Browsing by Author "Lin, Bor-Shing"

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Auto Defect Detection of A-IGZO Thin Film Transistor Backplane on Foldable Medical Array Panel Application
(2017-01-01)
Hung, Yu-Min
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Wang, Yao-Chin
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Yang, Yu-Ching
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Lin, Bor-Shyh
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Lin, Bor-Shing
This article reported on the pixel flaw detection properties of amorphous In-Ga-Zn-O-based active thin film transistors on flexible medical panel substrate application, which is using the non-invasive optoelectronic transforming inspection technique. The experimental data exactly showed good flaw detection rate > 90% and stable uniformity < 5%. Currently, it is a popular method with electrical testing and detection with advantages of ultrahigh resolution and small-pixel-sized medical panel, and no contact damaged to the application of flexible medical panel substrates.
Automatic Wheezing Detection Using Speech Recognition Technique
(2016-08)
Lin, Bor-Shing
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Lin, Bor-Shyh
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影像與生醫光電研究所
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Institute of Imaging and Biomedical Photonics
This study developed a speech recognition technique to detect wheezing. Wheezes are important in the diagnosis of pulmonary pathologies such as asthma. The acoustic features of wheezes are distinct in the frequency domain. Therefore, many studies have focused on detecting wheezing peaks in spectrograms through image processing. However, automated detection of wheezing peaks is difficult because of blurred edges and noise. This paper proposes an alternative approach for wheezing detection in which the mel frequency cepstral coefficients (MFCCs) are integrated into the Gaussian mixture model (GMM). The MFCCs reduce the short-term spectral information to a few coefficients, and the GMM recognizes the respiratory sounds. The respiratory sounds of 18 volunteers (9 asthmatic and 9 normal adults) were recorded for training and testing. The results of a qualitative analysis of wheeze recognition showed a good sensitivity of 0.881 and a high specificity of 0.995.
Data Hiding on Social Media Communications Using Text Steganography
(2018-01-01)
Shiu, Hung-Jr
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Lin, Bor-Shing
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Lin, Bor-Shyh
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Huang, Po-Yang
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Huang, Chien-Hung
This research work proposes a steganography on social media communications. Nowadays, people frequently use messenger or social network such as Skype, Line, Facebook, Whatsapp and Twitter, etc. to communicate with other people and these platforms become popular to be used to exchange secrets or preserve personal information. Personal information like accounts and passwords might not be seen and embedded to cover objects when two clients communicate to each other. Those objects shall be pictures, music, or text messages. The proposed scheme will be deployed on the cover text. A strategy is designed to increase the capacity of hidden data and try to make any simple piece of text to be the cover text; such as letters, article of news or the common messages. The new approach - Extended Line will be adopted, and then together with White Space between to increase the capacity of a text. The simulation results disclose that the algorithm not only increases the capacity, but also increases the efficiency of decoding. Moreover, it still works on any kind of cover text.
Design of a Mobile Brain Computer Interface-Based Smart Multimedia Controller
(2015-03-01)
Tseng, Kevin C.
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Lin, Bor-Shing
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Wong, Alice May-Kuen
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Lin, Bor-Shyh
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影像與生醫光電研究所
Music is a way of expressing our feelings and emotions. Suitable music can positively affect people. However, current multimedia control methods, such as manual selection or automatic random mechanisms, which are now applied broadly in MP3 and CD players, cannot adaptively select suitable music according to the user's physiological state. In this study, a brain computer interface-based smart multimedia controller was proposed to select music in different situations according to the user's physiological state. Here, a commercial mobile tablet was used as the multimedia platform, and a wireless multi-channel electroencephalograph (EEG) acquisition module was designed for real-time EEG monitoring. A smart multimedia control program built in the multimedia platform was developed to analyze the user's EEG feature and select music according his/her state. The relationship between the user's state and music sorted by listener's preference was also examined in this study. The experimental results show that real-time music biofeedback according a user's EEG feature may positively improve the user's attention state.
Design of a Wearable 12-Lead Noncontact Electrocardiogram Monitoring System
(2019-04-01)
Hsu, Chien-Chin
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Lin, Bor-Shing
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He, Ke-Yi
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Lin, Bor-Shyh
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影像與生醫光電研究所
A standard 12-lead electrocardiogram (ECG) is an important tool in the diagnosis of heart diseases. Here, Ag/AgCl electrodes with conductive gels are usually used in a 12-lead ECG system to access biopotentials. However, using Ag/AgCl electrodes with conductive gels might be inconvenient in a prehospital setting. In previous studies, several dry electrodes have been developed to improve this issue. However, these dry electrodes have contact with the skin directly, and they might be still unsuitable for patients with wounds. In this study, a wearable 12-lead electrocardiogram monitoring system was proposed to improve the above issue. Here, novel noncontact electrodes were also designed to access biopotentials without contact with the skin directly. Moreover, by using the mechanical design, this system allows the user to easily wear and take off the device and to adjust the locations of the noncontact electrodes. The experimental results showed that the proposed system could exactly provide a good ECG signal quality even while walking and could detect the ECG features of the patients with myocardial ischemia, installation pacemaker, and ventricular premature contraction.
Design of Novel Field Programmable Gate Array-Based Hearing Aid
(2019-01-01)
Lin, Bor-Shing
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Yang, Po-Yu
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Liu, Ching-Feng
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Huang, Yi-Chia
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Liu, Chengyu
Hearing loss is one of the most common chronic diseases. For people with hearing loss, communicating with other people, particularly in an environment with considerable background noise, is difficult. Recently, several hearing aids have been developed to improve speech comprehension in a noisy environment. The use of an adaptive beamformer is one of the alternative methods for improving speech intelligibility. However, the adaptive beamformer requires the location of the desired speaker to estimate the time differences of arrival (TDOAs) of speech sources to numerous spatially separated sensors in acoustics. In general, the technique of steered response power source localization was used to estimate the TDOA; however, this technique was easily affected by environmental reverberation. To overcome the aforementioned concern, a novel hearing aid is proposed in this paper. By using an image processing technology, the location of the desired speaker could be manually selected to provide precise information on the TDOA. Moreover, adaptive signal enhancements were implemented in a field-programmable gate array to enhance the speech of interest in real time. The experimental results indicate that the proposed system could improve speech intelligibility in various noisy environments. Therefore, the proposed system may be employed to improve the daily lives of people with hearing the loss in the future.
Design of smart EEG cap
(2019-09-01)
Lin, Bor-Shing
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Huang, Yao-Kuang
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Lin, Bor-Shyh
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影像與生醫光電研究所
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Institute of Imaging and Biomedical Photonics
Background and Objective: Brain machine interface (BMI) is a system which communicates the brain with the external machines. In general, an electroencephalograph (EEG) machine has to be used to monitor multi-channel brain responses to improve the BMI performance. However, the bulky size of the EEG machine and applying conductive gels in EEG electrodes also cause the inconvenience of daily life applications. How to select the relevant EEG channel and remove irrelevant channels is important and useful for the development of BMIs. Methods: In this research, a smart EEG cap was proposed to improve the above issues. Different from the conventional EEG machine, the proposed smart EEG cap contain a spatial filtering circuit to enhance EEG features in local area, and it could also select the relevant EEG channel automatically. Moreover, the novel dry active electrodes were also designed to acquire EEG without conductive gels in the hairy skin of the head, to improve the convenience in use. Results: Finally, the proposed smart EEG cap was applied in motion imagery-based BMI and several experiments were tested to valid the system performance. The proposed smart EEG cap could effectively enhance EEG features and select relevant EEG channel, and the information transfer rate of BMI was about 6.06 bits/min. Conclusions: The proposed smart EEG cap has advantages of measuring EEG without conductive gels and wireless transmission to effectively improve the convenience of use, and reduce the limitation of activity in daily life. In the future, it might be widely applied in other BMI applications. (C) 2019 Elsevier B.V. All rights reserved.
Design of wearable and wireless multi-parameter monitoring system for evaluating cardiopulmonary function
(2017-09-01)
Li, Shih-Hong
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Lin, Bor-Shing
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Wang, Chen-An
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Yang, Cheng-Ta
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Lin, Bor-Shyh
The 6-minute walking test (6MWT) is the test most commonly used to evaluate cardiopulmonary function in patients with respiratory or heart disease. However, there was previously no integrated monitoring system available to simultaneously record both the real-time cardiopulmonary physiological parameters and the walking information (i.e., walking distance, speed, and acceleration) during the 6MWT. In this study, then, a wearable and wireless multi-parameter monitoring system was proposed to simultaneously monitor oxygen saturation (SpO(2)), heart rhythm, and the walking information during the 6MWT. A multi parameter detection algorithm was also designed to estimate the heart rate effectively. The results of the study indicate that this system was able to reveal the dynamic changes and differences in walking speed and acceleration during the 6MWT. As such, the system has the potential to provide a more integrated approach to monitoring cardiopulmonary parameters and walking information simultaneously during the 6MWT. The proposed system warrants further investigation as an assistive assessment tool in evaluating cardiopulmonary function and may be widely applied in cardiopulmonary-related and sports medicine applications in the future. (C) 2017 IPEM. Published by Elsevier Ltd. All rights reserved.
Design of Wearable Breathing Sound Monitoring System for Real-Time Wheeze Detection
(2017-01-01)
Li, Shih-Hong
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Lin, Bor-Shing
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Tsai, Chen-Han
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Yang, Cheng-Ta
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Lin, Bor-Shyh
In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this study, a wearable and wireless breathing sound monitoring system for real-time wheeze detection was proposed. Moreover, a breathing sounds analysis algorithm was designed to continuously extract and analyze the features of breathing sounds to provide the objectively quantitative information of breathing sounds to professional physicians. Here, normalized spectral integration (NSI) was also designed and applied in wheeze detection. The proposed algorithm required only short-term data of breathing sounds and lower computational complexity to perform real-time wheeze detection, and is suitable to be implemented in a commercial portable device, which contains relatively low computing power and memory. From the experimental results, the proposed system could provide good performance on wheeze detection exactly and might be a useful assisting tool for analysis of breathing sounds in clinical diagnosis.
Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface
(2019-10-01)
Lin, Bor-Shyh
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Lin, Bor-Shing
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Yen, Tzu-Hsiang
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Hsu, Chien-Chin
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Wang, Yao-Chin
Brain-computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).
Development of a Wearable Motor-Imagery-Based Brain-Computer Interface
(2016-03-01)
Lin, Bor-Shing
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Pan, Jeng-Shyang
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Chu, Tso-Yao
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Lin, Bor-Shyh
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影像與生醫光電研究所
A motor-imagery-based brain-computer interface (BCI) is a translator that converts the motor intention of the brain into a control command to control external machines without muscles. Numerous motor-imagery-based BCIs have been successfully proposed in previous studies. However, several electroencephalogram (EEG) channels are typically required for providing sufficient information to maintain a specific accuracy and bit rate, and the bulk volume of these EEG machines is also inconvenient. A wearable motor imagery-based BCI system was proposed and implemented in this study. A wearable mechanical design with novel active comb-shaped dry electrodes was developed to measure EEG signals without conductive gels at hair sites, which is easy and convenient for users wearing the EEG machine. In addition, a wireless EEG acquisition module was also designed to measure EEG signals, which provides a user with more freedom of motion. The proposed wearable motor-imagery-based BCI system was validated using an electrical specifications test and a hand motor imagery experiment. Experimental results showed that the proposed wearable motor-imagery-based BCI system provides favorable signal quality for measuring EEG signals and detecting motor imagery.
Development of Novel Hearing Aids by Using Image Recognition Technology
(2019-05-01)
Lin, Bor-Shing
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Liu, Ching-Feng
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Cheng, Chih-Jen
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Wang, Jhi-Joung
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Liu, Chengyu
Speech is easily affected by different background noise in real environment to reduce the speech intelligibility, in particular, for hearing impaired listeners. In order to improve the above issue, several hearing aids have been developed to enhance the speech signal in noisy environment. Most of current hearing aids were designed to enhance the component of speech and suppress the component of noise. However, it is difficult to separate other speech sources. Adaptive signal enhancement with the beamforming technique might improve the above issue. However, how to distinguish the location of the desired speaker effectively is still a difficult challenge for adaptive beamforming method. A novel concept of hearing aid was proposed in this study. Different from the beamforming-based hearing aids, which use the cross-correlation-coefficient method to estimate time difference of arrival (TDOA), an image recognition technology was used to estimate the location of the desired speaker to obtain the more precise TDOA. An adaptive signal enhancement was also used to enhance the noisy speech sound. From the experimental results, the proposed system could provide a smaller absolute error of TDOA less than 1.25 x 10(-4) ms, and a clear speech sound from the target speaker who the user wants to listen to.
Development of Novel Lip-Reading Recognition Algorithm
(2017-01-01)
Lin, Bor-Shing
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Yao, Yu-Hsien
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Liu, Ching-Feng
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Lien, Ching-Feng
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Lin, Bor-Shyh
Total laryngectomy is a common treatment for patients with advanced laryngeal and hypopharyngeal cancer, but it is also a result from the loss of the natural voice and directly affects the basic communication functions in daily life. Reconstructing the basic communication function is an important issue for these patients after total laryngectomy surgery. Recently, the image processing technique for lip-reading recognition has been widely developed and applied in various kinds of applications. It is also one of the possibly alternative approaches to reconstructing the basic communication function for these patients after total laryngectomy surgery. Although many human lip-reading recognition methods have been developed to detect lip contour precisely, detecting pronouncing lip contour effectively is still a difficult challenge. In this paper, a novel lip-reading recognition algorithm was proposed to recognize English vowels from the lip contour when speaking. Here, several criteria for detecting the mouth region of interest (ROI) were designed to reduce the error rate of detecting the mouth ROI and lip contour. Moreover, several lip parameters, including the width, height, contour points, area, and the ratio (width/height) of lips, were used to recognize the lip contour and English vowels when speaking. The advantages of the proposed method are that it could detect the mouth ROI automatically, reduce the influence of individual differences, such as the individual lip shape or makeup effect, and it also could perform a good performance without pretraining. Finally, the performance of lip-reading recognition under different backgrounds and individual differences was also tested, and the accuracy of the proposed algorithm on lip-reading recognition was over 80%.
Novel Approach of Respiratory Sound Monitoring Under Motion
(2018-01-01)
Wang, Yan-Di
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Liu, Chun-Hui
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Jiang, Ren-Yi
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Lin, Bor-Shing
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Lin, Bor-Shyh
Electronic stethoscope system is the most frequently used approach to collect respiratory sound to evaluate the lung function of patients or investigate various kinds of lung diseases of in recent years. However, current electronic stethoscope systems are just suitable for measuring respiratory sounds under static state. Under motion, the respiratory sounds are easily affected by the vibration of the human body. Moreover, it is also inconvenient to wear the conventional electronic stethoscope system. In order to improve the above issues, a novel wireless respiratory sound recording system was proposed to collect respiratory sound under motion. Here, a wireless and wearable respiratory sound recording device was designed to collect respiratory sound wirelessly. It is also easy to wear and monitor respiratory sound under motion due to its advantages of small volume and wireless transmission. Moreover, the technique of adaptive filter was also applied to enhance the noisy respiratory sound from single channel trial. From the experimental results, the noisy respiratory sound can be effectively improved by the proposed adaptive filter. Therefore, the proposed system exactly contains the potential of being a good assisting tool for lung diseases and may be applied in the applications of lung and sports medicine in the future.
The use of surface electromyography in dysphagia evaluation
(2017-01-01)
Wang, Yao-Chin
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Chou, Willy
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Lin, Bor-Shing
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Wang, Jhi-Joung
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Lin, Bor-Shyh
Dysphagic patients usually have a variety of clinical problems such as malnutrition, significant weight loss, and aspiration pneumonia. Dysphagia is a complication commonly caused by strokes, and surface electromyography (sEMG) provides a simple, non-radioactive, and non-invasive method to measure the patterns of muscle activity during swallowing, allowing clinicians to describe the physiology of swallowing behavior. Most previous studies have described swallowing behavior in terms of amplitude and duration. However, there is no objective and precise approach available for the evaluation of swallowing coordination. In order to evaluate swallowing coordination more precisely, a wireless and wearable monitoring device for dysphagia evaluation was designed for the present study in order to measure four muscle groups simultaneously during swallowing. In this context, the variations of the cross-correlation coefficients were defined as the discoordination index, a metric which can effectively reflect the differences between the surface EMG patterns of the bilateral muscle groups. The experimental results of this study show that the discoordination indices for dysphagic patients are significantly larger than those for healthy subjects and that these discoordination indices can be used as an effective means of evaluating the coordination between bilateral swallowing muscles.
Using Back-Propagation Neural Network for Automatic Wheezing Detection
(2015)
Lin, Bor-Shing
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Jan, Gene Eu
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Wu, Huey-Dong
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Lin, Bor-Shyh
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Chen, Sao-Jie
This study describes the design of a fast and high performance wheeze recognition system. The proposed wheezing detection algorithm is based on order truncate average (OTA) and back-propagation neural network (BPNN). Some features are then extracted from the processed spectra to train a BPNN. Eventually, the new testing samples go through the trained BPNN to recognize whether they are wheezing sounds. Experimental results show a high sensitivity of 0.946 and a specificity of 1.0 in qualitative analysis of wheeze recognition.
Wearable Cardiopulmonary Function Evaluation System for Six-Minute Walking Test
(2019-11-01)
Lin, Bor-Shing
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Jhang, Ruei-Jie
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Lin, Bor-Shyh
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光電系統研究所
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影像與生醫光電研究所
As a submaximal exercise test, a 6-min walking test (6MWT) can be considered a suitable index for the exercise capacity of patients with a respiratory problem. Traditionally, medical staff manually collect cardiopulmonary information using different devices. However, no integrated monitoring system is currently available to simultaneously record the real-time breathing sound, heart rhythm, and precise walking information (i.e., walking distance, speed, and acceleration) during the 6MWT. In this study, a wearable and wireless multiparameter monitoring system is proposed to simultaneously monitor the breathing sound, oxygen saturation (SpO2), electrocardiograph (ECG) signals, and precise walking information during the 6MWT. Here, a wearable mechanical design was successfully used to reduce the effect of motion artifacts on the breathing sound and ECG signal. A multiparameter detection algorithm was designed to effectively estimate heart and breathing rates. Finally, the cardiopulmonary function of smokers was evaluated using the proposed system. The evaluation indicated that this system could reveal dynamic changes and differences in the breathing rate, heart rate, SpO2, walking speed, and acceleration during the 6MWT. The proposed system can serve as a more integrated approach to monitor cardiopulmonary parameters and obtain precise walking information simultaneously during the 6MWT.
Wireless Monitoring System for Oral-feeding Evaluation of Preterm Infants
(2014-01-01)
Wang, Chen-An
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Liao, Yi-Chien
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Wu, Pei-Jung
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Wang, Yu-Lin
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Lin, Bor-Shing
Feeding and swallowing disorders are relatively common in early infancy. In Clinical, it shows negative impacts on growth and neurodevelopmental, therefore it has become a high risk of neurodevelopmental delays in preterm infants. Oral feeding that requires suckling, swallowing, and breathing coordination, and it is the most complex sensorimotor process for the newborn infant. Currently, both preterm infant\'s oral feeding disorders and severity are dependent on subjective clinical experience. Directly monitoring sucking-swallowing-breathing activities of oral is difficult for preterm infants. In this study, a wireless monitoring system for oral feeding of preterm infants was developed to monitor the events of sucking-swallowing-breathing activities continuously and objectively. Finally, the experimental results show that the proposed system can detect the events of sucking, swallowing, and breathing activities effectively.