Abstract

Abstract
Over the years, air transport has increased gradually. This increase can cause difficulties in air traffic management, reducing its effectiveness. The use of intelligent agents in air traffic management seems to be a promising approach to overcome this difficulty. Thus this paper proposes the creation of a multi-agent system for autonomous air traffic management. Several concepts and proposals suggested by other authors about this topic are used in this implementation in an attempt to create a system based on the strengths of each work. This work is also part of a larger project, related to the coordination of intelligent agents in the execution of joint missions. The connection between the two systems is the management of the aircrafts that perform the missions. With this system, it is possible to manage the landings and departures of the aircrafts in an airport, manage the vehicles moving on the airport and even prevent collisions between aircrafts moving in the covered airspace.

Abstract
In this paper, a meta-learning approach is proposed to suggest the best optimization technique(s) for instances of the Traveling Salesman Problem. The problem is represented by a dataset where each example is associated with one of the instances. Thus, each example contains characteristics of an instance and is labeled with the name of the technique(s) that obtained the best solution for this instance. Since the best solution can be obtained by more than one technique, an example may have more than one label. Therefore, the meta-learning problem is addressed as a multi-label classification problem. Experiments with 535 instances of the problem were performed to evaluate the proposed approach, which produced promising results. © 2010 IEEE.

Abstract
This paper presents the design of a single feed multiband printed monopole antenna array using the 2nd generation of the Minkowski fractal geometry. The multiband operation is achieved by a suitable chosen of the size and iteration of the fractal geometry, which is optimized using the EM simulation tool Ansoft HFSS. During this work, it is found that adding a rectangular stub on the ground plane, the impedance match of the antenna can be improved with little influence on the original resonant frequencies. This finding has been confirmed by both simulation and measurement results. Meanwhile, the antenna array on a PDA size substrate was also designed and fabricated. The experimental results show that it can operate from 2.32 to 2.49 and from 5.1 to 5.88 GHz, which covers the required bands for IEEE 802.11a/b/g (2.41-2.48 GHz, 5.15-5.35 GHz and 5.725-5.875 GHz) applications. Measurements indicate that the maximum gain of this printed monopole array can reach 2.3 dBi at lower band and 5.6 dBi at upper band. The simulation results show that the radiation efficiency of this antenna array is 86% at 2.4 GHz, 82% at 5.2 GHz and 89% at 5.8 GHz. ©2010 IEEE.

Abstract

Abstract
Nowadays, microarray has become a fairly common tool for simultaneously inspecting the behavior of thousands of genes. Researchers have employed this technique to understand various biological phenomena. One straightforward use of such technology is identifying the class membership of the tissue samples based on their gene expression profiles. This task has been handled by a number of computational methods. In this paper, we provide a comprehensive evaluation of 7 commonly used algorithms over 6S publicly available gene expression datasets. The focus of the study was on comparing the performance of the algorithms in an efficient and sound manner, supporting the prospective users on how to proceed to choose the most adequate classification approach according to their investigation goals.