Relevant bibliographies by topics / Time-frequency Representations (TFRs)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Time-frequency Representations (TFRs)'

Author: Grafiati
Published: 6 September 2023
Last updated: 26 July 2025

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Journal articles on the topic "Time-frequency Representations (TFRs)"

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Bačnar, David, Nicoletta Saulig, Irena Petrijevčanin Vuksanović, and Jonatan Lerga. "Entropy-Based Concentration and Instantaneous Frequency of TFDs from Cohen’s, Affine, and Reassigned Classes." Sensors 22, no. 10 (2022): 3727. http://dx.doi.org/10.3390/s22103727.

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This paper explores three groups of time–frequency distributions: the Cohen’s, affine, and reassigned classes of time–frequency representations (TFRs). This study provides detailed insight into the theory behind the selected TFRs belonging to these classes. Extensive numerical simulations were performed with examples that illustrate the behavior of the analyzed TFR classes in the joint time–frequency domain. The methods were applied both on synthetic and real-life non-stationary signals. The obtained results were assessed with respect to time–frequency concentration (measured by the Rényi entr
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Pan, M. Ch, P. Sas, and H. Van Brussel. "Machine Condition Monitoring Using Signal Classification Techniques." Journal of Vibration and Control 9, no. 10 (2003): 1103–20. http://dx.doi.org/10.1177/107754603030683.

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Two signal classification approaches, based on Wigner-Ville distribution and extended symmetric Itakura distance, are proposed to post-process the time-frequency representations (TFRs) of vibration signatures, with the final aim to arrive at an automated procedure of machine condition monitoring. Three synthetical signals are used to evaluate and compare the classification performance of these techniques. Some related computation issues, such as characters of different TFRs and weighted window length, are discussed. Experimental case studies, joint fault diagnosis, are realized.
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MASRI, PAUL, ANDREW BATEMAN, and NISHAN CANAGARAJAH. "A review of time–frequency representations, with application to sound/music analysis–resynthesis." Organised Sound 2, no. 3 (1997): 193–205. http://dx.doi.org/10.1017/s1355771898009042.

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Analysis–resynthesis (A–R) systems gain their flexibility for creative transformation of sound by representing sound as a set of musically useful features. The analysis process extracts these features from the time domain signal by means of a time–frequency representation (TFR). The TFR provides an intermediate representation of sound that must make the features accessible and measurable to the rest of the analysis. Until very recently, the short-time Fourier transform (STFT) has been the obvious choice for time–frequency representation, despite its limitations in terms of resolution. Recent a
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Padovese, L. R., N. Martin, and F. Millioz. "Time—frequency and time-scale analysis of Barkhausen noise signals." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 223, no. 5 (2009): 577–88. http://dx.doi.org/10.1243/09544100jaero436.

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Carrying out information about the microstructure and stress behaviour of ferromagnetic steels, magnetic Barkhausen noise (MBN) has been used as a basis for effective non-destructive testing methods, opening new areas in industrial applications. One of the factors that determines the quality and reliability of the MBN analysis is the way information is extracted from the signal. Commonly, simple scalar parameters are used to characterize the information content, such as amplitude maxima and signal root mean square. This paper presents a new approach based on the time—frequency analysis. The ex
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Zhang, Guanghui, Xueyan Li, and Fengyu Cong. "Objective Extraction of Evoked Event-Related Oscillation from Time-Frequency Representation of Event-Related Potentials." Neural Plasticity 2020 (December 19, 2020): 1–20. http://dx.doi.org/10.1155/2020/8841354.

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Evoked event-related oscillations (EROs) have been widely used to explore the mechanisms of brain activities for both normal people and neuropsychiatric disease patients. In most previous studies, the calculation of the regions of evoked EROs of interest is commonly based on a predefined time window and a frequency range given by the experimenter, which tends to be subjective. Additionally, evoked EROs sometimes cannot be fully extracted using the conventional time-frequency analysis (TFA) because they may be overlapped with each other or with artifacts in time, frequency, and space domains. T
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Gaviria, Carlos A., and Luis A. Montejo. "Optimal Wavelet Parameters for System Identification of Civil Engineering Structures." Earthquake Spectra 34, no. 1 (2018): 197–216. http://dx.doi.org/10.1193/092016eqs154m.

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Damage-induced changes in structure dynamic properties are commonly tracked with time-frequency representations (TFRs). One of the most widely accepted tools for determining a TFR is the continuous wavelet transform (CWT). The success of CWT analysis is highly dependent on selecting the most appropriate values for the parameters that define the mother wavelet. This article presents a detailed analytical and numerical study to select optimal wavelet parameters using the complex Morlet wavelet (CMOR) and the Gabor wavelet. The results obtained suggest that it is possible to define optimal parame
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Li, B., P.-L. Zhang, Z.-J. Wang, S.-S. Mi, and D.-S. Liu. "Application of S transform and morphological pattern spectrum for gear fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 12 (2011): 2963–72. http://dx.doi.org/10.1177/0954406211408781.

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Time–frequency representations (TFR) have been intensively employed for analysing vibration signals in gear fault diagnosis. However, in many applications, TFR are simply utilized as a visual aid to detect gear defects. An attractive issue is to utilize the TFR for automatic classification of faults. A key step for this study is to extract discriminative features from TFR as input feature vector for classifiers. This article contributes to this ongoing investigation by applying morphological pattern spectrum (MPS) to characterize the TFR for gear fault diagnosis. The S transform, which combine
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Bagherzadeh, Seyed Amin, Hamed Mohammadkarimi, and Mohammad Hossein Alizadeh. "Enhanced Synchrosqueezing Transform for Detecting Non-Traditional Flight Modes in High Angle of Attack Maneuvers." Mathematical and Computational Applications 30, no. 2 (2025): 41. https://doi.org/10.3390/mca30020041.

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Due to nonlinear aerodynamics, “non-traditional” flight modes may appear in longitudinal and lateral/directional dynamics once an aircraft experiences a high angle of attack and rapid maneuvers. Signal decomposition techniques are required to uncover these modes since they are hidden in flight characteristics. This study represents the Enhanced SynchroSqueezing Transform (ESST) for the extraction of “non-traditional” flight modes from flight data. Developed in the framework of the SynchroSqueezing Transform (SST), the ESST decomposes an Amplitude- and Frequency-Modulated (AMFM) signal into Int
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Powell, Stephanie J., Srishti Nayak, and Cyrille L. Magne. "Examining the Neural Markers of Speech Rhythm in Silent Reading Using Mass Univariate Statistics of EEG Single Trials." Brain Sciences 14, no. 11 (2024): 1142. http://dx.doi.org/10.3390/brainsci14111142.

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Background/Objectives: The Implicit Prosody Hypothesis (IPH) posits that individuals generate internal prosodic representations during silent reading, mirroring those produced in spoken language. While converging behavioral evidence supports the IPH, the underlying neurocognitive mechanisms remain largely unknown. Therefore, this study investigated the neurophysiological markers of sensitivity to speech rhythm cues during silent word reading. Methods: EEGs were recorded while participants silently read four-word sequences, each composed of either trochaic words (stressed on the first syllable)
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DJEBBARI, ABDELGHANI, and F. BEREKSI-REGUIG. "SMOOTHED-PSEUDO WIGNER–VILLE DISTRIBUTION OF NORMAL AND AORTIC STENOSIS HEART SOUNDS." Journal of Mechanics in Medicine and Biology 05, no. 03 (2005): 415–28. http://dx.doi.org/10.1142/s0219519405001552.

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In this paper, we are interested in the acquisition and the time-frequency analysis of the Phonocardiogram (PCG) signal. The interactive software "PCG Recorder" we implemented in MATLAB, drives the sound card of a personal computer for acquisition purposes. Normal and abnormal heart sounds were acquired with 16 bits resolution and at high sampling frequencies; the value 2 kHz was selected as sampling rate to avoid spectral aliasing. For each patient, additional information like the age, the gender, the weight as well as the auscultation area can be introduced within the saved data file. The ao
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Dissertations / Theses on the topic "Time-frequency Representations (TFRs)"

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Pelluri, Sai Gunaranjan. "Joint Spectro-Temporal Analysis of Moving Acoustic Sources." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4279.

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Signals generated by fast moving acoustic sources are both challenging for analysis as well as rich in information. The motion is conceptually relative between the source and receiver i.e., either one of them is moving or both are moving. Thus, the receiver would gather information about the relative motion as well as the nature of source itself. For example, direction, velocity, acceleration, number of different sources, friend/foe, etc. are all information that can be gathered. All these parameters are inherently embedded in the received signal. Given the rich information content inherent in
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Viswanath, G. "Robustness And Localization In Time-Varying Spectral Estimation." Thesis, 1997. https://etd.iisc.ac.in/handle/2005/1814.

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Viswanath, G. "Robustness And Localization In Time-Varying Spectral Estimation." Thesis, 1997. http://etd.iisc.ernet.in/handle/2005/1814.

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Book chapters on the topic "Time-frequency Representations (TFRs)"

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Gupta, Priti, and Vijay Kumar. "Measuring of Time-Frequency Representation (TFR) Content – Using the Kapur’s Entropies." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14834-7_36.

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Roy, Nabamita Banerjee, and Kesab Bhattacharya. "Application of ST for Time Frequency Representations (TFRs) of Different Electrical Signals." In Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults. CRC Press, 2021. http://dx.doi.org/10.1201/9780367431143-4.

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Marks II, Robert J. "Time-Frequency Representations." In Handbook of Fourier Analysis & Its Applications. Oxford University Press, 2009. http://dx.doi.org/10.1093/oso/9780195335927.003.0014.

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The Fourier transform is not particularly conducive in the illustration of the evolution of frequency with respect to time. A representation of the temporal evolution of the spectral content of a signal is referred to as a time-frequency representation (TFR). The TFR, in essence, attempts to measure the instantaneous spectrum of a dynamic signal at each point in time. Musical scores, in their most fundamental interpretation, are TFR’s. The fundamental frequency of the note is represented by the vertical location of the note on the staff. Time progresses as we read notes from left to right. The
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Conference papers on the topic "Time-frequency Representations (TFRs)"

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Pan, Min-chun. "Mechanical Noise Identification Using Time-Frequency Representations." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21005.

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Abstract Three computation schemes of time-frequency representations (TFRs) have been developed and implemented to identify different components of mechanical noise originated from the transmission system of electrical vehicles. This study explores the close relationships between three TFRs, i.e. the spectrogram based on windowed Fourier transform (WFT), the Wigner-Ville distribution (WVD), and the smoothed WVD (SWVD). One main purpose is to pursue the efficiency of computing the SWVD of a dynamic signature. The revised scheme can tremendously reduce the computation time to a scale of around 1
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Liu, Zhiliang, Yaqiang Jin, and Ming J. Zuo. "Time-Frequency Representation Based on Robust Local Mean Decomposition." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-65184.

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Fourier transform based frequency representation makes an underlying assumption of stationarity and linearity for the target signal whose spectrum is to be computed, and thus it is unable to track time varying characteristics of non-stationary signals that also widely exist in the physical world. Time-frequency representation (TFR) is a technique to reveal useful information included in the signals, and thus the TFR methods are very attractive to the scientific and engineering world. Local mean decomposition (LMD) is a TFR technique used in many fields, e.g. machinery fault diagnosis. Similar
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Liu, Libin, and Ming J. Zuo. "Copula-Based Time-Frequency Distribution Analysis for Planetary Gearbox Fault Detection." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68060.

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Linear and bilinear time-frequency distributions (TFDs) have been employed in planetary gearbox fault diagnosis. For linear TFDs, there is a trade-off between the time localization and frequency resolution and the spectrogram may not have correct energy marginals. For bilinear TFDs, they cannot be interpreted as an energy distribution because of the existence of possible negative values even though they are designed for energy density representation. To overcome these shortcomings, TFDs based on copula theory have been reported in the literature. In this paper, we analyze two simulated data se
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Huang, Huan, Natalie Baddour, and Ming Liang. "Algorithm for Multiple Time-Frequency Curve Extraction From Time-Frequency Representation of Vibration Signals for Bearing Fault Diagnosis Under Time-Varying Speed Conditions." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67171.

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Bearing fault diagnosis under constant operational condition has been widely investigated. Monitoring the bearing vibration signal in the frequency domain is an effective approach to diagnose a bearing fault since each fault type has a specific Fault Characteristic Frequency (FCF). However, in real applications, bearings are often running under time-varying speed conditions which makes the signal non-stationary and the FCF time-varying. Order tracking is a commonly used method to resample the non-stationary signal to a stationary signal. However, the accuracy of order tracking is affected by m
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Shakir, Huzefa, and Won-Jong Kim. "Discrete-Time Closed-Loop Model Identification of Fixed-Structure Unstable Multivariable Systems." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41834.

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This paper presents improved empirical representations of a general class of open-loop unstable systems using closed-loop system identification. A multi-axis magnetic-levitation (maglev) nanopositioning system with an extended translational travel range is used as a test bed to verify the closed-loop system-identification method proposed in this paper. A closed-loop identification technique employing the Box-Jenkins (BJ) method and a known controller structure is developed for model identification and validation. Direct and coupling transfer functions (TFs) are then derived from the experiment
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Comandur, Vinodhini, Karen Feigh, J. V., and Robert Walters. "Development and Assessment of Flight Lead Cue for Real-time Guidance and Pilot Workload Reduction in Rotorcraft Shipboard Recovery." In Vertical Flight Society 76th Annual Forum & Technology Display. The Vertical Flight Society, 2020. http://dx.doi.org/10.4050/f-0076-2020-16398.

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Helicopter shipboard launch and recovery are some of the most challenging operations to date, owing to the pilot workload associated with the tasks. A variety of environmental conditions such as random deck motion, heavy sea states and unsteady aerodynamic interactions can be attributed to the same. This paper highlights the development and assessment of a visual flight lead cue for real-time guidance and pilot workload reduction. For a chosen approach-turnland maneuver, the pilot workload is assessed using data from pilot-in-the-loop (PIL) flight simulations. Quantitative metrics based on Tim
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Dib, Gerges, Oleksii Karpenko, Jacob Klaser, and Lalita Udpa. "WIRELESS SENSOR NETWORKS FOR ONLINE MONITORING OF HEAVY-DUTY VEHICLE SYSTEMS." In 2024 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium. National Defense Industrial Association, 2024. http://dx.doi.org/10.4271/2024-01-3329.

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<title>ABSTRACT</title> <p>Materials and parts in complex systems, such as ground vehicles, can suffer from fatigue due to use, age and other stresses experienced during service. It is therefore essential to evaluate damage and predict the remaining life, reliability and safety of the vehicle. This paper describes the design of a wireless system for real-time monitoring of ground vehicles using Lamb waves. The proposed approach integrates sensor technology, signal processing and wireless networking into a single solution for online structural health monitoring (SHM). Lamb wav
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