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In this paper, we assess the performance of an equal-gain combining (EGC) diversity receiver operating over composite Fisher-Snedecor fading channels. The Fisher-Snedecor fading model results from the amalgamation of two well-known physical phenomena that greatly degrade the wireless signal: multipath and shadowing. More precisely, the Fisher-Snedecor $\mathcal {F}$ distribution is formed by the ratio of two independent Nakagami-m random variables (RVs)—the Nakagami-m RV in the numerator models multipath fading while the Nakagami-m RV in the denominator characterizes shadowing fading. To evaluate the performance of the considered EGC diversity receiver, we first derive exact expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the sum of the ratio of independent Nakagami-m RVs. To achieve this goal, we utilize a Laplace- and residue-based approach. Additionally, we employ the moment-matching method and the Beta Prime distribution to effectively approximate the sum of the ratio of independent Nakagami-m RVs. Subsequently, we apply the obtained exact and approximate sums statistics to compute the performance metrics of the EGC receiver subject to Fisher-Snedecor $\mathcal {F}$ fading channels, such as the outage probability (OP) and the average bit-error rate (ABER). We aspire that the findings presented in this work not only enhance the theoretical comprehension of wireless communication systems but also offer valuable insights for optimizing the design and performance of these systems in real-world scenarios.

Spread-spectrum techniques have found extensive use in broadband communications, both in military and commercial applications, for their low interception probability. However, it has been shown that these techniques prove ineffective on non-coherent fading channels with very large bandwidths since its capacity decreases with the increase of the bandwidth, eventually going to zero as the bandwidth goes to infinity. Peaky frequency-shift keying is a promising alternative modulation technique expected to achieve high data rates while maintaining a low interception probability. The current study explores such modulation techniques and outlines the conditions for which the reliability and capacity of the system increase while reducing the probability of interception compared to typical spread spectrum schemes. We show that peaky frequency-shift keying achieves a considerably higher transmission rate than non-peaky signaling for any given target level of interception probability, especially for outdoor fading scenarios in the low signal-to-noise ratio regime.

The increasing domestic demand for electricity has required the scientific community to develop new technologies focused on energy expenditure minimization. In this context, Home Energy Management System (HEMS) is an element that can incorporate applications that aid in efficient energy management, such as load recognition. Load recognition allows HEMS to map all appliances, assisting in efficient management. In this regard, methods that solve the load recognition problem are vital for HEMS. The motivation of this work is to propose a novel system that enhances load recognition in HEMS applications. For this purpose, the proposed system contains an event detector stage based on the Wavelet transform and another stage for appliance identification consisting of two processing chains, one for high reliability and one for fast model training. The main novelties of our work are: (i) very-low training time via fast training processing chain, less than 1 second with $k$ -Nearest Neighbor (k-NN); (ii) the highest values for accuracy, F1, and the kappa index, 98.90%, 98.77%, and 0.9382, respectively, for the regularized Convolutional Neural Network (CNN-r) using the ‘Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology’ (REFIT) dataset; (iii) this is the first paper employing CNN-r and Vision Transformer (ViT) for load recognition problem; and (iv) the approach is applicable in households without any restrictions, as long as training the models. Regarding the tested scenarios, the results reveal a very efficient strategy yielding accuracy values greater than 98% for the REFIT dataset and the Reference Energy Disaggregation Dataset (REDD). Finally, the proposed approach provides the highest performance compared to other methods available in the literature.

The Reconfigurable Intelligent Surface (RIS) has recently gained traction as a promising, enabling technology for future sixth-generation (6G) mobile networks. Thereby, this paper presents a comprehensive statistical, secrecy, and performance analysis of RIS-assisted multiple-antenna wireless communication systems under Weibull fading. The system model encompasses an antenna array at the source transmitting to a typical single-antenna user through both a generally weaker direct link and an enhanced RIS-aided double-Weibull-fading linear cascaded communication channel. Moreover, the RIS is assumed to perform constructive interference towards the user through realistic non-ideal phase cancellation with residual phase errors that follow the von Mises distribution. Then, the statistical distribution of the resultant channel fading is accurately modeled by the analytically derived moment-generated Gamma distribution in conformity with the central limit theorem (CLT). While considering the existence of a malicious eavesdropper directly linked with the source, relevant performance metrics, expressly the secrecy outage probability (SOP), the symbol error rate (SER), and the spectral efficiency of the system, have their expressions determined for the specific Weibull-fading scenario and verified by Monte Carlo simulations. The study highlights the significant performance improvement of the system arising from a larger number of antenna elements of the RIS, as well as through a more accurate phase cancellation response associated with a higher von Mises concentration parameter, $\kappa $ . Furthermore, the effects of diverse fading scenarios and varying line-of-sight (LoS) propagation strength are evaluated through the Weibull shape parameter, $k$ .

This work introduces new effective methodologies for the detection, analysis, and classification of diverse defects that may occur throughout the production process of photovoltaic panels. In this context, this work proposes a novel approach that combines Image Processing and Vision Transformers (ViT) to address this challenge. The results of this work comprise a light flaw-type classifier based on ViT, along with computational tools to calculate the length of cracks and the proportional damaged area caused by flaws without requiring the training of other models. The proposed ViT-μ model achieved high accuracy in flaw detection and classification for solar cells, with rates of nearly 98% and 94%, respectively; achieved with a mere one-hour training duration. Moreover, this study introduces a weakly supervised method of visualizing the detected defects within a solar cell, by using attention maps.

This study evaluates the performance of large intelligent surface (LIS) technology in the context of a multi-user MIMO mobile communication system (Mu-MIMO) proposed for the sixth generation (6G). LIS employs digitally controlled reflectors to enhance Signal-to-Interference plus Noise Ratio (SINR) and establish line of sight (LoS) connectivity in non-LoS environments, improving transmission security. Analytical expressions are derived to assess LIS performance metrics, including distribution parameters, bit error probability, and secrecy outage probability, considering the presence of eavesdroppers and environmental fading. The study highlights the potential of LIS technology to enhance the confidentiality and reliability of digital communication systems in next-generation networks.

In a recent work, Fu et al. (2021) proposed an anomaly detector (AD) for hyperspectral images called DeCNN-AD, which decomposes the image in a low rank representation of the background and the anomalies. DeCNN-AD is regularized by an implicit plug and play prior (PnP), providing state-of-the-art anomaly detection performance in hyperspectral images. In this article, we propose a different AD using the Regularization by Denoising (RED) framework. The regularizer that emerges in RED algorithms is advantageous in the sense that it can be minimized by many solvers, unlike PnP, which has to be rederived for each specific solver like the alternating direction method of multipliers or the proximal gradient method. The proposed detector was compared to DeCNN-AD in several experiments, and was shown to present a more stable behavior better convergence properties, to be less sensitive to the choice of parameters, while leading to very similar performance in terms of optimal AUC of receiver operating characteristic curves.

A middleware software can be seem as an abstraction layer between hardware and user applications, that facilitates the development and deployment of services in various scenarios, such as those found in Home Energy Management Systems (HEMS). There are several middleware proposals for HEMS, with most of them taking the cloud computing approach. This approach is unconcerned about computing resources but raises a dependency on external connections. This paper presents a middleware for energy management systems, based on the concept of edge computing for smart homes. The paper presents a reference model for the proposed architecture, considering specific requirements for this type of application. The proposed architecture employs the concept of microservices for data access and system configuration. The proposed middleware is designed to work with embedded systems under computational constraints, such as processing capability and storage, to reduce costs and allow its application closer to the user. The middleware is open and customizable to meet the developer’s needs. The proposed solution was implemented and tested in a university laboratory, as well as at the Eldorado Research Institute to confirm the effectiveness of the middleware. The proposal stands out from others found in the literature as it can be implemented using low cost hardware. In addition to using microservices concepts, the proposed middleware is a valuable option for applications that need an edge computing approach. A performance analysis was carried out, using low cost hardware with limited resources. The results show that the proposal can handle a significant number of devices, offering low latency and low error rate, and consuming few processing resources and memory.

This work investigates the efficiency of the process of load disaggregation, considering only the values of active power. To perform the task, we use data collected from the NILM (Non-Intrusive Load Monitoring) measurement method, presented in the Rainforest Automation Energy Dataset (RAE) and Reference Energy Disagreggation Dataset (REDD) database. A strategy of assigning labels using combinations of equipment in use, by status ON/OFF, and also by choosing an appropriate temporal data window is discussed. Also, the performance of very well-known machine learning algorithms such as k-Nearest Neighbor (kNN), Decision Tree, and Random Forest are evaluated. The results show a very efficient and low computer complexity strategy presenting values of F1-Score above 95%, for RAE and REDD database. As presented in table I, the proposed approach presents the highest F1-Score, compared to other methods in the literature, considering all appliances in the REDD database. The greatest benefit of the approach consists in the possibility of applying the disaggregation process in a household without smart outlets, under the restriction that the training and test houses hold identical or similar appliances.

Presents corrections to the article “Anomaly Detection in Hyperspectral Images via Regularization by Denoising”.

In this work, we investigate the probability distribution function of the channel fading between a base station, an array of intelligent reflecting elements, known as large intelligent surfaces (LIS), and a single-antenna user. We assume that both fading channels, i.e., the channel between the base station and the LIS, and the channel between the LIS and the single user are Nakagami-m distributed. Additionally, we derive the exact bit error probability considering quadrature amplitude (M-QAM) and binary phase-shift keying (BPSK) modulations when the number of LIS elements, n, is equal to 2 and 3. We assume that the LIS can perform phase adjustment, but there is a residual phase error modeled by a Von Mises distribution. Based on the central limit theorem, and considering a large number of reflecting elements, we also present an accurate approximation and upper bounds for the bit error rate. Through several Monte Carlo simulations, we demonstrate that all derived expressions perfectly match the simulated results.

Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.

This paper presents the development of a methodology to detect and evaluate faults in cable-stayed towers, which are part of the infrastructure of Brazil’s interconnected electrical system. The proposed method increases system reliability and minimizes the risk of service failure and tower collapse through the introduction of predictive maintenance methods based on artificial intelligence, which will ultimately benefit the end consumer. The proposed signal processing and interpretation methods are based on a machine learning approach, where the tower vibration is acquired from accelerometers that measure the dynamic response caused by the effects of the environment on the towers through wind and weather conditions. Data-based models were developed to obtain a representation of health degradation, which is primarily based on the finite element model of the tower, subjected to wind excitation. This representation is also based on measurements using a mockup tower with different types of provoked degradation that was subjected to ambient changes in the laboratory. The sensor signals are preprocessed and submitted to an autoencoder neural network to minimize the dimensionality of the resources involved, being analyzed by a classifier, based on a Softmax configuration. The results of the proposed configuration indicate the possibility of early failure detection and evolution evaluation, providing an effective failure detection and monitoring system.

This article proposes a methodology for monitoring the structural stability of each tower of an electric power transmission line through sensor measurements which estimates the different situations that may indicate the need for intervention to prevent the structure collapsing. The extended Kalman filter was adopted to predict the failures, considering sensor fusion techniques such as the displacements of the upper central position of the tower above certain limits. The load of the stay cables is calculated from the natural frequencies, which are determined by the accelerometers connected to the cables. The average value of these forces, which must be higher than a normal limit, were calculated to predict a failure. All guyed towers of a power transmission line thousands of kilometers long will be individually monitored considering the methodology described in this study, which makes this article one of the first relevant research studies in this area. Typically, guyed towers must often be manually inspected to ensure that the stay cables have acceptable pretension to prevent a lack of stability in the transmission line towers.

Large intelligent surfaces (LIS) are a new trend to achieve higher spectral efficiency and signal-to-noise ratio in mobile communications. For this reason, this paper proposes metrics to analyze the performance of systems with multiple antennas aided by LIS and derive the spectral efficiency, secrecy outage probability, and bit error probability in an environment with Nakagami-m distributed fading. In addition to an eavesdropper, there is a single-antenna user, an array of antennas at the transmitter side and the possibility of a direct link between transmitter and receiver. This study assumes that the LIS performs non-ideal phase cancellation leading to a residual phase error that follows a Von Mises distribution, and shows that the resulting channel can be accurately approximated by a Gamma distributed SNR whose parameters are analytically derived. From these formulas, it is possible to evaluate the effect of the strength of the line-of-sight link by varying the Nakagami parameter, m.

This work considers a base station equipped with an M-antenna uniform linear array and L users under line-of-sight conditions. As a result, one can derive an exact series expansion necessary to calculate the mean sum-rate channel capacity. This scenario leads to a mathematical problem where the joint probability density function (JPDF) of the eigenvalues of a Vandermonde matrix WWH are necessary, where W is the channel matrix. However, differently from the channel Rayleigh distributed, this joint PDF is not known in the literature. To circumvent this problem, we employ Taylor’s series expansion and present a result where the moments of mn are computed. To calculate this quantity, we resort to the integer partition theory and present an exact expression for mn. Furthermore, we also find an upper bound for the mean sum-rate capacity through Jensen’s inequality. All the results were validated by Monte Carlo numerical simulation.

Large intelligent surfaces (LIS) promises not only to improve the signal to noise ratio, and spectral efficiency but also to reduce the energy consumption during the transmission. We consider a base station equipped with an antenna array using the maximum ratio transmission (MRT), and a large reflector array sending signals to a single user. Each subchannel is affected by the Rayleigh flat fading, and the reflecting elements perform non-perfect phase correction which introduces a Von Mises distributed phase error. Based on the central limit theorem (CLT), we conclude that the overall channel has an equivalent Gamma fading whose parameters are derived from the moments of the channel fading between the antenna array and LIS, and also from the LIS to the single user. Assuming that the equivalent channel can be modeled as a Gamma distribution, we propose very accurate closed-form expressions for the bit error probability and a very tight upper bound. For the case where the LIS is not able to perform perfect phase cancellation, that is, under phase errors, it is possible to analyze the system performance considering the analytical approximations and the simulated results obtained using the well known Monte Carlo method. The analytical expressions for the parameters of the Gamma distribution are very difficult to be obtained due to the complexity of the nonlinear transformations of random variables with non-zero mean and correlated terms. Even with perfect phase cancellation, all the fading coefficients are complex due to the link between the user and the base station that is not neglected in this paper.

We derive exact closed-form expressions for Long Range (LoRa) bit error probability and diversity order for channels subject to Nakagami-m, Rayleigh and Rician fading. Analytical expressions are compared with numerical results, showing the accuracy of our proposed exact expressions. In the limiting case of the Nakagami and Rice parameters, our bit error probability expressions specialize into the non-fading case.