Browsing by Author "Chen, Yan-Ann"

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A Flexible Analysis and Prediction Framework on Resource Usage in Public Clouds
(2012)
Lin, Chia-Yu
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Chen, Yan-Ann
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Tseng, Yu-Chee
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Wang, Li-Chun
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資訊工程學系
In cloud computing environments, users can rent virtual machines (VMs) from cloud providers to execute their programs or provide network services. While using this kind of cloud services, one of the biggest problems for the users is to determine the proper number of VMs to complete the jobs considering both budget and time. In this paper, we propose a resource prediction framework (RPF), which can help users choose the minimum number of virtual machines to complete their jobs within a user specified time constraint. In order to verify the feasibility of RPF, we have done three case studies, namely parallel frequent pattern growth (FP-Growth), parallel K-means, and Particle Swarm Optimization (PSO). FP-growth, K-means and PSO are data intensive algorithms. These algorithms are typically executed repeatedly with different execution parameters to find the optimal results. When evaluating RPF by these algorithms in cloud environments, we have to modify them to parallel versions. The evaluation results indicate that RPF can successfully obtain the minimum number of VMs with acceptable errors. According to our case studies, the proposed RPF can be adopted by data intensive jobs by providing flexibility to both end users and cloud system providers.
A Framework for Simultaneous Message Broadcasting Using CDMA-Based Visible Light Communications
(2015-12-01)
Chen, Yan-Ann
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Chang, Yi-Ting
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Tseng, Yu-Chee
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Chen, Wen-Tsuen
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資訊工程學系
Internet of Things applications are fast growing recently. One of the things that has a lot of potential is the lighting equipment since it is widely used in our daily life. Recently, the technology of visible light communication (VLC) has been widely discussed. VLC has several advantages, such as freedom of license, line-of-sight security, and less health concern compared with radio-based systems. In addition, the rapid progress of light emitting diode (LED) technology by solid-sate lighting allows VLC to be easily deployed and integrated with the existing lighting infrastructure at low costs. However, VLC, when integrated with lighting infrastructure, is usually for one-way communication and is highly sensitive to external interfering light. Thus, transmitting or broadcasting multiple messages simultaneously over a visible light channel without any preprocessing may result in serious collisions. In this paper, we propose a framework to tackle these problems by optical code division multiple access (CDMA) for VLC. With our approach, a VLC receiver can enter an environment without any prior configuration and can be designed with simple hardware. Even a mobile device with a high-resolution photodiode sensor can be used as a receiver. We demonstrate an application of indoor positioning by querying the location service provider on the Internet with the IDs decoded from the received light signals. The prototyping results reveal some communication properties of CDMA-based VLC and its potential for indoor positioning applications.
Appliance Control by TDoA-based Localization and Gesture Recognition
(2018-01-01)
Chiang, Ting-Hui
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Chen, Yan-Ann
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Chang, Chun-Ting
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Chen, Ling-Jyh
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Tseng, Yu-Chee
Smart home is one of the major Internet of Things (IoT) applications. When realizing a smart home, designing an intuitive interaction for appliance control has become a crucial issue. A control intention to an appliance comprises two main meanings, identification and control command. But, previous works seldom deliver these two meanings at the same time. To fully convey the control intention, this paper proposes a scheme of intuitive appliance control that tracks and recognizes hand moving trajectory and hand gestures respectively by exploiting acoustic information. We utilize a 3D positioning method based on time difference of arrival (TDoA) of acoustic signals to detect the trajectory of a hand movement for appliance identification and design a gesture recognition method by Doppler effect for control command. We validate the effectiveness of the proposed scheme via real-life experiments. The results show that average locating errors to the targets are 5.24 degrees and 10.35 degrees in the horizontal and vertical planes respectively and the accuracy of gesture recognition is 96.66%. Since human's eyesight is horizontal to the ground, locating an object in the vertical view is error-prone. We also provide a scroll gesture to assist the appliance selection.
Applying Machine Learning to Head Gesture Recognition using Wearables
(2017-01-01)
Wu, Cheng-Wei
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Yang, Hua-Zhi
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Chen, Yan-Ann
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Ensa, Bajo
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Ren, Yi
Recently, some studies for head gestures recognition have been proposed, but most of them are based on image processing technology. Moreover, there is less focus on the use of wearable devices in recognizing head gestures. In this paper, we apply machine learning techniques to recognize some common head activities using a head-mounted wearable device. We use the wearable device to collect sensor data related to user's head activities. Then, we apply energy-based segmentation method on the collected data to find out the data segments where the activities may occur. Finally, we extract candidate features from the segments and feed them into a pre-trained classifier to identify the type of head gesture. We implement the prototype of above methods on Arduino platform and evaluate the efficiency of the proposed methods on real datasets. The experiment results show that our proposed methods can effectively and efficiently identify different types of head gestures with an average accuracy rate of 95%.
Controlling the fluid factors of an environment by sensor and actuator networks
(2016-04)
Chen, Yan-Ann
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Yeh, Lun-Wu
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Tseng, Yu-Chee
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資訊工程學系
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Department of Computer Science
Recently, wireless sensor and actuator networks (WSANs) have been widely discussed. An important application of WSANs is the control of environmental fluid factors, such as temperatures, in an environment. In this paper, we study the control of temperatures in an indoor environment with energy saving in mind. Temperature sensors are deployed in a regular grid or a random manner. Actuators are controllable air conditioners with multiple outlets. Users may be located in any places in the environment. We consider two user satisfaction models: binary and continuous. Based on these assumptions, we present models and control mechanisms to adjust air conditioners to meet users\' requirements. Simulation results show that our mechanisms can indeed achieve our goals in an efficient way. (C) 2015 Elsevier B.V. All rights reserved.
Inference of Conversation Partners by Cooperative Acoustic Sensing in Smartphone Networks
(2016-06-01)
Chen, Yan-Ann
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Chen, Ji
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Tseng, Yu-Chee
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資訊工程學系
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Department of Computer Science
A lot of personal daily contexts and activities may be inferred by analyzing acoustic signals in vicinity. Conversations play an important role in one\'s social communications. In this work, we consider the inference of conversation partners via acoustic sensing conducted by a group of smartphones in vicinity. By considering the continuity and overlap of speeches, we propose novel inference methods to identify conversational relationships among co-located users. In our system, each smartphone individually processes the acoustic data to understand its owner\'s talking turns and emotions. Via direct wireless communications, smartphones then cooperatively conduct the inference to retrieve conversational groups. Compared to existing work, which only exploits peer-to-peer conversational relationships, our approach is able to capture group conversational relationships in a more real-time manner. A prototype on Android smartphones is demonstrated to verify the feasibility of our approach. We also collect conversation data from movie clips and real life with 2 to 14 speakers to validate our result, which shows promising performance.
On Common Profile Matching among Multiparty Users in Mobile D2D Social Networks
(2014)
Chen, Yan-Ann
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Lin, Wan-Hsuan
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Tseng, Yu-Chee
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資訊工程學系
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Department of Computer Science
Recently, mobile social networks (MSNs) have been widely discussed due to the rapid growth of smart mobile devices. This work focuses on mobile D2D social networks (MDSNs), where users in an MSN are physical neighbors. An important social application of MDSNs is common profile matching (CPM), which refers to the scenario where a group of smartphone users meet in a small region (such as a ball room) and these users are interested in identifying the common attributes among them from their personal profiles efficiently via short-range (such as D2D) communications. For example, a group of strangers may want to find common hobbies, friends, or countries they visited before, and a group of students may want to know the common courses they have ever taken. Assuming that users in an MDSN form a fully connected network, we formulate three versions, namely all common, beta-common, and top-gamma-popular, of the CPM problem. The first problem is an extension of an earlier work, while the latter two problems are newly defined. We present solutions based on the basic and the iterative Bloom filters. Evaluation results show that our mechanisms are quite communication-efficient.
The Privacy Exposure Problem in Mobile Location-based Services
(2016-01-01)
Wu, Fang-Jing
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Brust, Matthias R.
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Chen, Yan-Ann
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Luo, Tie
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資訊工程學系
Mobile location-based services (LBSs) empowered by mobile crowdsourcing provide users with context-aware intelligent services based on user locations. As smartphones are capable of collecting and disseminating massive user location-embedded sensing information, privacy preservation for mobile users has become a crucial issue. This paper proposes a metric called privacy exposure to quantify the notion of privacy, which is subjective and qualitative in nature, in order to support mobile LBSs to evaluate the effectiveness of privacy-preserving solutions. This metric incorporates activity coverage and activity uniformity to address two primary privacy threats, namely activity hotspot disclosure and activity transition disclosure. In addition, we propose an algorithm to minimize privacy exposure for mobile LBSs. We evaluate the proposed metric and the privacy-preserving sensing algorithm via extensive simulations. Moreover, we have also implemented the algorithm in an Android-based mobile system and conducted real-world experiments. Both our simulations and experimental results demonstrate that (1) the proposed metric can properly quantify the privacy exposure level of human activities in the spatial domain and (2) the proposed algorithm can effectively cloak users' activity hotspots and transitions at both high and low user-mobility levels.