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Abstract The marginal Fisher information (MFI) metric is used to design waveforms for the sake of informationally optimal adaptive‐on‐transmit radar operation. A framework for MFI waveform design is developed and the Polyphase‐Coded FM (PCFM) waveform model is utilised to produce a constant‐modulus, spectrally contained signal amenable to transmission with high‐power amplifiers. The efficacy of the MFI waveform design and minimum mean square error (MMSE) estimation is experimentally demonstrated and extended into an adaptive and dynamic sensing paradigm. The radar transmit waveform is optimised to maximise the Fisher information with respect to the range profile. Upon observing new information from radar echoes, the iterative MMSE (iMMSE) estimator then minimises the estimation error variance according to prior observations. Sequential information maximisation (via waveform design) and error minimisation (via iMMSE) tends towards the Cramér–Rao lower bound (CRLB) with additional measurements improving radar resolution and accuracy. These concepts maximise the information extracted by a radar operating in a congested spectrum where the available bandwidth is limited.

The marginal Fisher information (MFI) metric is used to design waveforms for the sake of informationally optimal adaptive‐on‐transmit radar operation. A framework for MFI waveform design is developed and the Polyphase‐Coded FM (PCFM) waveform model is utilised to produce a constant‐modulus, spectrally contained signal amenable to transmission with high‐power amplifiers. The efficacy of the MFI waveform design and minimum mean square error (MMSE) estimation is experimentally demonstrated and extended into an adaptive and dynamic sensing paradigm. The radar transmit waveform is optimised to maximise the Fisher information with respect to the range profile. Upon observing new information from radar echoes, the iterative MMSE (iMMSE) estimator then minimises the estimation error variance according to prior observations. Sequential information maximisation (via waveform design) and error minimisation (via iMMSE) tends towards the Cramér–Rao lower bound (CRLB) with additional measurements improving radar resolution and accuracy. These concepts maximise the information extracted by a radar operating in a congested spectrum where the available bandwidth is limited. [ABSTRACT FROM AUTHOR]

Abstract Partial discharge (PD) signals are an important index to evaluate the operation state of intelligent substations. The correct distinction of PD pulse and interference pulse has become a challenging task. Because of the noise and the low signal‐to‐noise ratio, the stable signals become non‐stationary. The selection of a wavelet basis, the selection rule of threshold λ and the design of the threshold function are the key factors affecting the final denoising effect. Therefore, an enhanced ant colony optimisition wavelet (ACOW) algorithm was applied to find the global optimal threshold through the continuous derivative threshold function and the ant colony optimisation (ACO) algorithm. At the same time the efficiency of adaptive search calculation, was also significantly improved. The method of the ACOW algorithm was compared with the soft wavelet method, gradient‐based wavelet method and the genetic optimisation wavelet (GOW) method. Using these four methods to denoise four typical signals, different mean square errors (MSE), magnitude errors (ME) and time costs were obtained. Interestingly, the results show that the ACOW method can achieve the minimum MSE and has less time cost. It generates significantly smaller waveform distortion than the other three threshold estimation methods. In addition, the high efficiency and good quality of the output signals are beneficial to the diagnosis of local discharge signals in intelligent substations.

Minimum mean-square error (MMSE) approaches have been shown to achieve state-of-the-art performance on the task of speech enhancement. However, MMSE approaches lack the ability to accurately estimate non-stationary noise sources. In this paper, a long short-term memory fully convolutional network (LSTM-FCN) is utilized to accurately estimate a priori signal-to-noise ratio (SNR) since the speech enhancement performance of an MMSE approach improves with the accuracy of the used a priori SNR estimator. The proposed MMSE approach makes no assumptions about the characteristics of the noise or the speech. MMSE approaches that utilize the LSTM-FCN estimator are evaluated using the mean opinion score of the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI) measures of speech. The experimental investigation shows that the speech enhancement performance of an MMSE approach that utilizes LSTM-FCN estimator significantly increases.

Abstract Dynamic thermal management (DTM) is applied to address the thermal problem of high performance very‐large‐scale integrated chips. The false alarm rate (FAR) can be used to evaluate the impact of full‐chip thermal field reconstruction accuracy on DTM. A low FAR relies on the accurate reconstruction of the full thermal field, especially near the temperature triggering threshold of DTM. However, little attention is currently being paid to such temperature ranges. To reduce FAR, a new full‐chip thermal field reconstruction strategy is proposed. A low‐dimensional linear model is used to accurately represent the thermal fields. The dictionary learning technology is exploited to train the model and the minimum weighted mean square error evaluation method is incorporated to improve the reconstruction accuracy near the temperature triggering threshold. A temperature sensor placement algorithm using the heuristic algorithm to solve the NP‐hard problem is also proposed. The experimental results show that the proposed strategy can reconstruct the full thermal field with a more precise accuracy near the triggering threshold and achieve the lowest FAR compared to the state of the art.

Abstract Predicting the correct values of stock prices in fast fluctuating high‐frequency financial data is always a challenging task. A deep learning‐based model for live predictions of stock values is aimed to be developed here. The authors' have proposed two models for different applications. The first one is based on Fast Recurrent Neural Networks (Fast RNNs). This model is used for stock price predictions for the first time in this work. The second model is a hybrid deep learning model developed by utilising the best features of FastRNNs, Convolutional Neural Networks, and Bi‐Directional Long Short Term Memory models to predict abrupt changes in the stock prices of a company. The 1‐min time interval stock data of four companies for a period of one and three days is considered. Along with the lower Root Mean Squared Error (RMSE), the proposed models have low computational complexity as well, so that they can also be used for live predictions. The models' performance is measured by the RMSE along with computation time. The model outperforms Auto Regressive Integrated Moving Average, FBProphet, LSTM, and other proposed hybrid models on both RMSE and computation time for live predictions of stock values.

Abstract Inclination is one of the important parameters of a target’s surface, which is closely related to the waveform of light detection and ranging. When the target is tilted, the laser spot on the target’s surface changes greatly, and the influence of the target on the echo signal is more complex. Here, the formation process of a laser’s echo signal is explored. On that basis, the spot distribution model in the case of a large incident angle is optimised, and a comparison is made with the conventional method. A simulation system which can produce and display the signal affected by the inclined target is designed. In view of the difficulty in extracting the target's inclination from the echo signal, a method based on waveform analysis is proposed in this article. Then a calculation model is built to retrieve the inclination of the target from the echo signal by data fitting. The maximum measured error is only 0.948°, and the maximum root‐mean‐squared‐error is only 0.45°.

Abstract Active localization of a stationary target is one key issue in applications of bistatic sonar based on bistatic range (BR) measurement. However, the complex underwater environment makes achieving the synchronization between the transmitted station and the received station difficult. For solving the synchronization problem, this study relies on mobile bistatic sonar to acquire measurement information at multiple time steps. After that, the target location and clock bias are jointly estimated by solving a non‐linear least square problem. To this end, a recursive scheme is proposed to optimise the measurement cost and reduce the impact of the initial value to achieve accurate estimation by taking full advantage of the redundant measurement information. Orthogonal transformation is used to ensure computational efficiency and numerical reliability. The Cramer–Rao Lower Bound (CRLB) is also derived as a lower bound on the error in estimating the target position and clock bias. Simulation results show that the proposed method achieves the CRLB performance and optimises the measurement cost over the small error region under Gaussian noise.

Abstract Shared control of mobile robots integrates manual input with auxiliary autonomous controllers to improve the overall system performance. However, prior work that seeks to find the optimal shared control ratio needs an accurate human model, which is usually challenging to obtain. In this study, the authors develop an extended Twin Delayed Deep Deterministic Policy Gradient (DDPG) (TD3X)‐based shared control framework that learns to assist a human operator in teleoperating mobile robots optimally. The robot's states, shared control ratio in the previous time step, and human's control input is used as inputs to the reinforcement learning (RL) agent, which then outputs the optimal shared control ratio between human input and autonomous controllers without knowing the human model. Noisy softmax policies are developed to make the TD3X algorithm feasible under the constraint of a shared control ratio. Furthermore, to accelerate the training process and protect the robot, a navigation demonstration policy and a safety guard are developed. A neural network (NN) structure is developed to maintain the correlation of sensor readings among heterogeneous input data and improve the learning speed. In addition, an extended DAGGER (DAGGERX) human agent is developed for training the RL agent to reduce human workload. Robot simulations and experiments with humans in the loop are conducted. The results show that the DAGGERX human agent can simulate real human inputs in the worst‐case scenarios with a mean square error of 0.0039. Compared to the original TD3 agent, the TD3X‐based shared control system decreased the average collision number from 387.3 to 44.4 in a simplistic environment and 394.2 to 171.2 in a more complex environment. The maximum average return increased from 1043 to 1187 with a faster converge speed in the simplistic environment, while the performance is equally good in the complex environment because of the use of an advanced human agent. In the human subject tests, participants' average perceived workload was significantly lower in shared control than that in exclusively manual control (26.90 vs. 40.07, p = 0.013).

Abstract In order to solve the problem of low accuracy and efficiency of soil moisture content prediction in tea plantations and improve the level of soil water content prediction, a soil moisture content prediction model for tea plantations based on the support vector machine (SVM)‐optimised bald eagle search (BES) algorithm (BES‐SVM) is proposed. Soil data and environmental data of tea plantations were transmitted to the server using sensor nodes and weather station nodes. The prediction models of soil moisture content and natural environmental parameters such as soil electrical conductivity, soil temperature, air temperature, air humidity, light intensity, and rainfall were developed using the SVM model optimised by the bald eagle search algorithm, and the mean square error (MSE) and coefficient of determination (R2) were calculated to evaluate the model performance. Meanwhile, the performance of the BES‐SVM model is compared with the particle swarm algorithm optimisation SVM (PSO‐SVM) and genetic algorithm optimised SVM (GA‐SVM) models. Results show that the proposed model has a mean coefficient of determination of 95.65%, and the prediction performance is better than the PSO‐SVM and GA‐SVM model, indicating that the BES‐SVM model has good performance and is a feasible prediction method for soil water content prediction and guiding irrigation and fertilisation management in tea plantations.

This article addresses the problem of threshold region characterization of the maximum likelihood (ML) sinusoid frequency estimation. We first study on the exact analytical expression of the probability of detection for the ML mean square error for all the signal-to-noise ranges. Then, we propose a simple asymptotic expression to this ML detection probability and propose a model for the characterization of the variance of the frequency estimation. We also provide asymptotic closed-form expressions to the threshold and the no-information signal-to-noise ratio breakdowns for the ML frequency estimation, respectively. Outcomes of extensive numerical simulations verify our proposed theoretical derivations. [ABSTRACT FROM AUTHOR]

Abstract In this study, efficient channel characterisation and modelling based on deep learning algorithms are developed and presented in a line‐of‐sight (LOS) scenario. The learning and the validation processes are performed using measurements from only one environment, enabling robust model learning and prediction results. Then, the model efficiency is analysed and validated using measurements from different environments that are not included in the learning process. Finally, the channel characterisation is made with the predicted and measured ones. The designed model achieved a highly accurate channel frequency response prediction within different environments without any prior information. The model root‐mean‐square error achieves up to 2% compared with the latest proposed models in the literature. Hence, an efficient modelling tool is provided for the future wireless communication design in a complex confined environment in LOS scenarios.

Abstract The issue of aircraft tracking using passive sensors with poor measurement precision and a variable update rate is investigated. First, the need for a time‐dependent Markov chain transition matrix for the Interacting Multiple Model (IMM) is thoroughly evaluated using simulation and real data from an experimental multilateration system. Three different methods for computing the time‐dependent matrix are compared, showing their superiority for highly aperiodic sensors. Second, the Variable Structure Interacting Multiple Model (VS‐IMM) for passive sensors with poor geometry is proposed, in which the mode set update is based on local dilution of precision or distance root mean square error. The performance of the estimator is compared with the IMM using real data. The VS‐IMM results in the best performance for areas of both high and low measurement precision.

Abstract The false alarm of a fault detection module in aircraft will interfere with flight. If there is a time‐offset between the inertial navigation system (INS) and air data system (ADS), the probability of a false alarm (PFA) of the fault detection module will increase, which is ignored in existing approaches. To address the problem, a fault detection approach is proposed applied to INS/ADS with a time‐offset. An INS/ADS fault detection model based on kinematic equations is developed, and we combine an unscented Kalman filter (UKF) with Runge‐Kutta to deal with the non‐linear and discretisation problem. A time‐offset estimator is designed and the observability of time‐offset is analysed, showing that time‐offset is observable only in the manoeuvre phase. A fault detection architecture applied to INS/ADS with a time‐offset is designed, which solves the problem of the high PFA of INS/ADS fault detection under a time‐offset. Simulation results show that under multiple time‐offset scenarios, the root mean square error of the proposed approach can reach 0.0134 s minimally, and after time alignment, the PFA of the fault detection can be reduced to 1.8%.

Existing crowd counting approaches predominantly perform well on the training-testing protocol. However, due to large style discrepancies not only among images but also within a single image, they suffer from obvious performance degradation when applied to unseen domains. In this paper, we aim to design a generalizable crowd counting framework which is trained on a source domain but can generalize well on the other domains. To reach this, we propose a gated ensemble learning framework. Specifically, we first propose a diverse fine-grained style attention model to help learn discriminative content feature representations, allowing for exploiting diverse features to improve generalization. We then introduce a channel-level binary gating ensemble model, where diverse feature prior, input-dependent guidance and density grade classification constraint are implemented, to optimally select diverse content features to participate in the ensemble, taking advantage of their complementary while avoiding redundancy. Extensive experiments show that our gating ensemble approach achieves superior generalization performance among four public datasets. Codes are publicly available at https://github.com/wdzhao123/DCSL. [ABSTRACT FROM AUTHOR]

The optimal strategy estimation of random evolutionary Boolean games (REBGs) is discussed in this article. First, using the minimum mean square error criterion, the optimal strategy estimator is proposed for REBGs. Then, a matrix approach is developed to calculate the optimal strategy estimator by the aid of a semitensor product of matrices, which includes the prediction matrix, updating distribution, and strategy iterative formula. Finally, an elucidative example is included to show the obtained results are valid. [ABSTRACT FROM AUTHOR]

In this article, an optimized non-uniformly distributed turn (NUDT) coil antenna is proposed to mitigate the lateral misalignment problem in wireless power transfer (WPT) systems. The proposed $ {Q}$ -assisted ${H}$ -field forming technique is analytically implemented using a minimum mean square error (mmse)-based algorithm. This results in an optimized transmitter (Tx) coil with clustered turns achieving a widespread uniformity of the ${H}$ -field in the receiver (Rx) working region. The proposed optimal design shows a consistent mutual inductance and power transfer efficiency (PTE) irrespective of the Rx misalignment which is realized using the circuit parameters. The analytical and simulated results of the proposed Tx coil designed using the $ {Q}$ -assisted mmse-based ${H}$ -field forming technique is experimentally verified. The misalignment tolerance of the proposed antenna is improved over the existing design by 224%. The proposed design proves its advantage to realize misalignment tolerant wireless charging platforms for WPT applications. [ABSTRACT FROM AUTHOR]

The problem of the optimal allocation (in the expected mean square error sense) of a measurement budget for particle filtering is addressed. We propose three different optimal intermittent filters, whose optimality criteria depend on the information available at the time of decision making. For the first, the stochastic program filter, the measurement times are given by a policy that determines whether a measurement should be taken based on the measurements already acquired. The second, called the offline filter, determines all measurement times at once by solving a combinatorial optimization program before any measurement acquisition. For the third one, which we call online filter, each time a new measurement is received, the next measurement time is recomputed to take all the information that is then available into account. We prove that in terms of expected mean square error, the stochastic program filter outperforms the online filter, which itself outperforms the offline filter. However, these filters are generally intractable. For this reason, the filter estimate is approximated by a particle filter. Moreover, the mean square error is approximated using a Monte-Carlo approach, and different optimization algorithms are compared to approximately solve the combinatorial programs (a random trial algorithm, greedy forward and backward algorithms, a simulated annealing algorithm, and a genetic algorithm). Finally, the performance of the proposed methods is illustrated on two examples: a tumor motion model and a common benchmark for particle filtering. [ABSTRACT FROM AUTHOR]

Abstract Zebra crossing detection is a fundamental function of the electronic travel aid. It can locate the zebra crossing and estimate its direction to help the visually impaired to cross the road safely. In contrast to the conventional methods, a regression approach is adopted to detect zebra crossing based on convolutional neural networks. Specifically, a fixed‐size window slides across the image captured at the intersection. The image patches are sequentially fed to the logistic regression model to identify the zebra crossing. Then the image patch of zebra crossing is fed to the regression model to predict the direction. The parameters of models are optimized by the backpropagation algorithm before predictions. Compared with existing methods, the proposed method can improve the precision‐recall performance of the zebra crossing identification and reduce the root mean square error of predicted directions.

Abstract In recent years, approximate circuit design targeting the error‐tolerant applications has gained significance. In this study, the authors propose a metric that ranks a stand‐alone approximate adder in terms of power savings obtained for a given mean error distance/mean square error (MSE). The authors demonstrate that this ranking of approximate adders can be used in applications that contain adder trees and registers. In applications that also have accurate multipliers interspersed with adders, the authors find that certain types of approximations in the adders result in more power‐efficient implementations of multipliers. Besides power savings, the other metrics of interest are noise floor and mean error in filtering applications and the compression achieved for a given peak signal‐to‐noise ratio (PSNR) in image compression applications. The authors also show that for the same overall MSE, there is a trade‐off between noise floor and mean error. This makes it possible to classify these adders based on whether they result in an increased noise floor or a mean error for the same overall MSE. Furthermore, the authors discuss the effect of using an approximate discrete cosine transform block to meet the reduced PSNR requirements, on the overall compression levels and the trade‐offs involved in the process.

Abstract In wireless localization systems, the performance enhancement of location estimation is an important goal. In recent years, different positioning systems using an ultra‐wide band (UWB) technology have been created, and always an evaluation metric to test such systems is needed for ensuring a suitable system for a specified application. Also, a non‐line‐of‐sight (NLOS) identification and mitigation method is needed usually when utilizing the UWB technology. The mean‐square‐error (MSE) and geometric dilution of precision (GDOP) evaluation metrics are widely implemented as standard for choosing a perfect system. In a harsh environment, a novel algorithm of indoor positioning (IP) system is presented using the UWB technology without implementing any NLOS identification and mitigation technique and the localization accuracy is evaluated online. The UWB is used to communicate a mobile station (M) with n anchor nodes distributed randomly and clustered by utilizing a combination method to create different groups, and then a conventional linearized least square (LLS) method is utilized by each group for locating M. A weighted GDOP metric is implemented online to assess the positioning accuracy of each group. Then, the group having the lowest positioning error among other groups is selected to relocate M using a proposed LLS, named modified LLS, for the selected group to enhance the positioning accuracy. The created system outperforms different IP systems in the market for the last decade in terms of time, complexity, and accuracy. The created IP system has a positioning error around 25 cm2 of MSE in a hard environment, which is less than those of different IP systems created recently in the market.

Dynamic thermal management (DTM) is applied to address the thermal problem of high performance very‐large‐scale integrated chips. The false alarm rate (FAR) can be used to evaluate the impact of full‐chip thermal field reconstruction accuracy on DTM. A low FAR relies on the accurate reconstruction of the full thermal field, especially near the temperature triggering threshold of DTM. However, little attention is currently being paid to such temperature ranges. To reduce FAR, a new full‐chip thermal field reconstruction strategy is proposed. A low‐dimensional linear model is used to accurately represent the thermal fields. The dictionary learning technology is exploited to train the model and the minimum weighted mean square error evaluation method is incorporated to improve the reconstruction accuracy near the temperature triggering threshold. A temperature sensor placement algorithm using the heuristic algorithm to solve the NP‐hard problem is also proposed. The experimental results show that the proposed strategy can reconstruct the full thermal field with a more precise accuracy near the triggering threshold and achieve the lowest FAR compared to the state of the art. [ABSTRACT FROM AUTHOR]

In this article, a planar distributed multicoil transmitter antenna is presented for wireless power transfer (WPT) applications. The proposed antenna is optimized to mitigate lateral misalignment problems and configured to operate in two different modes. The antenna switched to the normal mode excites a chief coil to form a concentrated $H$ -field within the perfectly aligned receiver area. Encircling the chief coil, a set of booster coils is incorporated in the design which is switched on in enhanced mode to stretch the $H$ -field uniformity whenever the receiver is misaligned. As a result, the proposed design evolves into a switchable planar distributed multicoil (SPDMC) antenna. Using a field forming technique based on a minimum mean square error algorithm, dimensional parameters of the proposed antenna are optimized. The proposed antenna improves stability in the power transfer efficiency which is verified by measuring the $S21$ parameter. The $S21$ stability achieved in the Rx working area is 98 $\times98$ mm2 which corresponds to a 255% improvement over an existing design of the same dimension in the literature. The proposed SPDMC antenna is proven to be highly insensitive to lateral misalignment and a good antenna candidate for various short- and medium-range WPT applications. [ABSTRACT FROM AUTHOR]

Motivated by the recent success of reinforcement learning algorithms, this paper studies a class of biased stochastic approximation (SA) procedures under a mild “ergodicity-like” assumption on the random noise sequence. Building on a multistep Lyapunov function that looks ahead to several future updates to accommodate the stochastic perturbations (thus gaining control over the bias), we prove a general result on the convergence of the SA iterates, and use it to derive non-asymptotic bounds on the mean-square error in the case of constant stepsizes. This novel viewpoint renders finite-time analysis of biased SA algorithms under a family of stochastic perturbations possible. For direct comparison with prior work, we demonstrate these bounds by applying them to TD-learning with linear function approximation, under the Markov chain observation model. The resultant finite-time error bound for TD-learning is the first of its kind, in the sense that it holds i) for the unmodified versions (i.e., without any modification to the updates) using even nonlinear approximators; as well as for Markov chains ii) under sublinear mixing conditions and iii) starting from any initial distribution, at least one of which has to be violated for existing results to be applicable. [ABSTRACT FROM AUTHOR]

Proportionate Maximum Versoria Criterion (P-MVC) based adaptive algorithms for unknown sparse system identification problem are proposed in this brief. The conventional proportionate type algorithms used for sparse system identification can work well only under Gaussian assumption due to the dependency on the least mean square error. However, in many real cases, the algorithms have to be also robust in impulsive noise environments. The Maximum Versoria Criteria based adaptive algorithms were found to have good robustness against impulsive noise while the proportionate term in the adaptive algorithm exploits the sparse nature to improve the convergence speed. Hence, to simultaneously have robustness under impulsive environment and improved convergence speed, the P-MVC algorithm and an improved tracking P-MVC version are proposed. The performance analysis indicates that the Excess Mean Square Error (EMSE) is the same as that of MVC adaptive algorithm. Furthermore, simulations in the context of sparse system identification scenario reveal that the proposed algorithms have both robustness and improved performance in impulsive noise environment. [ABSTRACT FROM AUTHOR]

Cell-free massive MIMO is one of the key technologies for future wireless communications, in which users are simultaneously and jointly served by all access points (APs). In this paper, we investigate the minimum mean square error (MMSE) estimation of effective channel coefficients in cell-free massive MIMO systems with massive connectivity. To facilitate the theoretical analysis, only single measurement vector (SMV) based MMSE estimation is considered in this paper, i.e., the MMSE estimation is performed based on the received pilot signals at each AP separately. Inspired by the decoupling principle of replica symmetric postulated MMSE estimation of sparse signal vectors with independent and identically distributed (i.i.d.) non-zero components, we develop the corresponding decoupling principle for the SMV based MMSE estimation of sparse signal vectors with independent and non-identically distributed (i.n.i.d.) non-zero components, which plays a key role in the theoretical analysis of SMV based MMSE estimation of the effective channel coefficients in cell-free massive MIMO systems with massive connectivity. Subsequently, based on the obtained decoupling principle of MMSE estimation, likelihood ratio test and the optimal fusion rule, we perform user activity detection based on the received pilot signals at only one AP, or cooperation among the entire set of APs for centralized or distributed detection. Via theoretical analysis, we show that the error probabilities of both centralized and distributed detection tend to zero when the number of APs tends to infinity while the asymptotic ratio between the number of users and pilots is kept constant. We also investigate the asymptotic behavior of oracle estimation in cell-free massive MIMO systems with massive connectivity via random matrix theory. Moreover, in order to demonstrate the potential performance loss of SMV based MMSE estimation, which does not employ the correlation between the received pilot signals at different APs, the multiple measurement vector (MMV) based MMSE estimation, i.e., joint MMSE estimation with pilot signals from all APs, is analyzed via numerical results. Numerical analysis shows that the theoretical analyze with our decoupling principle for the SMV based MMSE estimation of sparse signal vectors with i.n.i.d. non-zero components matches well with the numerical results. [ABSTRACT FROM AUTHOR]

Phase noise problem in oscillators can degrade the performance of high‐speed communication systems. The author analysed the impact of phase noise problem on multi‐input–multi‐output (MIMO) systems under common and independent oscillators. The estimation of system parameters (i.e. phase noise and channel gains) is a challenging task. In this study, a data‐aided least square estimator based compressed sensing Kalman filter (KF)‐based compressed sensing (CS) scheme is proposed for tracking phase parameters. The signal model and estimation problem for the system are mathematically derived. Also, Bayesian Cramér–Rao lower bound (BCRLB) scheme is also derived. For joint estimation, the mean square error (MSE) and bit error rate (BER) performances of the BCRLBs and proposed scheme are compared. Results demonstrate that the proposed KF‐based CS scheme gives low BER values and better performance compared to other estimation schemes. The utilisation of the proposed scheme helps in reducing the MSE of the MIMO system. Finally, the proposed scheme enhances the estimation of phase noise parameters for the MIMO system. [ABSTRACT FROM AUTHOR]

Adaptive pulse compression (APC) has been proposed based on the minimum mean square error (MMSE) criterion to effectively suppress range sidelobes of strong targets and retrieve all targets in multi-target scenarios. However, MMSE-based algorithms suffer from deteriorated performance in the presence of targets with range-straddling due to modelling mismatch. This degradation can be partially compensated by modified MMSE-based algorithms. To further suppress sidelobes when range straddling occurs, we propose a modified APC algorithm robust to targets with range-straddling, namely range-straddling-robust APC (RSR-APC). We first establish the signal model for targets with range-straddling and then derive the expressions of the MMSE filter based on matched filter outputs and the range-straddling offsets based on an improved Rife algorithm. Simulation results show that the proposed algorithm can suppress sidelobes of targets with range-straddling and improve estimation accuracy of target positions in different scenarios. [ABSTRACT FROM AUTHOR]

In this study, efficient channel characterisation and modelling based on deep learning algorithms are developed and presented in a line‐of‐sight (LOS) scenario. The learning and the validation processes are performed using measurements from only one environment, enabling robust model learning and prediction results. Then, the model efficiency is analysed and validated using measurements from different environments that are not included in the learning process. Finally, the channel characterisation is made with the predicted and measured ones. The designed model achieved a highly accurate channel frequency response prediction within different environments without any prior information. The model root‐mean‐square error achieves up to 2% compared with the latest proposed models in the literature. Hence, an efficient modelling tool is provided for the future wireless communication design in a complex confined environment in LOS scenarios. [ABSTRACT FROM AUTHOR]

Low complexity approximate message passing (AMP) orthogonal frequency division multiplexing combined with index modulation (OFDM-IM) detection algorithms are proposed, which exploit the sparse structure of the frequency domain (FD) OFDM-IM symbols. To circumvent the high root mean square error (RMSE) in the conventional AMP algorithm, a minimum mean square error (MMSE) denoiser is proposed based on the classic Bayesian approach and on the state evolution of AMP. Our simulation results demonstrate that it is capable of improving both the RMSE as well as the convergence rate. However, in practice, the channel's diagonal FD matrix may be a non-Gaussian sensing matrix, hence a damping strategy is conceived. In conclusion, the proposed MMSE denoiser based damping-assisted AMP-aided detector strikes a compelling bit error ratio vs. complexity trade-off. [ABSTRACT FROM AUTHOR]

Traditional deep learning assumes that the probability distributions of the test set and the training set are exactly the same. When the test samples and the training samples are from different working conditions, the distribution gap between different conditions might dramatically lower the accuracy. To address this problem, domain adaptation (DA) via joint using training and test sets is adopted to increase the final accuracy. The disadvantage of DA is that it requires test samples to participate in the training phase. However, sometimes it is impossible to collect the test samples beforehand. To solve this problem, this article proposes a conditional adversarial domain generalization with a single discriminator, which aims to extract domain-invariant features from different working conditions and generalize the features to unseen working conditions. To realize conditional adversarial training, a new conditional adversarial strategy that the feature extractor can allow the discriminator to distinguish the fault classes, but cannot distinguish the domains, is designed to better confuse the discriminator and generalize the features. Meanwhile, three loss functions, including mean square error (MSE), cross-entropy coupled with entropy, and cross-entropy combined with traditional adversarial methods, are proposed to achieve the new conditional adversarial strategy. The effectiveness of the conditional adversarial strategy is verified by training conditional generative adversarial networks (CGANs) on two image data sets, and the performance of the proposed domain generalization method for bearing fault diagnosis is also verified by bearing data sets. Compared with traditional conditional adversarial domain generalization, the proposed method can save computing resource consumption and improve accuracy that does not appear in the training phase. [ABSTRACT FROM AUTHOR]

The quaternion Gaussian kernel is usually used when solving quaternion nonlinear problems. However, how to choose a proper value of kernel width is still an important issue. In most previous studies, the kernel width was set manually or estimated in advance by using Silvermans rule based on the sample distribution, which can easily degrade the performance of algorithms. In this brief, the variable kernel width quaternion kernel least mean squares algorithm (VKW-QKLMS) aims to develop an online technique for optimizing the kernel width of the quaternion kernel LMS (QKLMS) algorithm, in which the filter weight and the kernel width are alternately updated by using stochastic gradient algorithm. Simulation results show that the performance of the VKW-QKLMS algorithm does not depend on the selection of initial value of the kernel width. The VKW-QKLMS algorithm can usually achieve better performances than other competing algorithms. Only when the kernel width in the QKLMS algorithm is empirically selected as a proper value, the QKLMS algorithm can achieve much the same performance as that of the VKW-QKLMS algorithm. [ABSTRACT FROM AUTHOR]

UAV communications based on antenna array entail a beam tracking technology for reliable link acquisition. Unlike conventional cellular communication, beam tracking in UAV communication addresses new issues such as mobility and abrupt channel disconnection from UAV's perturbation. To deal with these issues, we propose a beam tracking scheme based on extended Kalman filter (EKF) using a monopulse signal, which can provide (1) higher robustness by offering a reliable link in the estimated spatial direction and (2) lower complexity compared with the existing conventional beam tracking schemes. We point out the limitations of using a beamformed signal as a measurement model for a Kalman filter (KF) based scheme and instead utilize the monopulse signal as a more plausible model. For the performance evaluation, we derive the upper bound of the mean square error for spatial angle estimation of UAV and confirm that the proposed scheme is stable with a certain bounded error. We also show from various simulations that the proposed scheme can efficiently track UAV and detect beam disconnection every time frame using a beamformed signal. [ABSTRACT FROM AUTHOR]

Bearings-only filtering (BOF) is important in many practical applications. It is also a challenging nonlinear filtering problem due to limited information contained in highly nonlinear measurements. Researchers have proposed various nonlinear filters for BOF problems. We propose a new approach to nonlinear filtering using pseudomeasurement construction based uncorrelated conversion for BOF. This approach can more effectively utilize the measurement information than the original linear minimum mean square error estimator. The constructed pseudomeasurement is uncorrelated with the original measurement. Based on the recently proposed uncorrelated conversion based filter (UCF), we propose a UCF using pseudorange construction (UCF-PRC) for the BOF problem. An improved filter, the optimized UCF-PRC, is also proposed to minimize the mean square error. The effectiveness of the new filters is demonstrated by simulation results. Specifically, compared with the particle filter, the UCF-PRC has better estimation accuracy with nearly the same computational cost. [ABSTRACT FROM AUTHOR]

The Maximum Versoria Criterion (MVC) based adaptive algorithm has recently gained the attention of researchers because of its robustness against impulsive interference and reduced complexity. In this brief, the steady-state Excess Mean Square Error (EMSE) analysis of the standard MVC algorithm is done using the energy conservation relation. For the Gaussian noise case, quadratic equation that can provide the exact value of theoretical steady state EMSE is obtained without any assumptions other than the conventional ones and the Price’s theorem. For the non-Gaussian case, an approximate solution using Taylor’s expansion is derived. Experimental results validate the obtained theoretical findings. [ABSTRACT FROM AUTHOR]

In some system identification problems, the weight vector to be estimated can be represented by a tensor product of two low dimensional component vectors. This feature is useful for the design of adaptive filters with fast convergence rate and/or low computational complexity. The tensor LMS algorithm has been proposed under this scenario and its recent extensions find applications in several contexts. This brief analyzes the steady-state mean-square error (MSE) performance of the tensor LMS algorithm, which can provide performance prediction and design guideline for engineers. Since the updates of the weight-vectors of the component adaptive filters (CAFs) are coupled, it is challenging to perform steady-state MSE analysis. To address this problem, this brief first establishes the variance relation in steady state for the CAFs and then decouples their excess MSEs by solving a system of linear equations. Finally, the steady-state MSE of the reproduced adaptive filter is composed of the excess MSEs of the CAFs and the variance of the system noise. Simulations are performed to verify the accuracy of the theoretical findings. [ABSTRACT FROM AUTHOR]

In the current scenario of the deregulated Indian electricity market where the power demand and its availability vary remarkably, the factors playing a significant role in demand variations are often associated with the impact of unprecedented weather conditions and technological evolutions. To maintain grid security and discipline that yield to financial implications, there lies a great need to formulate an equilibrium between electricity supply and demand. Devising a model to anticipate the variations which are highly adaptive to such changes is the need of the hour. For this purpose, an algorithm has been proposed in this study, which is best suited for the day-ahead load forecast. The variables selected for the forecast are one-day-lagged demand statistics, seasonality trend, weather, and calendar variables. The proposed algorithm outperforms the existing benchmark model, which is evaluated through various statistical performance metrics such as mean absolute percentage error, mean absolute error, root-mean-square error, and coefficient of variation. The performance of the proposed methodology at the seasonal level is analysed and validated through uncertainty analysis with one post-sample year for the state of Delhi, India. This model presents its compatibility to prevalent grid regulations as well as shall hold good in the weather and demand variations possibly expected in the future.

The performance of neural networks with quadratic cost function (MSE cost function) is analyzed in terms of the adjustment rate of weights and its performance in multi-magnitude data processing using a qualitative mathematical method based on mean squared error. However, neural networks using quadratic cost functions exhibit low-weight updating rates and variations in performances in multi-magnitude data processing. This paper investigates the performance of neural networks using a quadratic relative error cost function (REMSE cost function). Two-node-to-one-node models are built to investigate the performance of the REMSE and MSE cost functions in adjustment rate of weights and multi-magnitude data processing. A three-layer neural network is employed to compare the training and prediction performances of the REMSE cost function and MSE cost function. Three LSTM networks are used to evaluate the differences between REMSE, MSE, and Logcosh in actual applications by learning stress and strain of soil. The results indicate that the REMSE cost function can notably accelerate the adjustment rate of weights and improve the performance of the neural network in small magnitude data regression. The applications of the REMSE cost function are also discussed.

Phase noise problem in oscillators can degrade the performance of high‐speed communication systems. The author analysed the impact of phase noise problem on multi‐input–multi‐output (MIMO) systems under common and independent oscillators. The estimation of system parameters (i.e. phase noise and channel gains) is a challenging task. In this study, a data‐aided least square estimator based compressed sensing Kalman filter (KF)‐based compressed sensing (CS) scheme is proposed for tracking phase parameters. The signal model and estimation problem for the system are mathematically derived. Also, Bayesian Cramér–Rao lower bound (BCRLB) scheme is also derived. For joint estimation, the mean square error (MSE) and bit error rate (BER) performances of the BCRLBs and proposed scheme are compared. Results demonstrate that the proposed KF‐based CS scheme gives low BER values and better performance compared to other estimation schemes. The utilisation of the proposed scheme helps in reducing the MSE of the MIMO system. Finally, the proposed scheme enhances the estimation of phase noise parameters for the MIMO system. [ABSTRACT FROM AUTHOR]

In this study, secure communications in a two‐way relaying wiretap system with a silent eavesdropper are studied. Under the assumption that the channel state information relating to the eavesdropper is unknown at legitimate nodes, the precoding vectors at the two source nodes are jointly designed to decrease the wiretap capacity at the eavesdropper. The derived relationship between the two precoding vectors is partially similar in nature to the linear minimum mean squared error (MMSE) equaliser. An iterative process is proposed to jointly achieve two precoding vectors, and its convergence is proved. Simulation results are presented to demonstrate that the proposed MMSE‐based precoding algorithm can be used for source nodes with any number of antennas, and outperforms its conventional zero‐forcing‐ and matched‐filter‐based counterparts. Signal leakage from the relay node to the eavesdropper is nearly completely suppressed by the proposed precoding design. [ABSTRACT FROM AUTHOR]

The extensive use of Internet of things (IoT) devices demonstrates an increasing demand for secure frameworks. Position altering attacks are amongst the most severe vulnerabilities in two‐way time of arrival distance measurement technique, which is the most common in distance measurements by IoT devices. The overshadowing attack as a distance enlargement attack replays the authentic signal with a certain amount of delay and sensible power amplification. Therefore, the attack detection is difficult and requires a precise model to estimate its behaviour. The contribution of this study is a location estimation method based on the overshadowing attack probabilistic model that estimates the attack outcome delay and corrects the erroneous measurements. The overshadow delay, which is the delay between the authentic signal and the attacked one, plays a key role in this process. The method has been evaluated by three attack scenarios and compared to the conventional maximum likelihood (CML) and mixture secure location of things (M‐SLOT) methods. The results of the proposed method demonstrate root mean squared error (RMSE) of 0.48 in position estimation. With the same dataset, RMSEs of M‐SLOT and CML methods are 4.69 and 9.84, respectively, which confirm the better performance of the proposed method. [ABSTRACT FROM AUTHOR]

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]