A5100337725- ECG Monitoring and Analysis
- Cardiac electrophysiology and arrhythmias
- Industrial Vision Systems and Defect Detection
- Seismic Imaging and Inversion Techniques
- Lipid metabolism and disorders
- EEG and Brain-Computer Interfaces
- Image Processing Techniques and Applications
- Adipose Tissue and Metabolism
- Neurological Disease Mechanisms and Treatments
- Robot Manipulation and Learning
- Exercise and Physiological Responses
- Analog and Mixed-Signal Circuit Design
- Medical Research and Treatments
- Advanced Measurement and Metrology Techniques
- Stroke Rehabilitation and Recovery
- Cardiovascular Function and Risk Factors
- Image and Signal Denoising Methods
- Iron Metabolism and Disorders
- Manufacturing Process and Optimization
- Kruppel-like factors research
- Adipokines, Inflammation, and Metabolic Diseases
University of Virginia
2024
University of Tennessee at Knoxville
2023
Beijing Advanced Sciences and Innovation Center
2023
Shihezi University
2023
Oklahoma State University
2022
Augusta University
2010
Atrial fibrillation (AF) is the most common cardiac arrhythmia, which clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, can eventually lead to heart failure, stroke, or even sudden death. Electrocardiography (ECG), involves acquiring bioelectrical signals from body surface reflect activity, standard procedure for detecting AF. However, occurrence often intermittent, costing significant amount time effort medical doctors...
The rapid development in advanced sensing and imaging brings about a data-rich environment, facilitating the effective modeling, monitoring, control of complex systems. For example, body-sensor network captures multi-channel information pertinent to electrical activity heart (i.e., electrocardiograms (ECG)), which enables medical scientists monitor detect abnormal cardiac conditions. However, high-dimensional data are generally complexly structured. Realizing full potential depends great...
Image denoising is a critical task in various scientific fields such as medical imaging and material characterization, where the accurate recovery of underlying structures from noisy data essential. Although supervised techniques have achieved significant advancements, they typically require large datasets paired clean-noisy images for training. Unsupervised methods, while not reliant on data, necessitate set unpaired clean training, which are always accessible. In this paper, we propose...
Stress-inducible interleukin 6 (IL-6) is generated in brown adipocytes via beta-3 adrenergic receptor (ADRB3) signaling, which necessary stress hyperglycemia, the kind of metabolic adaptation enabling "fight or flight" response by means liver gluconeogenesis. Nevertheless, mechanism ADRB3 signaling mediates IL-6 remains unclear. As a result, it critical to understand how produce signaling. We found that agonist and cold stimulation promoted expression KLF7 mice. In parallel these results...
Aim: Advanced sensing and imaging is capable to retrieve rich information of complex systems, which can be integrated with underlying physics develop a personalized simulation framework discern system internal property for defect localization. Simulation-based localization involves modeling the heterogeneous 3D physical systems calibrating spatial-varying model parameters. In this paper, we aim an effective simulation-based active learning in systems.Methods: We Hierarchical Gaussian Process...
Atrial fibrillation (AF) is the most common cardiac arrhythmia, which clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, can eventually lead to heart failure, stroke, or even sudden death. It critical importance develop an advanced analytical model that effectively interpret electrocardiography (ECG) signals provide decision support for accurate diagnostics. In this paper, we propose innovative deep-learning method automated...