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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Rapid Portable Electrical Biosensing Design With Dielectrophoresis and Its Application for Cardiac Biomarker Detection
(2020-08-01)
Chen, Chun-Chi
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Chan, Yun-Sheng
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Fang, Yu-Hao
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Wang, Yun-Ming
;
Lee, Chen-Yi
With the growing number of new biomarkers discovered for clinic diagnosis, there is an emerging need for the development of biosensing device. One important application of the biosensor in disease diagnosis is to detect biomarkers and the biosensor should be sensitive enough to measure the small variation in biomarker concentration. Besides, to support the point-of-care diagnostics that can facilitate emergency diagnosis and disease screening, it is important that the sensing device needs to be portable, user-friendly, and support the rapid biomarker detection. In this work, we propose a rapid portable sensing device by utilizing dielectrophoresis force to accelerate the centralization of the biomarkers and the comparator-based operational amplifier to enlarge small signal response. Furthermore, the testing results demonstrate the proposed biosensor can detect subtle admittance variation corresponding to the concentration change of the biomarkers in short assay time. The sensing device is able to detect cardiac troponins level with the limit of detection at 0.1 ng mL(-1), that can satisfy the actual requirements for diagnosis of cardiovascular disease. The proposed biosensor can provide an easy-to-use, cost-effective, and rapid biomarker detection that can be applied to clinical applications to support point-of-care diagnostics.
Short-time-span EEG-based personalized emotion recognition with deep convolutional neural network
(2019-01-01)
Cheah, Kit Hwa
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Nisar, Humaira
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Yap, Vooi Voon
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Lee, Chen-Yi
;
電子工程學系及電子研究所
Emotion recognition can be useful in various applications such as in neurofeedback training for functional enhancement. A practically realizable emotion recognition system should rely on as little physiological signals/modalities as possible. Also, emotion-related neurological activities may be vastly different from person to person. Hence, this paper presents the single-modal EEG-based personalized emotion recognition convolutional neural network (CNN) models working on the DEAP dataset. The valence and arousal level classification performance of our presented CNN classifiers have surpassed the other emotion classifiers working on the DEAP dataset based on our scope of literature reviewed. The models, which are deep CNN, rely on only plain EEG data and require no pre-extracted EEG features. The design and application of the CNN models is aimed at possible future work of identification of new emotion-related EEG features, relying on the automated feature extraction capability of the CNN. The two CNN models presented have achieved the 3-class valence classification test accuracy of 97.59% and 98.75% respectively, and the 3-class arousal classification test accuracy of 98.48% and 97.58%.
Reduction of Contact Resistivity by Nano-Textured Contact
(1970-01-01)
Tsui, Bing-Yue
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Lee, Ya-Hsin
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Lee, Chen-Yi
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
The feasibility of reducing contact resistance by adding nano-textures on the contact surface is evaluated by three-dimensional simulation. Upward and downward pyramids and hemispheres are considered, and the effects of contact area and electric field are discussed. The downward nano-texture has a stronger electric field than the upward nano-texture; thus the increasing contact area will be more effectively used and lead to a greater improvement effect. On the other hand, the electric field of the hemisphere nano-texture is lower than that of the pyramid nano-texture. With proper design, the nano-textured contact can effectively reduce contact resistance up to 30-50%. Therefore, the proposed nano-textured contact is a promising approach with contact resistivity approaching physical limits.
FADE: Feature Aggregation for Depth Estimation With Multi-View Stereo
(2020-01-01)
Yang, Hsiao-Chien
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Chen, Po-Heng
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Chen, Kuan-Wen
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Lee, Chen-Yi
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Chen, Yong-Sheng
Both structural and contextual information is essential and widely used in image analysis. However, current multi-view stereo (MVS) approaches usually use a single common pre-trained model as pixel descriptor to extract features, which mix structural and contextual information together and thus increase the difficulty of matching correspondence. In this paper, we propose FADE (feature aggregation for depth estimation), which treats spatial and context information separately and focuses on aggregating features for efficient learning of the MVS problem. Spatial information includes image details such as edges and corners, whereas context information comprises object features such as shapes and traits. To aggregate these multi-level features, we use an attention mechanism to select important features for matching. We then build a plane sweep volume by using a homography backward warping method to generate match candidates. Furthermore, we propose a novel cost volume regularization network aims to minimize the noise in the matching candidates. Finally, we take advantage of 3D stacked hourglass and regression to produces high-quality depth maps. With these well-aggregated features, FADE can efficiently perform dense depth reconstruction, achieving state-of-the-art performance in terms of accuracy and requiring the least amount of model parameters.
Convolutional neural networks for classification of music-listening EEG: comparing 1D convolutional kernels with 2D kernels and cerebral laterality of musical influence
(2020-07-01)
Cheah, Kit Hwa
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Nisar, Humaira
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Yap, Vooi Voon
;
Lee, Chen-Yi
;
電子工程學系及電子研究所
This paper highlights the ability of convolutional neural networks (CNNs) at classifying EEG data listening to different kinds of music without the requirement for handcrafted features. Deep learning architectures presented in this paper include CNN of different depths and different convolutional kernels. Support vector machine (SVM) taking in EEG features describing the frequency spectrum, signal regularity, and cross-channel correlation has been applied for performance comparison with CNN. The best performing CNN model presented in this paper achieves the tenfold cross-validation (CV) binary classification average accuracy of 98.94% (validation) and 97.46% (test), and the tenfold CV three-class classification accuracy of 97.68% (validation) and 95.71% (test). In comparison, the SVM classifier achieves tenfold CV binary classification accuracy of 80.23% (validation). The CNN model presented is able to not only differentiate EEG of subjects listening to music from that of subjects without auditory input, but it is also capable of accurately differentiating the EEG of subjects listening to different music. In the context of designing neural computing models for EEG analysis, this paper shows that decomposing two-dimensional spatiotemporal convolutional kernels into separate one-dimensional spatial and one-dimensional temporal kernels significantly reduces the number of trainable parameters (size) of the model while retaining the classification performance. This finding is useful, especially in designing CNN for memory-critical embedded systems for EEG processing. In neurological aspect, auditory stimulus is found to have altered the EEG pattern of the frontal lobe and the left cerebral hemisphere more than the other brain regions.
Deep learning assisted detection of glaucomatous optic neuropathy and potential designs for a generalizable model
(2020-05-14)
Ko, Yu-Chieh
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Wey, Shih-Yu
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Chen, Wei-Ta
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Chang, Yu-Fan
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Chen, Mei-Ju
Purpose To evaluate ways to improve the generalizability of a deep learning algorithm for identifying glaucomatous optic neuropathy (GON) using a limited number of fundus photographs, as well as the key features being used for classification. Methods A total of 944 fundus images from Taipei Veterans General Hospital (TVGH) were retrospectively collected. Clinical and demographic characteristics, including structural and functional measurements of the images with GON, were recorded. Transfer learning based on VGGNet was used to construct a convolutional neural network (CNN) to identify GON. To avoid missing cases with advanced GON, an ensemble model was adopted in which a support vector machine classifier would make final classification based on cup-to-disc ratio if the CNN classifier had low-confidence score. The CNN classifier was first established using TVGH dataset, and then fine-tuned by combining the training images of TVGH and DrishtiGS datasets. Class activation map (CAM) was used to identify key features used for CNN classification. Performance of each classifier was determined through area under receiver operating characteristic curve (AUC) and compared with the ensemble model by diagnostic accuracy. Results In 187 TVGH test images, the accuracy, sensitivity, and specificity of the CNN classifier were 95.0%, 95.7%, and 94.2%, respectively, and the AUC was 0.992 compared to the 92.8% accuracy rate of the ensemble model. For the Drishti-GS test images, the accuracy of the CNN, the fine-tuned CNN and ensemble model was 33.3%, 80.3%, and 80.3%, respectively. The CNN classifier did not misclassify images with moderate to severe diseases. Class-discriminative regions revealed by CAM co-localized with known characteristics of GON. Conclusions The ensemble model or a fine-tuned CNN classifier may be potential designs to build a generalizable deep learning model for glaucoma detection when large image databases are not available.
Sample Preparation for Multiple-Reactant Bioassays on Micro-Electrode-Dot-Array Biochips
(2019-01-01)
Liang, Tung-Che
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Chan, Yun-Sheng
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Ho, Tsung-Yi
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Chakrabarty, Krishnendu
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Lee, Chen-Yi
Sample preparation, as a key procedure in many biochemical protocols, mixes various samples and/or reagents into solutions that contain the target concentrations. Digital microfluidic biochips (DMFBs) have been adopted as a platform for sample preparation because they provide automatic procedures that require less reactant consumption and reduce human-induced errors. However, traditional DMFBs only utilize the (1:1) mixing model, i.e., only two droplets of the same volume can be mixed at a time, which results in higher completion time and the wastage of valuable reactants. To overcome this limitation, a next-generation micro-electrode-dot-array (MEDA) architecture that provides flexibility of mixing multiple droplets of different volumes in a single operation was proposed. In this paper, we present a generic multiple-reactant sample preparation algorithm that exploits the novel fluidic operations on MEDA biochips. Simulated experiments show that the proposed method outperforms existing methods in terms of saving reactant cost, minimizing the number of operations, and reducing the amount of waste.
Energy Harvesting For Wearable Devices: A Review
(2019-10-15)
Chong, Yung-Wey
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Ismail, Widad
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Ko, Kwangman
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Lee, Chen-Yi
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電子工程學系及電子研究所
In recent years, wearable devices have attracted attention because of their ability to enhance the quality of life. This disruptive technology has helped healthcare professionals with intervening early in chronic diseases, especially amongst independently living patients, and has facilitated real-time monitoring of patients' vital signs remotely. One of the major bottlenecks that hamper the adoption of wearable device is the continuous power supply. Most wearable devices solely depend on battery supply. When the energy stored in the battery is depleted, the operation of wearable devices is affected. To overcome this limitation, efficient energy harvesters for wearable devices are crucial. The paper primarily aims to present a comprehensive classification of different energy sources that can be capitalized to power wearable devices. In addition, this research paper deals with the key challenges that must be considered in the development of autonomous wearable devices for telemedicine applications with a proposed system design for wearable device that uses energy harvesting technology.
Centralized State Sensing Using Sensor Array on Wearable Device
(2019-01-01)
Lee, Eugene
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Hsu, Tsu-Jui
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Lee, Chen-Yi
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
Signal acquisition from the wrist using wearable device is usually corrupted by noise, sometimes up to a level where the noise completely dominates the signal. In this paper, we use the acquisition of photoplethysmography (PPG) signal as an example to demonstrate our proposed algorithm implemented on a sensor array. To increase the chances of acquiring clean PPG signal, we develop a uniform linear sensor array located above the radial artery. We also develop a wearable device to integrate our sensor array and our proposed algorithm. We propose centralized state sensing (CSS), a centralized algorithm suited to our sensor array, to increase the efficiency in multi sensor estimation. To support our proposed algorithm, we use heart rate estimation as an example and compare it with the estimation of a single sensor and the statistical mean of the estimation of multiple sensors. Experimental results demonstrate that our proposal achieve a reduction in mean absolute error of 26.8%, compared to the estimation result obtained using the average of the estimation of all sensors. Operating on a 80 MHz processor, our algorithm introduces a 8 ms overhead, making it highly suitable for wearable devices.
Joint Capacitive Sensing and Frequency Selection for Fast Medical Tests
(2019-01-01)
Chan, Yun-Sheng
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Lung, Kuan-Yu
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Wang, Yun-Ming
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Lee, Chen-Yi
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生物科技學系
This paper presents a new approach with frequency selection for fast medical tests. Many medical tests need high speed sensor because of its short reaction time. High frequency biosensor with high sensitivity plays a vital role in fast medical tests. Each medical test has its own reaction time. How to select proper frequency for specific medical test is an important issue. In this paper, one frequency-based capacitive sensor is proposed to prove this new approach with salt water. According to the experimental results, the limit of detection (LOD) can be improved if proper working frequency is selected. The 0.1% wt salt water in 20ul can be detected on proposed system and coefficient of variation (CV) is only 0.19% at 40MHz.

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