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As the global demand for data has continued to rise exponentially, some have begun turning to the idea of semantic communication as a means of efficiently meeting this demand. Pushing beyond the boundaries of conventional communication systems, semantic communication focuses on the accurate recovery of the meaning conveyed from source to receiver, as opposed to the accurate recovery of transmitted symbols. In this survey, we aim to provide a comprehensive view of the history and current state of semantic communication and the techniques for engineering this higher level of communication. A survey of the current literature reveals four broad approaches to engineering semantic communication. We term the earliest of these approaches classical semantic information, which seeks to extend information-theoretic results to include semantic information. A second approach makes use of knowledge graphs to achieve semantic communication, and a third utilizes the power of modern deep learning techniques to facilitate this communication. The fourth approach focuses on the significance of information, rather than its meaning, to achieve efficient, goal-oriented communication. We discuss each of these four approaches and their corresponding studies in detail, and provide some challenges and opportunities that pertain to each approach. Finally, we introduce a novel approach to semantic communication, which we term context-based semantic communication. Inspired by the way in which humans naturally communicate with one another, this context-based approach provides a general, optimization-based design framework for semantic communication systems. Together, this survey provides a useful guide for the design and implementation of semantic communication systems.

Distribution system planning and operation has seen many structural changes due to the increased participation of consumers in the energy market and the adaptation of new technologies such as distributed energy resources (DERs), electric vehicles (EV) and local energy storage systems (ESSs). Despite the convenience of such technologies and the gradual drop in their prices, new technical challenges (e.g., excessive power losses) have emerged at the system level. Over the past few decades, power loss minimization in distribution systems has gained popularity and the need for loss sensitivity analysis (LSA) frameworks has become a necessity for its successful implementation. Existing work on LSA mostly focuses on system planning aspects through DER optimal placement and sizing. However, enabling LSA-based system operational applications is a vital step toward the successful transition to modern distribution systems (MDSs). Therefore, this paper presents a comprehensive overview on the state of the art in LSA for MDSs. First, the theoretical formulations of existing LSA methods are summarized. Then, the applications of LSA in distribution systems are highlighted. Finally, based on the analysis of literature, open research gaps and future research pathways are discussed.

Improving the resilience of the power distribution networks is becoming a top priority for the utility companies. Robustness is a key part of resilience, and is often studied through failure-based analysis. Furthermore, with the increasingly dynamic nature of the grid, voltage fluctuation is an important factor to consider while assessing system robustness. Very few metrics exist for capturing robustness to voltage fluctuations, primarily due to their complex computation process. While several failure-based robustness metrics have been proposed in the literature, there is no strict consensus regarding the applicability of these metrics. Therefore, this paper addresses two key gaps in robustness analysis of power distribution networks. First, this paper presents a systematic study of different failure-based robustness metrics by comparing their similarity and dissimilarity in ranking critical nodes of the power distribution network. Secondly, the efficacy of these metrics in characterizing voltage fluctuations is evaluated by comparing their ranking to that of voltage influencing scores. From experimental results, it is shown that hybrid failure-based metrics can quantify voltage fluctuations to a reasonable extent. We highlight the major shortcomings of current metric formulations and discuss possible future research directions related to robustness characteristics and analysis.

Recognition of dichotomous emotional states such as happy and sad play important roles in many aspects of human life. Existing literature has recorded diverse attempts in extracting physiological and non-physiological traits to record these emotional states. Selection of the right instrumental approach for measuring these traits plays a critical role in emotion recognition. Moreover, various stimuli have been used to induce emotions. Therefore, there is a current need to perform a comprehensive overview of instrumental approaches and their outcomes for the new generation of researchers. In this direction, this study surveys the instrumental approaches in discriminating happy and sad emotional states that are elicited using audio-visual stimuli. A comprehensive literature review is performed using PubMed, Scopus, and ACM digital library repositories. The reviewed articles are classified with respect to the i) stimulation modality, ii) acquisition protocol, iii) instrumentation approaches, iv) feature extraction, and v) classification methods. In total, 39 research articles were published on the selected topic of instrumental approaches in differentiating dichotomous emotional states using audio-visual stimuli between January 2011 and April 2021. The majority of the papers used physiological traits, namely electrocardiogram, electrodermal activity, heart rate variability, photoplethysmogram, and electroencephalogram based instrumental approaches for recognizing the emotional states. The results show that only a few articles have focused on audio-visual stimuli for the elicitation of happy and sad emotional states. This review is expected to seed research in the areas of standardization of protocols, enhancing the diagnostic relevance of these instruments, and extraction of more reliable biomarkers.

The power distribution grid is typically unobservable due to a lack of measurements. While deploying more sensors can alleviate this issue, it also presents new challenges related to data aggregation and the underlying communication infrastructure. Therefore, developing state estimation methods that enhance situational awareness at the grid edge with compressed measurements is critical. For this purpose, a suite of sparsity-based approaches that exploit the correlation among states/measurements in spatial as well as temporal domains have been proposed recently. This article presents a systematic comparison and evaluation of these approaches. Specifically, the performance and complexity of spatial methods (1-D compressive sensing and matrix completion) and spatio-temporal methods (2-D compressive sensing and tensor completion) are compared using the IEEE 37 and IEEE 123 bus test systems. Additionally, new robust formulations of these sparsity-based methods are derived and shown to be robust to bad data and network parameter uncertainties. Among the sparsity-based approaches, compressive sensing methods tend to outperform matrix completion and tensor completion methods in terms of error performance.

Impulsive noise (IN) and Doppler shift can significantly degrade the performance of orthogonal frequency-division multiplexing (OFDM)-based underwater acoustic (UWA) communication systems. In this paper, we propose a receiver structure that deals efficiently with both these channel impairments in a coded OFDM-based UWA system. First, an analog nonlinear preprocessor (ANP) is proposed to efficiently detect and mitigate IN in an analog domain. The proposed ANP exhibits intermittent nonlinearity when there is impulsivity. Next, the impact of IN on a two-step Doppler shift compensation approach is quantified. Specifically, the ability of the ANP to improve the robustness of Doppler shift compensation in the presence of IN is highlighted. The performance improvement of the proposed receiver is due to the fact that unlike the other nonlinear methods, the ANP is implemented in the analog domain where the outliers are still broadband and distinguishable. The simulation results also demonstrate the superior bit-error-rate (BER) performance of our approach relative to classic approaches that use blanking and/or clipping for IN mitigation.

Non-intrusive sleep monitoring is critical for certain populations such as severely disabled autistic children. Nocturnal disturbance analysis is an important diagnostic tool for assessing sleep issues. The objective of this paper is to detect and minimize the effects of motion artifacts in signals recorded via an unobtrusive electromechanical film-based ballistocardiogram (BCG) sensor integrated into a smart bed system. The goal is to have a reliable estimation of beat-to-beat (B-B) interval. The proposed algorithm includes two main stages: a motion detection algorithm followed by a motion artifact removal system. Motion detection involves a sequential detection algorithm in which successive data frames are compared to two thresholds: upper and lower thresholds. Each motion corrupted frame can then be reconstructed by an approach that relies on a parametric model of the BCG signal. Exploiting the fact that the underlying BCG parameters (J-peak-to-J-peak interval, J-peak-to-K-peak amplitude, and the most significant frequency component) change slowly and are correlated across time; an autoregressive model-based tracking and Wiener smoother based parameter estimation strategies are proposed. The experimental results are presented to demonstrate the effectiveness of the proposed motion artifact detection and removal algorithms. The probability of detection of 96% and high average coverage of 84% with over 90% precision for the estimated B-B intervals are achieved on recordings for 19 h of sleep from three subjects. Our novel motion detection and removal methods demonstrate the feasibility of using bed-based BCG signals for providing a reliable unobtrusive way to estimate the B-B interval in the presence of motion.

With the overcrowded sub-6-GHz bands, millimeter wave (mmWave) bands offer a promising alternative for the next-generation wireless standard, i.e., 5-G. However, the susceptibility of mmWave signals to severe pathloss and shadowing requires the use of highly directional antennas to overcome such adverse characteristics. Building a network with directional beams changes the interference behavior, since, narrow beams are vulnerable to blockages. Such sensitivity to blockages causes uncertainty in the interfering node locations and not all of the potential interfering nodes actively contribute to the interference power level at a certain location of the network. Configuration uncertainty may also manifest in the spectral domain while applying dynamic channel and frequency assignment to support 5-G applications. In this paper, we first propose a blockage model considering mmWave specifications. Subsequently, using the proposed blockage model, we derive a spatial-spectral interference model for dense finite-area 5-G mmWave networks. The proposed interference model considers randomness of node configuration in both spatial and spectral domains. Finally, the error performance of the network from an arbitrarily located user perspective is calculated in terms of bit error rate and outage probability metrics. The analytical results are validated via Monte Carlo simulations. It is shown that considering mmWave specifications and also randomness in both spectral and spatial node configurations leads to a noticeably different interference profile.

Emerging fifth generation (5G) wireless networks require massive bandwidth in higher frequency bands, extreme network densities, and flexibility of supporting multiple wireless technologies in order to provide higher data rates and seamless coverage. It is expected that the utilization of the large bandwidth in the millimeter-wave (mmWave) band and deployment of heterogeneous networks (HetNets) will help address the data rate requirements of 5G networks. However, high pathloss and shadowing in the mmWave frequency band, strong interference in the HetNets due to massive network densification, and coordination of various air interfaces are challenges that must be addressed. In this paper, we consider a relay-based multiband orthogonal frequency division multiple access HetNet in which mmWave small cells are deployed within the service area of macro cells. In particular, we attempt to exploit the distinct propagation characteristics of mmWave bands (i.e., 60 GHz-the V-band and 70-80 GHz-the E-band) and the long term evolution band to maximize overall data rate of the network via efficient resource allocation. The problem is solved using a modified dual decomposition approach and then a low complexity greedy solution based on the iterative activity selection algorithm is presented. Simulation results show that the proposed approach outperforms conventional schemes.