Browsing by Author "Ko, Chun-Hsien"

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A CLOUD MODEL AND CONCEPT PROTOTYPE FOR COGNITIVE RADIO NETWORKS
(2012-08-01)
Wu, Sau-Hsuan
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Chao, Hsi-Lu
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Ko, Chun-Hsien
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Mo, Shang-Ru
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Jiang, Chung-Ting
The FCC's approval for the first commercial operation in TV white space gives new momentum to the development of cognitive radio in TVWS. On the other hand, the rapid growth of Cloud computing makes it possible and more economical to build a CR metropolitan area network with commodity hardware. In view of the opportunity and challenges brought about by these two technologies, we propose a CR cloud networking model that is able to support CR access in TVWS. Making use of the flexible and vast computing capacity of the cloud, a database and a cooperative spectrum sensing algorithm that estimates the radio power map of licensed users are realized on a CR cloud implemented with Microsoft's Windows Azure Cloud platform. The CRC can support CSS, dynamic spectrum access and mobility management. A medium access control protocol is also developed for this CRCN model to collect sensing reports and provide access to the TVWS and CRC services. Through this CRCN prototype, important network parameters such as the mean squared errors in CSS, the CR channel vacating delay, and the cloud-based handover time are measured for the design and deployment of the CRCN concept.
A Proactive Indoor Positioning System in Randomly Deployed Dense WiFi Networks
(2016-01-01)
Ko, Chun-Hsien
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Wu, Sau-Hsuan
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電信工程研究所
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Institute of Communications Engineering
A new approach for indoor positioning is presented, aimed at designing a WiFi positioning system that is feasible and convenient for both service providers and end users. In the proposed approach, only access points (APs) need to collect the received signal strengthes (RSS) of mobile devices, and use these RSS samples to jointly estimate the devices' locations. To enhance the accuracy of positioning, the relationship between the RSS samples and their geometrical locations is explored, leading to a sparse Bayesian model for the radio power map of the RSS observations of each AP. With more than 20 training anchors, the accuracy of the proposed model-based positioning method can be lower than 3.4 meters in an indoor space with only 4 randomly deployed APs, which outperforms the fingerprinting method by 0.4 meter. Extensive experimental results also verify that the proposed positioning service can offer considerable accuracy with only limited efforts in training, suggesting that the prototype is realistic for randomly deployed dense WiFi networks.
AF Detection by Exploiting the Spectral and Temporal Characteristics of ECG Signals With the LSTM Model
(2018-01-01)
Chang, Yen-Chun
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Wu, Sau-Hsuan
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Tseng, Li-Ming
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Chao, Hsi-Lu
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Ko, Chun-Hsien
This research reinvestigates the detection of atrial fibrillation (AF) from a recurrent neural network (RNN) viewpoint. In particular, a long short-term memory (LSTM) model of RNN is designed to exploit the high-order spectral and temporal features of the multi-lead electrocardiogram (ECG) signals of patients with AF. To verify thethe proposed method, the LSTM model is tested with ECG data available from the PhysioNet and some normal ECG data collected in our labs. The results show that not only the deviation of the so-called RR intervals of ECG signals but also its temporal variations are critical to AF detection. The accuracy of AF detection can reach up to 98.3 %, with an LSTM model of using 30 hidden units. Considering more realistic applications, we further tested the model with subjects different from that of the training data. The accuracy is about 87% with high sensitivity. The experimental results show that the proposed model is able to effectively extract both the long-term and short-term characteristics of the spectral content of the AF ECG signals, making it a good candidate model for AF detection.