Browsing by Author "Chung, Pei-Jung"

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ML estimation under misspecified number of signals
(2005)
Chung, Pei-Jung
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
The maximum likelihood (ML) approach for estimating direction of arrival (DOA) is a well known and popular technique in array processing. Its statistical properties such as consistency and efficiency have been extensively studied in the literature. A common assumption made in previous works is that the assumed number of signals equals the true one. However, this information is not always available and usually needs to be estimated together with the DOA parameter. Thus it is crucial to know whether ML estimator provides any significant information when we are not certain about the number signals. In this work, we show that ML estimator under misspecified number of signals converges to a well defined limit. In the case of well separated sources, components of ML estimates coincide with the true parameters. Our theoretical analysis is validated by numerical experiments.
On signal detection using the Benjamini-Hochberg procedure
(2005)
Chung, Pei-Jung
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Boehme, Johann E.
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Mecklenbraeuker, Christoph F.
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Hero, Alfred O.
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電子工程學系及電子研究所
We investigate a multiple hypothesis test designed for detecting signals embedded in noisy observations of a sensor array. The global level of the multiple test is controlled by the false discovery rate (FDR) criterion recently sugaested by Benjamini and Hochberg instead of the classical familywise error rate (FWE) criterion. In the previous study [3], the suggested procedure has shown promising results oil simulated data. Here we carefully examine the independence condition required by the Benjamini Hochberg procedure to ensure the control of FDR. Based oil the properties of beta distribution, we proved the applicability of the Benjamini Hochberg procedure to the underlying test. Further simulation results show that the false alarm rate is less than 0.02 for a choice of FDR as high as 0.1, implying the reliability of the test has not been affected by the increase in power.
SDN-Enabled Traffic-Aware Load Balancing for M2M Networks
(2018-06-01)
Chen, Yu-Jia
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Wang, Li-Chun
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Chen, Meng-Chieh
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Huang, Pin-Man
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Chung, Pei-Jung
This paper proposes a traffic-aware load balancing scheme for machine-to-machine (M2M) networks using software-defined networking (SDN). Load balancing techniques are essential for M2M networks to relieve the heavy loading caused by bursty traffic. Leveraging the capability of SDN to monitor and control the network, the proposed load balancing scheme can satisfy different quality of service requirements through traffic identification and rerouting. Experimental results show that the proposed scheme can reduce service response time up to 50% compared to the non-SDN load balancing scheme.
The Methodology of the Maximum Likelihood Approach Estimation, detection, and exploration of seismic events
(2012-05-01)
Chung, Pei-Jung
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Boehme, Johann F.
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交大名義發表
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National Chiao Tung University