This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
We consider the blind separation of sources with general (e.g., not necessarily stationary) temporal covariance structures. When the sources’ temporal covariance matrices are kn...
We consider the problem of diagnosing performance problems in distributed system and networks given end-to-end performance measurements provided by test transactions, or probes. C...
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can b...
Abstract. We present an approach for blindly decomposing an observed random vector x into f(As) where f is a diagonal function i.e. f = f1 × . . . × fm with one-dimensional funct...