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297 results on '"Huang, Chun-Hsiang"'

1. Improving Robustness and Clinical Applicability of Respiratory Sound Classification via Audio Enhancement

Author

Tzeng, Jing-Tong, Li, Jeng-Lin, Chen, Huan-Yu, Huang, Chun-Hsiang, Chen, Chi-Hsin, Fan, Cheng-Yi, Huang, Edward Pei-Chuan, and Lee, Chi-Chun

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Deep learning techniques have shown promising results in the automatic classification of respiratory sounds. However, accurately distinguishing these sounds in real-world noisy conditions poses challenges for clinical deployment. Additionally, predicting signals with only background noise could undermine user trust in the system. In this study, we propose an audio enhancement (AE) pipeline as a pre-processing step before respiratory sound classification, aiming to improve performance in noisy environments. Multiple experiments were conducted using different audio enhancement model structures, demonstrating improved classification performance compared to the baseline method of noise injection data augmentation. Specifically, the integration of the AE pipeline resulted in a 2.59% increase in the ICBHI classification score on the ICBHI respiratory sound dataset and a 2.51% improvement on our recently collected Formosa Archive of Breath Sounds (FABS) in multi-class noisy scenarios. Furthermore, a physician validation study assessed the clinical utility of our system. Quantitative analysis revealed enhancements in efficiency, diagnostic confidence, and trust during model-assisted diagnosis with our system compared to raw noisy recordings. Workflows integrating enhanced audio led to an 11.61% increase in diagnostic sensitivity and facilitated high-confidence diagnoses. Our findings demonstrate that incorporating an audio enhancement algorithm significantly enhances robustness and clinical utility., Comment: The following article has been submitted to The Journal of the Acoustical Society of America (JASA). After it is published, it will be found at https://pubs.aip.org/asa/jasa

Published
2024

8. Biopharmaceutical beamline TLS 15A1 for macromolecular crystallography at the National Synchrotron Radiation Research Center.

Author

Chiang, Cheng‐Hung, Chou, Chung‐Kuang, Tseng, Chien‐Chang, Chen, Yi‐Hui, Liu, Yi‐Chun, Huang, Chen‐Ying, Chao, Chun‐Hsiung, and Huang, Chun‐Hsiang

Subjects
PROTEIN crystallography, CRYSTALLOGRAPHY, RESEARCH institutes, ATOMIC structure, DATA quality, SYNCHROTRON radiation
Abstract

Synchrotron protein crystallography (PX) remains an indispensable tool for uncovering the atomic structures of biomolecules. TLS 15A1, operated by the protein diffraction group of NSRRC, has been accessible to both academic and industrial users since January 2013. This beamline is energy‐tunable within a range between 5.6 and 15.5 keV, which is suitable for phasing experiments. Over the past 11 years, TLS 15A1 has supported 657 projects and facilitated 1523 experiments across 41,656 h of beamtime. This article describes the beamline layout, studies of factors influencing data quality, and significant scientific contributions. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Biopharmaceutical beamline TLS 15A1for macromolecular crystallography at the National Synchrotron Radiation Research Center

Author

Chiang, Cheng‐Hung, Chou, Chung‐Kuang, Tseng, Chien‐Chang, Chen, Yi‐Hui, Liu, Yi‐Chun, Huang, Chen‐Ying, Chao, Chun‐Hsiung, and Huang, Chun‐Hsiang

Abstract

Synchrotron protein crystallography (PX) remains an indispensable tool for uncovering the atomic structures of biomolecules. TLS 15A1, operated by the protein diffraction group of NSRRC, has been accessible to both academic and industrial users since January 2013. This beamline is energy‐tunable within a range between 5.6 and 15.5 keV, which is suitable for phasing experiments. Over the past 11 years, TLS 15A1 has supported 657 projects and facilitated 1523 experiments across 41,656 h of beamtime. This article describes the beamline layout, studies of factors influencing data quality, and significant scientific contributions. Photograph of the sample environment set‐up in the TLS 15A1 experimental station. The experimental station is equipped with a high precision X‐ray MD2 micro‐diffractometer, a large area CCD detector, Cryojet, and an automatic sample mounting system (SAM) for highly efficient data collection.

Published
2024
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14. Smart Control of Home Appliances Using Hand Gesture Recognition in an IoT-Enabled System.

Author

Yang, Cheng-Ying, Lin, Yi-Nan, Wang, Sheng-Kuan, Shen, Victor R.L., Tung, Yi-Chih, Shen, Frank H.C., and Huang, Chun-Hsiang

Abstract

Recently, with the vigorous development of the Internet of Things (IoT) technology, all kinds of intelligent home appliances in the market are constantly innovating. The public requirements for residential safety and convenience are also increasing. Meanwhile, with the improvement of indigenous medical technology and quality of life, people's average lifespan is gradually increasing. However, countries around the world are facing the problem of aging societies. Hand gesture recognition is gaining popularity in the fields of gesture control, robotics, or medical applications. Therefore, how to create a convenient and smart control system of home appliances for the elderly or the disabled has become the objective of this study. It aims to use Google MediaPipe to develop a hand tracking system, which detected 21 key points of a hand through the camera lens of a mobile device and used a vector formula to calculate the angle of the intersection of two lines based on four key points. After the angle of bending finger is obtained, users' hand gesture can be recognized. Our experiments have confirmed that the recognition precision and recall values of hand gesture for numbers 0–9 reached 98.80% and 97.67%, respectively; and the recognition results were used to control home appliances through the low-cost IoT-Enabled system. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Information Delivery Systems for Car Passengers Without Networking Capabilities

Author

Huang, Chun-Hsiang, Chen, Po-Wei, Hsieh, Ping-Yen, Wu, Ja-Ling, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ip, Horace H.-S., editor, Au, Oscar C., editor, Leung, Howard, editor, Sun, Ming-Ting, editor, Ma, Wei-Ying, editor, and Hu, Shi-Min, editor

Published
2007
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24. A Colorization Based Animation Broadcast System with Traitor Tracing Capability

Author

Liu, Chih-Chieh, Kuo, Yu-Feng, Huang, Chun-Hsiang, Wu, Ja-Ling, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Shi, Yun Qing, editor, and Jeon, Byeungwoo, editor

Published
2006
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26. Fidelity-Controlled Robustness Enhancement of Blind Watermarking Schemes Using Evolutionary Computational Techniques

Author

Huang, Chun-Hsiang, Shen, Chih-Hao, Wu, Ja-Ling, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Cox, Ingemar J., editor, Kalker, Ton, editor, and Lee, Heung-Kyu, editor

Published
2005
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28. The highly efficient protein crystallography beamline TLS 13B1at the National Synchrotron Radiation Research Center

Author

Chen, Yi‐Hui, Tseng, Chien‐Chang, Chou, Chung‐Kuang, Liu, Yi‐Chun, Chiang, Cheng‐Hung, Huang, Chen‐Ying, Chao, Chun‐Hsiung, and Huang, Chun‐Hsiang

Abstract

Synchrotron protein crystallography (PX) is one of the most powerful and dominant tools for determining the three‐dimensional structures of biological macromolecules. At the National Synchrotron Radiation Research Center, TLS 13B1 is the first dedicated energy‐tunable and highly automated PX beamline for efficiently solving de novo protein structures in Taiwan and has been open for users since September 2005. With remote access, user support services, and annual PX training courses over 17 years, the number of user groups at TLS 13B1 has grown from 11 to 139. In total, 1,429 projects were supported, and 2090 experiments were hosted during 67,846 hr of beamtime. This paper describes the photon source, beamline components, experimental station equipment, beamline software, beamline application process, and results from our users. Photograph of the sample environment setup in the TLS 13B1 experimental station. The experimental station is equipped with a high‐precision X‐ray MD2 micro‐diffractometer, a large area CCD detector, Cryojet, and an automatic sample mounting system (SAM) for highly efficient data collection.

Published
2023
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36. Deep Reinforcement Learning-based Beam Tracking from mmWave Antennas Installed on Overhead Messenger Wires

Author

Huang Chun-Hsiang, Yusuke Koda, Masao Shinzaki, Shirato Yushi, Masahiro Morikura, Naoki Kita, Koji Yamamoto, and Takayuki Nishio

Subjects
Computer science, business.industry, 05 social sciences, 050801 communication & media studies, 020302 automobile design & engineering, 02 engineering and technology, Signal, Base station, 0508 media and communications, 0203 mechanical engineering, Electronic engineering, Reinforcement learning, Overhead (computing), Wireless, business, Beam (structure)
Abstract

To achieve reliable small cell millimeter-wave wireless backhauls, this study installs small cell base stations (SBSs) on overhead messenger wires to gain flexibility in physical deployments of SBSs ensuring in the line-of-sight connections between SBSs and gateway BSs. These installations pose challenges in aligning directional beams, whereby complicated wind-forced dynamics in on-wire SBSs require frequent beam training, and consequently, a large signaling overhead. To address this, this study aims at demonstrating the feasibility of learning-based beam tracking where a beam tracking policy is learned a priori to fix beam misalignment caused by the wind-forced dynamics. Because wind-forced dynamics in SBSs can be three-dimensional (3D), the proposed beam tracking newly exploits the 3D position/velocity of the SBS as state information. As a solution to fix beam misalignment, the beam tracking policy is learned via deep reinforcement learning wherein the 3D information and beam direction are regarded as a state and an action, respectively, and the received signal power at a gateway BS is maximized. The simulation results depict the feasibility of learning an appropriate beam tracking policy to prevent beam misalignment induced by wind-forced 3D dynamics in on-wire SBSs.

Published
2020

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