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

Are social exclusion and poverty measures interrelated? A study with Spanish data

One of the targets of Europe's growth strategy (Europe 2020) is "reduction of poverty by aiming to lift at least 20 million people out of the risk of poverty or social exclusion". Since poverty is a multidimensional concept, EUROSTAT proposes three indicators to calculate it: people at risk-of-poverty after social transfers (persons are at risk of poverty if their equivalent disposable income is below the risk-of-poverty threshold, which is set at 60 % of the national median after social transfers); severely materially deprived people (severely materially deprived persons have living conditions greatly constrained by a lack of resources and cannot afford at least four of the following: to pay rent or utility bills; to keep their home adequately warm; to pay unexpected expenses; to eat meat, fish or a protein equivalent every second day; a week holiday away from home; a car; a washing machine; a colour TV; or a telephone) and people living in households with very low work intensity (persons are defined as living in households with very low work intensity if they are aged 0-59 and the working age members in the household worked less than 20 % of their potential during the past year.). We concentrate on the first two indicators and analyze the relationships between them using the Spanish Survey on Living Conditions 2010. Following EUROSTAT methodology we found that 2,590,148 Spanish households can be considered poor and 504,227 are deprived. But only 262,280 are, at the same time, poor and deprived. In order to improve deprivation index we substitute EUROSTAT methodology by Fuzzy method but results do not get better. Additionally, we test both deprivation indicators with households' income distribution (percentiles) and find very significant inconsistencies: some deprived families belong to the highest income percentiles and some variables used to work out the indexes have sample problems. The main conclusion of the article is that in order to calculate a poverty multidimensional index we should take into account that social exclusion variables and indexes have to be analyzed very carefully before using them to classify people as deprived, at least in the Spanish case

A Study in Spanish Regions' Poverty: A New Methodological Perspective

The present article reviews poverty studies in Spanish Autonomous Communities using a new methodological approach. One of the main concerns in those studies is poverty concentration in specific Spanish Autonomous Communities. This can be the result of a bias due to use a unique national poverty line, since the circumstances of every family unit, specifically if it is poor or not, could be different depending on the Region it inhabited. In order to test this assertion we estimate a CHAID algorithm and CATPCA. The main conclusion of this analysis is that Spanish poverty is strongly influenced by regional differences. To correct this effect we define as Spanish poor households those that are, at the same time, under the national and regional poverty line. We call this group Real poverty. Finally, we calculate ten poverty indexes for this real poor population. The results show that it is no possible to categorize Spanish Autonomous Communities depending on poverty indexes, since they do not configure a specific distribution of those regions. On the contrary, every index establishes its own regional ordering. Therefore, we should introduce regional adjustments in data and focus in household characteristics -size, type, age, level of education…- to explain Spanish poverty. The information comes from the Spanish Survey on Income and Living Conditions (SILC) in 2008

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

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