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We study the list decodability of different ensembles of codes over the real alphabet under the assumption of an omniscient adversary. It is a well-known result that when the source and the adversary have power constraints $P $ and $N $ respectively, the list decoding capacity is equal to $\frac {1}{2}\log \frac {P}{N}$. Random spherical codes achieve constant list sizes, and the goal of the present paper is to obtain a better understanding of the smallest achievable list size as a function of the gap to capacity. We show a reduction from arbitrary codes to spherical codes, and derive a lower bound on the list size of typical random spherical codes. We also give an upper bound on the list size achievable using nested Construction-A lattices and infinite Construction-A lattices. We then define and study a class of infinite constellations that generalize Construction-A lattices and prove upper and lower bounds for the same. Other goodness properties such as packing goodness and AWGN goodness of infinite constellations are proved along the way. Finally, we consider random lattices sampled from the Haar distribution and show that if a certain conjecture that originates in analytic number theory is true, then the list size grows as a polynomial function of the gap-to-capacity. [ABSTRACT FROM AUTHOR]

This article addresses the opportunistic use of satellite downlink attenuation measurements for the site-specific estimation of rainfall rate. In particular, this article proposes a regression model characterized by the introduction of correction elements extracted from the frequency-domain analysis of the attenuation time series, and the use of a simple linear regression function with a small modification, which allows to using a negative intercept with the goal of minimizing the maximum error (large underestimation/overestimation). The performance, assessed using attenuation measurements of a satellite downlink in Q/V band shows that the proposed model allows to estimate the rainfall rate with low average error and low maximum error with respect to a more standard ITU model, so as to offer more reliable estimations. [ABSTRACT FROM AUTHOR]

Objective: Local activation time (LAT) mapping of cardiac chambers is vital for targeted treatment of cardiac arrhythmias in catheter ablation procedures. Current methods require too many LAT observations for an accurate interpolation of the necessarily sparse LAT signal extracted from intracardiac electrograms (EGMs). Additionally, conventional performance metrics for LAT interpolation algorithms do not accurately measure the quality of interpolated maps. We propose, first, a novel method for spatial interpolation of the LAT signal which requires relatively few observations; second, a realistic sub-sampling protocol for LAT interpolation testing; and third, a new color-based metric for evaluation of interpolation quality that quantifies perceived differences in LAT maps. Methods: We utilize a graph signal processing framework to reformulate the irregular spatial interpolation problem into a semi-supervised learning problem on the manifold with a closed-form solution. The metric proposed uses a color difference equation and color theory to quantify visual differences in generated LAT maps. Results: We evaluate our approach on a dataset consisting of seven LAT maps from four patients obtained by the CARTO electroanatomic mapping system during premature ventricular complex (PVC) ablation procedures. Random sub-sampling and re-interpolation of the LAT observations show excellent accuracy for relatively few observations, achieving on average 6% lower error than state-of-the-art techniques for only 100 observations. Conclusion: Our study suggests that graph signal processing methods can improve LAT mapping for cardiac ablation procedures. Significance: The proposed method can reduce patient time in surgery by decreasing the number of LAT observations needed for an accurate LAT map. [ABSTRACT FROM AUTHOR]

This article proposes a proportional-integral (PI) event-triggered control strategy for networked nonlinear systems subject to unmatched uncertainties. The influences of the unmatched uncertainties and external disturbances on the output channel are attenuated by an extended-state-observer-based output feedback controller through a state transformation. The outputs of the controller are transmitted in an event-triggered fashion by comparing the difference between the current sampling error and its integration with respect to time. We prove the stability of the closed-loop event-triggered control system by regulating the trajectories of the observation and tracking errors into a compact set. Further, the relationships between the control performance and designed parameters are investigated quantitatively. In addition, Zeno-freeness is guaranteed by proving the existence of a nonzero minimum inter-event time. Finally, the effectiveness of the proposed PI-type event-triggered control strategy in reducing signal transmission rate is validated through numerical examples and various experiments. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the mean of a symmetric log-concave distribution under the constraint that only a single bit per sample from this distribution is available to the estimator. We study the mean squared error as a function of the sample size (and hence the number of bits). We consider three settings: first, a centralized setting, where an encoder may release $n$ bits given a sample of size $n$ , and for which there is no asymptotic penalty for quantization; second, an adaptive setting in which each bit is a function of the current observation and previously recorded bits, where we show that the optimal relative efficiency compared to the sample mean is precisely the efficiency of the median; lastly, we show that in a distributed setting where each bit is only a function of a local sample, no estimator can achieve optimal efficiency uniformly over the parameter space. We additionally complement our results in the adaptive setting by showing that one round of adaptivity is sufficient to achieve optimal mean-square error. [ABSTRACT FROM AUTHOR]

Point cloud is a discrete and unordered expression of 3D data. A lot of methods have been proposed to solve the problem in 3D object classification and scene recognition. To handle the huge amount of unordered point cloud, down-sampling before processing is needed. The shortage of existing sampling methods is the lack of geometry information consideration, which is essential for point cloud classification and segmentation tasks. Our method is mainly motivated by the observation that points with a high curvature variation can depict the outlines of objects. Thus, we propose a curvature variation based sampling method for point cloud classification and segmentation tasks. We aim to sample points with high curvature variations, which are considered to be more suitable for classification and segmentation tasks than the traditional sampling method. We combine the proposed sampling algorithm with the existing sampling method for multiple information fusion, and a higher accuracy and mean IoU can be achieved. The experimental results verify the advantage of considering curvature variation in classification and segmentation tasks. [ABSTRACT FROM AUTHOR]

Low-rank tensor recovery (LRTR) is a natural extension of low-rank matrix recovery (LRMR) to high-dimensional arrays, which aims to reconstruct an underlying tensor $\boldsymbol{\mathcal {X}}$ X from incomplete linear measurements $\mathfrak {M}(\boldsymbol{\mathcal {X}})$ M (X) . However, LRTR ignores the error caused by quantization, limiting its application when the quantization is low-level. In this work, we take into account the impact of extreme quantization and suppose the quantizer degrades into a comparator that only acquires the signs of $\mathfrak {M}(\boldsymbol{\mathcal {X}})$ M (X) . We still hope to recover $\boldsymbol{\mathcal {X}}$ X from these binary measurements. Under the tensor Singular Value Decomposition (t-SVD) framework, two recovery methods are proposed—the first is a tensor hard singular tube thresholding method; the second is a constrained tensor nuclear norm minimization method. These methods can recover a real $n_1\times n_2\times n_3$ n 1 × n 2 × n 3 tensor $\boldsymbol{\mathcal {X}}$ X with tubal rank $r$ r from $m$ m random Gaussian binary measurements with errors decaying at a polynomial speed of the oversampling factor $\lambda :=m/((n_1+n_2)n_3r)$ λ : = m / ((n 1 + n 2) n 3 r) . To improve the convergence rate, we develop a new quantization scheme under which the convergence rate can be accelerated to an exponential function of $\lambda$ λ . Numerical experiments verify our results, and the applications to real-world data demonstrate the promising performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Predictive errors restrict high performance of finite control set model predictive control (FCS-MPC) in the cascaded H-bridge multilevel inverter.In order to solve this problem, the influence mechanisms of parameter mismatch, model discretization, and sampling error on voltage and current prediction errors are analyzed in this article. A hierarchical multifactorial error correction method is proposed. The known voltage prediction errors of the previous adjacent prediction periods are first fed back to calculate the voltage predictive error factors and self-correct the next voltage prediction errors. And then the current prediction errors are calibrated by the corrected voltage and the calculated current predictive error factor. This proposed method involves a simple structure and a good inhibitory ability on multifactorial prediction errors. Finally, experimental results in a three-phase five-level cascaded H-bridge multilevel inverter support the theoretical analysis and the proposed method. [ABSTRACT FROM AUTHOR]

This letter discusses performance degradation of traditional supervisory control and data acquisition (SCADA)-based static state estimation (SSE) caused by violation of the static assumption. The sensitivity of non-synchronized sampling errors against oscillations is tested through real measurement data and grid model from a subsystem of the North American power gird. The test results show that as an important reason leading to the failure of SCADA-based SSE, the non-synchronized sampling error of SCADA is sensitive to oscillations in that the error rapidly increases when oscillations occur and vanishes when the oscillations cease. [ABSTRACT FROM AUTHOR]

For AC microgrids with three-phase unbalanced loading conditions, the four-wire voltage source inverters (FW-VSIs) have become an advisable interfaced converter between the source and loads. Predictive control has been applied to FW-VSIs in recent years, but model errors are not solved well, which is caused through parameter mismatch, sampling error and time delay. Thus, the accurate following between reference value and predicted value is difficult to achieve. To this end, a dual-predictive control based on adaptive error correction (DPCEC) for the FW-VSIs is presented. Firstly, the state-space function of FW-VSIs under $\alpha$ $\beta$ 0 reference frame is built. Then, the DPCEC strategy is proposed based on the above issue. Therein, an adaptive error correction strategy is severally embedded into both the outer and inner prediction loop. Noting that the impacts of different negative factors can be simultaneously processed and corrected through adaptive error correction strategy. Not only does the proposed control strategy achieve better performance in steady state, but also retains fast dynamic response in transient state. Finally, simulation and experimental results which verify the high performance of the proposed control techniques are provided. [ABSTRACT FROM AUTHOR]

In the existing offline inductance identification methods of permanent magnet synchronous machines (PMSMs), dq-axis voltage equations are generally adopted and the inverter nonlinearity compensation is necessary, which complicates the identification process and restricts the generality. In order to solve the problem, an offline inverter-nonlinearity-immune inductance identification method is proposed in this article based on the PMSM equivalent impedance model. The method can identify the dq-axis inductances by injecting both dc and high frequency signals, where the magnetic saturation and the cross saturation are considered. Besides, an inductance surface forming algorithm is introduced based on the bilinear interpolation. The continuous dq-axis inductance surfaces can be then obtained by only a few identified data, so that the time of data sampling and processing is greatly shorten compared with the existing methods. To enhance the accuracy of the identification method, the digital delay error and the sampling error in zero current zone are investigated and compensated. The effectiveness of the proposed method is validated on three different test machines. [ABSTRACT FROM AUTHOR]

The extensive application of vector phase shift modulation method in permanent magnet synchronous motor drive systems with single dc-link current sensor results in obvious current ripple in the low-speed region. The patterns of these ripples vary greatly in different sectors, leading to nonnegligible phase current reconstruction errors. In this article, an estimation of the local average current change rate (ELAC2R) based reconstruction error suppression method within the switching cycle is proposed to resolve this problem. By comparing the actual and the ideal sampling points, the increase of current ripple and the reconstruction error caused by the combination of the vector phase shift and the single current sensor sampling in the low-speed region are modelled and analyzed. Then according to equivalent modelling of the current ripple between the ideal and the actual sampling points, the more accurate phase current information can be quickly obtained based on the sampling value. The compensation process is significantly simplified and the computation burden of the processor is reduced. Experimental results show that the performance of current reconstruction can be improved effectively with less harmonic content with the proposed method. [ABSTRACT FROM AUTHOR]

The output of renewable generation depends on the real-time weather conditions and changes rapidly; so the economic operating point of the power system varies over time. This paper aims to find the explicit mapping from variable renewable power to optimal power flow solutions. To this end, we propose a parametric distribution optimal power flow (P-DOPF) method, which gives the optimal dispatch strategy and power flow status as analytical functions of the renewable output. With the established distribution optimal power flow problem based on the relaxed Distflow model, the first step is to perform a global polyhedral approximation on the second-order cone constraints to develop a linearized formulation. The second step is to obtain the P-DOPF model by treating renewable power output as parameters; then, the P-DOPF problem gives rise to a multi-parametric linear program (mp-LP). Third, we prove that the optimal solution and optimal value of the P-DOPF are piecewise linear functions of the parameters and we design an adaptive-sampling algorithm to construct the optimal value and optimal solution functions, as well as the partition of the parameter set, subject to a given error tolerance; this algorithm is not influenced by model degeneracy, a common difficulty of existing mp-LP algorithms. The P-DOPF framework provides an explicit real-time control policy of generators in response to the renewable output. Case studies on the IEEE 33 and 69-bus systems verify the effectiveness and performance of the proposed method; by comparison, the proposed method outperforms the established affine policy method in computational efficiency and optimality by 24.5% and 4.62%, respectively. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering images of the illuminated area, playing a significant role in various applications. However, achieving focused SAR images is challenging under sparse sampling and phase error conditions. By exploiting the sparsity or compressibility priors, the state-of-the-art sparsity-driven SAR imaging methods can reconstruct images under the condition of sparse sampling. However, the handcrafted priors used in these methods limit the imaging performance, and the iterative solution schemes reduce the computational efficiency. Besides, the measurement inaccuracy introduced by the phase error also degrades the reconstruction performance of the sparsity-driven imaging methods. To address these issues, a deep network for SAR autofocus imaging is proposed, which alternately performs image reconstruction and phase error estimation. When performing image reconstruction, the sparsity-cognizant total least-square (S-TLS) model is introduced to handle the problem of measurement inaccuracy, contributing to robust reconstruction performance under the condition of phase error. During the implementation of the deep network, a feature transform operator is used to realize data-driven prior knowledge learning and overcome the limitations of handcrafted priors. Moreover, the deep network approach can significantly improve computational efficiency. Experiments on simulated and real data verify the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we design receivers for filter bank multicarrier-based (FBMC-based) massive MIMO considering practical aspects such as channel estimation and equalization. In particular, we propose a spectrally efficient pilot structure and a channel estimation technique in the uplink to jointly estimate all the users’ channel impulse responses. We mathematically analyze our proposed channel estimator and find the statistics of the channel estimation errors. These statistics are incorporated into our proposed equalizers to deal with the imperfect channel state information (CSI) effect. We revisit the channel equalization problem for FBMC-based massive MIMO, address the shortcomings of the existing equalizers in the literature, and make them more applicable to practical scenarios. The proposed receiver in this paper consists of two stages. In the first stage, a linear combining of the received signals at the base station (BS) antennas provides a coarse channel equalization and removes any multiuser interference. In the second stage, a per subcarrier fractionally spaced equalizer (FSE) takes care of any residual distortion of the channel for the user of interest. We propose an FSE design based on the equivalent channel at the linear combiner output. This enables the applicability of our proposed technique to small and/or distributed antenna setups such as cell-free massive MIMO. Finally, the efficacy of the proposed techniques is corroborated through numerical analysis. [ABSTRACT FROM AUTHOR]

Quantum error mitigation (QEM) is a class of promising techniques for reducing the computational error of variational quantum algorithms. In general, the computational error reduction comes at the cost of a sampling overhead due to the variance-boosting effect caused by the channel inversion operation, which ultimately limits the applicability of QEM. Existing sampling overhead analysis of QEM typically assumes exact channel inversion, which is unrealistic in practical scenarios. In this treatise, we consider a practical channel inversion strategy based on Monte Carlo sampling, which introduces additional computational error that in turn may be eliminated at the cost of an extra sampling overhead. In particular, we show that when the computational error is small compared to the dynamic range of the error-free results, it scales with the square root of the number of gates. By contrast, the error exhibits a linear scaling with the number of gates in the absence of QEM under the same assumptions. Hence, the error scaling of QEM remains to be preferable even without the extra sampling overhead. Our analytical results are accompanied by numerical examples. [ABSTRACT FROM AUTHOR]

This article reports on the experimental validation of a method to predict radiated emissions of equipment under test in a reverberation chamber where the multiple monopole sources stirring technique is implemented. The method is validated for three different case studies: a loop, a slot in a metal enclosure, and a realistic device comprised of the chassis of a power supply for a workstation. The reconstruction of the free space electromagnetic emissions starting from measurements in a reverberation chamber is compared with measurements in an anechoic environment. A statistical analysis of the uncertainty due to a positioning error of the field sample measurement points is also proposed. [ABSTRACT FROM AUTHOR]

The annual output value of Taiwan’s wafer foundry and IC packaging test ranks first worldwide. The electronics industry has a complete industrial ecological chain in the supply chain system of global information and communication technology. In order to increase market competitiveness and increase the operational flexibility, manufacturing process outsourcing has become a trend in the business model of the electronics industry. The quality of the outsourcing affects the quality and function of the final product, the outsourcer selection is crucial to ensure the quality of the final product. This article takes the wire bonding process as an example, and proposes a fuzzy selection model of quality-based IC packaging process outsourcers. Then, this article selects the six standard deviation indicator that can fully reflect the process yield and quality level as the evaluation tool. Since the indicator contains unknown parameter, in order to improve the accuracy of estimation and overcome the uncertainty of measurement data, this paper applies mathematical programming to derive the confidence interval of six sigma quality index, and proposes a confidence-interval-based fuzzy testing meth as a green outsourcer selection tool to reduce the risk of misjudgment due to sampling errors and improve the accuracy of the selection. [ABSTRACT FROM AUTHOR]

The current sampling errors in dual-motor group (DMG) cause more adverse effects on system performance than that in a single motor system. To solve this problem, in this article, a synergistic correction strategy for the abovementioned problems in DMG fed by double inverters is proposed. First, an orthogonal pulsewidth modulation carriers strategy is proposed to imitate the “shutdown” status of motor subsystems to obtain ascertainable current relations. Then, the current values with sampling errors in DMG at several “fixed” sampling points are derived, based on which the sampling errors in DMG are corrected. Meanwhile, the rules for the action time of active vectors and output voltage range in each inverter are analyzed for each “fixed” sampling point. Finally, the proposed strategy is verified by experimental results on a DMG system, which show that the sampling errors can be precisely estimated with the proposed strategy. [ABSTRACT FROM AUTHOR]

In theera of Internet of Things (IoT), sensor plays a more vital role and its quality has a great impact on final measurement. From the viewpoint of final measurement, there are two kinds of effects: bias-only measurement and error-growth measurement. The bias-only measurement can be directly read from a sensing device, and each measurement is only distorted by the inherited bias error of the sensor, e.g., measuring temperature by a thermometer sensor. On the other hand, the error-growth measurement cannot be read directly but be calculated by combining multiple former sampling data, therefore multiple bias errors are accumulated in the measurement. For instance, in inertial navigation, the raw data sampled from an inertial measurement unit (IMU) are converted into a trajectory by multiple integral operations, so the errors of new sampling data are continuously added into the trajectory, and the deviation of the traced trajectory will reach an unacceptable level over time. Clearly, a good IMU trajectory strategy is the one with a less error-accumulation effect. Unfortunately, the analysis of error-growth effect remains not well studied, which motivates this work. This letter first proposes a theoretical error-growth effect analysis framework. Next, we use it to analyze three typical inertial methods, namely single-IMU method, gyro-free-IMU (GF-IMU) method, and $\omega$ -free accelerometer pair (OFAP) method. Finally, the theoretical derivations of the three inertial methods are proved by simulation results. [ABSTRACT FROM AUTHOR]

In the voltage source inverter applications, inverter nonlinearities would affect the parameter identification process in many ways. Hence, this article proposes an offline identification method for resistance and dq-axis inductance surface by considering the inverter nonlinearity characteristics. A variable amplitude square-wave injection (VASI) scheme is proposed for the dq-axis inductance identification. The VASI method achieves the inductance identification with a novel data sampling strategy. Meanwhile, it can also establish the inductance surfaces by only a few identified data points with a polynomial fitting algorithm, which greatly reduces the identification time compared with the existing methods. The resistance identification is realized by a slope signal injection method, in which the effect of IGBT voltage drop is analyzed. In order to improve the identification accuracy, the inverter nonlinearities are compensated by a self-learning method considering the zero-axis voltage at different rotor positions. At the same time, the sampling error in zero current zones of abc-phases is researched. In order to verify the effectiveness and generality, the proposed method is carried out on two different test machines and confirmed by finite element analysis. [ABSTRACT FROM AUTHOR]

Recently, a connection between the age of information and remote estimation error was found in a sampling problem of Wiener processes: If the sampler has no knowledge of the signal being sampled, the optimal sampling strategy is to minimize the age of information; however, by exploiting causal knowledge of the signal values, it is possible to achieve a smaller estimation error. In this paper, we generalize the previous study by investigating a problem of sampling a stationary Gauss-Markov process named the Ornstein-Uhlenbeck (OU) process, where we aim to find useful insights for solving the problems of sampling more general signals. The optimal sampling problem is formulated as a constrained continuous-time Markov decision process (MDP) with an uncountable state space. We provide an exact solution to this MDP: The optimal sampling policy is a threshold policy on instantaneous estimation error and the threshold is found. Further, if the sampler has no knowledge of the OU process, the optimal sampling problem reduces to an MDP for minimizing a nonlinear age of information metric. The age-optimal sampling policy is a threshold policy on expected estimation error and the threshold is found. In both problems, the optimal sampling policies can be computed by low-complexity algorithms (e.g., bisection search and Newton’s method), and the curse of dimensionality is circumvented. These results hold for (i) general service time distributions of the queueing server and (ii) sampling problems both with and without a sampling rate constraint. Numerical results are provided to compare different sampling policies. [ABSTRACT FROM AUTHOR]