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Contemporary communications systems use large arrays in order to exploit the spatial domain requiring multiple radio-frequency (RF) chains leading to prohibitive cost and power. Spatial degrees of freedom are also achieved by utilizing the polarization states of an electromagnetic wave. To this end, we propose a polarization-state modulation system based on carrier wave reflections from a reconfigurable intelligent surface (RIS), where a sequence of data bits is mapped to polarization states. We consider a system with an upper millimeter-wave or low-THz RF source so that the channel model is line-of-sight-dominant. The data symbol constellation consists of sixteen quaternion-valued symbols, of which two correspond to linear and the remaining twelve represent elliptical polarizations. Our numerical experiments indicate that the RIS is capable of directing signal power toward the location of the receiver. The bit error rate performance of our rudimentary demodulator, although worse than OFDM-BPSK, is improved using a quaternion neural network.

High-breakdown-point estimators of multivariate location and shape matrices, such as the MM-SHR with smoothed hard rejection and the Rocke S-estimator, are generally designed to have high efficiency for Gaussian data. However, many phenomena are non-Gaussian, and these estimators can therefore have poor efficiency. This article proposes a new tunable S-estimator, termed the S-q-estimator, for the general class of symmetric elliptical distributions, a class containing many common families such as the multivariate Gaussian, t-, Cauchy, Laplace, hyperbolic, and normal inverse Gaussian distributions. Across this class, the S-q-estimator is shown to generally provide higher maximum efficiency than other leading high-breakdown estimators while maintaining the maximum breakdown point. Furthermore, the S-q-estimator is demonstrated to be distributionally robust, and its robustness to outliers is demonstrated to be on par with these leading estimators while also being more stable with respect to initial conditions. From a practical viewpoint, these properties make the S-q-estimator broadly applicable for practitioners. These advantages are demonstrated with an example application-the minimum-variance optimal allocation of financial portfolio investments.

Many applications in signal processing require the estimation of mean and covariance matrices of multivariate complex-valued data. Often, the data are non-Gaussian and are corrupted by outliers or impulsive noise. To mitigate this, robust estimators are employed. However, existing robust estimation techniques employed in signal processing, such as M-estimators, provide limited robustness in the multivariate case. For this reason, this paper introduces the signal processing community to the highly robust class of multivariate estimators called multivariate S-estimators. The paper extends multivariate S-estimation theory to the complex-valued domain. The theoretical performances of S-estimators are explored and compared with M-estimators through the practical lens of the minimum variance distortionless response (MVDR) beamformer, and the empirical finite-sample performances of the estimators are explored through the practical lens of direction-of-arrival (DOA) estimation using the multiple signal classification (MUSIC) algorithm.

Smart grids are typically modelled as cyber–physical power systems, with limited consideration given to the social aspects. Specifically, traditional power system studies tend to overlook the behaviour of stakeholders, such as end‐users. However, the impact of end‐users and their behaviour on power system operation and response to disturbances is significant, particularly with respect to demand response and distributed energy resources. Therefore, it is essential to plan and operate smart grids by taking into account both the technical and social aspects, given the crucial role of active and passive end‐users, as well as the intermittency of renewable energy sources. In order to optimize system efficiency, reliability, and resilience, it is important to consider the level of cooperation, flexibility, and other social features of various stakeholders, including consumers, prosumers, and microgrids. This article aims to address the gaps and challenges associated with modelling social behaviour in power systems, as well as the human‐centred approach for future development and validation of socio‐technical power system models. As the cyber–physical–social system of energy emerges as an important topic, it is imperative to adopt a human‐centred approach in this domain. Considering the significance of computational social science for power system applications, this article proposes a list of research topics that must be addressed to improve the reliability and resilience of power systems in terms of both operation and planning. Solving these problems could have far‐reaching implications for power systems, energy markets, community usage, and energy strategies.

Recent research in ultra-reliable and low latency communications (URLLC) for future wireless systems has spurred interest in short block-length codes. In this context, we analyze arbitrary harmonic bandwidth (BW) expansions for a class of high-dimension constant curvature curve codes for analog error correction of independent continuous-alphabet uniform sources. In particular, we employ the circumradius function from knot theory to prescribe insulating tubes about the centerline of constant curvature curves. We then use tube packing density within a hypersphere to optimize the curve parameters. The resulting constant curvature curve tube (C3T) codes possess the smallest possible latency, i.e., block-length is unity under BW expansion mapping. Further, the codes perform within 5 dB signal-to-distortion ratio of the optimal performance theoretically achievable at a signal-to-noise ratio (SNR) < -5 dB for BW expansion factor n ≤ 10. Furthermore, we propose a neural-network-based method to decode C3T codes. We show that, at low SNR, the neural-network-based C3T decoder outperforms the maximum likelihood and minimum mean-squared error decoders for all n. The best possible digital codes require two to three orders of magnitude higher latency compared to C3T codes, thereby demonstrating the latter’s utility for URLLC.

This paper proposes a new robust method for accurately and reliably estimating remote bus frequencies in a power system. Two different measurement sources for the remote bus frequency are considered, that is, the ideal Frequency Divider (FD) and the Synchronous Reference Frame Phase Locked Loops (SRF-PLLs). Each measurement signal encounters different uncertainties and data quality issues. In this paper, both data sources are employed and fused together to better estimate the remote bus frequencies. To this end, the model structure of the power system is selected and an Unscented Kalman Filter (UKF) is utilized together with a fusion covariance intersection method to enhance the accuracy of the estimated bus frequency. Since the fusion estimation method fails in case of quality issues on both channels, a robust Generalized Maximum-likelihood UKF (GM-UKF) using a novel outlier detection criteria is developed. The impact of the resulting robust fusion filter on the estimation of the remote bus frequencies and on the performance of WAPSS, which makes use of the estimated frequencies as the feedback signal, is examined via simulations. The results demonstrate the excellent performance and reliability of the proposed method in dealing with noise filtering and outlier suppression while ensuring a high statistical efficiency.

Recorded Electroencephalogram (EEG) signals are typically affected by interference and artifacts, which can both impact eye reading and computer analysis of the data. Artifacts are induced by physiological (noncerebral) activities of the patient, such as muscular activities of the eyes, or the heart, or the body, while interference may be of external or internal origin. External interference can be induced by electrical machines if the latter are in the same room as the patients, while internal interference can be caused by abnormal breathing, or body movement, or electrode malfunction, or headset movements. Interference may cause severe distortion of EEG signals, resulting in loss of some segments of brain signals, while artifacts are additive signals to brain signals. Therefore, in order to analyze the brain activity signals of a patient, we need to identify and eliminate interference and isolate artifacts. In this paper, we analyze the EEG signals that were recorded using a headset with fourteen channels placed on the heads of comatose patients at the National Institute of Neurology in Tunis, Tunisia. We identify the interference using a robust statistical method known as projection statistics and we separate the brain signals from the artifacts cited above by applying an independent component analysis method. Finally, we show the multifractal behavior of the EEG signals without interference by applying the wavelet leader method and analyze their properties using the singularity spectrum.

A prerequisite to dynamic state estimation of a stochastic nonlinear dynamic model of a power system is its observability analysis. However, due to the model nonlinearity, the traditional methods either suffer from a poor estimation accuracy if a linear approximation is performed or yield an extremely complicated procedure if the Lie-derivative method is applied to a large-scale power system. To address these weaknesses, we propose a new data-driven Koopman-based observability method by revealing the link that exists between the Koopman operator and the Lie-derivative in the Koopman canonical coordinates. This enables the proposed data-driven method not only to be fully derivative-free, which alleviates its implementation complexity but also overcomes the model nonlinearity and inaccuracy of the system. Furthermore, as an important byproduct, the proposed observability analysis scheme provides a valuable guide for the selection of the observables of the Koopman operator, which is a major difficulty for the application of this operator. Finally, we demonstrate the excellent performance of the proposed method on several IEEE standard test systems.

As a model reduction tool, coherency identification has been extensively investigated by power researchers using various model-driven and data-driven approaches. Model-driven approaches typically lose their accuracy due to linear assumptions and parameter uncertainties, while data-driven approaches inevitably suffer from bad data issues. To overcome these weaknesses, we propose a data-driven cumulant tensor-based approach that can identify coherent generators and detect anomalies simultaneously. More specifically, it converts the angular velocities of generators into a fourth-order cumulant tensor that can be decomposed to reflect the coherent generators. Also, using co-kurtosis in the cumulant tensor, anomalies can be detected as well. The simulations reveal its excellent performance.