Agent decision-making in open mixed networks

Computer systems increasingly carry out tasks in mixed networks, that is in group settings in which they interact both with other computer systems and with people. Participants in these heterogeneous human-computer groups vary in their capabilities, goals, and strategies; they may cooperate, collaborate, or compete. The presence of people in mixed networks raises challenges for the design and the evaluation of decision-making strategies for computer agents. This paper describes several new decision-making models that represent, learn and adapt to various social attributes that influence people's decision-making and presents a novel approach to evaluating such models. It identifies a range of social attributes in an open-network setting that influence people's decision-making and thus affect the performance of computer-agent strategies, and establishes the importance of learning and adaptation to the success of such strategies. The settings vary in the capabilities, goals, and strategies that people bring into their interactions. The studies deploy a configurable system called Colored Trails (CT) that generates a family of games. CT is an abstract, conceptually simple but highly versatile game in which players negotiate and exchange resources to enable them to achieve their individual or group goals. It provides a realistic analogue to multi-agent task domains, while not requiring extensive domain modeling. It is less abstract than payoff matrices, and people exhibit less strategic and more helpful behavior in CT than in the identical payoff matrix decision-making context. By not requiring extensive domain modeling, CT enables agent researchers to focus their attention on strategy design, and it provides an environment in which the influence of social factors can be better isolated and studied.     

Two-stage cascaded classification approach based on genetic fuzzy learning for speech/music discrimination

Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents a robust and effective approach for speech/music discrimination, which relies on a two-stage cascaded classification scheme. The cascaded classification scheme is composed of a statistical pattern recognition classifier followed by a genetic fuzzy system. For the first stage of the classification scheme, other widely used classifiers, such as neural networks and support vector machines, have also been considered in order to assess the robustness of the proposed classification scheme. Comparison with well-proven signal features is also performed. In this work, the most commonly used genetic learning algorithms (Michigan and Pittsburgh) have been evaluated in the proposed two-stage classification scheme. The genetic fuzzy system gives rise to an improvement of about 4% in the classification accuracy rate. Experimental results show the good performance of the proposed approach with a classification accuracy rate of about 97% for the best trial.     

Design of a novel monopulse antenna system using the time‐modulated antenna arrays

A novel monopulse antenna for a tracking radar system is proposed using the time modulated antenna array technique. The time modulation technique applied in the antenna arrays overcomes the usual need for compromise between sum and difference beams and avoids the need for implementation of two separate feed networks. The time modulated antenna array also relaxes the error tolerance of the tracking accuracy from the amplitude and phase errors, due to the new design freedom of “time” which can be controlled precisely and quickly. Moreover, the position of the target can be recovered by two reflected echoes from one previously transmitted pulse, thus the tracking errors resulting from the excitation errors and receiver noise in conventional antenna arrays can be greatly alleviated. Numerical results show that with the aid of time modulation technique, one sum beam and two difference beams can be simultaneously obtained either for the boresight or off‐boresight target tracking. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Efficient analysis of wireless communication antennas using an accurate [Z] matrix interpolation technique

An accurate impedance matrix interpolation technique based on the surface integral equation (SIE) is presented for the analysis of wireless communication antennas over wide frequency bands. The first‐order derivative of the impedance matrix at the internal frequency is considered in the cubic polynomial‐based interpolation scheme, thus the novel impedance matrix interpolation scheme will provide high accuracy and high efficiency over a frequency band. To demonstrate the efficiency and accuracy of the proposed method, numerical results for planar inverted F antennas (PIFA) and a wideband E‐shaped patch antenna are presented. Good agreement among the interpolation results, exact MoM solutions, finite element method (FEM) solutions, and measured data is observed over the bandwidth. Besides, dimensions of the feeding probe are also studied to investigate their effect on the input impedance and radiation patterns. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

A modelling framework to support farmers in designing agricultural production systems

Given the new challenges confronting world agriculture, innovative production systems need to be designed at the farm level. As experiments are not easy to conduct at this level, modelling is required to evaluate ex-ante the multiple impacts of proposed innovations. A conceptual framework based on three sub-systems (biophysical, technical, and decisional) interacting within the farm is proposed to guide this process. The advantages and limitations of the framework are illustrated with three examples. The first describes a conceptual, rule-based model representing management decisions for the cultivation of salad in southern France. The second uses a computerized model of the three sub-systems to study irrigation management strategies on farms producing hay in southern France. The third presents a spreadsheet model designed to assist dairy farms in Morocco to define new livestock and forage system strategies. The lessons learned concern the modelling process, the fit between the type of model and its use, and the advantage of involving stakeholders in the design process of tools and innovations.     

Of robot ants and elephants: A computational comparison

In the robotics community, there exist implicit assumptions concerning the computational capabilities of robots. Two computational classes of robots emerge as focal points of recent research: robot ants and robot elephants. Ants have poor memory and communication capabilities, but are able to communicate using pheromones, in effect, turning their work area into a shared memory. By comparison, elephants are computationally stronger, have large memory, and are equipped with strong sensing and communication capabilities. Unfortunately, not much is known about the relation between the capabilities of these models in terms of the tasks they can address. In this paper, we present formal models of both ants and elephants, and investigate if one dominates the other. We present two algorithms: AntEater, which allows elephant robots to execute ant algorithms and ElephantGun, which converts elephant algorithms-specified as Turing machines-into ant algorithms. By exploring the computational capabilities of these algorithms, we reach interesting conclusions regarding the computational power of both models.     

A framework for a video analysis tool for suspicious event detection

This paper proposes a framework to aid video analysts in detecting suspicious activity within the tremendous amounts of video data that exists in today’s world of omnipresent surveillance video. Ideas and techniques for closing the semantic gap between low-level machine readable features of video data and high-level events seen by a human observer are discussed. An evaluation of the event classification and detection technique is presented and a future experiment to refine this technique is proposed. These experiments are used as a lead to a discussion on the most optimal machine learning algorithm to learn the event representation scheme proposed in this paper.

Virtio network paravirtualization driver: Implementation and performance of a de-facto standard

One of the techniques used to improve I/O performance of virtual machines is paravirtualization. Paravirtualized devices are intended to reduce the performance overhead on full virtualization where all hardware devices are emulated. The interface of a paravirtualized device is not identical to that of the underlying hardware. The OS of the virtual guest machine must be ported in order to use a paravirtualized device. In this paper, the network virtualization done by the Kernel-based Virtual Machine (KVM) is described. The KVM model is different from other Virtual Machines Monitors (VMMs) because the KVM is a Linux kernel model and it depends on hardware support. In this work, the overhead of using such virtual networks is been measured. A paravirtualized model by using the virtio [38] network driver is described, and some performance results of web benchmark on the two models are presented.     

基于亲水表面处理的GaAs/GaN晶片直接键合

对晶片进行亲水表面处理,在氮气保护下500℃热处理10min,成功实现GaAs与GaN晶片的直接键合,键合质量较好.扫描电子显微镜观测结果表明,键合界面没有空洞.光致发光谱观测结果表明,键合工艺对晶体内部结构的影响很小.可见光透射谱测试结果表明,键合界面具有良好的透光特性.GaAs与GaN晶片直接键合的成功,为实现GaAs和GaN材料的集成提供了实验依据.     

试述冶炼防爆安全

本文阐述冶炼防爆的重要性、迫切性和艰巨性，剖析炉料爆炸物的爆炸机理，对冶炼防爆工作提出一些见解和措施。