"This paper introduces a new improved method for obtaining the oscillation of a second-order advanced difference equation of the form \begin{equation*} \Delta(\eta(n)\Delta\chi(n))+f(n)\chi(\sigma(n))=0 \end{equation*} where $\eta(n)>0,$ $\sum_{n=n_0}^{\infty}\frac{1}{\eta(n)}<\infty,$ $f(n)>0,$ $\sigma(n)\geq n+1,$ and $\{\sigma(n)\}$ is a monotonically increasing integer sequence. We derive new oscillation criteria by transforming the studied equation into the canonical form. The obtained results are original and improve on the existing criteria. Examples illustrating the main results are presented at the end of the paper."
The paper proposes a stream of courses in telecommunications that could fit in the academic curriculum of a typical 8-semester Electronic Engineering degree. By providing a sound and well-structured theoretical and technological background and by giving emphasis to digital techniques, broadband networks and market issues, the stream aims at preparing the students to meet the high and changing requirements of the digital, broadband and de-regulated telecom environment of today.
In this paper, we present a robust classification technique to disambiguate Greek polysemous words based on hierarchical probabilistic networks. Assuming that the linguistic data of a polysemous word is classified to equivalent classes according to the number of its senses, we try to disambiguate them by using a hierarchical mixture of experts' probabilistic model — a soft version of neural networks that permits overlapping between classes and which is successfully applied in classification tasks. The model is used in combination with an effective feature selection strategy based on a Chi-square test that enhances disambiguation performance. Due to the absence of previous similar work on Greek linguistic data, for comparison we also implement and apply the very popular naïve Bayes classifier to the same data. Comparing the two systems, we find that the Hierarchical Mixtures-of-Experts (HME) model is superior to the naïve Bayes classifier, mainly because of its ability to permit overlapping and the capture of non-linearity among the data. We believe that the system can be successfully applied on real linguistic data after training on a small amount of data.
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