Results 291 to 300 of about 1,601,810 (338)
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Statistical classification of chaotic signals
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002The classification of chaotic signals generated by low-dimensional deterministic models given a dictionary of possible models is considered. The proposed classification methods rely on the concept of "best predictor" of signal. A statistical interpretation of this concept based on the ergodic theory of chaotic systems is presented.
Christophe Couvreur +2 more
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Quantizing Signals for Linear Classification
2019 IEEE International Symposium on Information Theory (ISIT), 2019In many machine learning applications, once we have learned a classifier, in order to apply it, we may still need to gather features from distributed sensors over communication constrained channels. In this paper, we propose a polynomial complexity algorithm for feature quantization tailored to minimizing the classification error of a linear classifier.
Yahya H. Ezzeldin +2 more
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Automatic classification of electromyographic signals
Electroencephalography and Clinical Neurophysiology, 1983The results of the application of classification methods to electromyograph signals of weak contractions in normal and myopathic subjects are described. Methods of pattern recognition, previously presented, allow the selection of representative motor unit action potentials.
J L, Coatrieux +3 more
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Classification of cutaneous microcirculation signals
Proceedings of Computer Based Medical Systems, 2002The aim of our study is to develop a computer aided diagnosis tool, allowing characterization of blood tissues from laser Doppler signals of microcirculation. A learning population of four classes corresponding to various local treatments inducing vasoconstriction or vasodilation is available. Signals are modelled by ARMA processes estimated by maximum
Hitti, Eric +2 more
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Compressive Sampling for Signal Classification
2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006Compressive 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. +4 more
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Signal classification by probabilistic reasoning
2013 IEEE Radio and Wireless Symposium, 2013Much of the work into developing environmental and network awareness for cognitive radios has been focused on developing new metrics to identify the modulation schemes in use by neighboring radio nodes. Unfortunately, the metrics are used to derive only hard decisions which are often threshold-based and therefore unable to assign a measure of ...
Christopher Ian Phelps +1 more
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Structured neural networks for signal classification
Computer Standards & Interfaces, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
L. BRUZZONE +2 more
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Signal classification using Neural Networks
2002The 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 +4 more
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2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2005
The article describes the classification of simple movements using a system based on Hidden Markov Models (HMM). Brisk extensions and flexions of the index finger, and movements of the proximal arm (shoulder) and distal arm (finger) were classified using scalp EEG signals. The aim of our study was to develop a system for the classification of movements
J. Stastny, P. Sovka, A. Stancak
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The article describes the classification of simple movements using a system based on Hidden Markov Models (HMM). Brisk extensions and flexions of the index finger, and movements of the proximal arm (shoulder) and distal arm (finger) were classified using scalp EEG signals. The aim of our study was to develop a system for the classification of movements
J. Stastny, P. Sovka, A. Stancak
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Classification of cardiovascular signals
2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2021Tatiana Saraiva +3 more
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