Sparse Blind Source Separation (BSS) problems have recently received some attention. And some of them have been proposed for the unknown number of sources. However, they only consider the overdetermined case (i.e. with more sources than sensors). In the practical BSS, there are not prior assumptions on the number of sources. In this paper, we use cluster and Principal Component Analysis (PCA) t...

Recent work in blind source separation applied to anechoic mixtures of speech allows for reconstruction of sources that rarely overlap in a time-frequency representation. While the assumption that speech mixtures do not overlap significantly in time-frequency is reasonable, music mixtures rarely meet this constraint, requiring new approaches. We introduce a method that uses spatial cues from an...

The development of future software for proposed new computer architectures is expected to make significant demands on compiler technologies. Significant rewriting of applications will be likely to support the use of new hardware features and preserve the current software investment. Existing compilers provide little support for the broad range of research efforts addressing exascale challenges,...

OBJECTIVE One of the most widely used approaches in electroencephalography/magnetoencephalography (MEG) source imaging is application of an inverse technique (such as dipole modelling or sLORETA) on the component extracted by independent component analysis (ICA) (sensor-space ICA + inverse technique). The advantage of this approach over an inverse technique alone is that it can identify and loc...

source localization is an essential part of array signal processing including radar, sonar, seismology and oceanography applications. the performance of the presented source localization methods are degraded substantially in practical situations. one of the conditions which decreases the performance o