Browsing by Author "Wang, Hsiang-Yu"

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A PDMS-Based Cylindrical Hybrid Lens for Enhanced Fluorescence Detection in Microfluidic Systems
(2014-02-01)
Lin, Bor-Shyh
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Yang, Yu-Ching
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Ho, Chong-Yi
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Yang, Han-Yu
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Wang, Hsiang-Yu
Microfluidic systems based on fluorescence detection have been developed and applied for many biological and chemical applications. Because of the tiny amount of sample in the system; the induced fluorescence can be weak. Therefore, most microfluidic systems deploy multiple optical components or sophisticated equipment to enhance the efficiency of fluorescence detection. However, these strategies encounter common issues of complex manufacturing processes and high costs. In this study; a miniature, cylindrical and hybrid lens made of polydimethylsiloxane (PDMS) to improve the fluorescence detection in microfluidic systems is proposed. The hybrid lens integrates a laser focusing lens and a fluorescence collecting lens to achieve dual functions and simplify optical setup. Moreover, PDMS has advantages of low-cost and straightforward fabrication compared with conventional optical components. The performance of the proposed lens is first examined with two fluorescent dyes and the results show that the lens provides satisfactory enhancement for fluorescence detection of Rhodamine 6G and Nile Red. The overall increments in collected fluorescence signal and detection sensitivity are more than 220% of those without lens, and the detection limits of Rhodamine 6G and Nile red are lowered to 0.01 g/mL and 0.05 g/mL, respectively. The hybrid lens is further applied to the detection of Nile red-labeled Chlorella vulgaris cells and it increases both signal intensity and detection sensitivity by more than 520%. The proposed hybrid lens also dramatically reduces the variation in detected signal caused by the deviation in incident angle of excitation light.
Who Is the Designer? ARC-100 Database and Benchmark on Architecture Classification
(2020-01-01)
Huang, Yen-Chang
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Wang, Shih-Yuan
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Liong, Sze-Teng
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Huang, Chieh-En
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Hsieh, Yi-Chen
Architecture is about evolution, there exist many types of architectural styles that depend on the geography, traditions, and culture of the particular regions. An architectural designer may have a similar preference in creating the new architectural building, which can be easily recognized from the physical attributes and characteristics. This paper performs an architect classification based on the outward appearance of the building. An architecture database with 100 images (ARC-100) that have balanced class distribution is constructed. Among the architectural buildings, the best performance is 71% for 5-class classification. Convolutional neural networks (CNNs) have demonstrated breakthrough performance on various classification tasks in recent studies, and even outperform human experts in specific tasks. Thus, for the baseline evaluation, multiple pretrained CNN models are employed with slight modifications. Prior to the feature extraction and classification processes, the removal of background noise is performed using two approaches: manually and automatically. The former approach requires high human intervention, while the latter utilizes the cutting-edge object segmentation technology, namely mask regional convolutional neural network (R-CNN). The illustration of the experiment training progress and the confusion matrix are reported, to allow further interpretation and analysis for the model trained. Notably, this is the first work that performs automatic classification based on architectural styles. This framework can be used to improve the cultural understanding and practices in providing education for holistic development and enhance the learning experience and progressions from an aesthetic perspective. (C) 2020 The Authors. Published by Atlantis Press B.V.