Low-Noise Electronic System Design
Low-Noise Electronic System Design
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
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We present a novel concept for in vivo sensing of glucose using metamaterials in combination with automatic learning systems. In detail, we use the plasmonic analogue of electromagnetically induced transparency (EIT) as sensor and evaluate the acquired data with support vector machines. The metamaterial can be integrated into a contact lens. This sensor changes its optical properties such as reflectivity upon the ambient glucose concentration, which allows for in situ measurements in the eye. We demonstrate that estimation errors below 2% at physiological concentrations are possible using simulations of the optical properties of the metamaterial in combination with an appropriate electrical circuitry and signal processing scheme. In the future, functionalization of our sensor with hydrogel will allow for a glucose-specific detection which is insensitive to other tear liquid substances providing both excellent selectivity and sensitivity.