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Journal articles on the topic 'Signal'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 bloo

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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 signa

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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 s

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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 (

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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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11

Yoshida, Jungi. "Sound signal processor for extracting sound signals from a composite digital sound signal." Journal of the Acoustical Society of America 114, no. 1 (2003): 28. http://dx.doi.org/10.1121/1.1601078.

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12

Becker, Florent, Tom Besson, Jérôme Durand-Lose, et al. "Abstract Geometrical Computation 10." ACM Transactions on Computation Theory 13, no. 1 (2021): 1–31. http://dx.doi.org/10.1145/3442359.

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Signal machines form an abstract and idealized model of collision computing. Based on dimensionless signals moving on the real line, they model particle/signal dynamics in Cellular Automata. Each particle, or signal , moves at constant speed in continuous time and space. When signals meet, they get replaced by other signals. A signal machine defines the types of available signals, their speeds, and the rules for replacement in collision. A signal machine A simulates another one B if all the space-time diagrams of B can be generated from space-time diagrams of A by removing some signals and ren

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13

Song, Sanghun. "The Development of the Real Time Target Simulator for the RF Signal of Electronic Warfare using VST and FPGA." Journal of the Korea Institute of Military Science and Technology 26, no. 4 (2023): 324–34. http://dx.doi.org/10.9766/kimst.2023.26.4.324.

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In this paper, the target simulator for RF signals was developed by using VST(Vector Signal Transceiver) and set by real-time signal processing SW programs. A function to process RF signals using FPGA(Field Programmable Gate Array) board was designed. The system functions capable of data processing, raw signals monitoring, target signals(simulated range, velocity) generating and RF environments data analyzing were implemented. And the characteristics of modulated signal were analyzed in RF environment. All function of programs for processing RF signal have options to store signal data and to m

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14

Feng, Yongxin, Shunchao Fei, Fang Liu, and Bo Qian. "SSCM: An Unambiguous Acquisition Algorithm for CBOC Modulated Signal." Journal of Electrical and Computer Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5381789.

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Composite binary offset carrier (CBOC) signal has been widely researched in GNSS. The main ingredient of CBOC signal is BOC(1,1) signal. Usually, the acquisition method for BOC(1,1) signal is used to capture CBOC signal, while the research of special acquisition method for CBOC signal is rare. In this letter, according to the principle and characteristics of CBOC signal, a special side-peak cancellation method (SSCM) is proposed and simulated. In this method, two special auxiliary signals are introduced. And the local reference signals are obtained by multiplying the data channel signal and pi

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15

Luo, Shan, Guoan Bi, Tong Wu, Yong Xiao, and Rongping Lin. "An Effective LFM Signal Reconstruction Method for Signal Denoising." Journal of Circuits, Systems and Computers 27, no. 09 (2018): 1850140. http://dx.doi.org/10.1142/s0218126618501402.

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One of the main challenges in signal denoising is to accurately restore useful signals in low signal-to-noise ratio (SNR) scenarios. In this paper, we investigate the signal denoising problem for multi-component linear frequency modulated (LFM) signals. An effective time-frequency (TF) analysis-based approach is proposed. Compared to the existing approaches, our proposed one can further increase the noise suppressing performance and improve the quality of the reconstructed signal. Experimental results are presented to show that the proposed denoising approach is able to effectively separate th

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16

Liu, Haochen. "BPSK/BOC Modulation Signal System for GPS Satellite Navigation Signals." Journal of Physics: Conference Series 2384, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2384/1/012023.

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Abstract With the continuous research and development of the global positioning system in various countries, the requirements for the accuracy and efficiency of GPS signals are getting higher and higher. To improve the performance of GPS signal modulation, this research focuses on the BPSK modulation signal and the BOC modulation signal and introduces the basic principles and practical uses of the two modulation signals. Based on the disadvantage of weak anti-noise of BPSK modulated signal and high ambiguity of BOC modulated signal, two algorithms of signal-to-noise ratio estimation and parall

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17

Bateneva, T. V., N. S. Budvis, and N. P. Khmyrova. "SIGNAL-CODE CONSTRUCTIONS USING FREQUENCY-TIME SIGNALS." RADIO COMMUNICATION TECHNOLOGY, no. 38 (2018): 9–21. http://dx.doi.org/10.33286/2075-8693-2018-38-9-21.

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18

Mingjiang Shi, Xiaoyan Zhuang, and He Zhang. "Signal Reconstruction for Frequency Sparse Sampling Signals." Journal of Convergence Information Technology 8, no. 9 (2013): 1197–203. http://dx.doi.org/10.4156/jcit.vol8.issue9.147.

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19

Elgendi, Mohamed. "Optimal Signal Quality Index for Photoplethysmogram Signals." Bioengineering 3, no. 4 (2016): 21. http://dx.doi.org/10.3390/bioengineering3040021.

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20

Venkatachalam, K. L., Joel E. Herbrandson, and Samuel J. Asirvatham. "Signals and Signal Processing for the Electrophysiologist." Circulation: Arrhythmia and Electrophysiology 4, no. 6 (2011): 965–73. http://dx.doi.org/10.1161/circep.111.964304.

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21

Venkatachalam, K. L., Joel E. Herbrandson, and Samuel J. Asirvatham. "Signals and Signal Processing for the Electrophysiologist." Circulation: Arrhythmia and Electrophysiology 4, no. 6 (2011): 974–81. http://dx.doi.org/10.1161/circep.111.964973.

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22

Frey, Douglas R. "Signal conditioning circuit for compressing audio signals." Journal of the Acoustical Society of America 103, no. 1 (1998): 17. http://dx.doi.org/10.1121/1.423132.

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23

Lu, Jie, Naveen Verma, and Niraj K. Jha. "Compressed Signal Processing on Nyquist-Sampled Signals." IEEE Transactions on Computers 65, no. 11 (2016): 3293–303. http://dx.doi.org/10.1109/tc.2016.2532861.

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24

Ask, Per. "Ultrasound imaging. Waves, signals and signal processing." Ultrasound in Medicine & Biology 28, no. 3 (2002): 401–2. http://dx.doi.org/10.1016/s0301-5629(01)00520-8.

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25

Hoch, James A., and K. I. Varughese. "Keeping Signals Straight in Phosphorelay Signal Transduction." Journal of Bacteriology 183, no. 17 (2001): 4941–49. http://dx.doi.org/10.1128/jb.183.17.4941-4949.2001.

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26

Kuiper, D. "Signals and signal transduction pathways in plants." Scientia Horticulturae 68, no. 1-4 (1997): 258–59. http://dx.doi.org/10.1016/s0304-4238(96)00969-7.

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27

Vosvrda, Miloslav S. "Discrete random signals and statistical signal processing." Automatica 29, no. 6 (1993): 1617. http://dx.doi.org/10.1016/0005-1098(93)90033-p.

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28

Kale, Uma, and Edward Voigtman. "Signal processing of transient atomic absorption signals." Spectrochimica Acta Part B: Atomic Spectroscopy 50, no. 12 (1995): 1531–41. http://dx.doi.org/10.1016/0584-8547(95)01380-6.

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29

Dyrløv Bendtsen, Jannick, Henrik Nielsen, Gunnar von Heijne, and Søren Brunak. "Improved Prediction of Signal Peptides: SignalP 3.0." Journal of Molecular Biology 340, no. 4 (2004): 783–95. http://dx.doi.org/10.1016/j.jmb.2004.05.028.

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30

Munni, Pattan. "Simulation of Signals with Field Signal Simulator." IOSR Journal of Electronics and Communication Engineering 7, no. 3 (2013): 07–12. http://dx.doi.org/10.9790/2834-0730712.

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31

Birmes, Franziska S., and Susanne Fetzner. "Bakterielle Kommunikation: Signale und Signal-inaktivierende Enzyme." BIOspektrum 22, no. 3 (2016): 251–54. http://dx.doi.org/10.1007/s12268-016-0681-4.

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32

Pagot, Jean-Baptiste, Olivier Julien, Paul Thevenon, Francisco A. Fernandez, and Margaux Cabantous. "Signal Quality Monitoring for New GNSS Signals." Navigation 65, no. 1 (2018): 83–97. http://dx.doi.org/10.1002/navi.218.

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33

Kawamoto, Mitsuru, A. K. Barros, A. Mansour, Kiyotoshi Matsuoka, and Noboru Ohnishi. "Blind signal separation for convolved nonstationary signals." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 84, no. 2 (2000): 21–29. http://dx.doi.org/10.1002/1520-6440(200102)84:2<21::aid-ecjc3>3.0.co;2-p.

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34

Ryapolov, A. V., V. E. Mitrokhin, N. V. Fambulov, and D. A. Gredyaev. "DIGITAL SIMULATOR OF GPS C/A SIGNALS." RADIO COMMUNICATION TECHNOLOGY, no. 48 (June 16, 2021): 64–78. http://dx.doi.org/10.33286/2075-8693-2021-48-64-78.

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A structure of a digital signal simulator which allows generating testing GPS C/A signals or creating signal-like interference is observed. Proposed scheme of the simulator includes generators of navigation signals, a generator of noiselike signal, a signal summation block and a block of signal bit capacity transformation. A vari-ant of simulator hardware implementation in FPGA is showed. Examples of gener-ated signals are presented.

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35

Gelenbe, Erol. "Random Neural Networks with Negative and Positive Signals and Product Form Solution." Neural Computation 1, no. 4 (1989): 502–10. http://dx.doi.org/10.1162/neco.1989.1.4.502.

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We introduce a new class of random “neural” networks in which signals are either negative or positive. A positive signal arriving at a neuron increases its total signal count or potential by one; a negative signal reduces it by one if the potential is positive, and has no effect if it is zero. When its potential is positive, a neuron “fires,” sending positive or negative signals at random intervals to neurons or to the outside. Positive signals represent excitatory signals and negative signals represent inhibition. We show that this model, with exponential signal emission intervals, Poisson ex

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36

Xiang, Chengzhi, and Ailin Liang. "Analog and Photon Signal Splicing for CO2-DIAL Based on Piecewise Nonlinear Algorithm." Atmosphere 13, no. 1 (2022): 109. http://dx.doi.org/10.3390/atmos13010109.

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In the CO2 differential absorption lidar (DIAL) system, signals are simultaneously collected through analog detection (AD) and photon counting (PC). These two kinds of signals have their own characteristics. Therefore, a combination of AD and PC signals is of great importance to improve the detection capability (detection range and accuracy) of CO2-DIAL. The traditional signal splicing algorithm cannot meet the accuracy requirements of CO2 inversion due to unreasonable data fitting. In this paper, a piecewise least square splicing algorithm is developed to make signal splicing more flexible an

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37

Chen, Junzhi, Hongbo Li, Chunfang Ren, and Fan Hu. "Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues." Applied Sciences 13, no. 4 (2023): 2619. http://dx.doi.org/10.3390/app13042619.

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The microseismic signals of rock fractures indicate that the rock mass in a particular area is changing slowly, and the microseismic signals of rock blasting indicate that the rock mass in a particular area is changing violently. It is of great significance to accurately distinguish rock fracture signals and rock microseismic signals for analyzing the changes in the rock mass in the area where the signal occurs. Considering the microseismic signals of the Dahongshan Iron Mine, the time domain, frequency domain, energy characteristic distribution, and fractal features of each signal were analyz

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38

Desouza, Kevin, Tobin Hensgen, and J. Roberto Evaristo. "Signals, signal devices, and signal space in organisations: a conceptual lens to crisis evasion." International Journal of Emergency Management 2, no. 1/2 (2004): 1. http://dx.doi.org/10.1504/ijem.2004.005227.

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39

Li, Jiafeng. "A Review of the Basic Principles and Methods of Signal Sampling Technology." Transactions on Computer Science and Intelligent Systems Research 8 (October 24, 2024): 146–52. http://dx.doi.org/10.62051/edadp344.

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In modern life, communication is an essential human activity. In the process of communication, various information spreads around the world in the form of signals. In order to optimize the communication, the reasonable processing of the signal becomes an important research problem. Signal processing is an important step during signal processing. It is one of two processes that convert an analog signal into a digital equivalent signal. It can be considered that signal sampling transforms the time axis of the signal into a set of discrete time moments, that is, the process of converting a signal

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40

Vujović, Željko. "Magnetic resonance signal." Tehnika 74, no. 3 (2019): 415–21. http://dx.doi.org/10.5937/tehnika1903415v.

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41

Borawake, Prof Dr M. P. "Audio Signal Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 1495–96. http://dx.doi.org/10.22214/ijraset.2022.44063.

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Abstract: Audio Signal Processing is also known as Digital Analog Conversion (DAC). Sound waves are the most common example of longitudinal waves. The speed of sound waves is a particular medium depends on the properties of that temperature and the medium. Sound waves travel through air when the air elements vibrate to produce changes in pressure and density along the direction of the wave’s motion. It transforms the Analog Signal into Digital Signals, and then converted Digital Signals is sent to the Devices. Which can be used in Various things., Such as audio signal, RADAR, speed processing,

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42

Zhang, Zengmeng, Xing Cheng, Dayong Ning, Jiaoyi Hou, and Yongjun Gong. "Underwater acoustic beacon signal extraction based on dislocation superimposed method." Advances in Mechanical Engineering 9, no. 2 (2017): 168781401769167. http://dx.doi.org/10.1177/1687814017691671.

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Flight data are recorded in an acoustic beacon. A new signal extraction method led by random decrement technique is proposed to detect sound signals from thousands of meters under the sea. This method involves dislocation superimposed method and cross-correlation function to extract acoustic beacon signals with noise interference. First, the starting point is selected and the length of each segment is determined via two superposition ways. Second, the signal segment for linear superposition is intercepted to complete acoustic beacon signal extraction. Finally, the signals are subjected to cros

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43

Ashraf A. Ahmad, Mustapha M. Aji, Yusuf Abdulmumin, Ilyasu A. Jae, and Uthman I. Bello-Imokhuede. "Profiling radar signals based of pulse-to-pulse frequency agility." Global Journal of Engineering and Technology Advances 15, no. 2 (2023): 141–49. http://dx.doi.org/10.30574/gjeta.2023.15.2.0100.

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It is well known that the application of radar is becoming more and more popular with the development of signal technology progress. Therefore, this paper presents a first-stage process for radar signals analysis involving four different radar signals based on pulse-to-pulse frequency Agility. The radar signals include a normal radar signal (NRS), frequency hopping radar signal (FHRS), 2-frequency shift keying radar signal (2FSKRS), and a combination of frequency hopping radar signal (FHRS) and 2-frequency shift keying radar signal (2FSKRS). The process of modeling and generating the radar sig

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44

Ashraf, A. Ahmad, M. Aji Mustapha, Abdulmumin Yusuf, A. Jae Ilyasu, and I. Bello-Imokhuede Uthman. "Profiling radar signals based of pulse-to-pulse frequency agility." Global Journal of Engineering and Technology Advances 15, no. 2 (2023): 141–49. https://doi.org/10.5281/zenodo.8046831.

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It is well known that the application of radar is becoming more and more popular with the development of signal technology progress. Therefore, this paper presents a first-stage process for radar signals analysis involving four different radar signals based on pulse-to-pulse frequency Agility. The radar signals include a normal radar signal (NRS), frequency hopping radar signal (FHRS), 2-frequency shift keying radar signal (2FSKRS), and a combination of frequency hopping radar signal (FHRS) and 2-frequency shift keying radar signal (2FSKRS). The process of modeling and generating the radar sig

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45

Granados-Ruiz, Jackeline, David Asael Gutiérrez-Hernández, Carlos Lino-Ramírez, et al. "METHODOLOGICAL APPROACH FOR EXTRACTION OF CHARACTERISTICS OF BIOLOGICAL SIGNALS." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 02 (2019): 3011–20. https://doi.org/10.5281/zenodo.14811307.

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Generally, signal processing is applied to a set of data that is derived from the sampling of an acquired signal. This treatment is carried out with the help of a computer that in turn executes a series of logical and mathematical operations. The treatment of signals is linked to other techniques and scientific disciplines. Some of the applications of the signal treatments may be in the form of processing of audio signals, treatment of digital images, digital communications and biological signals. In this case, the treatment was applied to biological signals such as ECG (Electrocardiogram sign

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46

Yuan, Xiao Yan, Hong Fang, Xin Zhou, and Guo Chu Shou. "Designing Multisine Excitations for Measurement of Modern Wireless Communication System." Applied Mechanics and Materials 103 (September 2011): 25–29. http://dx.doi.org/10.4028/www.scientific.net/amm.103.25.

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Multisine signal is often employed as an appropriate excitation to represent the complex modulated RF signals for accurate measurement of modern wireless communication system. This paper presents a novel way of designing a multisine signal by using Discrete Fourier Transformation coefficients to represent the digital modulation signals. Investigations of the approach on the IS-95 reverse-link signal as original signal is demonstrated the designed multisine signal can approximate represent original signal.

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47

Davis, Daniel J., and John H. Challis. "Vertical Ground Reaction Force Estimation From Benchmark Nonstationary Kinematic Data." Journal of Applied Biomechanics 37, no. 3 (2021): 272–76. http://dx.doi.org/10.1123/jab.2020-0237.

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Time-differentiating kinematic signals from optical motion capture amplifies the inherent noise content of those signals. Commonly, biomechanists address this problem by applying a Butterworth filter with the same cutoff frequency to all noisy displacement signals prior to differentiation. Nonstationary signals, those with time-varying frequency content, are widespread in biomechanics (eg, those containing an impact) and may necessitate a different filtering approach. A recently introduced signal filtering approach wherein signals are divided into sections based on their energy content and the

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48

Liu, Yunjiang, Fuzhong Wang, Lu Liu, and Yamin Zhu. "Secondary signal-induced large-parameter stochastic resonance for feature extraction of mechanical faults." International Journal of Modern Physics B 33, no. 15 (2019): 1950157. http://dx.doi.org/10.1142/s0217979219501571.

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Aiming to solve the problem that it is difficult to extract large parameter signals from a strong noise background, a novel method of large parameter stochastic resonance (SR) induced by a secondary signal is proposed. The SR mechanism of high-frequency signals is expounded by analyzing the density distribution curve. High-frequency signals are converted to low-frequency signals using the scale transformation method, and then large-parameter SR is induced by the secondary signal. Ultimately, the method is applied to the feature extraction of mechanical faults. Simulation and experimental resul

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49

Zeng, Hai, Ning Zeng, Jin Han, and Yan Ding. "Engine Fault Detection Approach Based on Angle Domain Signal Model." Journal of Physics: Conference Series 2068, no. 1 (2021): 012034. http://dx.doi.org/10.1088/1742-6596/2068/1/012034.

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Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain sig

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Jalil Aklo, Nabil. "Design FIR Band Pass Filter With Centered At 50 Hz For Medical Application." University of Thi-Qar Journal for Engineering Sciences 8, no. 3 (2017): 55–65. http://dx.doi.org/10.31663/utjes.v8i3.98.

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Digital signal processing techniques used widely to cancel undesired parts of the signals, like that interference with other frequency range. Electromagnetic signals can be interference with human body signals such as Electrocardio signal Electroencephalogram and Electromyography. These interferences can be affect on the diagnosis signal during the examination and when these happen with different frequencies,such as (EMG) noise, devices that have high vibration which give fault data or effect the final result. Filtering of signals interference is very useful in the diagnosis of biomedical case

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