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Given a probability measure $\mu $ over $\mathbb {R}^{n}$ , it is often useful to approximate it by the convex combination of a small number of probability measures, such that each component is close to a product measure. Recently, Ronen Eldan used a stochastic localization argument to prove a general decomposition result of this type. In Eldan’s theorem, the ‘number of components’ is characterized by the entropy of the mixture, and ‘closeness to product’ is characterized by the covariance matrix of each component. We present an elementary proof of Eldan’s theorem which makes use of an information theory (or estimation theory) interpretation. The proof is analogous to the one of an earlier decomposition result known as the ‘pinning lemma.’ [ABSTRACT FROM AUTHOR]

Conventional radars repeat the transmission of the same waveform after a predefined interval of time called pulse-repetition-interval (PRI). This technique helps estimate the range and Doppler shift of the targets and suppress clutter. However, in dual-function radar communication (DFRC), a different symbol waveform is transmitted after each PRI. Depending on the number of targets, radar receiver output yields several peaks representing different targets’ ranges. Each peak comes with its side-lobes called range-side-lobes (RSL). In DFRC, due to different symbol waveform transmission, peaks and RSLs do not remain coherent, making Doppler shift estimation and clutter suppression challenging tasks. In most of the available literature, iterative receive filters have been designed for DFRC to minimize RSLs and achieve coherent output for different waveforms. However, the proposed receive filter does not guarantee coherent output for more than two waveforms. In contrast, we proposed two novel closed-form algorithms to design receive filters for DFRC that guarantee coherent output response for several waveforms and suppress RSLs. Simulation results demonstrate that the proposed receivers achieve full coherency, and the RSLs are significantly lower than the conventional method. Furthermore, the advantage of achieving coherent output response is shown in target detection and bit-error-rate improvement. [ABSTRACT FROM AUTHOR]

In a low-voltage islanded microgrid, the distribution line impedance and relatively large power angle may lead to active and reactive power coupling during voltage and frequency control actions, which cause errors for conventional droop control at interfacing inverters of distributed generation (DG) units. To overcome this issue, in this two-part article, a novel model predictive controller for DGs is proposed to regulate voltage and frequency at the point of common coupling in isolated microgrids. In Part 1 of the article, a data-driven predictive model is developed and parameterized by system identification approach using nonlinear least square (NLS) method. Four NLS optimization algorithms (Gauss–Newton (GN), adaptive GN, Levenberg–Marquardt, and trust region reflective)] are evaluated and GN is chosen as the best performing algorithm. To initialize the iteration for NLS, the Backcast technique is chosen after comparing with Zero and Estimate techniques. Two optimization methods, “simulation” and “prediction” focus, are considered; and the prediction focus shows higher prediction accuracy and faster convergence, and the ability to avoid the necessity of data prefiltering by introducing a built-in weighted filter in the objective function. The polynomial input–output Box–Jenkins model is chosen as the model structure. The model incorporates distribution line parameters into the control algorithm and allows a wider variation of power angle without initiating nonlinearity. Therefore, it can substantially reduce the controller size and complexity, and widen the controller's operational range. The model is further implemented in the proposed model predictive controller in Part 2 of the article. [ABSTRACT FROM AUTHOR]

Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Alongside with the development of more accurate algorithms, the research community focused on finding good strategies to estimate the reliability, i.e., the confidence, of estimated disparity maps. This information proves to be a powerful cue to naively find wrong matches as well as to improve the overall effectiveness of a variety of stereo algorithms according to different strategies. In this paper, we review more than ten years of developments in the field of confidence estimation for stereo matching. We extensively discuss and evaluate existing confidence measures and their variants, from hand-crafted ones to the most recent, state-of-the-art learning based methods. We study the different behaviors of each measure when applied to a pool of different stereo algorithms and, for the first time in literature, when paired with a state-of-the-art deep stereo network. Our experiments, carried out on five different standard datasets, provide a comprehensive overview of the field, highlighting in particular both strengths and limitations of learning-based strategies. [ABSTRACT FROM AUTHOR]

In high-dimensional sparse regression, would increasing the signal-to-noise ratio while fixing the sparsity level always lead to better model selection? For high-dimensional sparse regression problems, surprisingly, in this paper we answer this question in the negative in the regime of linear sparsity for the Lasso method, relying on a new concept we term effect size heterogeneity. Roughly speaking, a regression coefficient vector has high effect size heterogeneity if its nonzero entries have significantly different magnitudes. From the viewpoint of this new measure, we prove that the false and true positive rates achieve the optimal trade-off uniformly along the Lasso path when this measure is maximal in a certain sense, and the worst trade-off is achieved when it is minimal in the sense that all nonzero effect sizes are roughly equal. Moreover, we demonstrate that the first false selection occurs much earlier when effect size heterogeneity is minimal than when it is maximal. The underlying cause of these two phenomena is, metaphorically speaking, the “competition” among variables with effect sizes of the same magnitude in entering the model. Taken together, our findings suggest that effect size heterogeneity shall serve as an important complementary measure to the sparsity of regression coefficients in the analysis of high-dimensional regression problems. Our proofs use techniques from approximate message passing theory as well as a novel technique for estimating the rank of the first false variable. [ABSTRACT FROM AUTHOR]

The article proposes a method of estimating the dielectric constant or propagation velocity of a cylindrical object. The method is simple but robust, completely noninvasive, and suitable for in situ use. The method employs a set of transmission measurements, which record travel times through the object with various path lengths. The shape of the arrival time curve is evaluated and the dielectric constant that best describes the curve shape is obtained as the estimate. The measurement configuration can be seen similar to that of common shot, common midpoint (CMP), or multioffset measurement in ground-penetrating radar (GPR) measurements. The proposed method was demonstrated using synthetic data and in laboratory experiments using a tree log and cemented sand samples with various water contents. The results indicated that the dielectric constant was reasonably estimated by the proposed technique, with an estimation error at least within 10% for the experiments discussed in this article. [ABSTRACT FROM AUTHOR]

Data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning and data mining. However, in large-scale real-world scenarios, the exact similarity computation has become daunting due to “3V” nature (volume, velocity and variety) of big data. In this case, the hashing techniques have been verified to efficiently conduct similarity estimation in terms of both theory and practice. Currently, MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore, weighted MinHash is generalized to estimate the generalized Jaccard similarity of weighted sets. This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms. In this review, we mainly categorize the weighted MinHash algorithms into quantization-based approaches, “active index”-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash ones to the real-valued weighted MinHash ones. Also, we have developed a Python toolbox for the algorithms, and released it in our github. We experimentally conduct a comprehensive study of the standard MinHash algorithm and the weighted MinHash ones in the similarity estimation error and the information retrieval task. [ABSTRACT FROM AUTHOR]

Radio spectrum is a scarce resource. To meet demands, new wireless technologies must operate in shared spectrum over unlicensed bands (coexist). We consider coexistence of Long-Term Evolution (LTE) License-Assisted Access (LAA) with incumbent Wi-Fi systems. Our scenario consists of multiple LAA and Wi-Fi links sharing an unlicensed band; we aim to simultaneously optimize performance of both coexistence systems. To do this, we present a technique to continuously estimate the Pareto frontier of parameter sets (traces) which approximately maximize all convex combinations of network throughputs over network parameters. We use a dimensionality reduction approach known as active subspaces to determine that this near-optimal parameter set is primarily composed of two physically relevant parameters. A choice of two-dimensional subspace enables visualizations augmenting explainability and the reduced-dimension convex problem results in approximations which dominate random grid search. [ABSTRACT FROM AUTHOR]

This article presents a joint state-of-charge (SOC) and state-of-available-power (SOAP) estimation method based on online battery model parameter identification. First, the SOAP of lithium-ion batteries is analyzed thoroughly, and a safe operating area border-based (SOAB-based) SOAP estimation is proposed. Second, based on the adaptive battery-state estimator (ABSE) and improved ABSE, a joint SOC and SOAB-based SOAP estimation method is proposed. The joint estimation results show that the improved ABSE achieves higher accuracy than the ABSE at different battery aging states. The open-loop accuracy evaluation results show that the improved ABSE identifies the battery model parameters more accurately, and the ABSE algorithm error source lies in its identified Rp being much higher than the actual value when the battery is charged/discharged at a high current. The ABSE does not consider the influence of load current on the equivalent circuit model parameters, so it is not suitable for SOAP estimation in theory. The improved ABSE proposed by our team can eliminate this modeling error, identify the battery model parameters, and estimate the SOC and SOAB-based SOAP more accurately. This improved ABSE is an effective algorithm for estimating the battery state when the battery is charged/discharged with a high current. [ABSTRACT FROM AUTHOR]

The performance of a sensor-based rotor flux-oriented vector control induction machine drive is highly influenced by the estimated value of the rotor time constant/rotor resistance which varies continuously with temperature when it is driven with the variable loads. Therefore, to achieve accurate torque control, online tunning of the rotor time constant is necessary. This article proposes a novel and simple adaption technique to estimate the rotor time constant. In this proposed method, the reference model of the rotor flux vector is derived using the voltage model of the induction machine, which is independent of the rotor time constant. The current controller uses the model of the rotor flux vector that is derived using the current model of the induction machine. This current model is dependent on the rotor time constant. The error between the rotor flux vectors derived using the current model and the voltage model is minimized using the least-squares method. In this process, the rotor time constant is always tuned to its actual value. The article also presents a simple approach to estimate the parameters of induction machine like the stator resistance, the net leakage inductance, and the initial value of the rotor time constant in an offline mode at a standstill condition. To validate the proposed algorithm, two experimental prototypes are developed for two different power ratings of 3.7 and 67 kW. Experimental results from these prototypes confirm the effectiveness, reliability, and stability of the proposed algorithm both in the motoring and regenerative modes of operation. This method of the adaptation of the rotor time constant is simple and, therefore, useful for practicing engineers. [ABSTRACT FROM AUTHOR]

This article concentrates on finite-time stabilization and energy consumption estimation for nonlinear systems with and without delay. By constructing an appropriate controller and utilizing inequality techniques, sufficient conditions are proposed to guarantee the finite-time stability of the delayed nonlinear system. Furthermore, the energy consumption produced in system controlling is estimated by inequality techniques. Then, we formulate similar results for the delay-free case. Finally, numerical examples are presented to demonstrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]

This paper derives the optimal test channel distribution and the complete characterization of the classical Gorbunov and Pinsker (1973), Gorbunov and Pinsker (1974) nonanticipatory epsilon entropy of multivariate Gaussian Markov sources with square-error fidelity, which remained an open problem since 1974. The paper also formulates a state dependent nonanticipatory epsilon entropy, in which past reproductions are available to the decoder and not to the encoder, the test channel is specified with respect to an auxiliary (state) random process, and the reproduction process is a causal function of past reproduction and the auxiliary random process. This variation is analogous to the Wyner and Ziv (1976) and Wyner (1978) rate distortion function (RDF), of memoryless sources. It is shown that the operational rate of zero-delay codes, with past reproductions available to the decoder but not to the encoder is bounded below by the state dependent nonanticipatory epsilon entropy rate. For the case of multivariate Gaussian Markov sources with square-error fidelity, the optimal test channel distribution and the complete characterization of the state dependent of nonanticipatory epsilon entropy are derived, and also shown that that the two nonanticipatory epsilon entropies coincide. The derivations are new; they are based on structural properties of the stochastic realizations of the reproduction process that induce the optimal test channel distributions. They are derived using, achievable lower bounds on information theoretic measures, properties of mean-square estimation theory, Hadamard’s inequality, and canonical correlation coefficients of a tuple of multivariate jointly Gaussian random processes. Applications of the nonanticipatory epsilon entropy and its state dependent variation are discussed to the areas of control of unstable Gaussian systems over limited memory channels, design of causal estimators for Gaussian Markov sources with a fidelity criterion, computation of the rate loss of causal and zero-delay codes of Gaussian Markov sources with respect to non-causal codes. [ABSTRACT FROM AUTHOR]

The aluminum electrolytic capacitor (AEC) is one of the most important parts of a power electronic converter. Throughout its lifespan, the equivalent series resistance (ESR) of the AEC will increase, which will affect the performance of the power electronic converter. To guarantee the reliability of a converter, it is necessary to monitor the health state of the AEC, for which one common technique is to estimate the ESR of the AEC. Equipment with a high sampling rate is usually required because converter usually works at a high frequency. This requirement will increase the cost of ESR monitoring. To address this issue, this article proposes a novel method of estimating the ESR for an AEC. The proposed method uses an innovative compressed sensing approach to acquire the ripple voltage and current. Instead of complete reconstruction of the ripple voltage and current, only the high-frequency features in the wavelet subspace are reconstructed and used to calculate the ESR. The proposed method reduces the cost of data sampling, transmission, and storage. This method is not limited by the converter circuit structure and can be applied in various environments. Simulations and experiments were carried out to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Minimizing the symbol error in the uplink of multi-user multiple input multiple output systems is important, because the symbol error affects the achieved signal-to-interference-plus-noise ratio (SINR) and thereby the spectral efficiency of the system. Despite the vast literature available on minimum mean squared error (MMSE) receivers, previously proposed receivers for block fading channels do not minimize the symbol error in time-varying Rayleigh fading channels. Specifically, we show that the true MMSE receiver structure does not only depend on the statistics of the CSI error, but also on the autocorrelation coefficient of the time-variant channel. It turns out that calculating the average SINR when using the proposed receiver is highly non-trivial. In this paper, we employ a random matrix theoretical approach, which allows us to derive a quasi-closed form for the average SINR, which allows to obtain analytical exact results that give valuable insights into how the SINR depends on the number of antennas, employed pilot and data power and the covariance of the time-varying channel. We benchmark the performance of the proposed receiver against recently proposed receivers and find that the proposed MMSE receiver achieves higher SINR than the previously proposed ones, and this benefit increases with increasing autoregressive coefficient. [ABSTRACT FROM AUTHOR]

Track interruption will cause serious interference to target tracking, situation awareness, and information fusion. And it is often triggered by a variety of reasons, such as target maneuver, the blind spot of Doppler radar, and excessive long sampling interval, among other causes. All the existing algorithms are based on the hypothetical target motion models and adopt the theory of statistical estimation to measure the similarity between predicted tracks to associate, which have some disadvantages such as unreasonable hypotheses, inapplicable models, and uncertain thresholds. In order to address these problems, in this article, we propose a track segment association dual contrast neural network (TSADCNN) to complete track segment association tasks. First, based on deep metric learning, we propose a temporal and spatial information extraction module to extract track information, especially symmetric correlation information and raise dimension. Second, by constructing an appropriate optimization function, TSADCNN makes track segments belonging to the same target closer, track segments belonging to different targets farther in high-dimensional space. Finally, TSADCNN will pick the nearest neighbor vectors in high-dimensional space as associated tracks. Extensive experiments on simulation data are conducted to evaluate the effectiveness of TSADCNN, and the results demonstrate that this method has good adaptability and anti-noise ability. Furthermore, the contrast experiment shows that it can outperform other TSA methods in both quality and efficiency. Compared with the best performance, the average true association rate of TSADCNN can be increased by 1.3% in long interrupt intervals. [ABSTRACT FROM AUTHOR]

We present a general nonlinear Bayesian filter for high-dimensional state estimation using the theory of reproducing kernel Hilbert space (RKHS). By applying the kernel method and the representer theorem to perform linear quadratic estimation in a functional space, we derive a Bayesian recursive state estimator for a general nonlinear dynamical system in the original input space. Unlike existing nonlinear extensions of the Kalman filter where the system dynamics are assumed known, the state-space representation for the Functional Bayesian Filter (FBF) is completely learned online from measurement data in the form of an infinite impulse response (IIR) filter or recurrent network in the RKHS, with universal approximation property. Using a positive definite kernel function satisfying Mercer’s conditions to compute and evolve information quantities, the FBF exploits both the statistical and time-domain information about the signal, extracts higher-order moments, and preserves the properties of covariances without the ill effects due to conventional arithmetic operations. We apply this novel kernel adaptive filtering (KAF) to recurrent network training, chaotic time-series estimation and cooperative filtering using Gaussian and non-Gaussian noises, and inverse kinematics modeling. Simulation results show FBF outperforms existing Kalman-based algorithms. [ABSTRACT FROM AUTHOR]

The impulsive noise and the time-varying channel are two major detrimental factors which greatly constrain the performance of underwater acoustic communications (UACs). Utilizing the joint sparsity of the impulsive noise and the channel impulse response, the paper proposes the generalized approximate message passing (GAMP)-based sparse Bayesian learning (SBL) frameworks for joint impulsive noise mitigation and channel estimation and tracking for orthogonal frequency division multiplexing (OFDM) UACs. Firstly, the SBL framework for the joint estimation is employed. To reduce the computational complexity, the GAMP is introduced into the expectation-maximization (EM) algorithm, and a low-complexity GAMP based SBL framework is formulated without performance degradation. To further estimate and track the impulsive noise and channel state information in the slow time-varying scenarios, we propose a novel GAMP-based temporal SBL framework. The factor graph and GAMP are used to achieve the approximated estimation for the posterior statistics of both the channel state information and the impulsive noise. The algorithm formulates the message passing scheduling for the EM algorithm to solve the joint multiple sparse signal recovery problem. Simulations and sea-trial results demonstrate that the proposed algorithms significantly improve the performance in terms of the mean square error of channel estimation, impulsive noise estimation, bit error rate and computational complexity compared with their corresponding SBL-based counterparts. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is a classical and ubiquitous method for reducing data dimensionality, but it is suboptimal for heterogeneous data that are increasingly common in modern applications. PCA treats all samples uniformly so degrades when the noise is heteroscedastic across samples, as occurs, e.g., when samples come from sources of heterogeneous quality. This paper develops a probabilistic PCA variant that estimates and accounts for this heterogeneity by incorporating it in the statistical model. Unlike in the homoscedastic setting, the resulting nonconvex optimization problem is not seemingly solved by singular value decomposition. This paper develops a heteroscedastic probabilistic PCA technique (HePPCAT) that uses efficient alternating maximization algorithms to jointly estimate both the underlying factors and the unknown noise variances. Simulation experiments illustrate the comparative speed of the algorithms, the benefit of accounting for heteroscedasticity, and the seemingly favorable optimization landscape of this problem. Real data experiments on environmental air quality data show that HePPCAT can give a better PCA estimate than techniques that do not account for heteroscedasticity. [ABSTRACT FROM AUTHOR]

Objective: In this study, we have used whole heart simulations parameterized with large animal experiments to validate three techniques (two from the literature and one novel) for estimating epicardial and volumetric conduction velocity (CV). Methods: We used an eikonal-based simulation model to generate ground truth activation sequences with prescribed CVs. Using the sampling density achieved experimentally we examined the accuracy with which we could reconstruct the wavefront, and then examined the robustness of three CV estimation techniques to reconstruction related error. We examined a triangulation-based, inverse-gradient-based, and streamline-based techniques for estimating CV cross the surface and within the volume of the heart. Results: The reconstructed activation times agreed closely with simulated values, with 50-70% of the volumetric nodes and 97-99% of the epicardial nodes were within 1 ms of the ground truth. We found close agreement between the CVs calculated using reconstructed versus ground truth activation times, with differences in the median estimated CV on the order of 3-5% volumetrically and 1-2% superficially, regardless of what technique was used. Conclusion: Our results indicate that the wavefront reconstruction and CV estimation techniques are accurate, allowing us to examine changes in propagation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs. Significance: We implemented, validated, and compared the performance of a number of CV estimation techniques. The CV estimation techniques implemented in this study produce accurate, high-resolution CV fields that can be used to study propagation in the heart experimentally and clinically. [ABSTRACT FROM AUTHOR]

In this article, detection of power quality disturbances (PQD) in the power system utilizing mystery curves has been discussed. This technique first estimates the analytic signal of the real valued input signals based on Hilbert transform. By representing polar form of such a signal which is identified as magnitude and frequency variations, informative mystery curves in two-dimension can be discovered. Through the Euler's rotation hypothesis, the two-dimensional curves can be reached out to three dimension directly. The results obtained using the synthetic signals generated in MATLAB are validated experimentally on a prototype test bench to analyze the PQD events adhering to IEEE Std. 1159-1995. [ABSTRACT FROM AUTHOR]

The positioning problem addressed in this article amounts to finding the planar coordinates of a device from a collection of ranging measurements taken from other devices located at known positions. The solution based on weighted least square (WLS) is popular, but its accuracy depends from a number of factors only partially known. In this article, we explore the dependency of the uncertainty from the geometric configuration of the anchors. We show a refinement technique for the estimate produced by the WLS that compensates for the effects of geometry on the WLS and reduces the target uncertainty to a value very close to the Cramer–Rao Lower Bound. The resulting algorithm is called geometric WLS (G-WLS) and its application is particularly important in the most critical conditions for WLS (i.e., when the target is far apart from the anchors). The effectiveness of the G-WLS is proven theoretically and is demonstrated on a large number of experiments and simulations. [ABSTRACT FROM AUTHOR]

The ability of a sensor node to determine its location in a sensor network is important in many applications. We address the scenario, where malicious anchor node(s) attempt to disrupt, in an uncoordinated or coordinated manner, the localization process of a target node. We propose four localization techniques: weighted least square (WLS), secure WLS (SWLS), and $\ell _{1}$ -norm-based techniques LN-1 and LN-1E. WLS and SWLS techniques are shown to offer a significant advantage in uncoordinated attacks over existing techniques by detecting the malicious nodes and eliminating their measurements from the localization process and assigning larger weights to the measurements corresponding to anchor nodes that are closer to the target node. In a coordinated attack, the localization problem can be posed as a plane fitting problem, where the measurements from nonmalicious and malicious anchor nodes lie on two different planes. The LN-1E technique estimates the two planes and prevents disruption of the localization process. LN-1E can also determine the location where the malicious anchor nodes intend to make the target node appear to be located. The Cramer–Rao lower bound for the position estimate is also derived. We present simulation and experimental results, which demonstrate that the proposed techniques provide better localization accuracy than existing algorithms. [ABSTRACT FROM AUTHOR]

Objective: Continuous glucose monitoring (CGM) enables prediction of the future glucose concentration (GC) trajectory for making informed diabetes management decisions. The glucose concentration values are affected by various physiological and metabolic variations, such as physical activity (PA) and acute psychological stress (APS), in addition to meals and insulin. In this work, we extend our adaptive glucose modeling framework to incorporate the effects of PA and APS on the GC predictions. Methods: A wristband conducive of use by free-living ambulatory people is used. The measured physiological variables are analyzed to generate new quantifiable input features for PA and APS. Machine learning techniques estimate the type and intensity of the PA and APS when they occur individually and concurrently. Variables quantifying the characteristics of both PA and APS are integrated as exogenous inputs in an adaptive system identification technique for enhancing the accuracy of GC predictions. Data from clinical experiments illustrate the improvement in GC prediction accuracy. Results: The average mean absolute error (MAE) of one-hour-ahead GC predictions with testing data decreases from 35.1 to 31.9 mg/dL (p-value = 0.01) with the inclusion of PA information, and it decreases from 16.9 to 14.2 mg/dL (p-value = 0.006) with the inclusion of PA and APS information. Conclusion: The first-ever glucose prediction model is developed that incorporates measures of physical activity and acute psychological stress to improve GC prediction accuracy. Significance: Modeling the effects of physical activity and acute psychological stress on glucose concentration values will improve diabetes management and enable informed meal, activity and insulin dosing decisions. [ABSTRACT FROM AUTHOR]

The performance of the uplink of single and multiuser multiple input multiple output (MIMO) systems depends crucially on the receiver architecture and the quality of channel state information at the receiver. Therefore, several previous works have developed minimum mean squared error (MMSE) receivers and proposed balancing the resources spent on acquiring channel state information and transmitting the payload of data packets. Somewhat surprisingly, the most popular MIMO linear MMSE receivers do not exploit the correlation structure that is present in autoregressive Rayleigh fading environments. Therefore, in this article we first develop a new linear receiver that not only takes channel state information errors into account in minimizing the MSE of the received data symbols, but it also utilizes that the subsequent noisy channel coefficients are correlated. For this new linear MMSE receiver, we derive the achieved MSE as a function of the number of receive antennas and the pilot-to-data power ratio. Interestingly, we find that the pilot power that minimizes the MSE of the data symbols does not depend on the number of antennas and that the new linear MMSE receiver outperforms previously proposed MIMO receivers when the autocorrelation coefficient of the channel is high. [ABSTRACT FROM AUTHOR]

Utilization of abundant measurements provided by supervisory control and data acquisition (SCADA) system has attracted increasing attention. Real-time estimation of transmission line parameters, utilizing voltage and power flow measurements provided by remote terminal units (RTUs) located at two substations across the line, has been investigated, recently. This paper improves this approach by introducing a novel exact linear reformulation of the problem, which can be solved in closed form. The distributed-parameter model of long transmission lines is considered and its parameters are estimated in a non-iterative manner using RTU measurements. The method is also extended to estimate transformer series impedance and tap position by SCADA measurements, linearly. As such, the disadvantages associated with the previous iterative approach, e.g. concern over convergence, for transmission line parameters are avoided. Moreover, the novel technique for estimating transformer parameters allows to determine the tap position as well as updated transformer series impedance. Furthermore, a thorough analysis is presented to take the measurement accuracy into account. Simulation results for different transmission lines and transformers in the IEEE 118-bus test system are reported, where the result indicate successful performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Ultrasound elastography (USE) is an emerging noninvasive imaging technique in which pathological alterations can be visualized by revealing the mechanical properties of the tissue. Estimating tissue displacement in all directions is required to accurately estimate the mechanical properties. Despite capabilities of elastography techniques in estimating displacement in both axial and lateral directions, estimation of axial displacement is more accurate than lateral direction due to higher sampling frequency, higher resolution, and having a carrier signal propagating in the axial direction. Among different ultrasound imaging techniques, synthetic aperture (SA) has better lateral resolution than others, but it is not commonly used for USE due to its limitation in imaging depth of field. Virtual source synthetic aperture (VSSA) imaging is a technique to implement SA beamforming on the focused transmitted data to overcome the limitation of SA in depth of field while maintaining the same lateral resolution as SA. Besides lateral resolution, VSSA has the capability of increasing sampling frequency in the lateral direction without interpolation. In this article, we utilize VSSA to perform beamforming to enable higher resolution and sampling frequency in the lateral direction. The beamformed data are then processed using our recently published elastography technique, OVERWIND. Simulation and experimental results show substantial improvement in the estimation of lateral displacements. [ABSTRACT FROM AUTHOR]

This article presents a finite-difference time-domain technique to analyze the characteristics of the railway system emission to the outside environment. The 2-D simulation implements algorithms accommodating the interactions of Poisson's equation for the cross-component electrostatic coupling between the train and infrastructure with Maxwell's curl equations accounting for the time-varying wave propagation across the discontinuous boundaries excited by the on-board 400-MHz terrestrial trunked radio system. The 2-D near-field to far-field conversion using the generated surface currents concludes the investigation of system emission characteristics in terms of the radiation pattern and emission level in accordance with the EN 50121-2 criteria. The numerical results of the emission spectrum from 300 MHz to 1 GHz influenced by the integrated railway system components and on-site measurement records during the 750-Vdc metro system integration tests provide a validation of technique for estimating the railway system radiation prior to the start of the system installation phase. [ABSTRACT FROM AUTHOR]

Passive ultrasound imaging is of great interest for cavitation monitoring. Spatiotemporal monitoring of cavitation bubbles in therapeutic applications is possible using an ultrasound imaging probe to passively receive the acoustic signals from the bubbles. Fourier-domain (FD) beamformers have been proposed to process the signals received into maps of the spatial localization of cavitation activity, with reduced computing times with respect to the time-domain approach, and to take advantage of frequency selectivity for cavitation regime characterization. The approaches proposed have been mainly nonadaptive, and these have suffered from low resolution and contrast, due to the many reconstruction artifacts. Inspired by the array-processing literature and in the context of passive ultrasound imaging of cavitation, we propose here a robust estimation of the second-order statistics of data through spatial covariance matrices in the FD or cross-spectral density matrices (CSMs). The benefits of such formalism are illustrated using advanced reconstruction algorithms, such as the robust Capon beamformer, the Pisarenko class beamformer, and the multiple signal classification approach. Through both simulations and experiments in a water tank, we demonstrate that enhanced localization of cavitation activity (i.e., improved resolution and contrast with respect to nonadaptive approaches) is compatible with the rapid and frequency-selective approaches of the FD. Robust estimation of the CSM and the derived adaptive beamformers paves the way to the development of powerful passive ultrasound imaging tools. [ABSTRACT FROM AUTHOR]

Focused ultrasound is an emerging medical technique for transcranial procedures and requires the precise modeling of ultrasound signal propagation through the skull. To verify models, the onset time delay (OTD) between two signals measured at the same spatial location, with and without the presence of a skull in the path of the signal, is compared between simulations and experiments. Current methods to automatically identify OTD use correlation-based algorithms. However, these techniques suffer from poor results caused by signal distortion and low signal-to-noise ratios in experimental signals. In this study, we compare the effectiveness of machine learning (multiple linear regression) to three correlation-based time-delay estimation techniques in estimating the OTD of a signal pair. A sample of 1643 signal pairs, with the center frequencies of either 270 or 836 kHz, had their delays manually identified as a benchmark. Density, thickness, incidence angle, frequency, and x and y offsets from the center were used as predictors. We find that, compared with manual identification, machine learning is 80.4% more accurate than cross correlation across all test signals and is noise-independent through all noise bins. The median of the errors was less than 0.3 periods was observed for signals with a frequency of 270 kHz and less than 1.1 periods for signals with a frequency of 836 kHz, with little estimate bias. Overall, linear multivariable regression is determined to provide the best estimate of the OTD of two signals. [ABSTRACT FROM AUTHOR]

Tire slip angle is a vital parameter in tire/vehicle dynamics and control. This paper proposes an accurate estimation method by the fusion of intelligent tire technology and machine-learning techniques. The intelligent tire is equipped by MEMS accelerometers attached to its inner liner. First, we describe the intelligent tire system along with the implemented testing apparatus. Second, experimental results under different loading and velocity conditions are provided. Then, we show the procedure of data processing, which will be used for training five different machine learning techniques to estimate tire slip angles. The results show that the machine learning techniques, especially in frequency domain, can accurately estimate tire slip angles up to 10 degrees. More importantly, with the accurate tire slip angle estimation, all other states and parameters can be easily and precisely obtained, which is significant to vehicle advanced control, and thus this study has a high potential to obviously improve the vehicle safety especially in extreme maneuvers. [ABSTRACT FROM AUTHOR]

The problem of channel gain estimation in visible light communications and positioning is considered in this article. Pilot sequences are designed for simultaneously estimating the channel gains of multiple transmitters in a light system and satisfying the average and maximum transmit power constraint due to illumination purpose. This article illustrates how to design the optimal pilot sequences that minimise the noise variance experienced by a receiver although the optimisation problem is non-convex. Lower bounds on the noise variance are derived for systems with ambient light and systems without ambient light. These bounds reveal the relationship between noise variance, the average transmit power, the maximum transmit power, the number of light sources and pilot length. The necessary and sufficient conditions for the bounds to hold with equality are derived. These conditions establish a bridge between block design theory and channel estimation in visible light systems. Optimal pilot designs which achieve the lower bounds on noise variance can be obtained from balanced block designs and pairwise balanced designs. This article also shows how to optimise the average transmit power of a pilot design for minimising noise variance. [ABSTRACT FROM AUTHOR]

Demand for power electronics with smaller volume, lighter weight, and lower cost will eventually require new converter energy storage technologies with fundamentally higher power density and efficiency limits. This motivates investigation into piezoelectric resonators (PRs), which offer very high power density and efficiency capabilities and significantly improved scaling properties compared to magnetics. PRs have been used in power conversion previously, but the realm of possible converter implementations using only PRs for energy storage has seen little exploration. In this work, we enumerate and evaluate dc–dc converter topologies and switching sequences that best utilize a PR as the only energy storage component, and that enable low-loss resonant “soft charging” of the PR's input capacitance with voltage regulation capability. To compare these implementations, we present analysis techniques for their operation and periodic steady-state solutions considering practical constraints. In addition, we provide useful techniques for estimating PR utilization and efficiency, which we validate experimentally in a 200–100-V, 25-W rated prototype. This prototype exhibits peak efficiency > 99% as well as high efficiency (≥ 96%) across a wide range of operating conditions, illustrating the promise of PR-based converters for high voltage, low power applications. This article is accompanied by an instructional video for selecting and analyzing PR-based dc–dc converter implementations. [ABSTRACT FROM AUTHOR]

This letter proposes a novel technique for estimating the rate of change of frequency (RoCoF) of the center of inertia (CoI) in power systems. To offer a holistic picture of the system's frequency response, the proposed technique requires local frequency at the point of measurement, only. This enables the local estimation of active power deficit following a loss of generation (LoG) event, thus facilitating fast and effective remedial action. Extensive simulations conducted verify the effectiveness and applicability of the proposed technique. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is a noninvasive technique estimating the conductivity inside an observation domain from its peripheral measurements and owns many industrial and biomedical applications. Inclusion reconstruction is one of the key problems in EIT. However, the high-resolution inclusion reconstruction is challenging due to the ill-posedness and nonlinearity of EIT. The shape reconstruction methods focusing on recovering the shape of the inclusion is one kind of advanced methods to enhance the EIT resolution. To achieve high-performance shape reconstruction, a parametric geometric constraint boundary reconstruction (PGCBR) framework is proposed. The shape reconstruction is implemented by minimizing the energy function of the inclusion boundary represented by the parametric snake model. To accelerate the convergence, the Newton method is adopted to solve the energy minimization problem. The first- and second-order partial derivatives of the energy terms are calculated with the EIT shape sensitivity formula. A series of numerical tests evaluated the influences of the key factors, e.g., the basis function and the number of the knot and interpolating points, of PGCBR on its reconstruction performance and proved its robustness against the measurement noise. The following experimental tests, including both single- and multiphase conductivity cases, further proved the advantages of PGCBR over the nonparametric method. [ABSTRACT FROM AUTHOR]

This paper considers estimation of a random variable in Poisson noise with signal scaling coefficient and dark current as explicit parameters of the noise model. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the scaling coefficient, the dark current parameter, the distribution of the input random variable and channel realizations. With respect to the scaling coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the gradient of the conditional mean estimator with respect to the scaling coefficient and dark current parameter is proportional to the conditional variance. Moreover, a score function is proposed and a Tweedie-like formula for the conditional expectation is recovered. With respect to the distribution, several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in terms of mean squared error, then the input distribution is approximately gamma in the Lévy distance. Furthermore, sufficient and necessary conditions for linearity are found. Interestingly, it is shown that the conditional mean estimator cannot be linear when the dark current parameter of the Poisson noise is non-zero. [ABSTRACT FROM AUTHOR]

This letter proposes an optimization-free technique to estimate the parameters of voltage dependent loads through the measurements of only the load active and reactive power consumption and frequency deviations at the load bus. The technique is shown to be a relevant consequence of the dependency of power flow equations on the bus voltage phase angles. The WSCC 9-bus and the IEEE 14-bus systems serve to illustrate the proposed technique. [ABSTRACT FROM AUTHOR]

Phase unwrapping is an ill-posed classical problem in many practical applications of significance such as 3D profiling through fringe projection, synthetic aperture radar and magnetic resonance imaging. Conventional phase unwrapping techniques estimate the phase either by integrating through the confined path (referred to as path-following methods) or by minimizing the energy function between the wrapped phase and the approximated true phase (referred to as minimum-norm approaches). However, these conventional methods have some critical challenges like error accumulation and high computational time and often fail under low SNR conditions. To address these problems, this paper proposes a novel deep learning framework for unwrapping the phase and is referred to as “PhaseNet 2.0”. The phase unwrapping problem is formulated as a dense classification problem and a fully convolutional DenseNet based neural network is trained to predict the wrap-count at each pixel from the wrapped phase maps. To train this network, we simulate arbitrary shapes and propose new loss function that integrates the residues by minimizing the difference of gradients and also uses $L_{1}$ loss to overcome class imbalance problem. The proposed method, unlike our previous approach PhaseNet, does not require post-processing, highly robust to noise, accurately unwraps the phase even at the severe noise level of −5 dB, and can unwrap the phase maps even at relatively high dynamic ranges. Simulation results from the proposed framework are compared with different classes of existing phase unwrapping methods for varying SNR values and discontinuity, and these evaluations demonstrate the advantages of the proposed framework. We also demonstrate the generality of the proposed method on 3D reconstruction of synthetic CAD models that have diverse structures and finer geometric variations. Finally, the proposed method is applied to real-data for 3D profiling of objects using fringe projection technique and digital holographic interferometry. The proposed framework achieves significant improvements over existing methods while being highly efficient with interactive frame-rates on modern GPUs. [ABSTRACT FROM AUTHOR]

Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising solution, which we call fusion denoising. It is derived theoretically with a Gaussian image prior assumption. Synthetic experiments show our network’s generalization strength to unseen additive noise levels. We also adapt the fusion denoising network architecture for image denoising on real images. Our approach improves real-world grayscale additive image denoising PSNR results for training noise levels and further on noise levels not seen during training. It also improves state-of-the-art color image denoising performance on every single noise level, by an average of $0.1dB$ , whether trained on or not. [ABSTRACT FROM AUTHOR]

Capabilities for high-resistance determinations are essential for calibration of currents below 1 pA, as typically requested in several applications, including semiconductor device characterization, single-electron transport, and ion beam technologies. This need to calibrate low currents warrants the expansion of accessible values of high resistance. We present several methods for measuring resistances on the $\text{P}\Omega $ scale, namely potentiometry, dual-source bridge measurements, and teraohmmeter (TM) usage, all of which are subsequently compared to theoretical calculations. These methods were used to measure four 1- $\text{P}\Omega $ resistances, one 10- $\text{P}\Omega $ resistance, and one 100- $\text{P}\Omega $ resistance, all generated by wye-delta (Y- $\Delta $) networks containing three resistance elements. The differences between the experimentally obtained values and the theoretical values typically agree within 1% for 1-, 10-, and 100- $\text{P}\Omega $ resistances and the measurement uncertainties for the three techniques were estimated to be between 0.4%–4.8% for 1 $\text{P}\Omega $ , 2.8%–5.6% for 10 $\text{P}\Omega $ , and 4.4%–10.2% for 100 $\text{P}\Omega $. [ABSTRACT FROM AUTHOR]

In the field of vibration measurement, the vision-based approach can achieve high spatial resolution compared to traditional accelerometers and lasers and thus has attracted much attention during the past decades. A recently developed phase-based video motion magnification (PVMM) technique to estimate subtle motions from videos is only suitable for stationary vibration measurement, because it is limited to analyze the well-separated vibration modes. In this article, a time-varying motion filtering (TVMF) method is presented to endow the PVMM technique with the ability to decompose nonstationary vibrations and visualize the time-dependent mode shapes of nonstationary systems. Specifically, we first build a parameterized mathematical model for the video motions and then extract each vibration mode by optimizing a set of parameters. After modulating each mode with an amplification factor, the time-varying characteristics of the vibration mode shapes can be visualized to naked eyes. Compared to the traditional PVMM technique, the merit of the proposed TVMF is verified to be able to produce less noise and fewer artifacts in nonstationary vibration measurement. The performance of TVMF is demonstrated on both a simulated experiment and a real nonstationary moving mass system. [ABSTRACT FROM AUTHOR]

In the process of silicon single-crystal growth, the detection of melt level is an important step to ensure that the grown silicon single crystal is of high quality. Since there exist many mechanical motions, chemical interactions, thermal convection, and so on, it is extremely difficult to obtain the noise statistics of the liquid-level data. To attain a high-accuracy detection of liquid level with unknown noise statistics, we present an extended set-membership-based particle filter (PF). The key points are as follows. We construct a nonlinear state-space model for liquid-level detection system based on the kinematic principle and laser triangulation measurement principle and cast the detection problem of the liquid level into a state estimation problem. Based on this model, we use the extended set-membership estimation theory to generate an ellipsoid set that contains the true value of the liquid-level state. To overcome the difficulty of drawing particles under the case of unknown statistics noise, we draw particles from a Gaussian distribution in the obtained ellipsoidal set. In order to resample particles when the measurement noise statistic is unknown, we introduce a cost function to obtain the weights of the particles, and the ultimate estimation of liquid level is calculated by the weighted particles after the resampling procedure. Comparing with the extended Kalman PF (EPF) and the cost-reference PF (CRPF), the proposed algorithm has higher estimation accuracy. Some simulation and experiment results are presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) has become a widely accepted technique by worldwide utilities to detect winding and core deformations within power transformers. The main drawback of this technique is its reliance on the personnel level of expertise more than standard or automated codes. To establish reliable FRA interpretation codes, accurate high frequency transformer model that can emulate the frequency characteristics of real transformers in a wide frequency range is essential. The model can be used to investigate the impact of various winding and core deformations on the transformer FRA signature. The transformer equivalent high frequency electric circuit parameters can be calculated based on design data, which are rarely available, especially for old transformers. As such, this paper presents an artificial intelligence technique to estimate these parameters from the transformer FRA signature. The robustness of the proposed technique is assessed through its application on three, 3-phase power transformers of different ratings, sizes, and winding structures to estimate their high frequency electric circuit parameters during normal and fault conditions. Results show that the proposed technique can estimate equivalent circuit parameters with high accuracy and helps interpret the FRA signature based on the numerical changes of these parameters. The main advantage of this approach is the physical meaning of the model parameters facilitates reliable identification of various faults and hence aids in establishing reliable interpretation codes for transformer FRA signatures. [ABSTRACT FROM AUTHOR]

Primary channel statistics have recently gained increasing attention due to its remarkable role in the performance improvement of Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems. These statistics can be calculated from the outcomes of spectrum sensing, which is the well-known method used to identify the available instantaneous opportunities in the spectrum. Computing statistical information from spectrum sensing, however, may sometimes be unreliable due to the fact that spectrum sensing is imperfect in the real world and errors are likely to occur in the sensing decisions. In this context, this work provides a detailed analysis of a broad range of primary channel statistics under Imperfect Spectrum Sensing (ISS) and finds a set of closed-form expressions for the calculated statistics under ISS as a function of the original primary channel statistics, probability of error, and the employed sensing period. In addition, the obtained mathematical expressions are employed to find and propose novel estimators for the primary channel statistics, which outperform the existing estimators in the literature and can provide accurate estimations of the original statistics even under high probability of error of spectrum sensing. The correctness of the obtained analytical expressions and the accuracy of the proposed estimators are corroborated with both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Strict Minimum Message Length (SMML) is an information-theoretic statistical inference method widely cited (but only with informal arguments) as providing estimations that are consistent for general estimation problems. It is, however, almost invariably intractable to compute, for which reason only approximations of it (known as MML algorithms) are ever used in practice. Using novel techniques that allow for the first time direct, non-approximated analysis of SMML solutions, we investigate the Neyman-Scott estimation problem, an oft-cited showcase for the consistency of MML, and show that even with a natural choice of prior neither SMML nor its popular approximations are consistent for it, thereby providing a counterexample to the general claim. This is the first known explicit construction of an SMML solution for a natural, high-dimensional problem. [ABSTRACT FROM AUTHOR]

Objective: Vascular permeability (VP) is a mechanical parameter which plays an important role in cancer initiation, metastasis, and progression. To date, there are only a few non-invasive methods that can be used to image VP in solid tumors. Most of these methods require the use of contrast agents and are expensive, limiting widespread use. Methods: In this paper, we propose a new method to image VP in tumors, which is based on the use of ultrasound poroelastography. Estimation of VP by poroelastography requires knowledge of the Young's modulus (YM), Poisson's ratio (PR), and strain time constant (TC) in the tumors. In our method, we find the ellipse which best fits the tumor (regardless of its shape) using an eigen-system-based fitting technique and estimate the YM and PR using Eshelby's elliptic inclusion formulation. A Fourier method is used to estimate the axial strain TC, which does not require any initial guess and is highly robust to noise. Results: It is demonstrated that the proposed method can estimate VP in irregularly shaped tumors with an accuracy of above $\text{80}\%$ using ultrasound simulation data with signal-to-noise ratio of 20 dB or higher. In vivo feasibility of the proposed technique is demonstrated in an orthotopic mouse model of breast cancer. Conclusion: The proposed imaging method can provide accurate and localized estimation of VP in cancers non-invasively and cost-effectively. Significance: Accurate and non-invasive assessment of VP can have a significant impact on diagnosis, prognosis, and treatment of cancers. [ABSTRACT FROM AUTHOR]

With increasing improvements in the time of flight (TOF) resolution of positron emission tomography (PET) scanners, an accurate model of the TOF measurements is becoming increasingly important. This work considers two parameters of the TOF kernel; the relative positioning of the timing data-bins and the timing resolution along each line of response (LOR). Similar to an existing data-driven method, we assume that any shifts of data-bins along lines of response can be modelled as differences between crystal timing offsets. Inspired by this, timing resolutions of all LORs are modelled as the hypotenuse of timing resolutions of the crystal-pairs in coincidence. Furthermore, in order to mitigate the influence of potential inaccuracies of detector-pair sensitivities on crystal timing resolutions, relative LOR sensitivities are modelled as the product of efficiency factors for the two crystals in coincidence. We validate estimating maps of crystal timing offsets, timing resolutions and efficiencies from the emission data using noisy simulations of a brain phantom. Results are shown for phantom and patient data scanned on clinically available TOF-PET scanners. We find that the estimation of crystal timing resolutions is more sensitive to the data statistics than the estimation of crystal timing offsets. As a result, estimation of crystal timing properties could either be limited to high count emission data, or be obtained utilizing additional regularizations on the estimates. Using a more accurate model of the TOF acquisition, improvements are observed in standard activity reconstructions as well as joint reconstructions of activity and attenuation. [ABSTRACT FROM AUTHOR]

It is an effective approach to estimate rotor position of doubly salient electromagnetic machine (DSEM) from self-inductances, which changes with rotor position. To overcome the restrictions of low estimation accuracy as well as the inevitable commutation lag in the conventional sensorless startup method, this paper proposes a novel rotor angular position estimating technique for the DSEM sensorless startup. It starts with injecting three test pulses A + C- (phase A is connected to the positive rail of dc bus, and phase C is connected to the ground rail), B + A- and C + B- on armature windings, so two-phase series self-inductances can be identified according to the resultant currents. Then, the estimating expression of the DSEM rotor angular position is further obtained based on the geometrical relation in the rectangle composed of self-inductances, and the inevitable commutation lag in startup process is likely to be eliminated with the estimated rotor angular position. Moreover, the error of rotor angular position introduced by the nonideal linearity of self-inductances are analyzed in detail, and the boundaries of neighboring sectors are distinguished from normal sectors to compensate the estimation error of rotor angular position. Finally, to enhance the output torque performance during the DSEM startup process, a novel selection method for test pulses concerning rotor sector is developed. Compared to the conventional method, a quicker and superior startup performance is achieved since no negative torque is generated by the test pulses. The experiments on a 12/8-pole DSEM validate the correctness and feasibility of the proposed sensorless control methods. [ABSTRACT FROM AUTHOR]