Time-varying Measures Research Articles

Assessing the Effect of Abortion Restrictions: Improving Methodology and Expanding Outcomes.

Assessing the Effect of Abortion Restrictions: Improving Methodology and Expanding Outcomes.

Real-Time Complex Dielectric Constant Measurement of Liquids Using Epsilon-Near-Zero Supercoupling Channels

This article proposes a device for real-time measurement of the complex dielectric constant of liquids at X-band. The device consists of a narrow waveguide channel operating at the cut-off frequency to achieve a large field enhancement based on Epsilon-Near-Zero (ENZ) supercoupling theory. A micro-3D-printed sample holder is placed inside the channel and connected to feeding tubes that are designed to prevent energy leakage from the channel. The field enhancement and confinement inside the channel creates a strong interaction with the liquids under test, thereby providing very sensitive measurements over a wide range of permittivities (approx. 1-65). Two variations of the device are fabricated. One that provides a higher accuracy and another that provides a higher sensitivity. A full-wave simulation based forward model is developed and optimized using a constrained Nelder-Mead simplex. An inversion is performed based on the optimized forward model to find the complex permittivity from the measured scattering parameters. Two sets of experiments are performed, one of static liquid mixtures and another of time-varying liquid mixtures. The static measurements are done with binary mixtures of ethanol-water and methanol-water. The time-varying measurements are done with ethanol-water-sugar mixtures and ethanol-water mixtures. The measured results are consistent between the two devices and are in very good agreement with the published literature over the entire range of dielectric constants tested.

A Nonlinear Filter by Pseudo-Formal Linearization for Nonlinear Time-Varying Systems

This paper is concerned with a nonlinear filter for a general class of nonlinear time-varying systems, by making use of a pseudo-formal linearization method and Kalman filter approach. The given nonlinear time-varying dynamic and measurement equations are piecewisely linearized by the formal linearization method using Taylor expansion so that Kalman filter can be applied for each linearized time-varying equations. Then the resulting filters are smoothly united into a single nonlinear filter by an automatic choosing function. The efficiency of this filter is shown through numerical examples.

Distributed bearing-based localization under switching graph topologies

This paper addresses a distributed localization problem for n (n ≥ 2)-agent systems defined in d (d ≥ 2)-dimensional space under undirected switching graph topologies. It is assumed that each agent in the formation can measure the bearing to the neighboring agents and communicate with them, and at least one agent has access to its global position. Novel distributed position observers are designed under which each agent estimates its global position using its velocity, the relative bearing measurements, and the position estimates of neighboring agents received over a communication network. The main contribution of this work is that i) it relaxes the classical bearing rigidity assumption (typically required) for bearing-based localization problems addressed in the current literature and ii) it covers time-varying bearing measurements and/or time-varying graph topologies. Furthermore, the efficiency of the proposed observers is guaranteed by the rigorous mathematical analysis (i.e., uniform global exponential stability of the estimation error) along with illustrative simulation results.

A Novel Tightly-Coupled SINS/RCNS Integrated Navigation Method Considering Atmospheric Density Error

With the increasing demands for high-accuracy autonomous navigation, the integrated strapdown inertial navigation system and refractive celestial navigation system (SINS/RCNS) has received close attention. Aided by stellar refraction information, it can correct position and velocity errors. However, atmospheric density error is a critical element that restricts the accuracy of SINS/RCNS. To solve this problem, this paper proposes a novel tightly-coupled method. In this scheme, the atmospheric density error is estimated and compensated to accommodate for the unknown model perturbation. Additionally, a tight integration model based on star pixel coordinates is derived to handle the time-varying measurement noise. The proposed method is evaluated by representative suborbital flight vehicle navigation simulations, and the results indicate that the introduction of atmospheric density error compensation can improve the navigation accuracy without adding extra sensors and enhance the performance.

A time-delay approach to stabilization by extremum seeking controller via simplified ISS analysis

We study stabilization of linear uncertain systems under unknown control directions using a bounded extremum seeking controller with a small parameter. We consider a small time-varying measurement delay. By using the recently proposed time-delay approach to Lie-Brackets-based averaging, we transform the closed-loop system to a time-delay (neutral type) one, which has a form of perturbed Lie brackets system. The input-to-state stability (ISS) of the time-delay system guarantees the same for the original one. Differently from the existing analysis via Lyapunov-Krasovskii (L-K) method, we transform the neutral system to an ordinary differential equation (ODE) with delayed perturbations and employ variation of constants formula that greatly simplifies the analysis and leads to simpler stability conditions. Two numerical examples illustrate that the proposed method allows essentially larger parameter uncertainties with bounds on the small parameter and time-delay that are not too small.

Robust Adaptive Heading Tracking Fusion for Polarization Compass With Uncertain Dynamics and External Disturbances

For the polarization compass (PC), maintaining high-precision and high-robustness autonomous measurement of the heading information under conditions where the atmospheric polarized light is being blocked or disturbed represents a challenging problem. In this paper, the E-vector orientation algorithm, which provides high orientation accuracy and real-time performance in sunny weather, and the symmetric axis orientation algorithm, which offers a strong anti-interference ability in occluded environments, are integrated to solve this problem. Furthermore, an improved state error model is established to increase the accuracy of the heading estimation. To track the system state rapidly and circumvent the uncertainties caused by measurement noise variance, an improved variational Bayesian strong tracking cubature Kalman filter (VBSTCKF) is proposed to estimate both the state and the time-varying measurement noise variance accurately. In addition, a multi-frequency data fusion algorithm based on residual compensation is proposed to overcome the inconsistent sampling operation frequency problems of the two orientation algorithms. The outstanding feature is that the proposed multi-frequency VBSTCKF (MF-VBSTCKF) can improve the orientation precision and robustness while also ensuring the high-frequency output performance. Finally, experimental results acquired in occluded environments verify the superiority of the proposed method.

Invariant Error-Based Integrated Solution for SINS/DVL in Earth Frame: Extension and Comparison

The error propagation of traditional strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) is not autonomous because its error state model is trajectory-dependence. Recently, the invariant error defined on the Lie group has raised much attention due to its trajectory-independent and autonomous error propagation. In this article, the invariant error-based Kalman filter for the SINS/DVL integration solution is investigated with a main focus on its extension and comparison. The contributions of this study are threefold. First, the invariant error defined on the matrix Lie group (group of double direct isometrics) is extended to model the nongroup affine traditional SINS mechanism and the group affine transformed SINS mechanism; both of them are derived in Earth frame and augmented with the drift and bias of the inertial measurement units (IMUs). Then, the observation equations for different invariant error-based state models are derived for SINS/DVL application, a theoretical analysis is performed, and a comprehensive evaluation is conducted under different maneuvering conditions by lake field trial. Finally, the variational Bayesian approach is introduced into the invariant error-based Kalman filter to infer the inaccurate process noise covariance matrix (PNCM) and time-varying measurement noise covariance matrix (MNCM) from a practical perspective; experimental results demonstrate that it can improve the navigation accuracy significantly. This study is expected to facilitate the selection of appropriate invariant error to SINS/DVL applications.

The Stockholm-Gotland perinatal cohort-A population-based cohort including longitudinal data throughout pregnancy and the postpartum period.

Register-based reproductive and perinatal databases rarely contain detailed information from medical records or repeated measurements throughout pregnancy and delivery. This lack of enriched pregnancy and birth data led to the initiation of the Swedish Stockholm-Gotland Perinatal Cohort (SGPC). To describe the strengths of the SGPC, as well as the unique research questions that can be addressed using this cohort. The SGPC is a prospectively collected, population-based cohort that includes all births (from 22 completed gestational weeks onwards) between 1 January 2008 and 15 June 2020 in the Stockholm and Gotland regions of Sweden (335,153 singleton and 11,025 multiple pregnancies). Descriptive study. The SGPC is based on the electronic medical records of women and their infants. The medical record system is used for all antenatal clinic visits and admissions, delivery and neonatal admissions, as well as postpartum clinical visits. SGPC has been further enriched with data linkages to 10 Swedish National Health Care and Quality Registers. In contrast to other reproductive and perinatal databases available in Sweden, including the Medical Birth Register and the Pregnancy Register, SGPC contains highly detailed medical record data, including time-varying serial measurements for physiological parameters throughout pregnancy, delivery, and postpartum, for both mother and infant. These strengths have enabled studies that were previously inconceivable; the effects of serial measurements of pregnancy weight gain, changes in haemoglobin counts and blood pressure during pregnancy, fetal weight estimations by ultrasound, duration of stages and phases of labour, cervical dilatation and oxytocin use during delivery, and constructing reference curves for umbilical cord pH. The SGPC-with its rich content, repeated measurements and linkages to numerous health care and quality registers-is a unique cohort that enables high-quality perinatal studies that would otherwise not be possible.

Hesitancy or Resistance? Differential Changes in COVID-19 Vaccination Intention Between Black and White Americans.

The literature on COVID-19 vaccination has rarely taken a macro and longitudinal approach to investigate the nuanced racial and ethnic differences in vaccine hesitancy and refusal. To fill this gap, this study examines the relationships between race, time, and COVID-19 vaccine hesitancy and refusal using state-level data from the US Census Household Pulse Survey, 2020 US Decennial Census, and other sources (i.e., American Community Survey, Human Development Index database, and Centers for Disease Control and Prevention). Four longitudinal Generalized Estimating Equations (GEEs) were estimated to analyze how time-variant and time-invariant measures, and time itself influenced COVID-19 vaccine hesitancy and refusal rates, controlling for the effect of other relevant covariates. The results provide descriptive evidence that COVID-19 vaccine hesitancy had decreased in the USA, but vaccine refusal remained stable between January and October 2021. The GEEs further indicated that the proportion of the Black population was positively associated with both vaccine hesitancy and refusal rates, while the proportion of the White population was positively associated with the vaccine refusal rate but not associated with the vaccine hesitancy rate. In addition, over the 10-month period, COVID-19 vaccine hesitancy and refusal in the Black population declined rapidly, but vaccine refusal in the White population stayed fairly stable. More research and practical efforts are needed to understand and inform the public about these important but overlooked trends.

Non-Steady State NMR Effect and Application on Time-Varying Magnetic Field Measurement.

The measurement of a time-varying magnetic field is different from a constant magnetic field, due to its field intensity variation with time. Usually, the time-varying magnetic field measurement converts the solution of the magnetic induction intensity into the calculation of the induced electromotive force (EMF); then, the magnetic induction intensity is obtained by the time integration of the EMF, but the process is vulnerable to external interference. In this paper, a non-steady state nuclear magnetic resonance (NSS-NMR) scheme for the measurement of a time-varying magnetic field is proposed. In a time-varying magnetic field environment, an RF excitation signal with a certain frequency bandwidth is applied to excite the nuclear spin system. The NSS-NMR signal, which varies with time in the frequency range corresponding to the frequency bandwidth of the RF excitation, could finally be obtained after a series of processing of the probe output signal. During the NSS-NMR experiment, an orthogonal dual-coil probe is adopted to synchronously generate the RF excitation and induce the probe output signal. Moreover, a directional coupler that utilized in the experiment outputs a reference signal from the coupling port for the subsequent signal processing. The experimental results show that the weak NSS-NMR signal is indeed observed. The longitudinal time-varying magnetic field ranges from 0.576 T to 0.582 T, which is inverted by the Larmor precession relationship, have been successfully detected based on the so-called NSS-NMR effect.

Earth Velocity and Rigid-Body Attitude Estimation on SO(3) Using Biased Measurements

This article addresses the problem of simultaneously estimating: 1) the attitude of a rigid body on the special orthogonal group of order 3 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {SO}(3)$</tex-math></inline-formula> ); 2) the Earth’s spin vector expressed in body-fixed coordinates; and 3) a pair of time-varying sensor measurement biases. The available sensor readings include biased angular velocity measurements collected from a set of high-grade rate gyros sensitive to the rotation of the planet and biased vector observations of some known inertial reference field. The estimation solution consists of a Lagrangian-based pseudo Kalman filtering routine, which is partially constrained to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {SO}(3)$</tex-math></inline-formula> vis-á-vis the attitude. Most noticeably, the geometric relationship between the Earth’s angular velocity and the inertial field is leveraged to relax common practical, as well as theoretical, demands closely linked to conventional attitude estimation solutions, namely, the need for explicit body-fixed measurements of two inertial vector fields. The proposed algorithm is validated through a set of realisticsimulations. Experimental results are also included to further demonstrate the performance of this strategy in a real-world application.

Equity costs and risks in emerging markets: Are ESG and Sharia principles complementary?

By proposing a novel continuous and time-varying measure of Sharia compliance, we investigate whether it enhances the effects of corporate social responsibility, proxied by Environmental-Social-Governance scores, on firms' equity costs and market risks in emerging countries. We construct a large dataset of non-financial listed firms incorporated in eighteen emerging markets, both Sharia-compliant and conventional (4612 firm-year observations from 2002 to 2018), finding a consistent, statistically significant, and negative association between the interaction of ESG scores and the Sharia sensitivity with the cost of equity. Moreover, we reveal that this negative relationship is mediated by firms' market risk (risk channel).

Strong tracking square-root modified sliding-window variational adaptive Kalman filtering with unknown noise covariance matrices

The Kalman filter’s performance deteriorates in the existence of slowly time-varying and unknown measurement and process noise covariances. A simplified strong tracking square-root modified sliding window variational adaptive Kalman filter is proposed for the aforementioned challenges in this paper. A modified slidingwindow variational adaptive Kalman filtering is designed in the proposed algorithm capable of correcting and smoothing the previous states in accordance with the latter states and reducing the number of backward iterations to improve the filtering accuracy and computational efficiency of the algorithm. The multiple fading factors have been constructed to correct the one-step predicted error covariance matrix. Moreover, the square root decomposition approach is developed for decomposing the error covariance matrices to eliminate numerical rounding errors. The simulation results demonstrate that the proposed algorithm exhibits superior tracking capacity of the one-step predicted error covariance matrix and filtering accuracy compared with the existing filters.

A deep-learning approach for modelling pedestrian movement uncertainty in large- scale indoor areas

• Indoor trajectory reconstruction model using limited reference points is proposed. • Uncertainty prediction model uses the combination of 1D-CNN and LSTM is proposed. • Enhanced training dataset contains sampling and measurement errors is generated. • Uncertainty region considers both sampling and measurement errors is established. Modelling pedestrian movement uncertainty in complex urban environments is regarded as a meaningful and challenging task regarding the promotion of geospatial data mining and analysis. However, the traditional uncertainty prediction model only takes the movement distance or speed into consideration and is not able to adapt well to time-varying measurement errors. In this paper, a deep-learning framework is proposed for modelling pedestrian movement uncertainty in large-scale indoor areas, in which a hybrid deep-learning model combines a one-dimensional Convolutional Neural Network (1D-CNN) with a long short-term memory (LSTM) network is proposed for enhancing feature extraction performance and reducing time correlation errors. The proposed framework takes human motion related measurement features into consideration, in which the moving step-length and heading information during a time period are also reconstructed and modelled as the input to the deep-learning model. Compared with state-of-art algorithms applied to different real-world trajectory datasets, the proposed deep-learning approach demonstrates much better performance of uncertainty region prediction, including the different indexes (Euclidean error distance, completeness and density) This study has leaded to the provision of an effective and practical framework for modelling trajectory uncertainty of the pedestrian in challenging urban environments, and which is expected to benefit smart city and spatial perception related applications.

Effects of measurement noise on the construction of a transmission matrix.

The effects of time-varying measurement noise on transmission matrix acquisition processes are considered for the first time, to our knowledge. Dominant noise sources are discussed, and the noise properties of a typical interferometer system used for characterizing a multimode fiber transmission matrix are quantified. It is demonstrated that an appropriate choice of measurement basis allows a more accurate transmission matrix to be more quickly obtained in the presence of measurement noise. Finally, it is shown that characterizing the noise figure of the experimental system allows the inverse transmission matrix to be constructed with an ideal amount of regularization, which can in turn be used for optimal image acquisition.

Examining the asymmetries between equity and commodity ETFs during COVID-19

Previous studies have neither examined the volatility co-movements across stock and commodity markets in terms of both time and frequency nor differentiated between bad and good volatility and the potential asymmetric effect. To address this gap, we computed 5-min price data, the positive and negative semivariances on five leading Exchange Traded Funds (ETFs) covering the US equity market, crude oil, natural gas, gold, and silver markets from January 2, 2019 to May 29, 2020, and then draw on the wavelet coherency methodology and the time-varying wavelet coherence measure. The results show that the negative realized volatility co-movements are stronger during the COVID-19 outbreak, especially at short and medium frequencies. The US stock market leads energy and precious metals in the short-run frequency. However, over the long-run, the lead-lag pattern mostly alternates over time for all cases. Notably, the realized volatilities of US equities and precious metals are shaped by the COVID-19 outbreak, reflecting the quest of investors for protection from the market volatilities by investing in gold and silver. This latest finding is confirmed by the wavelet coherence measure. Further results show asymmetric co-movements emanating especially from realized negative semivariance of equities and energy markets around the pandemic outbreak across short-time horizons. We also notice that the COVID-19 outbreak increases the procyclical movement of all ETFs in the short-term. The effect is more pronounced among the US equity and precious metals markets, whereas no significant countercyclical connectedness is observed among these markets. Our findings support previous evidence showing that gold and silver can serve as safe-haven assets due to their low coherence with risky assets.

The Longitudinal Effect of Area Socioeconomic Changes on Obesity: a Longitudinal Cohort Study in the USA from 2003 to 2017.

Despite several dimensions of area socioeconomic status (SES), past literature has been dominated by the use of area socioeconomic position. We examined the longitudinal effect of three area SES measures (i.e., socioeconomic position, inequality, and segregation) on obesity. Using longitudinal data from the Fragile Families & Child Wellbeing Study (N = 1493), we estimated a linear mixed model to examine the effect of three time-varying area SES measures on time-varying measures of objectively measured body mass index z-score (BMIz) from ages 5years to 15years. Findings showed that BMIz increased steadily over time (B = 0.02, 95% CI = 0.02, 0.03). A significant interaction between time and area socioeconomic position indicates that children in areas with higher socioeconomic position had a smaller increase in BMIz than those in low socioeconomic areas (B = - 0.02, 95% CI = - 0.02, - 0.01). A non-linear relationship of area income inequality with BMIz such that BMIz was higher as area income inequality was greater, but the effect diminishes in magnitude with a higher level of area income inequality (linear term: B = 0.07; quadratic term: B = - 0.03). Area income segregation was associated with greater BMIz (B = 0.08, 95% CI = 0.03, 0.12). No time interaction effect was found for area income inequality and segregation. Results highlight a need for community health policy efforts and evidence-based interventions to address childhood obesity issues in low-SES areas.

An improved adaptive robust information filter for spacecraft relative navigation

This work concentrates on addressing the problem of time-varying measurement noise covariance and outliers (contaminated Gaussian distribution), which can significantly deteriorate the estimation accuracy of conventional Kalman filter. Adaptive filters or robust filters are only suitable for solving one of the above-mentioned problems. Meanwhile, the existing adaptive robust filters, performing unsatisfactorily, also can not meet the requirements of spacecraft relative navigation, which needs an accurate and reliable filter. This paper proposes an improved adaptive robust information filter, referred to as variational Bayesian (VB) adaptive dynamic-covariance-scaling (DCS)-based cubature information filter (VB-DCSCIF). The proposed filter, based on the cubature information filter framework, uses VB approximation to track measurement noise covariance with time-varying statistical characteristics, and relies on the improved DCS kernel function to suppress outliers in measurements. Therefore, VB-DCSCIF incorporates the advantages of VB approximation and the improved DCS kernel, exhibiting adaptivity and robustness. Spacecraft relative navigation simulations and Monte Carlo simulations demonstrate that VB-DCSCIF outperforms other filters for providing high-precision state estimation under time-varying measurement noise covariance and outliers.

Variational Bayesian implicit unscented Kalman filter for celestial navigation using time delay measurement

Sunlight arrival time delay is an innovative measurement for celestial navigation, which provides the distance information between the spacecraft and the nearby celestial body. For deep space detectors, the time delay measurement noise is time-varying, and its statistics cannot be accurately determined. The too-large deviation between the measured noise covariance matrix (R) and the noise statistics seriously reduces navigation accuracy. A variational Bayesian implicit unscented Kalman filter (VBIUKF) method is proposed and applied to celestial navigation using time delay measurement in this paper. VBIUKF adaptively estimates R using the innovation sequence and the predicted measurement covariance matrix to make R closest to the measurement noise statistics. Simulation results indicate that VBIUKF can obtain high accuracy navigation results under time-varying measurement noise.