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Millimeter-wave (mmWave) antenna arrays are pivotal components in modern wireless communication systems, offering high data rates and improved spectrum efficiency. However, the performance of mmWave antenna arrays can be significantly affected by structural distortions, such as mechanical deformations and environmental conditions, which may lead to deviations in beamforming characteristics and radiation patterns. In this paper, we present a comprehensive sensitivity study of mmWave antenna arrays to structural distortion, employing a coupled structural–electromagnetic statistical concept. The proposed model integrates structural analysis techniques with electromagnetic simulations to assess the impact of structural distortions on the performance of mmWave antenna arrays. In addition, the model incorporates random element positioning, making it easy to analyze radiation pattern sensitivity to structural deformation. Demonstrating the applicability of the model, a 10 × 10 microstrip patch antenna array is designed to assess the performance of the model with a random position error and saddle shape distortion. The results of the model are then compared against the acceptable results from the HFSS software (version 13.0), where a good agreement is observed between the two results. The results show the gain variation and sidelobe level under various degrees of distortion and random errors, respectively. These results provide a guide for design, deployment, and optimization of mmWave communication networks in real-world environments. In addition, the model provides valuable insights into the trade-offs between antenna performance, structural integrity, and system reliability, paving the way for more efficient and dependable mmWave communication systems in the era of 5G and beyond.

In this paper, we propose a novel approach to enhance spectral efficiency in communication systems by dynamically adjusting the mapping between cyclic permutation coding (CPC) and its injected form. By monitoring channel conditions such as interference levels and impulsive noise strength, the system optimises the coding scheme to maximise data transmission reliability and efficiency. The CPC method employed in this work maps information bits onto non-binary symbols in a cyclic manner, aiming to improve the Hamming distance between mapped symbols. To address challenges such as low data rates inherent in permutation coding, injection techniques are introduced by removing δ column(s) from the CPC codebook. Comparative analyses demonstrate that the proposed dynamic adaptation scheme outperforms conventional permutation coding and injection schemes. Additionally, we present a generalised mathematical expression to describe the relationship between the spectral efficiencies of both coding schemes. This dynamic approach ensures efficient and reliable communication in environments with varying levels of interference and impulsive noise, highlighting its potential applicability to systems like power line communications.

An effective way to perform maintenance on the wind turbine generator (WTG) blades installed in grid-connected wind farms is to inspect them using Unmanned Aerial Vehicles (UAV). The ability to detect wind turbine blade defects from these laser and RGB images captured by drones has been the subject of numerous studies. The issue that most applied techniques battle with is being able to locate different wind turbine blade defects with high confidence scores and precision. The accuracy of these models’ defect detection decreases due to varying testing image scales. This article proposes the Res-CNN3 technique for detecting wind turbine blade defects. In Res-CNN3, defect region detection is achieved through a bipartite process that processes the laser delta and RGB delta structure of a wind turbine blade image with an integration of residual networks and concatenated CNNs to determine the presence of typical defect regions in the image. The loss function is logistic regression, and a Selective Search (SS) algorithm is used to predict the regions of interest (RoI) of the input images for defects detection. Several experiments are conducted, and the outcomes prove that the proposed model has a high prospect for accuracy in solving the problem of defect detection in a manner similar to the advanced benchmark methods.

In this paper, the 3D localization and signal enhancement problem of a source in a noisy environment is addressed using an antenna array to ensure symmetry in communication engineering. The use of machine-learning-dependent convolutional recurrent neural networks (CRNN) and a minimum variance distortionless response (MVDR) beamformer for the localization of the source is developed. Furthermore, to ensure the adaptability of the signal enhancement module during deployment in a new environment or in new conditions, the training of a meta-learning model is conducted. At first, during the localization, the direction of arrival (DoA) estimation in both azimuth and elevation angles is generated. This is generated in a noisy three-dimensional plane and multi-source signal. Employing the DoA estimates, the MVDR is used for the enhancement of the signal source. Verifying the proposed method in the presence of mutual coupling, the two scenarios in communication engineering were simulated using a ray-tracing tool in the form of a real-world problem towards enhancing a signal source in a noisy environment and in the presence of various sources. The results obtained demonstrate how the proposed method outperforms the machine learning and parametric methods. In addition, the trained meta-learning model is employed to demonstrate how the proposed method is adaptable to any environment and still maintains an appreciable quality performance index after retraining with few data. Finally, the results obtained are motivating enough for the practical application of the proposed method.

In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable’s impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model’s multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively.

Within the power line communication (PLC) network, a large number of electronic devices are connected, and environmental factors can cause unusual behavior, leading to high-amplitude impulse noise in the received signal and, as a result, packet losses and burst errors in the data that are sent. Burst errors make it difficult to send data over power line channels efficiently and accurately. Analyzing error patterns with intelligent techniques can provide valuable insights into data transmission efficiency, enhance transmission quality, and optimize PLC systems. This research proposes a three-state Fritchman–Markov chain-based power line communication error model and develops a software-defined PLC system. The goal is to analyze and model the system’s statistical error process. The PLC system’s fundamental error pattern is deduced from the transmission and reception of data on our software-defined (SD) PLC platform. The system is designed with multi-state quadrature amplitude modulation (M-QAM) data transmission and reception techniques. An error pattern consisting of 50,000 bits is obtained by comparing the bits transmitted with those received using the in-house M-QAM-based PLC transceiver system. The error characteristics of the newly developed M-QAM SD-PLC system are precisely modeled using the error model. Examining the burst error statistics of the reference error sequences of the SD-PLC system and the three-state Fritchman–Markov error model reveals striking similarities. According to the results, the error model accurately represents the error characteristics of the developed M-QAM SD-PLC system. The proposed three-state Fritchman–Markov chain-based error model for PLC has the potential to provide a comprehensive understanding of the error process in PLC. Additionally, it can assess error control strategies with less computational complexity and a shorter simulation time.

A spherical antenna array (SAA) is an array-designed arrangement capable of scanning in almost all the radiation sphere with constant directivity. It finds recent applications in aerospace, spacecraft, vehicular and satellite communications. Therefore, estimation of the direction-of-arrival (DoA) of electromagnetic (EM) waves that impinge on an SAA with unknown mutual coupling called for research attention. This paper proposed a spherical harmonic atomic norm minimization (SHANM) for DoA estimation using an SAA configuration. The gridless sparse signal recovery problem is considered in the spherical harmonic (SH) domain in conjunction with the atomic norm minimization (ANM). Because of the unavailability of the Vandermonde structure in the SH domain, the theorem of Vandermonde decomposition that is the mathematical basis of the traditional ANM methods finds no application in SH. Addressing this challenge, a low-dimensional semidefinite programming (SDP) approach implementing the SHANM method is developed. This approach is independent of Vandermonde decomposition, and directly recovers the atomic decomposition in SH. The numerical experimental results show the superior performance of the proposed method against the previous methods. In addition, accounting for the impacts of mutual coupling, an experimental measured data, which is the generally accepted ground of testing any method, is employed to illustrate the efficacy and robustness of the proposed methods. Finally, for achieving DoA estimation with sufficient localization accuracy using a SAA, the proposed SHANM-based method is a better option.

Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to raise critical flags during network management. One aspect is malicious traffic identification, where zero-day attack detection is a critical problem of study. Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN–GRU combination sequences are presented to optimize the network parameters. In the simulation, the authors used the CICIDS-2017 benchmark dataset and used metrics such as precision, recall, False Positive Rate (FPR), True Positive Rate (TRP), and other aligned metrics. The results suggest a significant improvement, where many network attacks are detected with an accuracy of 98.73%, and an FPR rate of 0.075. We also performed a comparative analysis with other existing techniques, and the obtained results indicate the efficacy of the proposed IDS scheme in real cybersecurity setups.

Spherical antenna array (SAA) exhibits the ability to receive electromagnetic (EM) waves with the same signal strength, regardless of the direction-of-arrival (DoA), angle-of-arrival, and polarization. Hence, estimating the DoA of EM signals that impinge on SAA in the presence of mutual coupling requires research consideration. In this paper, a subspace pseudointensity vectors technique is proposed for DoA estimation using SAA with unknown mutual coupling. DoA estimation using an intensity vector technique is appealing due to its computational efficiency, particularly for SAAs. Two intensity vector-based techniques that operate with spherical harmonic decomposition (SHD) of an EM wave obtained from SAA are presented. The first technique employed pseudointensity vectors (PV) and operates quite well under EM conditions when one source is in operation each time, while the second technique employed subspace pseudointensity vectors (SPV) and operates under EM conditions when multi-sources and multiple reflection cause more challenging problems. The degree of correctness in the estimation of the DoA via the PVs and SPVs is measured using baseline methods in the literature via simulations, adding noise to stationary, single-source and multi-source methods. In addition, incorporating mutual coupling effects, data from experiments, which are the generally acceptable ground truth when examining any procedure, are further used to illustrate the robustness and efficiency of the proposed techniques. The results are sufficiently inspiring for the practical deployment of the proposed techniques.

In this paper, we explain the design and implementation of an intelligent irrigation control system based on fuzzy logic for the automatic control of water pumps used in farms and greenhouses. This system enables its user to save water and electricity and prevent over-watering and under-watering of the crop by taking into account the climatic parameters and soil moisture. The irrigation system works without human intervention. The climate sensors are packaged using electronic circuits, and the whole is interfaced with an Arduino and a Simulink model. These sensors provide information that is used by the Simulink model to control the water pump speed; the speed of the water pump is controlled to increase or decrease the amount of water that needs to be pushed by the pump. The Simulink model contains the fuzzy control logic that manages the data read by the Arduino through sensors and sends the command to change the pump speed to the Arduino by considering all the sensor data. The need for human intervention is eliminated by using this system and a more successful crop is produced by supplying the right amount of water to the crop when it is needed. The water supply is stopped when a sufficient amount of moisture is present in the soil and it is started as soon as the soil moisture levels drops below certain levels, depending upon the environmental factors.

The integration of solar photovoltaic (PV) systems and utility grids has gradually gained significant interest in improving the sustainability of clean power supply for society. However, power quality remains a challenge due to partial shading conditions and harmonics. To overcome these drawbacks, a flexible radial movement optimization based on a dynamic safety perimeter maximum power point tracking algorithm is employed to track maximum power out of a PV system and to ensure that the optimum voltage level at the common DC bus is obtained under partial shading conditions using fixed-tilt installation configuration. Furthermore, a novel inverter control loop system with a double second order generalized integrator phase-locked loop (DSOGI-PLL) is also proposed to mitigate harmonics and improve the power quality of the grid interfacing PV system using MATLAB SIMULINK software. The proposed system has several merits, such as better harmonic suppression capability, control adaptivity, rapid tracking speed, low computational burden and phase and grid synchronization.

The recent combination of ambient backscatter communication (ABC) with non-orthogonal multiple access (NOMA) has shown great potential for connecting large-scale Internet of Things (IoT) in future unmanned aerial vehicle (UAV) networks. The basic idea of ABC is to provide battery-free transmission by harvesting the energy of existing RF signals of WiFi, TV towers, and cellular base stations/UAV. ABC uses smart sensor tags to modulate and reflect data among wireless devices. On the other side, NOMA makes possible the communication of more than one IoT on the same frequency. In this work, we provide an energy efficient transmission design ABC-aided UAV network using NOMA. This work aims to optimize the power consumption of a UAV system while ensuring the minimum data rate of IoT. Specifically, the transmit power of UAVs and the reflection coefficient of the ABC system are simultaneously optimized under the assumption of imperfect channel state information (CSI). Due to co-channel interference among UAVs, imperfect CSI, and NOMA interference, the joint optimization problem is formulated as non-convex, which involves high complexity and makes it hard to obtain the optimal solution. Thus, it is first transformed and then solved by a sub-gradient method with low complexity. In addition, a conventional NOMA UAV framework is also studied for comparison without involving ABC. Numerical results demonstrate the benefits of using ABC in a NOMA UAV network compared to the conventional UAV framework.

Unplanned power plant failures have been seen to be a major cause of power shortages, and thus customer power cuts, in the South African power grid. These failures are measured as the unplanned capability loss factor (UCLF). The study of South Africa’s UCLF is almost non-existent. Parameters that affect the future UCLF are, thus, still not well understood, making it challenging to forecast when power shortages may be experienced. This paper presents a novel study of South African UCLF forecasting using state-of-the-art deep learning techniques. The study further introduces a novel deep learning ensemble South African UCLF forecasting system. The performance of three of the best recent forecasting techniques, namely, long short-term memory recurrent neural network (LSTM-RNN), deep belief network (DBN), and optimally pruned extreme learning machines (OP-ELM), as well as their aggregated ensembles, are investigated for South African UCLF forecasting. The impact of three key parameters (installed capacity, demand, and planned capability loss factor) on the future UCLF is investigated. The results showed that the exclusion of installed capacity in the LSTM-RNN, DBN, OP-ELM, and ensemble models doubled the UCLF forecasting error. It was also found that an ensemble model of two LSTM-RNN models achieved the lowest errors with a symmetric mean absolute percentage error (sMAPE) of 6.43%, mean absolute error (MAE) of 7.36%, and root-mean-square error (RMSE) of 9.21%. LSTM-RNN also achieved the lowest errors amongst the individual models.

Recently, the awareness of the severe consequences of greenhouse gases on the environment has escalated. This has encouraged the world to reduce the usage of fossil fuels for power generation and increase the use of cleaner sources, such as solar energy and wind energy. However, the conventional power system itself was designed as a passive power system, in which power generation is centralised, and power flows from substations towards the loads. Decentralised renewable energy sources, also called distributed generators, were introduced to create an active power system in which power generation can occur anywhere in the power system. Decentralised power generation creates challenges for the conventional power system, such as voltage fluctuations, high voltage magnitudes, reverse power flow, and low power factor. In this paper, an adaptive control system that coordinates different distributed generators for voltage regulation and power factor correction is introduced and designed. The control system will decrease the total reactive power that flows in the transmission network through a reactive power exchange between distributed generators. Therefore, power factor will improve, power system losses will reduce, and the total apparent power on lines will reduce, giving more room to active power to flow. The results obtained showed that the control system is effective in regulating voltage and improving the power factor when multiple distributed generators are connected.

Integrating cognitive radio into the current power grid is designed to enable smart communication and decisions within the grid. Communication within the grid is not feasible without channel(s) and most studies have emphasized the use of cellular spectrum. This study proposes a strategy that enables the use of television white space (TVWS) within the grid. To be specific, we propose using a next-generation utility network (Next-GUN), which leverages on the cognitive radio (CR) channel aggregation capability. This strategy enables the aggregation of idle TVWS into a usable channel, thus making the Next-GUN different from the traditional power network. Next-GUN differs in terms of security, reliability, self-awareness, and cross-layer compatibility to the interface, and conveys different traffic classes, thereby making it a hybrid system. It has no dedicated channel assigned to it, and hence, utilizes the idle TVWS opportunistically to transmit data. The proposed scheme was modelled as a Markovian process and analyzed using a continuous time Markov chain (CTMC). Extensive system simulations were performed to evaluate this proposed model and the corresponding comparison with the literature was done to see the improvement. The result of our comparisons shows that when channels are aggregated, more data/information are transmitted. In addition, the use of cognitive radios on a power network enables smart transaction because idle TVWS is utilized instead of congesting the GSM spectrum. Lastly, the power utility establishment can save the cost of paying for a licensed spectrum.