Relevant bibliographies by topics / Signal / Journal articles
To see the other types of publications on this topic, follow the link: Signal.

Journal articles on the topic 'Signal'

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

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Signal.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
13

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
14

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
15

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
16

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
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 (2011): 965–73. http://dx.doi.org/10.1161/circep.111.964304.

Full text
APA, Harvard, Vancouver, ISO, and other styles
18

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
19

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
20

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
21

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
22

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
23

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
24

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
25

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
26

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
27

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
28

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
29

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
30

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
31

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
32

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
33

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
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 (2023): 324–34. http://dx.doi.org/10.9766/kimst.2023.26.4.324.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
35

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
36

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
37

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
38

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
39

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
40

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 (2023): 958–63. http://dx.doi.org/10.55248/gengpi.4.623.44854.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
42

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
43

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.

Full text
Abstract:
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 in a continuous time domain to a discrete time domain through a series of methods. It is used to convert continuous time signals into digital signals for relevant processing and storage of digital signals in subsequent operations. This paper will summarize the basic principles and methods of signal sampling technology, introduce the sampling theorem, sampling frequency, sampling depth, sampling method and sampling error, and finally discuss the future development of signal adoption technology.
APA, Harvard, Vancouver, ISO, and other styles
44

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
45

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.

Full text
Abstract:
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).
APA, Harvard, Vancouver, ISO, and other styles
46

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.

Full text
Abstract:
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 then Butterworth filtered with section-specific cutoff frequencies improved second derivative estimates in a nonstationary kinematic signal. Utilizing this signal-section filtering approach for estimating running vertical ground reaction forces saw more of the signal’s high-frequency content surrounding heel strike maintained without allowing inappropriate amounts of noise contamination in the remainder of the signal. Thus, this signal-section filtering approach resulted in superior estimates of vertical ground reaction forces compared with approaches that either used the same filter cutoff frequency across the entirety of each signal or across the entirety of all signals. Filtering kinematic signals using this signal-section filtering approach is useful in processing data from tasks containing an impact when accurate signal second derivative estimation is of interest.
APA, Harvard, Vancouver, ISO, and other styles
47

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.

Full text
Abstract:
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 results indicate that (i) the effect of SR induced by the secondary signal is significantly enhanced when the frequency of the secondary signal is twice that of the signals to be detected after the scale transformation; (ii) when the frequency of secondary signal is twice the maximum frequency of the signals to be detected after the scale transformation, choosing an appropriate amplitude of secondary signal can alleviate the problem that the noise energy is excessively concentrated in the low-frequency channel with regard to the extraction of two-frequency or three-frequency high-frequency signals; and (iii) by adding the secondary signal to the engineering example, the fault power spectrum value of system output is 101% higher than that without the secondary signal.
APA, Harvard, Vancouver, ISO, and other styles
48

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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 signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.
APA, Harvard, Vancouver, ISO, and other styles
50

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

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography