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Journal articles on the topic 'Time–frequency reassignment'

Author: Grafiati
Published: 22 February 2023

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1

Nilsen, G. K. "Recursive Time-Frequency Reassignment." IEEE Transactions on Signal Processing 57, no. 8 (August 2009): 3283–87. http://dx.doi.org/10.1109/tsp.2009.2020355.

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2

Ahrabian, Alireza, and Danilo P. Mandic. "Selective Time-Frequency Reassignment Based on Synchrosqueezing." IEEE Signal Processing Letters 22, no. 11 (November 2015): 2039–43. http://dx.doi.org/10.1109/lsp.2015.2456097.

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3

Auger, Francois, Patrick Flandrin, Yu-Ting Lin, Stephen McLaughlin, Sylvain Meignen, Thomas Oberlin, and Hau-Tieng Wu. "Time-Frequency Reassignment and Synchrosqueezing: An Overview." IEEE Signal Processing Magazine 30, no. 6 (November 2013): 32–41. http://dx.doi.org/10.1109/msp.2013.2265316.

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4

Hao, Zhi Hua, Zhuang Ma, and Hao Miao Zhou. "Research on Fault Diagnosis Method Based on Empirical Mode Decomposition & Time-Frequency Reassignment." Advanced Materials Research 433-440 (January 2012): 6256–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6256.

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The reassignment method is a technique for sharpening a time-frequency representation by mapping the data to time-frequency coordinates that are nearer to the true region of support of the analyzed signal. The reassignment method has been proved to produce a better localization of the signal components and improve the readability of the time-frequency representation by concentrating its energy at a center of gravity. But there are still few cross-terms. Then, the empirical mode decomposition is introduced to the reassignment method to suppress the interference of the cross-term encountered in processing the multi-component signals. The multi-component signal can be decomposed into a finite number intrinsic mode function by using EMD. Then, the reassignment method can be calculated for each of the intrinsic mode function. Simulation analysis is presented to show that this method can improve the localization of time-frequency representation and reduce the cross terms. The vibration signals measured from diesel engine in the stage of deflagrate were analyzed with the reassignment method. Experimental results indicate that this method has good potential in mechanical fault feature extraction.
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Wei, Dahuan, Zhenfeng Huang, Hanling Mao, Xinxin Li, Huade Huang, Bang Wang, and Xiaoxu Yi. "Iterative reassignment: An energy-concentrated time-frequency analysis method." Mechanical Systems and Signal Processing 182 (January 2023): 109579. http://dx.doi.org/10.1016/j.ymssp.2022.109579.

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6

Bruni, Vittoria, Michela Tartaglione, and Domenico Vitulano. "A Fast and Robust Spectrogram Reassignment Method." Mathematics 7, no. 4 (April 19, 2019): 358. http://dx.doi.org/10.3390/math7040358.

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The improvement of the readability of time-frequency transforms is an important topic in the field of fast-oscillating signal processing. The reassignment method is often used due to its adaptivity to different transforms and nice formal properties. However, it strongly depends on the selection of the analysis window and it requires the computation of the same transform using three different but well-defined windows. The aim of this work is to provide a simple method for spectrogram reassignment, named FIRST (Fast Iterative and Robust Reassignment Thinning), with comparable or better precision than classical reassignment method, a reduced computational effort, and a near independence of the adopted analysis window. To this aim, the time-frequency evolution of a multicomponent signal is formally provided and, based on this law, only a subset of time-frequency points is used to improve spectrogram readability. Those points are the ones less influenced by interfering components. Preliminary results show that the proposed method can efficiently reassign spectrograms more accurately than the classical method in the case of interfering signal components, with a significant gain in terms of required computational effort.
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Samad, Salina Abdul, and Aqilah Baseri Huddin. "Improving spectrogram correlation filters with time-frequency reassignment for bio-acoustic signal classification." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (April 1, 2019): 59. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp59-64.

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<p>Spectrogram features have been used to automatically classify animals based on their vocalization. Usually, features are extracted and used as inputs to classifiers to distinguish between species. In this paper, a classifier based on Correlation Filters (CFs) is employed where the input features are the spectrogram image themselves. Spectrogram parameters are carefully selected based on the target dataset in order to obtain clear distinguishing images termed as call-prints. An even better representation of the call-prints is obtained using spectrogram Time-Frequency (TF) reassignment. To demonstrate the application of the proposed technique, two species of frogs are classified based on their vocalization spectrograms where for each species a correlation filter template is constructed from multiple call-prints using the Maximum Margin Correlation Filter (MMCF). The improved accuracy rate obtained with TF reassignment demonstrates that this is a viable method for bio-acoustic signal classification.</p>
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Wang, Hui, Xiu Wei Li, Yu Xin Yun, and Hai Yan Yuan. "Reassigned Time Frequency Analysis for PD Signals in GIS." Applied Mechanics and Materials 448-453 (October 2013): 1959–62. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1959.

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Partial discharge signal in GIS is a kind of typical non-stationary signal, using the time or frequency domain simply is not enough to describe the time-varying information of PD. Based on the reason above, this paper introduces a joint time-frequency analysis method according to the reassignment theory for analyzing the PD of GIS. After the processing of the PD signals simulated and on field, we conclude that this method provides a higher concentration in the time-frequency plane and reduces the most influence of the cross-interference terms.
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9

Lin, Hongbo, Yue Li, Baojun Yang, and Haitao Ma. "Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment." GEOPHYSICS 78, no. 6 (November 1, 2013): V229—V237. http://dx.doi.org/10.1190/geo2012-0432.1.

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Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.
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10

Xiao, Jun, and Patrick Flandrin. "Multitaper Time-Frequency Reassignment for Nonstationary Spectrum Estimation and Chirp Enhancement." IEEE Transactions on Signal Processing 55, no. 6 (June 2007): 2851–60. http://dx.doi.org/10.1109/tsp.2007.893961.

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11

Korycki, Rafał. "Methods of Time-Frequency Analysis in Authentication of Digital Audio Recordings." International Journal of Electronics and Telecommunications 56, no. 3 (September 1, 2010): 257–62. http://dx.doi.org/10.2478/v10177-010-0033-0.

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Methods of Time-Frequency Analysis in Authentication of Digital Audio RecordingsThis paper describes the problem of tampering detection and discusses the main methods used for authenticity analysis of digital audio recordings. For the first topic, two frequency measurement algorithms based on electric network frequency criterion are applied. Time-frequency analysis is used and improved with reassignment method for purpose of visual inspection of modified recordings. The algorithms are shortly described and exemplary plots are presented with interpretation. The last described method, recently proposed, is based on checking frame offsets in compressed audio files.
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12

Auger, F., and P. Flandrin. "Improving the readability of time-frequency and time-scale representations by the reassignment method." IEEE Transactions on Signal Processing 43, no. 5 (May 1995): 1068–89. http://dx.doi.org/10.1109/78.382394.

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13

CHO, Hyunjin, Wan Jin KIM, and Wooyoung HONG. "Underwater Signal Analysis in the Modulation Spectrogram with Time-Frequency Reassignment Technique." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E102.A, no. 11 (November 1, 2019): 1542–44. http://dx.doi.org/10.1587/transfun.e102.a.1542.

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14

Bernhardt, R. C. "Automatic time-slot reassignment in a frequency reuse TDMA portable radio system." IEEE Transactions on Vehicular Technology 41, no. 3 (1992): 296–304. http://dx.doi.org/10.1109/25.155976.

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15

Han, Li, Mauricio D. Sacchi, and Liguo Han. "Spectral decomposition and de-noising via time-frequency and space-wavenumber reassignment." Geophysical Prospecting 62, no. 2 (December 23, 2013): 244–57. http://dx.doi.org/10.1111/1365-2478.12088.

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16

Holighaus, Nicki, Zdenek Průša, and Peter L. Søndergaard. "Reassignment and synchrosqueezing for general time–frequency filter banks, subsampling and processing." Signal Processing 125 (August 2016): 1–8. http://dx.doi.org/10.1016/j.sigpro.2016.01.007.

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17

Oberlin, Thomas, Sylvain Meignen, and Valerie Perrier. "Second-Order Synchrosqueezing Transform or Invertible Reassignment? Towards Ideal Time-Frequency Representations." IEEE Transactions on Signal Processing 63, no. 5 (March 2015): 1335–44. http://dx.doi.org/10.1109/tsp.2015.2391077.

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18

Meignen, Sylvain, Thomas Oberlin, Philippe Depalle, Patrick Flandrin, and Stephen McLaughlin. "Adaptive multimode signal reconstruction from time–frequency representations." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2065 (April 13, 2016): 20150205. http://dx.doi.org/10.1098/rsta.2015.0205.

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This paper discusses methods for the adaptive reconstruction of the modes of multicomponent AM–FM signals by their time–frequency (TF) representation derived from their short-time Fourier transform (STFT). The STFT of an AM–FM component or mode spreads the information relative to that mode in the TF plane around curves commonly called ridges . An alternative view is to consider a mode as a particular TF domain termed a basin of attraction . Here we discuss two new approaches to mode reconstruction. The first determines the ridge associated with a mode by considering the location where the direction of the reassignment vector sharply changes, the technique used to determine the basin of attraction being directly derived from that used for ridge extraction. A second uses the fact that the STFT of a signal is fully characterized by its zeros (and then the particular distribution of these zeros for Gaussian noise) to deduce an algorithm to compute the mode domains. For both techniques, mode reconstruction is then carried out by simply integrating the information inside these basins of attraction or domains.
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19

Knigge, Bernhard, and Frank E. Talke. "Slider Vibration Analysis at Contact Using Time-Frequency Analysis and Wavelet Transforms." Journal of Tribology 123, no. 3 (July 6, 2000): 548–54. http://dx.doi.org/10.1115/1.1308035.

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Time-frequency analysis and wavelet transforms are employed to investigate transient contact dynamics at the head/disk interface of computer hard drives. Wavelet transforms are used to resolve multiple short consecutive contacts at high time resolution at high frequencies. The reassignment method is applied to the time-frequency distribution to enhance the time-frequency resolution, thereby allowing to resolve air-bearing and slider body frequencies simultaneously. The results indicate that strong impacts between slider and disk can lead to excitations of slider body and suspension vibrations. Finite element modal analysis of nano and pico glide sliders is found to be in good agreement with experimentally measured frequencies.
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20

Fitz, Kelly. "Rehabilitating the world’s worst sounds: Time‐frequency reassignment in sound modeling and morphing." Journal of the Acoustical Society of America 121, no. 5 (May 2007): 3131–32. http://dx.doi.org/10.1121/1.4782139.

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21

Chen, Xiaowang, and Zhipeng Feng. "Iterative generalized time–frequency reassignment for planetary gearbox fault diagnosis under nonstationary conditions." Mechanical Systems and Signal Processing 80 (December 2016): 429–44. http://dx.doi.org/10.1016/j.ymssp.2016.04.023.

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22

Sandoval, Steven, and Phillip L. De Leon. "Recasting the (Synchrosqueezed) Short-Time Fourier Transform as an Instantaneous Spectrum." Entropy 24, no. 4 (April 6, 2022): 518. http://dx.doi.org/10.3390/e24040518.

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In a previous work, we proposed a time-frequency analysis called instantaneous spectral analysis (ISA), which generalizes the notion of the Fourier spectrum and in which instantaneous frequency is utilized to the fullest extent. In this paper, we recast both the Fourier transform (FT) and filterbank (FB) interpretations of the short-time Fourier transform (STFT) as instantaneous spectra. We show that to recast the FB interpretation of STFT as an instantaneous spectrum with valid structure, frequency reassignment is a fundamental necessity, thus demonstrating that this IS is closely related to the synchrosqueezed STFT. This result provides a new theoretical motivation for the synchrosqueezed STFT. Finally, we illustrate through example the instantaneous spectra corresponding to the FT and FB interpretations of STFT using two closed-form examples.
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23

Herrera, Roberto H., Jiajun Han, and Mirko van der Baan. "Applications of the synchrosqueezing transform in seismic time-frequency analysis." GEOPHYSICS 79, no. 3 (May 1, 2014): V55—V64. http://dx.doi.org/10.1190/geo2013-0204.1.

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Time-frequency representation of seismic signals provides a source of information that is usually hidden in the Fourier spectrum. The short-time Fourier transform and the wavelet transform are the principal approaches to simultaneously decompose a signal into time and frequency components. Known limitations, such as trade-offs between time and frequency resolution, may be overcome by alternative techniques that extract instantaneous modal components. Empirical mode decomposition aims to decompose a signal into components that are well separated in the time-frequency plane allowing the reconstruction of these components. On the other hand, a recently proposed method called the “synchrosqueezing transform” (SST) is an extension of the wavelet transform incorporating elements of empirical mode decomposition and frequency reassignment techniques. This new tool produces a well-defined time-frequency representation allowing the identification of instantaneous frequencies in seismic signals to highlight individual components. We introduce the SST with applications for seismic signals and produced promising results on synthetic and field data examples.
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24

MORVIDONE, MARCELA. "TIME-SCALE APPROACH FOR CHIRP DETECTION II." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 01 (January 2010): 89–107. http://dx.doi.org/10.1142/s0219691310003407.

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A new approach for joint detection and estimation of signals embedded in stationary random noise is considered and compared to previously studied methods, for the subclass of amplitude and frequency modulated signals. The method is a "reassigned" version of the Hough-wavelet transform and it is compared to the matched filter approach and the Hough-wavelet transform itself. Extensions to previous results obtained with these two methods are also presented. As target application, the problem of gravitational waves at interferometric detectors is considered. Our main conclusion is that there is no gain in introducing the reassignment operation.
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25

Pedersen, Helle A., Jérôme I. Mars, and Pierre‐Olivier Amblard. "Improving surface‐wave group velocity measurements by energy reassignment." GEOPHYSICS 68, no. 2 (March 2003): 677–84. http://dx.doi.org/10.1190/1.1567238.

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Surface waves are increasingly used for shallow seismic surveys—in particular, in acoustic logging, environmental, and engineering applications. These waves are dispersive, and their dispersion curves are used to obtain shear velocity profiles with depth. The main obstacle to their more widespread use is the complexity of the associated data processing and interpretation of the results. Our objective is to show that energy reassignment in the time–frequency domain helps improve the precision of group velocity measurements of surface waves. To show this, full‐waveform seismograms with added white noise for a shallow flat‐layered earth model are analyzed by classic and reassigned multiple filter analysis (MFA). Classic MFA gives the expected smeared image of the group velocity dispersion curve, while the reassigned curve gives a very well‐constrained, narrow dispersion curve. Systematic errors from spectral fall‐off are largely corrected by the reassignment procedure. The subsequent inversion of the dispersion curve to obtain the shear‐wave velocity with depth is carried out through a procedure combining linearized inversion with a nonlinear Monte Carlo inversion. The diminished uncertainty obtained after reassignment introduces significantly better constraints on the earth model than by inverting the output of classic MFA. The reassignment is finally carried out on data from a shallow seismic survey in northern Belgium, with the aim of determining the shear‐wave velocities for seismic risk assessment. The reassignment is very stable in this case as well. The use of reassignment can make dispersion measurements highly automated, thereby facilitating the use of surface waves for shallow surveys.
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Czarnecki, Krzysztof, and Wojciech Leśniak. "Bearing Estimation Using Double Frequency Reassignment for a Linear Passive Array." Polish Maritime Research 24, no. 3 (September 1, 2017): 26–35. http://dx.doi.org/10.1515/pomr-2017-0087.

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AbstractThe paper demonstrates the use of frequency reassignment for bearing estimation. For this task, signals derived from a linear equispaced passive array are used. The presented method makes use of Fourier transformation based spatial spectrum estimation. It is further developed through the application of two-dimensional reassignment, which leads to obtaining highly concentrated energy distributions in the joint frequency-angle domain and sharp graphical imaging. The introduced method can be used for analysing,a priori, unknown signals of broadband, nonstationary, and/or multicomponent type. For such signals, the direction of arrival is obtained based upon the marginal energy distribution in the angle domain, through searching for arguments of its maxima. In the paper, bearing estimation of three popular types of sonar pulses, including linear and hyperbolic frequency modulated pulses, as well as no frequency modulation at all, is considered. The results of numerical experiments performed in the presence of additive white Gaussian noise are presented and compared to conventional digital sum-delay beamforming performed in the time domain. The root-mean-square error and the peak-to-average power ratio, also known as the crest factor, are introduced in order to estimate, respectively, the accuracy of the methods and the sharpness of the obtained energy distributions in the angle domain.
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27

Cao, Maosen, Shuaitao Hu, Xin Zhang, Shixiang Zhang, Dragoslav Sumarac, and Jiayi Peng. "Investigation of Time-Varying Cable Tension of Bridges Using Time-Frequency Reassignment Techniques Based on Structural Health Monitoring Data." Applied Sciences 12, no. 8 (April 15, 2022): 4008. http://dx.doi.org/10.3390/app12084008.

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Cables have been increasingly utilized in modern long-span or tied-arch bridges as the main bearing structures. Real-time identification of time-varying cable tension is essential for assessing the service performance of bridges. Vibration-based methods have been an increasing research focus in recent decades. However, a long time interval is needed to estimate structural frequency using vibration-based methods, increasing the calculating time of cable tension. The time-varying cable tension is thus difficult to extract. This study proposes a time-frequency reassignment-based algorithm to reduce the detection time to address this issue. Combined with a time-frequency analysis tool and vibration theory of cables, the algorithm can identify the time-varying frequency and further quickly calculate the time-varying cable tension within 12.8 s. The features of the proposed algorithm are mainly threefold: identifying the time-varying frequencies with high precision; without some prior knowledge of vibration; having no other requirements for sensor modes. Moreover, the experimental validation is conducted using a quasi-static loading in a workshop and a dynamic field test on Sutong Bridge, respectively. The results show that the proposed algorithm can be used to identify time-varying tension and assess the service performance of cables, providing a new path for real-time condition monitoring of bridges in service.
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28

Wang, Shibin, Chuanyi Cheng, Jianghan Zhou, Fuhua Qin, Yining Feng, Baoqing Ding, Zhibin Zhao, and Xuefeng Chen. "Reassignment-enable reweighted sparse time-frequency analysis for sparsity-assisted aeroengine rub-impact fault diagnosis." Mechanical Systems and Signal Processing 183 (January 2023): 109602. http://dx.doi.org/10.1016/j.ymssp.2022.109602.

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29

S. Sameh, Souli, and Lachiri Z. Zied. "On the Use of Time–Frequency Reassignment and SVM-Based Classifier for Audio Surveillance Applications." International Journal of Image, Graphics and Signal Processing 6, no. 12 (November 8, 2014): 17–25. http://dx.doi.org/10.5815/ijigsp.2014.12.03.

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30

Richard, Cédric, and Régis Lengellé. "Joint recursive implementation of time—frequency representations and their modified version by the reassignment method." Signal Processing 60, no. 2 (July 1997): 163–79. http://dx.doi.org/10.1016/s0165-1684(97)80004-7.

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31

Mather, T. William, and M. Ani Hsieh. "Macroscopic modeling of stochastic deployment policies with time delays for robot ensembles." International Journal of Robotics Research 30, no. 5 (March 14, 2011): 590–600. http://dx.doi.org/10.1177/0278364911401442.

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We consider the dynamic assignment and reassignment of a homogeneous robot ensemble to multiple spatially located tasks with deterministic or near-deterministic task execution times. Similar to Halasz et al. and Berman et al., we consider the development of agent-level, i.e. microscopic, stochastic control policies through the analysis of an appropriate macroscopic analytical model that describes the dynamics of the ensemble. Specifically, we present an approach to better approximate the effects of deterministic microscopic time delays at the macroscopic level based on Padé approximants. We present, analyze, and compare the frequency response of our approach to that presented by Berman et al. using different agent-based simulations.
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32

Ozdemir, Mehmet Akif, Ozlem Karabiber Cura, and Aydin Akan. "Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning." International Journal of Neural Systems 31, no. 08 (May 26, 2021): 2150026. http://dx.doi.org/10.1142/s012906572150026x.

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Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.
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Swiercz, Ewa. "Time-frequency tomographic imaging of a rotating object in a narrow-band radar." International Journal of Microwave and Wireless Technologies 8, no. 6 (April 15, 2016): 871–79. http://dx.doi.org/10.1017/s1759078716000404.

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The backscatter from radar object carries Doppler information of scatterers on the object determined by the radial velocity of scattering points and the radar transmitted frequency. For a rotating object this information is contained in the frequency characteristics over varying aspect angle. Frequency characteristics are used to create projections for Doppler radar tomographic imaging. This paper presents a method for high resolution imaging of a rotating target using a time-frequency transform of a returned signal as tomographic projections. The resolution of a tomographic image depends not only on radar system parameters but also depends on the resolution of input projections. The reassigned spectrogram is proposed for building of tomographic projections, due to its possibility of squeezing of frequency spread. The reassigned spectrogram is sensitive to noise so the denoising procedure in the time-frequency domain must be performed before the reassignment procedure. The denoising is performed by removing Short Time Fourier Transform (STFT) noise coefficients below the appropriate threshold. The STFT is a linear time-frequency transform and coefficients, which belong to the signal and coefficients which belong to noise can be analyzed separately. The efficiency of the proposed idea of imaging is supported by results of numerical experiments.
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34

Tary, Jean Baptiste, Roberto Henry Herrera, and Mirko van der Baan. "Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, no. 2126 (July 9, 2018): 20170254. http://dx.doi.org/10.1098/rsta.2017.0254.

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The continuous wavelet transform (CWT) has played a key role in the analysis of time-frequency information in many different fields of science and engineering. It builds on the classical short-time Fourier transform but allows for variable time-frequency resolution. Yet, interpretation of the resulting spectral decomposition is often hindered by smearing and leakage of individual frequency components. Computation of instantaneous frequencies, combined by frequency reassignment, may then be applied by highly localized techniques, such as the synchrosqueezing transform and ConceFT, in order to reduce these effects. In this paper, we present the synchrosqueezing transform together with the CWT and illustrate their relative performances using four signals from different fields, namely the LIGO signal showing gravitational waves, a ‘FanQuake’ signal displaying observed vibrations during an American football game, a seismic recording of the M w 8.2 Chiapas earthquake, Mexico, of 8 September 2017, followed by the Irma hurricane, and a volcano-seismic signal recorded at the Popocatépetl volcano showing a tremor followed by harmonic resonances. These examples illustrate how high-localization techniques improve analysis of the time-frequency information of time-varying signals. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.
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35

Michel, Clotaire, and Philippe Gueguen. "Time-Frequency Analysis of Small Frequency Variations in Civil Engineering Structures Under Weak and Strong Motions Using a Reassignment Method." Structural Health Monitoring: An International Journal 9, no. 2 (December 4, 2009): 159–71. http://dx.doi.org/10.1177/1475921709352146.

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36

Hua, Zehui, Juanjuan Shi, Xingxing Jiang, Yang Luo, and Zhongkui Zhu. "Matching and reassignment based time-frequency enhancement for rotating machinery fault diagnosis under nonstationary speed operations." Measurement Science and Technology 32, no. 8 (May 11, 2021): 084002. http://dx.doi.org/10.1088/1361-6501/abfa3e.

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37

Yi, Cancan, Xing Wang, Yajun Zhu, and Wei Ke. "A Novel Adaptive Mode Decomposition Method Based on Reassignment Vector and Its Application to Fault Diagnosis of Rolling Bearing." Applied Sciences 10, no. 16 (August 7, 2020): 5479. http://dx.doi.org/10.3390/app10165479.

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To solve the problem that the random distribution of noise in the time-frequency (TF) plane largely affects the readability of TF representations, a novel signal adaptive decomposition algorithm processed in TF domain, which provides adequate information about the time-varying instantaneous frequency, is presented in this paper. The theoretical basis of this algorithm is short-time Fourier transform (STFT). The research into the algorithm comprises two steps: the TF plane denoising takes sparse low-rank matrix estimation as a priority and then achieves signal decomposition based on reassignment vector (RV). A low-rank matrix approximation scheme, which exploits the sparse properties of the TF transformation coefficient and uses non-convex penalty, is put forward to obtain clean STFT. Then, a new approach called RV, which is different from the traditional mode decomposition methods such as Empirical Mode Decomposition (EMD), is used to estimate the characteristic curve corresponding to the TF ridges of the interested modes. Based on the classical reassignment method, RV has a solid theory foundation. Moreover, it can identify different signal components such as stationary signal, modulating signal and impulse characteristic. Combining the advantages of low-rank matrix approximation approach and those of RV defined in TF plane, a novel signal adaptive decomposition method is proposed in this paper to identify fault characteristics. To illustrate the effectiveness of the method, fault signals of rolling bearing under stationary condition and time-varying speed are respectively analyzed.
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38

Ma, Gary H. K., and Albert Y. Zomaya. "An Efficient Algorithm for Solving the Minimum Blocking Frequency Assignment Problem Using Channel Reassignment." Journal of Interconnection Networks 04, no. 02 (June 2003): 227–45. http://dx.doi.org/10.1142/s0219265903000842.

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The channel allocation problem (CAP) that involves the allocation a disjoint set of channels to meet the call demands for a cellular network is an NP-complete combinatorial optimisation problem [1]. The CAP can be viewed as: static (during the initial design/planning of the cellular network) and dynamic (when the network is operational). This paper presents a new algorithm designed to solve the online call control problem. This algorithm is a modified version of the maximum channel packing channel allocation (MCPCA) scheme, proposed by [19]. The original MCPCA scheme aims at maximising the reuse of channels (i.e. dense packing) and simulation results showed that it is more efficient than fixed channel assignment (FCA) or borrowing channel assignment (BCA) schemes for solving the class of minimum blocking frequency assignment problem (MB-FAP) [2]. The new algorithm, entitled maximum channel packing channel assignment with re-assignment (MCPCA-RA), takes the dense packing mechanism further by allowing a neighbouring cell to re-assigns a channel to an existing call and releases the channel previously used to the new request. Simulation results that the number of blocked calls is reduced by an average of 6% compared to the original MCPCA scheme, but at the cost of extra computations due to the reassignment mechanism. Since the reassignment computations only involve cells in the local neighbourhood and can be compute in a parallel manner, MCPCA-RA algorithm is practical and efficient in real-time.
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39

Sun, Haixin, Yongchun Miao, and Jie Qi. "Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data." Complexity 2018 (July 26, 2018): 1–15. http://dx.doi.org/10.1155/2018/8426790.

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We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data. We propose the kernel sparse learning (KSL), developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies (IFs) ridges of the overlapped multicomponent from a time-frequency representation (TFR). The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection. The IFs first extraction is decided using the framework of the intrinsic mode polynomial chirp transform (IMPCT), which obtains a brief local orthogonal TFR of signals. Then, the IFs curves of the multicomponent signal can be easily reconstructed by the T-F reassignment. After the IFs are extracted, component decomposition is performed through KSL. Finally, the performance of the method is compared when applied to several simulated micro-Doppler signals, which shows its effectiveness in various applications.
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40

Ponce de León, Jesús, José Ramón Beltrán, and Fernando Beltrán. "Instantaneous frequency estimation and representation of the audio signal through Complex Wavelet Additive Synthesis." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 03 (May 2014): 1450030. http://dx.doi.org/10.1142/s0219691314500301.

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In this work, an improvement of the Complex Wavelet Additive Synthesis (CWAS) algorithm is presented. This algorithm is based on a discrete version of the Complex Continuous Wavelet Transform (CCWT) which analyzes the input signal in a frame-to-frame approach and under variable frequency resolution per octave. After summarizing several Time-Frequency Distributions (TFD), concretely the standard Short Time Fourier Transform (STFT), the Pseudo Wigner–Ville Distribution (PWVD), reassignment and complex wavelets, a comparative study of the accuracy in the instantaneous frequency (IF) estimation is shown. The comparative study includes three different signal processing tools (based on the summarized TFD): the Time-Frequency Toolbox (TFTB) of François Auger, the High Resolution Spectrographic Routines (HRSR) of Sean Fulop and the proposed CWAS algorithm. A set of eight synthetic signals have been analyzed using six different methods: the regular STFT spectrogram, the PWVD, their corresponding reassigned versions, the Nelson crossed spectrum method and finally the Complex Continuous Wavelet Transform (CCWT). Finally, two- and three-dimensional Time-Frequency representations of the IF provided by the CWAS algorithm are presented.
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41

Płotka, Marek, Karol Abratkiewicz, Mateusz Malanowski, Piotr Samczyński, and Krzysztof Kulpa. "The Use of the Reassignment Technique in the Time-Frequency Analysis Applied in VHF-Based Passive Forward Scattering Radar." Sensors 20, no. 12 (June 17, 2020): 3434. http://dx.doi.org/10.3390/s20123434.

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This paper presents the application of the time-frequency (TF) reassignment technique in passive forward scattering radar (FSR) using Digital Video Broadcasting – Terrestrial (DVB-T) transmitters of opportunity operating in the Very High Frequency (VHF) band. The validation of the proposed technique was done using real-life signals collected by the passive radar demonstrator during a measurement campaign. The scenario was chosen to test detection ranges and the capability of estimating the kinematic parameters of a cooperative airborne target in passive FSR geometry. Additionally, in the experiment the possibility of utilizing FSR geometry in foliage penetration conditions taking advantage of the VHF band of a DVB-T illuminator of opportunity was tested. The results presented in this paper show that the concentrated (reassigned) energy distribution of the signal in the TF domain allows a more precise target Doppler rate to be estimated using the Hough transform.
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42

Mousavi, S. Mostafa, and Charles A. Langston. "Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data." GEOPHYSICS 82, no. 4 (July 1, 2017): V211—V227. http://dx.doi.org/10.1190/geo2016-0433.1.

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Recorded seismic signals are often corrupted by noise. We have developed an automatic noise-attenuation method for single-channel seismic data, based upon high-resolution time-frequency analysis. Synchrosqueezing is a time-frequency reassignment method aimed at sharpening a time-frequency picture. Noise can be distinguished from the signal and attenuated more easily in this reassigned domain. The threshold level is estimated using a general cross-validation approach that does not rely on any prior knowledge about the noise level. The efficiency of the thresholding has been improved by adding a preprocessing step based on kurtosis measurement and a postprocessing step based on adaptive hard thresholding. The proposed algorithm can either attenuate the noise (either white or colored) and keep the signal or remove the signal and keep the noise. Hence, it can be used in either normal denoising applications or preprocessing in ambient noise studies. We tested the performance of the proposed method on synthetic, microseismic, and earthquake seismograms.
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43

LIU, Zenghua. "Reassignment of Time-frequency Representations Based on Nonparametric Snakes and Its Application in Guided Wave Mode Identification of Steel Strands." Journal of Mechanical Engineering 47, no. 08 (2011): 47. http://dx.doi.org/10.3901/jme.2011.08.047.

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44

Abdelrhman, Ahmed M., M. Salman Leong, Lim Meng Hee, and Wai Keng Ngui. "A Comparative Study of Reassigned Conventional Wavelet Transform for Machinery Faults Detection." Applied Mechanics and Materials 773-774 (July 2015): 90–94. http://dx.doi.org/10.4028/www.scientific.net/amm.773-774.90.

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Application of Fast Fourier Transform (FFT) in machinery faults detection is known to be only effective if fault is of repetitive in nature and considering severe. While minor and transient faults are usually remain undetected based on vibration spectrum analysis. Wavelet analysis is relatively new technique which is still suffered from inadequately in its time-frequency resolution. In this paper, ahmedrabak_time wavelet is proposed based on the wavelet reassignment technique for Morlet mother wavelet. The proposed wavelet analysis is compared to the conventional wavelet analysis for machinery faults detection based on simulated signal. The results showed that the proposed wavelet has a better resolution than conventional wavelet analysis which could clearly indicate the presence and the location of the fault.
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45

Hazra, Budhaditya, and Sriram Narasimhan. "Gear Fault Diagnosis Using Synchro-Squeezing Transform Based Feature Analysis." Key Engineering Materials 569-570 (July 2013): 449–56. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.449.

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Synchro-squeezing transform has recently emerged as a powerful signal processing tool in non-stationary signal processing. Premised upon the concept of time-frequency (TF) reassignment, its basic objective is to provide a sharper representation of signals in the TF plane and extract the individual components of a non-stationary multi-component signal, akin to empirical mode decomposition (EMD). The rich mathematical structure based on continuous wavelet transform (CWT) makes synchro-squeezing powerful for gear fault diagnosis, as faulty gear signal is frequently constituted out of multiple amplitude-modulated and frequency-modulated signals embedded in noise. This work utilizes the decomposing power of synchro-squeezing transform to extract the IMFs from a gear signal followed by the application of standard gearbox condition indicators which promises greater prognostic power than that can be achieved by applying condition indictors directly to the inherently complex gear signals. The efficacy and the robustness of the algorithm are demonstrated with the aid of practical experimental data obtained from a helicopter gear box.
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46

Gharbi, Tijani, Dominique Barchiesi, Olivier Bergossi, Hervé Wioland, and Cédric Richard. "Optical near-field data analysis through time–frequency distributions: application to the characterization and separation of the image spectral content by reassignment." Journal of the Optical Society of America A 17, no. 12 (December 1, 2000): 2513. http://dx.doi.org/10.1364/josaa.17.002513.

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47

Laaboubi, M., E. Aassif, R. Latif, A. Dliou, G. Maze, and D. Décultot. "Application of the reassignment time–frequency method on an acoustic signals backscattered by an air-filled circular cylindrical shell immersed in water." Aerospace Science and Technology 27, no. 1 (June 2013): 216–24. http://dx.doi.org/10.1016/j.ast.2012.09.001.

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48

Hazra, B., A. Sadhu, and S. Narasimhan. "Fault detection of gearboxes using synchro-squeezing transform." Journal of Vibration and Control 23, no. 19 (January 20, 2016): 3108–27. http://dx.doi.org/10.1177/1077546315627242.

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This paper presents a novel fault detection method for gearbox vibration signatures using the synchro-squeezing transform (SST). Premised upon the concept of time-frequency (TF) reassignment, the SST provides a sharp representation of signals in the TF plane compared to many popular TF methods. Additionally, it can also extract the individual components, called intrinsic mode functions or IMFs, of a nonstationary multi-component signal, akin to empirical mode decomposition. The rich mathematical structure based on the continuous wavelet transform makes synchro-squeezing a promising candidate for gearbox diagnosis, as such signals are frequently constituted out of multiple amplitude and frequency modulated signals embedded in noise. This work utilizes the decomposing power of the SST to extract the IMFs from gearbox signals, followed by the application of both condition indicators and fault detection to gearbox vibration data. For robust detection of faults in gear-motors, a fault detection technique based on time-varying auto-regressive coefficients of IMFs as features is utilized. The sequential Karhunen–Loeve transform is employed on the condition indicators to select the appropriate window sizes on which the SST can be applied. This approach promises improved fault detection capability compared to applying condition indicators directly to the raw data. Laboratory experimental data obtained from a drivetrain diagnostics simulator and seeded fault tests from a helicopter gearbox provide test beds to demonstrate the robustness of the proposed algorithm.
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49

Hellinger, Walter C., Saiyid A. Hasan, Laura P. Bacalis, Deborah M. Thornblom, Susan C. Beckmann, Carina Blackmore, Terri S. Forster, et al. "Outbreak of Toxic Anterior Segment Syndrome Following Cataract Surgery Associated With Impurities in Autoclave Steam Moisture." Infection Control & Hospital Epidemiology 27, no. 3 (March 2006): 294–98. http://dx.doi.org/10.1086/501540.

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Background.Toxic anterior segment syndrome (TASS), a complication of cataract surgery, is a sterile inflammation of the anterior chamber of the eye. An outbreak of TASS was recognized at an outpatient surgical center and its affiliated hospital in December 2002.Methods.Medical records of patients who underwent cataract surgery during the outbreak were reviewed, and surgical team members who participated in the operations were interviewed. Potential causes of TASS were identified and eliminated. Feedwater from autoclave steam generators and steam condensates were analyzed by use of spectroscopy and ion chromatography.Results.During the outbreak, 8 (38%) of 21 cataract operations were complicated by TASS, compared with 2 (0.07%) of 2,713 operations performed from January 1996 through November 2002. Results of an initial investigation suggested that cataract surgical equipment may have been contaminated by suboptimal equipment reprocessing or as a result of personnel changes. The frequency of TASS decreased (1 of 44 cataract operations) after reassignment of personnel and revision of equipment reprocessing procedures. Further investigation identified the presence of impurities (eg, sulfates, copper, zinc, nickel, and silica) in autoclave steam moisture, which was attributed to improper maintenance of the autoclave steam generator in the outpatient surgical center. When impurities in autoclave steam moisture were eliminated, no cases of TASS were observed after more than 1,000 cataract operations.Conclusion.Suboptimal reprocessing of cataract surgical equipment may evolve over time in busy, multidisciplinary surgical centers. Clinically significant contamination of surgical equipment may result from inappropriate maintenance of steam sterilization systems. Standardization of protocols for reprocessing of cataract surgical equipment may prevent outbreaks of TASS and may be of assistance during outbreak investigations.
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McLarnon, Michael, Jane Thornton, Gail Knudson, Nigel Jones, Danny Glover, Andrew Murray, Michael Cummings, and Neil Heron. "A Scoping Review of Transgender Policies in the 15 Most Commonly Played UK Professional Sports." International Journal of Environmental Research and Public Health 20, no. 4 (February 17, 2023): 3568. http://dx.doi.org/10.3390/ijerph20043568.

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Introduction: There has been much debate recently on the participation of transgender and gender-diverse (TGD) athletes in sport, particularly in relation to fairness, safety and inclusion. The 2021 IOC Framework on Fairness, Inclusion and Non-discrimination acknowledges the central role that eligibility criteria play in ensuring fairness, particularly in the female category, and states that athletes should not be excluded solely on the basis of their TGD identity. Aims: To identify policies that address TGD athlete participation in the 15 major United Kingdom (UK) sporting organisations and to summarise the evidence for each of these policies. Methods: A scoping review of TGD policies from the 15 major UK sporting organisations. Results: Eleven of the governing bodies had publicly available TGD policies. Most of the sporting associations drew guidance from the official 2015 IOC Consensus Meeting on Sex Reassignment and Hyperandrogenism, particularly with regard to physiological testosterone levels. Many organisations referenced their policies as a guide for decision making but stated that they ultimately made case-by-case decisions on an athlete’s eligibility. Relevant considerations not addressed in most policies included pre- versus post-pubertal athletes, justification for testosterone thresholds, the length of time out of competitive action (if any) for transitioning athletes, the irreversible advantage from male puberty (if any), the responsibility for and frequency of follow up for hormonal testing and the consequences for athletes outside set testosterone limits. Conclusions: There is a lack of consensus among the top 15 UK sporting organizations relating to elite sport participation for TGD athletes. It would be useful for sport organizations to work together to develop greater standardization/consensus for TGD athlete policies, taking into consideration fairness, safety and inclusion in each sport.
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