Abstract

Abstract
The genetic algorithm (GA) is an optimization metaheuristic that relies on the evolution of a set of solutions (population) according to genetically inspired transformations. In the variant of this technique called cellular GA, the evolution is done separately for subgroups of solutions. This paper describes a hardware framework capable of efficiently supporting custom accelerators for this metaheuristic. This approach builds a regular array of problem-specific processing elements (PEs), which perform the genetic evolution, connected to shared memories holding the local subpopulations. To assist the design of the custom PEs, a methodology based on highlevel synthesis from C++ descriptions is used. The proposed architecture was applied to a spectrum allocation problem in cognitive radio networks. For an array of 5x5 PEs in a Virtex-6 FPGA, the results show a minimum speedup of 22x compared to a software version running on a PC and a speedup near 2000x over a MicroBlaze soft processor.

Abstract
A new smart structure based on fiber Bragg gratings (FBGs) embedded into composite laminates for temperature and strain simultaneous measurement has been designed and experimentally tested. Two holes have been drilled at preset locations in the composite plate to create different strain sensitivities at different locations. The proposed design has been compared to three reference sensing heads also based on embedding FBGs into composite materials. Experimental results agree remarkably well with mechanical simulations and validate all the tested designs for the temperature-strain discrimination. Based on the same principle, another sensing head with a long single FBG embedded has also been designed and experimentally tested, obtaining temperature independent strain measurement.

Abstract
Optical fibre Bragg grating (FBG) sensors are now quite established and widely used in strain measurements of composites. However, insufficient understanding of the limitations of the embedment and measuring techniques often lead to inaccurate and inconclusive results. In this study, a novel method to improve the reliability and accuracy of the strain measurements on unidirectional composites using embedded FBG sensors was successfully developed. Using a carbon/epoxy prepreg system, test specimens were manufactured with longitudinally embedded FBG sensors. The combined behaviour of the sensors and the host material was characterized and a calibration rule (correction factor) was determined for the chosen material. The consistency of the results with both theoretical and empirical assumptions suggests that the proposed method is applicable to a wide range of FBG sensors and host materials.

Abstract
Classification is one of the most important tasks of machine learning. Although the most well studied model is the two-class problem, in many scenarios there is the opportunity to label critical items for manual revision, instead of trying to automatically classify every item. In this paper we tailor a paradigm initially proposed for the classification of ordinal data to address the classification problem with reject option. The technique reduces the problem of classifying with reject option to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Finally, the framework is extended to multiclass ordinal data with reject option. An experimental study with synthetic and real datasets verifies the usefulness of the proposed approach.

Abstract
Decision making in complex, multi-agent and dynamic environments such as disaster spaces is a challenging problem in Artificial Intelligence. This research paper aims at developing distributed coordination and cooperation method based on reinforcement learning to enable team of homogeneous, autonomous fire fighter agents, with similar skills to accomplish complex task allocation, with emphasis on firefighting tasks in disaster space. The main contribution is applying reinforcement learning to solve the bottleneck caused by dynamicity and variety of conditions in such situations as well as improving the distributed coordination of fire fighter agent's to extinguish fires within a disaster zone. The proposed method increases the speed of learning; it has very low memory usage and has a good scalability and robustness in the case that the number of agents and complexity of task increases. The effectiveness of the proposed method is shown through simulation results. © 2013 Springer-Verlag.