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ABSTRACT This study considers the stationary higher‐order Markov process for circular data by employing the mixture transition distribution modeling. The underlying circular transition distribution is based on Wehrly and Johnson's bivariate joint circular models. The structures of the circular autocorrelation function, together with the circular partial autocorrelation function, are investigated. They are found to be similar to those of the autocorrelation and partial autocorrelation functions of the real‐valued autoregressive process when the underlying binding density has zero sine moments. The validity of the model is assessed by applying it to some Monte Carlo simulations and real directional data. [ABSTRACT FROM AUTHOR]

Welch's method provides an estimator of the power spectral density that is statistically consistent. This is achieved by averaging over periodograms calculated from overlapping segments of a time series. For a finite-length time series, while the variance of the estimator decreases as the number of segments increases, the magnitude of the estimator's bias increases: a bias-variance trade-off ensues when setting the segment number. We address this issue by providing a novel method for debiasing Welch's method that maintains the computational complexity and asymptotic consistency, and leads to improved finite-sample performance. Theoretical results are given for fourth-order stationary processes with finite fourth-order moments and an absolutely convergent fourth-order cumulant function. The significant bias reduction is demonstrated with numerical simulation and an application to real-world data. Our estimator also permits irregular spacing over frequency and we demonstrate how this may be employed for signal compression and further variance reduction. The code accompanying this work is available in R and python. [ABSTRACT FROM AUTHOR]

This paper develops a nonparametric method for estimating the spectral density of multivariate stationary time series using basis expansion. A likelihood-based approach is used to fit the model through the minimization of a penalized Whittle negative log-likelihood. Then, a Newton-type algorithm is developed for the computation. In this method, we smooth the Cholesky factors of the multivariate spectral density matrix in a way that the reconstructed estimate based on the smoothed Cholesky components is consistent and positive-definite. In a simulation study, we have illustrated and compared our proposed method with other competitive approaches. Finally, we apply our approach to two real-world problems, Electroencephalogram signals analysis, E l N i n ~ o Cycle. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AbstractTime series models (TSMs) are used to forecast events based on verified historical data that are widely applied to describe natural and social phenomena. The spectral density function (SDF) of the TSM has the benefit to determine trends, periodic patterns and behavior of observable events and is used in degree of memory (DOM) which is used to identify independent and dependent structures. Therefore, paying attention to the estimator of SDF (SDE) is the cornerstone and desired goal in the statistical properties of TSMs. This paper gives a novel perspective for improving the SDE then dependency structure of TSMs by integrating clustering and sequential analysis. The accuracy of the lag window estimator (LWE) and periodogram estimator (PE), which are commonly used as SDEs in practice, is examined both with and without integrating the new proposed technique in order to figure out the efficacy of the new technique. Several circumstances, including independent and dependent data structures, TSMs with long and short DOMs, different numbers of clusters, and different dataset sizes, are used to carry out the investigation. The main findings indicate that: (i) The SDEs (PE and LWE) perform better when combined with the new proposed technique than when used alone. (ii) Using the new technique in conjunction with the SDEs results in absolutely perfect estimators for independent processes. (iii) High efficiency estimators for short memory TSMs arise when the new proposed technique is combined with the SDEs in the dependent processes; however, high efficiency in long memory TSMs (LMTSMs) is not guaranteed. (iv) Using the new technique, the correlation estimator behavior between SDEs and frequencies improves significantly with increasing DOM values of LMTSMs. (v) By applying the new technique for LMTSMs, the DOM estimator provides results indicating significant dependence. This study demonstrates that there are other efficient ways to improve the estimator than estimate techniques. [ABSTRACT FROM AUTHOR]

We study the estimation problem of the spectral density function for harmonizable non‐stationary processes. More precisely, we consider spectrally correlated processes whose spectral measure has the support contained in the union of unknown lines with possibly non‐unit slopes. We propose the frequency‐smoothed periodogram along the estimated support line as an estimator of the spectral density function. We show the mean‐square consistency of the proposed estimator. Additionally, we discuss the estimation of the support line in a specific model with its applications in locating a moving source. Finally, we present simulations confirming the proven results. [ABSTRACT FROM AUTHOR]

A novel robust adaptive technique is proposed to estimate the instantaneous power system frequency using a momentum-learning-control-rate-based fractional-order least mean squares approach with enhanced Harris hawks optimization. The adaptive estimation comprises two modules, where the first part involves the design of the momentum-learning-control-term-based fractional-order least mean squares algorithm and second part focuses on parameter tuning of the algorithm through enhanced Harris hawks optimization incorporating chaotic mapping and opposition-based learning. This integration yields a robust and automated adaptive algorithm for frequency estimation with superior performance compared to traditional transform-based techniques, particularly in the presence of noise. The proposed method excels in scenarios where the estimator should manage multiple variables, including step size, fractional-order step constants, and momentum learning control terms. Moreover, it facilitates accurate power frequency estimation for real signals in multiarea power systems or microgrids. To validate the efficacy of the algorithm, computer-simulated data representing step and ramp changes in the frequency were processed. Additionally, the algorithm was tested with signals derived from a multiple-control-area, multisource renewable-based power system. Detailed comparative results were obtained and verified through MATLAB simulations and real-time experimental setup, demonstrating the superior performance of the adaptive model. [ABSTRACT FROM AUTHOR]

The article in the Journal of Fluid Mechanics delves into the equilibrium of fluid fluxes in the wake of a three-dimensional flat-back bluff body, analyzing the impact of base blowing on wake dynamics and drag reduction strategies. The study reveals that optimal blowing can reduce base drag by 9% and alter wake symmetry, leading to elongated shear layers and reduced bubble intensity. By examining wake mass fluxes and coherent structures associated with bistable flow and symmetric wake configurations under different blowing rates, the research aims to enhance understanding of drag reduction techniques and wake dynamics behind bluff bodies. [Extracted from the article]

In the realm of speaker identification, pitch frequency serves as a fundamental feature. However, this feature can be compromised when a speaker records his speech in a closed room, resulting in distorted signal features. This distortion not only reduces the effectiveness of speaker identification systems, but also opens the door for potential deception by hackers who exploit the reverberation effects in closed rooms. To address this concern, the correction of estimated pitch frequencies emerges as an essential step for the success of speaker identification systems. This paper presents a Hybrid Approach for Estimating Pitch Frequency (HAEPF) that integrates both the Zero Crossing Rate (ZCR) and Auto-Correlation Function (ACF) methods. Furthermore, the paper delves into the modeling of reverberant speech using comb filtering, shedding light on how multiple reflections impact the accuracy of pitch frequency estimation. Several simulation experiments were conducted to assess pitch frequency estimation for speech signals, both in the presence and absence of reverberation. The estimation errors were calculated for all three scenarios of reverberation (mild, moderate, and severe). The results clearly indicate that as the degree of reverberation, characterized by the comb filter order, increases, the pitch frequency estimation error also increases. The estimation accuracy of the proposed approach is calculated in terms of Pitch Frequency Estimation Error (PFEE), Gross Pitch Error (GPE) and Octave Error (OER) and is compared with those of several established pitch frequency estimation methods. The proposed approach exhibits a notable enhancement even in noisy environments, reducing PFEE by 43%, and achieving GPE and OER of less than 0.3 and 0.12, respectively, at a Signal-to-Noise Ratio (SNR) of 0 dB. [ABSTRACT FROM AUTHOR]

Spectral density estimation is a well-studied problem for the case of directly observed time series. If data are censored then add hoc methods, like ignoring censored observations or treatment them as missing and using imputation, are recommended. This article, for the first time in the literature, presents the theory and methodology of efficient non parametric spectral density estimation for censored time series. Recommended estimator is based on estimation of autocovariances for censored time series which is of interest on its own. Similarly to the case of direct observations, the theory and methodology are first developed for Gaussian time series, and then robustness of the estimator is verified for a larger class of mixing time series. Simulations and practical examples complement the theory. [ABSTRACT FROM AUTHOR]

To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as 1 × 10−4, with a false detection probability of 1 × 10−3, can be obtained under a signal-to-interference ratio (SIR) of −10 dB and Eb/N0 of 1 dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than 3%, with the condition of Eb/N0 being as low as −5 dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial intelligence and Internet of Things are playing an increasingly important role in monitoring beehives. In this paper, we propose a method for automatic recognition of honeybee type by analyzing the sound generated by worker bees and drone bees during their flight close to an entrance to a beehive. We conducted a wide comparative study to determine the most effective preprocessing of audio signals for the detection problem. We compared the results for several different methods for signal representation in the frequency domain, including mel-frequency cepstral coefficients (MFCCs), gammatone cepstral coefficients (GTCCs), the multiple signal classification method (MUSIC) and parametric estimation of power spectral density (PSD) by the Burg algorithm. The coefficients serve as inputs for an autoencoder neural network to discriminate drone bees from worker bees. The classification is based on the reconstruction error of the signal representations produced by the autoencoder. We propose a novel approach to class separation by the autoencoder neural network with various thresholds between decision areas, including the maximum likelihood threshold for the reconstruction error. By classifying real-life signals, we demonstrated that it is possible to differentiate drone bees and worker bees based solely on audio signals. The attained level of detection accuracy enables the creation of an efficient automatic system for beekeepers. [ABSTRACT FROM AUTHOR]

The beat signal obtained from frequency-modulated continuous-wave (FMCW) radar is a waveform that is corrupted by noise and requires filtering out interference components for frequency calibration. Traditional FFT methods are affected by the fence effect and spectral leakage, leading to a reduction in frequency estimation accuracy. Therefore, an improved double-spectrum-line interpolation frequency estimation algorithm is proposed in this paper, utilizing spectral refinement and phase interpolation. Firstly, the post-FFT spectral signal is refined to narrow the frequency search range and enhance frequency resolution, thereby separating the noise signal. Then, a frequency deviation factor is defined based on the relationship between adjacent phase angles. Finally, the signal's phase angles are interpolated using the frequency deviation factor to estimate the frequency of the beat signal. Experimental results demonstrate that the proposed algorithm reduces the impact of quantization on the frequency distribution and increases the signal's noise resistance. The proposed algorithm has a higher accuracy and lower standard deviation compared to the recently proposed algorithm. [ABSTRACT FROM AUTHOR]

Frequency is one of the most significant indicators of power system, which needs accurate estimation to provide a reliable basis for monitoring, control, and protection. Owning to the efficiency, DFT is widely used in the frequency measurement of power systems. However, the inherent defects, that is, the spectral leakage and picket fence effect, decrease the accuracy of DFT. One of the variants, named the windowed phase difference method (WPD), addresses the problem using the phase variation of two windowed sequences in the frequency domain and achieves accurate frequency estimation. Suffering from the ignored negative component and contamination of noise, there are still undesired errors in WPD-based frequency estimation. To reduce the influence of the undesired errors, this paper first analyzes the systematic error of WPD introduced by spectrum leakage and proposes an improved WPD (IWPD) through systematic error compensation to achieve more accurate frequency estimations. Then, the windowing effect on IWPD-based frequency estimation of a noisy signal is studied by deducing the theoretical expression of frequency estimation variance. Finally, the proposed IWPD is validated by extensive computer simulations and practical experiments. [ABSTRACT FROM AUTHOR]

In this article, we introduce a Gegenbauer autoregressive tempered fractionally integrated moving average process. We work on the spectral density and autocovariance function for the introduced process. The parameter estimation is done using the empirical spectral density with the help of the nonlinear least square technique and the Whittle likelihood estimation technique. The performance of the proposed estimation techniques is assessed on simulated data. Further, the introduced process is shown to better model the real-world data in comparison to other time series models. [ABSTRACT FROM AUTHOR]

Spectral density analysis plays an important role in studying a stationary random process on a real line. In this paper, we extend this discussion for the random process with stationary increments. We investigate the properties of the method of moments structure function estimation, and propose a nonparametric spectral density function estimator. Our numerical results show that the proposed spectral density estimator performs comparable with the parametric counterpart when the underlying process is assumed to be band‐limited. Additionally, this method is applied to analyze US Housing Starts Data, where the hidden periodicities are detected, providing consistent conclusions with previous economic studies. [ABSTRACT FROM AUTHOR]

A multichannel speech enhancement system usually consists of spatial filters such as adaptive beamformers followed by postfilters, which suppress remaining noise. Accurate estimation of the power spectral density (PSD) of the residual noise is crucial for successful noise reduction in the postfilters. In this paper, we propose a postfilter utilizing proposed a posteriori speech presence probability (SPP) and noise PSD estimators, which are based on both the coherence and the statistical models. We model the coherence-based a posteriori SPP as a simple function of the magnitude of coherence between two microphone signals and combine it with a single-channel SPP based on statistical models. The coherence-based estimator for the PSD of the noise remaining in the beamformer output in the presence of speech is derived using the pseudo-coherence considering the effect of the beamformers, which is used to construct the coherence-based noise PSD estimator. Then, the final noise PSD estimator is obtained by combining the coherence-based and statistical model-based noise PSD estimators with the proposed SPP. The spectral gain function is also modified, incorporating the proposed SPP. Experimental results demonstrate that the proposed method led to more accurate noise PSD estimation and perceptual evaluation of speech quality scores in various diffuse noise environments, and did not degrade the speech quality under the presence of directional interference, although the proposed method utilizes the coherence information. [ABSTRACT FROM AUTHOR]

We consider the estimation and uncertainty quantification of the tail spectral density, which provide a foundation for tail spectral analysis of tail dependent time series. The tail spectral density has a particular focus on serial dependence in the tail, and can reveal dependence information that is otherwise not discoverable by the traditional spectral analysis. Understanding the convergence rate of tail spectral density estimators and finding rigorous ways to quantify their statistical uncertainty, however, still stand as a somewhat open problem. The current article aims to fill this gap by providing a novel asymptotic theory on quadratic forms of tail statistics in the double asymptotic setting, based on which we develop the consistency and the long desired central limit theorem for tail spectral density estimators. The results are then used to devise a clean and effective method for constructing confidence intervals to gauge the statistical uncertainty of tail spectral density estimators, and it can be turned into a visualization tool to aid practitioners in examining the tail dependence for their data of interest. Numerical examples including data applications are presented to illustrate the developed results. for this article are available online. [ABSTRACT FROM AUTHOR]

Featured Application: Online thickness monitoring of transparent film. Thickness measurement of thin films is essential for quality control in the manufacturing process of the semiconductor and display industries. Real-time monitoring of film thickness during production is an urgent technical problem to be solved. In this study, a method for large-area thickness measurement of transparent films based on a multichannel spectral interference sensor is proposed. The sensor simultaneously acquires multichannel spectral interference signals through a combination of fan-out fiber optic bundles, detection probes, and an imaging spectrometer. The spectral data are calibrated and transformed into the wavenumber dimension, and then the power spectral density estimation method is used to demodulate the data frequency to swiftly derive the film thickness. The thickness measurement capacity of the proposed system is successfully validated on two standard film samples with a relative deviation of less than 0.38% and a relative standard deviation of less than 0.044%. The total spectral acquisition and calculation time for a single multichannel measurement was approximately 7.5 ms. The experimental results on polyimide films show that the measurement efficiency of the system is at least 4 times higher than that of the traditional system, indicating the potential of the multichannel spectral interference sensor for online monitoring in film production. [ABSTRACT FROM AUTHOR]

Passive wireless surface acoustic wave (SAW) resonant sensors are widely used in measuring pressure, temperature, and torque, typically detecting sensing parameters by measuring the echo signal frequency of SAW resonators. Therefore, the accuracy of echo signal frequency estimation directly affects the performance index of the sensor. Due to the exponential attenuation trend of the echo signal, the duration is generally approximately 10 μs, with conventional frequency domain analysis methods limited by the sampling frequency and data points. Thus, the resolution of frequency estimation is limited. Here, signal time-domain fitting combined with a genetic algorithm is used to estimate SAW echo signal frequency. To address the problem of slow estimation speed and poor timeliness caused by a conventional genetic algorithm, which needs to simultaneously estimate multiple parameters, such as signal amplitude, phase, frequency, and envelope, the Hilbert transform is proposed to remove the signal envelope and estimate its amplitude, and the fast Fourier transform subsection method is used to analyze the initial phase of the signal. The genetic algorithm is thereby optimized to realize the frequency estimation of SAW echo signals under a single parameter. The developed digital signal processing frequency detection system was monitored in real time to estimate the frequency of an SAW echo signal lasting 10 μs and found to have only 100 sampling points. The proposed method has a frequency estimation error within 3 kHz and a frequency estimation time of less than 1 s, which is eight times faster than the conventional genetic algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, two approaches are proposed for estimating the direction of arrival (DOA) and power spectral density (PSD) of stationary point sources by using a single, rotating, directional microphone. These approaches are based on a method previously presented by the authors, in which point source DOAs were estimated by using a broadband signal model and solving a group-sparse optimization problem, where the number of observations made by the rotating directional microphone can be lower than the number of candidate DOAs in an angular grid. The DOA estimation is followed by the estimation of the sources' PSDs through the solution of an overdetermined least squares problem. The first approach proposed in this paper includes the use of an additional nonnegativity constraint on the residual noise term when solving the group-sparse optimization problem and is referred to as the Group Lasso Least Squares (GL-LS) approach. The second proposed approach, in addition to the new nonnegativity constraint, employs a narrowband signal model when building the linear system of equations used for formulating the group-sparse optimization problem, where the DOAs and PSDs can be jointly estimated by iterative, group-wise reweighting. This is referred to as the Group-Lasso with l 1 -reweighting (GL-L1) approach. Both proposed approaches are implemented using the alternating direction method of multipliers (ADMM), and their performance is evaluated through simulations in which different setup conditions are considered, ranging from different types of model mismatch to variations in the acoustic scene and microphone directivity pattern. The results obtained show that in a scenario involving a microphone response mismatch between observed data and the signal model used, having the additional nonnegativity constraint on the residual noise can improve the DOA estimation for the case of GL-LS and the PSD estimation for the case of GL-L1. Moreover, the GL-L1 approach can present an advantage over GL-LS in terms of DOA estimation performance in scenarios with low SNR or where multiple sources are closely located to each other. Finally, it is shown that having the least squares PSD re-estimation step is beneficial in most scenarios, such that GL-LS outperformed GL-L1 in terms of PSD estimation errors. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive study of the dichotomous search iterative parabolic discrete time Fourier transform (Ds-IpDTFT) estimator, a novel approach for fine frequency estimation in noisy exponential signals. The proposed estimator leverages a dichotomous search process before iterative interpolation estimation, which significantly reduces computational complexity while maintaining high estimation accuracy. An in-depth exploration of the relationship between the optimal parameter p and the unknown parameter δ forms the backbone of the methodology. Through extensive simulations and real-world experiments, the Ds-IpDTFT estimator exhibits superior performance relative to other established estimators, demonstrating robustness in noisy conditions and stability across varying frequencies. This efficient and accurate estimation method is a significant contribution to the field of signal processing and offers promising potential for practical applications. [ABSTRACT FROM AUTHOR]

Various nonparametric approaches for Bayesian spectral density estimation of stationary time series have been suggested in the literature, mostly based on the Whittle likelihood approximation. A generalization of this approximation involving a nonparametric correction of a parametric likelihood has been proposed in the literature with a proof of posterior consistency for spectral density estimation in combination with the Bernstein–Dirichlet process prior for Gaussian time series. In this article, we will extend the posterior consistency result to non‐Gaussian time series by employing a general consistency theorem for dependent data and misspecified models. As a special case, posterior consistency for the spectral density under the Whittle likelihood is also extended to non‐Gaussian time series. Small sample properties of this approach are illustrated with several examples of non‐Gaussian time series. [ABSTRACT FROM AUTHOR]

We introduce a new approach for nonparametric spectral density estimation based on the subsampling technique, which we apply to the important class of nonstationary time series. These are almost periodically correlated sequences. In contrary to existing methods, our technique does not require demeaning of the data. On the simulated data examples, we compare our estimator of spectral density function with the classical one. Additionally, we propose a modified estimator, which allows to reduce the leakage effect. Moreover, in the , we provide a simulation study and two real data economic applications. for this article are available online. [ABSTRACT FROM AUTHOR]

There are some previous works on designing efficient and high-order numerical methods of density estimation for stochastic partial differential equation (SPDE) driven by multivariate Gaussian random variables. They mostly focus on proposing numerical methods of density estimation for SPDE with independent random variables and rarely research density estimation for SPDE is driven by multivariate Gaussian random variables. In this paper, we propose a high-order algorithm of gPC-based density estimation where SPDE driven by multivariate Gaussian random variables. Our main techniques are (1) we build a new multivariate orthogonal basis by adopting the Gauss–Schmidt orthogonalization; (2) with the newly constructed orthogonal basis in hand, we first assume the unknown function in the SPDE has the stochastic general polynomial chaos (gPC) expansion, second implement the stochastic gPC expansion for the SPDE in the multivariate Gaussian measure space, and third we obtain and numerical calculation deterministic differential equations for the coefficients of the expansion; (3) we used high-order algorithm of gPC-based for density estimation and moment estimation. We apply the newly proposed numerical method to a known random function, stochastic 1D wave equation, and stochastic 2D Schnakenberg model, respectively. All the presented stochastic equations are driven by bivariate Gaussian random variables. The efficiency is compared with the Monte-Carlo method based on the known random function. [ABSTRACT FROM AUTHOR]

Background: Biologgers have contributed greatly to studies of animal movement, behaviours and physiology. Accelerometers, among the various on-board sensors of biologgers, have mainly been used for animal behaviour classification and energy expenditure estimation. However, a general principle for the combined sampling duration and frequency for different taxa is lacking. In this study, we evaluated whether Nyquist–Shannon sampling theorem applies to accelerometer-based classification of animal behaviour and energy expenditure approximation. To evaluate the influence of accelerometer sampling frequency on behaviour classification, we annotated accelerometer data from seven European pied flycatchers (Ficedula hypoleuca) freely moving in aviaries. We also used simulated data to systematically evaluate the combined effect of sampling duration and sampling frequency on the performance of estimating signal frequency and amplitude. Results: We found that a sampling frequency higher than Nyquist frequency at 100 Hz was needed to classify fast, short-burst behavioural movements of pied flycatcher, such as swallowing food with a mean frequency of 28 Hz. In contrast, high frequency movements with longer durations such as flight could be characterized adequately using much lower sampling frequency of 12.5 Hz. To identify rapid transient prey catching manoeuvres within these flight bouts, again a high frequency sampling at 100 Hz was needed. For both the experimental data of the flycatchers and the simulated data, the combination of sampling frequency and sampling duration affected the accuracy of signal frequency and amplitude estimation. For long sampling durations, the sampling frequency equal to the Nyquist frequency was adequate for accurate signal frequency and amplitude estimation. Accuracy declined with decreasing sampling duration, especially for signal amplitude estimation with up to 40% standard deviation of normalized amplitude difference. To accurately estimate signal amplitude at low sampling duration, a sampling frequency of four times the signal frequency was necessary (two times the Nyquist frequency). Conclusions: The appropriate sampling frequency of accelerometers depends on the objective of the specific study and the characteristics of the behaviour. For studies with no constraints on device battery and storage, a sampling frequency of at least two times the Nyquist frequency will achieve relative optimal representative of signal information (i.e., frequency and amplitude). For classification and energy expenditure estimation of short-burst behaviours, 1.4 times the Nyquist frequency of behaviour is required. [ABSTRACT FROM AUTHOR]

With higher availability of WES, the diagnosis in our patient could have been established already at the time of onset of psychiatric symptoms or even earlier at the time of orthopaedic management of bone cysts with characteristics of internal fatty tissue indicative for NHD. In our opinion, clinical judgement in such cases cannot substitute genetic testing, as some diseases are rare and established red flags, such as positive family history, disease-specific MRI changes or "typical" symptoms are often absent or appear late in the disease course. Electroencephalography (EEG) revealed no epileptiform abnormalities but an asymmetric frontotemporal dysrhythmia during rest, with a pathological slowing of the dominant alpha frequency towards the theta band, and increased delta and beta power (Fig. Appendix EEG recording EEG recordings were analyzed using custom-written code in Python. [Extracted from the article]

Purpose: This study investigated EEG alpha rhythm spectral power in children with Specific Language Impairment (SLI) and compared it to typically developing children to better understand the electrophysiological characteristics of this disorder. Specifically, we explored resting-state EEG, because there are studies that point to it being linked to speech and language development. Methods: EEG recordings of 30 children diagnosed with specific language impairment and 30 typically developing children, aged 4.0-6.11 years, were carried out under eyes closed and eyes open conditions. Differences in alpha rhythm spectral power in relation to brain topography and experimental conditions were calculated. Results: In the eyes closed condition, alpha rhythm spectral power was statistically significantly lower in children with specific language impairment in the left temporal (T5) and occipital electrodes (O1, O2) than in typically developing children. In the eyes open condition, children with SLI showed significantly lower alpha rhythm spectral power in the left temporal (T3, T5), parietal (P3, Pz), and occipital electrodes (O1, O2). There were no statistically significant differences between the groups in relation to the relative change (the difference between average alpha rhythm spectral power during eyes closed condition and average alpha rhythm spectral power during eyes open condition divided by average alpha rhythm spectral power during eyes closed condition) in the alpha rhythm spectral power between the conditions. Conclusion: Lower alpha rhythm spectral power in the left temporal, left, midline parietal, and occipital brain regions could be a valuable electrophysiological marker in children with SLI. Further investigation is needed to examine the connection between EEG alpha spectral power and general processing and memory deficits in patients with SLI. [ABSTRACT FROM AUTHOR]

In many applications, including radar, radio and television, medical, industrial, and others, frequency estimate of a periodic sinusoidal signal is a crucial step in the signal processing process. Due to its simplicity of usage in digital systems, the interpolation-based signal frequency estimation algorithm is now frequently employed. Because of its performance speed, interpolation methods in the analysis rely on the fast Fourier transform (FFT). This paper proposes an approach for single-tone frequency estimate utilizing DFT interpolation with Parzen windowing in order to increase the accuracy of frequency estimation. In addition, compared to Li and Dian algorithm, the proposed method has a lower computing complexity and more steady performance. To minimize undesirable effects brought on by spectrum leakage from the FFT procedure, suitable windows have been investigated. To investigate the viability of the suggested method, three windows Flattop, Parzen, and Bohman, were applied to the simulation signal. When compared to the other windows, the Parzen window with the proposed algorithm outperformed them with a maximum frequency estimation error of 0.00003 compared to 0.0001 and 0.0002 for the Dian and Li algorithms, respectively, when the Sample Size was 8192. [ABSTRACT FROM AUTHOR]

Frequency estimation of sinusoidal signals is a critical task in various signal processing applications, including control systems, monitoring, radio broadcasts, and more. The Fast Fourier Transform (FFT) is a widely employed technique for signal analysis; however, it suffers from spectral leakage issues. To mitigate this problem, windowing functions are utilized, aiming to enhance frequency estimation accuracy through the combination of an optimal window and a precise frequency correction formula. In this study, a novel frequency estimation algorithm based on a three-point spectral interpolation method is proposed and compared with the Jacobsen algorithm. Simulation results demonstrate that the proposed algorithm exhibits superior performance in terms of frequency estimation errors. Specifically, the maximum frequency estimation error for the proposed algorithm, when using the Nuttall window, was found to be 0.001, representing a 29-fold reduction compared to the error of 0.029 for the Jacobsen algorithm. This improvement highlights the effectiveness of the proposed interpolation-based algorithm for accurate frequency estimation in signal processing applications. [ABSTRACT FROM AUTHOR]

In this paper, we study the asymptotic theory for spectral analysis of stationary random fields on Z 2 , including linear and nonlinear fields. Asymptotic properties of Fourier coefficients and periodograms, including limiting distributions of Fourier coefficients, and the uniform consistency of kernel spectral density estimators are obtained under various mild conditions on moments and dependence structures. The validity of the aforementioned asymptotic results for estimated spatial fields is also established. [ABSTRACT FROM AUTHOR]

Some green light emitting diodes (LEDs) based on GaN/InGaN multiquantum-well (MQW) structures exhibit strong frequency and temperature dependence of capacitance and prominent changes in capacitance–voltage profiles with temperature that make it difficult to obtain reliable deep level transient spectroscopy (DLTS) measurements. DLTS performed at low probing signal frequency and with constant capacitance between the measurements by controlling applied bias mitigates these issues. This allows measurement of deep electron and hole traps in specific quantum wells (QWs) in the MQW structure. The dominant electron and hole traps detected have levels near Ec− (0.45–0.5) eV and Ev+ (0.6–0.63) eV. Their density increases significantly after aging for a long period (2120 h) at high driving current and elevated temperature. The reason for the observed anomalies in DLTS spectra of these green LEDs is the high density of states in the QWs with activation energies near 0.08, 0.12–0.14, and 0.3 eV, detected in admittance spectra, and, for the 0.08 eV and 0.3 eV, these states are likely related to defects. [ABSTRACT FROM AUTHOR]

Spacecraft constellations consisting of multiple satellites are becoming more and more interesting not only for commercial use but also for space science missions. The proposed and accepted scientific multi-satellite missions that will operate within Earth's magnetospheric environment, like HelioSwarm, require researchers to extend established methods for the analysis of multi-spacecraft data to more than four spacecraft. The wave telescope is one of those methods. It is used to detect waves and characterize turbulence from multi-point magnetic field data, by providing spectra in reciprocal position space. The wave telescope can be applied to an arbitrary number of spacecraft already. However, the exact limits of the detection for such cases are not known if the spacecraft, acting as sampling points, are irregularly spaced. We extend the wave telescope technique to an arbitrary number of spatial dimensions and show how the characteristic upper detection limit in k space imposed by aliasing, the spatial Nyquist limit, behaves for irregularly spaced sampling points. This is done by analyzing wave telescope k -space spectra obtained from synthetic plane wave data in 1D up to 3D. As known from discrete Fourier transform methods, the spatial Nyquist limit can be expressed as the greatest common divisor in 1D. We extend this to arbitrary numbers of spatial dimensions and spacecraft. We show that the spatial Nyquist limit can be found by determining the shortest possible basis of the spacecraft distance vectors. This may be done using linear combination in position space and transforming the obtained shortest basis to k space. Alternatively, the shortest basis can be determined mathematically by applying the modified Lenstra–Lenstra–Lovász (MLLL) algorithm combined with a lattice enumeration algorithm. Thus, we give a generalized solution to the determination of the spatial Nyquist limit for arbitrary numbers of spacecraft and dimensions without any need of a priori knowledge of the measured data. Additionally, we give first insights into the application to real-world data incorporating spacecraft position errors and minimizing k -space aliasing. As the wave telescope is an estimator for a multi-dimensional power spectrum substituting spatial Fourier transform, the results of this analysis can be applied to power spectral density estimation via Fourier transform or other methods making use of irregular sampling points. Therefore, our findings are also of interest to other fields of signal processing. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is essential for obtaining intelligence in modern information warfare. Wideband chirp signals with a low signal-to-noise ratio (SNR) are widely used in SAR. Intercepting low-SNR wideband chirp signals is of great significance for anti-SAR reconnaissance. Digital channelization technology is an effective means to intercept wideband signals. The existing digital channelization methods have the following problems: the contradiction of reception blind zone and signal spectrum aliasing, high computational complexity, and low estimating accuracy for chirp signals with a low SNR. This paper proposes a non-critical sampling digital channelized receiver architecture to intercept chirp signals. The receiver architecture has no blind zone in channel division and no aliasing of signal spectrum in the channel, which can provide reliable instantaneous frequency measurements. An adaptive threshold generation algorithm is proposed to detect signals without prior information. In addition, an improved instantaneous frequency measurement (IFM) algorithm is proposed, improving low SNR chirp signals' frequency estimation accuracy. Moreover, a simple channel arbitration logic is proposed to complete the cross-channel combination of wideband signals. Simulations show that the proposed receiver architecture is reliable and robust for low SNR and wideband chirp signal detection. When the input SNR is 0 dB, the absolute frequency root-mean-square error (RMSE) of bandwidth and the center frequency is 0.57 MHz and 1.05 MHz, respectively. This frequency accuracy is great for radio frequency (RF) wideband systems. [ABSTRACT FROM AUTHOR]

In recent years, frequency-modulated continuous wave (FMCW) radar has been widely used in automatic driving, settlement monitoring and other fields. The range accuracy is determined by the estimation of the signal beat frequency. The existing algorithms are unable to distinguish between signal components with similar frequencies. To address this problem, this study proposed an enhanced root-MUSIC algorithm based on matrix reconstruction. Firstly, based on the sparsity of a singular value vector, a convex optimization problem was formulated to identify a singular value vector. Two algorithms were proposed to solve the convex optimization problem according to whether the standard deviation of noise needed to be estimated, from which an optimized singular value vector was obtained. Then, a signal matrix was reconstructed using an optimized singular value vector, and the Hankel structure of the signal matrix was restored by utilizing the properties of the Hankel matrix. Finally, the conventional root-MUSIC algorithm was utilized to estimate the signal beat frequency. The simulation results showed that the proposed algorithm improved the frequency resolution of multi-frequency signals in a noisy environment, which is beneficial to improve the multi-target range accuracy and resolution capabilities of FMCW radar. [ABSTRACT FROM AUTHOR]

• Power differences in the gamma frequency range between processing of foreign accent and native accent variations. • No accent-related power differences in low frequencies. • Suggests a difference in phoneme processing for foreign accent during extended speech. Previous studies indicate differences in native and foreign speech processing (Lev-Ari, 2018), with mixed evidence for differences between dialectal and foreign accent processing (Adank, Evans, Stuart-Smith, & Scott, 2009; Floccia et al., 2006, 2009; Girard, Floccia, & Goslin, 2008). Two theories have been proposed: The Perceptual Distance Hypothesis suggests that dialectal accent processing is an attenuated version of foreign accent processing (Clarke & Garrett, 2004), while the Different Processes Hypothesis argues that foreign and dialectal accents are processed via distinct mechanisms (Floccia, Butler, Girard, & Goslin, 2009). A recent single-word ERP study suggested flexibility in these mechanisms (Thomas, Martin, & Caffarra, 2022). The present study deepens this investigation by investigating differences in native, dialectal, and foreign accent processing across frequency bands during extended speech. Electroencephalographic data was recorded from 30 participants who listened to dialogues of approximately six minutes spoken in native, dialectal and foreign accents. Power spectral density estimation (1–35 Hz) was performed. Linear mixed models were done in frequency windows of particular relevance to discourse processing. Frequency bands associated with phoneme [gamma], syllable [theta], and prosody [delta] were considered along with those of general cognitive mechanisms [alpha and beta]. Results show power differences in the Gamma frequency range. While in higher frequency ranges foreign accent processing is differentiated from power amplitudes of native and dialectal accent processing, in low frequencies we do not see any accent-related power amplitude modulations. This suggests that there may be a difference in phoneme processing for native accent types and foreign accent, while we speculate that top-down mechanisms during discourse processing may mitigate the effects observed with short units of speech. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of disentangling nonoverlapping multicomponent signals from their observation being possibly contaminated by external additive noise. We aim to extract and to retrieve the elementary components (also called modes) present in an observed nonstationary mixture signal. To this end, we propose a new pseudo-Bayesian algorithm to perform the estimation of the instantaneous frequency of the signal modes from their time-frequency representation. In a second time, a detection algorithm is developed to restrict the time region where each signal component behaves, to enhance quality of the reconstructed signal. We finally deal with the presence of noise in the vicinity of the estimated instantaneous frequency by introducing a new reconstruction approach relying on nonbinary band-pass synthesis filters. We validate our methods by comparing their reconstruction performance to state-of-the-art approaches through several experiments involving both synthetic and real-world data under different experimental conditions. [ABSTRACT FROM AUTHOR]

Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation for LFM signals under additive white Gaussian noise (AWGN) and additive symmetric alpha stable noise (SαSN). DNN is composed of input, output, and two hidden layers, where several nodes in the first and second hidden layers are 25 and 8, respectively. CNN is the content input layer; many hidden layers include convolution, batch normalization, ReLU, max pooling, fully connected, and dropout. The output layer consists of a fully connected softmax and classification layers. SαS distributions are impulsive noise disturbances found in many communication environments such as marine systems, their distribution lacks a closed-form probability density function (PDF), except for specific cases, and infinite second-order statistics, hence geometric SNR (GSNR) is used in this work to determine the effect of noise in a mixture of Gaussian and SαS noise processes. DNN is a machine learning classifier with few layers for reducing FE and SE complexity. CNN is a deep learning classifier, designed with many layers, and proved to be more accurate than DNN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful to the FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption as compared to the classical FE based on time–frequency analysis, which are important requirements for many systems, such as some Internet of Things (IoT) sensor applications. After training CNN for frequency and slope estimation of LFM signals, the performance of CNN (in terms of accuracy) can give good results at very low signal-to-noise ratios where time–frequency distribution (TFD) fails, giving more than 20 dB difference in the GSNR working range as compared to the classical spectrogram-based estimation, and over 15 dB difference with Viterbi-based estimate. [ABSTRACT FROM AUTHOR]

As the current multi‐segment signal fusion algorithms cannot meet the requirement of high‐precision frequency estimation at different signal‐to‐noise ratios (SNRs), an adaptive fusion algorithm is proposed in this paper. At high SNR, an autocorrelation weighted fusion algorithm is utilised, which adopts the real‐time weighted fusion of multi‐segment autocorrelation signals, resulting in denoised sinusoidal signals. Then the frequency is obtained by minimising the error function based on coarse estimation. At low SNR, an improved spectrum fusion algorithm is adopted, which uses the three‐parameter sine‐fitting method to determine the phase difference compensation factor, and the multi‐segment signal spectrum is weighted to obtain a long signal spectrum with continuous phase. As a result, the frequency can be estimated by spectral peak search. Simulation results show that the proposed algorithm, compared with existing algorithms, not only improves the estimation accuracy by about 20%–50% but also can perform high‐precision frequency estimation for multi‐segment signals of different lengths, frequencies, and abnormal situations. [ABSTRACT FROM AUTHOR]

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The aim of this paper is to make inference about a general class of time series models including fractional Brownian motion. The spectral of these processes is supported on lines not parallel to the diagonal T 1 (x) = x , T j (x) = α j x ± β j , j = 2 , ... , m , in spectral square [ 0 , 2 π) × [ 0 , 2 π) , and this class includes stationary, cyclostationary, almost cyclostationary time series and specially fractional Brownian motions. First, the periodogram of these processes is defined and auxiliary operator is applied to explore the distribution of the periodogram. Then the asymptotical estimation for the spectral density function is proposed and asymptotical Wishart function is found. Finally, the validity of the theoretical results is studied using simulated data sets. [ABSTRACT FROM AUTHOR]

The study analyzed the spectra of acoustic signals obtained during uniaxial compression of artificial sandstone samples under continuous loading through the postultimate state up to failure. The authors attempt to reveal a shift towards lower values in the characteristic acoustic signal frequency as the load approaches the critical load state. The experiments were performed on a lever press in two modes: simple continuous loading and continuous loading with periodic impact on the sample by direct current. This was done in order to indicate the effect of electric current on the acoustic emission parameters. In both cases, when a load reached close-to-ultimate and postultimate states, the amplitude of the signals in the 10–20 kHz frequency range begins to significantly exceed the amplitude of the signals in frequency ranges above 20 kHz. At the final stage, immediately before the onset of an avalanche-like rise in acoustic emission activity, an increase in signals with frequencies in the 5–10 kHz range is also noted. Based on the identical behavior of the samples, the authors have concluded that the electric impact did not significantly affect the spectral acoustic signal characteristics. The shift of the frequency interval, which is accounted for the maximum RMS amplitudes of the acoustic signal, towards low frequencies may indicate either the formation of larger cracks or the appearance of additional cracks of a different mode than at lower loads (e.g., shear cracks). By itself, this phenomenon may indicate impending macrofailure. [ABSTRACT FROM AUTHOR]

This paper applies new and recently introduced approaches to study trends in gun violence in the United States. We use techniques in both the time and frequency domain to provide a more complete understanding of gun violence dynamics. We analyze gun violence incidents on a state-by-state basis as recorded by the Gun Violence Archive. We have numerous specific phenomena of focus, including periodicity of incidents, locations in time where behavioral changes occur, and shifts in gun violence patterns since April 2020. First, we implement a recently introduced method of spectral density estimation for nonstationary time series to investigate periodicity on a state-by-state basis, including revealing where periodic behaviors change with time. We can also classify different patterns of behavioral changes among the states. We then aim to understand the most significant shifts in gun violence since numerous key events in 2020, including the COVID-19 pandemic, lockdowns, and periods of civil unrest. Our dual-domain analysis provides a more thorough understanding and challenges numerous widely held conceptions regarding the prevalence of gun violence incidents. [ABSTRACT FROM AUTHOR]

This paper presents a method based on advanced signal processing techniques for the detection of inte-rturn short circuit faults in an induction machine. The techniques presented can be classified into power spectral density estimation techniques and time-frequency techniques. The theoretical principles of these methods are presented and their performances are compared from the stator current analysis (MCSA). This article proposes a comparative study of these signal processing techniques. One is based on the power spectral density estimation techniques, such as Periodogram and Welch periodogram. The second method is based on the Hilbert transform (HT) to extract the envelope for the stator current. Then, this signal is processed via discrete wavelet transform (DWT) for determining the faulty components in the spectrum of the stator current envelope and identifying the eigenvalues of energy levels (HDWT). This technique is powerful for the non stationary signal processing. Moreover, the combination HT-DWT has assured its ability to evaluate the severity in detecting faults of the shorted turns and it is effective for nonstationary and stationary signals to extract useful information for the detection of ITSC fault. The techniques have usd experimentally data issued from the induction motor. [ABSTRACT FROM AUTHOR]

Nonlinear wavelet-based estimators for spectral densities of non-Gaussian linear processes are considered. The convergence rates of mean integrated squared error (MISE) for those estimators over a large range of Besov function classes are derived, and it is shown that those rates are identical to minimax lower bounds in standard nonparametric regression model within a logarithmic term. Thus, those rates could be considered as nearly optimal. Therefore, the resulting wavelet-based estimators outperform traditional linear methods if the degree of smoothness of spectral densities varies considerably over the interval of interest, such as sharp spike, cusp, bump, etc., since linear estimators are not able to attain these rates. Unlike in classical nonparametric regression with Gaussian noise errors where thresholds are determined by normal distribution, we determine the thresholds based on a Bartlett type approximation of a quadratic form with dependent variables by its corresponding quadratic form with independent identically distributed (i.i.d.) random variables and Hanson-Wright inequality for quadratic forms in sub-gaussian random variables. The theory is illustrated with some numerical examples, and our simulation studies show that our proposed estimators are comparable to the current ones. [ABSTRACT FROM AUTHOR]

• The paper deals with real time song recognition based on new fingerprints which are extracted from audio samples. • Proposed fingerprints are based on short time power spectral density estimation and on a frequency variant threshold. • Corpus of songs can be expanded with new songs without modify the already evaluated fingerprints and data structures. • Performance was evaluated both in terms of recognition accuracy and time-precision using a corpus of 7000 songs. • Results outperform the state of the art song recognition method used by Shazam both in term of accuracy and time-precision. Music and song recognition is an activity of wide interest for researchers and companies due to the intrinsic challenges and the possible economic profits it can give. Despite basic algorithms about song recognition are simple in principle, it is quite difficult to obtain an efficient and robust approach able to generate an effective algorithm for identifying songs on the fly. This statement is proved by the fact that there are very few companies in the world having their core business into this field, even if the potential market is very huge. In this paper, we propose a new approach for generating fingerprints from excerpts of songs that is the first step in implementing a complete algorithm of song recognition. Their generation is based on the Welch's method for spectral density estimation, the use of a Mel filter bank and an exponential adaptive threshold curve in the frequency domain never used before. Even if the previous techniques are not new, at the best of our knowledge they are not used all together for fingerprint generation. Our main purpose is to show that the proposed fingerprint generation approach permits to obtain a very high accuracy in recognizing pieces of song and their position inside the song, as well as it appears robust compared to typical alteration of the audio signal. Specifically, the fingerprints we generate are highly insensitive to noise and audio lossy compression algorithms; moreover, we think the method is prone also to generate pitch insensitive fingerprints with a small modification. We show through an experimentation with a large database of songs the recognition accuracy obtained with our fingerprints is better than the landmark-based approach (already used by the famous Shazam application). This is not a negligible results because even small improvements means a very large number of more recognitions, with higher profit prospects in industrial applications. In order to better focus on the fingerprint structure and its generation algorithm, we don't discuss any specific search algorithm, that is a subject of further work, and we use a linear search only in our experiments; in such a way, we think the goodness of the fingerprint as such is better evinced. [ABSTRACT FROM AUTHOR]