Holographic 3DTV research within the european 3DTV project

A European project on 3DTV has been functional since September 2004. Holographic displays for 3DTV and signal processing issues associated with diffraction and holography are among research interests. The research has already generated interesting results. © 2008 Optical Society of America

Electric vehicle routing problem with backhauls considering the location of charging stations and the operation of the electric power distribution system

Las compañías logísticas están altamente motivadas en hacer que sus operaciones sean menos contaminantes a través de una solución eficiente con vehículos eléctricos (VEs). Sin embargo, el rango de conducción es uno de los aspectos limitantes en la inserción de los vehículos eléctricos en las flotas logísticas, debido a la baja capacidad proporcionada por las baterías para completar las rutas. En este sentido, es necesario desarrollar un marco de trabajo para incrementar de forma virtual la capacidad de la batería, por medio de la ubicación de estaciones de recarga a lo largo de la red de transporte, y completar las rutas satisfactoriamente. Por otro lado, los operadores de redes de distribución expresan su preocupación asociada a la inclusión de nuevas cargas eléctricas (estaciones de recarga de VEs), sin desmejorar la gestión óptima de suministro de energía a los usuarios finales. Bajo estas circunstancias, en este artículo se introduce el problema de ruteamiento de vehículos eléctricos con recogidas, formulado como un modelo de programación lineal entera mixta y considerando la operación del sistema de distribución en condiciones de máxima demanda. Se consideran diferentes puntos candidatos a estaciones de recarga de VEs para recargar la batería al final de una ruta linehaul o durante la ruta backhaul. El problema se formula con un enfoque multiobjetivo, donde se modela la operación de las redes de transporte y de distribución de energía eléctrica. El modelo propuesto es evaluado en instancias del VRPB (Vehicle Routing Problem with Backhauls) junto con sistemas de prueba de distribución de la literatura especializada. Para cada prueba, se presentan los correspondientes frentes de Pareto usando el método ε-constraint. Logistics companies are largely encouraged to make greener their operations through an efficient solution with electric vehicles (EVs). However, the driving range is one of the limiting aspects for the introduction of EVs in logistics fleet, due to the low capacity provided by the batteries to perform the routes. In this regards, it is necessary to set up a framework to virtually increase this battery capacity by locating EV charging stations (EVCSs) along the transportation network for the completion of their routes. By the other side, the Distribution Network Operators (DNOs) express the concern associated with the inclusion of new power demands to be attended (installation of EVCSs) in the Distribution Network (DN), without reducing the optimal power supply management for the end-users. Under these circumstances, in this paper the Electric Vehicle Routing Problem with Backhauls and optimal operation of the Distribution Network (EVRPB-DN) is introduced and formulated as a mixed-integer linear programming model, considering the operation of the DN in conditions of maximum power demand. Different candidate points for the EVs charging are considered to recharge the battery at the end of the linehaul route or during the backhaul route. The problem is formulated as a multi-objective approach where the transportation and power distribution networks operation are modeled. The performance and effectiveness of the proposed formulation is tested in VRPB instance datasets and DN test systems from the literature. Pareto fronts for each instance are presented, using the ε-constraint methodology

Naive possibilistic classifiers for imprecise or uncertain numerical data

International audienceIn real-world problems, input data may be pervaded with uncertainty. In this paper, we investigate the behavior of naive possibilistic classifiers, as a counterpart to naive Bayesian ones, for dealing with classification tasks in the presence of uncertainty. For this purpose, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. Here the possibility distributions that are used are supposed to encode the family of Gaussian probabilistic distributions that are compatible with the considered dataset. We consider two types of uncertainty: (i) the uncertainty associated with the class in the training set, which is modeled by a possibility distribution over class labels, and (ii) the imprecision pervading attribute values in the testing set represented under the form of intervals for continuous data. Moreover, the approach takes into account the uncertainty about the estimation of the Gaussian distribution parameters due to the limited amount of data available. We first adapt the possibilistic classification model, previously proposed for the certain case, in order to accommodate the uncertainty about class labels. Then, we propose an algorithm based on the extension principle to deal with imprecise attribute values. The experiments reported show the interest of possibilistic classifiers for handling uncertainty in data. In particular, the probability-to-possibility transform-based classifier shows a robust behavior when dealing with imperfect data

Emerging Technologies for Telemedicine

This paper focuses on new technologies that are practically useful for telemedicine. Three representative systems are introduced: a Digital Video Transport System (DVTS), an H.323 compatible videoconferencing system, and Vidyo. Based on some of our experiences, we highlight the advantages and disadvantages of each technology, and point out technologies that are especially targeted at doctors and technicians, so that those interested in using similar technologies can make appropriate choices and achieve their own goals depending on their specific conditions

Password-free network security through joint use of audio and video

10.1117/12.263383Proceedings of SPIE - The International Society for Optical Engineering2915120-125PSIS