144 citations · 228 across the 13 of their papers we have counts for
13 papers
Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
Moo K. Chung, Shih-Gu Huang, Ian C. Carroll +2
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. O…
Revisiting convolutional neural network on graphs with polynomial approximations of Laplace-Beltrami spectral filtering
Shih-Gu Huang, Moo K. Chung, Anqi Qiu +1
This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace-Beltrami CNN (LB-CNN) by replacing the graph Lapla…
Fast Mesh Data Augmentation via Chebyshev Polynomial of Spectral filtering
Shih-Gu Huang, Moo K. Chung, Anqi Qiu +1
Deep neural networks have recently been recognized as one of the powerful learning techniques in computer vision and medical image analysis. Trained deep neural networks need to be…
Statistical Analysis of Dynamic Functional Brain Networks in Twins
Moo K. Chung, Shih-Gu Huang, Tananun Songdechakraiwut +2
Recent studies have shown that functional brain brainwork is dynamic even during rest. A common approach to modeling the brain network in whole brain resting-state fMRI is to compu…
Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis
Shih-Gu Huang, Ilwoo Lyu, Anqi Qiu +1
Heat diffusion has been widely used in brain imaging for surface fairing, mesh regularization and cortical data smoothing. Motivated by diffusion wavelets and convolutional neural…
Statistical Model for Dynamically-Changing Correlation Matrices with Application to Brain Connectivity
Shih-Gu Huang, S. Balqis Samdin, Chee-Ming Ting +2
Background: Recent studies have indicated that functional connectivity is dynamic even during rest. A common approach to modeling the dynamic functional connectivity in whole-brain…
Rapid Acceleration of the Permutation Test via Slow Random Walks in the Permutation Group
Moo K. Chung, Yixian Wang, Shih-Gu Huang +1
The permutation test is an often used test procedure in brain imaging. Unfortunately, generating every possible permutation for large-scale brain image datasets such as HCP and ADN…
Fast and accurate computation of normalized Bargmann transform
Soo-Chang Pei, Shih-Gu Huang
The linear canonical transform (LCT) was extended to complex-valued parameters, called complex LCT, to describe the complex amplitude propagation through lossy or lossless optical…
Reversible Joint Hilbert and Linear Canonical Transform Without Distortion
Soo-Chang Pei, Shih-Gu Huang
Generalized analytic signal associated with the linear canonical transform (LCT) was proposed recently by Fu and Li ["Generalized Analytic Signal Associated With Linear Canonical T…
Fast Discrete Linear Canonical Transform Based on CM-CC-CM Decomposition and FFT
Soo-Chang Pei, Shih-Gu Huang
In this paper, a discrete LCT (DLCT) irrelevant to the sampling periods and without oversampling operation is developed. This DLCT is based on the well-known CM-CC-CM decomposition…
Two-dimensional nonseparable discrete linear canonical transform based on CM-CC-CM-CC decomposition
Soo-Chang Pei, Shih-Gu Huang
As a generalization of the two-dimensional Fourier transform (2D FT) and 2D fractional Fourier transform, the 2D nonseparable linear canonical transform (2D NsLCT) is useful in opt…
Discrete Gyrator Transforms: Computational Algorithms and Applications
Soo-Chang Pei, Shih-Gu Huang, Jian-Jiun Ding
As an extension of the 2D fractional Fourier transform (FRFT) and a special case of the 2D linear canonical transform (LCT), the gyrator transform was introduced to produce rotatio…
STFT with Adaptive Window Width Based on the Chirp Rate
Soo-Chang Pei, Shih-Gu Huang
An adaptive time-frequency representation (TFR) with higher energy concentration usually requires higher complexity. Recently, a low-complexity adaptive short-time Fourier transfor…