A comparative analysis of road financing approaches in Europe and the United States

• The viability of the present system of road funding has in recent years been widely questioned, for it has proved insufficient to support spending programs at their current levels. • Some transfers from the general fund have been needed since 2008 to keep the HTF solvent. • Fuel taxes, the primary source for funding roads in the US, have not kept pace with inflation for years. • A wide variety of measures to increase revenue has been presented: raising fuel taxes, extending toll highways, implementing a VMT fee, etc.

A novel FPGA-based evolvable hardware system based on multiple processing arrays

In this paper, an architecture based on a scalable and flexible set of Evolvable Processing arrays is presented. FPGA-native Dynamic Partial Reconfiguration (DPR) is used for evolution, which is done intrinsically, letting the system to adapt autonomously to variable run-time conditions, including the presence of transient and permanent faults. The architecture supports different modes of operation, namely: independent, parallel, cascaded or bypass mode. These modes of operation can be used during evolution time or during normal operation. The evolvability of the architecture is combined with fault-tolerance techniques, to enhance the platform with self-healing features, making it suitable for applications which require both high adaptability and reliability. Experimental results show that such a system may benefit from accelerated evolution times, increased performance and improved dependability, mainly by increasing fault tolerance for transient and permanent faults, as well as providing some fault identification possibilities. The evolvable HW array shown is tailored for window-based image processing applications.

Classical segmentation methods on novel MR imaging: a study of brain tissue segmentation of MP2RAGE vs MPRAGE

MP2RAGE has proven to be a bias-free MR acquisition with excellent contrast between grey and white matter. We investigated the ability of three state-of-the-art algorithms to automatically extract white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) from MPRAGE and MP2RAGE images: unified Segmentation (S) in SPM82 , its extension New Segment (NS), and an in-house Expectation-Maximization Markov Random Field tissue classification3 (EM-MRF) with Graph Cut (GC) optimization4 . Our goal is to quantify the differences between MPRAGE and MP2RAGE-based brain tissue probability maps.

Mechanical Degradation of Tungsten Alloys at Extreme Temperatures in Vacuum and Oxidation Atmospheres

Mechanical degradation of tungsten alloys at extreme temperatures in vacuum and oxidation atmospheres.

FK concentrator outdoor measurements

The FK is a two-stage optical concentrator for CPV, composed by a Fresnel lens working as POE and a refractive element working as SOE. Both elements perform Köhler integration, for uniform irradiance purposes. The FK has demonstrated that compares very well with other Fresnel-based concentrator optics. Recent on-sun measurements carried out on an FK mono-module prototype have already shown outstanding results, achieving electrical efficiencies over 34%. Further optimization of optical design together with application of AR coating on SOE will predictably lead to efficiencies over 35%.

A combined algorithm for analyzing structural controllability and observability of complex networks

In this paper a combined algorithm for analyzing structural controllability and observability of complex networks is presented. The algorithm addresses the two fundamental properties to guarantee structural controllability of a system: the absence of dilations and the accessibility of all nodes. The first problem is reformulated as a Maximum Matching search and it is addressed via the Hopcroft- Karp algorithm; the second problem is solved via a new wiring algorithm. Both algorithms can be combined to efficiently determine the number of required controllers and observers as well as the new required connections in order to guarantee controllability and observability in real complex networks. An application to a Twitter social network with over 100,000 nodes illustrates the proposed algorithms.

Dam seepage analysis based on artificial neural networks: the hysteresis phenomenon

Seepage flow measurement is an important behavior indicator when providing information about dam performance. The main objective of this study is to analyze seepage by means of an artificial neural network model. The model is trained and validated with data measured at a case study. The dam behavior towards different water level changes is reproduced by the model and a hysteresis phenomenon detected and studied. Artificial neural network models are shown to be a powerful tool for predicting and understanding seepage phenomenon.

Adaptive spatio-temporal filter for low-cost camera depth maps

In this paper we present an adaptive spatio-temporal filter that aims to improve low-cost depth camera accuracy and stability over time. The proposed system is composed by three blocks that are used to build a reliable depth map of static scenes. An adaptive joint-bilateral filter is used to obtain consistent depth maps by jointly considering depth and video information and by adapting its parameters to different levels of estimated noise. Kalman filters are used to reduce the temporal random fluctuations of the measurements. Finally an interpolation algorithm is used to obtain consistent depth maps in the regions where the depth information is not available. Results show that this approach allows to considerably improve the depth maps quality by considering spatio-temporal information and by adapting its parameters to different levels of noise.

High-quality real-time temporal segmentation tool for video editing software

The increasing use of video editing software has resulted in a necessity for faster and more efficient editing tools. Here, we propose a lightweight high-quality video indexing tool that is suitable for video editing software.

Low-cost efficient interactive whiteboard

In this paper we present a low-cost efficient Interactive Whiteboard that, by fusing depth and video information provided by a low-cost depth camera, is able to detect and track user movements.

Region-dependent vehicle classification using PCA features

Video-based vehicle detection is the focus of increasing interest due to its potential towards collision avoidance. In particular, vehicle verification is especially challenging due to the enormous variability of vehicles in size, color, pose, etc. In this paper, a new approach based on supervised learning using Principal Component Analysis (PCA) is proposed that addresses the main limitations of existing methods. Namely, in contrast to classical approaches which train a single classifier regardless of the relative position of the candidate (thus ignoring valuable pose information), a region-dependent analysis is performed by considering four different areas. In addition, a study on the evolution of the classification performance according to the dimensionality of the principal subspace is carried out using PCA features within a SVM-based classification scheme. Indeed, the experiments performed on a publicly available database prove that PCA dimensionality requirements are region-dependent. Hence, in this work, the optimal configuration is adapted to each of them, rendering very good vehicle verification results.

Accurate depth-color scene modeling for 3D contents generation with low cost depth cameras

In this paper, we present a depth-color scene modeling strategy for indoors 3D contents generation. It combines depth and visual information provided by a low-cost active depth camera to improve the accuracy of the acquired depth maps considering the different dynamic nature of the scene elements. Accurate depth and color models of the scene background are iteratively built, and used to detect moving elements in the scene. The acquired depth data is continuously processed with an innovative joint-bilateral filter that efficiently combines depth and visual information thanks to the analysis of an edge-uncertainty map and the detected foreground regions. The main advantages of the proposed approach are: removing depth maps spatial noise and temporal random fluctuations; refining depth data at object boundaries, generating iteratively a robust depth and color background model and an accurate moving object silhouette.

Adaptive protection scheme for MVC-encoded stereoscopic video streaming in IP-based networks

We present an adaptive unequal error protection (UEP) strategy built on the 1-D interleaved parity Application Layer Forward Error Correction (AL-FEC) code for protecting the transmission of stereoscopic 3D video content encoded with Multiview Video Coding (MVC) through IP-based networks. Our scheme targets the minimization of quality degradation produced by packet losses during video transmission in time-sensitive application scenarios. To that end, based on a novel packet-level distortion model, it selects in real time the most suitable packets within each Group of Pictures (GOP) to be protected and the most convenient FEC technique parameters, i.e., the size of the FEC generator matrix. In order to make these decisions, it considers the relevance of the packet, the behavior of the channel, and the available bitrate for protection purposes. Simulation results validate both the distortion model introduced to estimate the importance of packets and the optimization of the FEC technique parameter values.

Real-time determination of airspace wind speed field based on FDR = Determinación en tiempo real del campo de velocidad del viento basado en registradores de datos de vuelo

El estudio del comportamiento de la atmósfera ha resultado de especial importancia tanto en el programa SESAR como en NextGen, en los que la gestión actual del tránsito aéreo (ATM) está experimentando una profunda transformación hacia nuevos paradigmas tanto en Europa como en los EE.UU., respectivamente, para el guiado y seguimiento de las aeronaves en la realización de rutas más eficientes y con mayor precisión. La incertidumbre es una característica fundamental de los fenómenos meteorológicos que se transfiere a la separación de las aeronaves, las trayectorias de vuelo libres de conflictos y a la planificación de vuelos. En este sentido, el viento es un factor clave en cuanto a la predicción de la futura posición de la aeronave, por lo que tener un conocimiento más profundo y preciso de campo de viento reducirá las incertidumbres del ATC. El objetivo de esta tesis es el desarrollo de una nueva técnica operativa y útil destinada a proporcionar de forma adecuada y directa el campo de viento atmosférico en tiempo real, basada en datos de a bordo de la aeronave, con el fin de mejorar la predicción de las trayectorias de las aeronaves. Para lograr este objetivo se ha realizado el siguiente trabajo. Se han descrito y analizado los diferentes sistemas de la aeronave que proporcionan las variables necesarias para obtener la velocidad del viento, así como de las capacidades que permiten la presentación de esta información para sus aplicaciones en la gestión del tráfico aéreo. Se ha explorado el uso de aeronaves como los sensores de viento en un área terminal para la estimación del viento en tiempo real con el fin de mejorar la predicción de las trayectorias de aeronaves. Se han desarrollado métodos computacionalmente eficientes para estimar las componentes horizontales de la velocidad del viento a partir de las velocidades de las aeronaves (VGS, VCAS/VTAS), la presión y datos de temperatura. Estos datos de viento se han utilizado para estimar el campo de viento en tiempo real utilizando un sistema de procesamiento de datos a través de un método de mínima varianza. Por último, se ha evaluado la exactitud de este procedimiento para que esta información sea útil para el control del tráfico aéreo. La información inicial proviene de una muestra de datos de Registradores de Datos de Vuelo (FDR) de aviones que aterrizaron en el aeropuerto Madrid-Barajas. Se dispuso de datos de ciertas aeronaves durante un periodo de más de tres meses que se emplearon para calcular el vector viento en cada punto del espacio aéreo. Se utilizó un modelo matemático basado en diferentes métodos de interpolación para obtener los vectores de viento en áreas sin datos disponibles. Se han utilizado tres escenarios concretos para validar dos métodos de interpolación: uno de dos dimensiones que trabaja con ambas componentes horizontales de forma independiente, y otro basado en el uso de una variable compleja que relaciona ambas componentes. Esos métodos se han probado en diferentes escenarios con resultados dispares. Esta metodología se ha aplicado en un prototipo de herramienta en MATLAB © para analizar automáticamente los datos de FDR y determinar el campo vectorial del viento que encuentra la aeronave al volar en el espacio aéreo en estudio. Finalmente se han obtenido las condiciones requeridas y la precisión de los resultados para este modelo. El método desarrollado podría utilizar los datos de los aviones comerciales como inputs utilizando los datos actualmente disponibles y la capacidad computacional, para proporcionárselos a los sistemas ATM donde se podría ejecutar el método propuesto. Estas velocidades del viento calculadas, o bien la velocidad respecto al suelo y la velocidad verdadera, se podrían difundir, por ejemplo, a través del sistema de direccionamiento e informe para comunicaciones de aeronaves (ACARS), mensajes de ADS-B o Modo S. Esta nueva fuente ayudaría a actualizar la información del viento suministrada en los productos aeronáuticos meteorológicos (PAM), informes meteorológicos de aeródromos (AIRMET), e información meteorológica significativa (SIGMET). ABSTRACT The study of the atmosphere behaviour is been of particular importance both in SESAR and NextGen programs, where the current air traffic management (ATM) system is undergoing a profound transformation to the new paradigms both in Europe and the USA, respectively, to guide and track aircraft more precisely on more efficient routes. Uncertainty is a fundamental characteristic of weather phenomena which is transferred to separation assurance, flight path de-confliction and flight planning applications. In this respect, the wind is a key factor regarding the prediction of the future position of the aircraft, so that having a deeper and accurate knowledge of wind field will reduce ATC uncertainties. The purpose of this thesis is to develop a new and operationally useful technique intended to provide adequate and direct real-time atmospheric winds fields based on on-board aircraft data, in order to improve aircraft trajectory prediction. In order to achieve this objective the following work has been accomplished. The different sources in the aircraft systems that provide the variables needed to derivate the wind velocity have been described and analysed, as well as the capabilities which allow presenting this information for air traffic management applications. The use of aircraft as wind sensors in a terminal area for real-time wind estimation in order to improve aircraft trajectory prediction has been explored. Computationally efficient methods have been developed to estimate horizontal wind components from aircraft velocities (VGS, VCAS/VTAS), pressure, and temperature data. These wind data were utilized to estimate a real-time wind field using a data processing approach through a minimum variance method. Finally, the accuracy of this procedure has been evaluated for this information to be useful to air traffic control. The initial information comes from a Flight Data Recorder (FDR) sample of aircraft landing in Madrid-Barajas Airport. Data available for more than three months were exploited in order to derive the wind vector field in each point of the airspace. Mathematical model based on different interpolation methods were used in order to obtain wind vectors in void areas. Three particular scenarios were employed to test two interpolation methods: a two-dimensional one that works with both horizontal components in an independent way, and also a complex variable formulation that links both components. Those methods were tested using various scenarios with dissimilar results. This methodology has been implemented in a prototype tool in MATLAB © in order to automatically analyse FDR and determine the wind vector field that aircraft encounter when flying in the studied airspace. Required conditions and accuracy of the results were derived for this model. The method developed could be fed by commercial aircraft utilizing their currently available data sources and computational capabilities, and providing them to ATM system where the proposed method could be run. Computed wind velocities, or ground and true airspeeds, would then be broadcasted, for example, via the Aircraft Communication Addressing and Reporting System (ACARS), ADS-B out messages, or Mode S. This new source would help updating the wind information furnished in meteorological aeronautical products (PAM), meteorological aerodrome reports (AIRMET), and significant meteorological information (SIGMET).

BatNet: an implementation of a 6LoWPAN sensor and actuator network

This demo presents BatNet, a 6LoWPAN Wireless Transducer Network, in a Home Automation context. Its suitability for such application is shown by means of several performance and usability tests.