A blind source separation algorithm is used to estimate the mixing operator from electromagnetic emission signals through independent component analysis (ICA) technique. The mixing operator contains important information about the sources’ features and about the electromagnetic field propagation phenomena, thus, by properly interpreting the results given by the ICA algorithm a blind source loca...

The paper presents an approach that may enable the separation of the vibrations induced by underground traffic from the vibrations induced by other sources, based on Second Order Blind Identification (SOBI) algorithm. The signals recorded in different locations of an instrumented building are mixed signals from different internal and external vibration sources. The blind source separation algor...

When trying to understand the brain, it is of fundamental importance to analyse (e.g. from EEG/MEG measurements) what parts of the cortex interact with each other in order to infer more accurate models of brain activity. Common techniques like Blind Source Separation (BSS) can estimate brain sources and single out artifacts by using the underlying assumption of source signal independence. Howev...

Source separation is one of the signal processing’s main emerging domain. Many techniques such as maximum likelihood (ML), Infomax, cumulant matching, estimating function, etc. have been used to address this difficult problem. Unfortunately, up to now, many of these methods could not account completely for noise on the data, for different number of sources and sensors, for lack of spatial indep...