H.264/AVC inter prediction on accelerator-based multi-core systems

The AVC video coding standard adopts variable block sizes for inter frame coding to increase compression efficiency, among other new features. As a consequence of this, an AVC encoder has to employ a complex mode decision technique that requires high computational complexity. Several techniques aimed at accelerating the inter prediction process have been proposed in the literature in recent years. Recently, with the emergence of many-core processors or accelerators, a new way of supporting inter frame prediction has presented itself. In this paper, we present a step forward in the implementation of an AVC inter prediction algorithm in a graphics processing unit, using Compute Unified Device Architecture. The results show a negligible drop in rate distortion with a time reduction, on average, of over 98.8 % compared with full search and fast full search, and of over 80 % compared with UMHexagonS search

Polynomial-based surrogate modeling of microwave structures in frequency domain exploiting the multinomial theorem

We propose a methodology for developing EM-based polynomial surrogate models exploiting the multinomial theorem. Our methodology is compared against four EM surrogate modeling techniques: response surface modeling, support vector machines, generalized regression neural networks, and Kriging. Results show that the proposed polynomial surrogate modeling approach has the best performance among these techniques when using a very small amount of learning base points. The proposed methodology is illustrated by developing a surrogate model for a T-slot PIFA antenna simulated on a commercially available 3D FEM simulator

Polynomial-based surrogate modeling of RF and microwave circuits in frequency domain exploiting the multinomial theorem

A general formulation to develop EM-based polynomial surrogate models in frequency domain utilizing the multinomial theorem is presented in this paper. Our approach is especially suitable when the number of learning samples is very limited and no physics-based coarse model is available. We compare our methodology against other four surrogate modeling techniques: response surface modeling, support vector machines, generalized regression neural networks, and Kriging. Results confirm that our modeling approach has the best performance among these techniques when using a very small amount of learning base points on relatively small modeling regions. We illustrate our technique by developing a surrogate model for an SIW interconnect with transitions to microstrip lines, a dual band T-slot PIFA handset antenna, and a high-speed package interconnect. Examples are simulated on a commercially available 3D FEM simulator

Quantum chaos in the mesoscopic device for the Josephson flux qubit

We show that the three-junction SQUID device designed for the Josephson flux qubit can be used to study quantum chaos when operated at high energies. In the parameter region where the system is classically chaotic we analyze the spectral statistics. The nearest neighbor distributions $P(s)$ are well fitted by the Berry Robnik theory employing as free parameters the pure classical measures of the chaotic and regular regions of phase space in the different energy regions. The phase space representation of the wave functions is obtained via the Husimi distributions and the localization of the states on classical structures is analyzed.Comment: Final version, to be published in Phys. Rev. B. References added, introduction and conclusions improve

Extending the VEF traces framework to model data center network workloads

Producción CientíficaData centers are a fundamental infrastructure in the Big-Data era, where applications and services demand a high amount of data and minimum response times. The interconnection network is an essential subsystem in the data center, as it must guarantee high communication bandwidth and low latency to the communication operations of applications, otherwise becoming the system bottleneck. Simulation is widely used to model the network functionality and to evaluate its performance under specific workloads. Apart from the network modeling, it is essential to characterize the end-nodes communication pattern, which will help identify bottlenecks and flaws in the network architecture. In previous works, we proposed the VEF traces framework: a set of tools to capture communication traffic of MPI-based applications and generate traffic traces used to feed network simulator tools. In this paper, we extend the VEF traces framework with new communication workloads such as deep-learning training applications and online data-intensive workloads.Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033) R &D Project Grant (PID2019-109001RA-I00)Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Reliable full-wave EM simulation of a single-layer SIW interconnect with transitions to microstrip lines

We present a procedure to obtain reliable EM responses for a substrate integrated waveguide (SIW) interconnect with microstrip line transitions. The procedure focuses on two COMSOL configuration settings: meshing sizes and simulation bounding box. Once both are properly configured, the implemented structure is tested by perturbing the simulation bounding box to assure it has no effect on the EM responsesITESO, A.C

Microalga marina y su empleo en acuicultura y en la obtención de ácidos grasos poliinsaturados

Número de publicación: ES2088366 A1 (01.08.1996) También publicado como: ES2088366 B1 (01.03.1997) Número de Solicitud: Consulta de Expedientes OEPM (C.E.O.) P9500053 (13.01.1995)Una cepa de la microalga marina isochrysis galbana, depositada en la CCAP con el número de depósito CCAP 927/15 es capaz de producir elevadas cantidades de ácidos grasos poliinsaturados, especialmente de ácido eicosapentaenoico (EPA) y de ácido docosahexaenoico (DHA). La cepa microalgal crece adecuadamente a una temperatura de 18 c a 25 c, en un ph de 7 a 9,5, preferentemente a un ph de 7,65 a 8,00. La cepa microalgal, cultivada a 20 c en un fermentador de 5 litros agitado por paletas y con iluminación continua, produce EPA en una cantidad de, al menos, 39,5 mg por gramo de materia seca. Esta cepa es adecuada para su empleo en acuicultura (alimentación de larvas de peces y moluscos) y en la obtención de EPA y/o de un aceite rico en EPA y en DHA necesarios para la nutrición y salud humanas.Universidad de Almerí