Browsing by Author "Chang, Yi-Hsin"

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Applying Weighted Mean Aggregation to Edge Detection of Images
(2013)
Chang, Jyh-Yeong
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Chang, Yi-Hsin
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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
This paper applies weighted mean to construct interval-valued fuzzy relations for grayscale image edge detection. This fuzzy relation image shows the changes in intensity values between a 3x3 window central pixel and its eight neighbor pixels. We employ two weighting parameters, and perform the weighted mean aggregation for the central pixel and its eight neighbor pixels in a sliding window across the image to lead to the fuzzy edge images. Finally, the image edge map is obtained through a threshold operation. Moreover, to decrease the edge detection error, weighting parameters of the mean can be learned by the gradient method caste in discrete formulation. By the training results of eight grayscale synthetic images with adding random noises, we have shown that the integration of interval-valued fuzzy relations with the weighted mean aggregation algorithm will produce a more robust response in detecting the image edge. Finally, by applying the optimal edge detection parameters to natural images, we have found that it is better compared to the well-known Canny edge detector.
Profiling Energy Consumption of I/O Functions in Embedded Applications
(2013-01-01)
Tsao, Shiao-Li
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Yu, Cheng-Kun
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Chang, Yi-Hsin
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資訊工程學系
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Department of Computer Science
I/O operations consume a significant portion of energy of an embedded system. To profile the energy consumption of I/O requests issued by a specific application helps developers to understand the energy consumption of the software and to further optimize the energy efficiency of the designs. However, most of the existing energy profiling tools concentrate on the energy consumption analyses of processors and memory, and provide limited supports to associate the energy consumption of an I/O device with high-level I/O function calls. In this paper, we propose and implement a generic framework, called energy profiling module (EPROM), which can estimate the energy consumption of I/O requests in application processes. The experimental results based on two typical I/O devices, i.e. WLAN and TFT-LCD, demonstrate that our proposed framework can provide accurate estimates on the energy consumption of I/O function calls and the errors between the estimation and measurement results are below 4%.