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Language Identification in Unknown Signals
 IN PROCEEDING OF COLING000, 18TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS, PP10211026, ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND
, 2000
"... This paper describes algorithms and software developed to characterisc and detect generic intelligent languagelike features iu an input signal, using Natural Lauguage Learning techniques: looking for characteristic statistical "lauguagesignatures" in test corpora. As a first step towards ..."
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Cited by 5 (2 self)
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This paper describes algorithms and software developed to characterisc and detect generic intelligent languagelike features iu an input signal, using Natural Lauguage Learning techniques: looking for characteristic statistical "lauguagesignatures" in test corpora. As a first step
Language Identification In Unknown Signals
 In Proceeding of COLING000, 18th International Conference on Computational Linguistics, pp10211026, Association for Computational Linguistics (ACL) and
, 2000
"... This paper describes algorithms and software developed to characterise and detect generic intelligent languagelike features in an input signal, using Natural Language Learning techniques: looking for characteristic statistical "languagesignatures" in test corpora. As a first step towards ..."
Abstract
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This paper describes algorithms and software developed to characterise and detect generic intelligent languagelike features in an input signal, using Natural Language Learning techniques: looking for characteristic statistical "languagesignatures" in test corpora. As a first step
Linear Transformation Unknown Signal Processor Blind Adaptive
, 2001
"... 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 deconvolution, the transformation is a linear niteimpulse response (FIR) lter, and for blind source separation it is a matrix of mixing coecients ..."
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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 deconvolution, the transformation is a linear niteimpulse response (FIR) lter, and for blind source separation it is a matrix of mixing
DETECTION OF UNKNOWN SIGNALS BASED ON SPECTRAL CORRELATION MEASUREMENTS
"... 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 frequencycyclefrequency plane is proposed. An analytical characterization of the detector is provided, and its detection capability evaluated. T ..."
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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 frequencycyclefrequency plane is proposed. An analytical characterization of the detector is provided, and its detection capability evaluated
A new learning algorithm for blind signal separation

, 1996
"... A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of ..."
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Cited by 622 (80 self)
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A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number
The PapoulisGerchberg Algorithm with Unknown Signal Bandwidth
"... Abstract. The PapoulisGerchberg algorithm has been extensively used to solve the missing data problem in bandlimited signals. The interpolation of lowpass signals with this algorithm can be done if the signal bandwidth is known. In practice, the signal bandwidth is unknown and has to be estimated ..."
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Cited by 1 (0 self)
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Abstract. The PapoulisGerchberg algorithm has been extensively used to solve the missing data problem in bandlimited signals. The interpolation of lowpass signals with this algorithm can be done if the signal bandwidth is known. In practice, the signal bandwidth is unknown and has
Compressed sensing
, 2004
"... 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 numbe ..."
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Cited by 3625 (22 self)
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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
Capacity of a Mobile MultipleAntenna Communication Link in Rayleigh Flat Fading
"... We analyze a mobile wireless link comprising M transmitter and N receiver antennas operating in a Rayleigh flatfading environment. The propagation coefficients between every pair of transmitter and receiver antennas are statistically independent and unknown; they remain constant for a coherence int ..."
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Cited by 495 (22 self)
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We analyze a mobile wireless link comprising M transmitter and N receiver antennas operating in a Rayleigh flatfading environment. The propagation coefficients between every pair of transmitter and receiver antennas are statistically independent and unknown; they remain constant for a coherence
Decoding by Linear Programming
, 2004
"... 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 to rec ..."
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Cited by 1399 (16 self)
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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
Results 1  10
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7,714