Space mapping is a recognized method for speeding up electromagnetic (EM) optimization. Existing space-mapping approaches belong to the class of surrogate-based optimization methods. This paper proposes a cognition-driven formulation of space mapping that does not require explicit surrogates. The proposed method is applied to EM-based filter optimization. The new technique utilizes two sets of intermediate feature space parameters, including feature frequency parameters and ripple height parameters. The design variables are mapped to the feature frequency parameters, which are further mapped to the ripple height parameters. By formulating the cognition-driven optimization directly in the feature space, our method increases optimization efficiency and the ability to avoid being trapped in local minima. The technique is suitable for design of filters with equal-ripple responses. It is illustrated by two microwave filter examples.Index Terms-Cognition-driven design, computer-aided design (CAD), electromagnetic (EM) optimization, microwave filters, modeling, space mapping (SM).

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Objective— We sought to identify differentially expressed genes in human in stent stenosis (ISS) to provide insights into the mechanism of disease. Methods and Results— Using representation difference analysis, we examined differential gene expression between cultured normal human medial vascular smooth muscle cells (VSMCs) and cells from primary atherosclerotic plaques or ISS sites. Specific groups of genes were overexpressed in ISS and plaque VSMCs, including cell cycle regulatory proteins and cell matrix and contractile proteins. Differential expression was validated by virtual Northern analysis, reverse transcriptase-polymerase chain reaction, in situ hybridization, and immunohistochemistry. All ISS genes were expressed by normal intima and had even higher expression in primary plaque VSMCs. Conclusions— ISS VSMCs have a stable gene expression profile reflecting an intimal pattern, intermediate between normal medial and primary plaque VSMCs. Differential expression profiling may identify markers of disease that are overexpressed in ISS and also help elucidate the origin of the ISS lesion.