MISUSE OF SPEED-BUMPS ON TWO-LANE MAIN RURAL ROADS. A GENERALIZED PRACTICE IN VENEZUELA

[EN] Settlements of uncontrolled population on side of road in Venezuela originate the excessive use of traffic speed reducers to mitigate accidents. Misuse of these speed control devices generate problem of functionality in the two-lane main rural roads which requires to be studied to demonstrate its effect on the capacity and level of service. Although other factors may occur (i.e, environmental problems and health), the disproportionate use of speed-bumps worsens circulation quality by increase of travel time as most sensitive parameter. Where this effect not can be reversed it should be made efforts to mitigate speed using another traffic-calming device. The studied stretches are selected according to particular characteristics such as: urban settlement, isolated speed-bump and its installation in series, including case without speed-bumps which guarantees the proper contrast. Video cameras to detect the travel time of vehicles are used in each road section, it allow the measures of other parameters.The travel time distribution with or without speed-bumps and probability distribution that characterizes vehicle movement in each stretch allows the simulation and modeling with the ARENA software. Travel time allows obtain the speed which, together with the volume of traffic, determines the level of service according to the Highway Capacity Manual criterion. The economic cost of substitute measures versus travel time is evaluated and may be useful in decision-making or implementation of better policies by transport governmental institutions.The research is part of the program that leads the University of the Andes (Venezuela) in conjunction with the research Group in Transport, Infrastructure and Territory -GITIT- of the Bolivarian Pontifical University (Bucaramanga-Colombia).Calderas Volcanes, R.; Moreno González, E. (2016). MISUSE OF SPEED-BUMPS ON TWO-LANE MAIN RURAL ROADS. A GENERALIZED PRACTICE IN VENEZUELA. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 1676-1683. https://doi.org/10.4995/CIT2016.2016.2255OCS1676168

Tetrapirroles en superficies metálicas y óxidos: reactividad y estructura

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Facultad de Ciencias, Departamento de Física de la Materia Condensada. Fecha de lectura: 16-10-201

Presence and distribution of current-use pesticides in surface marine sediments from a Mediterranean coastal lagoon (SE Spain)

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Feature exploration for biometric recognition using millimetre wave body images

The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1186/s13640-015-0084-3The use of millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. Furthermore, the security community has started using millimetre wave screening scanners in order to detect concealed objects. We believe we can exploit the use of these devices by incorporating biometric functionalities. This paper proposes a biometric recognition system based on the information of the silhouette of the human body, which may be seen as a type of soft biometric trait. To this aim, we report experimental results on the BIOGIGA database with four feature extraction approaches (contour coordinates, shape contexts, Fourier descriptors and landmarks) and three classification methods (Euclidean distance, dynamic time warping and support vector machines). The best configuration of 1.33 % EER is achieved when using contour coordinates with dynamic time warping.This work has been partially supported by projects TeraSense (CSD2008-00068), Bio-Shield (TEC2012-34881) and BEAT (FP7-SEC-284989) from EU. E. Gonzalez-Sosa is supported by a PhD scholarship from Universidad Autonoma de Madrid

Occurrence and seasonal distribution of polycyclic aromatic hydrocarbons and legacy and current-use pesticides in air from a Mediterranean coastal lagoon (Mar Menor, SE Spain)

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Approach to an FPGA embedded, autonomous object recognition system: run-time learning and adaptation

Neural networks, widely used in pattern recognition, security applications and robot control have been chosen for the task of object recognition within this system. One of the main drawbacks of the implementation of traditional neural networks in reconfigurable hardware is the huge resource consuming demand. This is due not only to their intrinsic parallelism, but also to the traditional big networks designed. However, modern FPGA architectures are perfectly suited for this kind of massive parallel computational needs. Therefore, our proposal is the implementation of Tiny Neural Networks, TNN -self-coined term-, in reconfigurable architectures. One of most important features of TNNs is their learning ability. Therefore, what we show here is the attempt to rise the autonomy features of the system, triggering a new learning phase, at run-time, when necessary. In this way, autonomous adaptation of the system is achieved. The system performs shape identification by the interpretation of object singularities. This is achieved by interconnecting several specialized TNN that work cooperatively. In order to validate the research, the system has been implemented and configured as a perceptron-like TNN with backpropagation learning and applied to the recognition of shapes. Simulation results show that this architecture has significant performance benefit

Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training.

Neural networks are widely used in pattern recognition, security applications, and robot control. We propose a hardware architecture system using tiny neural networks (TNNs)specialized in image recognition. The generic TNN architecture allows for expandability by means of mapping several basic units(layers) and dynamic reconfiguration, depending on the application specific demands. One of the most important features of TNNs is their learning ability. Weight modification and architecture reconfiguration can be carried out at run-time. Our system performs objects identification by the interpretation of characteristics elements of their shapes. This is achieved by interconnecting several specialized TNNs. The results of several tests in different conditions are reported in this paper. The system accurately detects a test shape in most of the experiments performed. This paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and configured as a perceptron network with back-propagation learning, choosing as reference application the recognition of shapes. Simulation results show that this architecture has significant performance benefits

BioGiga: Base de datos de imágenes sintéticas de personas a 94 GHz con fines biométricos

Versión electrónica de la ponencia presentada en el XXVI Simposium Nacional de Union Cientifica Internacional de Radio, URSI 2011, celebrado en Madrid.The baseline corpus of a new database, called BioGiga, acquired in the framework of the Terasense Consolider Project, is presented. The corpus consists of synthetic images at 94 GHz of the body of 50 individuals. The images are the result of simulations carried out on corporal models at two types of scenarios (outdoors, indoors) and with two kinds of imaging systems (passive and active). These corporal models were previously generated based on body measurements taken from the subjects. In this contribution, the methodology followed and the tools used to generate the database are outlined. Furthermore, the contents of the corpus (data and statistics) as well as its applications are described.Este trabajo ha sido financiado parcialmente por los proyectos Bio-Challenge (TEC2009-11186), Contexts (S2009/TIC- 1485), TeraSense (CSD2008-00068) y ”Cátedra UAM-Telefónica"

Evolutionary design and optimization of Wavelet Transforms for image compression in embedded systems

This paper describes the initial studies of an Evolution Strategy aimed at implementation on embedded systems for the evolution of Wavelet Transforms for image compression. Previous works in the literature have already been proved useful for this application, but they are highly computationally intensive. Therefore, the work described here, deals with the simplifications made to those algorithms to reduce their computing requirements. Several optimizations have been done in the evaluation phase and in the EA operators. The results presented show how the proposed algorithm cut outs still allow for good results to be achieved, while effectively reducing the computing requirements