Periodic Signal Research Articles

Stochastic Resonance in a Single-Ion Nonlinear Mechanical Oscillator

Stochastic resonance is a counterintuitive phenomenon amplifying the weak periodic signal by application of external noise. We demonstrate the enhancement of a weak periodic signal by stochastic resonance in a trapped-ion oscillator when the oscillator is excited to the nonlinear regime and subject to an appropriate noise. Under the full control of the radio-frequency drive voltage, this amplification originates from the nonlinearity due to asymmetry of the trapping potential, which can be described by a forced Duffing oscillator model. Our scheme and results provide an interesting possibility to make use of controllable nonlinearity in the trapped ion, and pave the way toward a practical atomic sensor for sensitively detecting weak periodic signals from real noisy environment.

Contrast detection is enhanced by deterministic, high-frequency transcranial alternating current stimulation with triangle and sine waveform.

Stochastic resonance (SR) describes a phenomenon where an additive noise (stochastic carrier-wave) enhances the signal transmission in a nonlinear system. In the nervous system, nonlinear properties are present from the level of single ion channels all the way to perception and appear to support the emergence of SR. For example, SR has been repeatedly demonstrated for visual detection tasks, also by adding noise directly to cortical areas via transcranial random noise stimulation (tRNS). When dealing with nonlinear physical systems, it has been suggested that resonance can be induced not only by adding stochastic signals (i.e., noise) but also by adding a large class of signals that are not stochastic in nature that cause "deterministic amplitude resonance" (DAR). Here, we mathematically show that high-frequency, deterministic, periodic signals can yield resonance-like effects with linear transfer and infinite signal-to-noise ratio at the output. We tested this prediction empirically and investigated whether nonrandom, high-frequency, transcranial alternating current stimulation (tACS) applied to the visual cortex could induce resonance-like effects and enhance the performance of a visual detection task. We demonstrated in 28 participants that applying 80-Hz triangular-waves or sine-waves with tACS reduced the visual contrast detection threshold for optimal brain stimulation intensities. The influence of tACS on contrast sensitivity was equally effective to tRNS-induced modulation, demonstrating that both tACS and tRNS can reduce contrast detection thresholds. Our findings suggest that a resonance-like mechanism can also emerge when deterministic electrical waveforms are applied via tACS.NEW & NOTEWORTHY Our findings extend our understanding of neuromodulation induced by noninvasive electrical stimulation. We provide the first evidence showing acute online benefits of transcranial alternating current stimulation (tACS)triangle and tACSsine targeting the primary visual cortex (V1) on visual contrast detection in accordance with the resonance-like phenomenon. The "deterministic" tACS and "stochastic" high-frequency-transcranial random noise stimulation (tRNS) are equally effective in enhancing visual contrast detection.

Magnetic Resonance Imaging of Lung Perfusion.

"Lung perfusion" in the context of imaging conventionally refers to the delivery of blood to the pulmonary capillary bed through the pulmonary arteries originating from the right ventricle required for oxygenation. The most important physiological mechanism in the context of imaging is the so-called hypoxic pulmonary vasoconstriction (HPV, also known as "Euler-Liljestrand-Reflex"), which couples lung perfusion to lung ventilation. In obstructive airway diseases such as asthma, chronic-obstructive pulmonary disease (COPD), cystic fibrosis (CF), and asthma, HPV downregulates pulmonary perfusion in order to redistribute blood flow to functional lung areas in order to conserve optimal oxygenation. Imaging of lung perfusion can be seen as a reflection of lung ventilation in obstructive airway diseases. Other conditions that primarily affect lung perfusion are pulmonary vascular diseases, pulmonary hypertension, or (chronic) pulmonary embolism, which also lead to inhomogeneity in pulmonary capillary blood distribution. Several magnetic resonance imaging (MRI) techniques either dependent on exogenous contrast materials, exploiting periodical lung signal variations with cardiac action, or relying on intrinsic lung voxel attributes have been demonstrated to visualize lung perfusion. Additional post-processing may add temporal information and provide quantitative information related to blood flow. The most widely used and robust technique, dynamic-contrast enhanced MRI, is available in clinical routine assessment of COPD, CF, and pulmonary vascular disease. Non-contrast techniques are important research tools currently requiring clinical validation and cross-correlation in the absence of a viable standard of reference. First data on many of these techniques in the context of observational studies assessing therapy effects have just become available. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 5.

DMPP-4: candidate sub-Neptune mass planets orbiting a naked-eye star

ABSTRACT We present radial velocity measurements of the very bright (V ∼ 5.7) nearby F star, DMPP-4 (HD 184960). The anomalously low Ca ii H&K emission suggests mass-loss from planets orbiting a low activity host star. Periodic radial velocity variability with ∼10 m s−1 amplitude is found to persist over a >4 yr time-scale. Although the non-simultaneous photometric variability in four TESS sectors supports the view of an inactive star, we identify periodic photometric signals and also find spectroscopic evidence for stellar activity. We used a posterior sampling algorithm that includes the number of Keplerian signals, Np, as a free parameter to test and compare (1) purely Keplerian models (2) a Keplerian model with linear activity correlation and (3) Keplerian models with Gaussian processes. A preferred model, with one Keplerian and quasi-periodic Gaussian process indicates a planet with a period of $P_\textrm {b} = 3.4982^{+0.0015}_{-0.0027}$ d and corresponding minimum mass of $m_\textrm {b}\, \textrm {sin}\, i = 12.2^{+1.8}_{-1.9}$ M⊕. Without further high-time resolution observations over a longer time-scale, we cannot definitively rule out the purely Keplerian model with two candidates planets with $P_\textrm {b} = 2.4570^{+0.0026}_{-0.0462}$ d, minimum mass $m_\textrm {b}\, \textrm {sin}\, i = 8.0^{+1.1}_{-1.5}$ M⊕ and $P_\textrm {c} = 5.4196^{+0.6766}_{-0.0030}$ d and corresponding minimum mass of $m_\textrm {b}\, \textrm {sin}\, i = 12.2^{+1.4}_{-1.6}$ M⊕. The candidate planets lie in the region below the lower-envelope of the Neptune Desert. Continued mass-loss may originate from the highly irradiated planets or from an as yet undetected body in the system.

A Number Estimate of Detectable Detached Black Hole-star Binaries using a Photometric Telescope

Detached and wide-orbit black hole-star binaries (BHSBs) can generate three types of periodic photometric signals: Ellipsoidal Variation, Doppler beaming and Self-Lensing (SL), providing a proxy to discover these black holes. We estimate the relative amplitude of the three signals for such systems and the detectability for black holes of a photometric telescope like Kepler in several steps. We estimate the searchable star number by assuming every star has a black hole companion, and apply the occurrence of BHSBs in field stars to estimate the detectable black hole signals. We consider three types of Initial Mass Function (IMF) model with different high end exponential slopes. “When spot and white noise are both considered, there is about one detectable signal for SL and less than one event is expected for beaming and Ellipsoidal Variation signal in Kepler Input Catalog stars with the standard IMF model.” to “Due to contamination by stellar spots and white noise, one may expect one detectable signal for SL and less than one detectable signal for both beaming and Ellipsoidal Variation in Kepler Input Catalog stars with the standard IMF model.” On the other hand, if we assume that only white noise affects the detection efficiency of the BHSBs, we expect about 10 Ellipsoidal Variation signals and 17 beaming signals to be detectable while the number of SL signals remains unchanged.

Data-driven based cross-coupling compensation method for the piezoelectric tube scanner of atomic force microscopes

In this work, a data-driven based cross-coupling compensation (DD-CCC) method is proposed to suppress the cross-coupling effect of the scanner in atomic force microscopy (AFM). Firstly, the phase difference method is employed to accurately estimate the frequencies, amplitudes and phases of the harmonic components of the periodic compensation signal obtained from the fractional model-free repetitive control with the nonsynchronized sampling. Afterwards, the compensation signal is constructed analytically via summing the harmonic components, which requires very little memory units. In AFM scanning, the constructed compensation signal is applied in a feedforward manner, which is combined with a baseline tracking controller to follow the desired staircase trajectories. A series of scanning and imaging tests are conducted on an AFM with a fixed sampling frequency and different scanning rates. The results demonstrate that high performance AFM scanning and imaging can be achieved under the scanning rate of 120 Hz with the DD-CCC method.

Longitudinal, Latitudinal, and Local Time Variations of the 14.5‐Day Periodic Oscillation in the Ionosphere During 2014–2015 SSW

AbstractBased on global total electron content (TEC) maps from the International Global Navigation Satellite Systems Service (IGS), the ionospheric perturbations during the 2014–2015 sudden stratospheric warming (SSW) event were investigated. The analyses revealed both phase‐shifted semidiurnal perturbations and 14.5‐day periodic signal with zonal wavenumber 0 in the IGS TEC, indicating an enhanced lunar tidal impact on the ionosphere during the 2014–2015 SSW period. The 14.5‐day periodic oscillation in the IGS TEC presents an obvious latitudinal variation, with the maximum appearing in Equatorial Ionization Anomaly crest regions, which is due to the modulation of the equatorial ionospheric fountain effect by wind perturbations modulated by the lunar tide. Additionally, the 14.5‐day periodic oscillation presents a longitudinal pattern with three peaks in the northern hemisphere and four peaks in the southern hemisphere. Local time dependence of the 14.5‐day periodic perturbation is also significant.

The Companion of Enrico’s Chart for Phase Noise and Two-Sample Variances

Phase noise and frequency (in)stability both describe the fluctuation of stable periodic signals, from somewhat different standpoints. Frequency is unique compared to other domains of metrology, in that its fluctuations of interest span at least 14 orders of magnitude, from $10^{-4}$ in a mechanical watch to $10^{-18}$ in atomic clocks. The frequency span of interest is some 12-15 orders of magnitude, from $\mu$Hz to GHz Fourier frequency for phase noise, while the time span over which the fluctuations occur ranges from sub-$\mu$s to years integration time for variances. Because this domain is ubiquitous in science and technology, a common language and tools suitable to the variety mentioned are a challenge. This article is at once (1) a tutorial, (2) a review covering the most important facts about phase noise, frequency noise and two-sample (Allan and Allan-like) variances, and (3) a user guide to "Enrico's Chart of Phase Noise and Two-Sample Variances." In turn, the Chart is a reference card collecting the most useful concepts, formulas and plots in a single A4/A-size sheet, intended to be a staple on the desk of whoever works with these topics. The Chart is available under Creative Commons 4.0 CC-BY-NC-ND license from Zenodo, DOI 10.5281/zenodo.4399218. A wealth of auxiliary material is available for free on the Enrico's home page http://rubiola.org.

NEURAL NETWORK SIGNAL PROCESSING: THE TASKS WITHOUT “DEEP LEARNING”

The purpose of the study is to evaluate the possibility of using artificial neural networks for electrical signals processing.
 
 Methods. The application of a multilayer perceptron for these purposes as the simplest neural feed forward network is considered. Its difference as the basis of neural network algorithms is that when analyzing dynamic processes, signal processing should be carried out in a “sliding time window”.
 
 Results. It is shown that neural network processing makes it possible to approximate the shape of the signal with high accuracy and determine its parameters in real time. Using the example of periodic signals and transients in electrical circuits, accuracy assessments are made and the features of neural network processing are analyzed. The necessary sizes of the training sample of signals and the level of errors that occur when testing a neural network are discussed. Estimates of the required signal sampling frequency, the “sliding window” duration and the variation range of signal parameters when creating a training sample are given.
 
 Conclusions. It is shown that the proposed approach does not require “deep learning” of neural networks with complex architecture. It enables to create a signals training sample based on simple analytical formulas and to control the neural network algorithm quality at intermediate stages of calculations.

Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method.

Onboard electrostatic suspension inertial sensors are important applications for gravity satellites and space gravitational-wave detection missions, and it is important to suppress noise in the measurement signal. Due to the complex coupling between the working space environment and the satellite platform, the process of noise generation is extremely complex, and traditional noise modeling and subtraction methods have certain limitations. With the development of deep learning, applying it to high-precision inertial sensors to improve the signal-to-noise ratio is a practically meaningful task. Since there is a single noise sample and unknown true value in the measured data in orbit, odd-even sub-samplers and periodic sub-samplers are designed to process general signals and periodic signals, and adds reconstruction layers consisting of fully connected layers to the model. Experimental analysis and comparison are conducted based on simulation data, GRACE-FO acceleration data, and Taiji-1 acceleration data. The results show that the deep learning method is superior to traditional data smoothing processing solutions.

Applying wavelet analysis to the X-ray light curves of active galactic nuclei and quasi-periodic eruptions

ABSTRACT In this work, we examine the application of the wavelet transform to the X-ray timing analyses of active galactic nuclei (AGN) and quasi-periodic eruption sources (QPEs). Several scenarios are simulated to test the effectiveness of the wavelet analysis to stationary and non-stationary data. We find that the power spectral density (PSD) slope and the nature of the periodic signal can influence the ability to identify important features in the wavelet power spectrum. In general, weak and transient features can be discerned, which make the wavelet spectrum an important tool in examining AGN light curves. We carried out a wavelet analysis to four unique objects: Ark 120, IRAS 13224-3809, RE J1034+396, and the QPE GSN 069. The well-known quasi-periodic oscillation (QPO) in RE J1034 + 396 is significantly detected in the wavelet power spectrum. In IRAS 13224-3809, significant transient features appear during a flare at frequencies coincident with previously detected reverberation signals. Finally, the wavelet power spectrum of the QPE GSN 069 significantly reveals four persistent signals that exhibit a 3:2 ratio in oscillation frequencies, consistent with high-frequency QPOs in stellar mass X-ray binaries, but we cannot rule out the possibility this is an artefact of the calculation.

Temporal Variation of the Rotation in the Solar Transition Region

The temporal variations of solar rotation in the photosphere, chromosphere, and corona have been widely investigated, whereas the rotation of the solar transition region is rarely studied. Here, we perform a primary study about the long-term variation of the rotation in the transition region using Lyα irradiance from 1947 February 14 to 2023 February 20. Correlation techniques are used, and the main results are as follows. (1) The sidereal rotation period of the solar transition region varies between 22.24 and 31.49 days, and the mean sidereal rotation period is 25.50 days for the studied time interval 1947–2022. (2) The rotation period of the transition region exhibits a clear downward trend during 1947–2022, which might be caused by the reduced heliospheric pressure and the weaker solar global magnetic fields. (3) Significant periodic signal of the quasi-Schwabe cycle is found in the rotation periods of the transition region. (4) The cross-correlation between the rotation periods of the solar transition region and sunspot activity corroborates a strong correlation with the Schwabe cycle. Possible mechanisms responsible for these results are discussed.

Hydraulic circuit for pulse flow simulation in the tissue-mimicking aortic phantom

BACKGROUND: Computed tomographic angiography (CTA) is the gold standard in the diagnosis of most vascular pathologies. The optimal method to improve this technique is the use of anthropomorphic tissue-mimicking phantoms, since CTA is accompanied by radiation exposure and the risk of allergic reactions when using contrast agents. In addition to compliance with the X-ray properties of the vessel, the pulsations occurring in the aorta in vivo must be simulated. A review of existing solutions demonstrates a small number of national developments in this area at a relatively high cost of foreign analogues. Moreover, the lack of reproducible methodology for creating pulse flow simulation devices using available and inexpensive materials is worth noting.
 AIM: To develop a hydraulic circuit to simulate pulse blood flow in a tissue-mimicking aortic phantom.
 METHODS: A literature analysis of existing pulse flow simulation devices and tissue-mimicking phantoms of the abdominal aorta was conducted. The medical and technical requirements for the designed device were formulated. The control circuit was developed, the circuit element base was determined, and the hydraulic circuit prototype was assembled. Based on a literature review, a material suitable for reproducing the biomechanical characteristics of arterial tissue was selected. A simplified phantom of the abdominal aortic segment was made. The device model included a simplified abdominal aortic phantom, a control system, a pump, a pressure sensor, a flow meter, and a flow regulator. Initial testing of the developed circuit in the basic signal mode and in the real flow profile simulation mode was performed. The basic signals were periodic rectangular signals reproduced at different frequencies, simulating normal, rapid, and slow heart rate. Using pulse-width modulation, a profile of the pressure pulse wave was obtained.
 RESULTS: The developed hydraulic circuit allowed successful reproduction of pressure and flow velocity profiles in a tissue-mimicking aortic phantom. Further development of the project will involve fabrication and validation of the circuit (using anthropomorphic versions of the phantom) and simulation of the angiographic study.
 CONCLUSIONS: The results may be useful for the improvement of CTA techniques and the development of angiosurgical training stands.

Reflection loss-based roadway water depth measurement for driver safety

Flood fatalities generally occur in flood-prone areas such as low water bridges and tunnels, and most are vehiclerelated. The existing water depth measurement solutions are neither cost-efficient nor suitable for roadway scenarios. This paper proposes two water depth measurement methods that can be integrated into cellular networks. The proposed methods reuse the periodic communication signals of 5G and 6G to avoid allocating dedicated sensing signals and conflicting with the communication requirements. The main idea of these methods is based on the fact that the Reflection Loss (RL) induced by a water surface exhibits a strong dependence on the water depth. The peak counting method measures the water depth by counting the successive RL peaks separated by a constant spacing. The RL fingerprinting method integrates the RL peak positions and the measured RL values to determine the water depth. Several factors affecting the water depth measurement resolution are also analyzed. The simulation results show that the measurable water depth range of the proposed methods is larger than the water depth that may pose danger to vehicles, indicating that the proposed methods are feasible for roadway scenarios.

Motion magnification algorithms for video-based breathing monitoring

In this paper, we present two video processing techniques for contact-less estimation of the Respiratory Rate (RR) of framed subjects. Due to the modest extent of movements related to respiration in both infants and adults, specific algorithms to efficiently detect breathing are needed. For this reason, motion-related variations in video signals are exploited to identify respiration of the monitored patient and simultaneously estimate the RR over time. Our estimation methods rely on two motion magnification algorithms that are exploited to enhance the subtle respiration-related movements. In particular, amplitude- and phase-based algorithms for motion magnification are considered to extract reliable motion signals. The proposed estimation systems perform both spatial decomposition of the video frames combined with proper temporal filtering to extract breathing information. After periodic (or quasi-periodic) respiratory signals are extracted and jointly analysed, we apply the Maximum Likelihood (ML) criterion to estimate the fundamental frequency, corresponding to the RR. The performance of the presented methods is first assessed by comparison with reference data. Videos framing different subjects, i.e., newborns and adults, are tested. Finally, the RR estimation accuracy of both methods is measured in terms of normalized Root Mean Squared Error (RMSE), demonstrating the superiority, performance-wise, of the phase-based method.

Approximation of Aperiodic Signals Using Non-Integer Harmonic Series: The Generalized NAFASS Approach

In this paper, the non-orthogonal amplitude-frequency analysis of smoothed signals (NAFASS) method) is used to approximate discrete aperiodic signals from various complex systems with the non-integer harmonic series (NIHS). When approximating by the NIHS, there is a problem in determining the dispersion law for harmonic frequencies. In the original version of the NAFASS approach, the frequency dispersion law was determined from a linear-difference equation. However, many complex systems in nature have frequency distributions that differ from the linear law, which is used in the conventional Fourier analysis of periodic signals. This paper proposes a generalization of the NAFASS method for describing aperiodic signals by the NIHS with a frequency distribution that satisfies a recursive formula, which coincides with the local generalized geometric mean (GGM). The methodology of the generalized NAFASS method is demonstrated using descriptions of financial data (prices for metals) and sound data (sounds of insects) as examples. The results show the effectiveness of the generalized NAFASS approach for describing real-world time data. This discovery allows us to propose a new classification scheme for smoothed and aperiodic signals captured as responses and envelopes from various complex systems.

Solar and ENSO activity affecting Late Holocene carbon accumulation rates in peatlands from Northeast Asia: Evidence from periodic signal analysis

Peatlands are well developed in large areas of Northeast Asia, where changes in Late Holocene carbon accumulation rate (CAR) have played an important role in influencing the regional carbon cycle. However, the driving mechanisms and periodic signals of these important peatlands have rarely been investigated. The Zhibian peatland is a subalpine peatland, which has a continuous CAR record covering the entire Late Holocene. This peatland is located in Northeast Asia, its carbon dynamics were directly influenced by the East Asian summer monsoon. This peatland recorded direct interactions between the CAR dynamics, solar and El Niño–Southern Oscillation (ENSO) activity. Thus, we selected this peatland as a representative peatland for Northeast Asia. We used spectrum analyses and wavelet analyses to study the periodic signals of CAR dynamics, total solar irradiance (TSI), mean annual precipitation (Pann) and ENSO activity during the Late Holocene. The correlation relationships between the above parameters were investigated to explore the driving mechanisms for CAR dynamics in Northeast Asia peatlands. Our analyses indicate that CAR dynamics show 88a, 210a and 1000a periodicity over the Late Holocene. The potential driving factors, such as the Pann, ENSO and TSI, also indicate similar periodic signals. These relationships further indicate that the Pann, ENSO and TSI played an important role in controlling the CAR variations of Northeast Asia peatlands. Wavelet analyses results suggest that the CAR, Pann and TSI negatively corresponded with the ENSO variations. From the relationship model between these parameters, we concluded that strong solar activity resulted in fewer ENSO events and more precipitation in Northeast Asia. Wet conditions contributed to higher CAR, and vice versa.

Bipolar nonlinear photo-controlled thyristor with variable-resistance effect

With the advancement of artificial intelligence technology, volatile memristors emulating neurons have gained widespread attention in the field of neuromorphic computing. This paper proposes a volatile bipolar nonlinear photo-controlled thyristor (PCT) compatible with CMOS technology, providing a reference for the structural design of silicon-based neurons. Technology computer aided design (TCAD) simulation is used to analyze the variable-resistance mechanism and photoelectric effect of the device. The PCT fabricated based on the 0.18 μm Bipolar-CMOS-DMOS (BCD) process has two electrode ports. When a fixed amplitude bipolar periodic excitation signal is applied to the anode of the device, the output current and voltage of PCT show nonlinear changes at the same time due to the negative differential resistance effect, satisfying the fundamental requirements of an oscillatory neuron. By further coupling the photoelectric effect, the device can achieve continuous switching from high-resistance state to low-resistance state under light source driving conditions. Finally, the transient data of PCT is extracted and the hysteresis loop is fitted, which verifies the accuracy of the device theory and model simulation.Index Terms—CMOS technology, variable-resistance effect, photoelectric effect, TCAD simulation.

Unresolved Rossby and gravity modes in 214 A and F stars showing rotational modulation

ABSTRACT Here, we report an ensemble study of 214 A- and F-type stars observed by Kepler, exhibiting the so-called hump and spike periodic signal, explained by Rossby modes (r modes) – the hump – and magnetic stellar spots or overstable convective (OsC) modes – the spike, respectively. We determine the power confined in the non-resolved hump features and find additional gravity-mode (g-mode) humps always occurring at higher frequencies than the spike. Furthermore, we derive projected rotational velocities from FIES, SONG, and HERMES spectra for 28 stars and the stellar inclination angle for 89 stars. We find a strong correlation between the spike amplitude and the power in the r and g modes, which suggests that both types of oscillations are mechanically excited by either stellar spots or OsC modes. Our analysis suggests that stars with a higher power in m = 1 r-mode humps are more likely to also exhibit humps at higher azimuthal orders (m = 2, 3, or 4). Interestingly, all stars that show g-mode humps are hotter and more luminous than the observed red edge of the δ Scuti instability strip, suggesting that either magnetic fields or convection in the outer layers could play an important role.

DNA structural properties of DNA binding sites for 21 transcription factors in the mycobacterial genome.

Mycobacterium tuberculosis, the causative agent of tuberculosis, has evolved over time into a multidrug resistance strain that poses a serious global pandemic health threat. The ability to survive and remain dormant within the host macrophage relies on multiple transcription factors contributing to virulence. To date, very limited structural insights from crystallographic and NMR studies are available for TFs and TF-DNA binding events. Understanding the role of DNA structure in TF binding is critical to deciphering MTB pathogenicity and has yet to be resolved at the genome scale. In this work, we analyzed the compositional and conformational preference of 21 mycobacterial TFs, evident at their DNA binding sites, in local and global scales. Results suggest that most TFs prefer binding to genomic regions characterized by unique DNA structural signatures, namely, high electrostatic potential, narrow minor grooves, high propeller twist, helical twist, intrinsic curvature, and DNA rigidity compared to the flanking sequences. Additionally, preference for specific trinucleotide motifs, with clear periodic signals of tetranucleotide motifs, are observed in the vicinity of the TF-DNA interactions. Altogether, our study reports nuanced DNA shape and structural preferences of 21 TFs.