44 citations · 137 across the 11 of their papers we have counts for
12 papers
BrainCast: A Spatio-Temporal Forecasting Model for Whole-Brain fMRI Time Series Prediction
Yunlong Gao, Jinbo Yang, Li Xiao +5
Functional magnetic resonance imaging (fMRI) enables noninvasive investigation of brain function, while short clinical scan durations, arising from human and non-human factors, usu…
DDS-UDA: Dual-Domain Synergy for Unsupervised Domain Adaptation in Joint Segmentation of Optic Disc and Optic Cup
Yusong Xiao, Yuxuan Wu, Li Xiao +3
Convolutional neural networks (CNNs) have achieved exciting performance in joint segmentation of optic disc and optic cup on single-institution datasets. However, their clinical tr…
Sparse Phase Retrieval with Redundant Dictionary via -Analysis Model
Haiye Huo, Li Xiao
Sparse phase retrieval with redundant dictionary is to reconstruct the signals of interest that are (nearly) sparse in a redundant dictionary or frame from the phaseless measuremen…
Stable recovery of complex dictionary-sparse signals from phaseless measurements
Lianxing Xia, Haiye Huo
Dictionary-sparse phase retrieval, which is also known as phase retrieval with redundant dictionary, aims to reconstruct an original dictionary-sparse signal from its measurements…
Robust Multidimentional Chinese Remainder Theorem for Integer Vector Reconstruction
Li Xiao, Haiye Huo, Xiang-Gen Xia
The problem of robustly reconstructing an integer vector from its erroneous remainders appears in many applications in the field of multidimensional (MD) signal processing. To addr…
Multivariate Dynamical Sampling in and Shift-Invariant Spaces Associated with Linear Canonical Transform
Haiye Huo, Li Xiao
In this paper, we investigate the multivariate dynamical sampling problem in associated with the two-dimensional discrete time non-separable linear canonical tr…
Stable Recovery of Weighted Sparse Signals from Phaseless Measurements via Weighted l1 Minimization
Haiye Huo
The goal of phaseless compressed sensing is to recover an unknown sparse or approximately sparse signal from the magnitude of its measurements. However, it does not take advantage…
Nonuniform Sampling for Random Signals Bandlimited in the Linear Canonical Transform Domain
Haiye Huo, Wenchang Sun
In this paper, we mainly investigate the nonuniform sampling for random signals which are bandlimited in the linear canonical transform (LCT) domain. We show that the nonuniform sa…
Uncertainty Principles Associated with the Offset Linear Canonical Transform
Haiye Huo, Wenchang Sun, Li Xiao
As a time-shifted and frequency-modulated version of the linear canonical transform (LCT), the offset linear canonical transform (OLCT) provides a more general framework of most ex…
A new convolution theorem associated with the linear canonical transform
Haiye Huo
In this paper, we first introduce a new notion of canonical convolution operator, and show that it satisfies the commutative, associative, and distributive properties, which may be…
Uncertainty Principles for the Offset Linear Canonical Transform
Haiye Huo
The offset linear canonical transform (OLCT) provides a more general framework for a number of well known linear integral transforms in signal processing and optics, such as Fourie…
Towards Robustness in Residue Number Systems
Li Xiao, Xiang-Gen Xia, Haiye Huo
The problem of robustly reconstructing a large number from its erroneous remainders with respect to several moduli, namely the robust remaindering problem, may occur in many applic…