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Relevant bibliographies by topics / Synchroextracting transform

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Academic literature on the topic 'Synchroextracting transform'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Synchroextracting transform"

1

Yu, Gang, Mingjin Yu, and Chuanyan Xu. "Synchroextracting Transform." IEEE Transactions on Industrial Electronics 64, no. 10 (2017): 8042–54. http://dx.doi.org/10.1109/tie.2017.2696503.

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Lin, Ying, Siyuan Chen, Guangzhi Zhang, Minmin Huang, and Baoli Wang. "High-resolution time–frequency analysis based on a synchroextracting adaptive S-transform and its application." Journal of Geophysics and Engineering 19, no. 5 (2022): 1124–33. http://dx.doi.org/10.1093/jge/gxac068.

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Abstract We propose synchroextracting adaptive S-transform (SEAST) by combining the adaptivity provided by the recently introduced ‘Sparse Adaptive S-transform’ (SAST) with the high resolution of synchroextracting spectral decomposition method. Traditional synchroextracting transforms are based on short-time Fourier transforms (STFTs) and their application is limited by having a fixed analysis window size for high and low frequencies and no multi-resolution features. The arbitrary window functions used in the SAST vary with frequency and amplitude, making it more suitable for non-stationary se

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Li, Bei, and Zhuosheng Zhang. "Synchroextracting Transform Based on the Novel Short-Time Fractional Fourier Transform." Fractal and Fractional 8, no. 12 (2024): 736. https://doi.org/10.3390/fractalfract8120736.

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As a generalization of the short-time Fourier transform (STFT), the novel short-time fractional Fourier transform (NSTFRFT) has been introduced recently. In order to improve the concentration of the time–frequency representation (TFR) generated by the NSTFRFT, two post-processing time–frequency analysis methods, two synchroextracting transforms based on the NSTFRFT with two different fractional Fourier transform (FRFT) angles, are proposed in this paper. One is achieved via an equation where the instantaneous frequency satisfies the condition where the FRFT angle takes π2, and the other one is

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Hu, Zhifeng, Zhinong Li, Liying Ge, Qinghua Mao, and Xuhui Zhang. "Diagnosis Method for Mechanical Faults Based on Rotation Synchroextracting Chirplet Transform." Applied Sciences 12, no. 24 (2022): 12972. http://dx.doi.org/10.3390/app122412972.

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The problems of the synchroextracting transform method being unable to handle FM signals and being prone to time–frequency feature discontinuity in a strong noise environment are addressed by the construction of a novel rotation synchroextracting chirplet transform under the framework of the synchroextracting transform. The method retains the advantage of the generalized linear chirplet transform that can fit the time–frequency characteristics of the original signal and retains the high precision time–frequency analysis ability of the synchroextracting transform. The simulation results show th

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Zhang, Ran, Xingxing Liu, Yongjun Zheng, et al. "Time‐frequency synchroextracting transform." IET Signal Processing 16, no. 2 (2021): 117–31. http://dx.doi.org/10.1049/sil2.12073.

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Bao, Wenjie, Songyong Liu, Zhen Liu, and Fucai Li. "Generalized synchroextracting transform: Algorithm and applications." Mechanical Systems and Signal Processing 224 (February 2025): 112116. http://dx.doi.org/10.1016/j.ymssp.2024.112116.

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You, Guanghui, Yong Lv, Yefeng Jiang, and Cancan Yi. "A Novel Fault Diagnosis Scheme for Rolling Bearing Based on Convex Optimization in Synchroextracting Chirplet Transform." Sensors 20, no. 10 (2020): 2813. http://dx.doi.org/10.3390/s20102813.

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Synchroextracting transform (SET) developed from synchrosqueezing transform (SST) is a novel time-frequency (TF) analysis method. Its concentrated TF spectrum is obtained by applying a synchroextracting operator into TF transformation co-efficients on the TF plane. For this class of post-processing TF analysis methods, the main research focuses on the accurate estimation of instantaneous frequency (IF). However, the performance of TF analysis is greatly affected by the strong frequency modulation (FM) signal. In particular, the actual measured mechanical vibration signals always contain strong

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Shahin, M. Abdulla, and Jayakumari J. "High clarity speech separation using synchroextracting transform." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2621–30. https://doi.org/10.11591/ijece.v11i3.pp2621-2630.

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Degenerate unmixing estimation technique (DUET) is the most ideal blind source separation (BSS) method for underdetermined conditions with number of sources exceeds number of mixtures. Estimation of mixing parameters which is the most critical step in the DUET algorithm is developed based on the characteristic feature of sparseness of speech signals in time frequency (TF) domain. Hence, DUET relies on the clarity of time frequency representation (TFR) and even the slightest interference in the TF plane will be detrimental to the unmixing performance. In conventional DUET algorithm, short time

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Ashtari Jafari, Mohammad. "Comparative Application of Time-Frequency Methods on Strong Motion Signals." Advances in Civil Engineering 2021 (July 31, 2021): 1–14. http://dx.doi.org/10.1155/2021/9933078.

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Real-world physical signals are commonly nonstationary, and their frequency details change with time and do not remain constant. Fourier transform that uses infinite sine/cosine waves as basis functions represents frequency constituents of signals but does not show the variations of the signal frequency contents over time. Multiresolution demonstration of the time-frequency domain may be achieved by the techniques that can support adjustable resolution in time and frequency. Earthquake strong motion signals are nonstationary and indicate time-varying frequency content due to the scattering fro

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Li, Zhen, Jinghuai Gao, Hui Li, Zhuosheng Zhang, Naihao Liu, and Xiangxiang Zhu. "Synchroextracting transform: The theory analysis and comparisons with the synchrosqueezing transform." Signal Processing 166 (January 2020): 107243. http://dx.doi.org/10.1016/j.sigpro.2019.107243.

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Book chapters on the topic "Synchroextracting transform"

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Han, Lin, Liang Lu, Haoran Dong, et al. "Feature Extraction Method of EEG Signal Based on Synchroextracting Transform." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82565-2_38.

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Varanis, Marcus, Sadra Hemmati, Murilo Cesar Filipus, Felipe Lima de Abreu, Jose M. Balthazar, and C. Nataraj. "An Overview on Time-Frequency Effects of ECG Signals Using Synchroextracting Transform." In NODYCON Conference Proceedings Series. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50635-2_54.

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Li, Shuo, and Gang Yu. "Enhancing Muscle Signal Analysis: Insights from High-Order Synchroextracting Wavelet Transform on Electromyography Signals." In Mechanisms and Machine Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73407-6_28.

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Lv, Ming, Changfeng Yan, Bin Liu, Xiru Liu, Yuan Huang, and Bo Liu. "A method for estimating the size of rolling bearing defect based on synchroextracting transform and energy analysis." In Equipment Intelligent Operation and Maintenance. CRC Press, 2025. https://doi.org/10.1201/9781003470076-51.

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Conference papers on the topic "Synchroextracting transform"

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Tian, Hengyu, and Yajuan Xue. "Application of Second-Order Synchroextracting Transform Combined with Empirical Wavelet Transform for Seismic Data Analysis." In 2024 International Symposium on Electrical, Electronics and Information Engineering (ISEEIE). IEEE, 2024. https://doi.org/10.1109/iseeie62461.2024.00078.

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Wang, yaohui, yibin Rui, meng Xu, renhong Xie, and peng Li. "A modified second-order synchroextracting transform." In Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), edited by Zhenkai Zhang and Cheng Li. SPIE, 2024. http://dx.doi.org/10.1117/12.3022795.

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Yuan, Yufeng, Yibin Rui, Peng Li, Chengwei Yu, Renhong Xie, and Peng Li. "A Synchroextracting Transform Based on a Modified S-Transform." In 2022 14th International Conference on Signal Processing Systems (ICSPS). IEEE, 2022. http://dx.doi.org/10.1109/icsps58776.2022.00046.

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Zhang, Jie, and Dazhi Ding. "Maneuvering Target ISAR Imaging Based on Synchroextracting Transform." In 2019 International Applied Computational Electromagnetics Society Symposium - China (ACES). IEEE, 2019. http://dx.doi.org/10.23919/aces48530.2019.9060535.

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Han, Wenchang, and Feng Zhou. "Wideband Interference Suppression for SAR Based on Synchroextracting Transform." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883495.

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Wang, Q., S. Chen, B. Tang, and X. Han. "Synchroextracting S Transform and Its Application in Seismic Time-Frequency Analysis." In 82nd EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202010886.

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Shang, Jie, and Tian Ran Lin. "Varying Speed Bearing Fault Diagnosis Based on Synchroextracting Transform and Deep Residual Network." In 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM). IEEE, 2020. http://dx.doi.org/10.1109/aparm49247.2020.9209580.

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Zhang, Lin, and Yongqiang Liu. "Rolling Bearing Fault Diagnosis Based on Synchroextracting Transform Under Variable Rotational Speed Conditions." In 2018 Prognostics and System Health Management Conference (PHM-Chongqing). IEEE, 2018. http://dx.doi.org/10.1109/phm-chongqing.2018.00225.

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Yuan, Wang, Xiaolong Chen, Xiaolin Du, Gang Yu, and Xiaoyang He. "High-Precision UAV Micro-Motion Feature Extraction and Estimation Method Based on Local Maximum Synchroextracting Transform." In 2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2023. http://dx.doi.org/10.1109/iceict57916.2023.10245482.

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