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Journal articles on the topic 'Signal'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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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 (July 13, 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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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 (December 31, 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 (August 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 (November 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 (August 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 (February 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 (October 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 (January 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 (October 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 (March 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

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

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13

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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14

Mingjiang Shi, Xiaoyan Zhuang, and He Zhang. "Signal Reconstruction for Frequency Sparse Sampling Signals." Journal of Convergence Information Technology 8, no. 9 (May 15, 2013): 1197–203. http://dx.doi.org/10.4156/jcit.vol8.issue9.147.

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15

Elgendi, Mohamed. "Optimal Signal Quality Index for Photoplethysmogram Signals." Bioengineering 3, no. 4 (September 22, 2016): 21. http://dx.doi.org/10.3390/bioengineering3040021.

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16

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

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17

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

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18

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

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19

Lu, Jie, Naveen Verma, and Niraj K. Jha. "Compressed Signal Processing on Nyquist-Sampled Signals." IEEE Transactions on Computers 65, no. 11 (November 1, 2016): 3293–303. http://dx.doi.org/10.1109/tc.2016.2532861.

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20

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

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21

Hoch, James A., and K. I. Varughese. "Keeping Signals Straight in Phosphorelay Signal Transduction." Journal of Bacteriology 183, no. 17 (September 1, 2001): 4941–49. http://dx.doi.org/10.1128/jb.183.17.4941-4949.2001.

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22

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

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23

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

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24

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

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25

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 (July 2004): 783–95. http://dx.doi.org/10.1016/j.jmb.2004.05.028.

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26

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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27

Birmes, Franziska S., and Susanne Fetzner. "Bakterielle Kommunikation: Signale und Signal-inaktivierende Enzyme." BIOspektrum 22, no. 3 (April 30, 2016): 251–54. http://dx.doi.org/10.1007/s12268-016-0681-4.

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28

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

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29

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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30

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 (April 26, 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 the multi-component LFM signal from the strong noise environments.

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31

Becker, Florent, Tom Besson, Jérôme Durand-Lose, Aurélien Emmanuel, Mohammad-Hadi Foroughmand-Araabi, Sama Goliaei, and Shahrzad Heydarshahi. "Abstract Geometrical Computation 10." ACM Transactions on Computation Theory 13, no. 1 (March 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 renaming other signals according to local information. Given any finite set of speeds S we construct a signal machine that is able to simulate any signal machine whose speeds belong to S . Each signal is simulated by a macro-signal , a ray of parallel signals. Each macro-signal has a main signal located exactly where the simulated signal would be, as well as auxiliary signals that encode its id and the collision rules of the simulated machine. The simulation of a collision, a macro-collision , consists of two phases. In the first phase, macro-signals are shrunk, and then the macro-signals involved in the collision are identified and it is ensured that no other macro-signal comes too close. If some do, the process is aborted and the macro-signals are shrunk, so that the correct macro-collision will eventually be restarted and successfully initiated. Otherwise, the second phase starts: the appropriate collision rule is found and new macro-signals are generated accordingly. Considering all finite sets of speeds S and their corresponding simulators provides an intrinsically universal family of signal machines.

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32

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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33

Linh, Vuong Thuy, Nguyen Van Vu, and Le Ngoc Giang. "Voice Signal Quality Assessment Based on Signal Quality Standards and Analysis." International Journal of Research Publication and Reviews 4, no. 6 (June 2023): 958–63. http://dx.doi.org/10.55248/gengpi.4.623.44854.

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34

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 (August 5, 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 manage the data. The validity of the signal simulation was confirmed through verification of simulated signal results.

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35

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 pilot channel signal by the auxiliary signals. The cross-correlation results from the received pilot signal and the two local pilot signals with different auxiliary signals will subtract from one another. Then, side peaks of correlation function and in-band noise of pilot channel are suppressed, while the data channel has the same operation results. At last the outputs of pilot channel and data channel will be added up to make full use of the received signal power. By this way, the acquisition efficiency, accuracy, and adaptability to low signal-to-noise ratio (SNR) conditions for CBOC signal have been improved, alongside possible solution when the GNSS receiver works in a critical environment.

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36

Kweka, Maksi. "Good Signal? Bad Signal?" Future Healthcare Journal 5, no. 3 (October 2018): 231.2–232. http://dx.doi.org/10.7861/futurehosp.5-3-231a.

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37

Liu, Haochen. "BPSK/BOC Modulation Signal System for GPS Satellite Navigation Signals." Journal of Physics: Conference Series 2384, no. 1 (December 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 parallel phase code acquisition are introduced in this paper. By introducing the principles and functions of the two algorithms, the improvement of the optimization algorithm for the two modulated signals is expounded. This paper introduces the working principle of the GPS signal, shows the advantages and future development prospects of the BPSK modulation signal and the BOC signal based on the GPS modulation signal, and compares the two modulation signals with the optimization algorithm. The BOC modulated signal shows superior performance. Combined with the optimization algorithm, high-precision transmission signals can be achieved, but the requirements for the receiver are high. The BPSK modulation signal combined with the optimization algorithm can meet most of the needs in life. Still, it is slightly worse than the BOC modulation signal in the face of high-precision and high-demand signal transmission.

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38

Borawake, Prof Dr M. P. "Audio Signal Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 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, voice recognition, entertainment industry, and to find defected in machines using audio signals or frequencies. The signals pay important role in our day-to-day communication, perception of environment, and entertainment. A joint time-frequency (TF) approach would be better choice to effectively process this signal. The theory of signal processing and its application to audio was largely developed at Bell Labs in the mid-20th century. Claude Shannon and Harry Nyquist’s early work on communication theory and pulse-code modulation (PCM) laid the foundations for the field.

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39

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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40

Gelenbe, Erol. "Random Neural Networks with Negative and Positive Signals and Product Form Solution." Neural Computation 1, no. 4 (December 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 external signal arrivals, and Markovian signal movements between neurons, has a product form leading to simple analytical expressions for the system state.

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41

Xiang, Chengzhi, and Ailin Liang. "Analog and Photon Signal Splicing for CO2-DIAL Based on Piecewise Nonlinear Algorithm." Atmosphere 13, no. 1 (January 10, 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 and efficient. First, the lidar signal is segmented, and according to the characteristics of each signal, the best fitting parameters are obtained by using the least square fitting with different steps. Then, all the segmented and fitted signals are integrated to realize the effective splicing of the near-field AD signal and the far-field PC signal. A weight gradient strategy is also adopted in signal splicing, and the weights of the AD and PC signals in the spliced signal change with the height. The splicing effect of the improved algorithm is evaluated by the measured signal, which are obtained in Wuhan, China, and the splice of the AD and PC signals in the range of 800–1500 m are completed. Compared with the traditional method, the evaluation parameter R2 and the residual sum of squares of the spliced signal are greatly improved. The linear relationship between the AD and PC signals is improved, and the fitting R2 of differential absorption optical depth reaches 0.909, indicating that the improved signal splicing algorithm can well splice the near-field AD signal and the far-field PC signal.

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42

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 (February 17, 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 analyzed after noise reduction of the original signal. The results demonstrate that the signal duration and maximum amplitude of the signal could not accurately distinguish the two types of signals. However, the main frequency of the rock fracture signal after noise reduction is distributed above 500 HZ, and the main frequency of the rock blasting signal is mainly distributed below 500 HZ. After the denoised signal is decomposed by the ensemble empirical simulation decomposition, the energy of the IMF1 frequency band of the rock fracture signal occupies an absolute dominant position, and the sum of the energy of the IMF2–IMF4 frequency bands of the rock blasting signal occupies a dominant position. The fractal box dimension of the rock fracture signal is mainly below 1.1, and the fractal box dimension of the rock blasting signal is mainly above 1.25. According to the above research results, an automatic signal recognition system based on the BP neural network is established, and the recognition accuracy of the rock blasting and rock fracture signals reached 93% and 94% respectively, when this system was used.

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43

Wright, Beverly A. "Detectability of simultaneously masked signals as a function of signal bandwidth for different signal delays." Journal of the Acoustical Society of America 98, no. 5 (November 1995): 2493–503. http://dx.doi.org/10.1121/1.413280.

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Qi, Yang, Taichu Shi, and Ben Wu. "Wideband Mixed Signal Separation Based on Photonic Signal Processing." Telecom 2, no. 4 (November 2, 2021): 413–29. http://dx.doi.org/10.3390/telecom2040024.

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The growing needs for high-speed and secure communications create an increasing challenge to the contemporary framework of signal processing. The coexistence of multiple high-speed wireless communication systems generates wideband interference. To protect the security and especially the privacy of users’ communications requires stealth communication that hides and recovers private information against eavesdropping attacks. The major problem in interference management and stealth information recovery is to separate the signal of interest from wideband interference/noise. However, the increasing signal bandwidth presents a real challenge to existing capabilities in separating the mixed signal and results in unacceptable latency. The photonic circuit processes a signal in an analog way with a unanimous frequency response over GHz bandwidth. The digital processor measures the statistical patterns of the signals with sampling rate orders of magnitude smaller than the Nyquist frequency. Under-sampling the signals significantly reduces the workload of the digital processor while providing accurate control of the photonic circuit to perform the real-time signal separations. The wideband mixed signal separation, based on photonic signal processing is scalable to multiple stages with the performance of each stage accrued.

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Fikri, Muhammad Rausan, Indah Soesanti, and Hanung Adi Nugroho. "ECG Signal Classification Review." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 1 (June 18, 2021): 15. http://dx.doi.org/10.22146/ijitee.60295.

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The heart is an important part of the human body, functioning to pump blood through the circulatory system. Heartbeats generate a signal called an ECG signal. ECG signals or electrocardiogram signals are basic raw signals to identify and classify heart function based on heart rate. Its main task is to analyze each signal in the heart, whether normal or abnormal. This paper discusses some of the classification methods which most frequently used to classify ECG signals. These methods include pre-processing, feature extraction, and classification methods such as MLP, K-NN, SVM, CNN, and RNN. There were two stages of ECG classification, the feature extraction stage and the classification stage. Before ECG features were extracted, raw ECG signal data first processed in the pre-processing stage because ECG signals were not necessarily free of noise. Noise will cause a decrease in accuracy during the classification process. After features were extracted, ECG signals were then classified with the classification method. Neural Network methods such as CNN and RNN are best to use since they can give better accuracy. For further research, the machine learning method needs to be improved to get high accuracy and high precision in the ECG signals classification.

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Patil, Vrushal. "Traffic Signal Pattern Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (December 31, 2023): 126–28. http://dx.doi.org/10.22214/ijraset.2023.57249.

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Abstract: Every day we are witnessing a rapid increase in traffic volume on roads. Traffic signals are made to manage the traffic to get less disturbance during the journey and to avoid collisions. Sometimes these traffic signals might become a reason for a delay due to poor time management at signal timings. The old traffic signal patterns are the main cause of this issue and hence this project of new signalling patterns will help in using traffic signals more efficiently. In the traditional pattern at a crossover only one signal can be opened but using our pattern algorithm more than one signal can be opened and traffic could clear more easily. Even concepts of image processing are used to make the system more automated and intelligent.

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Lei, Yang. "Array Sensor Output Signal Detection System Signal Conditioning Circuit Design." Journal of Physics: Conference Series 2452, no. 1 (March 1, 2023): 012033. http://dx.doi.org/10.1088/1742-6596/2452/1/012033.

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Abstract The signal output by the array sensor is generally very weak, with a large dynamic range and a wide range of signal frequencies. In order to solve the problem of accurate measurement of weak signals with wide frequency and large dynamic range, this paper proposes a design method of sub-band filtering and variable gain amplifying circuit based on the analog switch, divides the signal into four frequency bands, and designs four groups of second-order voltage control filter, and adjust the magnification for different frequency signals, and only need to switch the corresponding resistance and capacitance to realize the switching of signal processing circuits of different frequency bands, which greatly optimizes the circuit structure. In order to reduce the interference in the transmission process, a single-ended differential circuit is designed to transmit the processed signal to the subsequent acquisition system for acquisition. After the simulation test, the signal conditioning circuit can effectively improve the signal-to-noise ratio of the detection signal and improve the measurement accuracy.

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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 (February 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 cross-correlation and energy analyses to determine the accuracy of interception signals. During the experiment, the collected acoustic beacon signal is used as the test signal, and the signal is obtained as the simulation signal on the basis of the parameters of acoustic beacons. Results show that the correlation between the synthetic and concerned signals is more than 80% after a number of superposition are performed and the extraction effect is remarkable. Dislocation superimposed method is simple and easily operated, and the extracted signal waveform yields a high accuracy.

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I. S. Amiri, I. S. Amiri, and J. Ali J. Ali. "Data signal processing via manchester coding-decoding method using chaotic signals generated by PANDA ring resonator." Chinese Optics Letters 11, no. 4 (2013): 041901–41904. http://dx.doi.org/10.3788/col201311.041901.

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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 (May 30, 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 signals is presented and thereafter, results on the outcome of this process and their implications are discussed. It is observed from the obtained results of an accurate depiction of key parameters of pulse width (PW) of 1 µs and frequency of 10 MHz of the radar signals among others, that the developed models of the radar signals are feasible for further analysis using robust model signal processing tools such as time-frequency analysis can be used. Hence, these models can be used in practical radar signal analysis such as electronic intelligence (ELINT) and electronic warfare support (ES).

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