This paper shows that parallel processing is useful for feature selection in brain-computer inter... more This paper shows that parallel processing is useful for feature selection in brain-computer interfacing (BCI) tasks. The classification problems arising in such application usually involve a relatively small number of high-dimensional patterns and, as curse of dimensionality issues have to be taken into account, feature selection is an important requirement to build suitable classifiers. As the number of features defining the search space is high, the distribution of the searching space among different processors would contribute to find better solutions, requiring similar or even smaller amount of execution time than sequential counterpart procedures. We have implemented a parallel evolutionary multiobjective optimization procedure for feature selection, based on the island model, in which the individuals are distributed among different subpopulations that independently evolve and interchange individuals after a given number of generations. The experimental results show improvements in both computing time and quality of EEG classification with features extracted by multiresolution analysis (MRA), an approach widely used in the BCI field with useful properties for both temporal and spectral signal analysis.
Multifactor authentication is a relevant tool in securing IT infrastructures combining two or mor... more Multifactor authentication is a relevant tool in securing IT infrastructures combining two or more credentials. We can find smartcards and hardware tokens to leverage the authentication process, but they have some limitations. Users connect these devices in the client node to log in or request access to services. Alternatively, if an application wants to use these resources, the code has to be amended with bespoke solutions to provide access. Thanks to advances in system-on-chip devices, we can integrate cryptographically robust, low-cost solutions. In this work, we present an autonomous device that allows multifactor authentication in client–server systems in a transparent way, which facilitates its integration in High-Performance Computing (HPC) and cloud systems, through a generic gateway. The proposed electronic token (eToken), based on the system-on-chip ESP32, provides an extra layer of security based on elliptic curve cryptography. Secure communications between elements use M...
Journal of computational biology : a journal of computational molecular cell biology, Jan 29, 2018
This article provides an insight on the power-performance issues related with the CPU-GPU (Centra... more This article provides an insight on the power-performance issues related with the CPU-GPU (Central Processing Unit-Graphics Processing Unit) parallel implementations of problems that frequently appear in the context of applications on bioinformatics and biomedical engineering. More specifically, we analyze the power-performance behavior of an evolutionary parallel multiobjective electroencephalogram feature selection procedure that evolves subpopulations of solutions with time-demanding fitness evaluation. The procedure has been implemented in OpenMP to dynamically distribute either subpopulations or individuals among devices, and uses OpenCL to evaluate the fitness of the individuals. The development of parallel codes usually implies to maximize the code efficiency, thus optimizing the achieved speedups. To follow the same trend, this article extends and provides a more complete analysis of our previous works about the power-performance characteristics in heterogeneous CPU-GPU plat...
Proceedings of the 6th International Work Conference on Artificial and Natural Neural Networks Bio Inspired Applications of Connectionism Part Ii, 2001
... M. Damas, M. Salmerón, J. Ortega, G. Olivares Department of Computer Architecture and Compute... more ... M. Damas, M. Salmerón, J. Ortega, G. Olivares Department of Computer Architecture and Computer Technology, University of Granada. Facultad de Ciencias. Campus Fuentenueva s/n. E-18071, Granada, Spain E-mail:{mdamas,moises,julio,gonzalo}@atc.ugr.es Abstract. ...
Xiii Jornadas De Paralelismo Lleida 9 10 Y 11 De Septiembre De 2002 Actas 2002 Isbn 84 8409 159 7 Pags 73 78, 2002
... CODE-2: un computador didáctico elemental. Autores: Alberto Prieto, Francisco Gómez-Mula, Jul... more ... CODE-2: un computador didáctico elemental. Autores: Alberto Prieto, Francisco Gómez-Mula, Julio Ortega, Héctor Pomares, Begoña del Pino, Antonio Cañas,Francisco Pelayo López, Antonio Díaz; Localización: XIII Jornadas ...
is an open problem with many useful applications in disciplines such as Medicine, Biology and Bio... more is an open problem with many useful applications in disciplines such as Medicine, Biology and Biochemistry. As this problem presents a vast search space where the analysis of each protein structure requires a significant amount of computing time, it is necessary to propose efficient search procedures in this very large space of possible protein conformations. Thus, an important issue is to add vital information (such as rotamers) to the process to decrease its active search space -rotamers give statistical information about torsional angles and conformations. In this paper, we propose a new method to refine the torsional angles of a protein to remake/reconstruct its structures with more resemblance to its original structure. This approach could be used to improve the accuracy of the rotamer libraries and/or to extract information from the Protein Data Bank to facilitate solution of the PSP problem.
The inherent fault tolerance of artificial neural networks (ANNs) is usually assumed, but several... more The inherent fault tolerance of artificial neural networks (ANNs) is usually assumed, but several authors have claimed that ANNs are not always fault tolerant and have demonstrated the need to evaluate their robustness by quantitative measures. For this purpose, various alternatives have been proposed. In this paper we show the direct relation between the mean square error (MSE) and the statistical sensitivity to weight deviations, defining a measure of tolerance based on statistical sentitivity that we have called Mean Square Sensitivity (MSS); this allows us to predict accurately the degradation of the MSE when the weight values change and so constitutes a useful parameter for choosing between different configurations of MLPs. The experimental results obtained for different MLPs are shown and demonstrate the validity of our model.
Feature selection is an important and active issue in clustering and classification problems. By ... more Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multiobjective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.
An analog CMOS implementation of a Cellular Neural Network with modifiable cloning templates is p... more An analog CMOS implementation of a Cellular Neural Network with modifiable cloning templates is proposed. One of the most important difficulties to hardware implement neural networks is their topological complexity which implies a high number of interconnections among cells. Nevertheless, as in cellular neural network each cell is only connected to its neighbour cells, their hardware implementation is easier. Even
Annals of Multicore and Gpu Programming, Mar 31, 2014
The use of auto-tuning techniques in a matrix multiplication routine for hybrid CPU+GPU platforms... more The use of auto-tuning techniques in a matrix multiplication routine for hybrid CPU+GPU platforms is analyzed. Basic models of the execution time of the hybrid routine and information obtained during its installation are used to optimize the execution time with a balanced assignation of the computation to the computing components in the heterogeneous system. Satisfactory results are obtained, with experimental execution times close to the lowest achievable.
International Journal of High Performance Systems Architecture, 2008
... in some scheduling problems, such as those appearing in parallel computing servers or in the ... more ... in some scheduling problems, such as those appearing in parallel computing servers or in the semiconductor industry (Gupta and Sivakumar, 2006), the ... the thermodynamic genetic algorithms (Mori et al., 1998) and the use of niching techniques (Cedeno and Vemuri, 1997) for ...
In Gigabit class networks, the physical transmission time is small compared to the time required ... more In Gigabit class networks, the physical transmission time is small compared to the time required to process the TCP/IP protocol stack. Thus, the usefulness of lightweight protocols that reduce the communication software overhead is even higher, as the performance demands shift to the network interface hardware and/or software. The communication protocol CLIC has recently been proposed for efficient communication in
Abstract. In this work we compare some of the freely available par-allel Toolboxes for Matlab, wh... more Abstract. In this work we compare some of the freely available par-allel Toolboxes for Matlab, which differ in purpose and implementa-tion details: while DP-Toolbox and MultiMatlab offer a higher-level parallel environment, the goals of PVMTB and MPITB, developed by ...
Proceedings of the 6th International Work Conference on Artificial and Natural Neural Networks Bio Inspired Applications of Connectionism Part Ii, 2001
Previous works have demonstrated that Mean Squared Sensitivity (MSS) constitutes a good aproximat... more Previous works have demonstrated that Mean Squared Sensitivity (MSS) constitutes a good aproximation to the performance degradation of a MLP affected by perturbations. In the present paper, the expression of MSS for Radial Basis Function Neural Networks affected by additive noise in their inputs is obtained. This expression is experimentally validated, allowing us to propose MSS as an effective measurement
In this paper we analyze parallel processing in clusters of computers of an improved prediction m... more In this paper we analyze parallel processing in clusters of computers of an improved prediction method based on RBF neural networks and matrix decomposition techniques (SVD and QR-cp). Parallel processing is required because of the extensive computation found in sucn an hybrid prediction technique, the reward being better prediction performance and also less network complexity. We discuss two alternatives of
Although there exist several approaches to rapidly solving the N-body problem, and a diversity of... more Although there exist several approaches to rapidly solving the N-body problem, and a diversity of implementation strategies, the performance tradeo s of the various strategies with respect to problem-speci c data distributions is poorly understood on a parallel computer. We present a synthetic workload model and a simulator that enables us to evaluate the performance tradeo s encountered in implementing particle methods on MIMD computers. These results can be used to evaluate designs early on in the implementation process.
This paper shows that parallel processing is useful for feature selection in brain-computer inter... more This paper shows that parallel processing is useful for feature selection in brain-computer interfacing (BCI) tasks. The classification problems arising in such application usually involve a relatively small number of high-dimensional patterns and, as curse of dimensionality issues have to be taken into account, feature selection is an important requirement to build suitable classifiers. As the number of features defining the search space is high, the distribution of the searching space among different processors would contribute to find better solutions, requiring similar or even smaller amount of execution time than sequential counterpart procedures. We have implemented a parallel evolutionary multiobjective optimization procedure for feature selection, based on the island model, in which the individuals are distributed among different subpopulations that independently evolve and interchange individuals after a given number of generations. The experimental results show improvements in both computing time and quality of EEG classification with features extracted by multiresolution analysis (MRA), an approach widely used in the BCI field with useful properties for both temporal and spectral signal analysis.
Multifactor authentication is a relevant tool in securing IT infrastructures combining two or mor... more Multifactor authentication is a relevant tool in securing IT infrastructures combining two or more credentials. We can find smartcards and hardware tokens to leverage the authentication process, but they have some limitations. Users connect these devices in the client node to log in or request access to services. Alternatively, if an application wants to use these resources, the code has to be amended with bespoke solutions to provide access. Thanks to advances in system-on-chip devices, we can integrate cryptographically robust, low-cost solutions. In this work, we present an autonomous device that allows multifactor authentication in client–server systems in a transparent way, which facilitates its integration in High-Performance Computing (HPC) and cloud systems, through a generic gateway. The proposed electronic token (eToken), based on the system-on-chip ESP32, provides an extra layer of security based on elliptic curve cryptography. Secure communications between elements use M...
Journal of computational biology : a journal of computational molecular cell biology, Jan 29, 2018
This article provides an insight on the power-performance issues related with the CPU-GPU (Centra... more This article provides an insight on the power-performance issues related with the CPU-GPU (Central Processing Unit-Graphics Processing Unit) parallel implementations of problems that frequently appear in the context of applications on bioinformatics and biomedical engineering. More specifically, we analyze the power-performance behavior of an evolutionary parallel multiobjective electroencephalogram feature selection procedure that evolves subpopulations of solutions with time-demanding fitness evaluation. The procedure has been implemented in OpenMP to dynamically distribute either subpopulations or individuals among devices, and uses OpenCL to evaluate the fitness of the individuals. The development of parallel codes usually implies to maximize the code efficiency, thus optimizing the achieved speedups. To follow the same trend, this article extends and provides a more complete analysis of our previous works about the power-performance characteristics in heterogeneous CPU-GPU plat...
Proceedings of the 6th International Work Conference on Artificial and Natural Neural Networks Bio Inspired Applications of Connectionism Part Ii, 2001
... M. Damas, M. Salmerón, J. Ortega, G. Olivares Department of Computer Architecture and Compute... more ... M. Damas, M. Salmerón, J. Ortega, G. Olivares Department of Computer Architecture and Computer Technology, University of Granada. Facultad de Ciencias. Campus Fuentenueva s/n. E-18071, Granada, Spain E-mail:{mdamas,moises,julio,gonzalo}@atc.ugr.es Abstract. ...
Xiii Jornadas De Paralelismo Lleida 9 10 Y 11 De Septiembre De 2002 Actas 2002 Isbn 84 8409 159 7 Pags 73 78, 2002
... CODE-2: un computador didáctico elemental. Autores: Alberto Prieto, Francisco Gómez-Mula, Jul... more ... CODE-2: un computador didáctico elemental. Autores: Alberto Prieto, Francisco Gómez-Mula, Julio Ortega, Héctor Pomares, Begoña del Pino, Antonio Cañas,Francisco Pelayo López, Antonio Díaz; Localización: XIII Jornadas ...
is an open problem with many useful applications in disciplines such as Medicine, Biology and Bio... more is an open problem with many useful applications in disciplines such as Medicine, Biology and Biochemistry. As this problem presents a vast search space where the analysis of each protein structure requires a significant amount of computing time, it is necessary to propose efficient search procedures in this very large space of possible protein conformations. Thus, an important issue is to add vital information (such as rotamers) to the process to decrease its active search space -rotamers give statistical information about torsional angles and conformations. In this paper, we propose a new method to refine the torsional angles of a protein to remake/reconstruct its structures with more resemblance to its original structure. This approach could be used to improve the accuracy of the rotamer libraries and/or to extract information from the Protein Data Bank to facilitate solution of the PSP problem.
The inherent fault tolerance of artificial neural networks (ANNs) is usually assumed, but several... more The inherent fault tolerance of artificial neural networks (ANNs) is usually assumed, but several authors have claimed that ANNs are not always fault tolerant and have demonstrated the need to evaluate their robustness by quantitative measures. For this purpose, various alternatives have been proposed. In this paper we show the direct relation between the mean square error (MSE) and the statistical sensitivity to weight deviations, defining a measure of tolerance based on statistical sentitivity that we have called Mean Square Sensitivity (MSS); this allows us to predict accurately the degradation of the MSE when the weight values change and so constitutes a useful parameter for choosing between different configurations of MLPs. The experimental results obtained for different MLPs are shown and demonstrate the validity of our model.
Feature selection is an important and active issue in clustering and classification problems. By ... more Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multiobjective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.
An analog CMOS implementation of a Cellular Neural Network with modifiable cloning templates is p... more An analog CMOS implementation of a Cellular Neural Network with modifiable cloning templates is proposed. One of the most important difficulties to hardware implement neural networks is their topological complexity which implies a high number of interconnections among cells. Nevertheless, as in cellular neural network each cell is only connected to its neighbour cells, their hardware implementation is easier. Even
Annals of Multicore and Gpu Programming, Mar 31, 2014
The use of auto-tuning techniques in a matrix multiplication routine for hybrid CPU+GPU platforms... more The use of auto-tuning techniques in a matrix multiplication routine for hybrid CPU+GPU platforms is analyzed. Basic models of the execution time of the hybrid routine and information obtained during its installation are used to optimize the execution time with a balanced assignation of the computation to the computing components in the heterogeneous system. Satisfactory results are obtained, with experimental execution times close to the lowest achievable.
International Journal of High Performance Systems Architecture, 2008
... in some scheduling problems, such as those appearing in parallel computing servers or in the ... more ... in some scheduling problems, such as those appearing in parallel computing servers or in the semiconductor industry (Gupta and Sivakumar, 2006), the ... the thermodynamic genetic algorithms (Mori et al., 1998) and the use of niching techniques (Cedeno and Vemuri, 1997) for ...
In Gigabit class networks, the physical transmission time is small compared to the time required ... more In Gigabit class networks, the physical transmission time is small compared to the time required to process the TCP/IP protocol stack. Thus, the usefulness of lightweight protocols that reduce the communication software overhead is even higher, as the performance demands shift to the network interface hardware and/or software. The communication protocol CLIC has recently been proposed for efficient communication in
Abstract. In this work we compare some of the freely available par-allel Toolboxes for Matlab, wh... more Abstract. In this work we compare some of the freely available par-allel Toolboxes for Matlab, which differ in purpose and implementa-tion details: while DP-Toolbox and MultiMatlab offer a higher-level parallel environment, the goals of PVMTB and MPITB, developed by ...
Proceedings of the 6th International Work Conference on Artificial and Natural Neural Networks Bio Inspired Applications of Connectionism Part Ii, 2001
Previous works have demonstrated that Mean Squared Sensitivity (MSS) constitutes a good aproximat... more Previous works have demonstrated that Mean Squared Sensitivity (MSS) constitutes a good aproximation to the performance degradation of a MLP affected by perturbations. In the present paper, the expression of MSS for Radial Basis Function Neural Networks affected by additive noise in their inputs is obtained. This expression is experimentally validated, allowing us to propose MSS as an effective measurement
In this paper we analyze parallel processing in clusters of computers of an improved prediction m... more In this paper we analyze parallel processing in clusters of computers of an improved prediction method based on RBF neural networks and matrix decomposition techniques (SVD and QR-cp). Parallel processing is required because of the extensive computation found in sucn an hybrid prediction technique, the reward being better prediction performance and also less network complexity. We discuss two alternatives of
Although there exist several approaches to rapidly solving the N-body problem, and a diversity of... more Although there exist several approaches to rapidly solving the N-body problem, and a diversity of implementation strategies, the performance tradeo s of the various strategies with respect to problem-speci c data distributions is poorly understood on a parallel computer. We present a synthetic workload model and a simulator that enables us to evaluate the performance tradeo s encountered in implementing particle methods on MIMD computers. These results can be used to evaluate designs early on in the implementation process.
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Papers by Julio Ortega