Juan José Escobar

Juan José Escobar
University of Granada | UGR · Department of Computer Architecture and Technology

Ph.D.

About

28
Publications
2,623
Reads
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107
Citations
Citations since 2016
28 Research Items
107 Citations
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2016201720182019202020212022051015202530
Introduction
My research interests include code optimization, energy-efficient parallel computing, and workload balancing strategies on heterogeneous and distributed systems, specially in issues related to evolutionary algorithms and multi-objective feature selection problems.

Publications

Publications (28)
Article
Full-text available
The demand for electricity related to Information and Communications Technologies is constantly growing and significantly contributes to the increase in global greenhouse gas emissions. To reduce this harmful growth, it is necessary to address this problem from different perspectives. Among these is changing the computing scale, such as migrating,...
Chapter
The search for a dyslexia diagnosis based on exclusively objective methods is currently a challenging task. Usually, this disorder is analyzed by means of behavioral tests prone to errors due to their subjective nature; e.g. the subject’s mood while doing the test can affect the results. Understanding the brain processes involved is key to proporti...
Chapter
Feature Selection (FS) is a contemporary challenge for the scientific community since new methods are being discovered and new forms of algorithmic design are required. In this sense, classic bio-inspired swarm intelligence algorithms can be explored to get a new suitable feature selection application where simple agents interact locally with each...
Chapter
Several methods have been developed to extract information from electroencephalograms (EEG). One of them is Phase-Amplitude Coupling (PAC) which is a type of Cross-Frequency Coupling (CFC) method, consisting in measure the synchronization of phase and amplitude for the different EEG bands and electrodes. This provides information regarding brain ar...
Chapter
The performance of neural networks has granted deep learning a place at the forefront of machine learning in the last decade. Although these models are computationally intensive, their advantage is recognized in a wide array of applications. Nonetheless, the large amount of learnable parameters in neural networks can be a disadvantage for small and...
Chapter
The Electroencephalography discipline studies a type of signals called Electroencephalograms (EEGs), which represent the electrical activity of different parts of the brain. EEGs are composed of a massive number of features that could be used to create an intelligent recognition system. Nevertheless, the high number of available features difficult...
Article
Full-text available
Combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) produces a powerful architecture for video classification problems as spatial–temporal information can be processed simultaneously and effectively. Using transfer learning, this paper presents a comparative study to investigate how temporal information can be utiliz...
Article
Full-text available
Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution profile, have been previously proposed....
Article
Full-text available
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 wa...
Article
Full-text available
Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in E...
Article
Full-text available
Present heterogeneous architectures interconnect nodes including multiple multi-core microprocessors and accelerators that allow different strategies to accelerate the applications and optimize their energy consumption according to the specific power-performance trade-offs. In this paper, a multilevel parallel procedure is proposed to take advantag...
Chapter
Training a deep neural network usually requires a high computational cost. Nowadays, the most common way to carry out this task is through the use of GPUs due to their efficiency implementing complicated algorithms for this kind of tasks. However, training several neural networks, each with different hyperparameters, is still a very heavy task. Typ...
Chapter
Convolutional Neural Networks (CNNs) have been demonstrated to be able to produce the best performance in image classification problems. Recurrent Neural Networks (RNNs) have been utilized to make use of temporal information for time series classification. The main goal of this paper is to examine how temporal information be- tween frame sequences...
Conference Paper
Many data mining applications on bioinformatics and bioengineering require solving problems with different profiles from the point of view of their implicit parallelism. In this context, heterogeneous architectures comprised by interconnected nodes with multiple multi-core microprocessors and accelerators, such as vector processors, Graphics Proces...
Conference Paper
Full-text available
The present trend in the development of computer architectures that offer improvements in both performance and energy efficiency has provided clusters with interconnected nodes including multiple multi-core microprocessors and accelerators. In these so-called heterogeneous computers, the applications can take advantage of different parallelism leve...
Article
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 evolu...
Article
By means of the availability of mechanisms such as Dynamic Voltage and Frequency Scaling (DVFS) and heterogeneous architectures including processors with different power consumption profiles, it is possible to devise scheduling algorithms that are aware of both runtime and energy consumption in parallel programs. In this paper, we propose and evalu...
Article
Full-text available
Many bioinformatics applications that analyse large volumes of high-dimensional data comprise complex problems requiring metaheuristics approaches with different types of implicit parallelism. For example, although functional parallelism would be used to accelerate evolutionary algorithms, the fitness evaluation of the population could imply the co...
Conference Paper
Heterogeneous CPU-GPU platforms include resources to benefit from different kinds of parallelism present in many data mining applications based on evolutionary algorithms that evolve solutions with time-demanding fitness evaluation. This paper describes an evolutionary parallel multi-objective feature selection procedure with subpopulations using t...
Conference Paper
Full-text available
The availability of mechanisms such as dynamic voltage and frequency scaling (DVFS) and heterogeneous architectures including processors with different power consumption profiles allow scheduling algorithms aware of both runtime and energy. In this paper, we propose and evaluate a scheduling strategy that takes into account the relative weights of...
Conference Paper
The realm of HPC systems lies in sharing computational resources efficiently. Their challenge is to turn massively large data into valuable information and meaningful knowledge. To accomplish this, I/O subsystems have to provide scalable bandwidth and capacity in order to deliver on the increasing demand for their requests. Emerging technologies, n...
Conference Paper
The interest on applications that analyse large volumes of high dimensional data has grown recently. Many of these applications related to the so-called Big Data show different implicit parallelism that can benefit from the efficient use, in terms of performance and power consumption, of Graphics Processing Unit (GPU) accelerators. Although the GPU...
Conference Paper
Bioinformatics applications that analyze large volumes of high-dimensional data and present different implicit parallelism can benefit from the efficient use, in performance terms, of heterogeneous parallel architectures, including accelerators such as graphics processing units (GPUs). This paper aims to take advantage of parallel codes to accelera...
Conference Paper
High-dimensional multi-objective optimization will open promising approaches to many applications on bioinformatics once efficient parallel procedures are available. These procedures have to take advantage of the present heterogeneous architectures comprising multicore CPUs and GPUs. In this paper, we describe and analyze several OpenCL implementat...

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Project (1)
Project
This project aims to model high dimensional biomedical data through multiobjective optimization techniques, taking advantage of heterogeneus computing architectures and paying special attention to energy consumption