Results 301 to 310 of about 1,601,810 (338)
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Classification and Parameters of Signals
2014We encounter signals in many fields of science, in particular in experimental sciences, which deal with examination of the reality that surrounds us. The information carried by the signals enables description and analysis of that reality, if one knows the mathematical relations concerning them. On the one hand, those relations should be general enough,
Edward Layer, Krzysztof Tomczyk
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Classification of EEG Signals by an Evolutionary Algorithm
2014The goal of this work is to predict the state of alertness of an individual by analyzing the brain activity through electroencephalographic data (EEG) captured with 58 electrodes. Alertness is characterized here as a binary variable that can be in a "normal" or "relaxed" state.
Vezard, Laurent +5 more
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Classification of spasticity affected EMG-signals
2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015Electromyography (EMG) is used as medical tool to display muscle activity and gain information about the health status of the patients muscle function, which may be affected by many kind of diseases. Spasticity is caused by injuries of the central nervous system, which may occur in consequence of stroke or as concomitant of multiple sclerosis.
Markus J. Lüken +2 more
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Classification of degraded signals by the method of invariants
Signal Processing, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jan Flusser, Tomás Suk
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Voiceband signal classification
The Journal of the Acoustical Society of America, 1991A 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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Classification of multichannel uterine EMG signals
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Classification 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).
Bassam Moslem +3 more
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Joint Deconvolution and Classification for Signals with Multipath
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007For many sensing modalities such as sonar, received signals are corrupted by multipath and can be challenging for automatic classification systems. An approach to jointly deconvolve and classify such signals is proposed. Specifically, a filter is estimated that minimizes the distortion between the received signal and a set of training signals, then the
Maya R. Gupta +2 more
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Blind Classification of MIMO Wireless Signals
2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019Classification of MIMO wireless signals with no prior information of the number of transmitter antenna elements and channel(s) is of tremendous importance in both military and civilian applications. In this research, we present a novel generalized algorithm for classification of digital modulation combined with the recognition of the number of transmit
Tejashri Kuber, Dola Saha, Ivan Seskar
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EEG Signal Classification Based On Fuzzy Classifiers
IEEE Transactions on Industrial Informatics, 2022Jan Rabcan +2 more
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