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The determination of a mobile terminal's position with high accuracy and ubiquitous coverage is still challenging. Global satellite navigation systems (GNSSs) provide sufficient accuracy in areas with a clear view to the sky. For GNSS-denied environments like indoors, complementary positioning technologies are required. A promising approach is to use the Earth's magnetic field for positioning. In open areas, the Earth's magnetic field is almost homogeneous, which makes it possible to determine the orientation of a mobile device using a compass. In more complex environments like indoors, ferromagnetic materials cause distortions of the Earth's magnetic field. A compass usually fails in such areas. However, these magnetic distortions are location dependent and therefore can be used for positioning. In this paper, we investigate the influence of elementary structures, in particular a sphere and a cylinder, on the achievable accuracy of magnetic positioning methods. In a first step, we analytically calculate the magnetic field around a sphere and a cylinder in an outer homogeneous magnetic field. Assuming a noisy magnetic field sensor, we investigate the achievable positioning accuracy when observing these resulting fields. For our analysis, we calculate the Cramér–Rao lower bound, which is a fundamental lower bound on the variance of an unbiased estimator. The results of our investigations show the dependency of the positioning error variance on the magnetic sensor properties, in particular the sensor noise variance and the material properties, i.e., the relative permeability of the sphere with respect to the cylinder and the location of the sensor relative to the sphere with respect to the cylinder. The insights provided in this work make it possible to evaluate experimental results from a theoretical perspective. [ABSTRACT FROM AUTHOR]

The localization of pollution sources is one of the main tasks in environmental engineering. For this paper, models of spatial distribution of nitrate concentration in groundwater were created, and the point of highest concentration was determined. This point represents the assumed location of the pollution source and differs from the actual location, so there is a certain distance between the measured and assumed location. This paper puts forward a new hypothesis that the distance between the measured and the assumed location is a function of the variance of the estimation error. The scientific contribution of this paper is based on the fact that the interaction of statistical and geostatistical methods can locate the dominant point source of pollution or narrow down the search area. The above hypothesis is confirmed by the example of the Varaždin wellfield, which was closed due to an excessively high groundwater nitrate concentration. Seven different interpolation methods were used to create spatial distribution models. Each method provides a different model, a different variance of the estimation error, and estimates of the location of the pollution source. The smallest value of variance of the estimation error of 1.65 was obtained for the minimum curvature interpolation method and the largest value of variance (24.49) was obtained for the kriging with logarithmic variogram. Our results show a nonlinear and monotonic relationship between the distance and the variance of the estimation error, so logarithmic and rational quadratic models were fitted to the scatter point data. The models were linearized, a t-test was performed, and the results show that the models can be considered reliable, which is confirmed by the values of the coefficients of determination of the linearized models, which are around 0.50. The obtained results can be used in planning additional research work to determine the measured location of the pollution source. The research methodology we used is universal and can be applied to other locations where high concentrations of certain contaminants have been detected in groundwater in alluvial aquifers. [ABSTRACT FROM AUTHOR]

Finite element analysis of complex bodies is frequently used in design to determine the size of deformations. Successive iterations, with progressive refinement of mesh densities, are most often required to obtain a sufficiently accurate convergent numerical solution. This process is costly, time consuming, and requires superior hardware and software. The paper presents a quick and effortless way to determine a sufficiently accurate value of the numerical solution. The mentioned solution is obtained by amending the numerical solution resulting for a certain value of the mesh density of the studied body with an adequate proportionality coefficient determined following the deformation study of simple bodies differently subject to external forces. It is assumed that the elastic displacement of the various bodies has a similar evolution as the mesh density increases and that the values of the proportionality coefficients considered are approximately equal for identical mesh densities. Examples presented are related to the reference body of the mechanical press PAI 25. [ABSTRACT FROM AUTHOR]

Integrated sensing and communications (ISAC) is emerging as a key technology of 6G. Owing to the low peak-to-average power ratio (PAPR) property, discrete Fourier transform spread orthogonal frequency-division multiplexing (DFT-s-OFDM) is helpful to improve the sensing range and suitable for high-frequency transmission. However, compared to orthogonal frequency-division multiplexing (OFDM), the sensing accuracy of DFT-s-OFDM is relatively poor. In this paper, frequency-domain spectral shaping (FDSS) is adopted to enhance the performances of DFT-s-OFDM including sensing accuracy and PAPR by adjusting the correlation of signals. Specifically, we first establish a signal model for the ISAC system, followed by the description of performance indicators. Then, we analyze the influence of amplitude fluctuation of frequency domain signals on sensing performance, which shows the design idea of FDSS-enhanced DFT-s-OFDM. Further, a FDSS-enhanced DFT-s-OFDM framework is introduced for ISAC, where two types of FDSS filters including a pre-equalization filter and an isotropic orthogonal transform algorithm (IOTA) filter are designed. The simulation results show that the proposed scheme can obtain about 4 dB performance gain in terms of sensing accuracy over DFT-s-OFDM. In addition, FDSS-enhanced DFT-s-OFDM can significantly reduce PAPR and improve the power amplifier efficiency. [ABSTRACT FROM AUTHOR]

Impaired baroreflex sensitivity (BRS) is partially responsible for erratic blood pressure fluctuations in End-Stage Renal Disease (ESRD) patients on chronic hemodialysis (HD), which is related to autonomic nervous dysfunction. The sequence method with delayed signals allows for the measurement of BRS in a non-invasive fashion and the investigation of alterations in this physiological feedback system that maintains BP within healthy limits. Our objective was to evaluate the modified delayed signals in the sequence method for BRS assessment in ESRD patients without pharmacological antihypertensive treatment and compare them with those of healthy subjects. We recruited 22 healthy volunteers and 18 patients with ESRD. We recorded continuous BP to obtain a 15-min time series of systolic blood pressure and interbeat intervals during the supine position (SP) and active standing (AS) position. The time series with delays from 0 to 5 heartbeats were used to calculate the BRS, number of data points, number of sequences, and estimation error. The BRS from the ESRD patients was smaller than in healthy subjects (p < 0.05). The BRS estimation with the delayed sequences also increased the number of data points and sequences and decreased the estimation error compared to the original time series. The modified sequence method with delayed signals may be useful for the measurement of baroreflex sensitivity in ESRD patients with a shorter recording time and maintaining an estimation error below 0.01 in both the supine and active standing positions. With this framework, it was corroborated that baroreflex sensitivity in ESRD is decreased when compared with healthy subjects. [ABSTRACT FROM AUTHOR]

In the process of grid connection of an LCL inverter, sudden changes in load, high harmonics of the grid voltage, sudden changes in power, and other disturbances often occur. These will cause sharp degradation of the grid-connected power quality, so this paper proposes a new solution to the problem of how to reduce and eliminate disturbances in power quality by using a first-order linear active disturbance rejection control (LADRC) strategy with precise compensation via splitting the total disturbance term. An improved overall direct compensation method for total disturbance was proposed. The use of a subdivision compensation term could avoid the misjudgment arising from the estimation of the overall perturbation by the linear expanded state observer (LESO) within the first few weeks of the feedback when the overall compensation was applied. It aimed to reduce the overshooting caused by the overcompensation of the estimated disturbance term and to shorten the system convergence speed. Backstepping control was introduced to optimize the intermediate quantities of LADRC to estimate the error design outer-loop control law. The controlled quantity tracking the input quantity had excellent characteristics, and could set the desired error range quantity as the purpose of approximation. Therefore, backstepping control was suitable as a feedforward control of the system to preprocess the error in the estimated total disturbance of LESO and feed it into the inner loop improvement LADRC. Secondly, an improved control cascade PWM modulation with a PLL phase-locked loop to regulate the inverter output resulted in the elimination of the effects of internal and external disturbances on the grid-connected current and voltage. Finally, the amplitude–frequency characteristics were analyzed and compared for the trackability and antidisturbance of the improved linear active disturbance rejection controller, showing a good performance of the improved active disturbance rejection. At the same time, comparative simulations were conducted to confirm that the grid-connected current of the LCL inverter could obtain a better stability and grid entry quality in the first-order improved linear active disturbance rejection control. [ABSTRACT FROM AUTHOR]

Here we introduce the new concept of computation coding. Similar to how rate-distortion theory is concerned with the lossy compression of data, computation coding deals with the lossy computation of functions. Particularizing to linear functions, we present an algorithmic approach to reduce the computational cost of multiplying a constant matrix with a variable vector, which requires neither a matrix nor vector having any particular structure or statistical properties. The algorithm decomposes the constant matrix into the product of codebook and wiring matrices whose entries are either zero or signed integer powers of two. For a typical application like the implementation of a deep neural network, the proposed algorithm reduces the number of required addition units several times. To achieve the accuracy of 16-bit signed integer arithmetic for 4k-vectors, no multipliers and only 1.5 adders per matrix entry are needed. [ABSTRACT FROM AUTHOR]

Accurate estimation of a neighbor's evolving position is essential to enhancing safety in intelligent transport systems. A vehicle can estimate a neighbor's evolving position via periodic beaconing wherein each vehicle periodically broadcasts a beacon including its own kinematic data (e.g., position, speed, and acceleration). Many researchers have proposed analytic models to describe periodic beaconing in vehicular ad-hoc networks (VANETs). However, those models have focused only on network performance, e.g., packet delivery ratio (PDR), or a delay, which fail to evaluate the accuracy of estimating a neighbor's evolving position. In this paper, we present a new analytic model capable of providing an estimation error of a neighbor's evolving position in VANET to assess the accuracy of the estimation. This model relies on a vehicle system using periodic beaconing and a constant speed and position estimator (CSPE) to estimate a neighbor's evolving position. To derive an estimation error, we first calculate the estimation error using a simple equation, which is associated with a probability of successful reception. Then, we derive the probability of successful reception that is applied onto the error model. To our knowledge, this is the first paper to establish a mathematical model to assess the accuracy of estimating a neighbor's evolving position. To validate the proposed model, we compared the numerical results of the model with those of the NS-2 simulation. We observed that numerical results of the proposed model were located within the 95% confidential intervals of simulations results. [ABSTRACT FROM AUTHOR]

The coherent receiving method can improve the sensitivity of an optical signal receiver for free space optical communication system effectively. To implement coherent receiving, the phase offset between the local laser in the receiver and the received optical signal through the atmosphere needs to be measured and estimated. The commonly used algorithm is the Viterbi-Viterbi phase offset estimation method (VVPE) and this method always produces great errors especially with low SNR (signal to noise ratio). To improve the estimation performance, we present a new method combing the VVPE with the Kalman filter (VVPE-KF) to estimate the phase offset. This method can lower the estimation error by no less than 60%, when the SNR is low. To verify the performance of this new method, the constant parameter channel and atmosphere turbulence channel are employed to evaluate the algorithm. The impact of the atmosphere turbulence intensity on the tracking error is discussed. [ABSTRACT FROM AUTHOR]

Impaired baroreflex sensitivity (BRS) is partially responsible for erratic blood pressure fluctuations in End-Stage Renal Disease (ESRD) patients on chronic hemodialysis (HD), which is related to autonomic nervous dysfunction. The sequence method with delayed signals allows for the measurement of BRS in a non-invasive fashion and the investigation of alterations in this physiological feedback system that maintains BP within healthy limits. Our objective was to evaluate the modified delayed signals in the sequence method for BRS assessment in ESRD patients without pharmacological antihypertensive treatment and compare them with those of healthy subjects. We recruited 22 healthy volunteers and 18 patients with ESRD. We recorded continuous BP to obtain a 15-min time series of systolic blood pressure and interbeat intervals during the supine position (SP) and active standing (AS) position. The time series with delays from 0 to 5 heartbeats were used to calculate the BRS, number of data points, number of sequences, and estimation error. The BRS from the ESRD patients was smaller than in healthy subjects (p < 0.05). The BRS estimation with the delayed sequences also increased the number of data points and sequences and decreased the estimation error compared to the original time series. The modified sequence method with delayed signals may be useful for the measurement of baroreflex sensitivity in ESRD patients with a shorter recording time and maintaining an estimation error below 0.01 in both the supine and active standing positions. With this framework, it was corroborated that baroreflex sensitivity in ESRD is decreased when compared with healthy subjects.

Data-driven decision making is the key to providing effective and efficient wildfire protection and sustainable use of natural resources. Due to the complexity of natural systems, management decision(s) require clear justification based on substantial amounts of information that are both accurate and precise at various spatial scales. To build information and incorporate it into decision making, new analytical frameworks are required that incorporate innovative computational, spatial, statistical, and machine-learning concepts with field data and expert knowledge in a manner that is easily digestible by natural resource managers and practitioners. We prototyped such an approach using function modeling and batch processing to describe wildfire risk and the condition and costs associated with implementing multiple prescriptions for risk mitigation in the Blue Mountains of Oregon, USA. Three key aspects of our approach included: (1) spatially quantifying existing fuel conditions using field plots and Sentinel 2 remotely sensed imagery; (2) spatially defining the desired future conditions with regards to fuel objectives; and (3) developing a cost/revenue assessment (CRA). Each of these components resulted in spatially explicit surfaces describing fuels, treatments, wildfire risk, costs of implementation, projected revenues associated with the removal of tree volume and biomass, and associated estimates of model error. From those spatially explicit surfaces, practitioners gain unique insights into tradeoffs among various described prescriptions and can further weigh those tradeoffs against financial and logistical constraints. These types of datasets, procedures, and comparisons provide managers with the information needed to identify, optimize, and justify prescriptions across the landscape. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are deployed in a range of modern applications. Their utilization is evolving with the aim of achieving a greener environment. Batteries are costly, and battery management systems (BMSs) ensure long life and proper battery utilization. Modern BMSs are complex and cause a notable overhead consumption on batteries. In this paper, the time-varying aspect of battery parameters is used to reduce the power consumption overhead of BMSs. The aim is to use event-driven processing to realize effective BMSs. Unlike the conventional approach, parameters of battery cells, such as voltages and currents, are no longer regularly measured at a predefined time step and are instead recorded on the basis of events. This renders a considerable real-time compression. An inventive event-driven coulomb counting method is then presented, which employs the irregularly sampled data information for an effective online state of charge (SOC) determination. A high energy battery model for electric vehicle (EV) applications is studied in this work. It is implemented by using the equivalent circuit modeling (ECM) approach. A comparison of the developed framework is made with conventional fixed-rate counterparts. The results show that, in terms of compression and computational complexities, the devised solution surpasses the second order of magnitude gain. The SOC estimation error is also quantified, and the system attains a ≤4% SOC estimation error bound. [ABSTRACT FROM AUTHOR]

In the design of multi-loop Networked Control Systems (NCSs), wherein each control system is characterized by heterogeneous dynamics and associated with a certain set of timing specifications, appropriate metrics need to be employed for the synthesis of control and networking policies to efficiently respond to the requirements of each control loop. The majority of the design approaches for sampling, scheduling, and control policies include either time-based or event-based metrics to perform pertinent actions in response to the changes of the parameters of interest. We specifically focus in this article on Age-of-Information (AoI) as a recently-developed time-based metric and threshold-based triggering function as a generic Event-Triggered (ET) metric. We consider multiple heterogeneous stochastic linear control systems that close their feedback loops over a shared communication network. We investigate the co-design across the NCS and discuss the pros and cons with the AoI and ET approaches in terms of asymptotic control performance measured by Linear-Quadratic Gaussian (LQG) cost functions. In particular, sampling and scheduling policies combining AoI and stochastic ET metrics are proposed. It is argued that pure AoI functions that generate decision variables solely upon minimizing the average age irrespective of control systems dynamics may not be able to improve the overall NCS performance even compared with purely randomized policies. Our theoretical analysis is validated through several simulation scenarios. [ABSTRACT FROM AUTHOR]

In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is analyzed. The considered data-generating processes include different autoregressive schemes with varying model orders, count models with overdispersion or zero inflation, counts with a bounded range, and counts exhibiting trend or seasonality. We conclude that Gaussian forecast approximations should be avoided. [ABSTRACT FROM AUTHOR]

In this paper, a novel finite-time nonlinear extended state observer (NLESO) is proposed and employed in active disturbance rejection control (ADRC) to stabilize a nonlinear system against system's uncertainties and discontinuous disturbances using output feedback based control. The first task was to aggregate the uncertainties, disturbances, and any other undesired nonlinearities in the system into a single term called the "generalized disturbance". Consequently, the NLESO estimates the generalized disturbance and cancel it from the input channel in an online fashion. A peaking phenomenon that existed in linear ESO (LESO) has been reduced significantly by adopting a saturation-like nonlinear function in the proposed nonlinear ESO (NLESO). Stability analysis of the NLEO is studied using finite-time Lyapunov theory, and the comparisons are presented over simulations on permanent magnet DC (PMDC) motor to confirm the effectiveness of the proposed observer concerning LESO. [ABSTRACT FROM AUTHOR]

Portfolio weights solely based on risk avoid estimation errors from the sample mean, but they are still affected from the misspecification in the sample covariance matrix. To solve this problem, we shrink the covariance matrix towards the Identity, the Variance Identity, the Single-index model, the Common Covariance, the Constant Correlation, and the Exponential Weighted Moving Average target matrices. Using an extensive Monte Carlo simulation, we offer a comparative study of these target estimators, testing their ability in reproducing the true portfolio weights. We control for the dataset dimensionality and the shrinkage intensity in the Minimum Variance (MV), Inverse Volatility (IV), Equal-Risk-Contribution (ERC), and Maximum Diversification (MD) portfolios. We find out that the Identity and Variance Identity have very good statistical properties, also being well conditioned in high-dimensional datasets. In addition, these two models are the best target towards which to shrink: they minimise the misspecification in risk-based portfolio weights, generating estimates very close to the population values. Overall, shrinking the sample covariance matrix helps to reduce weight misspecification, especially in the Minimum Variance and the Maximum Diversification portfolios. The Inverse Volatility and the Equal-Risk-Contribution portfolios are less sensitive to covariance misspecification and so benefit less from shrinkage. [ABSTRACT FROM AUTHOR]