Статті в журналах: "Transformation du signal"

1

Saidov, B. B., and V. F. Telezhkin. "Optimum ECG Signal Filtering Based on Wavelet Transformation." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 21, no. 4 (November 2021): 167–72. http://dx.doi.org/10.14529/ctcr210415.

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The development of digital signal processing and microprocessor technology creates conditions for improving methods for diagnosing the functional state of organs. Wavelet analysis is a modern and promising method of information processing. In order to determine the effective optimal filtering of the electrocardiography signal based on the wavelet transform, wavelet filtering was performed using wavelets of different families, the efficiency of using different levels of decomposition, me¬thods for calculating the threshold and types of the threshold function was investigated. Aim. Determination of effective optimal filtering of electrocardiography signal based on wavelet transform. Materials and methods. Cardiograms were taken for analysis. Then they were digitized and entered into a computer for processing. A program was written in the Matlab environment that implements continuous and discrete wavelet transform. Results. As a result of the research, 56 combinations of noise reduction parameters were tested for three noise levels. It was found that the maximum degree of signal purification from noise was obtained using the Coiflets 5 wavelet using a rigid thresholding method, with a heuristic method for calculating the threshold value. Wavelet Simlet 8 has lower correlation coefficient values than Coiflets 5, at 35 dB the best result is 97%, the noise level is 40 dB the best result is 98.7%, the noise level is 45 dB the best result is 99.3%, which is generally negligible differs from the correlation coefficients of the wavelet Coiflets 5. Conclusion. As a result of the study, the first and the present work, the following conclusions were made: the optimal level of the wavelet decomposition of the ECG signal N = 2; the maximum degree of signal cleaning from noise was obtained using the Coiflets 5 wavelet using a rigid thresholding method, with a heuristic method for calculating the threshold value; Simlet 8 wavelet using a soft thresholding method with a minimax thresholding method also shows noteworthy results, slightly inferior to Coiflets 5 wavelet results.

2

Shah, Yogendra Prasad. "Applications of Fourier Series and Fourier Transformation." Cognition 2, no. 1 (October 30, 2019): 145–56. http://dx.doi.org/10.3126/cognition.v2i1.55605.

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This paper investigates into the application of fourier transformation and series, which converts time domain signal to frequency domain signals, at which signals can be analyzed. Unlike Laplace transform, Fourier Transforms does not have full S plane, it just have the frequency j2πf plane. Fourier Transforms helps to analyze spectrum of the signals, helps in find the response of the LTI systems. (Continuous Time Fourier Transforms is for Analog signals and Discrete time Fourier Transforms is for discrete signals). Discrete Fourier Transforms are helpful in Digital signal processing for making convolution and many other signal manipulations. Overall, the paper will conclude the impact of Fourier Transforms in life.

3

Gong, Tao, Jianhua Yang, Miguel A. F. Sanjuán, Houguang Liu, and Zhen Shan. "Vibrational resonance by using a real-time scale transformation method." Physica Scripta 97, no. 4 (March 17, 2022): 045207. http://dx.doi.org/10.1088/1402-4896/ac5bc5.

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Abstract Vibrational resonance (VR) shows great advantages in signal enhancement. Nonlinear frequency modulated (NLFM) signals widely exist in various fields, so it is of great significance to enhance a NLFM signal. However, for the complex NLFM signal, where its instantaneous frequency of the signal varies nonlinearly, the traditional VR method is no longer applicable. To solve this problem, a rescaled VR method by a real-time scale transformation method is proposed. Its basic principle is to use the real-time scale coefficient and auxiliary signal parameters to match a NLFM signal in a nonlinear system. The corresponding numerical simulation is carried out to process three kinds of typical NLFM signals. The results manifest the excellent performance of the proposed method for the signal enhancement of NLFM signals. The method can process NLFM signals with an arbitrary frequency variation. Consequently, it has certain theoretical and practical values in some fields.

4

Gogolewski, Damian, Paweł Zmarzły, Tomasz Kozior, and Thomas G. Mathia. "Possibilities of a Hybrid Method for a Time-Scale-Frequency Analysis in the Aspect of Identifying Surface Topography Irregularities." Materials 16, no. 3 (January 31, 2023): 1228. http://dx.doi.org/10.3390/ma16031228.

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The article presents research results related to assessing the possibilities of applying modern filtration methods to diagnosing measurement signals. The Fourier transformation does not always provide full information about the signal. It is, therefore, appropriate to complement the methodology with a modern multiscale method: the wavelet transformation. A hybrid combination of two algorithms results in revealing additional signal components, which are invisible in the spectrum in the case of using only the harmonic analysis. The tests performed using both simulated signals and the measured roundness profiles of rollers in rolling bearings proved the advantages of using a complex approach. A combination of the Fourier and wavelet transformations resulted in the possibility to identify the components of the signal, which directly translates into better diagnostics. The tests fill a research gap in terms of complex diagnostics and assessment of profiles, which is very important from the standpoint of the precision industry.

5

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 (June 20, 2019): 1950157. http://dx.doi.org/10.1142/s0217979219501571.

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

6

Zhao Li, Feng Ji, Zhai Guang-Jie, and Zhang Li-Hua. "Wavelet transformation for magnetocardiography signal." Acta Physica Sinica 54, no. 4 (2005): 1943. http://dx.doi.org/10.7498/aps.54.1943.

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7

Zehner, William J., and R. Lee Thompson. "Signal transformation for aural classification." Journal of the Acoustical Society of America 112, no. 6 (2002): 2520. http://dx.doi.org/10.1121/1.1536506.

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8

Liu, Xiangying, and Elijah Kannatey-Asibu. "Acoustic Emission During Athermal Martensitic Transformation in Steels." Journal of Engineering for Industry 112, no. 1 (February 1, 1990): 84–91. http://dx.doi.org/10.1115/1.2899299.

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A relationship developed earlier between acoustic emission signals and the process of athermal martensitic transformation based on the free energy associated with the process is extended and verified experimentally. The relationship is found to model the process characteristics very well. The intensity of AE signal generated during transformation was found to be proportional to the temperature derivative of the fraction of martensite, the cooling rate, and volume of specimen. The AE signal was also found to be related to the carbon content of the steel. During transformation, the signal intensity was found to increase to a peak, and then tail off near the end of the transformation. Values of the martensite start temperature obtained from plots of the total RMS squared AE signals were also found to correlate well with values from the literature.

9

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

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

10

Guo, Ye Cai, and Zheng Xin Liu. "Fuzzy Neural Network Blind Equalization Algorithm Based on Signal Transformation." Applied Mechanics and Materials 44-47 (December 2010): 4146–50. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.4146.

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To recover QAM signals at the receiver of blind equalizer, a Fuzzy C-means clustering Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-FNN-BEA) is proposed. The proposed algorithm uses signal transformation method to debase the computational complexity of equalizer input signals and speed up the convergence rate, and makes use of fuzzy c-means clustering algorithm dividing the equalizer input signals into each cluster center with different membership values to improve the equalization performance. The proposed ST-FNN-BEA outperforms Neural Network Blind Equalization Algorithm (NN-BEA) and Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-NN-BEA) in improving convergence rates and reducing mean square error. The performance of ST-FNN-BEA is proved by the computer simulation with underwater acoustic channels.

11

Wang, Huan, Chang Xing Li, and Yong Zhuang Li. "Code Rate Estimation Algorithm for BPSK Signal Based on Wavelet Transformation." Advanced Materials Research 740 (August 2013): 178–82. http://dx.doi.org/10.4028/www.scientific.net/amr.740.178.

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A wavelet transformation based estimating algorithm for the symbol rate of BPSK signals is adopted . This method extracts baseband signal from the generated BPSK modulated signal by means of different ways, utilizing the modulus of wavelet transformation coefficients of this baseband signal to construct a singular pulse sequence consistent with the code rate of original modulated signal. Theoretical analysis on the power spectrum of this singular pulse sequence indicates that there exist discrete spectral lines in the integral multiples of baseband signal symbol rate, thus detecting these discrete spectral lines can achieve the estimation of signal symbol rate. Our proposed algorithm can estimate the signal symbol rate under the low SNR condition.

12

Suryani Faradisa, Irmalia, Ananda Ananda, Tri Arief Sardjono, and Mauridhi Hery Purnomo. "Denoising of Fetal Phonocardiogram Signal by Wavelet Transformation." E3S Web of Conferences 188 (2020): 00013. http://dx.doi.org/10.1051/e3sconf/202018800013.

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Auscultation is still one of the most basic analytical tools used to determine the fetal heart’s functional state as well as the first fetal well-being measure. It is called fetal phonocardiography (fPCG) in its modern form. The technique of fPCG is passive and can be used to track long-term. Robust signal processing techniques are required to denoise the signals in order to improve the diagnostic capabilities of fPCG. A linear filter is used to eliminate distortion and interference from the fPCG signals through conventional denoising techniques. This paper searched for optimal configuration of the wavelet based denoising system. Based on the experimental results, can be conclude that the signal should be decomposed on six levels. From this it can be seen that the lowest MSE (mean square error) value is the use of coiflets three with SURE threshold algorithm with hard threshold parameters.

13

Wu, Ling, Hong Chen, Shu Bin Gu, Lian Dong Lin, and Guo Qiang Lan. "Research on the Signal Encrypted by Unary Polynomial Transformation Chaos." Advanced Materials Research 846-847 (November 2013): 952–55. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.952.

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In terms of the chaotic signal needed by encryption, it is the more complicate the better. In addition, the frequency range of chaos should be wider than that of signal to be encrypted. However, chaotic spectrum usually is a narrow area in the low frequency region, and the complexity is not high enough. The signals encryption effects are affected. In order to solve this problem, this paper used the chaos transformed by unary polynomial to encrypt signals. Under the Matlab simulation environment, it is confirmed from time-domain and frequency-domain perspectives that the encryption effect of transformed chaos is better. Even the higher frequency signal can also be covered up completely. At the same time, the difficulty of decoding the encrypted signals is greatly increased, and the anti-attacking capability is strengthened.

14

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

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

15

Yao, J. "Complete Gabor transformation for signal representation." IEEE Transactions on Image Processing 2, no. 2 (April 1993): 152–59. http://dx.doi.org/10.1109/83.217220.

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16

Snášel, Václav, Karel Šin, and Karel Vlček. "Signal Watermarking by Wavelet Transformation Schemes." IFAC Proceedings Volumes 36, no. 1 (February 2003): 421–24. http://dx.doi.org/10.1016/s1474-6670(17)33787-4.

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17

Sebastian, Abu, and S. O. Reza Moheimani. "Signal transformation approach to fast nanopositioning." Review of Scientific Instruments 80, no. 7 (July 2009): 076101. http://dx.doi.org/10.1063/1.3160016.

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18

Yang, J. F., and M. Kaveh. "Coherent signal-subspace transformation beam former." IEE Proceedings F Radar and Signal Processing 137, no. 4 (1990): 267. http://dx.doi.org/10.1049/ip-f-2.1990.0040.

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19

Razani, B., A. Schlegel, and M. P. Lisanti. "Caveolin proteins in signaling, oncogenic transformation and muscular dystrophy." Journal of Cell Science 113, no. 12 (June 15, 2000): 2103–9. http://dx.doi.org/10.1242/jcs.113.12.2103.

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In adult animals and humans, signal transduction maintains homeostasis. When homeostatic mechanisms are interrupted, an illness or disease may ensue. Caveolae are plasma membrane specializations that contain the structural proteins caveolins, and appear to be important for normal signal transduction. The caveolin scaffolding domain interacts with several signaling molecules, sequestering them in the absence of activating signals, and thereby reducing the signal-to-noise ratio. Deletion and mutation of genes that encode caveolins is implicated in the pathogenesis of several human diseases. Down-regulation of caveolin-1 protein expression leads to deregulated signaling and consequently tumorigenesis, whereas naturally occurring dominant-negative caveolin-3 mutations cause muscular dystrophy.

20

Huang, Dawen, Jianhua Yang, Jingling Zhang, and Houguang Liu. "An improved adaptive stochastic resonance with general scale transformation to extract high-frequency characteristics in strong noise." International Journal of Modern Physics B 32, no. 15 (June 18, 2018): 1850185. http://dx.doi.org/10.1142/s0217979218501850.

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The idea of general scale transformation is introduced in detail. Based on this idea, an improved adaptive stochastic resonance (SR) method is proposed to extract weak signal features. Different periodic signals are considered to verify the proposed method. Compared with the normalized scale transformation, the output signal-to-noise ratio (SNR) of the proposed method is increased to a greater extent. Further, the influences of some key parameters on the responses of the two methods are discussed minutely. Results show that the improved adaptive SR method with general scale transformation is obviously superior to the traditional normalized scale transformation that is used in the former literatures. For different noise intensities and time scales, the proposed approach can always obtain the optimal response of SR to enhance the weak signal characteristics.

21

Christauskas, Julius, and Jurgis Stanaitis. "OPTIMIZATION OF SIGNAL FEATURES UNDER OBJECT'S DYNAMIC TEST." Aviation 12, no. 1 (March 31, 2008): 28–32. http://dx.doi.org/10.3846/1648-7788.2008.12.10-17.

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The results of investigating the influence of the time constant of acceleration signal transformation or vibration speed into a displacement signal are given. Regularities of displacement signal amplitude change after transformation of acceleration and speed signals have been determined. It is shown that the error of displacement signal amplitude change depends on the transformation time constant for the intended frequency of the input signal. Thus, its minimum value corresponds to the value of the transformation time constant, equal to half an input signal period. The results of displacement measurements obtained by using vibration speed sensors and the results of their comparison with data, obtained by employing the displacement master sensor, are given. Santrauka Šiame tyrime keleivių srauto dinamikos palyginimui buvo pasirinktas Tarptautinis Krokuvos aerouostas ir trys Baltijos šalių oro uostai. Per paskutiniuosius trejus metus (2004–2006) daugiausia keleivių buvo aptarnauta Tarptautiniame Rygos oro uoste – 5 433 461 keleivis; Tarptautiniame Krokuvos oro uoste – 4 416 896 keleiviai. Talino oro uostas aptarnavo 3 940 366 keleivius ir galiausiai Tarptautiniame Vilniaus oro uoste buvo aptarnautas 3 727 501 keleivis. Taip pat straipsnyje analizuojamos keleivių ir lėktuvų srauto Tarptautiniame Vilniaus oro uoste gerinimo galimybės.

22

Chen, Hong, Cheng Chen, and Shu Bin Gu. "UPT Chaos-Based Encryption Characteristic Analysis for Speech Signal." Applied Mechanics and Materials 713-715 (January 2015): 1456–59. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1456.

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In chaotic speech signal encryption, masking is the most direct and convenient method. But the chaotic signals usually have a narrow spectral range in low frequency region. In order to overcome the disadvantage that the higher frequency signals can not be completely covered up, UPT is used to conduct a nonlinear transformation to chaotic signal. After UPT, chaotic types are increased, chaotic characteristics are improved and spectral range is widened. Simulation experiments are done respectively from frequency domain and spectrogram perspectives. The chaos before and after transformation are used to mask speech signal. Decoding experiment is taken on the speech encrypted by transformed chaos. Experiment results show that, after UPT, the encryption effect of speech signal is improved obviously. This method has a high security and a strong anti-attack ability.

23

Yang, Zaixue, Bin Liu, Bing Chen, Qian Liang, Yao Zhang, and Yanming Chen. "Research on the Strategy for the Flexible Configuration of Chaotic Signal Probability Distribution and Its Application." Applied Sciences 14, no. 12 (June 14, 2024): 5181. http://dx.doi.org/10.3390/app14125181.

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Given the constraints on the invariant distribution in chaotic systems, flexibly setting the probability distribution of chaotic signals poses a significant challenge. To tackle this issue, this paper proposes a strategy that transforms the task into solving and modifying the probability density function of the chaotic intrinsic signal. Initially, kernel density estimation algorithms are employed to address the issue of obtaining smooth probability density functions for high-dimensional chaotic signals. Any chaotic signal can serve as the intrinsic signal source, with its probability density function and distribution function being solvable using this algorithm. Subsequently, a graph-based transformation algorithm is introduced for the flexible adjustment of chaotic signal probability distribution. This algorithm can convert the intrinsic signal into a chaotic signal with the desired distribution type based on the characteristics of the target distribution, providing an analytical expression for the transformation relationship. Finally, the effectiveness of this strategy is validated by generating uniform distribution chaotic signals using a Chua chaotic signal as the intrinsic source. The outstanding performance of this signal in suppressing common-mode conducted electromagnetic interference in high-frequency converters is highlighted. The experimental results demonstrate this strategy’s ability to flexibly configure probability distribution types of chaotic signals. Additionally, chaotic signals with a uniform distribution can achieve uniform power spectrum shaping, with a suppression effect on maximum common-mode conducted electromagnetic interference reaching 16.56 dB.

24

Zheleznjak, V. K., S. V. Lavrov, A. G. Filipovich, and M. M. Baranouski. "Synthesis of a measuring composite signal for assessing the security of speech signals during discrete-quantized transformation." Doklady BGUIR 18, no. 6 (October 1, 2020): 81–87. http://dx.doi.org/10.35596/1729-7648-2020-18-6-81-87.

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The purpose of the work is to systematically analyze and generalize a high-precision measuring signal for assessing the security in leakage channels in high-level noise by discrete-quantized representation of speech signals using the principles of amplitude-pulse modulation. It has been established that time sampling and level quantization of high-speed high-quality speech signals for digitalization are the main sources of information leakage. It is shown that to determine the degree of information security for high-quality high-speed transmission in broadband information transmission channels, it is necessary to use a complex measuring (test) composite signal. Requirements for the measuring signal are determined by the features of the discretequantized representation of speech signals. It is proposed to use a periodic pulse sequence of a triangular shape as a measuring signal. The triangular measurement signal has an advantage over the harmonic signal in the quantization noise extraction process, since allows you to achieve higher accuracy when processing it. To assess the security of the channel due to pulse-amplitude modulation, a harmonic signal is used, formed from a periodic pulse sequence of a triangular shape by the Fourier transform method. The use of the proposed measuring composite signal makes it possible to establish its numerical dependence with the numerical value of the signal taken as normalized and compare it to make a decision about the security of the speech signal. The materials presented in the article are original and can be used to assess the security of the channels of leakage of speech signals converted into digital form. In addition, the results obtained make it possible to carry out further studies of the security of speech signals during their reverse conversion from digital form to the original signal.

25

Tu, Yaqing, Huiyue Yang, Haitao Zhang, and Xiangyu Liu. "CMF Signal Processing Method Based on Feedback Corrected ANF and Hilbert Transformation." Measurement Science Review 14, no. 1 (February 1, 2014): 41–47. http://dx.doi.org/10.2478/msr-2014-0007.

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Abstract In this paper, we focus on CMF signal processing and aim to resolve the problems of precision sharp-decline occurrence when using adaptive notch filters (ANFs) for tracking the signal frequency for a long time and phase difference calculation depending on frequency by the sliding Goertzel algorithm (SGA) or the recursive DTFT algorithm with negative frequency contribution. A novel method is proposed based on feedback corrected ANF and Hilbert transformation. We design an index to evaluate whether the ANF loses the signal frequency or not, according to the correlation between the output and input signals. If the signal frequency is lost, the ANF parameters will be adjusted duly. At the same time, singular value decomposition (SVD) algorithm is introduced to reduce noise. And then, phase difference between the two signals is detected through trigonometry and Hilbert transformation. With the frequency and phase difference obtained, time interval of the two signals is calculated. Accordingly, the mass flow rate is derived. Simulation and experimental results show that the proposed method always preserves a constant high precision of frequency tracking and a better performance of phase difference measurement compared with the SGA or the recursive DTFT algorithm with negative frequency contribution

26

Gong, Tao, Jian-Hua Yang, Zhen Shan, Zhi-Le Wang, and Hou-Guang Liu. "Optimal resonance response of nonlinear system excited by nonlinear frequency modulation signal." Acta Physica Sinica 71, no. 5 (2022): 050503. http://dx.doi.org/10.7498/aps.71.20211959.

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Nonlinear frequency modulation (NLFM) signal is widely used in radar, communication and signal processing. The response of nonlinear system excited by this kind of signal has rich information. At the same time, enhancing different types of signals by resonance phenomenon has unique advantages in the field of signal processing. Compared with other signal processing methods, such as empirical mode decomposition, variational mode decomposition, wavelet transform, signal filtering, etc., this kind of method can not only enhance the signal, but also effectively suppress the interference noise. Therefore, it has certain significance to study the nonlinear system optimal response excited by different NLFM signals and enhance the NLFM signal through resonance phenomenon. In this paper, what is mainly studied is the nonlinear system resonance phenomenon excited by different NLFM signals, which is different from in previous studies. Firstly, a real-time scale transformation method is proposed to process the NLFM signals, and its basic principle is to match different NLFM signals by real-time scale coefficients and system parameters. The signal frequency at each time corresponds to the coefficients with different scales and system parameters, thereby realizing the optimal resonance response at each time. In order to describe the optimal resonance response excited by the NLFM signal more accurately, unlike the traditional spectral amplification factor, the real-time spectral amplification factor is introduced as an evaluation index. Then, the influence of system parameters on the optimal system resonance response is discussed, and the optimal resonance region is obtained, which means that the optimal resonance response can be achieved by selecting the parameters in a reasonable range. This method not only greatly enhances the signal characteristics, but also maintains the continuity of signal time-frequency characteristics. Finally, the real-time scale transformation method is compared with the general scale transformation method, showing the superiority of the proposed method in processing NLFM signal. The method and the results of this paper show some potential in dealing with complex NLFM, which provides a reference for NLFM signal enhancement and detection, and has a certain practical significance in signal enhancement. Furthermore, the relevant influence law of the system optimal response excited by the NLFM signal is given, which has a certain reference value for studying the system dynamic behavior under different signal excitations.

27

ARTEMYEV, Victor, Sergey MOKRUSHIN, Sergey SAVOSTIN, Artem MEDVEDEV, and Vitaly PANKOV. "PROCESSING OF TIME SIGNALS IN A DISCRETE TIME DOMAIN." Machine Science Journal 1, no. 1 (June 3, 2023): 46–54. http://dx.doi.org/10.61413/frcr4965.

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This article is devoted to the processing of time signals in a discrete time domain. Time signals are the main object of analysis in many areas, such as signal processing, communication, control and much more. Today, for efficient signal processing, it is necessary to use methods adapted to the discrete time domain, using the Z-transform method to solve difference equations in the discrete time domain. The Ztransform method is a powerful and most effective tool for analyzing and solving difference equations, widely used in control systems, signal processing and other fields. The main steps of applying the Z-transformation method are also presented, starting from the formulation of the difference equation to obtaining a solution in the original time domain. Special attention is paid to the Z-transformation process, where the difference equation turns into an algebraic equation with respect to the Z operator. The basic properties of the Z-transformation and the operations with them necessary for the successful application of the method are described.

28

Łopatka, Jerzy. "Recognition of narrowband rausing autoregressive models and pattern comparison approach." Journal of Telecommunications and Information Technology, no. 1 (March 30, 2002): 56–59. http://dx.doi.org/10.26636/jtit.2002.1.108.

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This paper presents an improved spectral recognition method for digitally modulated radio signals. It is based on a signal autoregressive (AR) model. Model poles are used as signal features for neural network based on recognition process. To reduce an influence of the signal noise and distortions introduced by the digital receiver, a nonlinear Z plane transformation is proposed.

29

Marmarelis, V. Z. "Signal transformation and coding in neural systems." IEEE Transactions on Biomedical Engineering 36, no. 1 (1989): 15–24. http://dx.doi.org/10.1109/10.16445.

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30

Bajcsy, Ruzena. "Signal-to-symbol transformation and vice versa." ACM Computing Surveys 27, no. 3 (September 1995): 310–13. http://dx.doi.org/10.1145/212094.212103.

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31

Isaksson, A. "Ubiquitin in signal transduction and cell transformation." Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1288, no. 1 (August 8, 1996): F21—F29. http://dx.doi.org/10.1016/0304-419x(96)00011-x.

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Brown, Mark A., Li Zhu, Christian Schmidt, and Philip W. Tucker. "Hsp90—From signal transduction to cell transformation." Biochemical and Biophysical Research Communications 363, no. 2 (November 2007): 241–46. http://dx.doi.org/10.1016/j.bbrc.2007.08.054.

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Kannatey-Asibu, Elijah, and Dong Pingsha. "Analysis of Acoustic Emission Signal Generation During Martensitic Transformation." Journal of Engineering for Industry 108, no. 4 (November 1, 1986): 328–31. http://dx.doi.org/10.1115/1.3187084.

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The formation of cold cracks during welding of high strength steels is almost always preceded by martensite formation. Real time detection of both the martensite and cracks formed is a basic necessity of automated welding systems. Acoustic emission has been found to be highly suited for this purpose. Unfortunately, most of the work done to date on AE generation during martensitic phase transformation has been qualitative in nature. This paper presents a quantitative analysis of AE signal generation during martensite formation, using an energy method. This formulation relates the chemical free energy change which is the driving force for the transformation to the RMS value which is a measure of the energy content of AE signals. The analysis shows that the RMS signal is dependent on carbon concentration, volume transformed, cooling rate, and temperature. This is consistent with previous experimental work.

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van Bemmel, J. H., Chr Zywietz, and J. A. Kors. "Signal Analysis for ECG Interpretation." Methods of Information in Medicine 29, no. 04 (1990): 317–29. http://dx.doi.org/10.1055/s-0038-1634807.

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AbstractIn ECG interpretation usually two main areas are discerned: the signal analysis and the diagnostic classification. This article reviews the major developments in the first area. ECG signal analysis itself is subdivided into the stages data acquisition, data transformation, feature selection, and data reduction. These stages are consecutively reviewed, while in the data transformation stage digital filtering, detection, wave typing, beat selection, and boundary recognition are discussed.

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Parfentyev, Nikolay, Schubert Maignan, and Irina Tkachenko. "Delta function of the Fourier transform." E3S Web of Conferences 535 (2024): 01006. http://dx.doi.org/10.1051/e3sconf/202453501006.

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In the analysis of the double window Fourier transform, it is found that both components of the direct transformation contain an equal amount of information sufficient to restore the original time signal. A double conversion diagram has been constructed. For any initial signal, there is a conjugate signal having the same frequency spectrum. Analysis of the harmonic signal transformation showed that at an arbitrary phase of the harmonic, the frequency representation contains two functions: conditionally unipolar with a maximum at the initial frequency and bipolar with a zero value at the specified frequency. For classical Fourier transformations with an infinite limit of integration, these functions turn into two delta functions with the same properties. The practical application of the found patterns is currently limited by the lack of an algorithm that allows the use of data from only one component.

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Bohari, Z. H., M. Isa, A. Z. Abdullah, P. J. Soh, and M. F. Sulaima. "A smart partial discharge classification SOM with optimized statistical transformation feature." Bulletin of Electrical Engineering and Informatics 10, no. 2 (April 1, 2021): 1054–62. http://dx.doi.org/10.11591/eei.v10i2.2751.

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Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised neural network is applied for PD classification. The methods combine the self-organizing maps (SOMs) and feature statistical transformation. By the combination of these methods, the ‘range’ normalization method produced the best classification outcomes. This development decided that PD information was effectively correlated and grouped by means of MATLAB’s SOM Toolbox and transformation device to discriminate the normal signal from the PD signal.

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Губін, Сергій Вікторович, Сергій Олександрович Тишко, Олег Євгенович Забула та Юрій Миколайович Черниченко. "ОСЦИЛОГРАФІЧНИЙ МЕТОД ВИМІРЮВАННЯ ФАЗОВОГО ЗСУВУ НА БАЗІ ДВОНАПІВПЕРІОДНОГО ПЕРЕТВОРЕННЯ". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 4 (25 грудня 2019): 47–54. http://dx.doi.org/10.32620/reks.2019.4.05.

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The subject matter of the article is the oscilloscope methods of measuring the phase shift of two harmonic signals, after carrying out their two-half-period transformation and summing. The goal is to develop ways to implement an oscilloscope method of measuring the phase shift of two harmonic signals, which will significantly reduce the component of measurement error caused by phase non-symmetry of the transmission channels, by reducing their length. Analyze the measurement error for each of the methods for determining the phase shift of two harmonic signals using their two-half-periodic transformation. The tasks: statement of measurement problem of determination of phase shift of two harmonic signals; analysis of known oscilloscope methods of phase shift measurement, development of methods for implementing the oscilloscope method based on the analysis of the characteristics of the total signal obtained during the two-half-period transformation; estimation of measurement errors for each method. The methods used are the methodology for estimating measurement errors in indirect measurements. The following results were obtained. Methods for implementing an oscilloscope measurement method using the total signal after a two-half-period transformation based on the analysis of temporal characteristics and local extrema of this signal are proposed. The list of measuring operations that implement each method is defined. The analysis of the components of measurement errors was performed and the degree of correlation was determined. Synthesized ratios for the calculation of measurement error. Conclusions. The scientific novelty of the obtained results is the following: an oscilloscopic method has been developed that will allow reducing substantially the component of the error caused by phase non-symmetry of the signal transmission channels; obtained ratios for the implementation of the oscilloscope measurement method using two-half-period conversion; obtained ratios to calculate the standard deviation of the total measurement error in each of the proposed methods.

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Efimov, S. A. "Synthesis of a mathematical model of the transformation core for processing vibroseismic data by reverse filtering." Interexpo GEO-Siberia 4 (May 18, 2022): 78–85. http://dx.doi.org/10.33764/2618-981x-2022-4-78-85.

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The vibroseismic method of studying the bowels of the earth is widely used to study the structural features of the bowels. The effectiveness of the method is due to the presence of controlled sources of seismic waves. In this case, the sources of seismic waves form a seismic signal, the parameters of which are determined and set by the researcher during the planning of the experiment. Frequency-modulated seismic signals - sweep signals - have become the most popular in field seismology. A special case of these signals are monochromatic signals. During the experiment, the seismic signal generated by the source is recorded by a seismic receiver. Thus, the researcher receives the initial seismic record - a seismogram. Processing of a seismogram, as an element of vibroseismic data, assumes the presence of information about the parameters of the seismic signal of the source. Based on this signal, a conversion core is formed for processing the seismogram. As a result of the operation of convolution of the seismogram with the core of the transformation, a vibrogram is formed. If it is necessary to increase the contrast (resolution) of the vibrogram, an additional filtering function is introduced into the conversion core, which performs the reverse filtering procedure. The purpose of this study is to form a filtering function and a mathematical model of the transformation core, which allows to increase the contrast of the vibrogram. The structurality and simplicity of the analytical expressions of the filtering function and the mathematical model of the transformation kernel is achieved by representing the function and model in operator form by means of the Laplace transform. The result of the study is analytical expressions in the operator form of the filtering function and a mathematical model of the transformation core to increase the contrast of vibrograms.

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Ali, Amjad, and Chen Sheng-Chang. "Seismic reflections de-noising and recognition using Empirical Mode Decomposition and Continuous Wavelet Transformation." Natural and Applied Sciences International Journal (NASIJ) 3, no. 1 (January 30, 2022): 1–12. http://dx.doi.org/10.47264/idea.nasij/3.1.1.

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Current developments in signal processing are allowing for enhanced seismic illustrations and investigation of subsurface structures. Recently, Empirical Mode Decomposition (EMD) and Continuous Wavelet Transformation (CWT) have been introduced to extract various features from a time series dataset. In this investigation, seismic signal with 10% Gaussian noise is transformed into sub-signals by EMD analysis to improve the Signal-to-Noise Ratio (SNR). Then, CWT is implemented for each sub-signal to identify the exact locations of seismic reflections. The main objective of this study is to utilize the EMD as a noise filter in the time-domain and CWT to recognize the anomalous zone in each sub-signal. Based on the results of EMD and CWT, the true representation of a seismic signal with minimum noise in the time domain has been achieved. The successful integration of EMD and CWT is achieved in terms of the identification of true seismic reflections as localized anomalous zones at 0.8 sec, 1 sec, and 1.07 sec.

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Stankovic, Ljubisa. "Noises in randomly sampled sparse signals." Facta universitatis - series: Electronics and Energetics 27, no. 3 (2014): 359–73. http://dx.doi.org/10.2298/fuee1403359s.

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Sparse signals can be recovered from a reduced set of randomly positioned samples by using compressive sensing algorithms. Two main reconstruction directions are in the sparse transformation domain analysis of signals and the gradient based algorithms. In the transformation domain analysis, that will be considered here, the estimation of nonzero signal coefficients is based on the signal transform calculated using available samples only. The missing samples manifest themselves as a noise. This kind of noise is analyzed in the case of random sampling, when the sampling instants do not coincide with the sampling theorem instants. Analysis of the external noise influence to the results, with randomly sampled sparse signals, is done as well. Theory is illustrated and checked on statistical examples.

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Mostarac, Petar, Roman Malarić, Katarina Mostarac, and Marko Jurčević. "Noise Reduction of Power Quality Measurements with Time-Frequency Depth Analysis." Energies 12, no. 6 (March 19, 2019): 1052. http://dx.doi.org/10.3390/en12061052.

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This paper presents the noise reduction of power quality measurement with time-frequency (T-F) depth analysis. Noise reduction is achieved with wavelet transformation by decomposition, thresholding and lossless reconstruction of signal. Three main problems with T-F noise reduction with wavelet transformation are: defining thresholding levels, level of decomposition and number of wavelet vanishing moment. In this analysis decomposition level and number of vanishing moments are defined via simulation for pure sinusoid signal, these values are used for signals with perturbations and they provide reasonable results. Analysis is conducted by simulating various change of parameters and then approved by laboratory measurement with calibrators and precision measurement equipment. The paper describes a method for noise reduction of signal without prior knowledge of noise level or signal amplitude. Proposed method is able to separate noise without adding phase shift for diverse signal conditions, harmonics, interharmonics, dips, swells and dynamic variations.

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Liu, Jiang-chao, and Wen-xue Gao. "Vibration Signal Analysis of Water Seal Blasting Based on Wavelet Threshold Denoising and HHT Transformation." Advances in Civil Engineering 2020 (March 9, 2020): 1–14. http://dx.doi.org/10.1155/2020/4381480.

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The blasting vibration signal obtained from tunnel construction monitoring is affected by the external environment, which contains a lot of noise that causes distortion during signal processing. To analyse the blasting vibration signal and determine the appropriate water seal blasting charge structure for construction, combined with wavelet threshold denoising method and HHT transformation, the blasting vibration signals of the four charge structures of conventional charge, water interval charge at both ends, water interval charge at the orifice, and water interval charge at the hole bottom are denoised and HHT is analysed. The results show that the wavelet threshold method can effectively eliminate high-frequency noise in the blasting vibration signals and retain information carried by the vibration signal itself. The frequency and energy of the blasting vibration signals of the water interval charge at both ends are densely distributed in the range of 0 s to 0.9 s and below 100 Hz. The frequency and energy of the blasting signals of the other three charging structures are reduced within the same range, sparse areas appear, and the instantaneous total energy is smaller than that with a water interval charge at both ends, which shows that the water interval charge at both ends can effectively apply explosive energy to the surrounding rock and reduce energy loss in the explosive. The blasting vibration signal energy of the water interval charge at both ends is mainly concentrated in components IMF2 to IMF5, and the corresponding frequencies are concentrated at 6 Hz to 11 Hz and 20 Hz to 70 Hz, while the blasting vibration signal energy of other three charge structures is mainly distributed in components IMF2 to IMF4, corresponding frequencies are concentrated within 20 Hz to 70 Hz, and the distribution at low frequencies is not obvious. Therefore, when using the water interval charge at both ends, it is necessary to increase the main vibration frequency of the original vibration signals by reducing the single section charge and using frequency shift technology to avoid the natural frequency of the structure and reduce resonance-induced damage.

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Hatipoglu, Bahar, Cagatay Murat Yilmaz, and Cemal Kose. "A signal-to-image transformation approach for EEG and MEG signal classification." Signal, Image and Video Processing 13, no. 3 (October 8, 2018): 483–90. http://dx.doi.org/10.1007/s11760-018-1373-y.

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Wang, Pengcheng, Sen Yan, and Xiuhua Li. "Research on Frequency Discrimination Method Using Multiplicative-Integral and Linear Transformation Network." Electronics 13, no. 9 (May 1, 2024): 1742. http://dx.doi.org/10.3390/electronics13091742.

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In this paper, a frequency discrimination method using a multiplicative-integral and linear transformation network is proposed. In this method, two preset differential frequency signals and frequency modulation signals are transformed by multiplication and integration, and then the instantaneous frequency parameters of the frequency modulation signal are accurately analyzed by the linear transformation network to restore the original modulation signal. Compared with the phase discriminator, the simulation results show that this method has a higher frequency discrimination bandwidth. In addition, this method has better anti-noise performance, and the frequency discrimination distortion caused by noise with a different Signal-to-Noise Ratio is reduced by 33.80% on average compared with the phase discriminator. What is more, the carrier center frequency error has little influence on the frequency discrimination quality of this method, which solves the problem that most common frequency discriminators are seriously affected by the carrier center frequency error. This method requires a low accuracy of carrier center frequency, which makes it extremely suitable for digital frequency discrimination technology and can meet the needs of various frequency discrimination occasions.

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Revunova, O. G., A. V. Tyshcuk, and О. О. Desiateryk. "On the Generalization of the Random Projection Method for Problems of the Recovery of Object Signal Described by Models of Convolution Type." Control Systems and Computers, no. 5-6 (295-296) (December 2021): 25–34. http://dx.doi.org/10.15407/csc.2021.05-06.025.

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Introduction. In technical systems, there is a common situation when transformation input-output is described by the integral equation of convolution type. This situation accurses if the object signal is recovered by the results of remote measurements. For example, in spectrometric tasks, for an image deblurring, etc. Matrices of the discrete representation for the output signal and the kernel of convolution are known. We need to find a matrix of the discrete representation of a signal of the object. The well known approach for solving this problem includes the next steps. First, the kernel matrix has to be represented as the Kroneker product. Second, the input-output transformation has to be presented with the usage of Kroneker product matrices. Third, the matrix of the discrete representation of the object has to be found. The object signal matrix estimation obtained with the help of pseudo inverting of Kroneker decomposition matrices is unstable. The instability of the object signal estimation in the case of usage of Kroneker decomposition matrices is caused by their discrete ill posed matrix properties (condition number is big and the series of the singular numbers smoothly decrease to zero). To find solutions of discrete ill-posed problems we developed methods based on the random projection and the random projection with an averaging by the random matrices. These methods provide a stable solutions with a small computational complexity. We consider the problem of object signals recovering in the systems where an input-output transformation is described by the integral equation of a convolution. To find a solution for these problems we need to build a generalization for two-dimensional signals case of the random projection method. Purpose. To develop a stable method of the recovery of object signal for the case in which an input-output transformation is described by the integral equation of a convolution. Results and conclusions. We developed the method of a stable recovery of object signal for the case in which an input-output transformation is described by the integral equation of a convolution. The stable estimation of the object signal is provided by Kroneker decomposition of the kernel matrix of convolution, computation of random projections for Kroneker factorization matrices, and a selection of the optimal dimension of a projector matrix. The method is illustrated by its application in technical problems.

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Xiang, Xin Jian, and Zhang Lin. "Arc-Fault Detection Method Research Based on Wavelet Transformation." Advanced Materials Research 646 (January 2013): 240–44. http://dx.doi.org/10.4028/www.scientific.net/amr.646.240.

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The arc-fault is the main reason that cause electric fires. The technology of arc-fault circuit interrupters (AFCI) is the new circuit protection technology and it could avoid arc-fault causing fire effectively. The appearance of arc-fault can not be predicted. The traditional time domain or frequency domain analysis method for arc-fault signal processing is not ideal because it’s inaccurate and not in time. This paper bases on characteristics of arc-fault signals and analyzes the series connection arc-fault signal by Daubechies wavelet transform in 4 orders. As a result, it can provide the characteristics of arc-fault and detect arc-fault effectively and timely. This method is confirmed reliability by the simulation result and provides the theoretical basis of the development of AFCI.

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Liu, Yang, Jigou Liu, and Ralph Kennel. "Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation." Machines 9, no. 6 (June 18, 2021): 123. http://dx.doi.org/10.3390/machines9060123.

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Precise frequency measurement plays an essential role in many industrial and robotic systems. However, different effects in the application’s environment cause signal noises, which make frequency measurement more difficult. In small signals or rough environments, even negative Signal-to-Noise Ratios (SNRs) are possible. Thus, frequency measuring methods, which are suited for low SNR signals, are in great demand. While denoising methods such as autocorrelation do not suffice for small signal with low SNR, frequency measurement methods such as Fast-Fourier Transformation or Continuous Wavelet Transformation suffer from Heisenberg’s uncertainty principle, which makes simultaneous high frequency and time resolutions impossible. In this paper, the cross-correlation spectrum is presented as a new frequency measuring method. It can be used in any frequency domain, and provides greater denoising than autocorrelation. Furthermore, frequency and time resolutions are independent from one another, and can be set separately by the user. In simulations, it achieves an average deviation of less than 0.1% on sinusoidal signals with a SNR of −10 dB and a signal length of 1000 data points. When applied to “self-mixing”-interferometry signals, the method can reach a normalized root-mean square error of 0.2% with the aid of an estimation method and an averaging algorithm. Therefore, further research of the method is recommended.

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Du, Cunpeng, Shengwen Yu, Haitao Yin, and Zhen Sun. "Microseismic Time Delay Estimation Method Based on Continuous Wavelet." Sensors 22, no. 8 (April 7, 2022): 2845. http://dx.doi.org/10.3390/s22082845.

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The microseismic signal is easily affected by observation noise and the inaccurate estimation of traditional methods will seriously reduce the location accuracy of the microseismic event. Therefore, based on the continuous wavelet spectrum and the similarity coefficient, a fast and efficient microseismic time delay estimation method is proposed. Firstly, the original signals are denoised by continuous wavelet transform. Subsequently, the time-frequency transform of the original signal by continuous wavelet transform, time-frequency signal extraction is the process of band-pass filtering, which can further reduce the influence of noise interference on the time delay estimation. Finally, we calculated the similarity between the time-frequency signals via the time domain and frequency domain integration. The similarity function is based on correlation and proposed according to the time-frequency transformation provided by the phase spectrum to evaluate the similarity between two noisy signals. The time delay estimation is determined by searching for the similarity function peak. The experimental results show the precision and accuracy of the method over the cross-correlation method and generalized cross-correlation phase transformation method, especially when the signal-to-noise ratio is low. Therefore, a new time delay estimation method for non-stationary random signals is presented in this paper.

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Lu, Zhiqi. "Review on mechano-electronic device fault diagnosis based on Hilbert-Huang Transformation." Journal of Physics: Conference Series 2351, no. 1 (October 1, 2022): 012033. http://dx.doi.org/10.1088/1742-6596/2351/1/012033.

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This paper reviews the new Hilbert-Huang Transformation (HHT for short) signal processing method, briefly discusses the significance of time-frequency analysis methods in electromechanical system fault diagnosis, points out the superiority of HHT for fault diagnosis with respect to the characteristics of nonstationary and nonlinear dynamic fault signals, and introduces the basic principles of HHT. The research applications of HHT in the field of fault diagnosis at home and abroad are discussed, showing that HHT is becoming an important signal processing method for mechanical fault diagnosis.

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Nikitenko, M. N. "Tem signal transformations to frequency domain for fast data inversion." Russian Journal of Geophysical Technologies, no. 2 (December 29, 2022): 15–29. http://dx.doi.org/10.18303/2619-1563-2022-2-15.

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The paper presents a new data inversion technique for the transient electromagnetic method (TEM) by converting the measured signals into the frequency domain. The inversion involves a search for such earth model parameters that there is a consistency between the synthetic and field data. We use an optimization method, where through numerical simulation at each iteration the synthetic data are determined in accordance with the changing model parameters. Numerical simulation of TEM signals is a computationally expensive procedure, since the time-domain signal is usually calculated via the inverse Fourier transform of the frequency signal. Consequently, compared to the frequency signal, the time needed to calculate the time signal increases hundreds of times. It is proposed to transform the measured signals into the frequency domain and perform inversion therein, which significantly reduces the time expenditures. Transition into the frequency domain by the Fourier transform includes the extraction of the primary field from the signal, calculated by means of a special algorithm. This fact makes it possible to employ for the transformation a relatively small time interval actually used in TEM instead of an infinite one.

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