Breaking the search space symmetry in partitioning problems: An application to the graph coloring problem

Many problems consist in splitting a set of objects into different groups so that each group verifies some properties. In practice, a partitioning is often encoded by an array mapping each object to its group numbering. In fact, the group number of an object does not really matter, and one can simply rename each group to obtain a new encoding. That is what we call the symmetry of the search space in a partitioning problem. This property may be prejudicial for optimization methods such as evolutionary algorithms (EA) which require some diversity during the search.     

Tree-decomposition based heuristics for the two-dimensional bin packing problem with conflicts

This paper deals with the two-dimensional bin packing problem with conflicts (BPC-2D). Given a finite set of rectangular items, an unlimited number of rectangular bins and a conflict graph, the goal is to find a conflict-free packing of the items minimizing the number of bins used. In this paper, we propose a new framework based on a tree-decomposition for solving this problem. It proceeds by decomposing a BPC-2D instance into subproblems to be solved independently. Applying this decomposition method is not straightforward, since merging partial solutions is hard. Several heuristic strategies are proposed to make an effective use of the decomposition. Computational experiments show the practical effectiveness of our approach.     

Multi‐objective optimization using metaheuristics: non‐standard algorithms

In recent years, the application of metaheuristic techniques to solve multi‐objective optimization problems has become an active research area. Solving this kind of problems involves obtaining a set of Pareto‐optimal solutions in such a way that the corresponding Pareto front fulfils the requirements of convergence to the true Pareto front and uniform diversity. Most of the studies on metaheuristics for multi‐objective optimization are focused on Evolutionary Algorithms, and some of the state‐of‐the‐art techniques belong this class of algorithms. Our goal in this paper is to study open research lines related to metaheuristics but focusing on less explored areas to provide new perspectives to those researchers interested in multi‐objective optimization. In particular, we focus on non‐evolutionary metaheuristics, hybrid multi‐objective metaheuristics, parallel multi‐objective optimization and multi‐objective optimization under uncertainty. We analyze these issues and discuss open research lines.     

Image-based control for cable-based robots

Some human robot interactive applications involved in tele-robotics, remote supervisory and unmanned systems require specific capabilities. This demand has promoted various interactive modes and high-level control techniques such as tele-manipulation, speech, vision, gesture, etc. Among these interactive modes, the image based control which is often named point and click control has proven to be the most appropriate one that offers multiple advantages. This mode consists of only and simply pointing in an appearing object of an image received from a remote site, to convert this click into a robot command towards the corresponding location in the real world space. This mode is suitable for remote applications, frees the human operator from being involved into the loop enabling him/her to use commands in the sense of click and forget. This paper presents, firstly, the design and the realization of an experimental planar cable-based robot constituted of four cables. Secondly, it presents the design and the implementation of a high-level image-based control. Some typical experiments which have been performed prove the simplicity and the effectiveness of the image-based control. Moreover, it opens perspectives for new applications with cable-based robots, particularity for rehabilitation applications.     

Sensitivity and specificity based multiobjective approach for feature selection: Application to cancer diagnosis

The study of the sensitivity and the specificity of a classification test constitute a powerful kind of analysis since it provides specialists with very detailed information useful for cancer diagnosis. In this work, we propose the use of a multiobjective genetic algorithm for gene selection of Microarray datasets. This algorithm performs gene selection from the point of view of the sensitivity and the specificity, both used as quality indicators of the classification test applied to the previously selected genes. In this algorithm, the classification task is accomplished by Support Vector Machines; in addition a 10-Fold Cross-Validation is applied to the resulting subsets. The emerging behavior of all these techniques used together is noticeable, since this approach is able to offer, in an original and easy way, a wide range of accurate solutions to professionals in this area. The effectiveness of this approach is proved on public cancer datasets by working out new and promising results. A comparative analysis of our approach using two and three objectives, and with other existing algorithms, suggest that our proposal is highly appropriate for solving this problem.     

Multi-environmental cooperative parallel metaheuristics for solving dynamic optimization problems

In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each sub-problem when a change happens, dealing always with one single environment at each time instant. In this paper, we propose a multi-environmental cooperative model for parallel meta-heuristics to tackle dynamic optimization problems. It consists in dealing with different environments at the same time, using different algorithms that exchange information coming from these environments. A parallel multi-swarm approach is presented for solving the Dynamic Vehicle Routing Problem. The effectiveness of the proposed approach is tested on a well-known set of benchmarks, and compared with other meta-heuristics from the literature. Experimental results show that our multi-environmental approach outperforms conventional meta-heuristics on this problem.     

Inhibition of small G proteins by clostridium sordellii lethal toxin activates cdc2 and MAP kinase in Xenopus oocytes

The lethal toxin (LT) from Clostridium sordellii is a glucosyltransferase that modifies and inhibits small G proteins of the Ras family, Ras and Rap, as well as Rac proteins. LT induces cdc2 kinase activation and germinal vesicle breakdown (GVBD) when microinjected into full-grown Xenopus oocytes. Toxin B from Clostridium difficile, that glucosylates and inactivates Rac proteins, does not induce cdc2 activation, indicating that proteins of the Ras family, Ras and/or Rap, negatively regulate cdc2 kinase activation in Xenopus oocyte. In oocyte extracts, LT catalyzes the incorporation of [14C]glucose into a group of proteins of 23 kDa and into one protein of 27 kDa. The 23-kDa proteins are recognized by anti-Rap1 and anti-Rap2 antibodies, whereas the 27-kDa protein is recognized by several anti-Ras antibodies and probably corresponds to K-Ras. Microinjection of LT into oocytes together with UDP-[14C]glucose results in a glucosylation pattern similar to the in vitro glucosylation, indicating that the 23- and 27-kDa proteins are in vivo substrates of LT. In vivo time-course analysis reveals that the 27-kDa protein glucosylation is completed within 2 h, well before cdc2 kinase activation, whereas the 23-kDa proteins are partially glucosylated at GVBD. This observation suggests that the 27-kDa Ras protein could be the in vivo target of LT allowing cdc2 kinase activation. Interestingly, inactivation of Ras proteins does not prevent the phosphorylation of c-Raf1 and the activation of MAP kinase that occurs normally around GVBD.     

A 4-91-GHz traveling-wave amplifier in a standard 0.12-/spl mu/m SOI CMOS microprocessor technology

This paper presents five-stage and seven-stage traveling-wave amplifiers (TWA) in a 0.12-/spl mu/m SOI CMOS technology. The five-stage TWA has a 4-91-GHz bandpass frequency with a gain of 5 dB. The seven-stage TWA has a 5-86-GHz bandpass frequency with a gain of 9 dB. The seven-stage TWA has a measured 18-GHz noise figure, output 1-dB compression point, and output third-order intercept point of 5.5 dB, 10 dBm, and 15.5 dBm, respectively. The power consumption is 90 and 130 mW for the five-stage and seven-stage TWA, respectively, at a voltage power supply of 2.6 V. The chips occupy an area of less than 0.82 and 1 mm for the five-stage and seven-stage TWA, respectively.     

Spatial and Temporal Variations of the Indoor Wireless EHF Channel

This paper reports line-of-sight propagation measurements performed at 37.2GHz between two terminals in an empty room in order to investigate the spatialand temporal fadings in terms of antenna directivity and human motion,respectively. The empirical data base consists of recordings of CW envelopefading waveforms with a fixed transmitter and either moving or stationaryreceiver. The reported results include amplitude fading distributions,propagation loss gradients, level crossing rates and fade durationsstatistics. These results can be used in the modeling, design reliableoperation of local multi-point communication systems.     

Study of Optical Properties of Nanocrystalline Zinc Phosphide Thin films

Silicon - Zinc phosphide (Zn3P2), a II–V group semiconductor compound has long been considered as a very interesting and suitable candidate for several applications especially in...