Large sets of multivariate measurement data are now routinely available through automated in-process measurement in many manufacturing industries. These data typically contain valuable information regarding the nature of each major source of process variability. In this article we assume that each variation source causes a distinct spatial variation pattern in the measurement data. The model th...

We assess the reliability of space-borne InSAR-derived deformation source parameters for volcanic and seismic events, with special focus on the future L-band data of the proposed Tandem-L mission [1-3]. Using representative simulation cases, the influence of certain characteristics of the InSAR measurements on the source model parameter precision is quantified. The performance drivers are asses...

A new blind source separation method is proposed to solve the single-channel mechanical signal separation. The new approach consists of ensemble ensemble empirical mode decomposition (EEMD) and blind source separation. Firstly the single-channel signal was decomposed into a set of proper intrinsic mode functions(IMF) by EEMD. A multi-dimensional signal was obtained by the combination of the den...

In order to locate source signal more accurately in authorized frequency bands, a novel primary user localization algorithm based on compressive sensing (PU-CSL) in cognitive radio networks (CRNs) is proposed in this paper. In comparison to existing centroid locating algorithms, PU-CSL shows higher locating accuracy for integrally exploring correlation between source signal and secondary users ...

Non-negative matrix factorization (NMF) is a dimensionality reduction method that usually leads to a part-based representation, and it is shown to be effective for source separation. However, the performance of the source separation degrades when one signal can be described with the bases for the other source signals. In this paper, we propose a discriminative NMF (DNMF) algorithm which exploit...

Initializing an unmixing matrix is an important problem in source separation since an objective function to be optimized is typically non-convex. In this paper, we consider the problem of two-source signal separation from a two-microphone array located on a mobile device, where a point source such as a speech signal is placed in front of the array, while no information is available about anothe...

Abstract: Given a set of M signal mixtures (x1, x2, . . . , xM ) (e.g. microphone outputs), each of which is a different mixture of a set of M statistically independent source signals (s1, s2, . . . , sM ) (e.g. voices), independent component analysis (ICA) recovers the source signals (voices) from the signal mixtures. ICA is based on the assumptions that source signals are statistically indepe...

The Subspace-based Reduced Rank and Polynomial Order (RRPO) methods were proposed recently [1, 2, 3], which estimate a reduced order linear prediction polynomial whose roots are the desired "signal roots". In this paper, we describe how to extend the RRPO methods to include constraints involving known signal information. Simulation results indicate that by incorporating known signal information...