Results 141 to 150 of about 1,572,835 (188)

Compressive Sampling for Signal Classification

2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Compressive sampling (CS), also called compressed sensing, entails making observations of an unknown signal by projecting it onto random vectors. Recent theoretical results show that if the signal is sparse (or nearly sparse) in some basis, then with high probability such observations essentially encode the salient information in the signal.
Haupt, J., Castro, R.M., Nowak, R., Fudge, G., Yeh, A.   +4 more
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Classification of transient signals (acoustic signals)

ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003
The authors are concerned with the classification of transient signals. Spectral ratio distance measures operating on the parametric spectra of the transient signals are used to perform classification. Performance of the classification algorithm is studied analytically and experimentally using both synthetic and real data.
K. Lashkari, B. Friedlander, J. Abel, B. McQuiston   +3 more
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Supervised radar signal classification

2016 International Joint Conference on Neural Networks (IJCNN), 2016
This work investigates radar signal classification and source identification using three classification models: Neural Networks (NN), Support Vector Machines (SVM) and Random Forests (RF). The available large dataset consists of pulse train characteristics such as signal frequencies, type of modulation, pulse repetition intervals, scanning type, scan ...
Ivan Jordanov, Nedyalko Petrov, Alessio Petrozziello   +2 more
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Signal classification using Neural Networks

2002
The aim of this paper is to classify two kind of signals recorded by seismic station: artificial explosions and seismic activity. The problem is approached from both the preprocessing and the classification point of view. For the preprocessing stage, instead of the conventional Fourier Transform, we use a Linear Prediction Coding (LPC) algorithm, which
Esposito A, FALANGA, Mariarosaria, Funaro M, MARINARO, Maria, SCARPETTA, Silvia   +4 more
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Voiceband signal classification

The Journal of the Acoustical Society of America, 1991
A signal is classified as one among a plurality of classifications by employing the autocorrelation of a complex low-pass version of the signal, i.e., the complex autocorrelation. The normalized magnitude of the complex autocorrelation obtained at a prescribed delay interval, i.e., "lag", is compared to predetermined threshold values to classify the ...
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EEG signal classification with different signal representations

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing, 2002
If several mental states can be reliably distinguished by recognizing patterns in EEG, then a paralyzed person could communicate to a device like a wheelchair by composing sequences of these mental states. In this article, the authors report on a study comparing four representations of EEG signals and their classification by a two-layer neural network ...
C.W. Anderson, S.V. Devulapalli, E.A. Stolz   +2 more
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Structured neural networks for signal classification

Computer Standards & Interfaces, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
L. BRUZZONE, F. ROLI, SERPICO, SEBASTIANO   +2 more
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Signals: Acquisition, Classification, Sampling

2021
In this chapter we learn what a signal is and how signals can be classified: continuous or discrete-time, with analog or quantized values, deterministic or random, 1D/2D/3D or multi-dimensional, real-world or technically synthesized. We will become familiar with basics of digital signal acquisition systems, mainly from the sampling theory point of view.
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Classification images for chromatic signal detection

Journal of the Optical Society of America A, 2005
The number and nature of the mechanisms for the detection of colored stimuli are still unclear. We use the paradigm of classification images to investigate the detection of a signal of homogeneous color added to a noisy texture. Both signal and noise colors were chosen from the isoluminant plane of the Derrington-Krauskopf-Lennie (DKL) color space. The
Thorsten, Hansen, Karl R, Gegenfurtner
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Classification of multichannel uterine EMG signals

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
Classification of multichannel uterine electromyogram (EMG) signals is addressed. Signals were recorded by a matrix of 16 electrodes. First, signals corresponding to each channel were individually classified using an artificial neural network (ANN) based on radial basis functions (RBF).
B, Moslem, M O, Diab, C, Marque, M, Khalil   +3 more
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