The distributed parameters of the transmission lines have the significant impact to the signal propagation. In the conventional method of the distributed parameter extraction,the discontinuity of inverse trigonometric...The distributed parameters of the transmission lines have the significant impact to the signal propagation. In the conventional method of the distributed parameter extraction,the discontinuity of inverse trigonometric or hyperbolic can arise the problem about phase ambiguity which causes significant errors for transmission models. A difference iteration method（ DIM） is proposed for extracting distributed parameters of high frequency transmission line structure in order to overcome the phase ambiguity in the conventional method（ CM）. The formulations of the proposed method are first derived for two-conductor and multi-conductor lines. Then the validation is performed for the models of micro-strip transmission line. Numerical results demonstrate that the proposed DIM can solve the problem about the phase ambiguity and the extracted distributed parameters are accurate and efficient for a wide range of the frequencies of interest and line lengths.展开更多
This study develops an optimized finite difference iterative （OFDI） scheme for the two-dimensional （2D） viscoelastic wave equation. The OFDI scheme is obtained using a proper orthogonal decomposition （POD） metho...This study develops an optimized finite difference iterative （OFDI） scheme for the two-dimensional （2D） viscoelastic wave equation. The OFDI scheme is obtained using a proper orthogonal decomposition （POD） method. It has sufficiently high accuracy with very few unknowns for the 2D viscoelastic wave equation. Existence, stability, and convergence of the OFDI solutions are analyzed. Numerical simulations verify efficiency and feasibility of the proposed scheme.展开更多
An algorithmic framework, based on the difference of convex functions algorithm （D- CA）, is proposed for minimizing a class of concave sparse metrics for compressed sensing problems. The resulting algorithm iterates...An algorithmic framework, based on the difference of convex functions algorithm （D- CA）, is proposed for minimizing a class of concave sparse metrics for compressed sensing problems. The resulting algorithm iterates a sequence ofl1 minimization problems. An exact sparse recovery theory is established to show that the proposed framework always improves on the basis pursuit （l1 minimization） and inherits robustness from it. Numerical examples on success rates of sparse solution recovery illustrate further that, unlike most existing non-convex compressed sensing solvers in the literature, our method always out- performs basis pursuit, no matter how ill-conditioned the measurement matrix is. Moreover, the iterative l1 （ILl） algorithm lead by a wide margin the state-of-the-art algorithms on l1/2 and logarithimic minimizations in the strongly coherent （highly ill-conditioned） regime, despite the same objective functions. Last but not least, in the application of magnetic resonance imaging （MRI）, IL1 algorithm easily recovers the phantom image with just 7 line projections.展开更多
基金supported by the National Natural Science Foundation of China(61201082)the Youth Science and Engineering Planning Project of Communication University of China(3132018XNG1817)
文摘The distributed parameters of the transmission lines have the significant impact to the signal propagation. In the conventional method of the distributed parameter extraction,the discontinuity of inverse trigonometric or hyperbolic can arise the problem about phase ambiguity which causes significant errors for transmission models. A difference iteration method（ DIM） is proposed for extracting distributed parameters of high frequency transmission line structure in order to overcome the phase ambiguity in the conventional method（ CM）. The formulations of the proposed method are first derived for two-conductor and multi-conductor lines. Then the validation is performed for the models of micro-strip transmission line. Numerical results demonstrate that the proposed DIM can solve the problem about the phase ambiguity and the extracted distributed parameters are accurate and efficient for a wide range of the frequencies of interest and line lengths.
基金Project supported by the National Natural Science Foundation of China(No.11671106)the Fundamental Research Funds for the Central Universities(No.2016MS33)
文摘This study develops an optimized finite difference iterative （OFDI） scheme for the two-dimensional （2D） viscoelastic wave equation. The OFDI scheme is obtained using a proper orthogonal decomposition （POD） method. It has sufficiently high accuracy with very few unknowns for the 2D viscoelastic wave equation. Existence, stability, and convergence of the OFDI solutions are analyzed. Numerical simulations verify efficiency and feasibility of the proposed scheme.
文摘An algorithmic framework, based on the difference of convex functions algorithm （D- CA）, is proposed for minimizing a class of concave sparse metrics for compressed sensing problems. The resulting algorithm iterates a sequence ofl1 minimization problems. An exact sparse recovery theory is established to show that the proposed framework always improves on the basis pursuit （l1 minimization） and inherits robustness from it. Numerical examples on success rates of sparse solution recovery illustrate further that, unlike most existing non-convex compressed sensing solvers in the literature, our method always out- performs basis pursuit, no matter how ill-conditioned the measurement matrix is. Moreover, the iterative l1 （ILl） algorithm lead by a wide margin the state-of-the-art algorithms on l1/2 and logarithimic minimizations in the strongly coherent （highly ill-conditioned） regime, despite the same objective functions. Last but not least, in the application of magnetic resonance imaging （MRI）, IL1 algorithm easily recovers the phantom image with just 7 line projections.
