The applications of error entropy (EE) are sometimes limited because its shape cannot be
flexibly adjusted by the default Gaussian kernel function to adapt to noise variation and thus …

Image reconstruction enhanced by regularizers, eg, to enforce sparsity, low rank or
smoothness priors on images, has many successful applications in vision tasks such as …

Distributed learning based on the divide and conquer approach is a powerful tool for big
data processing. We introduce a distributed kernel gradient descent algorithm for the …

Quantum error mitigation (QEM) is a class of promising techniques capable of reducing the
computational error of variational quantum algorithms tailored for current noisy intermediate …

The moving sofa problem, introduced by Leo Moser in 1966, seeks to determine the
maximal area of a 2D shape that can navigate an L-shaped corridor of unit width. Joseph …

This paper is devoted to presenting optimality conditions for the sufficiency of the maximum
principle for multiobjective optimal control problems with nonsmooth data. Such conditions …

Received signal strength (RSS)-based localization techniques have attracted a lot of interest
as they are easy to implement and do not require any localization-specific hardware …

Signal restoration is an important constrained optimization problem with significant
applications in various domains. Although non-convex constrained optimization problems …

提出了一种基于最大稳定极值区域(MSERs) 改进的亚像素级自动鲁棒配准算法.
该算法对灰度图像序列进行了MSERs 特征提取与椭圆拟合. 利用特征极线方位来代替该特征邻 …

Quantum computers have the potential of providing unprecedented computational power for
solving problems that are known to be difficult for classical computers, including integer …