Probability Distribution Of Signal Research Articles

Research on the Strategy for the Flexible Configuration of Chaotic Signal Probability Distribution and Its Application

Research on the Strategy for the Flexible Configuration of Chaotic Signal Probability Distribution and Its Application

Slice-Oriented Signal Probability Distribution Measure for Wind Turbine Generator Bearing Condition Monitoring Under Variable Speed Conditions

Slice-Oriented Signal Probability Distribution Measure for Wind Turbine Generator Bearing Condition Monitoring Under Variable Speed Conditions

Verifying turbulence models in the atmospheric boundary layer by acoustical means

The impacts of turbulence on sound propagation in the atmospheric boundary layer (ABL) are important in many practical applications such as acoustic source localization, sonic boom propagation, and auralization of flying aircraft. However, turbulence impacts can vary dramatically in response to changing meteorological conditions. To address this issue, a turbulence spectral model has been introduced that captures the complexities of turbulence generation by wind shear, buoyancy instabilities, and ground blocking in the ABL. An experiment on vertical and slanted sound propagation through the ABL was conducted to verify the model. Various statistical characteristics of sound signals, including variances of the log-amplitude and phase fluctuations, coherences, and signal probability distributions were measured and compared to theoretical predictions. The dependence of the statistics on meteorological conditions was found to be accurately predicted in conditions of well-developed turbulence, as occurs in windy conditions and the daytime. Predictions in conditions of weak and intermittent turbulence, as often occur at night and around sunrise and sunset, remain challenging.

Structural adaptive damage detection under uncertainty based on probability dissimilarity and moving average control chart

For engineering applications of guided-wave-based online structural damage detection, dealing with uncertainty caused by time-varying conditions (TVCs) is an issue of fundamental importance. In this study, a structural adaptive damage detection method based on probability dissimilarity and moving average control chart (MACC) is proposed. To suppress the uncertain effects of TVCs on signals, the probability dissimilarity-based damage index (PDDI) is extracted based on the proposed adaptive probability modeling of signal feature and probability distribution dissimilarity measure. Then, the automatic damage alarm mechanism based on MACC is proposed to determine alarm threshold adaptively and restrain the uncertain influence further by the moving average of PDDI. The proposed method is verified by the damage detection experiments on typical aircraft lug specimens under varying load and temperature, and then the method is applied to the full-scale aircraft fatigue test. The results demonstrate that the proposed method realizes reliable and adaptive structural damage detection under uncertain conditions.

Robust inference of ecosystem soil water stress from eddy covariance data

Eddy covariance data are invaluable for determining ecosystem water use strategies under soil water stress. However, existing stress inference methods require numerous subjective data processing and model specification assumptions whose effect on the inferred soil water stress signal is rarely quantified. These uncertainties may confound the stress inference and the generalization of ecosystem water use strategies across multiple sites and studies. In this research, we quantify the sensitivity of soil water stress signals inferred from eddy covariance data to the prevailing data and modeling assumptions (i.e., their robustness) to compile a comprehensive list of sites with robust soil water stress signals and assess the performance of current stress inference methods. To accomplish this, we identify the most prevalent assumptions from the literature and perform a digital factorial experiment to extract probability distributions of plausible soil water stress signals and model performance at 151 FLUXNET2015 and AmeriFlux-FLUXNET sites. We develop a new framework that summarizes these probability distributions to classify and rank the robustness of each site’s soil water stress signal, which we display with a user-friendly heat map. We estimate that only 5%–36% of sites exhibit a robust soil water stress signal due to deficient model performance and poorly constrained ecosystem water use parameters. We also find that the lack of robustness is site-specific, which undermines grouping stress signals by broad ecosystem categories or comparing results across studies with differing assumptions. Lastly, existing stress inference methods appear better suited for eddy covariance sites with grass/annual vegetation. Our findings call for more careful and consistent inference of ecosystem water stress from eddy covariance data.

Recursive Probability Mass Function Method to Calculate Probability Distributions of Pulse-Shaped Signals

This paper proposes an accurate and efficient method to calculate probability distributions of pulse-shaped complex signals. We show that the distribution over the in-phase and quadrature-phase (I/Q) complex plane is obtained by a recursive probability mass function of the accumulator for a pulse-shaping filter. In contrast to existing analytical methods, the proposed method provides complex-plane distributions in addition to instantaneous power distributions. Since digital signal processing generally deals with complex amplitude rather than power, the complex-plane distributions are more useful when considering digital signal processing. In addition, our approach is free from the derivation of signal-dependent functions. This fact results in its easy application to arbitrary constellations and pulse-shaping filters like Monte Carlo simulations. Since the proposed method works without numerical integrals and calculations of transcendental functions, the accuracy degradation caused by floating-point arithmetic is inherently reduced. Even though our method is faster than Monte Carlo simulations, the obtained distributions are more accurate. These features of the proposed method realize a novel framework for evaluating the characteristics of pulse-shaped signals, leading to new modulation, predistortion and peak-to-average power ratio (PAPR) reduction schemes.

Health indicator based on signal probability distribution measures for machinery condition monitoring

Health indicator (HI), which aims to make quantitative measures for machinery operating state at different degradation stages, is very critical in machinery condition monitoring. Some HIs from different aspects have been developed and reported in recent years. However, a preferable HI which is more robust to transient interferences, free of complicated model training and also sensitive to incipient defects in machinery condition monitoring still remains to be further investigated. To address these issues, a novel HI based on signal probability distribution measures is proposed in this paper. Firstly, characteristic parameters of the alpha stable distribution are preliminarily estimated based on the machinery degradation data, the consistency of which is quantitatively evaluated and optimized through the hypothesis test with a parameter calibration strategy. Afterwards, signal distribution models are accordingly constructed to describe the statistical characteristics of the machinery degradation data. On this basis, the deviation of the established signal distribution models between the current degradation state and the initial fault-free state is accordingly analyzed and quantified for machinery degradation assessment. Experimental validations by using simulated and industrial run-to-failure datasets demonstrate that the proposed HI can effectively recognize the state shift of the machinery during the degradation process and can be therefore applied for machinery condition monitoring.

Signal power distributions for simulated outdoor sound propagation in varying refractive conditions.

Probability distributions of acoustic signals propagating through the near-ground atmosphere are simulated by the parabolic equation method. The simulations involve propagation at four angles relative to the mean wind, with frequencies of 100, 200, 400, and 800 Hz. The environmental representation includes realistic atmospheric refractive profiles, turbulence, and ground interactions; cases are considered with and without parametric uncertainties in the wind velocity and surface heat flux. The simulated signals are found to span a broad range of scintillation indices, from near zero to exceeding ten. In the absence of uncertainties, the signal power (or intensity) is fit well by a two-parameter gamma distribution, regardless of the frequency and refractive conditions. When the uncertainties are included, three-parameter distributions, namely, the compound gamma or generalized gamma, are needed for a good fit to the simulation data. The compound gamma distribution appears preferable because its parameters have a straight forward interpretation related to the saturation and modulation of the signal by uncertainties.

Fatigue life prediction based on modified narrowband method under broadband random vibration loading

The vibration fatigue life and vibration response signal at the dangerous position of each specimen were obtained by the broadband random vibration fatigue test for 7075-T651 aluminum alloy, and the stress amplitude probability distribution of vibration response signal was counted by rainflow counting method. By comparing the probability density distribution functions of broadband random vibration signal by frequency-domain method and analyzing the fitting accuracy of different distribution models for rainflow distribution, a new model for frequency-domain fatigue life prediction based on modified narrowband distribution model was proposed. The proposed model takes the second-order spectral width parameter of vibration signal as the weight coefficient, and the life prediction results are more accurate and less dispersive.

Bayesian Inferences on Neural Activity in EEG-Based Brain-Computer Interface

A brain-computer interface (BCI) is a system that translates brain activity into commands to operate technology. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common non-target stimuli. Few existing ERP classifiers directly explore the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian analysis of the probability distribution of multi-channel real EEG signals under the P300 ERP-BCI design. We aim to identify relevant spatial temporal differences of the neural activity, which provides statistical evidence of P300 ERP responses and helps design individually efficient and accurate BCIs. As one key finding of our single participant analysis, there is a 90% posterior probability that the target ERPs of the channels around visual cortex reach their negative peaks around 200 milliseconds post-stimulus. Our analysis identifies five important channels (PO7, PO8, Oz, P4, Cz) for the BCI speller leading to a 100% prediction accuracy. From the analyses of nine other participants, we consistently select the identified five channels, and the selection frequencies are robust to small variations of bandpass filters and kernel hyper-parameters.

Notch fatigue behavior of needled C/SiC composite under random vibration loading

In order to study the properties of needled C/SiC composite under random vibration loading, a kind of plate specimen with arc notch was designed. Random vibration tests were carried out on a vibration table, the vibration fatigue life and vibration response signal at the dangerous position of each specimen were obtained. The strain amplitude probability distribution of vibration response signal was obtained by rainflow counting method, which can be approximately described by the Dirlik distribution. The attenuation history curve of natural frequency for the specimen presented obvious nonlinearity. In addition, the microscopic fracture morphology of failed specimens showed that the major failure mode of fibers in axial and transverse direction are pull-out and splitting, respectively. And the fiber bundles adhering strongly to the matrix can form a neat fracture surface.

Определение порогов обнаружения радиосигналов для метода энергетического детектора.

The estimation method for signal-to-noise ratio threshold values pertinent for low-level signals detection is developed and employed. The method is applicable for original energy detection technique based upon distribution-free statistics computed using spectrum samples provided by measuring equipment. Estimated thresholds are determined by measuring equipment inherent noise fluctuations and can be established in advance for certain hardware settings and sample lengths. For a typical spectrum analyzer model estimated sensitivity threshold varied from +0,6 dB to –11 dB for spectrum samples lengths ranged between 470 and 30 000 spectrums respectively. Experimental data confirmed estimated values and equivalence of sensitivity thresholds for white noise (generated by analog generator) and broadcasted LTE signals (generated by cellular base stations). The suggested energy detection technique is independent of signal's modulation, signal's probability distribution features, and intermittent or sporadic signal's total duration allotment profile within data acquisition period.

Low-level signals radiomonitoring using periodic spectrum samples analysis

The article presents practical implementation of energy detection method based upon non-parametric statistics computed using periodic spectrum samples provided by measuring equipment. The method enables efficient detection and monitoring for signals with low or negative signal-to-noise ratio. The method's sensitivity is limited by measuring equipment inherent noise fluctuations and can be a priory experimentally established for certain experimental hardware settings and desirable spectrum samples lengths. Sensitivity thresholds (in terms of signal-to-noise ratio) for reliable (with probability > 0,98) signal detection for a typical spectrum analyzer used in experiment varied from –11 dB to + 0,6 dB for spectrum samples lengths ranged between 30 000 and 470 spectrums respectively. The suggested energy detection method can be used for unstable and intermittent signals detection, which are active (or above sensitivity threshold) only for a fraction of spectrum sample recording time. The method is independent of signal's modulation (if any is used), amplitude variability profile and signal's probability distribution features. Experimentally determined sensitivity threshold levels for real radio frequency signals coincided within 1,9 dB tolerances with corresponding levels estimated from spectrum analyzer inherent noise fluctuations for all implemented spectrum samples lengths. The data recording time for abovementioned spectrum samples lengths ranged between 207 and 3,2 seconds respectively and was entirely hardware-dependent parameter. Experiment proved equal efficiency and reliability of the suggested method for reliable detection for both white noise signal (generated by analog generator) and broadcasted LTE signal (generated by cellular base stations), which were affected by multi-path propagation effects and average signal level instability due to subscribers time-varying activity. The experiment showed the proposed energy detection method besides detection of low-level radio frequency signals (down to –11 dB SNR) provides highly reliable assessment of the detected signal's signal-to-noise ratio with 0,6 dB tolerance and 0,95 probability. The energy detection method demonstrated zero level of false detections when there was no signal at the spectrum analyzer input (the input port of the instrument was terminated by a matched load), which is essential for method applicability in tasks of highly reliable detection of low-level signals from various types of sources. Taking into account specifications of available hardware, required sensitivity level and limits for data recording time it is possible to choose optimal length of spectrum sample for the energy detection method, which would be the most reasonable for any task in question. The energy detection method based upon non-parametric statistics computed using periodic spectrum samples can be effectively used in detection and radiomonitoring of low-level signals, in radio frequency electromagnetic compatibility research tasks and radio propagation path properties analysis in high loss environment.

Research on fault identification of gearbox bearing based on quantum theory

In view of the problem that the fault signal of bearing in automobile gearbox is easy to be covered by background noise, a fault signal recognition method based on quantum theory is proposed. This method will quantize the individual sampling signal, and take the vibration signal as the state superposition of fault signal and background noise, deeply study the randomness and local characteristics of sampling signal, and analyze the core information of sampling point, so that the amplitude characteristics of sampling point signal can be converted into the probability distribution of fault signal and noise signal in quantum domain, and change integrated attenuation of noise reduction for the fault signal, and realize the fine quantization noise as well as the highlight of the fault information. Experimental results show that the algorithm is feasible and efficient.

Clustering of Biomedical Signals in Recognition Systems with Supervised Learning

Recognition of biomedical signals by the characteristics of their shape using supervised learning algorithms in patient diagnostic systems is considered. The classification of signals is assumed to be known and training samples are assumed to be available. A statistical assessment of the decision fidelity of the types of signals in the control samples by indicators of sensitivity, specificity, general validity is assumed.The principle and procedure for improving the basic signal recognition procedure are disclosed. A software toolkit is developed and researched to identify and implement possible reserves in increasing the fidelity of decisions from a more detailed consideration of the nature and characteristics of the probability distributions of signals in their phase space. For this purpose, clustering of training samples is additionally carried out at locations of their aggregation, separately according to signal classes. Families of cluster standards attached to these locations are formed and they are used to determine the types of incoming signals.The procedures for their processing are getting more complicated. The complexity of training and decision-making is growing. Signal recognition training becomes combined. However, their initial classification, which carries a certain medical meaning, is not violated. The emphasis in decision-making is shifted to comparing recognized signals with nearest standards in their vicinity. Distant standards lose their influence.The development and research are illustrated by examples of recognition of three types of QRS complexes (N, A, and V) in the patient’s electrocardiogram record. The effectiveness of the developed tools is tested on concrete training and control samples in comparison with the basic algorithm.

Instrumental virtual tools for measuring random signal characteristics

Based on modern computer technologies developed by National Instruments, a virtual device for adjusting, controlling and studying various noise characteristics has been developed. Signs helping identify the laws of random signal probability distribution are smoothness or sharpen-ing, asymmetry and uncertainty. Examples of identification of noise characteristics of elec-tronic devices are provided; instrument technologies are used to monitor the technical condi-tion of such devices.

Muscle Fatigue Analysis by Using a Scale Mixture-based Stochastic Model of Surface EMG Signals.

This paper presents the estimation and analysis of surface electromyogram (EMG) signals during fatiguing contractions based on a stochastic EMG model. In the model, the probability distribution of EMG signals is assumed to be a mixture of Gaussians with the same mean but different variances, facilitating the representation of the variance distribution of EMG signals. The paper proposes a continuous estimation method for variance distribution parameters using a sliding window, enabling the evaluation of the time-varying stochastic properties of EMG signals. Estimation experiments were conducted on six healthy young adults to analyze changes in EMG variance distribution with the progression of muscle fatigue. The results reveal the linear and nonlinear relationships between muscle fatigue and variance distribution parameters.

The analysis of binary matrix symmetry properties in the tasks concerned with test control of digital devices

The object of research is the properties of binary matrix symmetry. The constructed model of a binary matrix enabled us to determine the measure of matrix symmetry. Entropy criteria are obtained on the basis of the symmetry measure, which allows us to rank objects, represented by binary structures, in the order of their preference, and to solve various optimization tasks. The use of the obtained results in the tasks concerned with test control of digital devices has been shown. The “black box” is type model of a digital device was described. The tasks were solved, relating to optimizing the probability distribution of input signals during random testing of discrete devices. With the help of the generator of pseudo-random numbers, the tests were devised covering single faults, modeled as «stuck-at faults» and «bridging faults» at control points. The resulting entropy criteria allowed us to reduce the duration of control tests of digital devices. The experimental data analysis enables us to make a conclusion about the expediency of using the entropy criteria in the theory and practice of digital device testing.

A Scale Mixture-Based Stochastic Model of Surface EMG Signals With Variable Variances.

Surface electromyogram (EMG) signals have typically been assumed to follow a Gaussian distribution. However, the presence of non-Gaussian signals associated with muscle activity has been reported in recent studies, and there is no general model of the distribution of EMG signals that can explain both non-Gaussian and Gaussian distributions within a unified scheme. In this paper, we describe the formulation of a non-Gaussian EMG model based on a scale mixture distribution. In the model, an EMG signal at a certain time follows a Gaussian distribution, and its variance is handled as a random variable that follows an inverse gamma distribution. Accordingly, the probability distribution of EMG signals is assumed to be a mixture of Gaussians with the same mean but different variances. The EMG variance distribution is estimated via marginal likelihood maximization. Experiments involving nine participants revealed that the proposed model provides a better fit to recorded EMG signals than conventional EMG models. It was also shown that variance distribution parameters may reflect underlying motor unit activity. This study proposed a scale mixture distribution-based stochastic EMG model capable of representing changes in non-Gaussianity associated with muscle activity. A series of experiments demonstrated the validity of the model and highlighted the relationship between the variance distribution and muscle force. The proposed model helps to clarify conventional wisdom regarding the probability distribution of surface EMG signals within a unified scheme.

Classification of pressure ulcer tissues with 3D convolutional neural network

A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising. Graphical Abstract The Classification of Pressure Ulcer Tissues Based on 3D Convolutional Neural Network.