1,721,051 research outputs found
Accurate vectorial finite element mode solver for magneto-optic and anisotropic waveguides
No full textIn this work, a dielectric waveguide mode solver is presented considering a general nonreciprocal permittivity tensor. The proposed method allows us to investigate important cases of practical interest in the field of integrated optics, such as magneto-optical isolators and anisotropic waveguides. Unlike the earlier developed mode solver, our approach allows for the precise computation of both forward and backward propagating modes in the nonreciprocal case, ensuring high accuracy and computational efficiency. As a result, the nonreciprocal loss/phase shift can be directly computed, avoiding the use of the perturbation method. To compute the electromagnetic modes, the Rayleigh-Ritz functional is derived for the non-self adjoint case, it is discretized using the node-based finite element method and the penalty function is added to remove the spurious solutions. The resulting quadratic eigenvalue problem is linearized and solved in terms of the propagation constant for a given frequency (i.e., γ–formulation). The main benefits of this formulation are that it avoids the time-consuming iterations and preserves the matrix sparsity. Finally, the method is used to study two examples of integrated optical isolators based on nonreciprocal phase shift and nonreciprocal loss effect, respectively. The developed method is then compared with the perturbation approach and its simplified formulation based on semivectorial approximation
Cryogenic optical data link for superconducting circuits
No full textAn optical fibre-fed superconducting electro-optic modulator with gigahertz bandwidth and attojoule per bit electric power consumption offers a fast, efficient means to connect superconducting circuits to the room temperature environment
Modelling of ring resonators with magneto-optic materials using the finite element method
No full textRelational space classification for malaria diagnosis
No full textWe present a study of sera derived from the malaria medical analysis of 189 subjects. The feature space is 18-dimensional and each serum is represented by a binary number. The subjects are divided into three different groups: no malaria, clinical malaria and asymptomatic subjects. We studied the main characteristics of the data and we selected 7 out of the 18 antigens as the most
important for group discrimination. We propose a novel representation of the data in the so-called relational space, where the coded data of pairs of patients are plotted. We are able to separate the groups with 58% accuracy, about 15% points better than several conventional methods with which we compare our results
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