Search

The article explains a newly developed noniterative, interpolation-based super-resolution (SR) technique, which uses the sampling theory for deriving the required SR algorithm. The approach is shown to be highly beneficial, as it minimizes the aliasing in the images and is also easily scalable.

We propose a novel hybrid aggressive space-mapping (HASM) optimization algorithm. HASM exploits both the trust-region aggressive space-mapping (TRASM) strategy and direct optimization. Severe differences between the coarse and fine models and nonuniqueness of the parameter extraction procedure may cause the TRASM algorithm to be trapped in local minima. The HASM algorithm is based on a novel lemma that enables smooth switching from the TRASM optimization to direct optimization if the TRASM algorithm is not converging. It also enables switching back from direct optimization to the TRASM algorithm in a smooth way. The uniqueness of the extraction step is improved by utilizing a good starting point. The algorithm does not assume that the final space-mapped design is the true optimal design and is robust against severe misalignment between the coarse and fine models. The examples include a seven-section waveguide transformer, the design of an H-plane waveguide filter, and a double-folded stub filter. Index Terms--CAD, design automation, electromagnetic simulation, iterative methods, microstrip filters, optimization methods, space mapping, waveguide filters.

A study proposed an effective approach for reducing blocking effects in transform image coding. The method requires two projection operators and the theory of projection onto convex sets (POCS) to ensure convergence of the iteration. However, the algorithm that was used is not strongly related to the theory of POCS. An alternative formal basis for establishing convergence and a more flexible theory for explaining the potential applications of the algorithm are presented.

The purpose of this paper is to report on the convergence rates of two iterative matrix solution methods individually and then to combine the two methods into a hybrid scheme to achieve additional convergence rate benefits. One interative matrix solver investigated is the sparse iterative method (SIM) which is a stationary, Jacobi-like solver but uses a sparse and not a banded matrix, with matrix elements corresponding to strong interactions, rather than position in the matrix. In this paper, SIM is modified to include an adaptive relaxation scheme to improve its convergence speed and numerical stability. Another iterative scheme investigated is the nonstationary biconjugate gradient stabilized (BiCGSTAB) method. It is shown that the BiCGSTAB is considerably improved when the method is preconditioned by the sparse matrix used in the SIM method. Finally, a hybrid scheme is proposed which combines both SIM-AR and BiCGSTAB-precon and it is shown that the hybrid gives best results on the problems considered. Examples giving convergence time versus accuracy are presented for two problems: a wire-grid plate, and a wire-grid partial helicopter. Index Terms--Iterative methods, simulation software, sparse matrices.