Relevant bibliographies by topics / Signal

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  1. Journal articles

Academic literature on the topic 'Signal'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 June 2025

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Journal articles on the topic "Signal"

1

Gudiškis, Andrius. "HEART BEAT DETECTION IN NOISY ECG SIGNALS USING STATISTICAL ANALYSIS OF THE AUTOMATICALLY DETECTED ANNOTATIONS / ŠIRDIES DŪŽIŲ NUSTATYMAS IŠ IŠKRAIPYTŲ EKG SIGNALŲ ATLIEKANT AUTOMATIŠKAI APTIKTŲ ATSKAITŲ STATISTINĘ ANALIZĘ." Mokslas – Lietuvos ateitis 7, no. 3 (2015): 300–303. http://dx.doi.org/10.3846/mla.2015.787.

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This paper proposes an algorithm to reduce the noise distortion influence in heartbeat annotation detection in electrocardiogram (ECG) signals. Boundary estimation module is based on energy detector. Heartbeat detection is usually performed by QRS detectors that are able to find QRS regions in a ECG signal that are a direct representation of a heartbeat. However, QRS performs as intended only in cases where ECG signals have high signal to noise ratio, when there are more noticeable signal distortion detectors accuracy decreases. Proposed algorithm uses additional data, taken from arterial blood pressure signal which was recorded in parallel to ECG signal, and uses it to support the QRS detection process in distorted signal areas. Proposed algorithm performs as well as classical QRS detectors in cases where signal to noise ratio is high, compared to the heartbeat annotations provided by experts. In signals with considerably lower signal to noise ratio proposed algorithm improved the detection accuracy to up to 6%. Širdies ritmas yra vienas svarbiausių ir daugiausia informacijos apie pacientų būklę teikiančių fiziologinių parametrų. Širdies ritmas nustatomas iš elektrokardiogramos (EKG), atliekant QRS regionų, kurie yra interpretuojami kaip širdies dūžio ãtskaitos, paiešką. QRS regionų aptikimas yra klasikinis uždavinys, nagrinėjamas jau keletą dešimtmečių, todėl širdies dūžių nustatymo iš EKG signalų metodų yra labai daug. Deja, šie metodai tikslūs ir patikimi tik esant dideliam signalo ir triukšmo santykiui. Kai EKG signalai labai iškraipomi, QRS aptiktuvai ne visada gali atskirti QRS regioną, o kartais jį randa ten, kur iš tikro jo būti neturėtų. Straipsnyje siūlomas algoritmas, kurį taikant sumažinama triukšmo įtaka nustatant iš EKG signalų QRS regionus. Tam naudojamas QRS aptiktuvas, kartu prognozuojantis širdies dūžio atskaitą. Remiamasi arterinio kraujo spaudimo signalo duomenimis, renkama atskaitų statistika ir atliekama jos analizė.
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2

Filonenko, Sergey, Tatiana Nimchenko, and Alexandr Kosmach. "MODEL OF ACOUSTIC EMISSION SIGNAL AT THE PREVAILING MECHANISM OF COMPOSITE MATERIAL MECHANICAL DESTRUCTION." Aviation 14, no. 4 (2010): 95–103. http://dx.doi.org/10.3846/aviation.2010.15.

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A model of acoustic emission signal formation at the prevailing mechanism of the destruction of composite materials is considered. The results of acoustic emission signal modelling are presented, taking into account the variable velocity of loading change. Acoustic emission signal experimental research results corresponding to theoretical research results are considered in this paper. It is shown that irregularity of the trailing edge of the acoustic emission signal is influenced by the change in the rate of the destruction process in composites. Santrauka Išnagrinetas akustines emisijos signalo modelis su vyraujančiu kompozitiniu medžiagu irimo mechanizmu. Pateikti akustines emisijos signalu modeliavimo rezultatai, ivertinant skirtinga apkrovos pasikeitimo greiti. Taip pat pateikti eksperimentinio akustines emisijos signalu tyrimo rezultatai, kurie sutampa su teoriniais tyrimais. Parodyta, kad akustines emisijos signalo galinio fronto netolygumas atsiranda tuomet, kai kinta kompozitines medžiagos irimo greitis.
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3

Shellenberger, Richard O., and Paul Lewis. "Signal Control by Six Signals." Psychological Reports 63, no. 1 (1988): 311–18. http://dx.doi.org/10.2466/pr0.1988.63.1.311.

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In previous signal-control experiments, several types of stimuli elicited pecking when paired with peck-contingent grain. Here, we compared the effectiveness of an auditory stimulus and five visual stimuli. For 12 pigeons, the first keypeck to follow the offset of a 4-sec. signal was reinforced with grain. We examined the following signals: a tone, a white keylight, a dark keylight, a keylight that changed from white to red, houselight onset, and houselight offset. All signals acquired strong control over responding. According to one measure, percent of signals with a peck, houselight offset showed less control than the others; according to another measure, pecking rate, the white keylight showed greater control than the others. In this experiment, we found that a wide variety of stimuli can elicit strong pecking in the signal-control procedure. The present findings increase the chances that in past conditioning experiments, some keypecks thought to be due to contingencies of reinforcement were in fact elicited.
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4

Hughes, Melissa. "Deception with honest signals: signal residuals and signal function in snapping shrimp." Behavioral Ecology 11, no. 6 (2000): 614–23. http://dx.doi.org/10.1093/beheco/11.6.614.

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5

Shelishiyah, R., M. Bharani Dharan, T. Kishore Kumar, R. Musaraf, and Thiyam Deepa Beeta. "Signal Processing for Hybrid BCI Signals." Journal of Physics: Conference Series 2318, no. 1 (2022): 012007. http://dx.doi.org/10.1088/1742-6596/2318/1/012007.

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Abstract The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). The data consists of brain signals for four tasks – Right/Left hand gripping and Right/Left arm raising. The EEG (brain activity) data were filtered using a bandpass filter to obtain the activity of mu (7-13 Hz) and beta (13-30 Hz) rhythm. The Oxy-haemoglobin and Deoxy-haemoglobin (HbO and HbR) concentration of the fNIRS signal was obtained with Modified Beer Lambert Law (MBLL). Both signals were filtered using a fifth-order Butterworth band pass filter and the performance of the filter is compared theoretically with the estimated signal-to-noise ratio. These results can be used further to improve feature extraction and classification accuracy of the signal.
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6

Minasian, R. A. "Photonic signal processing of microwave signals." IEEE Transactions on Microwave Theory and Techniques 54, no. 2 (2006): 832–46. http://dx.doi.org/10.1109/tmtt.2005.863060.

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7

Milligan, Graeme. "All the right signals Signal transduction." Trends in Biochemical Sciences 22, no. 10 (1997): 410. http://dx.doi.org/10.1016/s0968-0004(97)82532-7.

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8

Lessard, Charles S. "Signal Processing of Random Physiological Signals." Synthesis Lectures on Biomedical Engineering 1, no. 1 (2006): 1–232. http://dx.doi.org/10.2200/s00012ed1v01y200602bme001.

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9

Birdsall, Theodore G., Kurt Metzger, and Matthew A. Dzieciuch. "Signals, signal processing, and general results." Journal of the Acoustical Society of America 96, no. 4 (1994): 2343–52. http://dx.doi.org/10.1121/1.410106.

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10

Shinpaugh, K. A., R. L. Simpson, A. L. Wicks, S. M. Ha, and J. L. Fleming. "Signal-processing techniques for low signal-to-noise ratio laser Doppler velocimetry signals." Experiments in Fluids 12-12, no. 4-5 (1992): 319–28. http://dx.doi.org/10.1007/bf00187310.

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