Krishnan Chemmangat

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A new method for gradient-based optimization of electromagnetic systems using parametric sensitivity macromodels is presented. Parametric macromodels accurately describe the parameterized frequency behavior of electromagnetic systems and their corresponding parameterized sensitivity responses with respect to design parameters, such as layout and substrate(More)
Introduction: Efficient design of electromagnetic (EM) systems often requires expensive simulations using EM solvers which normally provide high accuracy at a significant cost in terms of memory storage and computing time. Alternatively, scalable macromodels can be used, which approximate the complex behavior of EM systems, characterized by frequency and(More)
3-D electromagnetic methods are fundamental design tools for complex high-speed systems. Among the integral equation-based techniques, the Partial Element Equivalent Circuit (PEEC) method has received a special attention in interconnect modeling, where mixed electromagnetic/circuit problems need to be solved. Retardation effects and the resulting delays(More)
This paper presents a design optimization approach for electromagnetic systems using parametric macromodels. The parametric macro-models are generated using an efficient sequential sampling of the design space of interest which ensures optimal sample selection for a required level of accuracy. The proposed method is validated on a microwave notch filter(More)
Large scale growth of wireless networks and the scarcity of the electromagnetic spectrum are imposing more interference to the wireless terminals which jeopardize the Quality of Service offered to the end users. In order to address this kind of performance degradation, this paper proposes a novel experimentally verified cognitive decision engine which aims(More)
This paper investigates the use of surrogate-based optimization to optimize the behavioral response of broadband microwave filters. The proposed method makes use of an efficient infill criterion (called expected improvement) that sequentially samples the response of the device at well-chosen regions in the design space. Based on these data samples,(More)
Categorisation of huge amount of data on the multimedia platform is a crucial task. In this work, we propose a novel approach to address the subtle problem of selfie detection for image database segregation on the web, given rapid rise in the number of selfies being clicked. A Convolutional Neural Network (CNN) is modeled to learn a synergy feature in the(More)
This letter presents a parametric macromodeling technique which accurately describes the parameterized frequency behavior of electromagnetic systems and their corresponding parameterized sensitivity responses with respect to design parameters. The technique is based on the interpolation of a set of state-space matrices with a proper choice of the(More)
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