Browsing by Author "Chung, Chun"

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Near-infrared bone densitometry: A feasibility study on distal radius measurement
(2018-07-01)
Chung, Chun
;
Chen, Yu-Pin
;
Leu, Tsai-Hsueh
;
Sun, Chia-Wei
;
光電工程學系
Osteoporosis, defined as decreased bone mineral density (BMD), poses patients in dangers for fracture risk and has become a major public health problem worldwide because of is associated morbidity, mortality and costs. Without doubt, early detection and timely intervention are important to successfully manage osteoporosis and its associated complications. The dual-energy x-ray absorptiometry (DXA) is the most popular and standard method to measure BMD. However, limitations including radiation exposure and availability restrict its application for osteoporosis screening among general population. In this study, we developed a simple method to detect human distal radius bone density based on near infrared (NIR) image system. Among 10 volunteers (including 5 young and 5 elderly participants) receiving bone density measurement using our NIR image system at the ultradistal part of bilateral distal radius, we demonstrated a strong correlation between the optical attenuation and BMD measured with DXA, which may facilitate predicting bone density status. We hope our potential NIR image system may open a new avenue for development of osteoporosis screening facilities and help in prevention of osteoporosis related fracture and its associated complications in the near future. Pearson's correlations between BMD values from the DXA and light intensity of NIR system.
Solution Strategies for Linear Inverse Problems in Spatial Audio Signal Processing
(2017-06-01)
Bai, Mingsian R.
;
Chung, Chun
;
Wu, Po-Chen
;
Chiang, Yi-Hao
;
Yang, Chun-May
The aim of this study was to compare algorithms for solving inverse problems generally encountered in spatial audio signal processing. Tikhonov regularization is typically utilized to solve overdetermined linear systems in which the regularization parameter is selected by the golden section search (GSS) algorithm. For underdetermined problems with sparse solutions, several iterative compressive sampling (CS) methods are suggested as alternatives to traditional convex optimization (CVX) methods that are computationally expensive. The focal underdetermined system solver (FOCUSS), the steepest descent (SD) method, Newton's (NT) method, and the conjugate gradient (CG) method were developed to solve CS problems more efficiently in this study. These algorithms were compared in terms of problems, including source localization and separation, noise source identification, and analysis and synthesis of sound fields, by using a uniform linear array (ULA), a uniform circular array (UCA), and a random array. The derived results are discussed herein and guidelines for the application of these algorithms are summarized.