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Coplanar waveguide (CPW)-fed vertically standing bow-tie antennas for 2.45 GHz ISM band applications for inter-/intraboard wireless communication are presented. The vertical antenna scheme provides wave propagation in the horizontal direction parallel to the substrate, allowing effective in-plane board-to-board communication. The monopole bow-tie antenna is implemented in an air-lifted fashion, resulting in high efficiency, broad bandwidth, and small footprint compared with the conventional surface mounted patch type antenna. The vertical bow-tie antenna with a height of 22.4 mm and a flare angle of 43° shows a 10-dB bandwidth of 20.4%, a footprint of ∼1 mm2, and a height reduction of 33% compared with a cylindrical monopole counterpart. A numerical analysis has been performed between 1 and 4 GHz and the transmission characteristics using two identical antennas have been performed at 2.45 GHz with a network analyzer. The numerical results are in good agreement with experimental data. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 266–269, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23988 [ABSTRACT FROM AUTHOR]

Bayesian networks is an emerging tool for a wide range of risk management applications, one of which is the modeling of operational risk. This comes at a time when changes in the supervision of financial institutions have resulted in increased scrutiny on the risk management of banks and insurance companies, thus giving the industry an impetus to measure and manage operational risk. The more established methods for risk quantification are linear models such as time series models, econometric models, empirical actuarial models, and extreme value theory. Due to data limitations and complex interaction between operational risk variables, various nonlinear methods have been proposed, one of which is the focus of this article: Bayesian networks. Using an idealized example of a fictitious on line business, we construct a Bayesian network that models various risk factors and their combination into an overall loss distribution. Using this model, we show how established Bayesian network methodology can be applied to: (1) form posterior marginal distributions of variables based on evidence, (2) simulate scenarios, (3) update the parameters of the model using data, and (4) quantify in real-time how well the model predictions compare to actual data. A specific example of Bayesian networks application to operational risk in an insurance setting is then suggested. [ABSTRACT FROM AUTHOR]