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

Author: Grafiati

Published: 6 September 2023

Last updated: 26 July 2025

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1

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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Bhuiyan, Moinuddin, Eugene V. Malyarenko, Mircea A. Pantea, Dante Capaldi, Alfred E. Baylor, and Roman Gr Maev. "Time-Frequency Analysis of Clinical Percussion Signals Using Matrix Pencil Method." Journal of Electrical and Computer Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/274541.

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This paper discusses time-frequency analysis of clinical percussion signals produced by tapping over human chest or abdomen with a neurological hammer and recorded with an air microphone. The analysis of short, highly damped percussion signals using conventional time-frequency distributions (TFDs) meets certain difficulties, such as poor time-frequency localization, cross terms, and masking of the lower energy features by the higher energy ones. The above shortcomings lead to inaccurate and ambiguous representation of the signal behavior in the time-frequency plane. This work describes an atte

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MASRI, PAUL, ANDREW BATEMAN, and NISHAN CANAGARAJAH. "The importance of the time–frequency representation for sound/music analysis–resynthesis." Organised Sound 2, no. 3 (1997): 207–14. http://dx.doi.org/10.1017/s1355771898009054.

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The time–frequency representation (TFR) is the initial stage of analysis in sound/music analysis–resynthesis (A–R) systems. Given a time-domain waveform, the TFR makes temporal and spectral detail available to the remainder of the analysis, so that the component features may be extracted. The resulting ‘feature set’ must represent the sound as completely as the original time-domain signal, if the A–R system is to be capable of effective transformation and good synthesis sound quality. Therefore the system as a whole is reliant upon the TFR to make the sound components detectable, separable and

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Pang, Yu, Limin Jia, and Zhan Liu. "Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings." Shock and Vibration 2020 (June 9, 2020): 1–14. http://dx.doi.org/10.1155/2020/8240168.

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In recent years, several time-frequency representation (TFR) and convolutional neural network- (CNN-) based approaches have been proposed to provide reliable remaining useful life (RUL) estimation for bearings. However, existing methods cannot tackle the spatiotemporal continuity between adjacent TFRs since temporal proposals are considered individually and their temporal dependencies are neglected. In allusion to this problem, a novel prognostic approach based on discrete cosine transformation (DCT) and temporal adjacent convolutional neural network (TACNN) is proposed. Wavelet transform (WT)

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Du, Yan, Yingpin Chen, Guoying Meng, Jun Ding, and Yajing Xiao. "Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images." Applied Sciences 8, no. 9 (2018): 1538. http://dx.doi.org/10.3390/app8091538.

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Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted. Currently, the bearing fault diagnosis method based on time–frequency image (TFI) recognition is attracting increasing attention. This paper contributes to the ongoing investigation by proposing a new approach for the fault severity monitoring of rolling bearings based on the texture feature extraction of sparse TFIs. The first and main step is to

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Yang, Yang, Yongqiang Cheng, Hao Wu, Zheng Yang, and Hongqiang Wang. "Enhanced Micro-Doppler Feature Extraction Using Adaptive Short-Time Kernel-Based Sparse Time-Frequency Distribution." Remote Sensing 16, no. 1 (2023): 146. http://dx.doi.org/10.3390/rs16010146.

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The extraction of the micro-Doppler (m-D) feature based on time-frequency distribution (TFD) is of great significance for target detection and identification. To improve the feature extraction performance, numerous TFDs have been developed, with the majority falling under Cohen’s class. Nevertheless, these TFDs basically face a trade-off between artifact suppression and energy concentration. The main reason is that each Cohen’s class TFD is constructed by applying the two-dimensional Fourier transform to a kerneled ambiguity function directly, while existing kernels generally attenuate artifac

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Jing Wei, Too, Abdul Rahim Bin Abdullah, Norhashimah Binti Mohd Saad, Nursabillilah Binti Mohd Ali, and Tengku Nor Shuhada Binti Tengku Zawawi. "Featureless EMG pattern recognition based on convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (2019): 1291. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1291-1297.

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In this paper, the performance of featureless EMG pattern recognition in classifying hand and wrist movements are presented. The time-frequency distribution (TFD), spectrogram is employed to transform the raw EMG signals into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the images without the need of manual feature extraction. The performance of CNN with different number of convolutional layers is examined. The proposed CN

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Cai, Fulin, Teresa Wu, and Fleming Y. M. Lure. "E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing." Sensors 24, no. 14 (2024): 4620. http://dx.doi.org/10.3390/s24144620.

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Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time–frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper

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Manap, M., Nur Sumayyah Ahmad, Abdul Rahim Abdullah, and Norhazilina Bahari. "Comparison of Open and Short-Circuit Switches Faults Voltage Source Inverter (VSI) Analysis Using Time-Frequency Distributions." Applied Mechanics and Materials 752-753 (April 2015): 1164–69. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1164.

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Voltage source inverter (VSI) plays an important roles in electrical drive systems. Consistently, expose to hash environmental condition, the lifespan of the electronic component such as insulated-gate bipolar transistor (IGBT) may shorten and many faults related to the inverter especially switches can be occur. The present of VSI switches faults causing equipment failure and increased the cost of manufacturing process. Therefore, faults detection analysis is mandatory to identify the VSI switches faults. This paper presents the analysis of VSI switches faults using time-frequency distribution

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19

Suraj, Purnendu Tiwari, Subhojit Ghosh, and Rakesh Kumar Sinha. "Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO BasedK-Means Clustering." Computational Intelligence and Neuroscience 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/945729.

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Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO basedK-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO basedK-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) basedK-mean

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Seninete, Sara, Mansour Abed, Azeddine Bendiabdellah, et al. "On the Use of High-resolution Time-frequency Distribution Based on a Polynomial Compact Support Kernel for Fault Detection in a Two-level Inverter." Periodica Polytechnica Electrical Engineering and Computer Science 64, no. 4 (2020): 352–65. http://dx.doi.org/10.3311/ppee.15469.

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Quadratic Time-Frequency Distributions (TFDs) become a standard tool in many fields producing nonstationary signatures. However, these representations suffer from two drawbacks: First, bad time-frequency localization of the signal's autoterms due to the unavoidable crossterms generated by the bilinear form of these distributions. This results on bad estimation of the Instantaneous Frequency (IF) laws and decreases, in our case, the ability to precisely decide the existence of a motor fault. Secondly, the TFD's parameterization is not always straightforward. This paper deals with faults' detect

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Pola, S., A. Macerata, M. Emdin, and C. Marchesi. "Estimation of the power spectral density in nonstationary cardiovascular time series: assessing the role of the time-frequency representations (TFR)." IEEE Transactions on Biomedical Engineering 43, no. 1 (1996): 46. http://dx.doi.org/10.1109/10.477700.

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Terrien, J., C. Marque, and G. Germain. "Ridge Extraction From the Time–frequency Representation (TFR) of Signals Based on an Image Processing Approach: Application to the Analysis of Uterine Electromyogram AR TFR." IEEE Transactions on Biomedical Engineering 55, no. 5 (2008): 1496–503. http://dx.doi.org/10.1109/tbme.2008.918556.

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Mohamad Basir, Muhammad Sufyan Safwan. "Window Optimisation of Power Quality Signal Detection using Gabor Transform." ASM Science Journal 14 (April 2, 2021): 1–10. http://dx.doi.org/10.32802/asmscj.2020.596.

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This paper presents power quality analysis on different signal characteristics, namely instantaneous sag, momentary sag, temporary sag, instantaneous swell, momentary swell, and temporary swell. Power quality signals were analyzed using linear time-frequency distribution (TFD) namely short-time Fourier transform (STFT) and proposed Gabor transform (GT), and the best technique for power quality detection was determined based on the performance analysis of varied window length. Optimum window length for different signal characteristics which are effective and reliable for developing real-time mo

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Zhang, Dahai, Yiming Wang, Yongjian Jiang, et al. "A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis." Energies 17, no. 4 (2024): 819. http://dx.doi.org/10.3390/en17040819.

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Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of

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Bârzan, Harald, Ana-Maria Ichim, Vasile Vlad Moca, and Raul Cristian Mureşan. "Time-Frequency Representations of Brain Oscillations: Which One Is Better?" Frontiers in Neuroinformatics 16 (April 14, 2022). http://dx.doi.org/10.3389/fninf.2022.871904.

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Brain oscillations are thought to subserve important functions by organizing the dynamical landscape of neural circuits. The expression of such oscillations in neural signals is usually evaluated using time-frequency representations (TFR), which resolve oscillatory processes in both time and frequency. While a vast number of methods exist to compute TFRs, there is often no objective criterion to decide which one is better. In feature-rich data, such as that recorded from the brain, sources of noise and unrelated processes abound and contaminate results. The impact of these distractor sources i

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Krishna, B. Murali, B. T. Krishna, and K. Babulu. "Design and Implementation of Time-Frequency Distributions for Real-Time Applications Using Field Programmable Gate Array." Journal of Circuits, Systems and Computers, May 9, 2022. http://dx.doi.org/10.1142/s0218126622502176.

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In this paper, time-frequency distributions (TFDs) and their hardware implementation on FPGA are presented. TFDs are evolved due to disadvantage of Fourier Transform (FT), which cannot provide time information in spectrum representation. Time-Frequency Representations (TFRs) are helpful in providing simultaneous information about spectral contents of a signal with respect to time period axis. The major problem associated with hardware implementation of TFDs is limited on-board memory. Forward and backward register allocation method (FBRA) is employed to obtain the optimum register occupation.

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Zhang, Dong, and Zhipeng Feng. "Wind Turbine Planetary Gearbox Fault Diagnosis via Proportion-Extracting Synchrosqueezing Chirplet Transform." Journal of Dynamics, Monitoring and Diagnostics, July 12, 2023. http://dx.doi.org/10.37965/jdmd.2023.151.

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Wind turbine planetary gearboxes usually work under time-varying conditions, leading to nonstationary vibration signals. These signals often consist of multiple time-varying components with close instantaneous frequencies. Therefore, high-quality time-frequency analysis (TFA) is needed to extract the time-frequency feature from such nonstationary signals for fault diagnosis. However, it is difficult to obtain high-quality time-frequency representations (TFRs) through conventional TFA methods due to low resolution and time-frequency blurs. To address this issue, we propose a new TFA method term

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V, Srimadumathi, and MachiReddy Ramasubba Reddy. "Classification of Motor Imagery EEG signals using high resolution Time-Frequency Representations and Convolutional Neural network." Biomedical Physics & Engineering Express, March 21, 2024. http://dx.doi.org/10.1088/2057-1976/ad3647.

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Abstract A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG si

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Klein, Renata. "A Method for Anomaly Detection for Non-stationary Vibration Signatures." Annual Conference of the PHM Society 5, no. 1 (2013). http://dx.doi.org/10.36001/phmconf.2013.v5i1.2250.

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   Vibration signatures contain information regarding the health status of the machine components. One approach to assess the health of the components is to search systematically for a list of specific failure patterns, based on the physical specifications of the known components (e.g. the physical specifications of the bearings, the gearwheels or the shafts). It is possible to do so, since the manifestation of the possible failures in the vibration signature is known a priory. The problem is that such a list is not comprehensive, and may not cover all possible failures. The

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Pereira Soares, Sergio Miguel, Yanina Prystauka, Vincent DeLuca, and Jason Rothman. "Type of bilingualism conditions individual differences in the oscillatory dynamics of inhibitory control." Frontiers in Human Neuroscience 16 (July 28, 2022). http://dx.doi.org/10.3389/fnhum.2022.910910.

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The present study uses EEG time-frequency representations (TFRs) with a Flanker task to investigate if and how individual differences in bilingual language experience modulate neurocognitive outcomes (oscillatory dynamics) in two bilingual group types: late bilinguals (L2 learners) and early bilinguals (heritage speakers—HSs). TFRs were computed for both incongruent and congruent trials. The difference between the two (Flanker effect vis-à-vis cognitive interference) was then (1) compared between the HSs and the L2 learners, (2) modeled as a function of individual differences with bilingual ex

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Wu, Hengshan, Shaodan Zhi, Qiqiang Fang, et al. "Synchronous Decomposition Match-Reassigning Transform and Its Application in Planetary Gearbox Fault Diagnosis." Measurement Science and Technology, October 8, 2024. http://dx.doi.org/10.1088/1361-6501/ad846a.

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Abstract Under time-varying operating conditions, the instantaneous frequency (IF) of the vibration signal of the planetary gearbox exhibits non-stationary time-varying closely spaced characteristics as well as non-proportional and non-synchronous characteristics, making it difficult for traditional time-frequency analysis (TFA) methods to accurately identify its fault features and obtain accurate time-frequency representations (TFR). To address this challenge, this paper proposes a TFA method based on Synchronous Decomposition Match-Reassigning Transform (SDMRT). Firstly, the successive varia

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Anjana, Goen, and C. Tiwari D. "Pattern Recognition Based Prosthesis Control for Movement of Forearms Using Surface and Intramuscular EMG Signals." November 2, 2015. https://doi.org/10.5281/zenodo.1110922.

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Myoelectric control system is the fundamental component of modern prostheses, which uses the myoelectric signals from an individual’s muscles to control the prosthesis movements. The surface electromyogram signal (sEMG) being noninvasive has been used as an input to prostheses controllers for many years. Recent technological advances has led to the development of implantable myoelectric sensors which enable the internal myoelectric signal (MES) to be used as input to these prostheses controllers. The intramuscular measurement can provide focal recordings from deep muscles of the forearm

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Xin, Zhang, Shao Jie, An Wenwei, Yang Tiantian, and Reza Malekian. "An Improved Time-Frequency Representation Based on Nonlinear Mode Decomposition and Adaptive Optimal Kernel." Elektronika ir Elektrotechnika 22, no. 4 (2017). https://doi.org/10.5755/j01.eie.22.4.15918.

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Time-frequency representation (TFR) based on Adaptive Optimal Kernel (AOK) normally performs well only for monocomponent signals and has poor noise robustness. To overcome the shortcomings of AOK TFR mentioned above, a new TFR algorithm is proposed here by integrating nonlinear mode decomposition (NMD) with AOK TFR. NMD is used to decompose multicomponent signals into a bundle of meaningful oscillations and then AOK is applied to compute the TFR of individual oscillations, finally all these TFRs are summed together to generate one TFR. Through quantitative comparison with other TFR methods to

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Xu, Chao, Congru Lin, Jiawei Peng, et al. "On-the-fly simulation of time-resolved fluorescence spectra and anisotropy." Journal of Chemical Physics 160, no. 10 (2024). http://dx.doi.org/10.1063/5.0201204.

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We combine on-the-fly trajectory surface hopping simulations and the doorway–window representation of nonlinear optical response functions to create an efficient protocol for the evaluation of time- and frequency-resolved fluorescence (TFRF) spectra and anisotropies of the realistic polyatomic systems. This approach gives the effective description of the proper (e.g., experimental) pulse envelopes, laser field polarizations, and the proper orientational averaging of TFRF signals directly from the well-established on-the-fly nonadiabatic dynamic simulations without extra computational cost. To

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liu, kangning, Juanjuan Shi, Changqing Shen, Weiguo Huang, and Zhongkui Zhu. "Synchronous fault feature extraction for rolling bearings in a generalized demodulation framework." Measurement Science and Technology, May 5, 2023. http://dx.doi.org/10.1088/1361-6501/acd2f5.

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Abstract Generalized demodulation (GD) has the potential of processing non-stationary vibration signals of rolling bearing under time-varying speed conditions as it can demodulate a signal with a curved time-frequency (TF) ridge into the signal with the TF ridge paralleling to time axis with an improved time frequency representation (TFR) energy concentration level. However, current GD methods require iteration operations and cannot simultaneously deal with vibrations with multiple components from rolling bearings. This paper proposes a method based on the GD framework, which can simultaneousl

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Bing, Pinging, Wei Liu, Zhixing Zhai, et al. "A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme." Frontiers in Cardiovascular Medicine 11 (April 4, 2024). http://dx.doi.org/10.3389/fcvm.2024.1277123.

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BackgroundElectrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis.MethodsIn this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM). In the method, the ST maps an ECG signal into a subspace in the time frequency dom

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