Retrieving ocean surface current by 4-D variational assimilation of sea surface temperature images

In this article we propose a new method to estimate ocean mesoscale structures of the surface current velocity by processing sea surface satellite images. Assuming that the intensity level can be described by a transport-diffusion equation, the proposed approach is based on variational assimilation of image observations within a simple transport-diffusion model. This approach permits to retrieve the current velocity field from a sequence of satellite images. Results of processing synthetic data and real NOAA-AVHRR satellite images are presented and commented.     

High Q 2D-length extension mode resonators for potential time–frequency applications

Microsystem Technologies - This paper presents recent advances on two dimensional length-extension mode (2D-LEM) quartz resonators providing high quality (Q) factor on resonances at a few MHz. The...     

Coherent addition of adjacent lasers by forked eigenstate operation

Forked laser eigenstates are shown to provide a powerful tool both to phase lock spatially separated laser oscillators and to add their powers coherently into a TEM(00) output beam. Coherent addition of the powers extracted from two fiber-coupled diode-pumped Nd:YAG channels is theoretically and experimentally demonstrated. Pure TEM(00) oscillation is obtained with a 20% optical-to-optical conversion efficiency. The coherence of the two-propagation-axis laser is proved, and single-frequency operation is demonstrated. The scalability of the scheme is discussed.     

涉密文档违规处理系统在保密部门的应用

计算机网络技术的高速发展大幅提高了人们的工作效率和信息获取量,但是提供便利的同时也隐藏着安全隐患。各地保密部门始终高度重视涉密文件、机密数据及个人隐私的安全防护,以防患于未然。因此针对堵截涉密信息泄露扩散的最佳时机研制出监测涉密文档监控系统。系统可通过安装在外网客户端上的监控程序,对客户机上文档的编辑工作进行监控,将编辑的文档标题自动发送给监视控制台或保密局等主管单位,并对编辑的文档自动进行涉密分析,一旦发现该文档可能包含有涉密信息,即刻屏蔽该机的网络连接,并立即报警。     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

Architectural Methodology Based on Intentional Configuration of Behaviors

Intelligence has been an object of study for a long time. Different architectures try to capture and reproduce these aspects into artificial systems (or agents), but there is still no agreement on how to integrate them into a general framework. With this objective in mind, we propose an architectural methodology based on the idea of intentional configuration of behaviors. Behavior‐producing modules are used as basic control components that are selected and modified dynamically according to the intentions of the agent. These intentions are influenced by the situation perceived, knowledge about the world, and internal variables that monitor the state of the agent. The architectural methodology preserves the emergence of functionality associated with the behavior‐based paradigm in the more abstract levels involved in configuring the behaviors. Validation of this architecture is done using a simulated world for mobile robots, in which the agent must deal with various goals such as managing its energy and its well‐being, finding targets, and acquiring knowledge about its environment. Fuzzy logic, a topologic map learning algorithm, and activation variables with a propagation mechanism are used to implement the architecture for this agent.     

Performance lower bounds in stochastic robust and adaptive control

The fundamental issues of capability of robust and adaptive control in dealing with uncertainty are investigated in stochastic systems. It is revealed that to capture the intrinsic limitations of adaptive control, it is necessary to use sup_t types of transient and persistent performance, rather than lim sup_t types which reflect only asymptotic behavior of a system. For clarity and technical tractability, a simple first-order linear time-varying system is employed as a vehicle to explore performance lower bounds of robust and adaptive control. Optimal performances of nominal, robust and adaptive control are explicitly derived and their implications are discussed in an information framework. An adaptive strategy is scrutinized for its achievable performance bounds. The results indicate that intimate interaction and inherent conflict between identification and control result in a certain performance lower bound which does not approach the nominal performance even when the system varies very slowly. Explicit lower bounds are obtained when disturbances are either normally or uniformly distributed     

Preparation of acidic or bifunctional catalysts by means of straightforward calcination of as-synthesized [Ga]-ZSM-5 zeolites obtained from alkali-free media. Propane aromatization

Propane aromatization (530°C, 1 atm) was used as a reaction model to evaluate the effect of the calcination temperature on the catalytic properties of an as-synthesized [Ga_1.3]-ZSM-5 zeolite obtained from alkali-free media and calcined at two different temperatures: 530°C (C-530) and 750°C (C-750). Results show that in spite of its lower acidity, C-750 is more active and selective toward aromatics than C-530. This is probably due to the fact that at higher temperature the decomposition of organic compounds used during the zeolite synthesis is accompanied by a partial degalliation of the zeolitic support leading to the production of a bifunctional xGa₂O₃ /H-[Ga_y-ZSM-5(2x+y=1.3)catalyst.