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Pattern users are faced with difficulties in understanding when and how to use the increasing number of available design patterns. This is mainly due to the inherent ambiguity in the existing means (textual and graphical) of describing them. Hence, there is a need to introduce formalism in order to describe them accurately and allow rigorous reasoning about them. The main problem of existing formal specification languages for design patterns is their lack of completeness. This is mainly because they tend to focus on specifying either the structural or the behavioral aspect of design patterns but not both of them. We propose a simple yet Balanced Pattern Specification Language (BPSL) that is aimed to achieve equilibrium by specifying both aspects of design patterns. BPSL combines two subsets of logic, one from First Order Logic (FOL) and one from Temporal Logic of Actions (TLA).

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Several contributions have shown that fusion of decisions or scores obtained from various single-modal biometrics verification systems often enhances the overall system performance. A recent approach of multimodal biometric systems with the use of single sensor has received significant attention among researchers. In this paper, a combination of hand geometry and palmprint verification system is being developed. This system uses a scanner as sole sensor to obtain the hands images. First, the hand geometry verification system performs the feature extraction to obtain the geometrical information of the fingers and palm. Second, the region of interest (ROI) is detected and cropped by palmprint verification system. This ROI acts as the base for palmprint feature extraction by using Linear Discriminant Analysis (LDA). Lastly, the matching scores of the two individual classifiers is fused by several fusion algorithms namely sum rule, weighted sum rule and Support Vector Machine (SVM). The results of the fusion algorithms are being compared with the outcomes of the individual palm and hand geometry classifiers. We are able to show that fusion using SVM with Radial Basis Function (RBF) kernel has outperformed other combined and individual classifiers.