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For OAM-based communications and target detection purposes, the OAM-generating method based on planar antenna and the concentric-ring array is proposed, which can collimate the main-lobe direction and suppress the side lobes simultaneously. The total radiation pattern is derived firstly and the feeding coefficient for each ring is optimized utilizing the genetic algorithm (GA). Subsequently, the planar...
Functional network analysis based on matrix decomposition/factorization methods including ICA and dictionary learning models have become a popular approach in fMRI study. Yet it is still a challenging issue in interpreting the result networks because of the inter-subject variability and image noises, thus in many cases, manual inspection on the obtained networks is needed. Aiming to provide a fast...
For the electromagnetic (EM) vortex imaging purposes, the sidelobe suppression and the beam collimation method in the generation of orbital angular momentum (OAM) beams is proposed. Based on the concentric ring array, the objective function for the generic algorithm is defined to calculate the signal amplitude for each ring. Comprehensive simulations are conducted to validate the effectiveness of...
In natural stimulus fMRI during video watching, it is natural to postulate that a human participant's attention system would respond to shot changes of the video stream. However, quantitative assessment of the relationship between the functional activities of the attention system and the dynamics of video shot changes has been rarely explored yet. This paper presents a novel framework for modeling...
Dynamic functional interaction has received much attention recently in the field of neuroimaging. Past studies reveal that the dynamics of functional interactions only exists in part of brain. In this paper, a novel Bayesian inference model is developed to bi-partition the brain regions into dynamic/stable sub networks and to simultaneously segment the temporal sequence of dynamic network into several...
In the human brain mapping field, virtually most existing fMRI activation detection methods, such as the general linear model (GLM), have assumed that the fMRI signal magnitude should follow the alternations of baseline and task periods. However, our extensive observation shows that different brain regions or networks exhibit quite dissimilar temporal activation patterns. Inspired by this observation,...
Multiple recent neuroimaging studies have demonstrated that the human brain's function undergoes remarkable temporal dynamics. However, quantitative characterization and modeling of such functional dynamics have been rarely explored. To fill this gap, we presents a novel Bayesian connectivity change point model (BCCPM), to analyze the joint probabilities among the nodes of brain networks between different...
Based on the structural connectomes constructed from diffusion tensor imaging (DTI) data, we present a novel framework to discover functional connectomics signatures from resting-state fMRI (R-fMRI) data for the characterization of brain conditions. First, by applying a sliding time window approach, the brain states represented by functional connectomes were automatically divided into temporal quasi-stable...
Human brain function has been widely believed as a network behavior. However, most previous activation detection methods in the task-based fMRI field were voxel-based, instead of network-based. For instance, the general linear model (GLM) has been widely used to fit the external stimulus curve with the fMRI BOLD signal of each voxel. In this paper, we present a novel network-based activation detection...
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