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It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the interaction between vehicles must be considered. Interaction aware prediction methods track the previous trajectories of both the target vehicle and its surrounding vehicles. In this study, the authors consider trajectory prediction as a sequence‐to‐sequence prediction problem. They tackle this problem with an LSTM encoder–decoder framework. Moreover, they propose two spatial‐attention mechanisms to account for the interaction between vehicles, i.e. context attention and lane attention. Spatial‐attention mechanisms adopt the selective‐attention mechanism of human drivers. They choose context vectors to help the model understand the surrounding environment better and thus improve its prediction accuracy. They evaluate the authors' methods on the highD data set recorded in German highways with root mean squared error metric. Their experimental results show superior performance to other state‐of‐the‐art methods. Code is available at https://github.com/momo1986/Spatial‐attention. [ABSTRACT FROM AUTHOR]

Respiratory rate is an essential indicator of serious illness. Compared to heart rate or systolic blood pressure, its change is more evident, hence is a better means to discern stable patients from those at risks. In addition, the respiration rate is a crucial indication of sleep quality associated with sleep disorders such as obstructive sleep apnea. Oronasal pressure, as a clinical respiratory signal for sleep analysis, can be used in polysomnography both in labs of hospital and homes. Besides, it is often taken as a reference signal in research as opposed to the estimated respiration rate. This study aims to provide an automated respiratory rate estimation system with signals taken from oronasal pressure transducer that can cope with noises and is adaptive to various respiratory frequencies. A robust approach is presented here that employs Ensemble empirical mode decomposition method to remove signal noise, together with Butterworth band‐pass filter to obtain the breathing frequency by means of zero‐crossing. Among 97.6% of the test data, the study yields a root mean square error of 1.031. Compared to other methods, the current approach provides a more accurate respiration rate estimation in the application of orinasal pressure to sleep analysis. [ABSTRACT FROM AUTHOR]

In recent times, several applications requiring highly accurate indoor positioning systems have been developed. Since the global positioning system is unavailable/less accurate in the indoor environment, alternative techniques such as visible light positioning (VLP) are considered. The VLP system benefits from the wide availability of illumination infrastructure, energy efficiency and the absence of electromagnetic interference. However, there is a limited number of studies on three‐dimensional (3D) VLP and the effect of multipath propagation on the accuracy of the 3D VLP. This study proposes a supervised artificial neural network to provide accurate 3D VLP whilst considering multipath propagation using receiver diversity. The results show that the proposed system can accurately estimate the 3D position with an average root mean square (RMS) error of 0.0198 and 0.021 m for line‐of‐sight (LOS) and non‐LOS link, respectively. For 2D localisation, the average RMS errors are 0.0103 and 0.0133 m, respectively. [ABSTRACT FROM AUTHOR]

An accurate power output prediction of the photovoltaic system is pivotal to eliminate the extra cost and the negative impact in the utility grid integrated with photovoltaic power sources. The power output of a photovoltaic system is predicted by introducing a long short‐term memory method. Moreover, the influence of noise data on prediction results is eliminated with the empirical mode decomposition. To further improve the accuracy and stability of the prediction method, the parameters of long short‐term memory neural networks are determined with a sine cosine algorithm. The performances of the long short‐term memory method in terms of root mean square error, mean absolute error, and coefficient of determination in January and August are analysed, respectively. Compared with other prediction schemes, the long short‐term memory method provides superior accuracy for photovoltaic power output prediction. [ABSTRACT FROM AUTHOR]

Estimating Kullback-Leibler divergence from identical and independently distributed samples is an important problem in various domains. One simple and effective estimator is based on the $k$ nearest neighbor distances between these samples. In this paper, we analyze the convergence rates of the bias and variance of this estimator. Furthermore, we derive a lower bound of the minimax mean square error and show that kNN method is asymptotically rate optimal. [ABSTRACT FROM AUTHOR]

Recently, widely linear adaptive filtering with noisy input and output has attracted much attention. As an unbiased estimation method, the widely linear complex-valued estimated input least mean square (WLC-EILMS) algorithm has shown its superiority in the noisy inputs and outputs. However, WLC-EILMS is not robust to impulsive noises. To overcome this defect, this brief uses the maximum complex correntropy criterion (MCCC) to generate a novel widely linear complex-valued estimated-input MCCC (WLC-EIMCCC) algorithm. The proposed WLC-EIMCCC algorithm exhibits superior performance especially in dealing with impulsive noises, and its performance analysis is also given for theoretical analysis. Simulations on system identification and channel equalization validate the correctness of the theoretical steady-state excess mean square error (EMSE) and the superior performance of WLC- EIMCCC. [ABSTRACT FROM AUTHOR]

To improve the stability and accuracy of manoeuvring target tracking in three‐dimensional space based on the angle of arrival (AOA) and its rate of change observations, this study presents a new observation fusion method by fusing the received signal strength (RSS) with AOA and the rate of change of AOA. To enhance the adaptive ability of traditional strong tracking extended particle filter (TSTEPF) against model mismatch, this study re‐determines the position of the fading factor in the strong tracking extended Kalman filter based on the orthogonal principle and gives the calculating method. And by combining the method with the particle filter, a new strong tracking extended particle filter (STEPF) algorithm is proposed. Simulation results show that after fusing RSS into the observation model, the tracking speed and precision are both improved, especially precision, as the position root‐mean‐square error has a 58% decline on average. And it is found that STEPF proposed in this study has a more stable adaptive ability than TSTEPF, and is superior in terms of position, velocity, and acceleration estimation accuracy. [ABSTRACT FROM AUTHOR]

In this study, the authors present a novel speech enhancement method by exploring the benefits of non‐local means (NLM) estimation and optimised empirical mode decomposition (OEMD) adopting cubic‐spline interpolation. The optimal parameters responsible for improving the performance are estimated using the path‐finder algorithm. At first, the noisy speech signal is decomposed into many scaled signals called intrinsic‐mode functions (IMFs) through the use of a temporary decomposition method is called sifting process in OEMD approach. The obtained IMFs are processed by NLM estimation technique in terms of non‐local similarities present in each IMF, to reduce the ill‐effects caused by interfering noise. The proposed NLM‐based method is effective to eliminate the noise of less‐frequency. Each IMF contains essential information about the signals, on some scale or frequency band. Field programmable gate array architecture is implemented on a Xilinx ISE 14.5 and the result of the proposed method offers good performance with a high signal‐to‐noise ratio (SNR) and low mean‐square error compared to other approaches. The performance evolution is carried out for different speech signals taken from the TIMIT database and noises taken from the NOISEX‐92 database in different SNR stages of 0, 5 and 10 dB, respectively. [ABSTRACT FROM AUTHOR]

In this study, a novel approach based on multi‐objective genetic algorithm (MO‐GA) is used for simultaneous tuning the unscented Kalman Filter (UKF) parameters and camera and inertial measurement unit camera calibration in a vision inertial navigation system (VINS). This system consists of visual odometry and inertial navigation system (INS) which integrates with a UKF. In order to obtain simultaneous tuning and calibration of the parameters and variables, the MO‐GA minimises the root mean square error of the position and velocity of the vehicle on a selected trajectory of the benchmark data set. Then, the tuned parameters and calibrated variables are placed in the VINS and an adjusted VINS (AVINS) is obtained. For investigating the AVINS, the mentioned system is compared with INS only, VINS based on calibration data of the benchmark data set, and GPS/INS as Real Data on the identical trajectory. Furthermore, in order to evaluate the results of the proposed approach, the AVINS is examined in the second trajectory. The results indicate the proper performance of the presented approach in the simultaneous tuning the filter parameters and calibrating the variables of sensors that are used in the uncalibrated VINS. [ABSTRACT FROM AUTHOR]

The recently proposed Zero-Attracting Proportionate Normalized Least Mean Square (ZA-PNLMS) algorithm improves the performance of the PNLMS algorithm for identifying sparse systems. In particular, it keeps the fast initial convergence rate of the PNLMS algorithm, and improves its transient performance by arresting the fall in the convergence rate at the later stage of the adaptation process, and it also improves the steady-state mean square error (MSE). However, the improvement in the steady-state performance is marginal. In this brief, we propose a novel gain function for the ZA-PNLMS algorithm by introducing an adaptive upper threshold parameter. It has two fold implications. First, the proposed threshold parameter truncates the proportional gains of highly active taps when they approach to their steady-states and helps the lesser active taps to converge faster by providing them more gains, and thereby the overall transient performance is further improved. Secondly, it also improves steady-state MSE significantly by reducing the fluctuation of the active taps at the steady-state. Extensive simulation studies have been carried out to justify the improvement obtained by the proposed algorithm. [ABSTRACT FROM AUTHOR]

This study presents a novel concept of consensus for multi‐agent systems with a two‐layer framework, namely, the node‐to‐node bipartite consensus. The agents are divided into the virtual leaders and followers, and the signed graph representing communication channels of followers is supposed to be structurally balanced. Moreover, only a small part of the followers are pinned by their corresponding leaders. In addition, the proposed control is subjected to Gaussian disturbances or norm‐bounded disturbances, and two extended forms of node‐to‐node bipartite consensus are correspondingly defined. By utilising the signed graph theory, the M ‐matrix theory, and the Lyapunov function approach, the consensus error between an arbitrary follower and its leader is proved to converge to zero in the mean square sense or to be ultimately bounded. The proposed conditions for consensus mainly depend on the selection of pinned followers and the coupling strength in each layer. Finally, the theoretical results are illustrated by detailed numerical examples. [ABSTRACT FROM AUTHOR]

In this study, an improved cuckoo search (ICS) algorithm is presented to solve the parameter estimation problem of soil structure. For 1D soil parameters estimation, the objective function is constructed based on the relative root‐mean‐square error between measured and calculated soil apparent resistivity curves. The ICS algorithm extends the search range of soil model parameters through variable control parameters. Optimal resistivity and thickness of each layer are obtained by several different algorithms under the same test conditions based on two known four‐layer soil models without noise. The results show that the ICS algorithm performs better in minimising error. This study also tests the optimisation performance of the ICS algorithm in two actual high‐quality data cases, comparative results of the ICS algorithm and existing literature validate the effectiveness and accuracy of ICS. Moreover, in order to make soil estimation results more visible in a specific area, the combination of the proposed estimated algorithm and a new arrangement way of measurement lines is proposed to estimate 3D soil electric parameter structure in Chengdu, China. [ABSTRACT FROM AUTHOR]

Portrait matting is of vital importance for many applications such as portrait editing, background replacement, ecommerce demonstration, and augmented reality. The portrait matt can be accessed by predicting the α value of the original picture. Previous deep matting methods usually adopt a segmentation network to tackle portrait matting tasks. However, these traditional methods will introduce unpleasant blemishes in the matting results sometimes. The authors find that the key factor behind this phenomenon is how they model the matting problem. On the one hand, α value predicting can be modelled as a regression task. On the other hand, it can be viewed as a classification task of predicting background or foreground. To solve this problem, they explore different methods to model the nature of the α matting problem and propose a novel quantisation‐based adaption. Their method comes up with an α quantisation loss to achieve multi‐threshold filtering. Furthermore, they apply an α merging block to improve conventional regression methods. With their method, the gradient loss is reduced by 7.53% relatively, with mean square error and sum of absolute difference decreased by 14.7% relatively, leading to a more visually pleasant α matt in several segmentation backbones. [ABSTRACT FROM AUTHOR]

We propose finite-alphabet equalization, a new paradigm that restricts the entries of the spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost hardware equalizers. To minimize the performance loss of this paradigm, we introduce FAME, short for finite-alphabet minimum mean-square error (MMSE) equalization, which is able to significantly outperform a naïve quantization of the linear MMSE matrix. We develop efficient algorithms to approximately solve the NP-hard FAME problem and showcase that near-optimal performance can be achieved with equalization coefficients quantized to only 1–3 bits for massive multi-user multiple-input multiple-output (MU-MIMO) millimeter-wave (mmWave) systems. We provide very-large scale integration (VLSI) results that demonstrate a reduction in equalization power and area by at least a factor of $ {3.9} {\times }$ and $ {5.8} {\times }$ , respectively. [ABSTRACT FROM AUTHOR]

For harmonic control of a power grid, it is prerequisite to accurately estimate and follow the utility harmonic impedance at the point of common coupling (PCC). The existing estimation methods generally consider that the utility harmonic impedance is constant, which causes the estimation error and fail in tracking of impedance fluctuations, since the utility harmonic impedance is fluctuant in practice. Actually, the harmonic impedance fluctuations will lead to the transient variation of the harmonic voltage and current which is bad for harmonic control. In this study, to follow the harmonic impedance fluctuations, a longer observation period is divided into a series of shorter period, and the fluctuations of the utility harmonic impedance over a short period are regarded as a stationary random process. Then, for each short period, the utility harmonic impedance is estimated by minimizing the fluctuation variance of the utility harmonic impedance. Besides the high estimation precision and good tracking performance of impedance fluctuations, other advantages of the proposed method are not limited by the traditional assumptions, such as the background harmonics are stable and small, the customer harmonic impedance is much larger than the utility, and the harmonic sources of both sides are independent, and so on. [ABSTRACT FROM AUTHOR]

We investigate channel estimation for an intelligent reflecting surface (IRS) aided wireless communication system with a multi-antenna access point (AP), an multi-element IRS, and a set of multiple users, each with a single antenna. Our goal is to minimize mean square error (MSE) of estimating the channels of direct and cascaded links from the AP to each user via the IRS elements by jointly optimizing the training sequence of the AP and reflection pattern of the IRS. To solve this challenging problem, we first find the joint optimal structure and reflection pattern for the training sequence, and then, we convert the problem into a convex form to design the optimal training sequence efficiently. To reduce the computational complexity, we also propose a suboptimal training sequence in closed form. Numerical results show that the proposed scheme outperforms baseline schemes. [ABSTRACT FROM AUTHOR]

Conventional least-mean-square (LMS) algorithm is one of the most popular algorithms, which is widely used for adaptive beamforming. But the performance of the LMS algorithm degrades significantly because the constant step size is not suitable for varying signal-to-noise ratio (SNR) scenarios. Although numerous variable-step-size LMS (VSS-LMS) algorithms were proposed to improve the performance of the LMS algorithm; however, most of these VSS-LMS algorithms are either computationally complex or not reliable in practical scenarios since they depend on many parameters that are not easy to tune manually. In this letter, a fast and robust VSS-LMS algorithm is proposed for adaptive beamforming. The VSS is obtained based on normalized sigmoid function, where the sigmoid function is calculated by using the mean of instantaneous error first and then normalized by the squared cumulative sum of instantaneous error and estimated signal power. The proposed algorithm can update the step size adaptively without tuning any parameter and outperform state-of-the-art algorithms with low computational complexity. The simulation results show better performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The nearest-instance-centroid-estimation kernel least mean-square (NICE-KLMS) algorithm has been proposed to balance the time and space requirements in kernel adaptive filters. However, the minimum mean square error (MMSE) criterion used in NICE-KLMS leads to performance degradation in some nonlinear problems. In this brief, the NICE is developed under the least-squares errors in the kernel space, generating a novel NICE kernel recursive least squares (NICE-KRLS) algorithm for performance improvement of NICE-KLMS. The weight update form for the solution to the least-squares errors existing in NICE-KRLS is therefore obtained recursively. To obtain a sparsification network, the vector quantization is combined into NICE-KRLS for online applications. Simulations on chaotic time-series prediction validate the superiorities of the proposed NICE-KRLS and its sparsification version. [ABSTRACT FROM AUTHOR]

A cyclic sub‐vector optimisation (CSVO) beamforming approach is investigated. With the proposed algorithm, an array beamforming vector is partitioned into a number of sub‐vectors of small sizes, allowing reduced‐dimension processing. Then multiple optimisation cycles are carried out by the block coordinate descent method, which leads to an optimal beamforming vector. The proposed scheme still needs to compute the matrix inversion, but the size of the matrix can be flexibly chosen and the computational complexity is manageable. The proof of the convergence and complexity analysis is given. The simulation results demonstrate the effectiveness and fine features of the proposed algorithm. Although the convergence rate of the CSVO is slightly slower, the CSVO has lower computational complexity than that of the diagonal loading conjugate gradient applied to normal equations algorithm. In comparison with other sub‐vector approaches, the proposed algorithm gains a faster convergence rate and improved stability. [ABSTRACT FROM AUTHOR]

Herein, the authors present a robust estimator of range against the impulsive noise using only the received signal's magnitude. The M estimator has been widely used in robust signal processing. However, the existing M estimator requires statistical testing involving a threshold which has an optimality that varies with time, hence algorithmically challenging and computationally burdensome. The statistical testing is utilised for discerning the inlier and outlier. Further, statistical testing renders the computational burden of the algorithm high since the testing must be performed for each observation. Therefore, they propose the M estimator based on the hyper‐tangent loss function, which does not demand statistical testing. Conventional M estimator employing information theoretic learning also does not call for statistical testing, but the mean square error (MSE) performance for the range estimation is inferior to that of the proposed method. Furthermore, they perform an analysis for the MSE for the proposed algorithm. Monte Carlo simulations not only validate their theoretical analysis, but also demonstrate the MSE performance of the proposed method is nearly same as the existing skipped filter although it does not require the statistical testing and optimal threshold selection. [ABSTRACT FROM AUTHOR]

A fixed‐width multiplier receives two n‐bit operands and generates an approximate n‐bit product as the output. It truncates part of the partial products and employs an appropriate error compensation circuit in order to reduce the approximation error. In this study, a new error compensation circuit for the fixed‐width multiplier has been proposed which utilises the correction vector (CV) and modified minor CV. The proposed error compensation circuit is capable of minimising both the mean error and the mean‐square error. Post‐synthesis results for 16‐bit of fixed‐width multiplier demonstrate that the proposed circuit has 3.50, 39.24, 42.91 and 44.91% reduced delay, area, power consumption and power–delay product when compared with the existing design reported in the literature. [ABSTRACT FROM AUTHOR]

In this study a new algorithm is proposed for distributed blind sensor macro‐calibration in networked control systems robust to noise. The proposed distributed algorithm for estimation of gain and offset correction parameters is of stochastic approximation type, with local non‐linear transformations of residuals. Convergence of the algorithm in mean‐square and with probability one to consensus is proved for a large class of non‐linear transformations, network properties and communication and measurement noise characteristics. The choice of the introduced non‐linear transformations in accordance with the theory of robust statistics leads to the proposal of new calibration algorithms robustified w.r.t. noise. It is demonstrated by Monte Carlo simulation that the proposed algorithms are very efficient in the presence of large outliers from the point of view of both achievement of high convergence rate and adequate values of convergence points, outperforming the existing linear algorithms. [ABSTRACT FROM AUTHOR]

Correntropy has recently attracted great attention because of its robustness to non-Gaussian noise. Inspired by this robustness property, a spline adaptive filtering algorithm based on the maximum correntropy criterion (SAF-MCC) has been proposed. However, the steady-state convergence performance of the SAF-MCC has not been studied. This brief presents a steady-state performance analysis of the SAF-MCC for Gaussian and non-Gaussian noise. We derive and discuss the theoretical excess mean square error (EMSE) under these cases. Monte Carlo simulations are presented to corroborate the theoretical performance analysis results developed in this brief. [ABSTRACT FROM AUTHOR]

Image denoising under low signal‐to‐noise ratio (SNR) and non‐Gaussian noise is still a challenging problem in image processing. In this study, the authors prose a kind of improved convolution neural network auto‐encoders for image denoising. Different from other priors based methods, the denoising auto‐encoders (DAEs) can learn end‐to‐end mappings from noisy images to the target ones. This study research statistical features of image residual between the restored images and target images. According to the maximum entropy principle, the training loss function of the ordinary DAEs was modified with residual statistics as the constraint condition, and an improved training algorithm was proposed based on augmented Lagrange function method. Thus, the quality of restored image can be improved through removing image information from residual more efficiently. Experiments show not only the denoising effects of improved DAEs is superior to the original mean‐square‐error loss function DAEs in both peak SNR and Riesz feature similarity metric indexes, but also has the ability to suppress the different types of noises with different levels through a single model. [ABSTRACT FROM AUTHOR]

This paper provides conditions on the observation probability distribution in Bayesian localization and optimal filtering so that the conditional mean estimate satisfies convex stochastic dominance. Convex dominance allows us to compare the unconditional mean square error between two optimal Bayesian state estimators over arbitrary time horizons instead of using brute force Monte-Carlo computations. The proof uses two key ideas from microeconomics, namely, integral precision dominance and aggregation of single crossing. The convex dominance result is then used to give sufficient conditions so that the optimal policy of a controlled sensing two-state partially observed Markov decision process (POMDP) is lower bounded by a myopic policy. Numerical examples are presented where the Shannon capacity of the observation distribution using one sensor dominates that of another, and convex dominance holds but Blackwell dominance does not hold. These illustrate the usefulness of the main result in localization, filtering and controlled sensing applications. [ABSTRACT FROM AUTHOR]

This study proposed methods based on Sentinel‐2 and Landsat‐8 cross‐fusion for improving water quality mapping (WQM). Therefore, four traditional fusion methods including intensity–hue–saturation, Gram–Schmidt transform, wavelet transform and Brovey transform and different scenarios of cross‐fusion have been implemented. The proposed cross‐fusion methods highly improved the correlation coefficient (CR) between the images and the water quality parameter (WQP). Considering the higher CR values, the created WQP maps showed very good accuracy, in which the root‐mean‐square error values were 0.03, 0.59, 0.96, 0.26 and 279.76 for potential hydrogen (PH), dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD) and electrical conductivity (EC) maps, respectively. Also, the effect of considering 1 px value or the mean of a 3×3 window of the input images for calculating the regression models on the accuracy of the final maps was tested. Only the best outputs for mapping PH and DO parameters were based on applying the mean of a 3×3 window. The results also showed that increasing the window size could increase the computational complexity and decrease the WQM accuracy. Comparing the output maps with the traditional maps confirmed the higher accuracy of the proposed methods. [ABSTRACT FROM AUTHOR]

In this study, the authors focus on the selection of instantaneous frequency estimates in the quasi‐maximum likelihood estimator for polynomial phase signal. More specifically, they are interested in the case of short signal observations at low signal‐to‐noise ratios. In the first part of this work, the benefit of instantaneous frequency estimates selection for the above estimator performances is highlighted. Then the authors propose a modified quasi‐maximum likelihood estimator based on a determinist instantaneous frequency estimates selection. Numerical studies provided at the end of this study showed clearly that the performances of the proposal in terms of root mean square error and signal to noise ratio threshold compared to the classical approach was better, in the considered case of short signal observation in a noisy environment. [ABSTRACT FROM AUTHOR]

Parametric spectral estimation techniques are widely used to estimate the parameters of sums of complex sinusoids corrupted by noise. In this work, the authors show that the numerical stability of the estimated frequencies not only depends on the size of the amplitudes associated with the real frequencies, but also to the distance among frequencies. Therefore, for closely spaced frequencies, the estimates are vulnerable to large deviate from their true values. To overcome this problem, they propose a strategy to artificially increase the frequency separation by downsampling the baseband equivalent of the noisy signal before applying a spectral estimation technique. This methodology significantly improves the estimation performance especially in the low signal to noise ratio regime. The performance of the technique is assessed in terms of the root mean square error and it is compared to results obtained in previous publications. [ABSTRACT FROM AUTHOR]

In this Letter, the authors present a study on linear channel estimators and their respective mean square error expressions acknowledging spatially correlated channels and pilot contamination. They also investigate the impact of imperfect channel covariance matrix knowledge. [ABSTRACT FROM AUTHOR]

Temperature is an important determinant of bacterial growth. While the dependence of bacterial growth on different temperatures has been well studied for many bacterial species, prediction of bacterial growth rate for dynamic temperature changes is relatively unclear. Here, the authors address this issue using a combination of experimental measurements of the growth, at the resolution of 5 min, of Escherichia coli and mathematical models. They measure growth curves at different temperatures and estimate model parameters to predict bacterial growth profiles subject to dynamic temperature changes. They compared these predicted growth profiles for various step‐like temperature changes with experimental measurements using the coefficient of determination and mean square error and based on this comparison, ranked the different growth models, finding that the generalised logistic growth model gave the smallest error. They note that as the maximum specific growth increases the duration of this growth predominantly decreases. These results provide a basis to compute the dependence of the growth rate parameter in biomolecular circuits on dynamic temperatures and may be useful for designing biomolecular circuits that are robust to temperature. [ABSTRACT FROM AUTHOR]

Recently, the improved simple set‐membership affine projection (IS‐SM‐AP) algorithm has been proposed in order to exploit sparsity in system models. Although its update equation resembles that of the set‐membership affine projection (SM‐AP) algorithm, the IS‐SM‐AP algorithm has two fundamental advantages over the SM‐AP algorithm: (i) it can exploit sparsity in system models and (ii) its computational complexity is lower. Up to now, the properties of this algorithm have been addressed only through numerical simulations, and no analytical study has been presented. To fill this gap, in this study, the authors analyse the steady‐state mean squared error of the IS‐SM‐AP algorithm using the energy conservation method. Furthermore, some important implementation issues are addressed, and a time‐varying parameter for the discard function is proposed. Finally, the authors present numerical results corroborating the theoretical analysis and the effectiveness of the proposed time‐varying parameter. [ABSTRACT FROM AUTHOR]

A Gaussian process regression (GPR) with active learning is proposed for developing the solar irradiance point and interval forecasting models, which consider the spatial‐temporal information collected from a targeted site and a number of neighbouring sites. To enhance the performance of the GPR‐based model an active learning process is developed for constructing an ad‐hoc input feature set, selecting training data points, and optimising hyper‐parameters of GPR models. To validate the advantages of the proposed method, a comprehensive computational study is conducted based on solar irradiance data collected from the northwest California area. In the point forecasting, the proposed method beats the state‐of‐the‐art benchmarking methods including classical statistical models and data‐driven models according to values of the normalised root mean squared error, normalised mean absolute error, normalised mean bias error, and coefficient of determination. In the interval forecasting, the proposed method outperforms the persistence model, autoregressive model with exogenous inputs, generic GPR, as well as two recently reported forecasting methods, the bootstrap‐based extreme learning machine and quantile regression, in terms of the forecasting reliability. Computational results show that the proposed method is more effective than well‐known existing benchmarks in the point and interval forecasting of the solar irradiance. [ABSTRACT FROM AUTHOR]

In this study, a new distributed filtering approach is proposed for the load frequency control of uncertain non‐linear power systems with cyber‐physical attacks. Specifically, the non‐linear power system is firstly modelled under interval type‐2 Takagi–Sugeno fuzzy framework and the uncertainty therein is captured by designing corresponding membership functions. Both denial of service cyber attack and physical sensor attack are considered and modelled as independent Bernoulli process. Based on the Lyapunov stability theory, less conservative sufficient conditions have been derived to guarantee the robustly mean‐square asymptotic stability with an average H∞ performance standard γ for the dynamic filtering error system. Moreover, artful matrix transformation techniques have been adopted to decouple the intertwined matrix variables in designing the distributed filter gains. In simulations, a three‐area non‐linear power system with internal uncertainties is used to validate the robustness of the proposed distributed filtering strategy to system parametric uncertainties and cyber‐physical attacks. [ABSTRACT FROM AUTHOR]

Video summarisation and key‐frame extraction play a vital role in video processing and content‐based video analysis. Key‐frame extraction facilitates rapid browsing and proficient video ordering in numerous applications such as multimedia, gaming etc. The application of 'effective key‐frame extraction' can be used in the field of object detection and tracking, and it is found to be helpful in providing some sort of signal to visually impaired persons. In this study, the advantages of Thepade's sorted ternary block truncation coding (TSTBTC) along with binary bat algorithm (BBA) are used to overcome the limitations by extracting an effective key frame from the videos. Static images in the form of frames are extracted from input video database and processed through TSTBTC algorithm for calculating similarity measures amongst two consecutive frames. The input data is processed using four colour spaces, namely red–green–blue colour space, Kekre's LUV colour space, YCbCr colour space, YUV colour space and BBA are used to optimise the threshold value. Furthermore, five similarity measures indices are used to calculate the number of frames, and the result obtained shows that TSTBTC–BBA algorithm deployed in YUV colour space provides better results compared with other existing techniques regarding precision, F‐measure and colour spaces. [ABSTRACT FROM AUTHOR]

The past decade has seen a rapid application of information theoretic learning (ITL) criteria in robust signal processing and machine learning problems. Generally, in ITL's literature, it is seen that, under non-Gaussian assumptions, especially when the data are corrupted by heavy-tailed or multi-modal non-Gaussian distributions, information theoretic criteria [such as minimum error entropy (MEE)] outperform second order statistical ones. The objective of this research is to investigate this better performance of MEE criterion against that of minimum mean square error. Having found similar results for MEEand MSE-based methods, in the non-Gaussian environment under particular conditions, we need a precise demarcation between this occasional similarity and occasional outperformance. Based on the theoretic findings, we reveal a better touchstone for the outperformance of MEE versus MSE.

Objected to the planetary gear transmission, research on the fault diagnosis of gear cracks is mainly carried out with method of EEMD decomposition and Envelope demodulation. During the process, EEMD decomposition is used to the experiment data and continuously parameters of the root mean square (RMS) value, peak-to-peak (PtP) value, kurtosis (Kr), and other parameters of the IMF components are computed. According those parameters, the signal is reconstructed and after band-pass filtering the Hilbert transform and the envelope demodulation are carried out. The results showed that the power spectrum information obtained by using the EEMD decomposition and Hilbert transform could effectively response fault characteristics of the gear crack in the planetary transmission. The research is beneficial to the gear meshing fault diagnosis of the planetary transmission quickly and accurately and has a positive engineering value.

Unmanned aerial vehicle (UAV) is a power-driven aircraft that is unmanned and reusable. The purpose of this study is to accurately estimate the state of charge (SOC) of lithium-ion batteries for UAVs. A support vector machine (SVM) method, SVM is a type of learning machine based on statistical learning, is used as the input variable of the battery charging discharge data (current, voltage and temperature). The kernel of the radial basis function is the best kernel of authors’ experiment, where the C, [inline-formula] and g values are 1, 0.012 and 0.0125, respectively. The experimental results from the lithium-ion battery data at NASA Ames Prognostics Center of Excellence demonstrate the potential application of the proposed method as an effective tool for battery SOC prediction. The accuracy of the whole experiment is 98.42%. Mean-squared error is 1.783%. The experimental results show that the model has higher accuracy in predicting the discharge capacity of lithium battery SOC-training samples.

In radar systems, transmitting and receiving waveforms with different polarisations benefit to acquire complete information about the target and its environment, ensuring significant enhancement of the radar's performance. Here, the authors consider the optimisation problem of jointly designed transmitter and receiver (TR) polarisation from the view of target scattering estimation. Under the condition that the target and clutter scattering covariance is known as a prior, they first use sequential linear minimum mean square error estimation to obtain the target scattering matrix and then optimise the sensing vector, which contains the polarisation information under the constraint of minimising the mean square error. The mentioned problem can be solved by eigenvector decomposition. Then, a method to get the TR polarisation parameters is provided. Finally, they use numerical simulations to show the advantage of the proposed methods.

Insect migration influences population dynamics, provides ecosystem services, causes heavy crop damages and spreads diseases. The identification of insect migrants is critically important for the study of insect migration. Radar is an effective means of detecting nocturnal insect migrants. The mass, body shape and wingbeat frequency could be measured by the special-purpose entomological radar. These features are potentially valuable for species identification. This paper proposed a method to extract the mass of migratory insects based on ellipsoid scattering model. This model was used to approximate the small insects in the Rayleigh region, and then the inversion formula of the mass was developed based on ellipsoid scattering characteristics. Finally, through analysing statistical data of insect migrants, the formula was modified to fit small migratory insects. The root mean square percent error is 34.92% for estimates of mass. The given method can promote the species identification and biomass quantification of over-flying migratory insects, so as to study the role of insect migration in ecosystems and the pest control.

The authors present a deep learning algorithm for the automatic centroid localisation of out-of-plane US needle reflections to produce a semi-automatic ultrasound (US) probe calibration algorithm. A convolutional neural network was trained on a dataset of 3825 images at a 6 cm imaging depth to predict the position of the centroid of a needle reflection. Applying the automatic centroid localisation algorithm to a test set of 614 annotated images produced a root mean squared error of 0.62 and 0.74 mm (6.08 and 7.62 pixels) in the axial and lateral directions, respectively. The mean absolute errors associated with the test set were 0.50 ± 0.40 mm and 0.51 ± 0.54 mm (4.9 ± 3.96 pixels and 5.24 ± 5.52 pixels) for the axial and lateral directions, respectively. The trained model was able to produce visually validated US probe calibrations at imaging depths on the range of 4–8 cm, despite being solely trained at 6 cm. This work has automated the pixel localisation required for the guided-US calibration algorithm producing a semi-automatic implementation available open-source through 3D Slicer. The automatic needle centroid localisation improves the usability of the algorithm and has the potential to decrease the fiducial localisation and target registration errors associated with the guided-US calibration method.

Most existing velocity measurement methods can only calculate the radial velocity by Doppler effect. Here, the authors study the method of measuring the lateral velocity. It is found that the lateral velocity influences the changing rate of Doppler frequency over time, which inspires them to measure the lateral velocity by analysing the Doppler spectrum or the quadratic phase of the echo. The relationship between the echoes and the lateral velocity is modelled in this study. Also the mean square error of lateral velocity measurement, including the model error and the estimation error, is also deduced. The influence of the observation time is studied according to the mean square error.

In this study, the problem of low-complexity direction-of-arrival (DOA) estimation is addressed, and a novel real-valued propagator method (PM) is presented with a uniform linear array. The covariance matrix is divided into two subarrays and an equivalent noise subspace is obtained by exploiting the standard PM algorithm without eigenvalue decomposition. By a coordinate mapping technique, the complex PM cost function has been converted into a real-valued polynomial whose order only rely on the number of arrays. Using such a mathematical fact, source DOAs can be estimated by polynomial rooting instead of peak searching. The proposed method is able to reduce significant complexity with comparable root-mean-square error performance to the standard PM, which is finally verified by numerical simulations.

The problem of DOA estimation for sub-array multiple-input multiple-output radar is concerned. Employing compressive sampling concept and the minimum mean-square error (MMSE) technology, the proposed algorithm alternates between updating sample covariance matrix and the MMSE filter bank values until convergence. Simplified array manifolds are considered to decrease the computational complexity. The core idea of the algorithm is to determine the DOA by calculating the spatial distribution of signal power adaptively. Simulation results show that the new algorithm performs well both in a wide SNR range and limited samples, compared with the MUSIC, PIAA-APES, and OGSBI algorithms. The most outstanding advantage of the new algorithm is that it can maintain high estimation accuracy under limited samples without knowing the number of targets.

This study investigates the waveform design problem for a distributed multiple-input-multiple-output (MIMO) radar operating in spectrally crowded environments. The goal is to synthesise waveforms to minimise the target estimation error and improve the spectral coexistence with other radio frequency systems (e.g. communication systems). To this end, a waveform design problem based on minimising the mean square error (MSE) of target estimation has been formulated under the energy and spectral constraints. An algorithm to synthesise the optimal spectrally constrained waveforms with the minimum MSE has been proposed. It has been shown that the synthesised waveforms have superior estimation performance (in terms of MSE) and ensure significantly enhanced spectral compatibility.

The concept of missing data is important to apply statistical methods on the dataset. Statisticians and researchers may end up to an inaccurate illation about the data if the missing data are not handled properly. Of late, Python and R provide diverse packages for handling missing data. In this study, an imputation algorithm, cumulative linear regression, is proposed. The proposed algorithm depends on the linear regression technique. It differs from the existing methods, in that it cumulates the imputed variables; those variables will be incorporated in the linear regression equation to filling in the missing values in the next incomplete variable. The author performed a comparative study of the proposed method and those packages. The performance was measured in terms of imputation time, root-mean-square error, mean absolute error, and coefficient of determination [inline-formula]. On analysing on five datasets with different missing values generated from different mechanisms, it was observed that the performances vary depending on the size, missing percentage, and the missingness mechanism. The results showed that the performance of the proposed method is slightly better.

Wavelet transform profilometry is a three‐dimensional (3D) reconstruction method based on the structured light technique of fringe pattern projection, widely used because it is a non‐invasive, high‐performance 3D reconstruction method. The presence of shadows created by the object in the image capture process is an obstacle in obtaining accurate 3D reconstructions, as they add noise to the phase data, leading to artefacts in object reconstruction, even when using robust phase‐unwrapping algorithms. Since shadows present diverse intensities and shapes, detecting and eliminating their effects are challenging tasks. This work presents a novel method to detect shadow regions and reduce their effects in 3D reconstruction. The proposed method uses coloured fringe patterns to detect the shadows and mathematical morphology to condition the outlines of the shadow regions. The shadow outline information is used to interpolate the background‐plane fringe pattern onto the captured scene, where the shadows are detected. The mean squared error (MSE) of the reconstructed objects is reduced to 25% of the MSE without shadow removal, on an average, when using the Bioucas phase‐unwrapping method. When using the Ghiglia phase‐unwrapping method, the MSE reduction is to 8.3%, on an average, of the MSE in the shadow case. [ABSTRACT FROM AUTHOR]

The big video data size today imposes huge pressure on storage. The variation and aging induced memory failures significantly influence the video output quality. Recently, researchers have developed different memory designs for videos, deep learning, and other data-intensive applications, which enables better energy-quality tradeoffs with design constraints. Unfortunately, designing memory has been proven to be a very challenging problem due to: 1) various design constraints; 2) multiple memory bitcell design options; and 3) challenging layout integration and cost analysis using different memory technologies. In this paper, we develop novel mathematical models for optimizing embedded video memory design without applying a time-consuming and laborious ASIC design process. The problems are formulated as nonlinear programs and integer linear programs. Different SRAM designs and hybrid SRAM and DRAM designs are considered in our models. The results of the numerical studies show that by applying our proposed method the average mean square error of the video storage can be greatly reduced, even by more than 90% in many cases. [ABSTRACT FROM AUTHOR]