Browsing by Author "Juan, Hung-Hui"

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Analytical Model for Wireless Communication Systems
(2010-06-01)
Juan, Hung-Hui
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Huang, ChingYao
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交大名義發表
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National Chiao Tung University
In this paper, a user capacity model, combining both the modulation and coding scheme (MCS) distribution model and resource allocation model, is developed to analyze the system performance of wireless communication systems. From the model, we can intuitively calculate the system capacity from MCS distribution. An example based on relay networks is used to quantify the benefit of deploying relay stations.
Cross-layer mobile WiMAX MAC designs for the H.264/AVC scalable video coding
(2010-01-01)
Juan, Hung-Hui
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Huang, Hsiang-Chun
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Huang, ChingYao
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Chiang, Tihao
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電子工程學系及電子研究所
To effectively provide the QoS of streaming services in mobile WiMAX systems. this paper proposes it cross-layer design between the streaming server and mobile WiMAX base stations. We will show that for each user the Implementation Of Multiple connections with feedback Information of the available transmission bandwidth is critical for supporting H.264/AVC-based scalable video coding in which the transmission packets call be further separated into multiple levels of importance. To quantify the performance, a mobile WiMAX simulation platform including both the video encoder/decoder and IEEE 802.16e MAC controls is developed.
IMPROVED SPECTRAL MATTING BY ITERATIVE K-MEANS CLUSTERING AND THE MODULARITY MEASURE
(2012)
Wu, Tung-Yu
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Juan, Hung-Hui
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Lu, Henry Horng-Shing
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統計學研究所
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Institute of Statistics
Spectral matting is a useful technique for image matting problem. A crucial issue of spectral matting is to determine the number of matting components which has large impacts on the matting performance. In this paper, we propose an improved framework based on spectral matting in order to solve this limitation. Iterative K-means clustering with the assistance of the modularity measure is adopted to obtain the hard segmentation that can be used as the initial guess of soft matting components. The number of matting components can be determined automatically because the improved framework will search possible image components by iteratively dividing image subgraphs.
Radio resource management of heterogeneous services in mobile WiMax systems
(2007-02-01)
Huang, ChingYao
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Juan, Hung-Hui
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Lin, Meng-Shiang
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Chang, Chung-Ju
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電子工程學系及電子研究所
IEEE 802.16e, known as Mobile WiMAX, has gained much attention recently for its capability to support high transmission rates in cellular environments and QoS for different applications. Beyond what the standard can define, in order to effectively support video streaming, VoIP, and data services, proprietary radio resource management, including multi-connection assignment, scheduling controls, and call admission controls, are essential. In this study we evaluate the downlink performance of a Mobile WiMAX cellular system with different radio resource management, especially the scheduler for QoS control and the implementation of multiconnection for streaming applications.
Scalable video streaming over mobile WiMAX
(2007)
Juan, Hung-Hui
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Huang, Hsiang-Chun
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Huang, ChingYao
;
Chiang, Tihao
;
電子工程學系及電子研究所
In this paper, we will investigate the performance of scalable video streaming services in mobile WiMAX systems. We will show that for each user the implementation of multiple connections with feedback information of the available transmission bandwidth is critical for supporting H.264/AVC-based scalable video streaming in which the transmission packets can be further separated into multiple levels of importance. Based on a mobile WiMAX simulation platform including both the video encoder/decoder server and IEEE 802.16e MAC controls, the MAC performance evaluation and subjective test are used to quantify the visual performance between two-connection and one-connection scenarios.
Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning
(2018-01-11)
Chen, Chien-Chang
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Juan, Hung-Hui
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Tsai, Meng-Yuan
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Lu, Henry Horng-Shing
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統計學研究所
By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher's iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.