Juan M. Sánchez-Pérez

Detecting skin in face recognition systems: A colour spaces study

Skin colour detection is a technique very used in most of face detectors to find faces in images or videos. However, there is not a common opinion about which colour space is the best choice to do this task. Therefore, the motivation for our study is to discover which colour model is the best option to build an efficient face detector which can be embedded in a functional face recognition system. We have studied 10 of the most common and used colour spaces doing different comparisons among them, in order to know which one is the best option for human skin colour detection. In concrete, we have studied the models: RGB, CMY, YUV, YIQ, YPbPr, YCbCr, YCgCr, YDbDr, HSV-or HSI-and CIE-XYZ. To make the comparison among them, we have used 15 truth images where the skin colour of a face is clearly separated from the rest of the image (background, eyes, lips, hair, etc.). Thus we can compare at level pixel each colour model, doing a detailed study of each format. We present the final conclusions comparing different results, such as: right detections, false positives and false negatives for each colour space. According to the obtained results, the most appropriate colour spaces for skin colour detection are HSV model (the winner in our study), and the models YCgCr and YDbDr.

A new methodology to implement the AES algorithm using partial and dynamic reconfiguration

Wireless networks are very widespread nowadays, so secure and fast cryptographic algorithms are needed. The most widely used security technology in wireless computer networks is WPA2, which employs the AES algorithm, a powerful and robust cryptographic algorithm. In order not to degrade the Quality of Service (QoS) of these networks, the encryption speed is very important, for which reason we have implemented the AES algorithm in an FPGA, taking advantage of the hardware characteristics and the software-like flexibility of these devices. In this paper, we propose our own methodology for doing an FPGA-based AES implementation. This methodology combines the use of three hardware languages (Handel-C, VHDL and JBits) with partial and dynamic reconfiguration, and a pipelined and parallel implementation. The same design methodology could be extended to other cryptographic algorithms. Thanks to all these improvements our pipelined and parallel implementation reaches a very high throughput (24.922Gb/s) and the best efficiency (throughput/area ratio) of all the related works found in the literature (6.97Mb/s per slice).

Experimental Study of Multipopulation Parallel Genetic Programming

The parallel execution of several populations in evolutionary algorithms has usually given good results. Nevertheless, researchers have to date drawn conflicting conclusions when using some of the parallel genetic programming models. One aspect of the conflict is population size, since published GP works do not agree about whether to use large or small populations. This paper presents an experimental study of a number of common GP test problems. Via our experiments, we discovered that an optimal range of values exists. This assists us in our choice of population size and in the selection of an appropriate parallel genetic programming model. Finding efficient parameters helps us to speed up our search for solutions. At the same time, it allows us to locate features that are common to parallel genetic programming and the classic genetic programming technique.

Differential evolution for solving the mobile location management

In this work we present two new approaches to solve the location management problem, respectively, based on the location areas and the reporting cells strategies. The location management problem corresponds to the management of the network configuration with the objective of minimizing the costs involved. We use the differential evolution algorithm to find the best configuration for the location areas and the reporting cells strategies, which principally considers the location update and paging costs. With this work we want to define the best values to the differential evolution configuration, using test networks and also realistic networks, as well as compare our results with the ones obtained by other authors. These two new approaches applied to this problem have given us very good results, when compared with those obtained by other authors.

Genetic algorithms using parallelism and FPGAs: the TSP as case study

In this work a detailed study about the implementation of genetic algorithms (GAs) using parallelism and field programmable gate arrays (FPGAs) is presented. Concretely, we use the traveling salesman problem (TSP) as case study. First at all, the TSP is described as well as the GA used for solving it. Afterwards, we present the hardware implementation of this algorithm. We detail 13 different hardware versions, searching that each new version improves the previous one. Many of these improvements are based on the use of parallelism techniques. Finally, the found results are shown and analysed: hardware/software comparisons, resource use, operation frequency, etc. We conclude indicating the parallelism techniques that obtain better results and stating FPGA implementation is better when the problem size increases or when better solutions (nearer to the optimum) must be found.

Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem

The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.

Multiobjective Metaheuristics for Traffic Grooming in Optical Networks

Currently, wavelength division multiplexing technology is widely used for exploiting the huge bandwidth of optical networks. It allows simultaneous transmission of traffic on many nonoverlapping channels (wavelengths). These channels support traffic demands in the gigabits per second (Gb/s) range; however, since the majority of devices or applications only require a bandwidth of megabits per second (Mb/s), this is a waste of bandwidth. This problem is efficiently solved by multiplexing a number of low-speed traffic demands (Mb/s) onto a high-speed wavelength channel (Gb/s). This is known as the traffic grooming problem. Since traffic grooming is an NP-hard problem, in this paper, we propose two novel multiobjective evolutionary algorithms for solving it. The selected algorithms are multiobjective variants of the standard differential evolution (DEPT) and variable neighborhood search. With the aim of ensuring the performance of our proposals, we have made comparisons with the well-known fast Nondominated Sort Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm 2, and other approaches published in the literature. After performing diverse comparisons, we can conclude that our novel approaches obtain promising results, highlighting in particular the performance of the DEPT algorithm.

A Comparative Study on Multiobjective Swarm Intelligence for the Routing and Wavelength Assignment Problem

The future of designing optical networks is focused on the wavelength division multiplexing (WDM) technology. This technology divides the huge bandwidth of an optical fiber into different wavelengths, providing different available channels per link of fiber. However, when it is necessary to establish a set of demands, a problem comes up. This problem is known as a routing and wavelength assignment (RWA) problem. Depending on the traffic pattern, two varieties of a RWA problem have been considered in the literature: static and dynamic. In this paper, we present a comparative study among three multiobjective evolutionary algorithms (MOEAs) based on swarm intelligence to solve the RWA problem in real-world optical networks. Artificial bee colony (ABC) algorithm, gravitational search algorithm (GSA), and firefly algorithm (FA) are the selected evolutionary algorithms, but are adapted to multiobjective domain (MO-ABC, MO-GSA, and MO-FA, respectively). In order to prove the goodness of the swarm proposals, we have compared them with a standard MOEA: fast nondominated sorting genetic algorithm. Finally, we present a comparison among the metaheuristics based on swarm intelligence and several techniques published in the literature, coming to the conclusion that swarm intelligence is very suitable to solve the RWA problem, and presumably that it may obtain such quality results not only in diverse telecommunication optimization problems, but also in other engineering optimization problems.

Predicting DNA Motifs by Using Evolutionary Multiobjective Optimization

Bioinformatics and computational biology include researchers from many areas: biochemists, physicists, mathematicians, and engineers. The scale of the problems that are discussed ranges from small molecules to complex systems, where many organisms coexist. However, among all these issues, we can highlight genomics, which studies the genomes of microorganisms, plants, and animals. Predicting common patterns, i.e., motifs, in a set of deoxyribonucleic acid (DNA) sequences is one of the important sequence analysis problems, and it has not yet been resolved in an efficient manner. In this study, we study the application of evolutionary multiobjective optimization to solve the motif discovery problem, applied to the specific task of discovering novel transcription factor binding sites in DNA sequences. For this, we have designed, adapted, configured, and evaluated several types of multiobjective metaheuristics. After a detailed study, the results indicate that these metaheuristics are appropriate for discovering motifs. To find good approximations to the Pareto front, we use the hypervolume indicator, which has been successfully integrated into evolutionary algorithms. Besides the hypervolume indicator, we also use the coverage relation to ensure: Which is the best Pareto front? New results have been obtained, which significantly improve those published in previous research works.

A Differential Evolution with Pareto Tournaments for solving the Routing and Wavelength Assignment problem in WDM networks

The technology based on Wavelength Division Multiplexing (WDM) applied to optical networks has resolved the bandwidth waste in this kind of networks. WDM divides the bandwidth of an optical fiber in different wavelengths that can be used by electronic devices to send and receive data without bottlenecks. Another problem appears when the necessity of choice of the path and the wavelengths to interconnect a set of source-destination pairs comes up. This problem is known as Routing and Wavelength Assignment (RWA) and there are two types, depending on the demands: Static-RWA and Dynamic-RWA. In this paper we present a multiobjective evolutionary algorithm to solve this problem. We choose the Differential Evolution (DE), incorporating the concept of Pareto Tournament (DEPT). To determine the parameters of the algorithm, we used two real different topologies (the first is a topology from USA, NSF network; and the second is a topology from Japan, NTT network) and six sets of source-destination pairs for each topology, that is, a total of twelve instances. After all experiments, we can conclude that with this multiobjective evolutionary algorithm, we have obtained better results than the other approaches published in the literature.