Design of asymmetrical RF and microwave bandpass filters by computer optimization

This paper presents an optimization-based approach to the design of asymmetrical filter structures having the maximum number of return- or insertion-loss ripples in the passband such as those based upon Chebyshev function prototypes. The proposed approach. has the following advantages over the general purpose optimization techniques adopted previously such as: less frequency sampling is required, optimization is carried out with respect to the Chebyshev (or minimax) criterion, the problem of local minima does not arise, and optimization is usually only required for the passband. When implemented around an accurate circuit simulation, the method can be used to include all the effects of discontinuities, junctions, fringing, etc. to reduce the amount of tuning required in the final filter. The design of asymmetrical ridged-waveguide bandpass filters is considered as an example. Measurements on a fabricated filter confirm the accuracy of the design procedure.

An Ambiguity Aversion Framework of Security Game under Ambiguities

Security is a critical concern around the world. Since resources for security are always limited, lots of interest have arisen in using game theory to handle security resource allocation problems. However, most of the existing work does not address adequately how a defender chooses his optimal strategy in a game with absent, inaccurate, uncertain, and even ambiguous strategy profiles' payoffs. To address this issue, we propose a general framework of security games under ambiguities based on Dempster-Shafer theory and the ambiguity aversion principle of minimax regret. Then, we reveal some properties of this framework. Also, we present two methods to reduce the influence of complete ignorance. Our investigation shows that this new framework is better in handling security resource allocation problems under ambiguities.

Game-­theoretic Resource Allocation with Real-­time Probabilistic Surveillance Information

Game-theoretic security resource allocation problems have generated significant interest in the area of designing and developing security systems. These approaches traditionally utilize the Stackelberg game model for security resource scheduling in order to improve the protection of critical assets. The basic assumption in Stackelberg games is that a defender will act first, then an attacker will choose their best response after observing the defender’s strategy commitment (e.g., protecting a specific asset). Thus, it requires an attacker’s full or partial observation of a defender’s strategy. This assumption is unrealistic in real-time threat recognition and prevention. In this paper, we propose a new solution concept (i.e., a method to predict how a game will be played) for deriving the defender’s optimal strategy based on the principle of acceptable costs of minimax regret. Moreover, we demonstrate the advantages of this solution concept by analyzing its properties.