In this study, we investigate a non-orthogonal multiple access (NOMA)-assisted dual-hop underwater wireless optical communication-radio frequency (UWOC-RF) network. Specifically, an optical source forwards a NOMA signal to a marine relay, which first deploys either the heterodyne detection or intensity modulation direct detection to process the received signal, and then encodes and re-transmits a superimposed signal to two terrestrial destinations. Assuming the UWOC channel is subject to a unified mixture Exponential-Generalized Gamma (mEGG) distribution and the RF channels experience Nakagami-${m}$fading, the analytical as well as asymptotic expression for outage probability is obtained in the form of the Meijer’s$G$-function. Moreover, the average achievable rate is also investigated by utilizing the bivariate Fox’s$H$-function. In addition, the accuracy of those expressions is verified with Monte-Carlo simulations.

In this work, a stable and accurate model order reduction method for large-scale partial element equivalent circuit (PEEC) models with many delays is proposed. The method reduces the dimension of the original model by interpolating the original transfer function. The interpolation points are iteratively selected using a greedy algorithm. An efficient error estimator for the reduced transfer function makes the greedy algorithm very successful in both, properly selecting the interpolation points and tightly measuring the error of the reduced transfer function. The resulting reduced-order model is accurate both in the frequency and in the time domain. Numerical tests have shown that the reduced-order model successfully filters the instability behavior of the original model and exhibits stability over a large time interval.

In this paper, a wavelet based approach is proposed for the model order reduction of linear circuits in time domain. Compared with Chebyshev reduction method, the wavelet reduction approach can achieve smaller reduced order circuits with very high accuracy, especially for those circuits with strong singularities. Furthermore, to compute the basis function coefficient vectors, a fast Sylvester equation solver is proposed, which works more than one or two orders faster than the vector equation solver employed by Chebyshev reduction method. The proposed wavelet method is also compared with the frequency domain model reduction method, which may loose accuracy in time domain. Both theoretical analysis and experiment results have demonstrated the high speed and high accuracy of the proposed method.

With the development of IC technology, it becomes urgent to investigate model reduction method for interconnects with process variations. In this paper, a one-shot projection algorithm (OPM) is proposed to generate a projection matrix that is independent of statistically varying parameters. As a result, construction of the reduced system can be decoupled with the Monte Carlo analysis in either frequency domain or time domain. Therefore, without loss of accuracy, OPM can obtain a reduced system in much less CPU time compared with the previous perturbation scheme. Numerical results have demonstrated the advantages of the proposed OPM

Compact thermal models are often used during joint electrothermal simulation of microelectromechanical systems (MEMS) and circuits. Formal model reduction allows generation of compact thermal models automatically from high-dimensional finite-element models. Unfortunately, it requires fixing a film coefficient employed to describe the convection boundary conditions. As a result, compact models produced by model reduction do not comply with the requirements of being boundary condition independent. In the present paper, the authors suggest an approach of successive series expansion with respect to the film coefficient as well as to the frequency during model reduction that allows to overcome the problem and keep the film coefficient as a symbolic parameter in the reduced model. The approach is justified with a numerical example of electrothermal simulation of a microthruster unit.

The variational analysis has been employed in for order reduction of weakly nonlinear systems. For a relatively strong nonlinear system, this method will mostly lose efficiency because of the exponentially increased number of inputs in higher order variational equations caused by the individual reduction process of the variational systems. Moreover, the inexact inputs into the higher order variational equations indispensably introduce extra errors in the order reduction process. Inspired by the variational analysis, we propose a direct model order reduction method. The order of the approximate polynomial system of the original nonlinear system is directly reduced by one project space. The proposed direct reduction technique can easily avoid the errors brought by inexact inputs and the exponentially increased inputs. We show theoretically and experimentally that the proposed method can achieve much more accurate reduced system with smaller order size than the conventional variational equation order reduction method.