This paper describes algorithms and software developed to characterisc and detect generic intelligent language-like features iu an input signal, using Natural Lauguage Learning techniques: looking for characteristic statistical "lauguage-signatures" in test corpora. As a first step

This paper describes algorithms and software developed to characterise and detect generic intelligent language-like features in an input signal, using Natural Language Learning techniques: looking for characteristic statistical "language-signatures" in test corpora. As a first step

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The goal of blind deconvolution and source separation is to unravel the eects of an unknown linear transformation on a unknown signal source. For blind de-convolution, the transformation is a linear nite-impulse response (FIR) lter, and for blind source separation it is a matrix of mixing

The problem of detecting an unknown signal embedded in white Gaussian noise is addressed. A CFAR detector based on combining the information of the whole frequency-cyclefrequency plane is proposed. An analytical characteri-zation of the detector is provided, and its detection capability evaluated

A new on-line learning algorithm which minimizes a statistical de-pendency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual in-formation (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number

Abstract. The Papoulis-Gerchberg algorithm has been extensively used to solve the missing data problem in band-limited signals. The interpolation of low-pass signals with this algorithm can be done if the signal bandwidth is known. In practice, the signal bandwidth is unknown and has

We study the notion of Compressed Sensing (CS) as put forward in [14] and related work [20, 3, 4]. The basic idea behind CS is that a signal or image, unknown but supposed to be compressible by a known transform, (eg. wavelet or Fourier), can be subjected to fewer measurements than the nominal

We analyze a mobile wireless link comprising M transmitter and N receiver antennas operating in a Rayleigh flat-fading environment. The propagation coefficients between every pair of transmitter and receiver antennas are statistically independent and unknown; they remain constant for a coherence

This paper considers the classical error correcting problem which is frequently discussed in coding theory. We wish to recover an input vector f ∈ Rn from corrupted measurements y = Af + e. Here, A is an m by n (coding) matrix and e is an arbitrary and unknown vector of errors. Is it possible