Browsing by Author "Wu, Chien-Hsien"

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Distributed Estimation for Vector Signal in Linear Coherent Sensor Networks
(2012-02-01)
Wu, Chien-Hsien
;
Lin, Ching-An
;
電控工程研究所
;
Institute of Electrical and Control Engineering
We introduce the distributed estimation of a random vector signal in wireless sensor networks that follow coherent multiple access channel model. We adopt the linear minimum mean squared error fusion rule. The problem of interest is to design linear coding matrices for those sensors in the network so as to minimize mean squared error of the estimated vector signal under a total power constraint. We show that the problem can be formulated as a convex optimization problem and we obtain closed form expressions of the coding matrices. Numerical results are used to illustrate the performance of the proposed method.
Dustiness test of nanopowders using a standard rotating drum with a modified sampling train
(2009-01-01)
Tsai, Chuen-Jinn
;
Wu, Chien-Hsien
;
Leu, Ming-Long
;
Chen, Sheng-Chieh
;
Huang, Cheng-Yu
The standard rotating drum tester was used to determine the dustiness of two nanopowders, nano-TiO(2) and fine ZnO, in standard 1-min tests. Then, the sampling train was modified to determine the number and mass distributions of the generated particles in the respirable size range using a Scanning Mobility Particle Sizer (SMPS), an Aerodynamic Particle Sizer (APS) and a Multi-orifice Uniform Deposit Impactor (MOUDI) in the 30-min tests. It was found that very few particles below 100 nm were generated and the released rate of particles decreased with increasing rotation time for both nanopowders in the 30-min tests. Due to the fluffy structure of the released TiO(2) agglomerated particles, the mass distributions measured by the MOUDI showed large differences with those determined by the APS assuming the apparent bulk densities of the powders. The differences were small for the ZnO agglomerates, which were more compact than the TiO(2) agglomerates.
Linear coherent distributed estimation over unknown channels
(2011-04-01)
Wu, Chien-Hsien
;
Lin, Ching-An
;
電控工程研究所
;
Institute of Electrical and Control Engineering
We study linear distributed estimation with coherent multiple access channel model and MMSE fusion rule. The flat fading channels are assumed unknown at the fusion center and need to be estimated. We adopt a two-phase approach, which first estimates channels and then estimates the source signal, to minimize the MSE of the estimated signal. We study optimal power allocation under a total network power constraint. We consider the optimal power allocation scheme in which training power and data power for each sensor are optimized, and the equal power allocation scheme in which training power is optimized while data power for each sensor is set equal. In both schemes, the problem is formulated as a constrained optimization problem and analytical closed-form solution is obtained. Analytic results reveal that (i) with estimated channels, the MSE approaches to a finite nonzero value as the number of sensors increases: (ii) the optimal training powers are the same in both schemes: (iii) the MSE performance compared with the case when channels are known shows the penalty caused by channel estimation becomes worse as the number of sensors increases. Simulation results verify our findings. (C) 2010 Elsevier B.V. All rights reserved.