F
Francisco V. Fernández
Researcher at Spanish National Research Council
Publications - 305
Citations - 4003
Francisco V. Fernández is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Neutron & Symbolic data analysis. The author has an hindex of 31, co-authored 281 publications receiving 3510 citations. Previous affiliations of Francisco V. Fernández include Autonomous University of Barcelona & Katholieke Universiteit Leuven.
Papers
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Analog circuit optimization system based on hybrid evolutionary algorithms
TL;DR: A new algorithm, called competitive co-evolutionary differential evolution (CODE), is proposed to design analog ICs with practical user-defined specifications, and it is shown that the proposed algorithm offers important advantages in terms of optimization quality and robustness.
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FRUIT: An operational tool for multisphere neutron spectrometry in workplaces
TL;DR: FrIT as discussed by the authors is an unfolding code for Bonner sphere spectrometers (BSS) developed, under the Labview environment, at the INFN-Frascati National Laboratory, which models a generic neutron spectrum as the superposition of up to four components.
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High-level synthesis of switched-capacitor, switched-current and continuous-time /spl Sigma//spl Delta/ modulators using SIMULINK-based time-domain behavioral models
J. Ruiz-Amaya,J.M. de la Rosa,Francisco V. Fernández,Fernando Medeiro,R. del Rio,Belén Pérez-Verdú,Ángel Rodríguez-Vázquez +6 more
TL;DR: This paper presents a high-level synthesis tool that combines an accurate SIMULINK-based time-domain behavioral simulator with a statistical optimization core and is the first tool dealing with the synthesis of /spl Sigma//spl Delta/Ms using both discrete-time and continuous-time circuit techniques.
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Pathological Element-Based Active Device Models and Their Application to Symbolic Analysis
TL;DR: The improved formulation method is compared with traditional formulation methods, showing that the NA matrix is more compact and the generation of nonzero coefficients is reduced, since the CPU time and memory consumption is reduced when recursive determinant-expansion techniques are used to solve theNA matrix.
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Efficient and Accurate Statistical Analog Yield Optimization and Variation-Aware Circuit Sizing Based on Computational Intelligence Techniques
TL;DR: Techniques inspired by computational intelligence are used to speed up yield optimization without sacrificing accuracy, and the resulting ORDE algorithm can achieve approximately a tenfold improvement in computational effort compared to an improved MC-based yield optimization algorithm integrating the infeasible sampling and Latin-hypercube sampling techniques.