Person:
Wang, Yan

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Sound-based image and positon recognition system: SIPReS

2018-06 , Uno, Shin’ichiro , Suzuki, Yasuo , Watanabe, Takashi , Matsumoto, Miku , Wang, Yan

We developed software called SIPReS, which describes two-dimensional images with sound. With this system, visually-impaired people can tell the location of a certain point in an image just by hearing notes of frequency each assigned according to the brightness of the point a user touches on. It can run on Android smartphones and tablets. We conducted a small-scale experiment to see if a visually-impaired person can recognize images with SIPReS. In the experiment, the subject successfully recognized if there is an object or not. He also recognized the location information. The experiment suggests this application’s potential as image recognition software.

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First-principles study of the hydrogen-metal system

1993-05 , Wang, Yan

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Subdivisions of complete graphs

2017-05-23 , Wang, Yan

A subdivision of a graph G, also known as a topological G and denoted by TG, is a graph obtained from G by replacing certain edges of G with internally vertex-disjoint paths. This dissertation studies a problem in structural graph theory regarding subdivisions of a complete graph in graphs. In this dissertation, we focus on TK_5, or subdivisions of K_5. A well known theorem of Kuratowski in 1932 states that a graph is planar if, and only if, it does not contain a subdivision of K_5 or K_{3,3}. Wagner proved in 1937 that if a graph other than K_5 does not contain any subdivision of K_{3,3} then it is planar or it admits a cut of size at most 2. Kelmans and, independently, Seymour conjectured in the 1970s that if a graph does not contain any subdivision of K_5 then it is planar or it admits a cut of size at most 4. In this dissertation, we give a proof of the Kelmans-Seymour conjecture.

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Asymptotic theory for decentralized sequential hypothesis testing problems and sequential minimum energy design algorithm

2011-05-19 , Wang, Yan

The dissertation investigates asymptotic theory of decentralized sequential hypothesis testing problems as well as asymptotic behaviors of the Sequential Minimum Energy Design (SMED). The main results are summarized as follows. 1.We develop the first-order asymptotic optimality theory for decentralized sequential multi-hypothesis testing under a Bayes framework. Asymptotically optimal tests are obtained from the class of "two-stage" procedures and the optimal local quantizers are shown to be the "maximin" quantizers that are characterized as a randomization of at most M-1 Unambiguous Likelihood Quantizers (ULQ) when testing M >= 2 hypotheses. 2. We generalize the classical Kullback-Leibler inequality to investigate the quantization effects on the second-order and other general-order moments of log-likelihood ratios. It is shown that a quantization may increase these quantities, but such an increase is bounded by a universal constant that depends on the order of the moment. This result provides a simpler sufficient condition for asymptotic theory of decentralized sequential detection. 3. We propose a class of multi-stage tests for decentralized sequential multi-hypothesis testing problems, and show that with suitably chosen thresholds at different stages, it can hold the second-order asymptotic optimality properties when the hypotheses testing problem is "asymmetric." 4. We characterize the asymptotic behaviors of SMED algorithm, particularly the denseness and distributions of the design points. In addition, we propose a simplified version of SMED that is computationally more efficient.

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