Period Estimation Method Research Articles

Dynamic estimation method for pulsar periods based on photon energy distribution folding and image template matching

The accuracy of the pulsar period estimation directly affects the restoration effect of the signal profile. A more accurate pulsar profile will help improve the accuracy of pulsar delay estimation and thereby improve the performance of X-ray pulsar navigation. This paper proposes a pulsar period estimation method based on photon energy distribution folding and image template matching (PETM). This method uses the probability distribution information of photon energy for weighted epoch folding. The one-dimensional (1D) profile information was converted into two-dimensional (2D) image information through reverse space-filling curve (SFC) encoding. Then, a feature matching was performed between the target structure and the template structure. At the same time, the criterion of Pearson correlation coefficient (PCC) was used to quantitatively evaluate the matching effect to estimate the optimal period. The simulation results show that the period estimation accuracy of the PETM method is significantly improved, as compared with the traditional \( method. This work also analyzes the folding effect based on the photon energy distribution model and conducts simulation experiments and comparisons on influencing factors, such as noise interference and data quality. At the same time, we also specifically demonstrated the effectiveness of the PETM method for the glitch phenomenon (i.e., a sudden change in period) of pulsar periods. Finally, we also used China's XPNAV-1 satellite to conduct experiments and analysis of the actual observation data of PSR B0531+21 pulsar within a fixed period of time. The results show that the period estimation accuracy of this method is 4.8190$ns$, which is 50.23<!PCT!> higher than the traditional \( method. The method proposed in this article has the advantages of high estimation accuracy, stable estimation performance, strong anti-interference ability, and excellent dynamic period estimation performance. Therefore, it can further improve the navigation performance of X-ray pulsars.

An energy-based pulsar period estimation method using Hilbert curve and double CNNs

In order to accurately estimate the period of X-ray pulsars to improve navigation performance, this paper proposes a pulsar period estimation method based on photon energy using reverse Hilbert coding and double Convolutional Neural Networks (CNNs) discrimination. This method uses photon energy information to perform epoch folding. It converts one-dimensional contour information into two-dimensional image signals through reverse Hilbert curve and recognizes contour image features through CNN models to determine the optimal period. According to the simulation results, the period estimation accuracy of this method is 0.5350 ns, which is 65.71% higher than the χ2 epoch folding method. The contour phase accuracy is 0.0004767, which is 41.93% higher than the χ2 epoch folding method. At the same time, the observation time and noise interference that may affect the accuracy of period estimation are also quantitatively analyzed. In addition, in order to simulate real navigation scenarios, this paper also conducts dynamic period estimation experiments on PSR B0531+21 pulsar in a specific period of time. The method proposed in this paper has the advantages of high precision, fast calculation speed, strong anti-interference ability and accurate dynamic period estimation, which can significantly improve the navigation performance of X-ray pulsars.

A novel symbol period estimation method of typical digital communication signal based on the characteristics of periodic modulation and mono-frequency

A novel symbol period estimation method of typical digital communication signal based on the characteristics of periodic modulation and mono-frequency

Period Estimation of Spread Spectrum Codes Based on ResNet.

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from -20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities.

Incipient detection of bearing fault using impulse feature enhanced weighted sparse representation

The bearing fault impact impulses induced by the contact between components with drawback, is difficult to be detected at sprouting stage due to the interference of background noise, harmonics, random shocks, etc. In this paper, an impulse feature enhanced weighted sparse representation (IFEWSR) algorithm is proposed to accurately detect the weak bearing fault impact feature from incipient stage condition monitoring (CM) signal. Firstly, a modified fault period estimation method is presented to improve the robustness and reduce the computational complexity of recently proposed algorithms. Secondly, a novel weighting strategy on wavelet coefficients, indicated by the period-assisted corelated kurtosis of envelope spectrum (CKSES), is presented to denote the contribution of subband signals for sparse representation calculation framework. In addition, the mean normalized energy-weight deviation (MNEWD) rule is proposed to evaluate the performance of the weighting algorithm on subband signals which is blank at present. Thirdly, a novel fault feature enhancement technique is developed to better capture the bearing fault feature information. The effectiveness and superiority of the proposed method are proved by simulation and experiments. Results show that the proposed IFEWSR method provides higher accuracy for incipient fault feature extraction and outperforms other state-of-the-art methods.

Edge-Intelligence-Based Condition Monitoring of Beam Pumping Units Under Heavy Noise in Industrial Internet of Things for Industry 4.0

Accurately estimating the state of equipment plays an important role in ensuring the efficient operation of Industrial 4.0 systems. This article focuses on monitoring the operating state and detecting the faults of beam pumping units under the condition of heavy noise within the Industrial Internet of Things. On the one hand, the equipment operating state monitoring system designed in this article uses an acceleration sensor, the signal of which contains considerable noise that greatly reduces the motion state estimation accuracy. On the other hand, the complexity of the indicator diagrams of beam pumping units makes it difficult to extract features, which limits the ability to improve the fault detection accuracy. To overcome these issues, first, a period estimation method based on self-checking that employs acceleration data is proposed to effectively overcome the influence of complex noise on the estimated data period; second, a denoising method based on a physical model is proposed to effectively reduce the influence of complex noise on the acceleration-based displacement estimation; and third, a method for detecting the faults of beam pumping units based on edge intelligence is proposed to effectively improve the fault detection accuracy while maintaining a low computational demand. Extensive experiments on real data verify the effectiveness of the proposed method. To the best of our knowledge, this is the first work to discuss the impact of the quality of data on the performance of fault detection of beam pump units.

Fast period estimation of X-ray pulsar signals using an improved fast folding algorithm

An accurate period is important to recover the pulse profile from a recorded photon event series of an X-ray pulsar and to estimate the pulse time of arrival, which is the measurement of X-ray pulsar navigation. Epoch folding is a classical period estimation method in the time domain; however, its computational complexity grows as the number of trail periods increases. In order to reduce the computational complexity, this paper improves the fast folding algorithm through segment correlation and amplitude accumulation, which is based on the post-order traversal of a binary tree. Compared with epoch folding, the improved fast folding algorithm can achieve a similar accuracy at the cost of a lower computational burden. Compared with the original fast folding algorithm, the improved algorithm can be applied to detectors with a much smaller effective area. The performance of the method is investigated by simulation data and observation data from the Neutron star Interior Composition Explorer (NICER).

Research on the Sparse Optimization Method of Periodic Weights and its Application in Bearing Fault Diagnosis

The vibration state monitoring signal of rolling bearings usually shows periodic transient impulse characteristics at the time of the fault, but in the fault sprouting stage and actual monitoring, its impulse components are easily disturbed by random pulses, harmonics, background noise, etc., which increases the difficulty of extracting the periodic shock characteristics of the fault. This paper proposed a rolling bearing fault feature extraction method based on sparse coefficient weighting theory and periodic enhancement strategy to effectively extract weak periodic shock features with a low signal-to-noise ratio. In the proposed period weighted sparse wavelet representation (PWSWR) method, firstly, based on the sparse wavelet model, the mean kurtosis (MK) index was introduced as a weight to distinguish the contribution of the wavelet coefficients of each signal component. Secondly, a sparse signal period enhancement strategy was proposed using the improved envelope harmonic product spectrum period estimation method. The strategy is embedded into the sparse representation model to achieve the periodic enhancement of filtered signals. Finally, the envelope detection of the sparse signal was carried out to identify the fault. The results of bearing fault simulation signals and two engineering test signals showed that the proposed method could effectively extract weak fault features of bearings and had certain advantages compared with other sparse methods.

Time-domain modelling and performance research of millimeter-wave all-digital phase-locked loop

In this paper, a modified time-domain model of millimeter-wave all-digital phase-locked loop (ADPLL) is implemented. In order to reflect the true behaviour of ADPLL, a quantified output digitally controlled-oscillator (DCO) with time domain jitter is proposed. In this ADPLL time-domain model, the DCO model can only output discrete frequency points to imitate the quantization effect of true DCO, and the overlap of different level tuning band is added into this model to imitate the true situation. In addition, the DCO time domain jitter and wander are also added into this model by using the Box-Muller method. Finally, a period estimation method is used to calculate the phase power spectrum density of the output signal, and then the phase noise is obtained through subsequent processing.

Comparison of Deterministic Methods to Estimate Sidereal Rotation Period from Light Curves

The importance of determining the sidereal rotation period of an astronomical object on future investigations pertaining to said object has been well documented in the literature. Researchers, however, have differed in their techniques used to estimate and model objects in the space catalog. In this paper, several period-estimation methods will be explored ranging across Fourier and phase-folding techniques. These methods will be tested using ground-based observations of light curve data for various resident space objects that fall under a rigid body context (i.e., asteroids, satellites, probes, rocket bodies) and celestial objects like stars and extrasolar planets. The effect of varying sample size, the inadequacies in unevenly sampled data processing, autonomy of the method, and complexity of parameters are investigated. For the models of artificial space objects that are not open source, a simulation is used to generate synthetic light curves with which all of the above-mentioned techniques are also employed. To account for heterogeneity in method parameters, each technique is tested with a range of values to optimize the rotational period. Results for uniformly sampled asteroid data as well as nonuniformly sampled stellar objects and generic sinusoidal data show variances in accuracy of the methods, but certain methods stand out.

Non-Invasive Monitoring of Respiratory Rate and Respiratory Status during Sleep Using a Passive Radio-Frequency Identification System

In recent years, checking sleep quality has become essential from a healthcare perspective. In this paper, we propose a respiratory rate (RR) monitoring system that can be used in the bedroom without wearing any sensor devices directly. To develop the system, passive radio-frequency identification (RFID) tags are introduced and attached to a blanket, instead of attaching them to the human body. The received signal strength indicator (RSSI) and phase values of the passive RFID tags are continuously obtained using an RFID reader through antennas located at the bedside. The RSSI and phase values change depending on the respiration of the person wearing the blanket. Thus, we can estimate the RR using these values. After providing an overview of the proposed system, the RR estimation flow is explained in detail. The processing flow includes noise elimination and irregular breathing period estimation methods. The evaluation demonstrates that the proposed system can estimate the RR and respiratory status without considering the user's body posture, body type, gender, or change in the RR.

Multiple Person Detection by Footsteps Sounds Using GMRS

This article targets footsteps sounds produced by multiple persons aiming to detect the multiple persons separately contained in the sound. To the best of authors’ knowledge, there have been no research on the detection of multiple persons from the footsteps sounds. For data processing, we propose to apply the geometric mean for real-time serial-to-parallel converter (GMRS). GMRS is a period estimation method applicable to a composite signal consisting of multiple periodic signals, such like a footsteps sound produced by multiple persons. In particular, GMRS is capable of estimating the period of each of the periodic signals while large power differences exist among the periodic signals. This article provides the performance evaluation of GMRS through computer simulations for the detection of multiple persons from the footsteps sounds as well as experimental results. As a result, it is confirmed that GMRS successfully detects 3 persons.

Changes in data management contribute to temporal variation in gestational duration distribution in the Swedish Medical Birth Registry.

Multiple factors contribute to gestational duration variability. Understanding the sources of variability allows to design better association studies and assess public health measures. Here, we aimed to assess geographical and temporal changes in the determination of gestational duration and its reporting in Sweden between 1973 and 2012. Singleton live births between 1973 and 2012 were retrieved from the Swedish Medical Birth Register. Gestational duration trends in percentiles and rates of pre- and post-term deliveries were analyzed by plotting the values over time. Temporal changes in gestational duration based on ultrasound and last menstrual period (LMP) estimation methods were compared. Intervals between LMP date and LMP-based due date were analyzed to assess changes in expected gestational duration. In total, 3 940 577 pregnancies were included. From 1973 until 1985, the median of gestational duration estimated based on LMP or ultrasound decreased from 283 to 278 days, and remained stable until 2012. The distribution was relatively stable when ultrasound-based estimates were used. Until the mid-1990s, there was a higher incidence than expected of births occurring on every seventh gestational day from day 157 onward. On an average, these gestational durations were reported 1.8 times more often than adjacent durations. Until 1989, the most common expected gestational duration was 280 days, and thereafter, it was 279 days. The expected gestational duration varied from 279 to 281 days across different Swedish counties. During leap years, the expected gestational duration was one day longer. Consequently, leap years were also associated with significantly higher preterm and lower post-term delivery rates than non-leap years. Changes in data handling and obstetrical practices over the years contribute to gestational duration variation. The resulting increase in variability might reduce precision in association studies and hamper the assessment of public health measures aimed to improve pregnancy outcomes.

Fast Butterfly Epoch Folding-Based X-Ray Pulsar Period Estimation With a Few Distorted Profiles

The traditional pulsar period estimation method using a single distorted profile averts multiple epoch folding processes, but still has space for accuracy to improve. To give consideration to both accuracy and computational load, we combine the fast butterfly epoch folding (FBEF) with the single distorted profile (SDP)-based pulsar period estimation, and propose the FBEF-based pulsar period estimation with a few distorted profiles (FBEF-FDP), which merely uses a few epoch folding processes to enhance accuracy sharply. Firstly, the fast butterfly epoch folding is used to produce a few distorted pulsar profiles with different periods. And then the single distorted profile-based pulsar period estimation explores each distorted prolife to provide a corresponding distortion degree. Finally, using these distortion degree values, the nonlinear search method for multiple minimums can get an optimal distortion degree estimation. Besides, we prove that distortion degree estimation errors of different accumulative distorted profiles from the same pulsar photon sequence are incompletely correlated. This is the theorical basis that the distortion degree estimation values from the same pulsar photon sequence can be fitted to obtain more highly accurate distortion degree estimation. The computational complexity demonstrates that the computational load increases with the number of groups. Thus, to ensure the real-time of the FBEF-FDP, the number of groups should not be too large. Simulation results demonstrate that the accuracy of the fast butterfly epoch folding-based pulsar period estimation using a few distorted profiles enhances at the cost of a small computational load.

The Identification and Assessment of Rail Corrugation Based on Computer Vision

The identification and assessment of rail corrugation are two of the essential tasks of daily railway inspection to guarantee the safety of train operation and promote the development of an efficient maintenance strategy. In view of the requirements for automatic identification and smart decision-making, computer vision-based rail corrugation identification and assessment methods are presented in this paper. Firstly, an improved Spatial Pyramid Matching (SPM) model, integrating multi-features and locality-constrained linear coding (IMFLLC), is proposed for rail corrugation identification. After that, an innovative period estimation method for rail corrugation is proposed based on the frequency domain analysis of each column in the corrugation region. Finally, the severity of the rail corrugation is assessed with the help of the wear saliency calculation and fuzzy theory. The experiment results demonstrate that the proposed corrugation identification method achieves a higher precision rate and recall rate than those of traditional methods, reaching 99.67% and 98.34%, respectively. Besides, the validity and feasibility of the proposed methods for the rail corrugation period estimation and severity assessment are also investigated.

Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. Therefore, the production of methods and systems for the automated classification of time-domain astronomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure, the Small Telescopes Installed at the Liverpool Telescope. These instruments have been in operation since March 2009 gathering data of large areas of sky around the current field of view of the main telescope generating a large dataset containing millions of light sources. The instruments are inexpensive to run as they do not require a separate telescope to operate but this style of surveying the sky introduces structured artifacts into our data due to the variable cadence at which sky fields are resampled. These artifacts can make light sources appear variable and must be addressed in any processing method.The data from large sky surveys can lead to the discovery of interesting new variable objects. Efficient software and analysis tools are required to rapidly determine which potentially variable objects are worthy of further telescope time. Machine learning offers a solution to the quick detection of variability by characterising the detected signals relative to previously seen exemplars. In this paper, we introduce a processing system designed for use with the Liverpool Telescope identifying potentially interesting objects through the application of a novel representation learning approach to data collected automatically from the wide-field instruments. Our method automatically produces a set of classification features by applying Principal Component Analysis on set of variable light curves using a piecewise polynomial fitted via a genetic algorithm applied to the epoch-folded data. The epoch-folding requires the selection of a candidate period for variable light curves identified using a genetic algorithm period estimation method specifically developed for this dataset. A Random Forest classifier is then used to classify the learned features to determine if a light curve is generated by an object of interest. This system allows for the telescope to automatically identify new targets through passive observations which do not affect day-to-day operations as the unique artifacts resulting from such a survey method are incorporated into the methods.We demonstrate the power of this feature extraction method compared to feature engineering performed by previous studies by training classification models on 859 light curves of 12 known variable star classes from our dataset. We show that our new features produce a model with a superior mean cross-validation F1 score of 0.4729 with a standard deviation of 0.0931 compared with the engineered features at 0.3902 with a standard deviation of 0.0619. We show that the features extracted from the representation learning are given relatively high importance in the final classification model. Additionally, we compare engineered features computed on the interpolated polynomial fits and show that they produce more reliable distributions than those fit to the raw light curve when the period estimation is correct.

Fast CS-based pulsar period estimation method without tentative epoch folding and its CRLB

The traditional pulsar period estimation methods have high computational loads as a result of the tentative epoch folding. The tentative epoch folding means that the massive pulsar signals are folded multiple times with different periods. However, due to the low computing power of on-board computer, the tentative epoch folding with high computational load must be avoided. To achieve this objective, we propose a fast compressive sensing-based pulsar period estimation method, which directly estimates the pulsar period according to the distortion degree of the accumulated profile. The compressive sensing (CS) includes three aspects: the dictionary, the measurement matrix and the sparse recovery algorithm. In the proposed method, the pulsar profiles with different distortion degrees comprise the distorted profile dictionary; the low-frequency Fourier transform matrix is developed as the measurement matrix; the super-resolution sparse recovery using the maximal elements is proposed to estimate the pulsar period. Besides, the pulsar period estimation performance against the Cramér-Rao lower bound (CRLB) is studied. And the computational complexity analysis is also provided. The simulation results demonstrate that the fast CS-based pulsar period estimation method approaches the CRLB and its computational time is only 37 ms.

Analysis of Rolling Shutter Effect on ENF-Based Video Forensics

Electric network frequency (ENF) is a time-varying signal of the frequency of mains electricity in a power grid. It continuously fluctuates around a nominal value (50/60 Hz) due to changes in the supply and demand of power over time. Depending on these ENF variations, the luminous intensity of a mains-powered light source also fluctuates. These fluctuations in luminance can be captured by video recordings. Accordingly, the ENF can be estimated from such videos by the analysis of steady content in the video scene. When videos are captured by using a rolling shutter sampling mechanism, as is done mostly with CMOS cameras, there is an idle period between successive frames. Consequently, a number of illumination samples of the scene are effectively lost due to the idle period. These missing samples affect the ENF estimation, in the sense of the frequency shift caused and the power attenuation that results. This paper develops an analytical model for videos captured using a rolling shutter mechanism. This model illustrates how the frequency of the main ENF harmonic varies depending on the idle period length, and how the power of the captured ENF attenuates as idle period increases. Based on this, a novel idle period estimation method for potential use in camera forensics that is able to operate independently of video frame rate is proposed. Finally, a novel time-of-recording verification approach based on the use of multiple ENF components, idle period assumptions, and the interpolation of missing ENF samples is also proposed.

Robust Period Estimation Using Mutual Information for Multiband Light Curves in the Synoptic Survey Era

The Large Synoptic Survey Telescope (LSST) will produce an unprecedented amount of light curves using six optical bands. Robust and efficient methods that can aggregate data from multidimensional sparsely-sampled time series are needed. In this paper we present a new method for light curve period estimation based on the quadratic mutual information (QMI). The proposed method does not assume a particular model for the light curve nor its underlying probability density and it is robust to non-Gaussian noise and outliers. By combining the QMI from several bands the true period can be estimated even when no single-band QMI yields the period. Period recovery performance as a function of average magnitude and sample size is measured using 30,000 synthetic multi-band light curves of RR Lyrae and Cepheid variables generated by the LSST Operations and Catalog simulators. The results show that aggregating information from several bands is highly beneficial in LSST sparsely-sampled time series, obtaining an absolute increase in period recovery rate up to 50%. We also show that the QMI is more robust to noise and light curve length (sample size) than the multiband generalizations of the Lomb Scargle and Analysis of Variance periodograms, recovering the true period in 10-30% more cases than its competitors. A python package containing efficient Cython implementations of the QMI and other methods is provided.

Multiple interferences suppression method based on adaptive signal data regrouping for GNSS receivers

The number of wide-band interfering signals which can be suppressed by an antenna array is limited to the number of antenna elements. To address this problem, a multiple interferences suppression method based on adaptive signal data regrouping for global navigation satellite system (GNSS) receivers is proposed on the basis of analysing the generalised periodic and sparse property of the wide-band periodic frequency modulated (WBPFM) signals. By exploiting the feature of the singular value ratio (SVR) spectrum of received signals containing periodic components, the authors introduce an improved SVR-based period estimation method to estimate the minimum common period (MCP) of WBPFM interfering signals. Regroup the signal data to construct space-time (ST) matrixes according to the estimated MCP. Then, an orthogonal minimum power distortionless response-based ST processor is adopted to nullify the interferences and protect GNSS signals. Simulation results show that the proposed method can suppress more wide-band interferences without increasing the number of antenna elements.