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Results 1 - 10
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7,714
Language Identification in Unknown Signals
- IN PROCEEDING OF COLING000, 18TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS, PP1021-1026, ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND
, 2000
"... 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 towards ..."
Abstract
-
Cited by 5 (2 self)
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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
Language Identification In Unknown Signals
- In Proceeding of COLING000, 18th International Conference on Computational Linguistics, pp1021-1026, Association for Computational Linguistics (ACL) and
, 2000
"... 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 towards ..."
Abstract
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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
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 de-convolution, the transformation is a linear nite-impulse response (FIR) lter, and for blind source separation it is a matrix of mixing coecients ..."
Abstract
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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
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 frequency-cyclefrequency plane is proposed. An analytical characteri-zation of the detector is provided, and its detection capability evaluated. T ..."
Abstract
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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 frequency-cyclefrequency plane is proposed. An analytical characteri-zation of the detector is provided, and its detection capability evaluated
A new learning algorithm for blind signal separation
-
, 1996
"... 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 of ..."
Abstract
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Cited by 622 (80 self)
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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
The Papoulis-Gerchberg Algorithm with Unknown Signal Bandwidth
"... 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 to be estimated ..."
Abstract
-
Cited by 1 (0 self)
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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
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 ..."
Abstract
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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 Multiple-Antenna Communication Link in Rayleigh Flat Fading
"... 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 int ..."
Abstract
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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 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
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 ..."
Abstract
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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
of
7,714
