By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators publish new report on Information Theory. According to news reporting from New York City, New York, by VerticalNews journalists, research stated, "We consider the problem of decentralized scalar parameter estimation using wireless sensor networks with Gaussian noise. Specifically, we propose a novel framework based on level-triggered sampling, a non-uniform sampling strategy, and sequential estimation."
The news correspondents obtained a quote from the research from Columbia University, "The proposed estimator can be used as an asymptotically optimal fixed-sample-size decentralized estimator when the observed Fisher information, i.e., Fisher information without expectation, is deterministic, as an alternative to the one-shot estimators commonly found in the literature. It can also be used as an asymptotically optimal sequential decentralized estimator when the observed Fisher information is random. We show that the optimal centralized estimator under Gaussian noise, which is the maximum likelihood estimator, is characterized by two processes, namely the observed Fisher information U-t and the observed correlation V-t. It is noted that V-t is always random even when U-t is not. In the proposed scheme, each sensor computes its local random processes, and sends a single bit to the fusion center (FC) whenever the local random processes passes certain predefined levels. The FC, upon receiving a bit from a sensor, updates its approximation to the corresponding global random process and, accordingly, its estimate. The sequential estimation process terminates when U-t (or the approximation to it) reaches a target value. We provide an asymptotic analysis for the proposed estimator and the one based on conventional uniform-in-time sampling under both deterministic and random U-t, and determine the conditions under which they are asymptotically optimal, consistent, and asymptotically unbiased."
According to the news reporters, the research concluded: "Analytical results, together with simulation results, demonstrate the superiority of the proposed estimator based on level-triggered sampling over the traditional decentralized estimator based on uniform sampling."
For more information on this research see: Sequential Decentralized Parameter Estimation Under Randomly Observed Fisher Information. IEEE Transactions on Information Theory, 2014;60(2):1281-1300. IEEE Transactions on Information Theory can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - www.ieee.org/; IEEE Transactions on Information Theory - ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=18)
Our news journalists report that additional information may be obtained by contacting Y. Yilmaz, Columbia University, Dept. of Elect Engn, New York, NY 10027, United States.
Keywords for this news article include: New York City, United States, Information Theory, North and Central America
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