Fast pseudorandom generator based on packed matrices

Pseudorandom generators are a basic foundation of many cryptographic services and information security protocols. We propose a modification of a previously published matricial pseudorandom generator that significantly improves performance and security. The resulting generator is successfully compared to world class standards.

An optimized pseudorandom generator using packed matrices

Most cryptographic services and information security protocols require a dependable source of random data; pseudorandom generators are convenient and efficient for this application working as one of the basic foundation blocks on which to build the required security infrastructure. We propose a modification of a previously published matricial pseudorandom generator that significantly improves performance and security by using word packed matrices and modifying key scheduling and bit extraction schemes. The resulting generator is then successfully compared to world class standards.

Incorporating global positioning data in real time P2P audio/video streams for mobile devices

We propose an original method to geoposition an audio/video stream with multiple emitters that are at the same time receivers of the mixed signal. The achieved method is suitable for those comes where a list of positions within a designated area is encoded with a degree of precision adjusted to the visualization capabilities; and is also easily extensible to support new requirements. This method extends a previously proposed protocol, without incurring in any performance penalty.

Evaluating approximations generated by the GNG3D method for mesh simplification

In this paper we present different error measurements with the aim to evaluate the quality of the approximations generated by the GNG3D method for mesh simplification. The first phase of this method consists on the execution of the GNG3D algorithm, described in the paper. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The implementation of three error functions, named Eavg, Emax, Esur, permitts us to control the error of the simplified model, as it is shown in the examples studied.

Computational cost of GNG3D algorithm for mesh simplification

In this paper we present a study of the computational cost of the GNG3D algorithm for mesh optimization. This algorithm has been implemented taking as a basis a new method which is based on neural networks and consists on two differentiated phases: an optimization phase and a reconstruction phase. The optimization phase is developed applying an optimization algorithm based on the Growing Neural Gas model, which constitutes an unsupervised incremental clustering algorithm. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The computational cost of both phases is calculated, showing some examples.

P2P audio/video protocol with global positioning data in real time for mobile devices

In this paper, we propose an original method to geoposition an audio/video stream with multiple emitters that are at the same time receivers of the mixed signal. The obtained method is suitable when a list of positions within a known area is encoded with precision tailored to the visualization capabilities of the target device. Nevertheless, it is easily adaptable to new precision requirements, as well as parameterized data precision. This method extends a previously proposed protocol, without incurring in any performance penalty.

A secret sharing scheme based on exponentiation in Galois fields

To provide more efficient and flexible alternatives for the applications of secret sharing schemes, this paper describes a threshold sharing scheme based on exponentiation of matrices in Galois fields. A significant characteristic of the proposed scheme is that each participant has to keep only one master secret share which can be used to reconstruct different group secrets according to the number of threshold values.

A neural network algorithm to simplify 2D meshes

In this paper we propose a neural network model to simplify and 2D meshes. This model is based on the Growing Neural Gas model and is able to simplify any mesh with different topologies and sizes. A triangulation process is included with the objective to reconstruct the mesh. This model is applied to some problems related to urban networks.

TimeBar for JavaVis

A TimeBar for JavaVis.

Spline for JavaVis 2

A Spline for camera movement in JavaVis.

Spline in JavaVis

A Spline for camera movement in JavaVis.

Red de Seguimiento e indicadores en el Máster Universitario en Ingeniería de Telecomunicación

El profesorado de la red docente, que forma parte de la comisión académica del Máster, realizó durante el curso 2011/12 un proyecto para el estudio de los indicadores de calidad del Máster, en función de los indicadores de calidad de las Agencias acreditadoras y dependiendo de las tasas de éxito y eficacias durante el primer curso de implantación del Máster.

Coordinación y seguimiento de los tres primeros cursos del Grado en Ing. Sonido e Imagen en Telecomunicación

El reto de implantar los nuevos grados exige un continuado esfuerzo de coordinación de las asignaturas de cada curso y de los diferentes cursos entres sí. En este trabajo se presentan los resultados de los diferentes proyectos que se han realizado para coordinar las asignaturas de los tres primeros cursos del Grado en Ingeniería en Sonido e Imagen en Telecomunicación de la Escuela Politécnica Superior. Además se analiza la coordinación de los proyectos entre sí, analizando los cambios surgidos en las fichas de las asignaturas, evaluación, metodología, etc. También se presenta una puesta en común con los coordinadores de todos los cursos para realizar las recomendaciones de matriculación a los estudiantes que realizan su matrícula a tiempo parcial o no superan cada curso todos los créditos matriculados. Y por último, se estudia la continuidad con los contenidos de las asignaturas que comienzan su implantación en el siguiente curso y por otro lado la coordinación en la evaluación para eliminar las numerosas coincidencias de evaluaciones continuas, de diferentes actividades en cada semana.

A study of a soft computing based method for 3D scenario reconstruction

Several recent works deal with 3D data in mobile robotic problems, e.g., mapping. Data comes from any kind of sensor (time of flight, Kinect or 3D lasers) that provide a huge amount of unorganized 3D data. In this paper we detail an efficient approach to build complete 3D models using a soft computing method, the Growing Neural Gas (GNG). As neural models deal easily with noise, imprecision, uncertainty or partial data, GNG provides better results than other approaches. The GNG obtained is then applied to a sequence. We present a comprehensive study on GNG parameters to ensure the best result at the lowest time cost. From this GNG structure, we propose to calculate planar patches and thus obtaining a fast method to compute the movement performed by a mobile robot by means of a 3D models registration algorithm. Final results of 3D mapping are also shown.

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D feature extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown.