Browsing by Author "Chen, Yi-Cheng"

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A 100 MHZ 1920x1080 HD-PHOTO 20 FRAMES/SEC JPEG XR ENCODER DESIGN
(2008)
Chien, Ching-Yen
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Huang, Sheng-Chieh
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Lin, Shih-Hsiang
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Huang, Yu-Chieh
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Chen, Yi-Cheng
A JPEG XR chip for HD-Photo is implemented with 25 MM area in TSMC 0.18um CMOS 1P6M technology at 100MHz According to the simulation results, the 4:4:4 1920x1080 HD-Photo 20 frames/sec can be encoded smoothly.
A BIOMEDICAL ENTERTAINMENT PLATFORM DESIGN BASED ON MUSICAL RHYTHM CHARACTERISTICS AND HEART RATE VARIABILITY (HRV)
(2008)
Huang, Yu-Chieh
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Lin, Shih-Hsiang
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Chien, Ching-Yen
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Chen, Yi-Cheng
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Chou, Lei-Chun
Heart rate variability (HRV) is a measure of variations in the heart rate. Over the last 25 years, HRV analysis has became popular as a non-invasive research and clinical tool for indirectly investigating both cardiac and autonomic nervous system (ANS) function in both health and disease area. How the musical rhythmic characteristics, tempo and complexity, affect the performance of HRV is studied in this work. By understanding the relationship between music and the function of ANS, the novel biomedical entertainment platform is proposed and used for relaxation.
A Novel System for Extracting Useful Correlation in Smart Home Environment
(2013)
Chen, Yi-Cheng
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Peng, Wen-Chih
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Lee, Wang-Chien
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資訊工程學系
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Department of Computer Science
Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this paper, a novel system, namely, Correlation Pattern Mining System (CPMS), is developed to capture the usage patterns and correlations among appliances. With several new optimization techniques, CPMS can reduce the search space effectively and efficiently. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.
A study of the relationship between two musical rhythm characteristics and heart rate variability (HRV)
(2008)
Lin, Shih-Hsiang
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Huang, Yu-Chieh
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Chien, Ching-Yen
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Chen, Yi-Cheng
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Chou, Lei-Chun
Heart rate variability (HRV) is a measure of variations in the heart rate. Over the last 25 years, HRV analysis has became more and more popular as a non-invasive research and clinical tool for indirectly investigating both cardiac and autonomic nervous system (ANS) function in both health and disease area. How the musical rhythmic characteristics, tempo and complexity, affect the performance of HRV is studied in this work. By understanding the relationship between music and the function of ANS, we can improve our life and health by music - non-invasively and simply.
An Intelligent System for Mining Usage Patterns from Appliance Data in Smart Home Environment
(2012)
Chen, Yi-Cheng
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Ko, Yu-Lun
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Peng, Wen-Chih
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資訊工程學系
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Department of Computer Science
In the last decade, considerable concern has arisen over the electricity saving due to the issue of reducing greenhouse gases. Previous studies on usage pattern utilization mainly are focused on power disaggregation and appliance recognition. Little attention has been paid to utilizing pattern mining for the target of energy saving. In this paper, we develop an intelligent system which analyzes appliance usage to extract users' behavior patterns in a smart home environment. With the proposed system, users can acquire the electricity consumption of each appliance for energy saving easily. In advance, if the electricity cost is high, users can observe the abnormal usage of appliances from the proposed system. Furthermore, we also apply our system on real-world dataset to show the practicability of mining usage pattern in smart home environment.
CIM: Community-Based Influence Maximization in Social Networks
(2014-04-01)
Chen, Yi-Cheng
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Zhu, Wen-Yuan
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Peng, Wen-Chih
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Lee, Wang-Chien
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Lee, Suh-Yin
Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.
Dependence of GaN Defect Structure on the Growth Temperature of the AlN Buffer Layer
(2008)
Wong, Yuen-Yee
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Chang, Edward Yi
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Yang, Tsung-Hsi
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Chang, Jet-Rung
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Chen, Yi-Cheng
The defect structure of the GaN film grown on sapphire by plasma-assisted molecular beam epitaxy (PAMBE) technique was found to be dependent on the AlN buffer layer growth temperature. This buffer growth temperature controlled the defect density in GaN film but had shown contrary effects on the density of screw threading dislocation (TD) and edge TD. The density of screw TD was high on lower temperature buffer but low on the higher temperature buffer. Meanwhile the density of edge TD had shown the opposite. Further examinations have suggested that the defect structure was closely related to the stress in the GaN film, which can be controlled by the growth temperature of the AlN buffer. Using the 525 degrees C AlN buffer, optimum quality GaN Film with relatively low screw and edge TDs were achieved.
Efficient algorithms for influence maximization in social networks
(2012-12-01)
Chen, Yi-Cheng
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Peng, Wen-Chih
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Lee, Suh-Yin
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資訊工程學系
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Department of Computer Science
In recent years, due to the surge in popularity of social-networking web sites, considerable interest has arisen regarding influence maximization in social networks. Given a social network structure, the problem of influence maximization is to determine a minimum set of nodes that could maximize the spread of influences. With a large-scale social network, the efficiency and practicability of such algorithms are critical. Although many recent studies have focused on the problem of influence maximization, these works in general are time-consuming when a social network is large-scale. In this paper, we propose two novel algorithms, CDH-Kcut and Community and Degree Heuristic on Kcut/SHRINK, to solve the influence maximization problem based on a realistic model. The algorithms utilize the community structure, which significantly decreases the number of candidates of influential nodes, to avoid information overlap. The experimental results on both synthetic and real datasets indicate that our algorithms not only significantly outperform the state-of-the-art algorithms in efficiency but also possess graceful scalability.
Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams
(2008)
Li, Hua-Fu
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Huang, Hsin-Yun
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Chen, Yi-Cheng
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Liu, Yu-Jiun
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Lee, Suh-Yin
Efficient mining of high utility itemsets has become one of the most interesting data mining tasks with broad applications. In this paper, we proposed two efficient one-pass algorithms, MHUI-BIT and MHUI-TID, for mining high utility itemsets from data streams within a transaction-sensitive sliding window. Two effective representations of item information and an extended lexicographical tree-based summary data structure are developed to improve the efficiency of mining high utility itemsets. Experimental results show that the proposed algorithms outperform than the existing algorithms for mining high utility itemsets from data streams.
Five-element circuit model using linear-regression method to correct the admittance measurement of metal-oxide-semiconductor capacitor
(2009-01-01)
Cheng, Chao-Ching
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Chien, Chao-Hsin
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Luo, Guang-Li
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Liu, Jun-Cheng
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Chen, Yi-Cheng
The authors present a linear-regression method based on a five-element circuit model to correct measured capacitance-voltage and conductance-voltage curves. This model explains the effects of series resistance and parasitic capacitance/inductance on the frequency dispersion of measured capacitance and the magnification of measured conductance. These extracted parasitic components show significant dependencies on the geometry of capacitor structure, thereby causing different frequency-dependent capacitance characteristics in measurements. (C) 2009 American Vacuum Society. [DOI: 10.1116/1.3058724]
Growth of free-standing GaN layer on Si(111) substrate
(2009-03-15)
Yang, Tsung Hsi
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Ku, Jui Tai
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Chang, Jet-Rung
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Shen, Shih-Guo
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Chen, Yi-Cheng
This investigation demonstrates the epitaxial growth of a free-standing GaN layer on Si(1 1 1) using a funnel-like GaN nano-rod buffer structure. The funnel-like GaN nano-rods were directly grown on Si substrates by RF-plasma molecular beam epitaxy. Free-standing GaN layers were achieved through the coalescence of funnel-like GaN nano-rods by the metalorganic chemical vapor deposition. This study examines the structure, optical characteristics and stress of GaN nano-rods and free-standing GaN layers. The c-axis lattice constant of the strain-free Ga-face GaN layer on Si is 5.1844 angstrom, as determined by high-resolution X-ray diffraction. The fully relaxed band edge at 3.468 eV without deep-level emission around 2.3 eV, was revealed in a free-standing GaN layer on Si, using photoluminescence. (C) 2008 Elsevier B.V. All rights reserved.
Helicity-dependent terahertz emission spectroscopy of topological insulator Sb2Te3 thin films
(2017-11-06)
Tu, Chien-Ming
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Chen, Yi-Cheng
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Huang, Ping
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Chuang, Pei-Yu
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Lin, Ming-Yu
We report on helicity-dependent terahertz emissions that originate from the helicity-dependent photocurrents in topological insulator Sb2Te3 thin films due to ultrafast optical excitation. The polarity of the emitted terahertz radiation is controlled by both the incident angle and the helicity of optical pulses. Using an unprecedented decomposition-recombination procedure in the time domain, the signals of the Dirac fermions are fully separated from bulk contributions. These results provide insights into the optical coupling of topological surface states and open up opportunities for applying helicity-dependent terahertz emission spectroscopy in spintronics.
Helicity-Dependent Terahertz Radiation from Topological Insulator Sb2Te3 Thin Film by Femtosecond Optical Excitation
(2016)
Tu, Chien-Ming
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Chen, Yi-Cheng
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Huang, Ping
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Chuang, Pei-Yu
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Lin, Ming-Yu
We report on terahertz radiation from topological insulator (TI)Sb2Te3 thin film under ultrafast optical excitation with different helicity. Polarity-reversals of the emitted THz radiation were observed as the helicity of optical pulses reversed. The observed phenomena are coincident with the characteristics of the helicity-dependent photocurrent on TIs. Our work demonstrates the potential applications of surface photocurrents on TIs for opto-spintronics devices.
Heteroepitaxy of Co-Based Heusler Compound/Muscovite for Flexible Spintronics
(2019-09-25)
Chen, Yi-Cheng
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Yen, Min
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Lai, Yu-Hong
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Markou, Anastasios
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Zhang, Liguo
Materials with high spin-polarization play an important role in the development of spintronics. Co-based Heusler compounds are a promising candidate for practical applications because of their high Curie temperature and tunable half-metallicity. However, it is a challenge to integrate Heusler compounds into thin film heterostructures because of the lack of control on crystallinity and chemical disorder, critical factors of novel behaviors. Here, muscovite is introduced as a growth substrate to fabricate epitaxial Co2MnGa films with mechanical flexibility. The feature of heteroepitaxy is evidenced by the results of X-ray diffraction and transmission electron microscopy. Moreover, high chemical ordering with superior properties is delivered according to the observation of large Hall conductivity (680 Omega(-1) cm(-1)) and highly saturated magnetic moment (similar to 3.93 mu(B)/f.u.), matching well with bulk crystals. Furthermore, the excellence of magnetic and electrical properties is retained under the various mechanical bending conditions. Such a result suggests that the development of Co2MnGa/muscovite heteroepitaxy provides not only a pathway to the thin film heterostructure based on high-quality Heusler compounds but also a new aspect of spintronic applications on flexible substrates.
Incremental mining of temporal patterns in interval-based database
(2016-02-01)
Hui, Lin
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Chen, Yi-Cheng
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Weng, Julia Tzu-Ya
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Lee, Suh-Yin
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資訊工程學系
In several real-life applications, sequence databases, in general, are updated incrementally with time. Some discovered sequential patterns may be invalidated and some new ones may be introduced by the evolution of the database. When a small set of sequences grow, or when some new sequences are added into the database, re-mining sequential patterns from scratch each time is usually inefficient and thus not feasible. Although there have been several recent studies on the maintenance of sequential patterns in an incremental manner, these works only consider the patterns extracted from time point-based data. Few research efforts have been elaborated on maintaining time interval-based sequential patterns, also called temporal patterns, where each datum persists for a period of time. In this paper, an efficient algorithm, Inc_TPMiner (Incremental Temporal Pattern Miner) is developed to incrementally discover temporal patterns from interval-based data. Moreover, the algorithm employs some optimization techniques to reduce the search space effectively. The experimental results on both synthetic and real datasets indicate that Inc_TPMiner significantly outperforms re-mining with static algorithms in execution time and possesses graceful scalability. Furthermore, we also apply Inc_TPMiner on a real dataset to show the practicability of incremental mining of temporal patterns.
Incrementally Mining Temporal Patterns in Interval-based Databases
(2014)
Chen, Yi-Cheng
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Weng, Julia Tzu-Ya
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Wang, Jun-Zhe
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Chou, Chien-Li
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Huang, Jiun-Long
In several applications, sequence databases generally update incrementally with time. Obviously, it is impractical and inefficient to re-mine sequential patterns from scratch every time a number of new sequences are added into the database. Some recent studies have focused on mining sequential patterns in an incremental manner; however, most of them only considered patterns extracted from time point-based data. In this paper, we proposed an efficient algorithm, Inc_TPMiner, to incrementally mine sequential patterns from interval-based data. We also employ some optimization techniques to reduce the search space effectively. The experimental results indicate that Inc_TPMiner is efficient in execution time and possesses scalability. Finally, we show the practicability of incremental mining of interval-based sequential patterns on real datasets.
Interactive Visual Analysis for Vehicle Detector Data
(2015-06-01)
Chen, Yi-Cheng
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Wang, Yu-Shuen
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Lin, Wen-Chieh
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Huang, Wei-Xiang
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Lin, I-Chen
Visualization of vehicle detection (VD) data is essential because the data play an important role in traffic control and policy development. Most previous works focus on visualizing trajectories obtained from global positioning system (GPS), which are detailed but less representative. In contrast, VD data report the traffic statistic at each sensing site during a time span, including speed, flow, and occupancy of each lane, which contain comprehensive traffic information for analysis. In this work, we visualize three-year VD data of freeways in Taiwan. The visualization depicts the traffic situation at a site over time using a color-coded chart that extends from left to right over time. The charts are vertically stacked and horizontally aligned according to VD\'s located mileage and data time, respectively, to provide global insight. Our system allows semantic zoom, which changes the chart appearance in a continuous manner, to enable macro- and micro- scopic visualizations. Analysts can explore events that span an area with different sizes and that persist a time span with various lengths. To ensure the feasibility of our visualization, before the system design, we conducted a study with experts who work in the national freeway bureau and the institute of transportation of Taiwan. We also showed our results to the experts after the prototype system was built. The feedback shows that our VD data visualization is helpful to traffic control and policy development.
Large anomalous Nernst effect in thin films of the Weyl semimetal Co2MnGa
(2018-11-19)
Reichlova, Helena
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Schlitz, Richard
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Beckert, Sebastian
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Swekis, Peter
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Markou, Anastasios
The magneto-thermoelectric properties of Heusler compound thin films are very diverse. Here, we discuss the anomalous Nernst response of Co2MnGa thin films. We systematically study the anomalous Nernst coefficient as a function of temperature, and we show that unlike the anomalous Hall effect, the anomalous Nernst effect in Co2MnGa strongly varies with temperature. We exploit the on-chip thermometry technique to quantify the thermal gradient, which enables us to directly evaluate the anomalous Nernst coefficient. We compare these results to a reference CoFeB thin film. We show that the 50-nm-thick Co2MnGa films exhibit a large anomalous Nernst effect of -2 mu V/K at 300 K, whereas the 10-nm-thick Co2MnGa film exhibits a significantly smaller anomalous Nernst coefficient despite having similar volume magnetizations. These findings suggest that the microscopic origin of the anomalous Nernst effect in Co2MnGa is complex and may contain contributions from skew-scattering, side-jump, or intrinsic Berry phase. In any case, the observed anomalous Nernst coefficient of -2 mu V/K at 300 K is large compared to the values measured in other thin films and makes this material system a very promising candidate for efficient spin-caloritronic devices. Published by AIP Publishing.
Mining Temporal Patterns in Interval-Based Data
(2016)
Chen, Yi-Cheng
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Peng, Wen-Chih
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Lee, Suh-Yin
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資訊工程學系
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Department of Computer Science
Sequential pattern mining is an important subfield in data mining. Recently, discovering patterns from interval events has attracted considerable efforts due to its widespread applications. However, due to the complex relation between two intervals, mining interval-based sequences efficiently is a challenging issue. In this paper, we develop a novel algorithm, P-TPMiner, to efficiently discover two types of interval-based sequential patterns. Some pruning techniques are proposed to further reduce the search space of the mining process. Experimental studies show that proposed algorithm is efficient and scalable. Furthermore, we apply proposed method to real datasets to demonstrate the practicability of discussed patterns.
Mining Temporal Patterns in Time Interval-Based Data
(2015-12-01)
Chen, Yi-Cheng
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Peng, Wen-Chih
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Lee, Suh-Yin
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資訊工程學系
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Department of Computer Science
Sequential pattern mining is an important subfield in data mining. Recently, applications using time interval-based event data have attracted considerable efforts in discovering patterns from events that persist for some duration. Since the relationship between two intervals is intrinsically complex, how to effectively and efficiently mine interval-based sequences is a challenging issue. In this paper, two novel representations, endpoint representation and endtime representation, are proposed to simplify the processing of complex relationships among event intervals. Based on the proposed representations, three types of interval-based patterns: temporal pattern, occurrence-probabilistic temporal pattern, and duration-probabilistic temporal pattern, are defined. In addition, we develop two novel algorithms, Temporal Pattern Miner (TPMiner) and Probabilistic Temporal Pattern Miner (P-TPMiner), to discover three types of interval-based sequential patterns. We also propose three pruning techniques to further reduce the search space of the mining process. Experimental studies show that both algorithms are able to find three types of patterns efficiently. Furthermore, we apply proposed algorithms to real datasets to demonstrate the effectiveness and validate the practicability of proposed patterns.