Search Results (36)

Fine-grained sedimentary rocks are ideal carriers for astronomical cycle analysis as they can record and preserve significant astronomical cycle signals. Spectral analysis using the Multi-taper Method (MTM) and Evolutionary Harmonic Analysis (EHA) using the Fast Fourier Transform (FFT) were conducted on natural gamma data from key wells in the Es₃l sub-member in the Bonan Sag, Bohai Bay Basin, China. Gaussian bandpass filtering was applied using a short eccentricity cycle of 100 ka, and a “floating” astronomical time scale for the Es₃l sub-member (Lower 3rd sub-member of Shahejie Formation in Eocene) was established using magnetic stratigraphic ages as boundaries. Stratigraphic divisions were made for single wells in the Es₃l of the Bonan Sag, and a stratigraphic framework was established based on correlations between key wells. The research results indicate the following: Firstly, the Es₃l of the Bonan Sag records significant astronomical cycle signals, with an optimal sedimentation rate of 8.39 cm/ka identified. Secondly, the cyclical thicknesses corresponding to long eccentricity, short eccentricity, obliquity, and precession cycles are 38.9 m, 9.7 m, 4.6–3.4 m, and 1.96–1.66 m, respectively. Thirdly, the Es₃l sub-member stably records 6 long eccentricity cycles and 26 short eccentricity cycles, and the short eccentricity curve is used as a basis for stratigraphic division for high-precision stratigraphic correlations. Fourthly, the quality of sandstone-interbedded mudrock is jointly controlled by the short eccentricity and precession. Eccentricity maximum values result in thicker sandstone interlayers, while minimum precession values promote the thickness of sandstone interlayers. Through astronomical cycle analysis, the depositional evolution mechanism of sandstone-interbedded mudrock is revealed. Combined with the results of high-precision stratigraphic division, this can provide a basis for fine evaluation and “sweet spot” prediction of lacustrine shale oil reservoirs. Full article

Understanding precipitation changes over a long period of time can provide valuable insights into global climate change. Taking the forest–steppe ecotone of North China as the research area, based on the tree ring width index of Carya cathayensis Sarg (Carya cathayensis), the relationship between tree growth and climate factors is analyzed, and the annual precipitation is reconstructed from data from the nearest five weather stations from AD 1540 to 2019. The results show that the growth of trees was affected by the changes in precipitation. The precipitation was divided into three dry periods and three wet periods over 480 years, based on wavelet analysis. There were 328 years of precipitation within the mean plus or minus one standard deviation (SD) (accounting for 68.3% of 480 years), indicating that relatively stable climate conditions exist in the study area, which has become one of the main agricultural areas in China. Each period lasted 2–7 years according to the multi-taper method, indicating that precipitation change was closely related to the El Niño–Southern Oscillation (ENSO) on a short time scale and affected by the Atlantic Multidecadal Oscillation (AMO) on a medium time scale during the period of 60–80 years based on wavelet analysis. Full article

The power spectral density estimation algorithms, logarithmic frequency axis for power spectral density (LPSD), and the LISA-LPSD algorithm are widely utilized in the implementation of system evaluations for space-based gravitational-wave-detection projects, particularly in the low-frequency band ranging from 0.1 mHz to 1 Hz. However, existing adaptive sine multi-taper algorithms suffer from low resolution and high computational complexity in obtaining the optimal cone number across the entire frequency domain, which has hindered its application in this field. These algorithms often face challenges related to inadequate resolution when dealing with low-frequency signals, as well as the issue of high computational demands. In response to these challenges, this paper introduces an advanced adaptive sine multi-taper algorithm designed to optimize the determination of the cone number. By balancing the relationship between bias and variance, this approach facilitates gradient processing of the cone number specifically tailored for low-frequency signals. Comparative evaluations against the LPSD algorithm, the original adaptive sine multi-taper algorithm, and the LISA-LPSD algorithm reveal that the proposed method demonstrates superior spectral resolution and reduced algorithmic complexity. This improvement offers a more effective solution for the system evaluation of low-frequency gravitational-wave-detection projects. Full article

The early prediction of Alzheimer’s disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT). Time–frequency features were extracted using the Multitaper method, followed by multidimensional reduction techniques. Subspace features (F24 and F216) were selected via t-tests and False Discovery Rate (FDR) multiple comparisons correction, and subsequently analyzed in the Time–Frequency Area Average Test (TFAAT) and Prefrontal Beta Time Series Test (PBTST). The experimental results reveal that the MSIT task achieves optimal cross-subject classification performance using the Support Vector Machine (SVM) approach with the TFAAT feature set, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 58%. Similarly, the Sternberg Memory Task demonstrates classification ability with the logistic regression model applied to the PBTST feature set, emphasizing the beta band power spectrum in the prefrontal cortex as a potential marker of AD risk. These findings confirm that task-state EEG provides stronger classification potential compared to resting-state EEG, offering valuable insights for advancing early AD prediction research. Full article

Polysomnography (PSG) measures brain activity during sleep via electroencephalography (EEG) using six leads. Artifacts caused by movement or loose leads distort EEG measurements. We developed a method to automatically identify such artifacts in a PSG EEG trace. After preprocessing, we extracted power levels at frequencies of 0.5–32.5 Hz with multitaper spectral analysis using 4 s windows with 3 s overlap. For each resulting 1 s segment, we computed segment-specific correlations between power levels for all pairs of leads. We then averaged all pairwise correlation coefficients involving each lead, creating a time series of segment-specific average correlations for each lead. Our algorithm scans each averaged time series separately for “bad” segments using a local moving window. In a second pass, any segment whose averaged correlation is less than a global threshold among all remaining good segments is declared an outlier. We mark all segments between two outlier segments fewer than 300 s apart as artifact regions. This process is repeated, removing a channel with excessive outliers in each iteration. We compared artifact regions discovered by our algorithm to expert-assessed ground truth, achieving sensitivity and specificity of 80% and 91%, respectively. Our algorithm is an open-source tool, either as a Python package or a Docker. Full article

In 2023, we proposed the modified delay and Doppler profiler (mDDP) as an inter-carrier interference (ICI) countermeasure for underwater acoustic orthogonal frequency division multiplexing (OFDM) mobile communications in a multipath environment. However, the performance improvement in the computer simulation and pool experiments was not significant. In a subsequent study, the accuracy of the channel transfer function (CTF), which is the input for the mDDP channel parameter estimation, was considered insufficient. Then a sequential ICI canceling mDDP was devised. This paper presents simulations of underwater OFDM communications using an iterative one- to three-step mDDP. The non-reflective pool experiment conditions are a two-wave multipath environment where the receiving transducer moves at a speed of 0.25 m/s and is subjected to a Doppler shift in the opposite direction. As NumCOL, the number of taps in the multitap equalizer which removes ICI, was increased, the bit error rate (BER) of 0.0526661 at NumCOL = 1 was significantly reduced by a factor of approximately 45 to a BER of 0.0011655 at NumCOL = 51 for the sequential ICI canceling mDDP. Full article

In this paper, we propose a new test for the detection of polynomial modulated signals, as well as an aggregated test based on the new test. The test statistics are developed under the multitaper spectral framework, and are designed to be robust to the choice of the multitaper order K. The proposed tests are based on a modification of a previous test statistic developed by the authors, denoted by $F_4$. We review the $F_4$ test, and discuss some of its shortcomings, then propose our two tests. We illustrate performance via simulations, and apply our tests to SoHO GOLF optical solar time series. Full article

Currently there are several precipitation reconstructions for northern Mexico; however, there is a lack of temperature reconstructions to understand past climate change, the impact on ecosystems and societies, etc. The central region of Chihuahua is located in a transition zone between the Sierra Madre Occidental and the Great Northern Plain, characterized by extreme temperatures and marked seasonal variability. The objectives of this study were (1) to generate a climatic association between variables from reanalysis models and the earlywood series for the center of Chihuahua, (2) to generate a reconstruction of mean temperature, (3) to determine extreme events, and (4) to identify the influence of ocean–atmosphere phenomena. Chronologies were downloaded from the International Tree-Ring Data Bank and climate information from the NLDAS-2 and ClimateNA reanalysis models. The response function was performed using climate models and regional dendrochronological series. A reconstruction of mean temperature was generated, and extreme periods were identified. The representativeness of the reconstruction was evaluated through spatial correlation, and low-frequency events were determined through multitaper spectral analysis and wavelet analysis. The influence of ocean–atmosphere phenomena on temperature reconstruction was analyzed using Pearson correlation, and the influence of ENSO was examined through wavelet coherence analysis. Highly significant correlations were found for maximum, minimum, and mean temperature, as well as for precipitation and relative humidity, before and after the growth year. However, the seasonal period with the highest correlation was found from May to June for mean temperature, which was used to generate the reconstruction from 1775 to 2022. The most extreme periods were 1775, 1801, 1805, 1860, 1892–1894, 1951, 1953–1954, and 2011–2012. Spectral analysis showed significant frequencies of 56.53 and 2.09 years, and wavelet analysis from 0 to 2 years from 1970 to 1980, from 8 to 11 years from 1890 to 1910, and from 30 to 70 years from 1860 to 2022. A significant association was found with the Multivariate ENSO Index phenomenon (r = 0.40; p = 0.009) and Pacific Decadal Oscillation (r = −0.38; p = 0.000). Regarding the ENSO phenomenon, an antiphase association of r = −0.34; p = 0.000 was found, with significant periods of 1 to 4 years from 1770 to 1800, 1845 to 1850, and 1860 to 1900, with periods of 6 to 10 years from 1875 to 1920, and from 6 to 8 years from 1990 to 2000. This study allowed a reconstruction of mean temperature through reanalysis data, as well as a historical characterization of temperature for central Chihuahua beyond the observed records. Full article

In this study, we performed periodicity analyses of selected daily solar (flare index, coronal index, number of coronal mass ejections), geomagnetic (planetary equivalent range index, disturbance storm time index, interplanetary magnetic field) and cosmic ray indices for the last Solar Cycle 24 (from December 2008 to December 2019). To study the periodic variation of the above-listed datasets, the following analysis methods were applied; multi-taper method, Morlet wavelet, cross-wavelet transform and wavelet coherence analysis. The outcome of our analyses revealed the following. (i) The 25–33 days solar rotation periodicities exist in all datasets without any exception in the MTM power spectra. (ii) Except for the solar rotation periodicity, all periods show data preference, and they appear around the investigated cycle’s maximum phase. (iii) When comparing the phase relations between periodicities in the used datasets, they exhibit a gradual transition from small to large periods. For short-term periodicities, there are no phase relations but a mixed phase, whereas for high periodicities, there are complete phase/antiphase transitions. (iv) All identified flare index periodicities are common to all other datasets examined in this investigation. Full article

The interference cancellation ratio (ICR) is a key performance indicator of digital-to-analog hybrid radio frequency (RF) interference cancellation systems. Aiming at the low convergence speed of a digital-to-analog hybrid RF interference cancellation system based on a multi-tap structure (MDARFICS), a novel, fast, and robust variable-step-size least-mean-square (LMS) algorithm based on an improved hyperbolic tangent function (IHVSS-LMS) is proposed. The IHVSS-LMS algorithm adopts an improved hyperbolic tangent function and uses adjustable parameters and the iteration number to jointly adjust the step size, which improves the convergence speed and reduces the computational complexity. Moreover, by using the prior information of the input signal, the non-linear relationship between the step size and the input signal power is established, which enhances the robustness and the ability to suppress interference with mutable power. The IHVSS-LMS algorithm is applied to the MDARFICS. Through theoretical derivation, the convergence speed and the steady-state expressions of the interference cancellation ratio of the MDARFICS are obtained. The simulation results show that under the conditions of high and low signal-to-noise ratio (SNR), the robustness, convergence speed, and steady-state error performance of the IHVSS-LMS algorithm are better than the existing variable-step-size algorithm. The experimental results show that using the IHVSS-LMS algorithm, the MDARFICS can not only effectively accelerate the convergence speed by at least three times but can also improve the ICR by more than 3 dB. Full article

A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies $36\pm 6$ min of REM in one night of recorded sleep, while incorrectly labeling less than $10\%$ of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near $80\%$), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night’s worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data. Full article

In the context of global warming, climate change in river headwater regions and its drivers have attracted increasing attention. In this study, tree-ring width (TRW) chronology was constructed using tree-ring samples of fir (Abies faxoniana) in Dadu River Basin in the central part of the western Sichuan Plateau, China. Correlation analysis with climatic factors implies that the radial growth of trees in the region is mainly limited by temperature and has the highest correlation with the mean minimum temperature in summer (June and July) (R = 0.602, p < 0.001). On this basis, the TRW chronology was adopted to reconstruct variations in the mean minimum temperatures in summer from 1733 to 2010 in the upper reaches of Dadu River. The reconstruction equation was stable and reliable and offered a variance explanation rate of 36.2% in the observed period (1962~2010). In the past 278 years, the region experienced nine warm periods and ten cold periods. The warmest and coldest years occurred in 2010 and 1798, respectively, with values of 13.6 °C and 11.0 °C. The reconstruction was highly spatiotemporally representative and verified by temperatures reconstructed using other tree-ring data in surrounding areas. A significant warming trend was found in the last few decades. Moreover, the multi-taper method (MTM) analysis indicated significant periodic changes in quasi-2-year and 21–35-year periods, for which the El Niño Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) could be the key controlling factors. Full article

The conventional heating system is inefficient as the major part of the heating coil lies out-side the vessel it is placed on. This research article proposes a new single-coil multi-tapped induction heating system. This novel induction heating system is facilitated by a half-bridge resonant converter controlled by zero-voltage switching (ZVS). The multi-tapping winding system ensures an effective heat transfer between the coil and the working vessel with the windings of the induction coil segmented to an equivalent size of the vessel. The pulse density modulation (PDM) scheme employed here as the control proves to be the most versatile one. The whole system is duly simulated for an 850 W IH setup in MATLAB Simulink and implemented as a hardware prototype using a half-bridge resonant converter. The control pulses are developed through the PDM in a PIC16F877A controller. The simulation and experimental results prove the credibility of the proposed induction heating (IH) scheme, and during heavy loading conditions, it outperforms the single-coil IH system by gaining an efficiency of 89.29% Full article

Assume $X_1,X_2,\dots ,X_N$ are realizations of N observations from a real-valued discrete parameter third-order stationary process $X_t,t=0\pm 1,\pm 2,\dots$, with bispectrum $f_{XXX}({\lambda }_1,{\lambda }_2)$ where “$-\pi \le {\lambda }_1,{\lambda }_2\le \pi$”. Based on the previous assumption, L different multitapered biperiodograms $I_{XXX}^{{\left(mt\right)}_j}({\lambda }_1,{\lambda }_2);j=1,2,\dots ,L$ on overlapped segments ($X_t^{\left(j\right)};1\le t<N$) can be constructed. Further, the mean and variance of the average of these different multitapered biperiodograms can be expressed as asymptotic expressions. According to different bispectral windows/kernels ($W_{\beta }^{\left(j\right)}({\alpha }_1,{\alpha }_2)$, where “$-\pi ⩽{\alpha }_1,{\alpha }_2⩽\pi$” and$\beta$ is the bandwidth) and $I_{XXX}^{{\left(mt\right)}_j}({\lambda }_1,{\lambda }_2)$, the bispectrum $f_{XXX}({\lambda }_1,{\lambda }_2)$ can be estimated. The asymptotic expressions of the first- and second-ordered moments as well as the integrated relative mean squared error (IMSE) of this estimate are derived. Finally, some estimation results based on numerically generated data from the selected process “DCGINAR(1)” are presented and discussed in detail. Full article

Periodic behaviors of solar magnetic indicators might provide a clue for the understanding of solar dynamic processes. Combining with a Lomb–Scargle periodogram, the concentration of frequency and time via a multitapered synchrosqueezed transform is applied to investigate the periodic variations of modified coronal index for the time interval from 1 January 1939 to 31 August 2020. The main results are as follows: (1) During solar cycles 19 to 23, the Schwabe cycle of the modified coronal index is operating with its length variating between 10.5 and 11-yr, and the average value of length is 10.67-yr with standard deviation of 0.14-yr. (2) The Rieger-type periods are mainly distributed in a range from 120 to 200 days. In addition, the periods vary somewhat intermittently during cycles 18 to 24, which are operating with the highest power in cycles 21 and 22 while the power is much lower in cycles 23 and 24. (3) For rotation periods, the temporal variation exhibits a highly intermittent pattern as an asymmetrical distribution with its 25th, 50th, and 75th quantile of 26, 27.8, and 31-day, respectively. (4) Other mid-range periods are also detected with an average period length of 8.07, 5.44, 3.42, 2.3, and 1.01-yr. Full article