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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 (May 13, 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 entropy), instantaneous frequency (IF) estimation accuracy, cross-term presence in the TFRs, and the computational cost of the TFRs. This study gives valuable insight into the advantages and limitations of the analyzed TFRs and assists in selecting the proper distribution when analyzing given non-stationary signals in the time–frequency domain.
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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 (October 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 (November 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 and ongoing developments are providing several alternative schemes that allow for a more considered choice of TFR. This paper reviews these contemporary approaches in comparison with the more classical ones and with reference to their applicability, merits and shortcomings for application to sound analysis. (Where they have been successfully applied, details are provided.) The techniques reviewed include linear, bilinear and higher-order spectra, nonparametric and parametric methods and some sound-model-specific TFRs.
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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 (April 30, 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 experimental test case relates the use of MBN signals to characterize hardness gradients in a AISI4140 steel. To that purpose different time—frequency (TFR) and time-scale (TSR) representations such as the spectrogram, the Wigner-Ville distribution, the Capongram, the ARgram obtained from an AutoRegressive model, the scalogram, and the Mellingram obtained from a Mellin transform are assessed. It is shown that, due to nonstationary characteristics of the MBN, TFRs can provide a rich and new panorama of these signals. Extraction techniques of some time—frequency parameters are used to allow a diagnostic process. Comparison with results obtained by the classical method highlights the improvement on the diagnosis provided by the method proposed.
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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. To further investigate the related neuronal processes, a novel approach was proposed including three steps: (1) extract the temporal and spatial components of interest simultaneously by temporal principal component analysis (PCA) and Promax rotation and project them to the electrode fields for correcting their variance and polarity indeterminacies, (2) calculate the time-frequency representations (TFRs) of the back-projected components, and (3) compute the regions of evoked EROs of interest on TFRs objectively using the edge detection algorithm. We performed this novel approach, conventional TFA, and TFA-PCA to analyse both the synthetic datasets with different levels of SNR and an actual ERP dataset in a two-factor paradigm of waiting time (short/long) and feedback (loss/gain) separately. Synthetic datasets results indicated that N2-theta and P3-delta oscillations can be stably detected from different SNR-simulated datasets using the proposed approach, but, by comparison, only one oscillation was obtained via the last two approaches. Furthermore, regarding the actual dataset, the statistical results for the proposed approach revealed that P3-delta was sensitive to the waiting time but not for that of the other approaches. This study manifested that the proposed approach could objectively extract evoked EROs of interest, which allows a better understanding of the modulations of the oscillatory responses.
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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 (February 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 parameter values based on identification target, instantaneous frequency, or average damping ratio. This reduces the computational cost of a reliable CWT analysis when compared with currently employed iterative methodologies based on minimal Shannon entropy criteria.
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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 (July 6, 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 combines the separate strengths of the short-time Fourier transform and wavelet transforms, is chosen to perform the time–frequency analysis of vibration signals from gear. Then, the MPS scheme is applied to extract the discriminative features from the TFR. The promise of MPS is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experiment results demonstrate the MPS to be a satisfactory scheme for characterizing TFRs for an accurate classification of gear faults.
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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 (September 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 aortic, the tricuspid, the mitral and the pulmonic areas are considered for the acquisition task. The Smoothed-Pseudo Wigner–Ville Distribution (SPWVD) yield adequate Time-Frequency Representations (TFRs) of such non-stationary signal as heart sounds. Moreover, by taking into account the corresponding auscultation area for each obtained TFR, we adopt exclusion reasoning to attribute each burst to its origins within the myocardium. Furthermore, the alternating functioning of heart valves and cavities in systole and diastole was characterized in the time and frequency domains. Aortic stenosis heart sounds were involved in our study in a view to confirm their pathological nature towards the normal heart sounds findings. Indeed, the weakened S1 and S2 heart sounds and the strong systolic ejection murmur which dominates the overall systole, confirm our hypotheses. Thus, modulations laws relating to the systolic ejection of blood through the stenosed orifice were characterized by means of the reliable SPWVD approach. A third heart sound (S3) which is an indicator of the presence of systolic dysfunction and the elevated filling pressure for aortic stenosis lesion was also characterized.
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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 attempt to construct a TF representation specifically tailored to clinical percussion signals to achieve better resolution of individual components corresponding to physical oscillation modes. Matrix Pencil Method (MPM) is used to decompose the signal into a set of exponentially damped sinusoids, which are then plotted in the time-frequency plane. Such representation provides better visualization of the signal structure than the commonly used frequency-amplitude plots and facilitates tracking subtle changes in the signal for diagnostic purposes. The performance of our approach has been verified on both ideal and real percussion signals. The MPM-based time-frequency analysis appears to be a better choice for clinical percussion signals than conventional TFDs, while its ability to visualize damping has immediate practical applications.
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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 (November 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 measurable. Yet the standard TFR to-date is the short-time Fourier transform (STFT), of which the shortcomings, in terms of resolution, are well recognised. The purpose of this paper is to demonstrate the importance of the TFR to system function and system design. Poor feature extraction is shown to result from the use of inappropriate TFRs, whose underlying assumptions and expectations do not match those of the system. Existing models are used as case studies, with examples of performance for different sound types. A philosophy for A–R system design that includes TFR design is presented and a methodology for implementing it is proposed.
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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) is applied to effectively map the raw signals to the time frequency domain. Considering the high load and complexity of model computation, bilinear interpolation and DCT algorithm are introduced to convert TFRs into low-dimensional DCT spectrum coding matrix with strong sparsity. Furthermore, the TACNN model is proposed which is capable of learning discriminative features for temporal adjacent DCT spectrum coding matrix. Effectiveness of the proposed method is verified on the PRONOSTIA dataset, and experiment results show that the proposed model is able to realize automatic high-precision estimation of bearings RUL with high efficiency.
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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 (September 3, 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 obtain accurate TFIs from the vibration signals of rolling bearings. Traditional time–frequency analysis methods have disadvantages such as low resolution and cross-term interference. Therefore, the TFIs obtained cannot satisfactorily express the time–frequency characteristics of bearing vibration signals. To solve this problem, a sparse time–frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) was developed in this paper. Unlike traditional time–frequency analysis methods, the time–frequency analysis model of the STFA-PD method is based on the theory of sparse representation, and is solved using the first-order primal-dual algorithm. For employing the sparse constraint in the frequency domain, the STFA-PD obtains a higher time–frequency resolution and is free from cross-term interference, as the model is based on a linear time–frequency analysis method. The gray level co-occurrence matrix is then employed to extract texture features from the sparse TFIs as input features for classifiers. Vibration signals of rolling bearings with different fault severity degrees are used to validate the proposed approach. The experimental results show that the developed STFA-PD outperforms traditional time–frequency analysis methods in terms of the accuracy and effectiveness for the fault severity monitoring of rolling bearings.
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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 (June 1, 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 CNN models are evaluated using the EMG data from 10 intact and 11 amputee subjects through the publicly access NinaPro database. Our results show that CNN classifier offered the best mean classification accuracy of 88.04% in recognizing hand and wrist movements.
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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 distributions (TFDs) which are short times Fourier transform (STFT) and spectrogram. From time-frequency representation (TFR) obtained by using the TFDs, parameters of the faults signal are estimated such as instantaneous of average, root mean square (RMS), fundamental, Total Waveform Distortion (TWD), Total Harmonics Distortion (THD) and Total non-Harmonic Distortion (TnHD) of current signals. Then, based on the characteristics of the faults calculated from the signal parameters, VSI switches faults can be detected and identified. The performance of TFD for the faults analysis is also demonstrated to obtain the best TFD for switches faults detection and identification system. The results show that, STFT is the best technique to classify and identify VSI switches faults and can be implemented for automated system.
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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-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.
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Seninete, Sara, Mansour Abed, Azeddine Bendiabdellah, Malika Mimi, Adel Belouchrani, Abdelaziz Ould Ali, and Bilal Djamal Eddine Cherif. "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 (August 31, 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' detection in two-level inverter feeding induction motors, in particular open-circuit Insulated Gate Bipolar Transistor (IGBT) faults. For this purpose, we propose the use of a recent high-resolution TFD, referred as PCBD for Polynomial Cheriet-Belouchrani Distribution. The latter is adjusted using only a single integer that is automatically optimized using the Stankovic concentration measure, otherwise, no external windows are needed to perform the highest time-frequency resolution. The performance of the PCBD is compared to the best-known quadratic representations using a test bench. Experimental results show that the frequency components characterizing open-circuit faults are best detected using the PCBD thanks to its ability to suppress interferences while maintaining the signal's proper terms.
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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 (January 1996): 46. http://dx.doi.org/10.1109/10.477700.
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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 monitoring system was employed using MATLAB. From a time-frequency representation (TFR) results based on STFT and GT, parameters such as instantaneous RMS voltage, VRMS(t) and instantaneous total waveform distortion, VTWD(t) had been extracted. In finding the best technique for power quality, the TFDs had been compared in terms of accuracy, memory and computational complexity of the analysis. Based on the performance analysis conducted, GT was able to compute with high accuracy with 94% averagely as well as low memory size by 6% compared to STFT. Hence, GT is considered as the best TFD, and recommended for low cost PQ monitoring system.
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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 (May 2008): 1496–503. http://dx.doi.org/10.1109/tbme.2008.918556.
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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 is especially problematic, such that TFRs that are more robust to contaminants are expected to provide more useful representations. In addition, the minutiae of the techniques themselves impart better or worse time and frequency resolutions, which also influence the usefulness of the TFRs. Here, we introduce a methodology to evaluate the “quality” of TFRs of neural signals by quantifying how much information they retain about the experimental condition during visual stimulation and recognition tasks, in mice and humans, respectively. We used machine learning to discriminate between various experimental conditions based on TFRs computed with different methods. We found that various methods provide more or less informative TFRs depending on the characteristics of the data. In general, however, more advanced techniques, such as the superlet transform, seem to provide better results for complex time-frequency landscapes, such as those extracted from electroencephalography signals. Finally, we introduce a method based on feature perturbation that is able to quantify how much time-frequency components contribute to the correct discrimination among experimental conditions. The methodology introduced in the present study may be extended to other analyses of neural data, enabling the discovery of data features that are modulated by the experimental manipulation.
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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. A register of length 32-bit is considered for the input signal representation. The stored register values are applied to the proposed TFDs and computed using real-time hardware. FBRA is implemented during the computation of FFT in all TFDs. All the transforms are modeled using Verilog code and implemented on SPARTA-6 FPGA. A real-time ECG, earthquake and a quad chirp signals are taken as input to test the designed TFDs. Finally, a comparison of different hardware resources utilized on FPGA with earlier conventional methods for better real-time applications was made.
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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 termed the proportion-extracting synchrosqueezing chirplet transform (PESCT). Firstly, the proportion-extracting chirplet transform (PECT) is employed to generate high-resolution underlying TFRs. Then, the energy concentration of the underlying TFRs is enhanced via the synchrosqueezing transform. Finally, wind turbine planetary gearbox fault can be diagnosed by analysis of the dominant time-varying components revealed by the concentrated TFRs with high resolution. The proposed PESCT is suitable for achieving high-quality TFRs for complicated nonstationary signals. Numerical and experimental analyses validate the effectiveness of the PESCT in characterizing the nonstationary signals from wind turbine planetary gearboxes.
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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 experience within each group separately and (3) probed for its potential (a)symmetry between brain and behavioral data. We found no differences at the behavioral and neural levels for the between-groups comparisons. However, oscillatory dynamics (mainly theta increase and alpha suppression) of inhibition and cognitive control were found to be modulated by individual differences in bilingual language experience, albeit distinctly within each bilingual group. While the results indicate adaptations toward differential brain recruitment in line with bilingual language experience variation overall, this does not manifest uniformly. Rather, earlier versus later onset to bilingualism—the bilingual type—seems to constitute an independent qualifier to how individual differences play out.
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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 simultaneously demodulate multiple components of interests, by Hadamard products between matrices. A synchronous extractor is also constructed to post-process TFRs of GD-ed signals to further improve the TF aggregation. Unlike the conventional synchronous extraction transform, the synchronous extractor in this paper is directly applied to TF ridges paralleling to time axis without the estimation of instantaneous frequencies (IFs). Then, the post-processed TF ridges are backward demodulated to restore the actual IF. The proposed synchronous fault feature extraction method in the GD framework also allows for the signal reconstruction. Both simulated and experimental signals are applied to validating the effectiveness of the proposed method for rolling bearing fault diagnosis.