Time Frequency Signal Analysis Research Papers (original) (raw)

2025, 21st European Signal Processing Conference (EUSIPCO 2013)

In this paper, we examine the time-frequency representation (TFR) and sparse reconstruction of non-stationary signals in the presence of missing data samples. These samples lend themselves to missing entries in the instantaneous auto-correlation function (IAF) which, in turn, induce artifacts in the time-frequency distribution and ambiguity function. The artifacts are additive noise-like and, as such, can be mitigated by using proper time-frequency kernels. We show that the sparse signal reconstruction methods applied to the time-lag domain improve the TFR over the direct application of Fourier transform to the IAF. Additionally, the paper demonstrates that the use of signal-adaptive kernels provides superior performance compared to data-independent kernels when missing data are present.

2025, Review of Scientific Instruments

Scalogram is widely used to measure instantaneous frequencies of non-stationary signals. However, the basic property of the scalogram is observed only for stationary sinusoidal functions. A property of the scalogram for non-stationary signal is analytically derived in this paper. Based on the property, a new frequency measurement algorithm is proposed. In addition, a filter that can separate two similar frequency signals is developed based on the wavelet transform.

2025, Jurnal Tribologi

The bearing reliability plays major role in obtaining the desired performance of any machine. A continuous condition monitoring of machine is required in certain applications where failure of machine leads to loss of production, human safety and precision. Machine faults are often linked to the bearing faults. Condition monitoring of machine involves continuous watch on the performance of bearings and predicting the faults of bearing before it cause any adversity. This paper investigates an experimental study to diagnose the fault while bearing is in operation. An acoustic emission technique is used in the experimentation. An algorithm is developed to process various types of signals generated from different bearing defects. The algorithm uses time domain analysis along with combination low frequency analysis technique such as fast Fourier transform and high frequency envelope detection. Two methods have adopted for envelope detection which are Hilbert transform and order analysis. ...

2025

Wavelet transforms introduce new classes of basis functions for time-frequency signal analysis and have inherent properties that are particularly useful for the compression of analogue point on wave transient and disturbance power system signals. This paper examines the compression properties of the discrete wavelet and evaluates the transform using actual power system data. The results presented in the paper indicate that compression ratios up to 10:1 with acceptable distortion are achievable. The paper discusses the application of wavelet compression for rapid information access and exchange in network operation and control.

2025

Protection of Direct Current (DC) microgrids is a challenging task because of large currents within a few milliseconds after the occurrence of a DC fault, which causes serious damage to converters. The present study introduces a protection algorithm based on Traveling Waves (TWs) to detect and locate faults. The proposed method implements the High-order Synchrosqueezing Transform (HOSST) to capture TWs using local measurements. Unlike conventional methods, HOSST can analyze signals in both time and frequency domains by creating a concentrated energy in the time–frequency representation. By extracting impulse features, HOSST detects TWs with high accuracy even in a noisy environment for different types of DC faults with various fault resistances. The proposed method can identify external and internal faults by determining the polarity of TWs, which makes the relays operate properly within their protection zone and prevents mal-operation. To demonstrate the efficiency of the proposed method, it is applied to a DC microgrid. The results show the high accuracy of the presented approach in the fault location with a slight error with selectivity, reliability, sensitivity, and high speed, which is independent of the voltage level and configuration. In addition, the certainty of the proposed method is higher than the previous studies due to the simultaneous use of current and voltage.

2024

The paper is concerned with the analysis of instantaneous values and evaluation of certain power quality indices corresponding to phase currents and voltages acquired from the primary winding of a locomotive transformer. Representative data sets acquired during all operating regimes (acceleration, constant speed, normal and respectively regenerative braking) were analyzed. Firstly the background noise was estimated by using a wavelet thrashing tree considering a wavelet mother with a short filter. Specific computational aspects related to the use of the Stationary Wavelet Transform were presented and it was used to evaluate the instantaneous values of the fundamental harmonic, respectively the distorting residue. Discrete Wavelet Transform was used for 3 situations to deal with time-frequency localization of deviations from stationarity. A nodal analysis was also made with an original implementation of the Wavelet Packet Transform in a special situation, when the deviation from stat...

2024

This paper presents a new three phase approach based on space vector discrete wavelet transform to detect and localize power quality disturbances (PQD). This approach provides high resolution time frequency representation used to detect and localize the disturbances. Supplementary information about detected disturbances (duration and frequency spectrum) extracted in order to characterize them. From the monitored three phase voltage signals a space vector is generated using Clarke Transformation. For normal system voltage the space vector is of constant magnitude signal of 1.5pu. If PQD occurs in any one or all phases of system, results in change of magnitude or frequency or both of the space vector. The space vector is decomposed using Discrete Wavelet Transform (DWT) and the magnitude of detail coefficients is used to detect and localize the PQ disturbances. The proposed technique monitors all three phase voltages simultaneously therefore can offer fast detection than existing sing...

2024, IEEE Transactions on Signal Processing

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2024

The paper deals with the non-destructive testing of structural elements by means of the acoustic response using the jointed time and frequency transforms. These methods make it possible to localise the beginning and the end of frequency components contained in the measured signal, and in this way, they enable us to analyse perfectly the spectrum of the non-stationary noise. In this way, this mathematical procedure enables us to distinguish a good specimen from a defective one.

2024

Direction of arrival (DOA) estimation is a well studied topic and used in many different applications. Although it is usually based on time-difference-of-arrival (TDOA) of coherent signals, it is sometimes possible to also use frequency-difference-of-arrival (FDOA). By combining TDOA and FDOA a more reliable estimate of the DOA can be obtained. This can be useful in military applications such as reconnaissance and surveillance where the objective is to locate unknown transmitters. It is however not always possible to obtain accurate TDOA and FDOA estimates. In this study the conditions for this are examined for the airborne case when the receivers are on the same aerial vehicle. A method for simulating and estimating TDOAs and FDOAs is also presented. The results are based on simulations with three different signal types and conditions are found for which accurate TDOA and FDOA estimation can be achieved. These conditions are obtained from the Cramér-Rao bounds for TDOA and FDOA. It is shown that FDOA can in some cases yield a higher accuracy in the DOA estimate than what is possible with TDOA. Sammanfattning Riktning av ankomst (förkortat DOA)är ett väl studeratämne och används i många olika sammanhang.Även om det oftaär baserat på tidsskillnader i ankomst (förkortat TDOA) hos koherenta vågor,är det ibland möjligt attäven använda frekvensskillnader i ankomst (förkortat FDOA). Genom att kombinera TDOA och FDOA kan ett mer pålitligt estimat av DOA fås. Detta kan vara användbart i militära sammanhang så som spaning ochövervakning då måletär att lokalisera okända sändare. Detär dock inte alltid möjligt att uppnå noggranna TDOA och FDOA estimat. I denna studie undersöks villkoren för detta för fallet då mottagarnaär på samma luftburna farkost. En metod för att simulera och estimera TDOA och FDOA visas också. Resultaten baseras på simuleringar med tre olika signaltyper och villkor för vilka noggranna TDOA och FDOA estimat kan uppnås tas fram. Dessa villkorär tagna från Cramér-Raos sats för TDOA och FDOA. Det visas att FDOA kan i vissa sammanhang ge en högre noggrannhet i DOA estimatetän vad somär möjligt med TDOA.

2024, IEEE Transactions on Signal Processing

The polynomial Wigner-Ville distribution (PWVD) is a time-frequency signal analysis tool for representing polynomial frequency modulated (FM) signals. In this correspondence, we present an optimization procedure to design PWVD's and... more

2024, Analele Universităţii "Constantin Brâncuşi" din Târgu Jiu: Seria Inginerie

One method of identifying defects is to directly solve the optimization problem to obtain a set of parameters for locating and quantifying damages in the structure. Numeric or digital simulation uses computerized modelling and analysis to test static or dynamic behaviour of components subjected to stress,under different operating conditions.This type of analysis involves, however, the assimilation of operating modes with a specialized assisted design program as well as finite element simulation and analysis programs.Method used in present paper for analysing defects on components such L-shaped platens assembly is one of finite element, by means of SolidWork software. The purpose is to obtain Eigen modes ofL-shaped platens assembly embedded on a surface in order to use the results for further analysis in technical applications.

2024, Analele Universităţii "Constantin Brâncuşi" din Târgu Jiu: Seria Inginerie

This study proposes a method for locating and evaluating plate damage based on the determination of natural frequencies and modal forms of defective plates using an optimization approach. For simulations finite element method is used, form SolidWorks software. The efficiency of the presented method is explored by a comparison of the results obtained for several case studies of L-shaped plates assemblies having different defects and positions on plates. The results obtained confirm the applicability and effectiveness of the suggested method for solving the problems of localizing and quantifying defects on plates.

2024, Analele Universităţii "Constantin Brâncuşi" din Târgu Jiu: Seria Inginerie

In present paper we have conducted a study for locating and evaluating plate damage based on the determination of geometrical percentage deviation. Study of defects is of great importance because by a proper identification we are able to prevent loss part of component characteristics and endurance. We considered rectangular thin plates of various dimensions, under the influence of temperature. Impact of considered factor decreases with thickness of plate and in this scenario geometrical imperfections measured as deviations from ideal plate surface play a fundamental role in predicting the non-linear response.

2024, IEEE Transactions on Acoustics, Speech, and Signal Processing

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2024, Signal Processing

A new signal-adaptive joint time-frequency distribution for the analysis of nonstationary signals is proposed. It is based on a fractional-Fourier-domain realization of the weighted Wigner distribution producing auto-terms close to the ones in the Wigner distribution itself, but with reduced cross-terms. Improvement over the standard time-frequency representations is achieved when the principal axes of a signal (defined as mutually orthogonal directions in the time-frequency plane for which the width of the signal's fractional power spectrum is minimum or maximum) do not correspond to time and frequency. The computational cost of this fractional-domain realization is the same as the computational cost of the realizations in the time or the frequency domain, since the windowed Fourier transform of the fractional Fourier transform of a signal corresponds to the short-time Fourier transform of the signal itself, with the window being the fractional Fourier transform of the initial one. The appropriate fractional domain is found from the knowledge of three second-order fractional Fourier transform moments. Numerical simulations confirm a qualitative advantage in the time-frequency representation, when the calculation is done in the optimal fractional domain. The approach can be generalized to the time-frequency distributions from the Cohen class.

2024, IEEE Signal Processing Letters

2024, IEEE Transactions on Medical Imaging

Ultrasound shear wave elastography (SWE) is a noninvasive approach for evaluating mechanical properties of soft tissues. In SWE either group velocity measured in the time-domain or phase velocity measured in the frequency-domain can be reported. Frequency-domain methods have the advantage over time-domain methods in providing a response for a specific frequency, while timedomain methods average the wave velocity over the entire frequency band. Current frequency-domain approaches struggle to reconstruct SWE images over full frequency bandwidth. This is especially important in the case of viscoelastic tissues, where tissue viscoelasticity is often studied by analyzing the shear wave phase velocity dispersion. For characterizing cancerous lesions, it has been shown that considerable biases can occur with group velocity-based measurements. However, using phase velocities at higher frequencies can provide more accurate evaluations. In this paper, we propose a new method called Ultrasound Shear Elastography with Expanded Bandwidth (USEWEB) used for two-dimensional (2D) shear wave phase velocity imaging. We tested the USEWEB method on data from homogeneous tissue-mimicking liver fibrosis phantoms, custom-made viscoelastic phantom measurements, phantoms with cylindrical inclusions experiments, and in vivo renal transplants scanned with a clinical scanner. We compared results from the USEWEB method with a Local Phase Velocity Imaging (LPVI) approach over a wide frequency range, i.e., up to 200-2000 Hz. Tests carried out revealed that the USEWEB approach provides 2D phase velocity images with a coefficient of variation below 5% over a wider frequency band for smaller processing window size in comparison to LPVI, especially in viscoelastic materials. In addition, USEWEB can produce correct phase velocity images for much higher frequencies, up to 1800 Hz, compared to LPVI, which can be used to characterize viscoelastic materials and elastic inclusions.

2024

Frequency domain analysis and Fourier transforms are a cornerstone of signal and system analysis. These ideas are also one of the conceptual pillars within electrical engineering. Among all of the mathematical tools utilized in electrical engineering, frequency domain analysis is arguably the most far-reaching. In fact, these ideas are so important that they are widely used in many fields-not just in electrical engineering, but in practically all branches of engineering and science, and several areas of mathematics.

2024, Journal of the Optical Society of America A

A concise introduction to the concept of fractional Fourier transforms is followed by a discussion of their relation to chirp and wavelet transforms. The notion of fractional Fourier domains is developed in conjunction with the Wigner distribution of a signal. Convolution, filtering, and multiplexing of signals in fractional domains are discussed, revealing that under certain conditions one can improve on the special cases of these operations in the conventional space and frequency domains. Because of the ease of performing the fractional Fourier transform optically, these operations are relevant for optical information processing.

2024

This work presents a short review of the state-of-the-art on the subject of Instantaneous Angular Speed (IAS) estimation. Departing from the definition of IAS as a fundamental frequency of a superposition of sinusoids, convolved with a transfer function plus noise. With this in mind, the discussed methodologies are restricted to the ones for IF estimation on multicomponent signals, more precisely, to the fundamental frequency estimation ones. For an experimental validation, we study three measured signals, a vibration signal from a test rig affected by a resonance frequency, a vibration and an acoustic signal from an aircraft engine. As a conclusion, with an expert initial examination of the signal and a priori knowledge of the machine, the IAS can be accurately estimated from the vibration signal in both cases, in contrast, the IAS estimation using the acoustic signal needs to be improved.

2024, Periodica Polytechnica Electrical Engineering and Computer Science

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 meas...

2024, IEEE Geoscience and Remote Sensing Letters

This letter presents the use of polynomial Wigner-Ville distribution (PWVD) for accurate velocity estimation as used in remote road traffic management applications. In such applications based on inverse synthetic aperture radar, velocity estimation is central to obtain a suitable level of performance of the signal processing. Moreover, the precision of this velocity estimation is crucial in order to achieve the best detection and estimation of the gauge of the vehicles through the use of radar images. Hence, the PWVD is applied as an instantaneous frequency estimator used in this traffic surveillance application.

2024, Signal Processing

Adaptive directional time-frequency distribution (ADTFD) is an efficient TFD, which has outperformed most of the adaptive and fixed TFDs. However, two drawbacks of ADTFD have made this method inconvenient for processing real-world signals, such as biomedical signals. First, the high computational cost of the ADTFD and second, the sensitivity of the ADTFD to the presence of noise. This paper addresses these problems and introduces a computationally efficient variant of the ADTFD with robust construction in low SNR. In the proposed method, instead of iterative filtering in different directions, which is computationally expensive, the optimized directions are estimated using Radon transform of the modulus of the signal's ambiguity function. The achieved results show an improvement inversely proportional to the number of signal's auto-terms in contrast to the ADTFD.

2024, 2017 22nd International Conference on Digital Signal Processing (DSP)

A time-frequency analysis based approach for the decomposition of bivariate signals is presented. In particular, the well-known problem of two components overlapping in the time-frequency plane while having non-linear instantaneous frequencies is considered. The bivariate form of data leads to a significant modification of the Wigner distribution cross-terms. Therefore, the eigenvalue decomposition of Wigner distribution based signal autocorrelation matrix produces two significant eigenvalues instead of one in the common Wigner distribution. It is shown that the two corresponding eigenvectors can be linearly combined in order to produce fully separated signal components. The unknown coefficients are found by minimizing the timefrequency concentration measure of these particular eigenvectors linear combination. The presented approach is illustrated on the decomposition of a fast-varying real-valued signal with small instantaneous frequencies, so that its positive and negative frequency parts are so close that they degrade the analytical signal representation.

2024, 2018 7th Mediterranean Conference on Embedded Computing (MECO)

Decomposition of multicomponent signals overlapping in the time-frequency domain is a challenging research topic. To solve this problem, many approaches have been proposed so far, but only to be efficient for some particular signal classes. Recently, we have proposed a decomposition approach for multivariate multicomponent signals, based on the time-frequency signal analysis and concentration measures. The proposed solution is efficient for multivariate signals partially overlapped in the time-frequency plane regardless of the non-stationarity type of particular signal components. This decomposition approach is shown to be also efficient in noisy environments. In this paper, we investigate the limits of the decomposition efficiency subject to the signal-to-noise ratio and initial phase differences between the signals from different channels. The paper is focused on the decomposition of bivariate two-component signals.

2024, 2018 26th European Signal Processing Conference (EUSIPCO)

The paper examines the exact error of randomly sampled reconstructed nonsparse signals having a sparsity constraint. When signal is randomly sampled, it looses the property of sparsity. It is considered that the signal is reconstructed as sparse in the joint time-frequency domain. Under this assumption, the signal can be reconstructed by a reduced set of measurements. It is shown that the error can be calculated from the unavailable samples and assumed sparsity. Unavailable samples degrade the sparsity constraint. The error is examined on nonstationary signals, with the short-time Fourier transform acting as a representative domain of signal sparsity. The presented theory is verified on numerical examples.

2024, 2016 5th Mediterranean Conference on Embedded Computing (MECO)

Sparse signal processing and the reconstruction of missing samples of signals exhibiting sparsity in a transform domain have been emerging research topics during the last decade. In this paper, we present the proof of the sparsity measure convexity, when considering the missing samples as minimization variables. The sparsity measure can be directly exploited in the reconstruction procedures, such as in the recently proposed gradient-based reconstruction algorithm. It makes the proof of sparsity measure convexity with respect to the missing samples as minimization variables especially interesting for signal processing. The minimal value of the sparsity measure corresponds to the set of missing sample values representing the sparsest possible solution, assuming that the reconstruction conditions are met. Convexity, along with recently presented proof of the uniqueness of the acquired solution, makes the gradient-based algorithm with missing samples as variables, a complete approach to the signal reconstruction. If the sparsity measure is convex, then we can guarantee that the solution corresponds to the global minimum of the sparsity measure, since the local minima do not exist in that case.

2024, 2015 4th Mediterranean Conference on Embedded Computing (MECO)

Instantaneous frequency estimation of signals in a high noise environment is analyzed in the paper. An algorithm based on Ant colony optimization and Wigner distribution is proposed for solving the considered estimation problem. The proposed approach has been applied and tested on mono-component frequency-modulated signals. Numerical examples are given in order to demonstrate the algorithm's performances in the analyzed framework.

2024, IEEE Transactions on Signal Processing

The Cone-Kernel representation (CKR) and the instantaneous power spectrum (IPS) are two time-frequency representations (TFR's) where the cross-terms are localized in the region of auto-terms. Exploring their relationship for discrete sequences, we show that the IPS kernel is only two extreme terms of the CKR kernel. Further comparing other properties of these two TFR's, viz., invertibility, aliasing, and noise robustness, we show that there is aliasing in some transform domains for signals sampled at Nyquist rate and that the CKR has better noise robustness than the IPS.

2024, International Journal of Adaptive Control and Signal Processing

A novel method is presented for the instantaneous frequency estimation of multi-component signals with crossing signatures in the time-frequency domain. The proposed method uses a combination of Eigen decomposition of time-frequency distributions and time-frequency filtering to recursively extract signal components from the original mixture and estimate their instantaneous frequencies. The proposed algorithm outperforms other algorithms of similar complexity in terms of mean square error accuracy.

2024, Mathematics

Electroencephalogram (EEG) signals are known to contain signatures of stimuli that induce brain activities. However, detecting these signatures to classify captured EEG waveforms is one of the most challenging tasks of EEG analysis. This paper proposes a novel time–frequency-based method for EEG analysis and characterization implemented in a computer-aided decision-support system that can be used to assist medical experts in interpreting EEG patterns. The computerized method utilizes EEG spectral non-stationarity, which is clearly revealed in the time–frequency distributions (TFDs) of multicomponent signals. The proposed algorithm, which is based on the modification of the Rényi entropy, called local or short-term Rényi entropy (STRE), was upgraded with a blind component separation procedure and instantaneous frequency (IF) estimation. The method was applied to EEGs of both forward and backward movements of the left and right hands, as well as to EEGs of imagined hand movements, whi...

2024, IEEE Signal Processing Letters

Vertex-frequency analysis of graph signals is a chal-3 lenging topic for research and applications. Counterparts of 4 the short-time Fourier transform, the wavelet transform, and 5 the Rihaczek distribution have recently been introduced to the 6 graph-signal analysis. In this letter, we have extended the energy 7 distributions to a general reduced interference distributions class. 8 It can improve the vertex-frequency representation of a graph sig-9 nal while preserving the marginal properties. This class is related to 10 the spectrogram of graph signals as well. Efficiency of the proposed 11 representations is illustrated in examples. 12 Index Terms-Energy distributions, graph-signal processing, 13 time-frequency analysis, vertex-frequency analysis. 14 I. INTRODUCTION 15 G RAPH-SIGNAL processing has become an active re-16 search area in recent years, resulting in many advanced 17 solutions in various applications. In many practical cases, the 18 signal domain is not a set of equidistant instants in time or a 19 set of points in two-dimensional (2-D) or 3-D space placed on 20 a regular rectangular grid. The data-sensing domain is then re-21 lated to other parameters of the considered system/network. For 22 example, in many social or web-related networks the sensing 23 points and their connectivity are related to specific objects and 24 their links. In some physical processes, other properties than 25 the space or time coordinates define the relation between points 26 where the signal is sensed. Even for the data sensed in the well-27 defined time and space domains, the introduction of relations 28 between the sensing points in a form of graph may produce new 29 insights and more advanced data-processing methods [1]-[5]. 30 Spectral characteristics of graph signals can be vertex-31 varying. This corresponds to the time-varying signals and 32 time-frequency analysis in classical signal processing [6]-[10]. 33 Linear vertex-frequency analysis is introduced using strong 34 correspondence with the short-time Fourier transform and the 35 wavelet transform [11]-[15]. A different line of work has gener-36 alized the notion of time stationarity to signals defined on graphs 37 [16], [17], developing windowing and energy spectral estima-38 tion schemes for graph-stationary signals [17]. In general, the 39 classical time-frequency representations have many important 40 properties whose extension to the graphs is not guaranteed, for 41 example, the uncertainty principle.

2024, 2013 21st Telecommunications Forum Telfor (TELFOR)

In this paper we present a time-frequency plane tiling (splitting) approach for the local polynomial Fourier transform. Comparison of the proposed approach with the one based on the short-time Fourier transform is given. Advantages of the first order local polynomial Fourier transform in the localization and analysis of LFM signals are shown. Signals that can locally be approximated by the LFM signals are also considered. Theory is illustrated by several examples.

2024

S-transform, which is a powerful time frequency analysis method, has found applications in diverse areas of science and technology. S-transform is a time-frequency spectral localization technique that combines elements of wavelet transform (WT) and Short-Time Fourier Transform (STFT). It is a generalized form of the Gabor transformation where the width of the Gaussian window scales inversely and the height of the window scales linearly with frequency. In this Paper We Will get familiar with general S-Transform and its Inverse STransform. Then will see some applications like Power Frequency Measurement, Signal Filtering, Power System Disturbance Recognition, Fault Detection and Diagnosis of Grid-Connected Power Inverters, Geophysical Signal Analysis, EEG analysis, Optical 3-D Surface Profile Measurement, Magnetic Resonance Imaging (MRI).Traditionally S-transform is use to measure time-frequency so will compare S-transform with Gabor Transform, Wigner Transform, short-time Fourier tra...

2024, Renewable Energy and Power Quality Journal

The present works deals with the presentation and test of a novel Power Quality index, based in Higher-Order cumulants. Synthetics are used for test different start point, amplitude and length for the most common Power Grid disturbances (DIP, Oscillatory Transient, Harmonic Temporal Distortion and Impulsive Transient), obtaining a high accuracy (over 99% in some disturbances). Then real signals are used for confirm synthetics results. Finally, the Power Quality index presented, is confirmed with real signals as a good tool for detect imperfections in the power waveform.

2024, Epilepsia

In this study, we seek to analyze the determinants of the intracranial electroencephalography seizure onset pattern (SOP) and the impact of the SOP in predicting postsurgical seizure outcome. Methods: To this end, we analyzed 820 seizures from 252 consecutive patients explored by stereo-electroencephalography (total of 2148 electrodes), including various forms of focal refractory epilepsies. We used a reproducible method combining visual and time-frequency analyses. Results: We described eight SOPs: low-voltage fast activity (LVFA), preictal spiking followed by LVFA, burst of polyspikes followed by LVFA, slow wave/ DC shift followed by LVFA, sharp theta/alpha waves, beta sharp waves, rhythmic spikes/spike-waves, and delta-brush. LVFA occurred in 79% of patients. The seizure onset pattern was significantly associated with (1) underlying etiology (burst of polyspikes followed by LVFA with the presence of a focal cortical dysplasia, LVFA with malformation of cortical development, postvascular and undetermined epilepsies), (2) spatial organization of the epileptogenic zone (EZ; burst of polyspikes followed by LVFA with focal organization, slow wave/DC shift followed by LVFA with network organization), and (3) postsurgical seizure outcome (better outcome when LVFA present). Significance: This study demonstrates that the main determinants of the SOP are the underlying etiology and the spatial organization of the EZ. Concerning the postsurgical seizure outcome, the main determinant factor is the spatial organization of the EZ, but the SOP plays also a role, conferring better prognosis when LVFA is present.

2024, Journal of Neurology

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2024

Note: Publie sous les auspices du Centre National de la Recherche Scientifique (CNRS), France et de la Rice University, USA Reference Record created on 2004-09-07, modified on 2016-08-08

2024, Journal of the Acoustical Society of America

It is argued here that owing to an improper comparison with the "classical" discrete-time cross-ambiguity function (DTXAF), Bekir [J. Acoust. Soc. Am. 94, 817-826 (1993)] failed to demonstrate the alleged unaliasing property of his DTXAF.

2024

We report the results of an investigation into the signal characteristics and behavior of an instrument used to calibrate Optical Time Domain Reflectometers. This instrument implements the Telecommunications Industry Association standard TIA/EIA-455-226 "External Source Method." Results of calibrations performed at various US Air Force Precision Measurement Equipment Laboratories have included some anomalous pulse delays and our efforts were focused on identifying the cause and developing corrective procedures for this anomalous behavior. We also describe the measurement method and associated uncertainty analysis used to calibrate optical fiber delay lines employed in the calibration of the OTDR calibrator.

2024

Physiological data is generated by process that are either nonlinear deterministic or nondeterministic. The lempel-ziv complexity and non-extensive entropy measurement has been used to quantify information in physiological data like EEG and EMG. When the functions of brain cells are affected by damage caused by several disease it is observed changes in the features of the EEG providing useful insight into brain functions and playing a useful role as a first line of decision-support tool for early detection and diagnosis in brain diseases. This paper uses a method to identify the q-index in those signals by using the relationships between entropy definitions given by Lempel-ziv and those given by Tsallis methods. After all, this article shows that, the q-index can be used to characterize EEG seizure quantifying changes related to the q-entropic index.

2023, Frequenz

The research analyses the application of particle filters in estimating and extracting the features of radar signal time-frequency energy distribution. Time-frequency representation is calculated using modified B distribution, where the estimation process model represents one time bin. An adaptive criterion for the calculation of particle weighted coefficients whose main parameters are frequency integral squared error and estimated maximum of mean power spectral density per one time bin is proposed. The analysis of the suggested estimation application has been performed on a generated signal in the absence of any noise, and consequently on modelled and recorded real radar signals. The advantage of the suggested method is in the solution of the issue of interrupted estimations of instantaneous frequencies which appears when these estimations are determined according to maximum energy distribution, as in the case of intersecting frequency components in a multicomponent signal.

2023, IEEE Transactions on Instrumentation and Measurement

Accurate and fast characterization of voltage variations helps to evaluate their severity on equipment and activate protections. In this paper, a methodology for tracking and characterization of voltage variations, sample to sample, is presented. It consists of a Hilbert transform to estimate the voltage of the signal's envelope, a fuzzy logic system to track down the type of voltage variation, and a rule-based method for the final identification and decision making according to IEEE Std 1159-2009. Unlike some techniques presented in the literature for tracking voltage variations such as the Kalman filter and adaptive linear network techniques, the proposed methodology requires neither a harmonic model nor an algorithm to adjust the model parameters, which in many cases increases the computational burden and time tracking. It is worth mentioning that the proposed classification stage does not need a training stage; therefore, its development is easier and its efficiency does not depend on a data training set. The performance of the proposed methodology is validated and tested using synthetic signals as well as real measurements of voltage variations. In addition, an implementation of our methodology into an field-programmable gate array based system is performed in an effort to offer a low-cost and portable system-on-a-chip solution for online and real-time monitoring of voltage variations. Index Terms-Field-programmable gate array (FPGA), fuzzy logic (FL) system, Hilbert transform (HT), power quality, voltage variations. I. INTRODUCTION W ITH the increasing proliferation of nonlinear loads and the susceptibility of equipment to variations in power supply lines, power quality (PQ) monitoring has become a very interesting topic for many researchers around the world [1]-[4]. Among the different disturbances that adversely affect the PQ, voltage variations (sags, swells, and interruptions) usually associated with motor starting, transformer Manuscript

2023

We present a multi-window method for obtaining the time-frequency spectrum of a non-stationary signal. This method is based on optimal combination of evolutionary spectra that are calculated by using multiwindow Gabor expansion. The optimal weights are obtained by using a least square estimation method. An error criterion that is the difference between a reference time-frequency distribution and the combination of evolutionary spectra is minimized to determine the weights. Examples are given to illustrate the effectiveness of the proposed method.

2023

We present a multi-window method for obtaining the time-frequency spectrum of of nonstationary signals such as speech and music. This method is based on optimal combination of evolutionary spectra that are calculated by using multi-window Gabor expansion. The optimal weights are obtained by using a least square estimation method. An error criterion that is the squared distance between a reference time-frequency distribution and the combination of evolutionary spectra is minimized to determine the weights. Examples are given to illustrate the effectiveness of the proposed method.

2023

We present a method for estimating the instantaneous frequency of a signal. This method involves the calculation of a time-frequency energy density of the signal, then obtaining an instantaneous frequency estimate from this joint density. Time-frequency energy density is calculated as a least squares optimal combination of multi-window Gabor based evolutionary spectra. The optimal weights are obtained by minimizing an error criterion that is the difference between a reference time-frequency distribution and the combination of evolutionary spectra. Then instantaneous frequency of the signal is estimated from the final evolutionary spectrum as time conditional average frequency. Examples are given to illustrate the performance of our method.

2023, International Journal of Wavelets, Multiresolution and Information Processing

Two different approaches for joint detection and estimation of signals embedded in stationary random noise are considered and compared, for the subclass of amplitude and frequency modulated signals. Matched filter approaches are compared to time-frequency and time scale based approaches. Particular attention is paid to the case of the so-called "power-law chirps", characterized by monomial and polynomial amplitude and frequency functions. As target application, the problem of gravitational waves at interferometric detectors is considered.

2023, Bulletin of Electrical Engineering and Informatics

The identification of multiple harmonic sources (MHS) is vital to identify the root causes and the mitigation technique for a harmonic disturbance. This paper introduces an identification technique of MHS in a power distribution system by using a time-frequency distribution (TFD) analysis known as a spectrogram. The spectrogram has advantages in term of its accuracy, a less complex algorithm, and use of low memory size compared to previous methods such as probabilistic and harmonic power flow direction. The identification of MHS is based on the significant relationship of spectral impedances, which are the fundamental impedance (Z 1) and harmonic impedance (Z h) that estimate the time-frequency representation (TFR). To verify the performance of the proposed method, an IEEE test feeder with several different harmonic producing loads is simulated. It is shown that the suggested method is excellent with 100% correct identification of MHS. The method is accurate, fast and cost-efficient in the identification of MHS in power distribution arrangement.

2023, IEEE Transactions on Energy Conversion

This paper presents an improved method for the broken rotor bar detection in a squirrel-cage induction motor (IM). The method is based on the spectral analysis of the transient stator current signal during the counter-current braking (CCB). Contrary to the classical CCB, the proposed method results in the low braking current, which is a small fraction of the rated value and serves as a broken bar detection test signal only. This kind of the broken rotor bar fault diagnosis is independent of loading conditions and can be carried out even for a free shaft motor. The existence of spectral components in the low CCB current signal indicating the faulty conditions is first proven with the generalized theory of symmetrical components. The method is then verified via simulations, using an IM model based on the finite element analysis (FEA) and the magnetically coupled multiple circuits approach (MCMCA). Afterward, the experiments are performed, showing a good agreement with both the theoretical prediction and the simulation results, confirming the presence of the fault-induced components in the stator current spectra.