Path Loss Prediction Using Fuzzy Inference System and Ellipsoidal Rules

It is well known as the prediction of radio wave path loss in urban environment plays a key role in order to correctly plan wireless systems and mobile communication networks. To obtain more flexible prediction models able to give accurate results, in recent years Soft computing Techniques has been exploited. In this study, a novel approach based on ellipsoidal fuzzy inference system EFIS is investigated. Results compared with those provided by the Okumura Hata model and the standard Fuzzy Inference System approach (FIS) show superior performances of the EFIS approach

INDEPENDENT COMPONENT ANALYSIS AND DISCRETE WAVELET TRANSFORM FOR ARTIFACT REMOVAL IN BIOMEDICAL SIGNAL PROCESSING

Recent works have shown that artifact removal in bi omedical signals can be performed by using Discrete Wavelet Transform (DWT) or Independent Component Analysis (ICA). It results often very difficult to remove some artifacts because they could be superimposed on the recordings and they could corrupt the signals in the frequency domain. The two conditions could compromise the performance of both DWT and ICA methods. In this study we show that if the two methods are jointly implemented, it is possible to improve the performances for the artifact rejection procedure. We discuss in detail the new method and we also show how this method provides advantages with respect to DWT of ICA procedure. Finally, we tested the new approach on real data