A multiobjective approach based on artificial bee colony for the static routing and wavelength assignment problem

Nowadays, the most promising technology for designing optical networks is the wavelength division multiplexing. This technique divides the huge bandwidth of an optical fiber link into different wavelengths, providing different available channels per link. However, a problem comes up when it is necessary to interconnect a set of traffic demands. This problem is known as routing and wavelength assignment problem, and due to its complexity (NP-hard problem) it is very suitable for being solved using evolutionary computation. The selected heuristics is the artificial bee colony algorithm, an heuristics based on the behavior of honeybee foraging for nectar. Therefore, we have applied multiobjective optimization to solve the static routing and wavelength assignment problem, and adapted this algorithm to the multiobjective context. New results have been obtained that significantly improve those published in previous researches.     

Fault tolerant embedded systems design by multi-objective optimization

The design of fault tolerant systems is gaining importance in large domains of embedded applications where design constrains are as important as reliability. New software techniques, based on selective application of redundancy, have shown remarkable fault coverage with reduced costs and overheads. However, the large number of different solutions provided by these techniques, and the costly process to assess their reliability, make the design space exploration a very difficult and time-consuming task. This paper proposes the integration of a multi-objective optimization tool with a software hardening environment to perform an automatic design space exploration in the search for the best trade-offs between reliability, cost, and performance. The first tool is commanded by a genetic algorithm which can simultaneously fulfill many design goals thanks to the use of the NSGA-II multi-objective algorithm. The second is a compiler-based infrastructure that automatically produces selective protected (hardened) versions of the software and generates accurate overhead reports and fault coverage estimations. The advantages of our proposal are illustrated by means of a complex and detailed case study involving a typical embedded application, the AES (Advanced Encryption Standard).     

Perspectives on control-relevant identification through the use of interactive tools

System identification is an essential part of control design. Control engineers must devote substantial effort to identification issues in order to obtain suitable models for closed-loop control. Control-relevant identification seeks to both simplify the modelling task and improve the usefulness of the model by taking into account controller requirements during system identification. The advantages of this methodology can be better understood and appreciated through the interactive software tool described in this paper. The Interactive Tool for Control Relevant Identification (ITCRI) comprehensively captures the control-relevant identification process for the monovariable problem, from input design to closed-loop control, depicting these stages simultaneously and interactively in one screen. By simultaneously displaying both open- and closed-loop responses of the estimated models, ITCRI enables the user to readily assess how design variable choices during identification and control performance requirements impact model error and ultimately, closed-loop performance. Moreover, the work presents several examples which the aim to illustrate the tool and the considerations that arise when control requirements are taken into account during the identification stage.     

A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery

When solving a wide range of complex scenarios of a given optimization problem, it is very difficult, if not impossible, to develop a single technique or algorithm that is able to solve all of them adequately. In this case, it is necessary to combine several algorithms by applying the most appropriate one in each case. Parallel computing can be used to improve the quality of the solutions obtained in a cooperative algorithms model. Exchanging information between parallel cooperative algorithms will alter their behavior in terms of solution searching, and it may be more effective than a sequential metaheuristic. For demonstrating this, a parallel cooperative team of four multiobjective evolutionary algorithms based on OpenMP is proposed for solving different scenarios of the Motif Discovery Problem (MDP), which is an important real-world problem in the biological domain. As we will see, the results show that the application of a properly configured parallel cooperative team achieves high quality solutions when solving the addressed problem, improving those achieved by the algorithms executed independently for a much longer time.     

A multi-start tabu search method for a single-machine scheduling problem with periodic maintenance and sequence-dependent set-up times

In this paper we study a problem of sequencing jobs in a machine with programmed preventive maintenance and sequence-dependent set-up times. The problem combines two NP-hard problems, so we propose a heuristic method for solving it, which hybridizes multi-start strategies with Tabu Search. We compare our method with the only published metaheuristic algorithm for this problem on a set of 420 instances. The comparison favors the method developed in this work, showing that is able to find high quality solutions in very short computational times.     

Improving spectral results in a GTS context

This paper shows the advantages of post-processing spectral classifications in a Geographical Information System (GIS) context in order to improve results. A maximum-likelihood algorithm was used to classify(both supervised and non-supervised) a Landsat TM sub-image in Central Mexico. Purely spectral processing yielded poor accuracy results, showing the spectral limitation to distinguish classes; as a consequence, merging classes was necessary in order to increase accuracy (from less than 55 to 82 per cent). GIS rules were finally applied based on ancillary data (terrain mapping units and elevation data) improving the final accuracy to 88.2 and 83.0 per cent (supervised and non-supervised classifications).     

Retraction of “Using ergonomic digital human modeling in evaluation of workplace design and prevention of workrelated musculoskeletal disorders onboard small fishing vessel”

The following article from Human Factors and Ergonomics in Manufacturing & Service Industries, “Using ergonomic digital human modeling in evaluation of workplace design and prevention of workrelated musculoskeletal disorders onboard small fishing vessel,” by Enrique Álvarez‐Casado, Bing Zhang, Sonia Tello Sandoval and Mondelo Pedro, published online on October 11, 2011 in Wiley Online Library ( www.onlinelibrary.wiley.com ), has been retracted by agreement between the authors, the journal Editors‐in‐Chief, Waldemar Karwowski and Gavriel Salvendy, and Publisher Wiley Periodicals, Inc. The retraction has been agreed as these articles were mistakenly published after being rejected.     

Authentication of Individuals using Hand Geometry Biometrics: A Neural Network Approach

Biometric based systems for individual authentication are increasingly becoming indispensable for protecting life and property. They provide ways for uniquely and reliably authenticating people, and are difficult to counterfeit. Biometric based authenticity systems are currently used in governmental, commercial and public sectors. However, these systems can be expensive to put in place and often impose physical constraint to the users. This paper introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system using neural networks. The proposed approach followed to construct this system consists of an acquisition device, a pre-processing stage, and a neural network based classifier. One of the novelties of this work comprises on the introduction of hand geometry’s related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Another novelty of this research comprises on the use of error correction codes to enhance the level of performance of the neural network model. A dataset made of scanned images of the right hand of fifty different people was created for this study. Identification rates and Detection Cost Function (DCF) values obtained with the system were evaluated. Several strategies for coding the outputs of the neural networks were studied. Experimental results show that, when using Error Correction Output Codes (ECOC), up to 100% identification rates and 0% DCF can be obtained. For comparison purposes, results are also given for the Support Vector Machine method.