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Journal articles on the topic 'Signals’ analysis methods'

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Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

Ngui, Wai Keng, M. Salman Leong, Lim Meng Hee, and Ahmed M. Abdelrhman. "Wavelet Analysis: Mother Wavelet Selection Methods." Applied Mechanics and Materials 393 (September 2013): 953–58. http://dx.doi.org/10.4028/www.scientific.net/amm.393.953.

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Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.

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Qu, Yanhuai, Shuai Zhang, and Qingkai Han. "Comparison of Non-linear Signals Analysis Methods." MATEC Web of Conferences 232 (2018): 01014. http://dx.doi.org/10.1051/matecconf/201823201014.

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In modern engineering, linear is relative, while non-linear and non-stationary is absolute. There are many methods in non-linear signals processing. How to select a most suitable analysis method quickly for the nonlinear signal is particularly important, which can improve the signal processing efficiently. In this paper, three common analysis methods for nonlinear signals, Wavelet spectrum, Hilbert spectral analysis (HSA) and Poincaré mapping are researched and analyzed by some typical nonlinear signals from the complex electromechanical model test system. The effectiveness and application scopes of these approaches are obtained, which can provide a theoretical and practical basis for engineering application.

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Garg, Malika. "Methods for the Analysis of EEG signals: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 873–76. http://dx.doi.org/10.22214/ijraset.2021.38072.

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Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification

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4

Muthuswamy, Jitendran, and Nitish V. Thakor. "Spectral analysis methods for neurological signals." Journal of Neuroscience Methods 83, no. 1 (August 1998): 1–14. http://dx.doi.org/10.1016/s0165-0270(98)00065-x.

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5

A, Mohammed. "Deconvolution methods for biomedical signals analysis." Indian Journal of Science and Technology 3, no. 2 (February 20, 2010): 105–9. http://dx.doi.org/10.17485/ijst/2010/v3i2.1.

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6

Musha, Takaaki, and Tatsuya Kumazawa. "Intensity analysis methods for transient signals." Applied Acoustics 69, no. 1 (January 2008): 60–67. http://dx.doi.org/10.1016/j.apacoust.2006.08.011.

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7

Debbal, S. M. "Pathological Electromyogram (EMG) Signal Analysis Parameters." Clinical Cardiology and Cardiovascular Interventions 4, no. 13 (August 9, 2021): 01–14. http://dx.doi.org/10.31579/2641-0419/185.

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Clinical analysis of the electromyogram is a powerful tool for diagnosis of neuromuscular diseases. There fore, the detection and the analysis of electromyogram signals has he attracted much attention over the years. Several methods based on modern signal Processing techniques such as temporal analysis, spectro-temporel analysis ..., have been investigated for electromyogram signal treatment. However, many of these analysis methods are not highly successful due to their complexity and non-stationarity. The aim of this study is to analyse the EMGs signals using nonlinear analysis. This analysis can provide a wide range of information’s related to the type of signal (normal and pathological).

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Iwaniec, Joanna, Marek Iwaniec, and Antoni Kalukiewicz. "Application of vectorcardiography and recurrence-based methods to analysis of ECG signals." MATEC Web of Conferences 241 (2018): 01015. http://dx.doi.org/10.1051/matecconf/201824101015.

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The paper concerns application of vectorcardiography and recurrence-based methods to analyze ECG signals. The first method involves recording the direction and magnitude of electrical forces of the heart by means of a continuous series of vectors that form a curving line around a center, while the second consists in representing the ECG signals in the phase space and searching for the recurrence properties of the registered signals. In both methods, change of analysis domain offers new opportunities for early diagnosis. In this work, discussed methods have been used for the purposes of analysis of selected ECG signals stored in the medical database. Vectorcardiograms were determined on the basis of ECG signals for the Farnk's leads. Signal analysis was carried out with the application of the recurrence plots (RP) method. The proposed approach simplifies assessment of the signal information content by providing patterns characteristic for different myocardial injury. Carried out research requires complementation by results of statistical analysis for various age groups and degrees of myocardial injury.

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9

Nam, Ki Woo, Seok Hwan Ahn, and Jin Wook Kim. "Nondestructive Evaluation in Materials Using Time-Frequency Analysis Methods." Key Engineering Materials 297-300 (November 2005): 2090–95. http://dx.doi.org/10.4028/www.scientific.net/kem.297-300.2090.

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Application of signal processing techniques to nondestructive evaluation (NDE) in general has become a standard tool in determining the frequency characteristics of the signals and relating these characteristics to the integrity of the structure under consideration. The joint time-frequency analysis techniques are applied to analyze ultrasonic signals in degraded austenite stainless 316 steel, to study the evaluation of damage in this material. It is demonstrated that the nonstationary characteristics of ultrasonic signals could be analyzed effectively by these methods. WVD was found to be more effective for analyzing the attenuation and frequency characteristics of degraded materials through ultrasonic. It is indicated that the time-frequency analysis methods should also be useful in evaluating various damages and defects in structural members.

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10

Gao, Yuan Sheng, Qiang Chen, Qiang Sun, Zhong Chen, and Wen Hai Zhang. "The Impedance Calculation Methods Using Damped Sinusoidal Signal." Advanced Materials Research 860-863 (December 2013): 2003–6. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2003.

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There are large number of transient signals will be produced when the operation mode is changed. And the frequency of the transient signals distributed in wide range. The most of them are damped and oscillated. The impedance is a popular feature in power system, and it has been widely used in relay protection and fault location. So the impedance calculation based on the damped sinusoidal signal have been the important way to diagnosis the fault, locate the fault and so on. But the methods used now are based on the fundamental impedance calculation theory, lacking theoretical basis. In fact, the traditional methods for fundamental signal calculation are not appropriate for the quantitative analysis of damped sinusoidal signal. The paper analyzed the impedance calculation based on damped sinusoidal signal, combined the features of damped sinusoidal signals and the traditional impedance calculation method. The two typical signal analysis methods for damped sinusoidal signal extraction are used to calculate the accurate impedance based on the different extraction results. And the analysis laid a foundation for the impedance calculation using damped sinusoidal signals.

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11

ROMERO, Julian, Luis DIAGO, Junichi SHINODA, and Ichiro HAGIWARA. "Comparison of Data Reduction Methods for the Analysis of Iyashi Expressions using Brain Signals." Journal of Advanced Simulation in Science and Engineering 2, no. 2 (2015): 349–66. http://dx.doi.org/10.15748/jasse.2.349.

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12

DELLANDRÉA, E., P. MAKRIS, and N. VINCENT. "ZIPF ANALYSIS OF AUDIO SIGNALS." Fractals 12, no. 01 (March 2004): 73–85. http://dx.doi.org/10.1142/s0218348x04002380.

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This paper deals with several of the possible uses of Zipf and inverse Zipf laws in the field of audio signal analysis. We show that these laws are powerful analysis tools allowing the extraction of information not available by standard methods. The adaptation of Zipf and inverse Zipf laws to audio signals requires a coding of these signals into text-like data, considered as sequences of patterns. Because these codings are of first importance since they have to bring to the fore relevant information within signals, three types of codings have been developed, depending on the representation of the audio signal it is based on: temporal, frequential and time-scale representations. Once audio signal has been coded, features linked to Zipf and inverse Zipf approaches are computed. Finally, the classification step aims at the identification of signals. Four classification methods have been considered as well as a fusion method that combines these classifiers. In order to evaluate our method, we apply it on medical acoustical signals. They occur when swallowing and contain xiphoidal sounds. The problem is to extract and characterize xiphoidal sounds according to the gastro-oesophageal reflux pathological state. The aim is to help medical doctors to characterize and diagnose this pathology, and to give, in the end, a decision help tool as efficient as possible.

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Němcová, Andrea, Radovan Smíšek, Lucie Maršánová, Lukáš Smital, and Martin Vítek. "A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression." BioMed Research International 2018 (July 18, 2018): 1–26. http://dx.doi.org/10.1155/2018/1868519.

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The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts’ classification, we determined corresponding ranges of selected quality evaluation methods’ values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.

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14

Germán-Salló, Zoltán, and Zoltán Germán-Salló. "Methods in Complexity Analysis of Discrete Time Signals." Acta Marisiensis. Seria Technologica 17, no. 2 (December 1, 2020): 54–56. http://dx.doi.org/10.2478/amset-2020-0020.

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AbstractDiscrete time signals carry information about systems and their internal functional mechanisms which characterize their complexity. Complexity measures are strongly related to information content and evaluations have been made on various signals in many ways in last few years. This paper uses information theory estimates of complexity as different types of entropies in order to estimate the complexity of various time discrete synthesized signals. Results show that this kind of indices can be a useful tool in diagnostic, fault detection and further development.

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15

Bertocco, M., C. Narduzzi, D. Petri, and F. E. Zanin. "Accuracy of Autoregressive Methods in Exponential Signals Analysis." IFAC Proceedings Volumes 27, no. 8 (July 1994): 89–93. http://dx.doi.org/10.1016/s1474-6670(17)47697-x.

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16

Wei, Shuang, De Fu Jiang, and Michel Fattouche. "A Review of Super-Resolution Methods for Nonorthogonal Analysis of Highly Correlated Signals in Noise." Applied Mechanics and Materials 543-547 (March 2014): 2201–4. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2201.

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Highly correlated signals exist in many applications of different fields of physics and engineering. But existing techniques for signal estimation and detection are either too complex or do not perform adequately when the observations consist of highly correlated signals in noise. Thus, this paper presents a review of three important super-resolution methods to discuss their characteristics and limitation for extracting the highly correlated signals in noise.

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17

Xie, Jingsong, Wei Cheng, Yanyang Zi, and Mingquan Zhang. "Phase-based spectrum analysis method for identifying weak harmonics." Journal of Vibration and Control 24, no. 23 (March 16, 2018): 5585–96. http://dx.doi.org/10.1177/1077546318760904.

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Fault characteristic frequency extraction is an important means for the fault diagnosis of rotating machineries. Traditional signal processing methods commonly use the amplitude information of signals to detect damages. However, when the amplitudes of characteristic frequencies are weak, the recognition effects of traditional methods may be unsatisfactory. Therefore, this paper proposes the phase-based enhanced phase waterfall plot (EPWP) method and frequency equal ratio line (FERL) method for identifying weak harmonics. Taking a cracked rotor as an example, the characteristic frequency detection performances of the EPWP and FERL methods are compared with that of the traditional signal processing methods namely fast Fourier transform, short-time Fourier transform, discrete wavelet transform, continuous wavelet transform, ensemble empirical mode decomposition, and Hilbert–Huang transform. Research results demonstrate that the effects of EPWP and FERL for the recognitions of weak harmonics which are contained in steady signals and transient signals are better than that of the traditional signal processing methods. The accurate identification of weak characteristic frequencies in the vibration signals can provide an important reference for damage detections and improve the diagnostic accuracy.

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18

Moskalets, Mykola, and Svitlana Teplytska. "ANALYSIS OF METHODS USING FOR SUPERRESOLUTION OF THE SIGNALS IN THE TASKS OF SPACE-TIME ACCESS." EUREKA: Physics and Engineering 5 (September 30, 2016): 16–24. http://dx.doi.org/10.21303/2461-4262.2016.00154.

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Six methods are considered in the analysis of the methods of angular superresolution of the signals: non-adaptive beamforming, Kapon, thermal noise, Bordzhotti-Lagunas, maximal entropy and multiple signal classification (MUSIC). The comparative characteristic of the methods with assessing their advantages disadvantages and limitations is given. Theoretical resolution of these methods is assessed in the article. Numerical evaluation of resolution ability of the methods of angular superresolution of the signal are obtained based on the simulation of various scenarios of signal-to-noise ratio, taking into account the use of correlated and uncorrelated signals, a different number of antenna elements and the values of the signal / interference + noise ratio. These estimations show the ultimate theoretical accuracy of the methods and the potential for their use in problems of space-time access with set limits. The simulation results confirmed the statistical consistency of these methods of estimation of arrival direction angles of correlated and uncorrelated signals from subscriber stations for the space-time sampling in the output of the linear equidistant antenna array. The present analysis and research results make it possible to select the most effective method for determining the arrival of signals in accordance with the given parameters of signal-to-noise ratio under restrictions.

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19

Lukovenkova, Olga, Yury Senkevich, Alexandra Solodchuk, and Albert Shcherbina. "Overview of processing and analysis methods for pulse geophysical signals." E3S Web of Conferences 196 (2020): 02023. http://dx.doi.org/10.1051/e3sconf/202019602023.

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The paper discusses the processing and analysis methods for the geoacoustic and electromagnetic emission pulse signals recorded for more than 20 years at the IKIR FEB RAS geodynamic proving ground (Kamchatka Peninsula). The methods for pulse detection, waveform reconstruction, pulse time-frequency analysis using adaptive sparse approximation, structural description of pulse waveforms and pulse classification are proposed. To detect pulses, the adaptive threshold scheme is used. It adjusts to the noise level of a processed signal. To analyze time-frequency structure of the pulses, the adaptive matching pursuit algorithm is used. To identify pulse waveform, the structural description method is proposed. It encodes pulses with special image matrices. The method of the identified pulses classification is considered. Since the methods for pulse structure analysis are sensitive to noise and distortions, the authors propose the method for pulse waveform reconstruction based on wavelet filtering. The geophysical signal information features determined during the analysis can be used to search for anomalies in the data, and then establish a relationship between these anomalies and deformation process dynamics, in particular, with earthquake development processes.

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20

W. Arrasmith, William, and Everett R. Coots. "Analyzing Infrasound and Seismic Signals Emanating from a Waterborne System Using Canonical Modeling and Analysis Methods." International Journal of Modeling and Optimization 4, no. 3 (June 2014): 176–81. http://dx.doi.org/10.7763/ijmo.2014.v4.369.

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21

Wang, Zuo-Cai, Feng Wu, and Wei-Xin Ren. "Stationarity test of vibration signals with surrogate data and time–frequency analysis." Advances in Structural Engineering 20, no. 8 (November 21, 2016): 1143–54. http://dx.doi.org/10.1177/1369433216677602.

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The stationarity test of vibration signals is critical for the extraction of the signal features. In this article, the surrogate data with various time–frequency analysis methods are proposed for stationary test of vibration signals. The surrogate data are first generated from the Fourier spectrum of the original signal with keeping the magnitude of the spectrum unchanged and replacing its phase by a random sequence. The local and global spectra of the original signal and the surrogate data are then estimated by four time–frequency analysis methods, which are short-time Fourier transform, multitaper spectrograms, wavelet transform, and S-transform methods. The index of nonstationarity is then defined based on the distances between the local and global spectra. Three kinds of synthetic signals, which are stationary signals, frequency-modulated signals, and amplitude-modulated signals, are tested to compare the efficiency of the four time–frequency analysis methods as mentioned. The results show that with a certain observation scale value, the index of nonstationarity based on the short-time Fourier transform or wavelet transform method may fail to test the stationarity of the signal. The parametric studies and sensitivity analysis of the observation scale and noise-level effect are also extensively conducted. The results show that the index of nonstationarity calculated using the multitaper spectrograms’ method is more suitable for stationarity test of frequency-modulated signals, while the index of nonstationarity calculated using the S-transform method is more suitable for stationarity test of amplitude-modulated signals. The results also show that the noise has a significant effect on the stationarity test results. Finally, the stationarity of a real vibration signal measured from a cable is tested, and the results show that the proposed index of nonstationarity can effectively test the stationarity of real vibration signals.

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Lu, Zhenyu, Tingya Yang, and Min Zhu. "Study of the Method of Multi-Frequency Signal Detection Based on the Adaptive Stochastic Resonance." Abstract and Applied Analysis 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/420605.

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Recently, the stochastic resonance effect has been widely used by the method of discovering and extracting weak periodic signals from strong noise through the stochastic resonance effect. The detection of the single-frequency weak signals by using stochastic resonance effect is widely used. However, the detection methods of the multifrequency weak signals need to be researched. According to the different frequency input signals of a given system, this paper puts forward a detection method of multifrequency signal by using adaptive stochastic resonance, which analyzed the frequency characteristics and the parallel number of the input signals, adjusted system parameters automatically to the low frequency signals in the fixed step size, and then measured the stochastic resonance phenomenon based on the frequency of the periodic signals to select the most appropriate indicators in the middle or high frequency. Finally, the optimized system parameters are founded and the frequency of the given signals is extracted in the frequency domain of the stochastic resonance output signals. Compared with the traditional detection methods, the method in this paper not only improves the work efficiency but also makes it more accurate by using the color noise, the frequency is more accurate being extracted from the measured signal. The consistency between the simulation results and analysis shows that this method is effective and feasible.

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23

Borowska, Marta. "Entropy-Based Algorithms in the Analysis of Biomedical Signals." Studies in Logic, Grammar and Rhetoric 43, no. 1 (December 1, 2015): 21–32. http://dx.doi.org/10.1515/slgr-2015-0039.

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Abstract Biomedical signals are frequently noisy and incomplete. They produce complex and high-dimensional data sets. In these mentioned cases, the results of traditional methods of signal processing can be skewed by noise or interference present in the signal. Information entropy, as a measure of disorder or uncertainty in the data, was introduced by Shannon. To date, many different types of entropy methods have appeared with many different application areas. The purpose of this paper is to present a short overview of some methods of entropy analysis and to discuss their suitability for use in the analysis of biomedical signals.

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Zaynidinov, Hakimjon, Sayfiddin Bakhromov, Bunyod Azimov, and Sarvar Makhmudjanov. "Comparative Analysis Spline Methods in Digital Processing of Signals." Advances in Science, Technology and Engineering Systems Journal 5, no. 6 (December 2020): 1499–510. http://dx.doi.org/10.25046/aj0506180.

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25

Yanhuai, Qu, Shuai Zhang, and Qingkai Han. "Withdrawn article: Comparison of Non-linear Signals Analysis Methods." MATEC Web of Conferences 175 (2018): 03034. http://dx.doi.org/10.1051/matecconf/201817503034.

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26

Saxe, R. F. "The analysis of SEQUOYAH fluctuation signals by ‘unusual’ methods." Annals of Nuclear Energy 13, no. 7 (January 1986): 399–412. http://dx.doi.org/10.1016/0306-4549(86)90038-1.

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27

Zhang, Hui Xing, Jie Li, Qi Lin, Jian Zhi Qu, and Qi Zheng Yang. "Contrast of Time-Frequency Analysis Methods and Fusion of Wigner-Ville Distribution and Wavelet Transform." Advanced Materials Research 805-806 (September 2013): 1962–65. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.1962.

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Time-frequency analysis is a powerful tool for analyzing non-stationary signals, which can describe the signals frequency varying with time and provide us the joint information of time domain and frequency domain of the signal. We use a synthetic signal to realize the time-frequency analysis methods of wavelet transform, S transform and Wigner-Ville distribution. Through comparing and analyzing those time-frequency distributions, we propose a new method of integrating wavelet transform and Wigner-Ville distribution. This new method gives a better result than that of wavelet transform and Wigner-Ville distribution and increases the time-frequency resolution.

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Chen, Yanguang, and Yuqing Long. "Spatial Signal Analysis Based on Wave-Spectral Fractal Scaling: A Case of Urban Street Networks." Applied Sciences 11, no. 1 (December 24, 2020): 87. http://dx.doi.org/10.3390/app11010087.

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A number of mathematical methods have been developed to make temporal signal analyses based on time series. However, no effective method for spatial signal analysis, which are as important as temporal signal analyses for geographical systems, has been devised. Nonstationary spatial and temporal processes are associated with nonlinearity, and cannot be effectively analyzed by conventional analytical approaches. Fractal theory provides a powerful tool for exploring spatial complexity and is helpful for spatio-temporal signal analysis. This paper is devoted to developing an approach for analyzing spatial signals of geographical systems by means of wave-spectrum scaling. The traffic networks of 10 Chinese cities are taken as cases for positive studies. Fast Fourier transform (FFT) and ordinary least squares (OLS) regression methods are employed to calculate spectral exponents. The results show that the wave-spectrum density distribution of all these urban traffic networks follows scaling law, and that the spectral scaling exponents can be converted into fractal dimension values. Using the fractal parameters, we can make spatial analyses for the geographical signals. The wave-spectrum scaling methods can be applied to both self-similar fractal signals and self-affine fractal signals in the geographical world. This study has implications for the further development of fractal-based spatiotemporal signal analysis in the future.

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Wu, Xin Hui. "The Simulation Analysis of the Signals Collection Model for Railway Locomotive Remote Communication." Applied Mechanics and Materials 644-650 (September 2014): 4132–35. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4132.

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The locomotive frequency shift signals carry important operation information. In order to achieve reliable detection of the locomotive signals, this paper analyzes the signal characteristics of the locomotive and proposes a railway remote communication signals collection model. The model uses the data mining methods to extract the locomotive frequency shift signals by analyzing the locomotive frequency shift with signal classification technology. The separated signals can obtain the low frequency signals with low-pass filtering and shaping. The algorithm is simulated by MATLAB software. The results illustrate the proposed method can effectively separate and collect the shifted frequency signals with some application value.

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Brajkovič, Robert, Tomaž Žagar, and Dejan Križaj. "Frequency Synchronization Analysis in Digital lock-in Methods for Bio-impedance Determination." Measurement Science Review 14, no. 6 (December 15, 2014): 343–49. http://dx.doi.org/10.2478/msr-2014-0047.

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Abstract The lock-in method is one of the most frequently used methods for reconstruction of measured signals and as such frequently applied in the (bio)impedance method to determine the modulus and phase of the (bio)impedance. In implementation of the method in a (bio)impedance measurement device one has to consider possible non synchronized frequencies of the reference and the analyzed signals as well as potential sources of noise. In this work we analyzed these errors theoretically and experimentally. We show that both amplitude and phase errors depend on the relative difference of the frequencies of the reference and investigated signal as well as the number of integration periods. Theoretically, these errors vanish during the determination of the (bio)impedance modulus and phase. In practical implementation the inaccuracies appear at points of very low determined signal amplitudes due to the limited accuracy of analog to digital converters and are distributed around these points due to other sources of noise inherent in implementation of the measurement device.

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Buhaiov, М. V., V. V. Branovytskyi, and Y. O. Khorenko. "ANALYSIS OF THE EFFICIENCY OF SPECTRAL ESTIMATION METHODS FOR DETECTION OF UAV ACOUSTIC SIGNALS." Проблеми створення, випробування, застосування та експлуатації складних інформаційних систем, no. 19 (January 15, 2021): 4–15. http://dx.doi.org/10.46972/2076-1546.2020.19.01.

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One of the most important components of counteracting small unmanned aerial vehicles is their reliable detection. You can use propeller noise to detect such objects at short distances. An energy or harmonic detector is used to receive unmanned aerial vehicles acoustic emission. At low signal-to-noise ratios , which is most common in practice, the harmonic detector provides a higher probability of detection compared to energy. The principle of the harmonic detector is based on spectral analysis of acoustic signals. A mathematical model of the acoustic signal of an aircraft-type unmanned aerial vehicles is proposed. It is shown that at short time intervals (tens of milliseconds) such signals can be considered as stationary and for its analysis can be used known methods of spectral estimation. Nonparametric, parametric and subspace methods of spectral estimation are considered for processing of acoustic emission of unmanned aerial vehicles. To conduct a comparative analysis of different methods of spectral estimation, a statistical quality index was used, which can be calculated as a variation of the estimation of power spectral density. This index characterizes the method of spectral estimation in terms of frequency resolution and the ability to detect harmonic components of the signal into noise and not create interference that exceeds the amplitude of the signal. As a result of researches it was established that at high signal-to-noise ratios parametric methods are more effective in comparison with nonparametric. However, such a statement will be valid only if the correct order of the model. It is shown that the use of spatial methods is impractical for the analysis of acoustic signals of unmanned aerial vehicles. The use of the value of the statistical quality indicator as a threshold for deciding on the presence or absence of the acoustic signal of the unmanned aerial vehicles in the adopted implementation and its further processing should be used at SNR values greater than 5 dB.

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Mursaev, A. N., S. V. Zinkin, and D. A. Novichkov. "Theoretical study of methods of finding signals and synchronization of the earth stations of satellite communications." Informatization and communication, no. 2 (February 16, 2021): 7–14. http://dx.doi.org/10.34219/2078-8320-2021-12-2-7-14.

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When using spread-spectrum signals, the problem of searching by frequency and delay must be solved before the signal is processed. For demodulation of simple signals with AM, CHM and FM, it is fundamentally necessary to provide only a frequency search, and clock synchronization can be carried out after the first decision circuit, i.e. in the signal conversion equipment, usually placed in the interface equipment or channel-forming equipment. The paper presents a theoretical analysis of the methods of the search of signals and synchronization in earth stations - search and tracking of singaporemetro signals with an expanded range and features auto scan and auto-tracking frequency when receiving simple signals.

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Martinek, Radek, Martina Ladrova, Michaela Sidikova, Rene Jaros, Khosrow Behbehani, Radana Kahankova, and Aleksandra Kawala-Sterniuk. "Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals." Sensors 21, no. 18 (September 10, 2021): 6064. http://dx.doi.org/10.3390/s21186064.

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Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

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Wang, Yantong, and Lingyi Zhou. "Innovative Analysis and Application on Magnetograms Signal." Journal of Physics: Conference Series 2174, no. 1 (January 1, 2022): 012087. http://dx.doi.org/10.1088/1742-6596/2174/1/012087.

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Abstract This study aims to make research on magnetocardiography (magnetograms) signals. Magnetocardiogram is a non-invasive method to record and analyze the magnetic components of the electromagnetic field generated by cardiac electrical activity in the cardiac cycle. We conclude the basic characteristics of the magnetograms signal to summarize the existing magnetograms measurement principles. Then we combine the initial development process and detection means of magnetograms by discussing the signal shielding of magnetograms from two aspects of hardware and software. We compare several methods of shielding electromagnetic signals on hardware and finally obtain an excellent means to shield external signals at this stage. We also conclude the current application of magnetograms in medical treatment.

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Ulyashin, Aleksander, and Aleksander Velichko. "Comparative analysis of methods for constructing analog-to-digital converters." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 4 (December 18, 2020): 38–49. http://dx.doi.org/10.17212/2307-6879-2020-4-38-49.

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This paper is devoted to the comparative analysis of modern integrated analog-to-digital converters (ADCs). At the moment, a number of foreign companies, such as Analog Devices, Texas Instruments and Microchip, produce ADCs in integrated design. Each manufacturer uses its own method of implementing the device. The main task of such devices is to convert voltage to binary code. ADCs are used wherever it is necessary to receive an analog signal and process it in digital form. Examples include applications such as communications and telecommunications, various radio systems, and measurement technology. Very important characteristics of such equipment are dynamic range, ease of implementation and speed. The means of analog-to-digital conversion are constantly being improved, which leads to an increase in the speed of the converters and the frequency band of the converted signals, an increase in the dynamic range, sensitivity and accuracy of the ADC. Significant interest in high-speed ADCs with a large dynamic range is explained by the fact that in the vast majority of telecommunications and radio engineering systems, direct signal conversion schemes without intermediate frequency conversion are increasingly used. Broadband applications have also been developed. The main requirement in these applications is the high sensitivity and wide dynamic range of the transducer for simultaneous detection of strong and weak signals. In this paper, a comparative analysis of the main types of analog-to-digital converters offered on the market is carried out in order to identify the most optimal construction method for using it in modern equipment.

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Romaniszyn-Kania, Patrycja, Anita Pollak, Marcin D. Bugdol, Monika N. Bugdol, Damian Kania, Anna Mańka, Marta Danch-Wierzchowska, and Andrzej W. Mitas. "Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods." Sensors 21, no. 14 (July 16, 2021): 4853. http://dx.doi.org/10.3390/s21144853.

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Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%.

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Bueno-López, Maximiliano, and Johinner Mauricio Sanabria Villamizar. "A Comparative Study of Signal Analysis Methods Applied in the Detection of Instantaneous Frequency." Transactions on Energy Systems and Engineering Applications 1, no. 1 (December 16, 2020): 1–11. http://dx.doi.org/10.32397/tesea.vol1.n1.1.

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The smart grid concept is being applied more and more frequently and this is due to the need to integrate all the components that are part of power systems today, starting from generation units, storage systems, communications and connected loads. Non-linear and non-stationary signals have been obtained in this type of systems, which have high penetration of non-conventional energy sources (NCSRE) and non-linear loads. The power quality criterion has had to be adapted to the new conditions of the electrical systems and this has led to the need to search for new analysis methodologies for the acquired signals. In this article we present a review on non-linear and non-stationary signal analysis methods in electrical systems with high NCSRE penetration. To this end we explore the application of the Hilbert-Huang Transform (HHT), Wavelet Transform (WT) and Wigner-Ville Distribution (WVD), exposing each of the advantages and disadvantages of these methods. To validate the methodology, we have selected some synthetic signals that adequately describe the typical behaviors in these systems.

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Orikhovska, K., and L. Fainzilberg. "Comparative Analysis of Estimation Methods of the Physiological Signals Variability." Kibernetika i vyčislitelʹnaâ tehnika 2017, no. 3(189) (September 19, 2017): 5–28. http://dx.doi.org/10.15407/kvt189.03.005.

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Massaro, E., S. Fabiani, R. Campana, E. Costa, E. Del Monte, F. Muleri, and P. Soffitta. "Correlation methods for the analysis of X-ray polarimetric signals." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 885 (March 2018): 7–14. http://dx.doi.org/10.1016/j.nima.2017.12.056.

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Szcześniak, Adam, and Zbigniew Szcześniak. "Methods and analysis of processing signals of incremental optoelectronic transducer." Review of Scientific Instruments 80, no. 9 (September 2009): 094701. http://dx.doi.org/10.1063/1.3202327.

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Daszykowski, M., and B. Walczak. "Methods for the exploratory analysis of two-dimensional chromatographic signals." Talanta 83, no. 4 (January 2011): 1088–97. http://dx.doi.org/10.1016/j.talanta.2010.08.032.

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Hammond, J. K., and P. R. White. "THE ANALYSIS OF NON-STATIONARY SIGNALS USING TIME-FREQUENCY METHODS." Journal of Sound and Vibration 190, no. 3 (February 1996): 419–47. http://dx.doi.org/10.1006/jsvi.1996.0072.

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Badeau, Roland, and Bertrand David. "Adaptive subspace methods for high resolution analysis of music signals." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3803. http://dx.doi.org/10.1121/1.2935501.

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Lin, Zhiyue, and J. D. Z. Chen. "Comparison of three running spectral analysis methods for electrogastrographic signals." Medical & Biological Engineering & Computing 33, no. 4 (July 1995): 596–604. http://dx.doi.org/10.1007/bf02522520.

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Slesarev, D. A., and G. P. Gaev. "Comparison of certain methods of analysis of multicomponent nonstationary signals." Measurement Techniques 39, no. 10 (October 1996): 1052–56. http://dx.doi.org/10.1007/bf02377476.

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Atefvahid, Parham, Kamran Hassani, Kamal Jafarian, D. John Doyle, and Hessam Ahmadi. "Analysis of central venous pressure (CVP) signals using mathematical methods." Journal of Clinical Monitoring and Computing 31, no. 3 (May 3, 2016): 607–16. http://dx.doi.org/10.1007/s10877-016-9882-0.

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Setlak, Lucjan, and Rafał Kowalik. "Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver." Sensors 21, no. 14 (July 19, 2021): 4901. http://dx.doi.org/10.3390/s21144901.

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Sometimes, it is impossible to conduct tests with the use of the GNSS system, or the obtained results of the measurements made differ significantly from the predicted accuracy. The most common cause of the problems (external factors, faulty results) are interference disturbances from other radio telecommunication systems. The subject of this paper is to conduct research, the essence of which is an in-depth analysis in the field of elimination of LTE interference signals of the GNSS receiver, that is based on the developed effective methods on counteracting the phenomenon of interference signals coming from this system and transmitted on the same frequency. Interference signals are signals transmitted in the GNSS operating band, and unwanted signals may cause incorrect processing of the information provided to the end-user about his position, speed, and current time. This article presents methods of identifying and detecting interference signals, with particular emphasis on methods based on spatial processing of signals transmitted by the LTE system. A comparative analysis of the methods of detecting an unwanted signal was made in terms of their effectiveness and complexity of their implementation. Moreover, the concept of a new comprehensive anti-interference solution was proposed. It includes, among others, information on the various stages of GNSS signal processing in the proposed system, in relation to the algorithms used in traditional GNSS receivers. The final part of the article presents the obtained research results and the resulting significant observations and practical conclusions.

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Liu, Boquan, Evan Polce, Julien C. Sprott, and Jack J. Jiang. "Applied Chaos Level Test for Validation of Signal Conditions Underlying Optimal Performance of Voice Classification Methods." Journal of Speech, Language, and Hearing Research 61, no. 5 (May 17, 2018): 1130–39. http://dx.doi.org/10.1044/2018_jslhr-s-17-0250.

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Purpose The purpose of this study is to introduce a chaos level test to evaluate linear and nonlinear voice type classification method performances under varying signal chaos conditions without subjective impression. Study Design Voice signals were constructed with differing degrees of noise to model signal chaos. Within each noise power, 100 Monte Carlo experiments were applied to analyze the output of jitter, shimmer, correlation dimension, and spectrum convergence ratio. The computational output of the 4 classifiers was then plotted against signal chaos level to investigate the performance of these acoustic analysis methods under varying degrees of signal chaos. Method A diffusive behavior detection–based chaos level test was used to investigate the performances of different voice classification methods. Voice signals were constructed by varying the signal-to-noise ratio to establish differing signal chaos conditions. Results Chaos level increased sigmoidally with increasing noise power. Jitter and shimmer performed optimally when the chaos level was less than or equal to 0.01, whereas correlation dimension was capable of analyzing signals with chaos levels of less than or equal to 0.0179. Spectrum convergence ratio demonstrated proficiency in analyzing voice signals with all chaos levels investigated in this study. Conclusion The results of this study corroborate the performance relationships observed in previous studies and, therefore, demonstrate the validity of the validation test method. The presented chaos level validation test could be broadly utilized to evaluate acoustic analysis methods and establish the most appropriate methodology for objective voice analysis in clinical practice.

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Rejfek, Luboš, Zbyšek Mošna, Jaroslav Urbář, and Petra Koucká Knížová. "System for Automatic Detection and Analysis of Targets in FMICW Radar Signal." Journal of Electrical Engineering 67, no. 1 (January 1, 2016): 36–41. http://dx.doi.org/10.1515/jee-2016-0005.

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Abstract This paper presents the automatic system for the processing of the signals from the frequency modulated interrupted continuous wave (FMICW) radar and describes methods for the primary signal processing. Further, we present methods for the detection of the targets in strong noise. These methods are tested both on the real and simulated signals. The real signals were measured using the developed at the IAP CAS experimental prototype of FMICW radar with operational frequency 35.4 GHz. The measurement campaign took place at the TU Delft, the Netherlands. The obtained results were used for development of the system for the automatic detection and analysis of the targets measured by the FMICW radar.

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Vartiainen, Johanna, Janne Lehtomäki, Harri Saarnisaari, and Markku Juntti. "Analysis of the Consecutive Mean Excision Algorithms." Journal of Electrical and Computer Engineering 2010 (2010): 1–13. http://dx.doi.org/10.1155/2010/459623.

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The backward and forward consecutive mean excision (CME/FCME) algorithms are diagnostic methods for outlier (signal) detection. Since they are computationally simple, they have applications for both narrowband signal detection in cognitive radios and interference suppression. In this paper, a theoretical performance analysis framework of the CME algorithms is presented. The analysis provides simple tests of the detectability of the signals based on their shape in the considered domain (e.g., spectrum). As a consequence, results can be used to quickly check whether the CME/FCME algorithms are usable for a given problem or not without the need to resort to time consuming computer simulations. The computer simulations for random and orthogonal frequency division multiplexing (OFDM) signals show that the presented analysis is able to predict the detectability of signals well.

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