Browsing by Author "Chen, Chien-Nan"

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A Vital Wearing System with Wireless Capability
(2008)
Cheng, Mu-Huo
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Chen, Li-Chung
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Hung, Ying-Che
;
Chen, Chien-Nan
;
Yang, Chang Ming
A system with wearable sensors for detecting and sensing vital and physical motion signals such as the ECG, respiration, temperature, sweatiness, three-axis accelerations, and the motions of both knee ankles and elbows is presented. The system is also equipped with a bluetooth transceiver such that the obtained sensor signals can be transmitted to mobile phones or remote computers for either monitoring or post signal processing to extract knowledges or information. The presented system is embedded with signal processing algorithms such as the heart rate, the respiratory period, the body temperature, the degree of sweatiness, and the posture of wearing person are demonstrated. The heart rate and heart rate variation from the measured ECG data are estimated using an adaptive ESPRIT algorithm. This system is highly suitable for applications of remote healthcare and wellness.
Optimal Resource Allocation for Increasing Strictly Concave Utility Functions in Wireless Networks
(2012-03-01)
Lee, Tsern-Huei
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Huang, Yu-Wen
;
Chen, Chien-Nan
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傳播研究所
;
Institute of Communication Studies
Utility functions are widely used to model user-perceived service quality. For elastic traffic, the utility function is often concave. An elastic allocation (EA) algorithm has recently been proposed to maximize the total utility obtained by users, assuming resource is infinitesimally divisible and queues are constantly backlogged. We found that the EA algorithm is not always optimal. In fact, the solution it obtains can be infeasible. In this paper, we present a modified EA algorithm that is guaranteed to find the optimal solution under the same assumptions. The result is generalized for a system where queues are generally backlogged. In a real system, there is normally a basic unit for resources. Therefore, we further extend the designs to such a system for both constantly backlogged and generally backlogged queues. To reduce computational complexity, we also propose suboptimal resource allocation algorithms. Simulations are conducted to evaluate the proposed algorithms in terms of utility sum and execution time. Results show that our proposed algorithms perform better than previous works. Moreover, the performances of the proposed suboptimal algorithms are close to those of the optimal algorithms.