Results 251 to 260 of about 1,588,862 (288)
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Recognition of noisy peaks in ECG waveforms
Computers and Biomedical Research, 1984A new method for recognizing noisy peaks in ECG waveforms is presented. This method is based on the principle that most noisy peaks in ECG waveforms appear as pairs of adjacent peaks which satisfy certain criteria. These pairs are called "noisy peak pairs" in this paper.
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Stability of phase recognition in complex spatial waveforms
Vision Research, 1984Observers viewed 200 msec presentations of gratings containing first (0.5 c/deg) and third (1.5 c/deg) harmonic components. The phase of the third harmonic and the absolute position of the grating on the screen varied randomly from trial to trial. Classification of the phase relation (0, 90, 180 or 270 deg was 99% perfect.
M A, Georgeson, R S, Turner
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Cooperative Environment Recognition Utilizing UWB Waveforms and CNNs
2020 European Navigation Conference (ENC), 2020Cooperative navigation enhances localization performance and situational awareness in challenging conditions, such as in tactical and first responder operations. In this work we demonstrate how the waveform of the Ultra Wideband (UWB) signal used for ranging in cooperative navigation can also be used to detect the environment surrounding the user of ...
Maija Mäkelä +5 more
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Speech waveform envelope cues for consonant recognition
The Journal of the Acoustical Society of America, 1987This study investigated the cues for consonant recognition that are available in the time-intensity envelope of speech. Twelve normal-hearing subjects listened to three sets of spectrally identical noise stimuli created by multiplying noise with the speech envelopes of 19 /aCa/ natural-speech nonsense syllables.
D J, Van Tasell +3 more
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Waveform recognition in the presence of domain and amplitude noise
IEEE Transactions on Information Theory, 1997Summary: In this paper, we discuss the problem of recognizing single-dimensional, real-valued, functions in the presence of domain noise (i.e., noise that affects the domain rather than the amplitude). This problem is inspired by the field of on-line character recognition where it is more natural to view the hand as deforming the domain of the ...
Mohamad A. Akra, Sanjoy K. Mitter
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Multivariate Autoregressive Feature Extraction and the Recognition of Multichannel Waveforms
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1979It is proposed that the autoregressive coefficient matrices appearing in a multivariate autoregressive model fitting may be used for feature extraction purposes in problems concerning recognition of multichannel waveforms. It is demonstrated how the information contained in the autoregressive parameters may be further compressed by applying the ...
Dag Tjøstheim, Ottar Sandvin
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1977
A new system of peak component recognition and measurement in digitized waveforms is detailed. Two input parameters identify waveform context (scale and noise content), and a third specifies baseline tolerance (if applicable). The input waveform is preprocessed by a discrete linear piecewise approximation algorithm yielding a segmentation in endpoint ...
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A new system of peak component recognition and measurement in digitized waveforms is detailed. Two input parameters identify waveform context (scale and noise content), and a third specifies baseline tolerance (if applicable). The input waveform is preprocessed by a discrete linear piecewise approximation algorithm yielding a segmentation in endpoint ...
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An Automatic Recognition for the Auditory Brainstem Response Waveform
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015The Auditory Brainstem Response (ABR) is Brainstem Auditory Evoked potentials and often used in the neurophysiology. The waveform of ABR is usually recorded right after stimulation applied, as a response characteristic with a five peaks. These each peaks from the recording electrodes is identified by (a) neural transmission times and (b) amplitude in ...
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Waveform recognition using neural networks
The Leading Edge, 1990Pattern recognition plays an important role in a wide variety of applications from robot vision to predicting stock market trends. Efforts to automate the pattern recognition process go back in time to an early stage in the development of the modern digital computer. In the intervening years, a number of approaches have been developed.
Ibrahim Palaz, Ronald C. Weger
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Recognition of Waveforms Using Autoregressive Feature Extraction
IEEE Transactions on Computers, 1977It is proposed that the autoregressive coefficients (as computed from the estimated autocorrelation function) in an autoregressive spectral estimation scheme may be used for feature extraction purposes. An example is presented where such extractors are used to describe seismic wave traces originating from shallow earthquakes and underground nuclear ...
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