Sparsity has been a great issue in the design of FIR filter. The objective of a Sparse FIR filter design is to reduce the implementation complexity as the number of nonzero-coefficients is reduced. By increasing the number of zero-valued coefficients, the implementation of the filter becomes simple as the additions and multiplications corresponding to zero-valued coefficients are omitted. The p...

Filter methods realize a projection from superposed quantum state onto target state, which can be efficient if two states have sufficient overlap. Here we propose Gaussian filter (QGF) with the operator being function of system Hamiltonian. A hybrid quantum-classical algorithm feasible on near-term computers is developed, implements as linear combination Hamiltonian evolution at various times. ...

An iterative method is introduced for solving noisy, ill-conditioned inverse problems. Analysis of the semi-convergence behavior identifies three error components iteration error, noise error, and initial guess error. A derived expression explains how the three errors are related to each other relative to the number of iterations. The Standard Tikhonov regularization method is just the first it...

Methods for `1-type regularization have been widely used in Gaussian graphical model selection tasks to encourage sparse structures. However, often we would like to include more structural information than mere sparsity. In this work, we focus on learning so-called “scale-free” models, a common feature that appears in many real-work networks. We replace the `1 regularization with a power law re...

in order to obtain maximum information from magnetic and gravity anomaly maps, application of an edgedetection method is necessary. in this regard two commonly used methods are derivative filters and local phase filters. in this paper, a matlab code is expanded to combine an analytic signal filter and a tilt angle filter as a new edge detection filter called asta filter. this method was demonst...

A robust filter is designed for uncertain discrete time models. The filter is based on a regularized solution and guarantees minimum state error variance. Simulation results confirm its superior performance over other robust filter designs. keywords: regularization, least-squares, robust filter, regularization parameter, parametric uncertainty.

Ultrasound image deconvolution has been widely investigated in the literature. Among the existing approaches, the most common are based on l2-norm regularization (or Tikhonov optimization) or the well-known Wiener filtering. However, the success of the Wiener filter in practical situations largely depends on the choice of the regularization hyperparameter. An appropriate choice is necessary to ...