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1

Model of line-of-sight ultraviolet propagation

Li B., Wang Hongbo, Mao Z., Dong Z., Zhang X.

Optica Applicata

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2014

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Vol. 44, nr 4

601--610

EN

The traditional line-of-sight ultraviolet model cannot serve better for link performance study for the reason that the scattering characteristic is often ignored in the modelling process. Therefore, a line-of-sight ultraviolet bipyramid model in combination with transceiver full beam angles and geometrical relationship of the transceiver field of view intersection is established. The theoretical rationality of the bipyramid model in comparison with a traditional line-of-sight model is demonstrated by the classically analytical model for line-of-sight scenario. Based on a bit error rate requirement of voice communication, the effects of transmitting power versus range for three line-of-sight ultraviolet communication modes are further analyzed.

2

Lattice Machine Classification based on Contextual Probability

Wang Hongbo, Düntsch I., Trindade L.

Fundamenta Informaticae

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2013

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Vol. 127, nr 1-4

241--256

EN

In this paper we review Lattice Machine, a learning paradigm that “learns” by generalising data in a consistent, conservative and parsimonious way, and has the advantage of being able to provide additional reliability information for any classification. More specifically, we review the related concepts such as hyper tuple and hyper relation, the three generalising criteria (equilabelledness, maximality, and supportedness) as well as the modelling and classifying algorithms. In an attempt to find a better method for classification in Lattice Machine, we consider the contextual probability which was originally proposed as a measure for approximate reasoning when there is insufficient data. It was later found to be a probability function that has the same classification ability as the data generating probability called primary probability. It was also found to be an alternative way of estimating the primary probability without much model assumption. Consequently, a contextual probability based Bayes classifier can be designed. In this paper we present a new classifier that utilises the Lattice Machine model and generalises the contextual probability based Bayes classifier. We interpret the model as a dense set of data points in the data space and then apply the contextual probability based Bayes classifier. A theorem is presented that allows efficient estimation of the contextual probability based on this interpretation. The proposed classifier is illustrated by examples.

3

Multichannel comb filter based on linearly chirped fiber Bragg grating and DC phase shift

Sun G., Wang R., Pu Tao, Wang Hongbo, Wei Zhihu

Optica Applicata

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2011

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Vol. 41, nr 4

791--797

EN

A novel approach to implement high channel-count comb filters based on a DC phase shift is proposed. Various channel spacings can be achieved by a single chirped phase mask and a submicrometer-precision translation stage. Simulation and experiment show that arbitrary phase shifts introduced by DC refractive index modulation can be achieved. An multichannel comb filter with channel spacing of 100 GHz, 50 GHz and 40 GHz is implemented with the same phase mask. A comparison between the proposed DC phase shift and traditional discrete phase shift is made.