Until now, it is still difficult to identify different kinds of celestial bodies depending on their spectra, because it needs a lot of astronomers' manual work of measuring, marking and identifying, which is gener...
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ISBN:
(纸本)0819445606
Until now, it is still difficult to identify different kinds of celestial bodies depending on their spectra, because it needs a lot of astronomers' manual work of measuring, marking and identifying, which is generally very hard and time-consuming. And with the exploding spectral data from all kinds of telescopes, it is becoming more and more urgent to find a thoroughly automatic way to deal with such a kind of problem. In fact, when we change our viewpoint, we can find that it is a traditional problem in patternrecognition field when considering the whole process of dealing with spectral signals: filtering noises, extracting features, constructing classifiers, etc. The main purpose for automatic classification and recognition of spectra in LAMOST (Large Sky Area Multi-Object Fibre Spectroscopic Telescope) project is to identify a celestial body's type only based on its spectrum. For this purpose, one of the key steps is to establish a good model to describe all kinds of spectra and thus it will be available to construct some excellent classifiers. In this paper, we present a novel describing language to represent spectra. And then, based on the language, we use some algorithms to extract classifying rules from raw spectra datasets and then construct classifiers to identify spectra by using rough set method. Compared with other methods, our technique is more similar to man's thinking way, and to some extent, efficient.
In underwater acoustic communication, (ACOMMs), ocean surface and bottom conditions create multi path propagation's for the transmitted signal that result in Inter symbol Interference (ISI) at the receiver. Genera...
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In underwater acoustic communication, (ACOMMs), ocean surface and bottom conditions create multi path propagation's for the transmitted signal that result in Inter symbol Interference (ISI) at the receiver. Generally, Equalization, Diversity / Beam forming, and Channel Coding are three independent techniques that are used to improve received signal quality. Equalization compensates for ISI created by a band-limited, time-dispersive channel through implementation of specialized filtering schemes within the receiver. The coefficients of the equalizer need to be continuously adjusted to compensate for the variability in the channel. Since the number of states required by the equalizer (or beam former) is finite, and may be described over a limited set of operating conditions (environments), it is worthwhile to consider a patternrecognition approach for identifying channel conditions and subsequent equalizer state specifications. This paper describes the approach and preliminary results obtained form the use of patternrecognition techniques to select a set of `best choice' equalizer coefficients and to decode a signal sequence directly. The method does not rely on the application of any adaptive algorithm for estimation of the equalizer coefficients during the actual data transmission or reception. Expectations are that performance benefits may be gained in those cases where adaptive algorithms fail to select the optimal filter coefficients due to computational complexity or other factors.
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