Aperiodic Signals Research Articles

Harmonic Detection for Active Power Filter Based on Two-Step Improved EEMD

The performance of active power filter (APF) mainly depends on its harmonic detection method. Empirical mode decomposition (EMD) is applied to APF because of its effectiveness for any complicated signal analysis. Ensemble empirical mode decomposition (EEMD) can suppress mode mixing caused by EMD to a certain extent, but the amplitude and energy of fundamental is severely attenuated. To solve this problem, in this article, the causes of attenuation are discussed from the perspective of intrinsic mode functions (IMFs) energy, and an approach based on two-step improved EEMD is proposed. The first improved step (FI-EEMD) is to suppress the mode mixing and fundamental attenuation by injecting white noise and analytical signal with determined parameters into the target signal. The parameters of the analytical signal are selected according to the power grid conditions and signal characteristics. The second improved step (SI-EEMD) puts forward a signal reconstruction method based on the distribution of IMF energy extreme points and the distinguishing law of signal and noise to reduce the influence of noise-dominated components. The results show that the two-step improved EEMD not only has high-quality extracted fundamental with low total harmonic distortion (THD) and energy attenuation, but also has good applicability to aperiodic and nonstationary signals, which greatly improve the harmonic detection accuracy of APF.

The Generalized Nafass Method: How to Fit a Discrete Aperiodic Signal by a Finite Set of Harmonic Components?

The Generalized Nafass Method: How to Fit a Discrete Aperiodic Signal by a Finite Set of Harmonic Components?

Connectomics of human electrophysiology

We present both a scientific overview and conceptual positions concerning the challenges and assets of electrophysiological measurements in the search for the nature and functions of the human connectome. We discuss how the field has been inspired by findings and approaches from functional magnetic resonance imaging (fMRI) and informed by a small number of significant multimodal empirical studies, which show that the canonical networks that are commonplace in fMRI are in fact rooted in electrophysiological processes. This review is also an opportunity to produce a brief, up-to-date critical survey of current data modalities and analytical methods available for deriving both static and dynamic connectomes from electrophysiology. We review hurdles that challenge the significance and impact of current electrophysiology connectome research. We then encourage the field to take a leap of faith and embrace the wealth of electrophysiological signals, despite their apparent, disconcerting complexity. Our position is that electrophysiology connectomics is poised to inform testable mechanistic models of information integration in hierarchical brain networks, constructed from observable oscillatory and aperiodic signal components and their polyrhythmic interactions.

A high dimensional stochastic resonance system and its application in signal processing

At present, the stochastic resonance (SR) theory based on the traditional model with periodic input signal has been extensively studied. To further investigate dynamic process and improve the universality of SR system, a high-dimensional SR model is proposed in this paper. By analyzing the dynamic characteristics, the SR phenomenon is explained from the perspective of equilibrium point which is driven by the input signal. The expression of some key parameters is derived, and the noise reduction of aperiodic image signal is studied. Experimental results show that the SR phenomenon can be induced by appropriate input signal, and the proposed high-dimensional SR system has obvious noise reduction effect on aperiodic image signals, which further widens the application of SR theory in signal processing.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Cortical correlation structure of aperiodic neuronal population activity

Electrophysiological population signals contain oscillatory and non-oscillatory aperiodic (1/frequency-like) components. So far research has largely focused on oscillatory activity, and only recently, interest in aperiodic population activity has gained momentum. Accordingly, while the cortical correlation structure of oscillatory population activity has been characterized, little is known about the correlation of aperiodic neuronal activity. To address this, we investigated aperiodic neuronal population activity in the human brain using resting-state magnetoencephalography (MEG). We combined source-analysis, signal orthogonalization and irregular-resampling auto-spectral analysis (IRASA) to systematically characterize the cortical distribution and correlation of aperiodic neuronal activity. We found that aperiodic population activity is robustly correlated across the cortex and that this correlation is spatially well structured. Furthermore, we found that the cortical correlation structure of aperiodic activity is similar but distinct from the correlation structure of oscillatory neuronal activity. Anterior cortical regions showed the strongest differences between oscillatory and aperiodic correlation patterns. Our results suggest that correlations of aperiodic population activity serve as robust markers of cortical network interactions. Furthermore, our results show that aperiodic and oscillatory signal components provide non-redundant information about large-scale neuronal correlations. This may reflect at least partly distinct neuronal mechanisms underlying and reflected by oscillatory and aperiodic neuronal population activity.

Variation of Harmonics to Noise Ratio from the Age Range of 9-18 Years Old in both the Genders.

Voice is the perception of physical complexity of the laryngeal tone modified by a resonating cavity. The voice parameters are the vital signs of an individual to project their personality and gender. The basic acoustical parameters of voice are: Fundamental frequency, Intensity, Jitter, Shimmer and Harmonics to noise ratio (HNR) and among these parameters HNR is the most significant. The voice component represents the periodic and aperiodic signals and predominantly aperiodic signals. There are many research work have been documented on acoustical analysis of voice with narrowed age range at different zonals in India. The motive of the study was mainly focused on the ratio between periodic and aperiodic components of voice and its changes from pre pubertal to post pubertal stages in both the genders. Due to the paucity of literature on changes in HNR in developing children, the present study was conducted to find out how do the changes in HNR occur in both the genders. Observational study design with convenient sampling method was used for the collection of raw data. A total of 198 subjects comprising of group I males (n = 99) and group II females (n = 99). These groups were further subdivided into 9 sub groups in males (n = 99) and females (n = 99) based on the age range. Sustained phonation of /a/ was used as a stimulus for voice sample. The data was analyzed using PRAAT software. The over mean Harmonic to Noise Ratio was found to be higher in males compared to the females. When age wise comparison was done, a significant difference in the Harmonic to Noise Ratio was found in males with no significant changes noticed in females. The transition in the Harmonic to Noise Ratio in males was seen to start at the age of 14-15years.

Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh–Nagumo stochastic resonance

Objective. Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain–computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio. Approach. Using the principle of nonlinear aperiodic FitzHugh–Nagumo (FHN) model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times. Results: A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise. Significance. Compared with the conventional average and average-singular spectrum analysis-independent component analysis(average-SSA-ICA) method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.

Amplitude and frequency estimator for aperiodic multi-frequency noisy vibration signals of a tram gearbox

Sinusoidal parameter estimation for determining frequency position and amplitude is challenging for noisy short vibration signals, e.g. from machines or human vibrations. In this paper, we propose the “Trimmed Window Discrete Fourier Transform” (TWDFT) estimator, which uses for every frequency a one-point discrete Fourier transform (DFT) to determine the corresponding spectral amplitude. To avoid leakage effects, it cuts the time interval so that it corresponds to an integer number of period durations. To evaluate the estimator performance, we compare it with relevant estimators such as the Cramer-Rao lower bound (CRLB) and the spectral spline interpolation applied on a noisy mono-frequent test signal with a fractional frequency. For the estimated parameters, the mean squared errors (MSE) are calculated and compared as a function of the signal-to-noise ratio (SNR). The advantages of the TWDFT estimator can be seen over the whole SNR range. The TWDFT estimates are better than the fast Fourier transform (FFT) starting at a SNR of –6 dB. At a SNR of 30 dB, the estimator meets the real value of the frequency and reaches similar results as the CRLB. The application of the TWDFT estimator as a short-time analysis on a vibration signal of a tram gearbox shows a significantly more differentiated time-frequency analysis compared to a short-time Fourier transform (STFT).

The development of theta and alpha neural oscillations from ages 3 to 24 years

Intrinsic, unconstrained neural activity exhibits rich spatial, temporal, and spectral organization that undergoes continuous refinement from childhood through adolescence. The goal of this study was to investigate the development of theta (4−8 Hertz) and alpha (8−12 Hertz) oscillations from early childhood to adulthood (years 3–24), as these oscillations play a fundamental role in cognitive function. We analyzed eyes-open, resting-state EEG data from 96 participants to estimate genuine oscillations separately from the aperiodic (1/f) signal. We examined age-related differences in the aperiodic signal (slope and offset), as well as the peak frequency and power of the dominant posterior oscillation. For the aperiodic signal, we found that both the aperiodic slope and offset decreased with age. For the dominant oscillation, we found that peak frequency, but not power, increased with age. Critically, early childhood (ages 3–7) was characterized by a dominance of theta oscillations in posterior electrodes, whereas peak frequency of the dominant oscillation in the alpha range increased between ages 7 and 24. Furthermore, theta oscillations displayed a topographical transition from dominance in posterior electrodes in early childhood to anterior electrodes in adulthood. Our results provide a quantitative description of the development of theta and alpha oscillations.

A Complex-Valued Oscillatory Neural Network for Storage and Retrieval of Multidimensional Aperiodic Signals.

Recurrent neural networks with associative memory properties are typically based on fixed-point dynamics, which is fundamentally distinct from the oscillatory dynamics of the brain. There have been proposals for oscillatory associative memories, but here too, in the majority of cases, only binary patterns are stored as oscillatory states in the network. Oscillatory neural network models typically operate at a single/common frequency. At multiple frequencies, even a pair of oscillators with real coupling exhibits rich dynamics of Arnold tongues, not easily harnessed to achieve reliable memory storage and retrieval. Since real brain dynamics comprises of a wide range of spectral components, there is a need for oscillatory neural network models that operate at multiple frequencies. We propose an oscillatory neural network that can model multiple time series simultaneously by performing a Fourier-like decomposition of the signals. We show that these enhanced properties of a network of Hopf oscillators become possible by operating in the complex-variable domain. In this model, the single neural oscillator is modeled as a Hopf oscillator, with adaptive frequency and dynamics described over the complex domain. We propose a novel form of coupling, dubbed “power coupling,” between complex Hopf oscillators. With power coupling, expressed naturally only in the complex-variable domain, it is possible to achieve stable (normalized) phase relationships in a network of multifrequency oscillators. Network connections are trained either by Hebb-like learning or by delta rule, adapted to the complex domain. The network is capable of modeling N-channel electroencephalogram time series with high accuracy and shows the potential as an effective model of large-scale brain dynamics.

Analog-to-Information Conversion with Random Interval Integration

A novel method of analog-to-information conversion—the random interval integration—is proposed and studied in this paper. This method is intended primarily for compressed sensing of aperiodic or quasiperiodic signals acquired by commonly used sensors such as ECG, environmental, and other sensors, the output of which can be modeled by multi-harmonic signals. The main idea of the method is based on input signal integration by a randomly resettable integrator before the AD conversion. The integrator’s reset is controlled by a random sequence generator. The signal reconstruction employs a commonly used algorithm based on the minimalization of a distance norm between the original measurement vector and vector calculated from the reconstructed signal. The signal reconstruction is performed by solving an overdetermined problem, which is considered a state-of-the-art approach. The notable advantage of random interval integration is simple hardware implementation with commonly used components. The performance of the proposed method was evaluated using ECG signals from the MIT-BIH database, multi-sine, and own database of environmental test signals. The proposed method performance is compared to commonly used analog-to-information conversion methods: random sampling, random demodulation, and random modulation pre-integration. A comparison of the mentioned methods is performed by simulation in LabVIEW software. The achieved results suggest that the random interval integration outperforms other single-channel architectures. In certain situations, it can reach the performance of a much-more complex, but commonly used random modulation pre-integrator.

Prescribed chattering reduction control for quadrotors using aperiodic signal updating

In the paper, a prescribed chattering reduction control using aperiodic signal updating is presented for quadrotors subject to parameter uncertainties and external disturbances. Using estimation errors instead of tracking errors to update adaptive laws, estimator-based minimum learning parameter (EMLP) observers capable of relaxing computational complexity are respectively explored in translational and rotational loops to reject fast time-varying disturbances, such that transient oscillations can be efficiently mitigated even with a large adaptive gain. Meanwhile, quantitative analysis for transient learning performance is characterized by means of L2 norms of time differential of neural network weights. With the aid of disturbance estimates, a relative event-triggered robust control law is derived by inserting a compensation term to guarantee a favorable trajectory tracking with Zeno free behaviors and decreased sampling cost. Besides, an appointed-time prescribed performance control (APPC) is established, enforcing trajectory tracking errors to evolve within pre-given regions even in face of triggering errors, where a piecewise and continuous finite-time behavior function, rather than an exponential decaying function, is applied to enable a preassigned fast convergence time without retuning controller parameters. Finally, the stability of closed-loop system is proved via Lyapunov synthesis, while comparative studies are provided to validate the effectiveness of presented control method.

Multi-chromosomal CGP-evolved RNN for signal reconstruction

A multi-chromosomal structure for neuro-evolution of recurrent neural networks (RNNs) with Cartesian genetic programming (CGP) architecture is presented to develop a signal reconstruction model. The group behaviour of multi-chromosomes evolved together is explored in terms of computational and structural complexity, and accurate reconstruction of the corrupted signal. Signal reconstruction modelling is done through prediction of missing samples, and a sliding window mechanism is utilized to recover long segments of missing samples in periodic and aperiodic corrupted signals. The proposed model is compared with the conventional single chromosome-based Cartesian Genetic Programming-evolved Recurrent Neural Network (CGPRNN) architecture as well as state-of-the-art machine learning algorithms, i.e. linear regression, random forest and long short-term memory deep learning neural network. Diverse network architectures of the proposed Multi-Chromosomal Cartesian Genetic Programming-evolved Recurrent Neural Network (MC-CGPRNN) are evolved to explore the robustness of algorithm in terms of optimization of hyper parameters, evolutionary speed and generalization ability. The proposed algorithm is trained and tested on speech signal with both periodic and aperiodic noise. The best trained network is also evaluated on standard music signals achieving remarkable results. Experimental results show that the proposed MC-CGPRNN algorithm achieved SNR of 25.23 dB for speech signal, 29.92 dB for guitar signal and 28.45 dB for flute signal in case of 50% missing samples showing best SNR improvement in comparison with its counterparts for time domain signal reconstruction.

A novel stochastic resonance model based on bistable stochastic pooling network and its application

• A novel stochastic resonance model based on bistable stochastic pooling network is proposed. • The least mean square algorithm is used to optimize the BSPN output vector optimized by random noise with linear weighting. • The SNR of the output signal is deduced, and the change of SNR with the number of network nodes is discussed. • Some examples are used to verify the weak signal detection capability of the BSPN model. Analysing the vibration and sound signals of machine components is the primary approach for machine condition monitoring and fault diagnosis. However, due to the special working operating conditions of rotating machinery, the collected signals often contain strong noise components generated by other parts of the machine and harsh environment. These noises severely affect the analysis and processing of the target signal. Stochastic resonance (SR) is an effective technique to extract and enhance periodic or aperiodic signals submerged in noise. Consequently, SR has been widely used for fault diagnosis of rotating machinery. In this study, a bistable stochastic pooling network (BSPN) model based on the traditional SR model is proposed to improve the efficiency of weak fault diagnosis. The least mean square algorithm is used to perform linear weighted optimization on the output vector of random noise-optimized BSPN. At the same time, the optimal weight vector of the random stochastic pooling networks with any number of nodes is obtained. Subsequently, analog signals are used to examine the output signal-to-noise ratio (SNR) of the BSPN. Finally, the efficacy of BSPN system is validated through bearing data collected by two different experimental systems. The experimental results indicate that ordinary array system cannot avoid frequency conversion interference, so it is unable to extract extremely weak fault signals. On the contrary, the BSPN system can accurately detect the weak.

Experimental increase of blood glucose alters resting state EEG measures of excitation-inhibition balance.

What is the central question of this study? Glucose is the dominant energy source for the brain. However, little is known about how glucose metabolism impacts the coordination of network activity in the brain in healthy adults. What is the main finding and its importance? We demonstrate that both α oscillations and the aperiodic signal components of the resting EEG are modulated by experimentally elevated blood glucose concentrations. Our findings suggest that glucose increases measures associated with excitation-inhibition (E:I) balance, but that the effect on α oscillations might plateau. Understanding the relationship between glucose consumption and E:I balance is crucial to developing our understanding of how metabolism shapes human brain activity. Brain network oscillations can be divided broadly into periodic and aperiodic signal components, which are sensitive to state-dependent changes in network coordination and excitation-inhibition (E:I) balance. We sought to address whether the dominant energy source of the brain, glucose, is implicated in the regulation of network activity and excitability. We conducted an experimenter-blind, crossover study of the effect of blood glucose level (BGL) on the resting EEG frequency spectrum. Participants consumed a glucose drink (75g glucose) or an equivalent volume of water on two separate visits. EEG data were sampled before and ≤3h after the drink. We found that the experimentally induced changes in BGL exhibited an inverted U-shaped relationship, with changes in the individual α frequency peak, whereas the slope of the aperiodic signal component of the frequency spectrum showed a positive linear association suggestive of greater excitation. In contrast, peak α power, which is typically associated with top-down inhibitory processes, was negatively associated with changes in BGL. Collectively, these results suggest that high BGL alters brain network coordination in the form of α oscillations and measures associated with E:I balance.

Recovery and enhancement of unknown aperiodic binary signal by adaptive aperiodic stochastic resonance

In this study, the system with fractional power nonlinearity is introduced into the theory of aperiodic stochastic resonance (ASR). The fractional exponent is a key parameter and its effect on the ASR phenomenon excited by aperiodic binary signal is investigated in this system. Compared to the classical bistable system, the system with fractional power nonlinearity shows better performance. It can adjust not only the noise intensity but also the fractional exponent to enhance weak signal. In the field of signal transmission, pure aperiodic binary signal is usually submerged in the noise and the signal is unknown. Thus, an effective method is proposed based on ASR and moving average. By the method, the unknown aperiodic binary signal can be recovered in the noise background. To improve the efficiency of the signal recovery, the adaptive ASR is realised with the help of adaptive particle swarm optimisation (APSO) algorithm to optimise the parameters. The method may provide some reference to the engineering field.

PeriodNet: A Non-Autoregressive Raw Waveform Generative Model With a Structure Separating Periodic and Aperiodic Components

This paper presents PeriodNet, a non-autoregressive (non-AR) waveform generative model with a new model structure for modeling periodic and aperiodic components in speech waveforms. Non-AR raw waveform generative models have enabled the fast generation of high-quality waveforms. However, the variations of waveforms that these models can reconstruct are limited by training data. In addition, typical non-AR models reconstruct a speech waveform from a single Gaussian input despite the mixture of periodic and aperiodic signals in speech. These may significantly affect the waveform generation process in some applications such as singing voice synthesis systems, which require reproducing accurate pitch and natural sounds with less periodicity, including husky and breath sounds. PeriodNet uses a parallel or series model structure to model a speech waveform to tackle these problems. Two sub-generators connected in parallel or in series take an explicit periodic and aperiodic signal (sine wave and Gaussian noise) as an input. Since PeriodNet models periodic and aperiodic components by focusing on whether these input signals are autocorrelated or not, it does not require external periodic/aperiodic decomposition during training. Experimental results show that our proposed structure improves the naturalness of generated waveforms. We also show that speech waveforms with a pitch outside of the training data range can be generated with more naturalness.

A Biproportional Construction Algorithm for Correctly Calculating Fourier Series of Aperiodic Non-Sinusoidal Signal

The Fourier series (FS) applies to a periodic non-sinusoidal function satisfying the Dirichlet conditions, whereas the being-processed function in practical applications is usually an aperiodic non-sinusoidal signal. When is aperiodic, its calculated FS is not correct, which is still a challenging problem. To overcome the problem, we derive a direct calculation algorithm, a constant iteration algorithm, and an optimal iteration algorithm. The direct calculation algorithm correctly calculates its Fourier coefficients (FCs) when is periodic and satisfies the Dirichlet conditions. Both the constant iteration algorithm and the optimal iteration algorithm provide an idea of determining the states of . From the idea, we obtain an algorithm for determining the states of based on the optimal iteration algorithm. In the algorithm, the variable iteration step is introduced; thus, we present an algorithm for determining the states of based on the variable iteration step. The presented algorithm accurately determines the states of . On the basis of these algorithms, we build a biproportional construction theory. The theory consists of a first and a second proportional construction theory. The former correctly calculates the FCs of at the present sampling time

Problems of Insulation Diagnostics of Power Equipment by the Method of Partial Discharges

Target. High information value and reliability of diagnostics of isolation by partial discharges. Methods. Simulation of PD in Multisim and calculation of electrical fields of insulation defects by finite element method in the program ComSol. Results. A strong influence of the power supply on the value of voltage surge in case of emergency has been established. The method of adjustment of this influence is proposed. It was found that the oscillatory form of the PD is mainly determined not by the parameters of the insulation defect, but by the parameters of the energy object in which this defect is observed. Method for separation of aperiodic signal of PD from oscillatory component of system transient process in PD is proposed. It is shown that in operating turbogenerators and power transformers the unambiguous connection between the phase of PD occurrence and the voltage of PD occurrence is broken. Method of determination of PD occurrence place in slot part of turbogenerators is proposed. It is shown that in order to increase the reliability of comparing PD in polymer insulation of power cables of different voltage classes and geometric dimensions, when calibrating the measuring system, it is necessary to take into account the speed of propagation of the PD pulse through the cable and the capacity of the cable length unit. The concept of a corrective coefficient for calibration has been introduced and a method for determining it has been proposed. Conclusions. Taking into account the differences in PD parameters in real objects of the electric power industry, proposed in the article, from the parameters obtained in the standard laboratory model of the PD should increase the information and reliability of the PD method, as well as make it possible to conduct a comparative analysis of the results obtained under various observation conditions.