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This talk is concerned with a direct sampling method for imaging the support of a frequency-dependent source term embedded in a homogeneous and isotropic medium. The source term is given by the Fourie...
We present a real-time version of the DUET algorithm forthe blind separation of any number of sources using only two mixtures. The method applies when sources are Wdisjoint orthogonal, that is, when t...
There is increased interest in using microphone arrays in a variety of audio source separation and consequently speech processing applications. In particular, small arrays of two to four microphones a...
In this paper we present a robustness study of the two channel echoic parametric demixing problem. More specific, assume an oracle (or a perfect estimator) is providing a truncated estimate of the mix...
We investigate the assumption that sources have disjoint support in the time domain, time-frequency domain, or frequency domain. We call such signals disjoint orthogonal. The class of signals that app...
Few source separation and independent component analysis approaches attempt to deal with noisy data. Weconsider an additive noise mixing model with an arbitrary number of sensors and possibly more sou...
To be applicable in realistic scenarios, blind source separation approaches should deal evenly with non-square cases and the presence of noise. We consider an additive noisemixing model with an arbitr...
Sparse constraints on signal decompositions are justified bytypical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to ...
SOURCE SEPARATION USING SPARSE DISCRETE PRIOR MODELS     SOURCE SEPARATION  SPARSE DISCRETE  PRIOR MODELS       font style='font-size:12px;'> 2015/9/29
In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variablewith ...
In this paper we continue our treatment of source separation based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable...
In this paper I present a Minimum Description Length Estimator for number of sources in an anechoic mixture of sparse signals.The criterion is roughly equal to the sum of negative normalized maximum l...
The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the M...
We consider parabolic equations with mixed boundary conditions and domain inhomogeneities supported on a lower dimensional hypersurface, enforcing a jump in the conormal derivative. Only minimal regul...
In this paper, we investigate the quenching phenomena of the Cauchy problem for the second-order nonlinear parabolic equation on unbounded domain. It is shown that the solution quenches in finite time...
In this paper we have presented two source programs with FORTRAN 90 language to calculate the distribution of prime numbers in the sequence of odd numbers and the Goldbach-Xu’s numbers for every even ...

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