Results 231 to 240 of about 83,689 (283)

Waveform recognition using neural networks

The Leading Edge, 1990
Pattern 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
openaire   +1 more source

EMG burst waveform recognition procedure

Proceedings of the Fifteenth Annual Northeast Bioengineering Conference, 2003
A fast, simple EMG (electromyogram) burst waveform recognition algorithm has been developed for a personal computer. Raw EMG data are detrended, squared, and filtered. A sample EMG waveform segment, known as a template, is drawn from this data. A recognition signal is constructed as the convolution of the template and the prepared EMG data.
G. Dwyer, Y. Noguchi, H.H. Szeto
openaire   +1 more source

Respiratory waveform pattern recognition using digital techniques

Computers in Biology and Medicine, 1989
An algorithm for detection of ventilatory events using digital signal processing techniques is described. Start and end of inspiration of each breath are detected using a combination of first derivative peak detection and second derivative analysis for edge detection.
J B, Korten, G G, Haddad
openaire   +2 more sources

Cooperative Environment Recognition Utilizing UWB Waveforms and CNNs

2020 European Navigation Conference (ENC), 2020
Cooperative 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 ...
Rantanen Jesperi, Ilinca Julian, Mäkelä Maija, Kirkko-Jaakkola Martti, Kaasalainen Sanna, Ruotsalainen L.   +5 more
openaire   +2 more sources

Optimal recognition of neuronal waveforms

Biological Cybernetics, 1979
Statistically optimal methods for identifying single unit activity in multiple unit recordings are discussed. These methods take into account both the nerve impulse waveforms and the firing patterns of the units. A generalized least-squares fit procedure is shown to be the optimal recognition scheme under some reasonable statistical assumptions, but ...
openaire   +3 more sources

Speech waveform envelope cues for consonant recognition

The Journal of the Acoustical Society of America, 1987
This 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, S D, Soli, V M, Kirby, G P, Widin   +3 more
openaire   +2 more sources

Optimal Recognition Of Neural Waveforms

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991, 2005
The investigation of biological neural networks requires reliable classification of neural action potentials in extracellular recordings. When the signal-to-noise ratio is bw, when the spike waveforms gradually change shape and amplitude, or when spikes overlap, the current sorting techniques cannot provide robust on-line operation due to their noise ...
I.N. Bankman, K.O. Johnson, W. Schneider
openaire   +1 more source

Automatic Radar Waveform Recognition Using SVM

Applied Mechanics and Materials, 2012
In this paper, a new feature for radar waveform recognition based on the instantaneous frequency is proposed. It is especially utilized for discriminating phase coded signals from other signals. Maximum likelihood estimation (MLE), autocorrelation algorithm, and likelihood ratio test are exploited in the algorithm. In the classification system, support
Hao Gao, Xu Dong Zhang
openaire   +1 more source

Sequential feature extraction for waveform recognition

Proceedings of the May 5-7, 1970, spring joint computer conference on - AFIPS '70 (Spring), 1970
Many practical waveform recognition problems involve a sequential structure in time. One obvious example is speech. The information in speech can be assumed to be transmitted sequentially through a phonetic structure. Other examples are seismograms, radar signals, or television signals.
W. J. Steingrandt, S. S. Yau
openaire   +1 more source

Stability of phase recognition in complex spatial waveforms

Vision Research, 1984
Observers 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
openaire   +2 more sources