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Author

Francisco Sandoval

Other affiliations: ETSI, Technical University of Madrid
Bio: Francisco Sandoval is an academic researcher from University of Málaga. The author has contributed to research in topics: Artificial neural network & Mobile robot. The author has an hindex of 27, co-authored 228 publications receiving 3037 citations. Previous affiliations of Francisco Sandoval include ETSI & Technical University of Madrid.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors studied the application of Hop"eld-like neural networks to optimization problems and proposed an efficient local minima avoidance strategy based on the continuous dynamics.

234 citations

Journal ArticleDOI
TL;DR: Books and internet are the recommended media to help you improving your quality and performance.

168 citations

Journal ArticleDOI
TL;DR: In this paper, a genetic algorithm-based procedure for solving the optimal phasor measurement units (PMUs) placement problem is presented, which determines the minimal number of PMUs and their geographic distribution making the network observable, and shows the relationship between the number of current phasors that must be measured on each PMU and the necessary number of units for a given network.
Abstract: A genetic algorithm-based procedure for solving the optimal phasor measurement units (PMUs) placement problem is presented. It determines the minimal number of PMUs and their geographic distribution making the network observable, and it shows the relationship between the number of current phasors that must be measured on each PMU and the necessary number of PMUs for a given network.

143 citations

Journal ArticleDOI
TL;DR: In the present issue of Neurocomputing, it is a pleasure to present a collection of 12 extended versions of selected papers from the 10 theition of the International Work Conference on Artificial Neural Networks (IWANN2009).

115 citations


Cited by
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01 Mar 1987
TL;DR: The variable-order Adams method (SIVA/DIVA) package as discussed by the authors is a collection of subroutines for solution of non-stiff ODEs.
Abstract: Initial-value ordinary differential equation solution via variable order Adams method (SIVA/DIVA) package is collection of subroutines for solution of nonstiff ordinary differential equations. There are versions for single-precision and double-precision arithmetic. Requires fewer evaluations of derivatives than other variable-order Adams predictor/ corrector methods. Option for direct integration of second-order equations makes integration of trajectory problems significantly more efficient. Written in FORTRAN 77.

1,955 citations

Journal ArticleDOI
TL;DR: A new heuristic for feature detection is presented and, using machine learning, a feature detector is derived from this which can fully process live PAL video using less than 5 percent of the available processing time.
Abstract: The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.

1,847 citations

Book ChapterDOI
11 Dec 2012

1,704 citations

Journal ArticleDOI
TL;DR: An analysis of comparative surveys done in the field of gesture based HCI and an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters are provided.
Abstract: As computers become more pervasive in society, facilitating natural human---computer interaction (HCI) will have a positive impact on their use. Hence, there has been growing interest in the development of new approaches and technologies for bridging the human---computer barrier. The ultimate aim is to bring HCI to a regime where interactions with computers will be as natural as an interaction between humans, and to this end, incorporating gestures in HCI is an important research area. Gestures have long been considered as an interaction technique that can potentially deliver more natural, creative and intuitive methods for communicating with our computers. This paper provides an analysis of comparative surveys done in this area. The use of hand gestures as a natural interface serves as a motivating force for research in gesture taxonomies, its representations and recognition techniques, software platforms and frameworks which is discussed briefly in this paper. It focuses on the three main phases of hand gesture recognition i.e. detection, tracking and recognition. Different application which employs hand gestures for efficient interaction has been discussed under core and advanced application domains. This paper also provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters. It further discusses the advances that are needed to further improvise the present hand gesture recognition systems for future perspective that can be widely used for efficient human computer interaction. The main goal of this survey is to provide researchers in the field of gesture based HCI with a summary of progress achieved to date and to help identify areas where further research is needed.

1,338 citations

Book ChapterDOI
01 Jan 1996
TL;DR: Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariateData and the comparative lack of parametric models to represent it.
Abstract: Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

920 citations