Time-varying Phase Research Articles

Multi-objective optimization of switched reluctance motor driving electric vehicles based on vehicle operating condition

To improve the comprehensive performance of the switched reluctance motor (SRM) for electric vehicles (EVs) under actual driving conditions, a novel geometrical optimization method based on the time-varying phase current characteristics and the comprehensive indicator with justified weight coefficients is proposed in this paper. First, based on the established dynamic models of EV and SRM, the actual operating phase currents of the motor working under the New European Driving Cycle (NEDC) are extracted and classified to develop the representative optimization conditions. Then, considering the requirements of EV, five indicators are defined to evaluate the comprehensive performance of SRM, and a new multi-objective synchronization optimization function is proposed with five weight coefficients obtained by the statistical analysis method for the sample data of the indicators. Furthermore, the geometrical optimization for a four-phase 8/6 SRM is carried out by the combination of Particle Swarm Optimization (PSO) and BP neural network algorithms, after analyzing the effects of key geometrical parameters on the indicators. Finally, the work performance of the optimized SRM is evaluated and compared with that of the initial prototype, and the results show that the optimized SRM with the proposed multi-objective synchronization optimization method can improve the comprehensive indicator by 59.59%, and can significantly enhance the vehicle actual working economy by 50.74% under NEDC cycle.

Numerical simulation of wind–snow flow on a roof considering time-varying snow phase boundaries

Large-span roofs can be damaged by uneven snow loads caused by snow drifting. To predict wind-induced snow loads more accurately, this paper proposes a numerical simulation method. The method considers the interaction of wind and snow, the changing characteristics of snow phase boundaries over time, and corrects for snow deposition and erosion flux. The effectiveness of the method was confirmed by simulating wind–snow flow on a three-centered cylindrical shell and comparing the results with wind tunnel measurements. The simulation considered different snowfall conditions and time-varying snow phase boundaries on a large-span shell-shaped roof. The study investigated the influence of snowfall conditions and dynamic changes in snow phase boundaries on snow cover distribution by comparing simulation results.

Transient surface pressure of a rectangular cylinder subjected to downburst-like winds

Thunderstorm downbursts are transient in nature and have been responsible for a variety of structural damages in recent years. Currently, the researchers have done several works on the characteristics of downburst wind speed. Nonetheless, rare attention has been placed on the structural aerodynamics characteristics subjected to downburst winds. Based on this, an experimental investigation is performed to reproduce downburst-like winds physically and to study the transient surface pressures (SPs) on a 5:1 rectangular cylinder (RC). The experiment is conducted within a multiple-fan active control wind tunnel (MFACWT) and mainly focuses on simulating the transient characteristics of downburst-like flow, including time-varying mean (TVM) wind speed and nonstationary wind fluctuation. The resulting SPs are measured to understand the influence of transient wind on the aerodynamic behavior of bluff bodies. The spatiotemporal characteristics of the SPs are analyzed using wavelet transform and Priestley's classic spectral theory. The results indicate that the transient nature of the downburst-like flow can be physically reproduced by a MFACWT. The instantaneous pressures of a RC are illustrated by both the turbulence parameters of the transient flow and the flow-separation characteristics. The pressure coefficients normalized by the TVM of the downburst-like winds remain constant, which provides a more appropriate way to estimate the transient gust loading in a quasi-steady manner. Interestingly, the phenomenon of the time-varying phase shift and time-varying correlation of chordwise SPs is observed when the turbulent velocity changes dramatically. In addition, the normalized surface pressure can be regarded as a stationary stochastic process, which provides a significant basis for further establishing the theoretical model of nonstationary gust-loading.

Dynamic quantitative phase microscopy: a single-shot approach using geometric phase interferometry

There is a significant gap in cost-effective quantitative phase microscopy (QPM) systems for studying dynamic cellular processes while maintaining accuracy for long-term cellular monitoring. Current QPM systems often rely on complex and expensive voltage-controllable components like Spatial Light Modulators or two-beam interferometry. To address this, we introduce a QPM system optimized for time-varying phase samples using azobenzene liquid crystal as a Zernike filter with a polarization-sensing camera. This system operates without input voltage or moving components, reducing complexity and cost. Optimized for gentle illumination to minimize phototoxicity, it achieves a 1 Hz frame rate for prolonged monitoring. The system demonstrated accuracy with a maximum standard deviation of ±42 nm and low noise fluctuations of ±2.5 nm. Designed for simplicity and single-shot operations, our QPM system is efficient, robust, and precisely calibrated for reliable measurements. Using inexpensive optical components, it offers an economical solution for long-term, noninvasive biological monitoring and research applications.

Ionospheric contributions to the excess power in high-redshift 21-cm power-spectrum observations with LOFAR

ABSTRACT The turbulent ionosphere causes phase shifts to incoming radio waves on a broad range of temporal and spatial scales. When an interferometer is not sufficiently calibrated for the direction-dependent ionospheric effects, the time-varying phase shifts can cause the signal to decorrelate. The ionosphere’s influence over various spatiotemporal scales introduces a baseline-dependent effect on the interferometric array. We study the impact of baseline-dependent decorrelation on high-redshift observations with the Low Frequency Array (LOFAR). Data sets with a range of ionospheric corruptions are simulated using a thin-screen ionosphere model, and calibrated using the state-of-the-art LOFAR epoch of reionization pipeline. For the first time ever, we show the ionospheric impact on various stages of the calibration process including an analysis of the transfer of gain errors from longer to shorter baselines using realistic end-to-end simulations. We find that direction-dependent calibration for source subtraction leaves excess power of up to two orders of magnitude above the thermal noise at the largest spectral scales in the cylindrically averaged autopower spectrum under normal ionospheric conditions. However, we demonstrate that this excess power can be removed through Gaussian process regression, leaving no excess power above the 10 per cent level for a $5~$ km diffractive scale. We conclude that ionospheric errors, in the absence of interactions with other aggravating effects, do not constitute a dominant component in the excess power observed in LOFAR epoch of reionization observations of the North Celestial Pole. Future work should therefore focus on less spectrally smooth effects, such as beam modelling errors.

Neural network enhanced time-varying parameter estimation via weak measurement.

Weak measurement is employed to measure faint signals due to its capability to amplify detection results above technical noise. However, achieving high amplification effects requires accurate adjustment to the experimental system. Estimating unknown time-varying phases, accurately estimating phases, and sensitively perceiving phase changes pose challenges, demanding the system to continuously remain at the appropriate working range. To address this issue, we propose a neural network-based adaptive weak measurement scheme via single-channel light intensity detection. Through machine learning calibrating the experimental system, the reference phase can be dynamically and accurately adjusted, accommodating time-varying phase changes and ensuring the system operates optimally. Compared with traditional dual-channel weak measurement systems, the scheme reduces experimental complexity. Meanwhile, by accurately adjusting the reference phase, the scheme has higher sensitivity and estimation precision compared to the non-modulated scheme. We validate the effectiveness of the scheme in estimating the period and stochastic time-varying phase. The proposed method highlights the advancement of machine learning in weak measurement systems and can also be applied to other quantum-enhanced measurement schemes.

Two-ray channel models with doppler effects for LEO satellite communications

In this paper we present some two-ray models with Doppler effects for Low Earth Orbit (LEO) satellite links. We show that satellite motion-caused Doppler shifts are different along the two rays, resulting in a time-varying phase shift. This is quantified with a few Doppler models and approximations. The combined interference effects, along with the path length difference caused phase shift, are calculated using a generic LEO pass-over. Channel gains are computed and compared using various antenna patterns and system parameters. The models show good agreements except for very low elevation angles. They demonstrate that a tracking antenna is effective in reducing fading for moderate to high elevation angles. Fixed patch antennas perform well. Omnidirectional and dipole antennas perform poorly. Higher carrier frequency and higher antenna height lead to faster fades. The fading becomes deeper at low elevation angles. Very fast fading is observed near the ends of a pass-over.

Motor Current Time-Varying Quadratic Phase Coupling Analysis and Its Application in Traction Motor Fault Detection Under Varying-Speed Condition

Motor Current Time-Varying Quadratic Phase Coupling Analysis and Its Application in Traction Motor Fault Detection Under Varying-Speed Condition

A time-variant cross-track error phase removal method for ATI-SAR

A time-variant cross-track error phase removal method for ATI-SAR

Adaptive estimation of time-varying phase via weak value amplification

The real-time estimation of a time-varying phase is of great importance in several fields. In order to estimate a time-varying phase with high precision and sensitivity, we propose and experimentally demonstrate an adaptive phase estimation method via weak value amplification by splitting the light into sensing path and monitoring path. In the proposed method, the phase shift can be amplified, an adjustable sensitivity and linear dynamic range can be obtained through the insertion of the reference phase, and a full-range phase estimation could be realized via an appropriate adjustment strategy. Furthermore, detectors with a higher resolution could be selected to detect weak signal by the adjustment of attenuation. Potentially, the phase estimation precision of about 10−8 rad could be achieved with the available equipment.

Intermittent phase dynamics of non-autonomous oscillators through time-varying phase

Oscillatory dynamics pervades the universe, appearing in systems of all scales. Whilst autonomous oscillatory dynamics has been extensively studied and is well understood, the very important problem of non-autonomous oscillatory dynamics is less well understood. Here, we provide a framework for non-autonomous oscillatory dynamics, within which we can define intermittent phenomena such as intermittent phase synchronisation. Moreover, we demonstrate this framework with a coupled pair of non-autonomous phase oscillators as well as a higher-dimensional system comprising of two interacting phase-oscillator networks.

Time-varying phase synchronization of resting-state functional magnetic resonance imaging reveals a shift toward self-referential processes during sustained pain.

Growing evidence has suggested that time-varying functional connectivity between different brain regions might underlie the dynamic experience of pain. This study used a novel, data-driven framework to characterize the dynamic interactions of large-scale brain networks during sustained pain by estimating recurrent patterns of phase-synchronization. Resting-state functional magnetic resonance imaging signals were collected from 50 healthy participants before (once) and after (twice) the onset of sustained pain that was induced by topical application of capsaicin cream. We first decoded the instantaneous phase of neural activity and then applied leading eigenvector dynamic analysis on the time-varying phase-synchronization. We identified 3 recurrent brain states that show distinctive phase-synchronization. The presence of state 1, characterized by phase-synchronization between the default mode network and auditory, visual, and sensorimotor networks, together with transitions towards this brain state, increased during sustained pain. These changes can account for the perceived pain intensity and reported unpleasantness induced by capsaicin application. In contrast, state 3, characterized by phase-synchronization between the cognitive control network and sensory networks, decreased after the onset of sustained pain. These results are indicative of a shift toward internally directed self-referential processes (state 1) and away from externally directed cognitive control processes (state 3) during sustained pain.

Dynamic Short Circuit Ratio for Stability Assessment of Grid Connected VSC-HVDC Systems Considering the Impact of Time-Varying Phase Shift

As more renewable resources are integrated into the receiving-end AC grid through VSC-HVDC, the grid connected VSC-HVDC system is facing a critical dynamic stability issue involving multiple-frequency oscillatory behaviors, which mainly stems from the low-system-strength and the time-varying phase shift. This paper proposes a general stability analysis method to measure the dynamic stability of grid connected VSC-HVDC system from the point of view of input to state stability (ISS). A nonlinear time-varying discrete-time state space model (NTDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M) of the grid connected VSC-HVDC system is built considering the impact of the time-varying phase shift. Then, the NTDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M is decomposed into several interconnected subsystems. The general asymptotic gain matrix (GAGM), whose elements are derived from the proposed dissipative-form sum-type inequalities (DSIs), is constructed to quantify the interactions between multiple subsystems. Finally, a general dynamic stability analysis index, called dynamic short circuit ratio (DSCR), is proposed to effectively explain the critical dynamic instability point of the grid connected VSC-HVDC system, based on the spectral radius property of the GAGM. Moreover, the real-time simulations and multiple comparative studies are implemented in this paper to verify the effectiveness of the proposed NTDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M and stability analysis method by using RT-LAB platform.

Weak signal extraction in non-stationary channel with weak measurement

An emerging challenge of integrated communication and sensing is the extraction of weak sensing signals transmitted through an unknown non-stationary channel. In this work, we propose a weak signal extraction method with weak measurement. Taking advantage of time division multiplexing, we preliminarily estimate the channel via adjustable finite impulse response filter, further suppressing the interfering signal caused by background noises via spectrum shift. By subsequently using the time-varying phase estimation method via weak measurement, the real-time detection of weak signals in the non-stationary channel is achieved. We demonstrate via theoretical analysis and confirmatory experiment that our method is able to amplify the phase shift, to suppress technical noise and to improve detection resolution limit, while proving robust against light source fluctuations, initial phase differences and detector saturation. The method hence enables weak sensing signal extraction with a low signal-to-noise ratio non-stationary channel. Furthermore, we interface our measurement method to squeezed light sources, offering the possibility of surpassing standard quantum limit.

Spatio-temporal coherent snapshot compressed imaging with time-varying composite phase masks

Snapshot compressed imaging (SCI) can encode an image sequence into a single-frame measurement and has the advantages of low requirements for physical sensors, low bandwidth/ memory requirements, and a fast acquisition speed. However, existing snapshot compressed imaging mainly focuses on incoherent imaging, such as video/hyperspectral imaging, and research on coherent imaging is rare. In this paper, snapshot compressed sensing is introduced for coherent imaging conditions, and a spatio-temporal coherent snapshot compressed imaging method with time-varying composite phase masks is proposed. Numerical experiments demonstrate the effectiveness and superiority of our proposed spatio-temporal coherent snapshot compressed imaging with time-varying composite phase masks method (CSCI-CPM) in terms of the reconstruction quality and robustness for both self-sparse and complex cases.

Error correction algorithm of array time-varying amplitude and phase based on autoencoder

As array antennas are widely used in various mobile platforms, the time-varying amplitude and phase error has become an important factor affecting the application of array signal processing technology. A deep learning-based algorithm for the correction of time-varying amplitude and phase errors in arrays is proposed in terms of the idea of autoencoder. The algorithm makes full use of the data feature extraction and reconstruction capability of the autoencoder network, designs a deep learning network for the correction of time-varying amplitude phase error of the channel, gives a double-driven learning mechanism without time-varying amplitude phase error data (unperturbed data) and time-varying amplitude phase error data (perturbed data), completes the extraction of the array stream shape hidden features based on the principle of minimising the mean square error of the desired output and the ideal model. The simulated experiments show that the algorithm can effectively correct the time-varying amplitude and phase errors of each channel, and the mean square error of the corrected amplitude and phase errors are within 0.5% and 1.5% respectively when there are ±80% random time-varying amplitude errors and ±5° random time-varying phase errors. The effectiveness of the proposed algorithm is verified.

Dynamic Functional Connectivity in Pediatric Mild Traumatic Brain Injury

Resting-state fMRI can be used to identify recurrent oscillatory patterns of functional connectivity within the human brain, also known as dynamic brain states. Alterations in dynamic brain states are highly likely to occur following pediatric mild traumatic brain injury (pmTBI) due to the active developmental changes. The current study used resting-state fMRI to investigate dynamic brain states in 200 patients with pmTBI (ages 8-18 years, median = 14 years) at the subacute (∼1-week post-injury) and early chronic (∼ 4 months post-injury) stages, and in 179 age- and sex-matched healthy controls (HC). A k-means clustering analysis was applied to the dominant time-varying phase coherence patterns to obtain dynamic brain states. In addition, correlations between brain signals were computed as measures of static functional connectivity. Dynamic connectivity analyses showed that patients with pmTBI spend less time in a frontotemporal default mode/limbic brain state, with no evidence of change as a function of recovery post-injury. Consistent with models showing traumatic strain convergence in deep grey matter and midline regions, static interhemispheric connectivity was affected between the left and right precuneus and thalamus, and between the right supplementary motor area and contralateral cerebellum. Changes in static or dynamic connectivity were not related to symptom burden or injury severity measures, such as loss of consciousness and post-traumatic amnesia. In aggregate, our study shows that brain dynamics are altered up to 4 months after pmTBI, in brain areas that are known to be vulnerable to TBI. Future longitudinal studies are warranted to examine the significance of our findings in terms of long-term neurodevelopment.

Moving sound source localization and tracking for an autonomous robot equipped with a self-rotating bi-microphone array.

In this paper, we present two approaches to localizing and tracking a sound source that moves in a three-dimensional (3D) space. The sound signal was captured by a unique bi-microphone system that rotates at a constant angular velocity. The motion of the sound source along with the rotation of the bi-microphone array produces a sinusoidal inter-channel distance difference (ICDD) signal with time-varying amplitude and phase. Four state-space models were developed and employed to design extended Kalman filters (EKFs) that identify instantaneous amplitude and phase of the ICDD signal. Both theoretical and numerical observability analyses of the four state-space models were performed to reveal singularities of the proposed EKFs in the domain of interest. We also developed a Hilbert-transform based method that localizes the sound source by comparing the true analytic ICDD signal to a virtual reference signal with zero elevation and azimuth angles. A moving average filter is then applied to reduce the noise and the effect of the artifacts at the beginning and the ending portions of the estimates. The effectiveness of the proposed methods was evaluated using comparison studies in simulation.

Short-wavelength radiation pulses with time-varying orbital angular momentum from tailored relativistic electron beams.

In this Letter, we propose a novel, to the best of our knowledge, technique to generate short-wavelength radiation carrying time-varying orbital angular momentum (OAM) by tailoring relativistic beams in free-electron lasers. To manipulate the temporal properties of OAM beams, two time-delayed seed lasers with different OAM values are used to interact with the electron beam in the undulator. With this method, high-harmonic electron beam microbunching with a time-varying helical distribution can be tailored to match the time-varying instantaneous helical phase structure of the x ray beams. Theoretical and simulation results demonstrate that high-power x ray beams with time-varying OAM can be produced by the proposed technique, which opens new routes to scientific research in x ray science.

Passive Tone Detection for Moving Targets Based on Long-Time Coherent Integration

The detection of sinusoidal signals embedded in noise is an important topic in passive sonar signal processing. The performance of existing tone detection techniques is limited by the Doppler frequency shift, which is caused by the relative motion between moving targets and the receiver. In this study, an algorithm based on long-time coherent integration is proposed to achieve robust tonal signal detection under the influence of a Doppler frequency shift. First, the received narrowband signals radiated by moving targets are split into multiple segments through a window-length constraint. The results obtained by applying a discrete Fourier transform (DFT) to the data segments are modeled as a polynomial phase signal in the frequency domain. Then, the polynomial Radon-polynomial Fourier transform (PRPFT) is applied to simultaneously compensate the time-variant frequency drift and phase difference. To avoid the gain loss due to the discretization of the phase difference compensation in PRPFT, a phase compensation factor searching algorithm is proposed. The simulation results show that the proposed algorithm can provide higher frequency resolution and higher coherent integration gain even in the case of a severe Doppler shift. Furthermore, the results of sea trials demonstrate that the proposed method can perfectly achieve coherent integration for signals with a duration of several hundred seconds under different experimental conditions.