variability of our posterior emissions suggest an overestimation of agricultural CH₄ emissions by 10 to 20 % in the most recent SGHGI, which is likely due to an overestimation of emissions from manure handling. Urban areas do not appear as emission hotspots in our posterior results, suggesting that leakages from natural gas distribution are only a minor source of CH₄ in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr⁻¹ reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr⁻¹ implied by the EDGARv4.2 inventory for this sector. Increased CH₄ emissions (up to 30 % compared to the prior) were deduced for the north-eastern parts of Switzerland. This feature was common to most sensitivity inversions, which is a strong indicator that it is a real feature and not an artefact of the transport model and the inversion system. However, it was not possible to assign an unambiguous source process to the region. The observations of the CarboCount-CH network provided invaluable and independent information for the validation of the national bottom-up inventory. Similar systems need to be sustained to provide independent monitoring of future climate agreements.

Modelling the population dynamics of the Queensland fruit fly, Bactrocera (Dacus) tryoni: a cohort-based approach incorporating the effects of weather

Queensland fruit fly, Bactrocera (Dacus) tryoni (QFF) is arguably the most costly horticultural insect pest in Australia. Despite this, no model is available to describe its population dynamics and aid in its management. This paper describes a cohort-based model of the population dynamics of the Queensland fruit fly. The model is primarily driven by weather variables, and so can be used at any location where appropriate meteorological data are available. In the model, the life cycle is divided into a number of discreet stages to allow physiological processes to be defined as accurately as possible. Eggs develop and hatch into larvae, which develop into pupae, which emerge as either teneral females or males. Both females and males can enter reproductive and over-wintering life stages, and there is a trapped male life stage to allow model predictions to be compared with trap catch data. All development rates are temperature-dependent. Daily mortality rates are temperature-dependent, but may also be influenced by moisture, density of larvae in fruit, fruit suitability, and age. Eggs, larvae and pupae all have constant establishment mortalities, causing a defined proportion of individuals to die upon entering that life stage. Transfer from one immature stage to the next is based on physiological age. In the adult life stages, transfer between stages may require additional and/or alternative functions. Maximum fecundity is 1400 eggs per female per day, and maximum daily oviposition rate is 80 eggs/female per day. The actual number of eggs laid by a female on any given day is restricted by temperature, density of larva in fruit, suitability of fruit for oviposition, and female activity. Activity of reproductive females and males, which affects reproduction and trapping, decreases with rainfall. Trapping of reproductive males is determined by activity, temperature and the proportion of males in the active population. Limitations of the model are discussed. Despite these, the model provides a useful agreement with trap catch data, and allows key areas for future research to be identified. These critical gaps in the current state of knowledge exist despite over 50 years of research on this key pest. By explicitly attempting to model the population dynamics of this pest we have clearly identified the research areas that must be addressed before progress can be made in developing the model into an operational tool for the management of Queensland fruit fly. (C) 2003 Published by Elsevier B.V.