Browsing by Author "He, Guan-Wei"

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Full Model for Sensors Placement and Activities Recognition
(2017-01-01)
Ching, Yu-Tai
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He, Guan-Wei
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Cheng, Chang-Chieh
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Yang, Yu-Jin
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資訊工程學系
We implemented a wired sensors system that supports activities identification. The system consists of Raspberry Pi, MPU6050 (accelerometers and gyrometers), and TCA9548 (1 to 8 multiplexer). Our experimental results show that when 6 MPU6050 attached to the right arm, right wrist. chest, waist, right thigh, and right ankle, the activities of standing, sitting, lying, walking, running, going upstairs, going downstairs. drinking water, and dumbbells activities could be identified with high accuracy. The system can connect up to 128 sensors, but under a practical sampling rate, the number of sensors should not be greater than 15. The system shall be used for finding the optimal locations for a multi -sensor wearable system (for examples, clothes or shoes).
Kaleido: Visualizing Big Brain Data with Automatic Color Assignment for Single-Neuron Images
(2018-04-01)
Wang, Ting-Yuan
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Chen, Nan-Yow
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He, Guan-Wei
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Wang, Guo-Tzau
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Shih, Chi-Tin
Effective 3D visualization is essential for connectomics analysis, where the number of neural images easily reaches over tens of thousands. A formidable challenge is to simultaneously visualize a large number of distinguishable single-neuron images, with reasonable processing time and memory for file management and 3D rendering. In the present study, we proposed an algorithm named "Kaleido" that can visualize up to at least ten thousand single neurons from the Drosophila brain using only a fraction of the memory traditionally required, without increasing computing time. Adding more brain neurons increases memory only nominally. Importantly, Kaleido maximizes color contrast between neighboring neurons so that individual neurons can be easily distinguished. Colors can also be assigned to neurons based on biological relevance, such as gene expression, neurotransmitters, and/or development history. For cross-lab examination, the identity of every neuron is retrievable from the displayed image. To demonstrate the effectiveness and tractability of the method, we applied Kaleido to visualize the 10,000 Drosophila brain neurons obtained from the FlyCircuit database Thus, Kaleido visualization requires only sensible computer memory for manual examination of big connectomics data.
Soma Detection in 3D Images of Neurons using Machine Learning Technique
(2018-01-01)
He, Guan-Wei
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Wang, Ting-Yuan
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Chiang, Ann-Shyn
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Ching, Yu-Tai
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資訊工程學系
Computing and analyzing the neuronal structure is essential to studying connectome. Two important tasks for such analysis are finding the soma and constructing the neuronal structure. Finding the soma is considered more important because it is required for some neuron tracing algorithms. We describe a robust automatic soma detection method developed based on the machine learning technique. Images of neurons were three-dimensional confocal microscopic images in the FlyCircuit database. The testing data were randomly selected raw images that contained noises and partial neuronal structures. The number of somas in the images was not known in advance. Our method tries to identify all the somas in the images. Experimental results showed that the method is efficient and robust.