There are two main approaches for the enhancement of data transfer rates in fiber-optical communication systems: by
increasing the number of WDM-channels or simply by increasing the bit rate of each channel. At present, a key factor
which restricts the date rate of WDM-channels is polarization mode dispersion (PMD). PMD has a significant influence
on the signal quality at bit rates of more than 10 Gbps per channel.
An adaptive optical compensator based on controlled birefringent elements for mitigation of higher order PMD is posed
a challenging method.
Using the EMTY model in combination with nonlinear optimization to adjust the compensator parameters leads to good
results (in given test - average PMD value is reduced about 2,5 times) and can be used as basis of an algorithm for a
reconfigurable optical compensator.
There is comparative analysis of methods for estimation and definition of Hoerst index (index of self-similarity) and
comparative analysis of wavelet types using for image decomposition are offered. Five types of compared wavelets are
used for analysis: Haar wavelets, Daubechies wavelets, Discrete Meyer wavelets, symplets and coiflets. Best quality of
restored image Meyer and Haar wavelets demonstrate, because of they are characterised by minimal errors of
recomposition. But compression index for these types smaller, than for Daubechies wavelets, symplets and coiflets.
Contrariwise the latter obtain less precision of decompression.
As it is necessary to take into consideration the complexity of realization some wavelet transformation on digital signal
processors (DSP), simplest method is Haar wavelet transformation.
Receiving of remote sensed data's signals in urban space information reception centers is usually difficult, because of
complex electromagnetic situation in cities and insufficient EMC. Traditional methods for digital reconstruction of
images use smoothing and autoregressive forecasting. In this case anomalous spikes on the image are lost. Alternative
method based on Kolmogorov-Wiener's filters and fractal properties of satellite images is proposed in this article.
Offered method allows to predict pixel values of satellite images with normalized RMS error of the order of 20-30%.
There is methodology of multiscale compression for satellite images in article. This methodology based on system integration multiscale signal's analisys concepts. This system use quasicontinuous scannigs of Peano-Hilbert, discrete wavelet transforms and fractal sets.
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