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In the field of paper conservation and archival research, identifying the raw materials of paper is important to elucidate its history and culture. As the most basic element of the raw materials for paper, fibers have not been sufficiently investigated. In this study, we propose a nondestructive method for estimating paper fibers from macro photographs ( $4000\times3000$ pixels) captured using a digital camera. The proposed method consists of background patch (500 pixels per side) detection (BPD), wherein background regions with no text are identified; patch fiber classification (PFC), wherein background patches obtained after BPD are analyzed for fiber classification; and paper fiber estimation (PFE), wherein macro images obtained after PFC are analyzed for fiber estimation. BPD and PFC are employed to perform patch-based classification on segmented macro images, which are reconstructed during PFE to obtain the final fiber estimation results. We performed experiments using 1337 macro images (64176 patches) to evaluate the fiber estimation accuracy for kozo, mitsumata, and gampi via three-fold cross-validation. The average fiber classification accuracy for patch images was observed to be 79.1%; accordingly, the average fiber estimation accuracy for macro images was 85.8%. Experimental results indicated that PFE can be realized in a nondestructive manner on macro images of paper captured using a digital camera.

Computer scientists often publish their source code accompanying their publications, prominently using code repositories across various domains. Despite the concurrent existence of scholarly articles and their associated official code repositories, explicit references linking the two are often missing. Traditionally, identifying whether scholarly content and code repositories pertain to the same research project requires manual inspection, a time-consuming task. This paper proposes a deep learning-based algorithm for automatically matching scholarly articles with their corresponding official code repositories. Our findings indicate that the most common linking information includes the paper title and BibTeX entries, typically found in the repository’s readme document. In this study, we employed SPECTER for vector embedding of paper and repository metadata. Utilizing these embedding representations with the Light Gradient Boosting Machine (LGBM), our method achieved an F1 score of 0.94. Moreover, combining our best model with a rule-based approach improved performance by 5.31%. This study successfully delineates a connection between academic papers and associated official code repositories, minimizing reliance on explicit bibliographic information in repositories.

In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment’s safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.

Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.

This special section of the IEEE Open Journal of Circuits and Systems (OJCAS) aims to highlight a selection of papers from 2022 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). Due to COVID-19 and travel restrictions, APCCAS 2022 was organised as a hybrid conference during 11 - 13 November 2022 in Shenzhen China. As the regional flagship conference of IEEE Circuits and Systems Society, APCCAS 2022 was driven by the theme “Building a Fully-connected AIoT World” to emphasize the CAS Society’s potential for finding multidisciplinary solutions to societal and industrial challenges. The papers in this special issue were selected from a comprehensive list of papers presented in the sessions of APCCAS 2022.

The Editorial Board of the IEEE Open Access Journal of Power and Energy (OAJPE) would like to recognize the following best papers selected from all papers published between October 1, 2020 and September 30, 2023 in OAJPE.

Welcome to Volume 4 of IEEE Journal of Microwaves! In this issue, we bring you 11 quality papers: two invited and nine regular submissions. We also celebrate six of our Outstanding Reviewers of 2023 and announce our IEEE Journal of Microwaves 2022 Best Paper prize. Our author and reader pools continue to expand and our usage counts per article published now exceed 1530. All of our 232 published articles from 2021 to 2023 now appear on Clarivate's Web of Science and we are looking forward to possibly receiving an impact factor this July. Remember to take a look at our solicitation for papers that can fit into our Microwaves in Climate Change special issue, which we intend to release before the end of 2024.

This research proposes an inkjet printed dual-band dual-sense circularly polarized antenna using CPW-feeding on PET substrate. The antenna is designed and optimized using ANSYS HFSS, which operates at 4.01 GHz - 5.05 GHz (22.96%) and 6.23 GHz - 7.58 GHz (19.55%) with a return loss of $0.44\lambda $ x $0.52\lambda $ x $0.002\lambda $ , where $\lambda $ is the free-space wavelength at the lowest circular polarization frequency. Measurement of the fabricated version shows good agreement with the simulated version. To the best of author’s knowledge, this proposed design is the first circularly polarized antenna integrating flexibility with dual-band and dual-sensing characteristics.

Biometric identification technologies are crucial for enhancing security through reliable personal authentication methods. Among these modalities, finger knuckle biometrics stands out for its distinctive and consistent features, offering a valuable alternative to more commonly used biometric traits. Unlike fingerprints, which are easily captured from the surface of the skin, knuckle prints present a unique challenge. Knuckle prints are not as readily accessible from surface scans due to their position and the intricacy of their features, which require specialized techniques for accurate capture and recognition. The paper comprehensively reviews the evolution from traditional geometric methods to advanced deep learning techniques in finger knuckle recognition. Our review covers both unimodal and multimodal approaches, discussing various recognition strategies and their effectiveness. We also discussed the performance of knuckle biometric systems using metrics such as accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (EER). The paper also highlights the importance of publicly available knuckle datasets, which are essential for developing and evaluating FKP biometric systems. These datasets enable researchers to benchmark and improve recognition algorithms. This review is aimed at researchers, practitioners, and academics interested in biometric technologies, offering insights into current advancements and future directions in finger knuckle biometrics.

Light Field (LF) processing is a critical step in advancing immersive experiences beyond two-dimensional (2D) images. While LF-based technology facilitates a smooth transition from 2D to three-dimensional (3D) media, processing LFs composed of numerous images remains challenging. Most existing research has provided only limited processing capabilities for low-resolution LFs. For instance, LF editing typically offers basic functions like color changes and object removal. This paper proposes micro-cell-based processing for spatially and angularly large-scale LFs. By partitioning and rearranging the LF into micro-cells, which are then used as input for a vision transformer (ViT), we enable spatio-angular joint attention that significantly enhances performance. The local attention applied to small-sized micro-cells allows for high-resolution LF processing without excessive computational costs. This paper utilizes this combination of micro-cells and the ViT for LF editing applications. Experimental results demonstrate that the proposed LF editing system delivers exceptional performance both quantitatively and qualitatively, enabling high-resolution LF processing with a variety of editing functions.

This paper investigates the phenomenon of flicker induced in the Tunisian steel factory’s electric arc furnace (EAF). Therefore, several solutions have been proposed in the literature in order to mitigate this phenomenon. We cite the three famous techniques; the addition of an inductor in series with the electric arc furnace, the use of the Flexible Alternating Current Transmission Systems (FACTS) systems and the increase in the short-circuit power of the electric arc furnace. The final solution was selected based on a number of factors, including its lower cost and simpler plant installation dimension in comparison to the other two options. Above all, the high maintenance costs associated with the FACTS and the extremely careful sizing of the complete plant infrastructure. In fact, this study is subdivided into two parts. First, the first part will be devoted to the theoretical study of flicker while describing its admissible limits according to the standards in order not to disturb equipment or human beings. However, the 2nd part of the paper is subdivided into two sections. The first section deals with the evolution of flicker severity levels short and long term before and after increasing the short-circuit power of the electric arc furnace. However, the second section deals with the experimental tests and implements the measurement of the flicker severity generated by the electric arc furnace using a Megger PA-9 Plus flickermeter after increasing the short-circuit power of the electric arc furnace. In fact, the implementation of these tests was carried out following the cooperation of the Tunisian electricity and gas company and the national higher school of engineers of Tunis on the one hand and the steel factory in Manzel Bourguiba Tunisia on the other hand.

Order Fulfillment is a critical performance metric in company operations, directly linked to the effectiveness of process optimization and information integration. The ISA-95 standard sets a foundational guideline for the flow of information between control and management systems within organizations. At the core of this integration is the Manufacturing Execution System (MES), which functions as a shop floor information management system, capturing, integrating, and analyzing data to provide real-time insights into factory status on machine conditions, work-in-process, and production progress. This paper utilizes MES as a reference model, adopting the principles outlined in the ISA-95 standard to develop a framework for production process reengineering aimed at enhancing the order fulfillment. In this paper, a three-step guideline for reengineering the production process will be presented, with a machining factory serving as a practical case study. Through the reengineering of processes at this machining factory, this case company’s order fulfillment will be improved. While actual process reengineering has not yet been implemented at the case company, the proposed process reengineering plan has been officially endorsed by this case company, setting a path forward for future enhancement. An interview summary with the case company is provided in Section IV.

As university information management grows increasingly complex, the demand for seamless data sharing and interaction across various departments and faculties has surged. However, many universities lack a unified platform for data traceability and sensitive information protection, heightening security risks during frequent data exchanges. Furthermore, the uneven allocation of network resources and the absence of effective traffic analysis tools hinder universities from efficiently managing these resources, exacerbating the challenges of network management. Tight budgets further complicate cybersecurity infrastructure development, particularly as universities expand and diversify their disciplines, leading to even more imbalanced funding and constrained investment in network security.To address these challenges, this paper presents a data traceability system integrating Deep Packet Inspection (DPI) and Deep Flow Inspection (DFI) technologies. While DPI and DFI have been extensively studied in the literature, the innovation of this system lies in its hybrid approach, which combines the strengths of both technologies to form a complementary mechanism. This approach is particularly suited to the financial and technical limitations faced by universities. The system is designed for ease of implementation, management, and use, and has been optimized for the unique operational needs of higher education institutions.This research adopts a combination of action research and case study methodologies, starting with small-scale trials to assess system performance before expanding the scope to gather data from broader applications. In addition, this paper introduces the concept of tiered data management for the education sector, offering valuable insights into data classification and traceability within university information systems. The proposed system not only enhances the efficiency of data security management but also provides a practical solution for building future cybersecurity frameworks in higher education.

The demand for low-cost healthcare solutions has been increasing and the use of Internet of Things (IoT) devices is becoming more common. IoT devices have the potential to monitor vital health parameters of patients remotely, providing early detection of health problems and reducing medical costs. Chipless RFID sensors, combined with IoT technology, offer cost-effective solutions for healthcare applications. These sensors have significance for remote data collection, object tracking, authentication, and security. In recent years, there has been a growing attention on the design and development of chipless sensors for monitoring health. This paper provides an extensive review of chipless RFID sensors, including their functioning, fabrication techniques, and classifications. The paper further addresses the various smart sensing materials used in chipless RFID sensors, along with challenges and future trends. The IoT-based health monitoring system permits patients to be tracked 24/7 and provides rapid health consultations from doctors in distant places. The technology also allows guardians or family members to remotely monitor patients, which is regarded as one of the most significant benefits of saving human lives. Overall, this review article provides a comprehensive investigation of the potential advantages of chipless RFID sensors and IoT-based systems for healthcare monitoring, paving the way for cost-effective, easily accessible, and reliable healthcare solutions.

Human Activity Recognition (HAR) has seen remarkable advances in recent years, driven by the widespread use of wearable devices and the increasing demand for personalized healthcare and activity tracking. Federated Learning (FL) is a promising paradigm for HAR that enables the collaborative training of machine learning models on decentralized devices while preserving data privacy. It improves not only data privacy but also training efficiency as it utilizes the computing power and data of potentially millions of smart devices for parallel training. In addition, it helps end-user devices avoid sending users’ private data to the cloud, eliminates the need for a network connection, and saves the latency of back-and-forth communication. FL also offers significant advantages for communication by reducing the amount of data transmitted over the network, alleviating network congestion and reducing communication costs. By distributing the training process across devices, FL minimizes the need for centralized data storage and processing, leading to more scalable and resilient systems. This paper provides a comprehensive survey of the integration of FL into HAR applications. Unlike existing reviews, this paper uniquely focuses on the intersection of FL and HAR, providing an in-depth analysis of recent advances and their practical implications. We explore key advances in FL-based HAR methodologies, including model architectures, optimization techniques, and different applications. Furthermore, we highlight the major challenges and future research questions in this domain, such as model personalization and robustness, privacy concerns, concept drift, and the limited capacity of edge devices.

The paper presents a non-invasive wireless power transfer technique that facilitates horizontal propagation of electromagnetic (EM) waves within the tissue layer of the human body. The horizontal propagation is achieved by using a modified aperture open-ended circular waveguide as the radio front-end and a wave launcher for impinging the EM wave at a desired angle. The EM wave, then with a dominant horizontal component, takes advantage of the sub-dermal fat channel, a parallel plate-like channel formed by the sandwiched low dielectric fat layer between the high dielectric skin and muscle layer. This horizontally propagating wave can be utilized to power multiple implants from a single external source for overcoming the challenges associated with transmitter-receiver alignment in medical implants. The wave launcher is designed for an operating frequency of 2.4 GHz, which is in compliance with the ISM band, and attributed to the optimal frequency for through-body power transfer. This paper further contains an elaborate discussion on the design of the wave launcher including the SMA to wave launcher transition and the results of its numerical study. The design is then validated based on the return loss and electric field intensity in the multilayer heterogeneous medium replicating the human tissues.

Lexical Substitution (LS) replaces the target word or phrase with its synonym alternatives that are equivalent in meaning. Despite the richness of the Arabic language, Arabic LS received little attention as there are no benchmark evaluation datasets, even though researchers in many languages showed interest in this task. This paper presents an LS pipeline, AraLexSubPro, which provides different techniques for generating, selecting, and ranking substitution. To make a thorough comparison, AraLexSubPro uses four different methods as baselines to generate substitution candidates for the target words: a synonym dictionary approach (AWN), a pre-trained language model approach (AraBERT), AraBERT dropout approach (partial masking), and a hybrid approach using AraBERT and AWN. The generated substitutions are filtered and then ranked based on three high-quality features to compare thoroughly: word similarity, word frequency, and the BERT score. The substitutions are then reranked based on our AraLexSubPro ranker. The AraLexSubPro pipeline was evaluated using the first Arabic LS benchmark, the AraLexSubD dataset. This paper presents the first comprehensive study on the Arabic LS task.

Presently, with the increase in the operating speed of a train, the off-line phenomenon of its pantograph and the contact wire occurs intermittently, which affects the quality of its receiving current. In this paper, a distributed parameter-based Habedank arc model for a fully parallel AT power supply network is developed to examine the electrical characteristics under the typical operating conditions, including the normal travel and entry/exit stations. The incorporation of the train acceleration parameter facilitates further enhancement of the Habedank arc model and the equivalent dynamic resistance, also validating them. The results show that the off-line discharge arc voltage of the train pantograph and the locomotive voltage waveform during the overcurrent phase separation interval show a decreasing and increasing trend respectively when entering and leaving the station. Due to the nonlinear dynamic characteristics of the arc, the overall waveform contains many harmonics. The advantage of this model is that it is a simple, rapid, and highly accurate method for constructing an off-line discharge circuit model of the pantograph and catenary (PAC) under the working conditions. This paper introduces a novel approach to studying the electrical characteristics of the PAC discharge model with distributed parameters in a fully parallel AT power supply network. Also, it considers the effects of the operational conditions of the train, such as the entering and leaving stations, on the discharge characteristics. The research results of this paper can provide a reference for the identification of the electrical characteristics and protection of a PAC equipment during the operation of the train under the working conditions.

Android’s open-source nature has contributed to the platform’s rapid growth and its widespread adoption. However, this widespread adoption of the Android operating system (OS) has also attracted the attention of malicious actors who develop malware targeting these devices. Android malware threatens users’ privacy, data security, and overall device performance. Machine learning (ML) plays a significant role in malware analysis and detection because it can process huge amounts of data, identify complex patterns, and adjust to changing threats. The purpose of this paper is to provide a comprehensive review of the existing research on ML-based techniques used to detect and analyze Android malware. In this paper, the security weaknesses in Android OS are explored and the reasons why these weaknesses do not exist in the iPhone operating system (iOS) are discussed. Further, the authors examine the existing studies that have been proposed by researchers and outlines their strengths and limitations. The findings reveal that the existing researches utilize different ML models, features, and detection techniques, including static, dynamic, and hybrid approaches. Moreover, directions for future research and potential areas that require more attention and improvement in this field are highlighted.

The rapid advancements in wireless communications have prompted a surge in mobile data traffic, necessitating innovative solutions for 5G and beyond. This paper introduces a two-tier Heterogeneous Cloud Radio Access Network (HC-RAN) model leveraging millimeter Wave (mmWave) and sub-6 GHz frequencies to address this need. It integrates User-RRH associations to mitigate interference, enhance network throughput (via Heuristic Algorithm) and RRH-BBU clustering (via k-means) to manage resources in the network. The study evaluates SINR and rate coverage probabilities across various deployment scenarios, including Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions, as well as random and edge-based deployments. Results demonstrate that strategic placement of Remote Radio Heads (RRHs) and efficient clustering significantly improve network efficiency and user connectivity. In LOS conditions, random RRH deployments deliver superior coverage and throughput due to spatial diversity and reduced path loss. Conversely, edge-based deployments necessitate more resources to handle traffic demands but can excel in controlled scenarios. The proposed joint User-RRH association with RRH-BBU k-means clustering algorithm effectively manages interference, also maintains a balance between quality of service and efficient resource management. The proposed User-RRH association sub problem scheme that based on minimum path loss as a basic criterion outperforms on Limited Capacity User-RRH Association scheme (LC UA) in both the random and edge deployment scenarios and yield increasing in average throughput by approximately 38% and 27%, respectively. In other hand, the adaptive solution of RRH-BBU k-means clustering sub problem depend on actual load and number of active RRHs in the network to find the number of k RRH-BBU clusters, which manage resource consumption. This highlights the challenges in resource allocation and management with and without clustering. This paper concludes that optimized cell site deployment combined with association and clustering algorithms can significantly enhance 5G network performance, particularly in dense urban environments. These insights help network operators balance high service quality with efficient resource utilization.

The Industrial Internet of Things Networks (IIoT-N) have revolutionized industrial systems by connecting sensors, devices, and data analytics, creating complex, data-driven environments. However, key challenges persist, such as data diversity, scalability issues, sparse data, anomaly detection, and adapting to changing conditions while managing limited resources. To address these, Transfer Learning, a machine learning technique, has become a practical solution, enhancing IIoT-N by enabling better data integration, real-time analytics, improved fault detection, and adaptable models with minimal retraining, all while optimizing resource usage. This paper examines the role of Transfer Learning in IIoT-N, identifying research gaps and challenges, and highlights its potential across various industrial applications, including predictive maintenance, anomaly detection, edge computing, process optimization, and cross-domain knowledge transfer. Special attention is given to manufacturing, where Transfer Learning shows significant promise. The study also proposes a taxonomy of approaches for integrating Transfer Learning into IIoT-N. These include Domain Adaptation Techniques, Data Augmentation and Synthesis, Hybrid Models using Ensemble Learning, and practical strategies for successful implementation. Additionally, a detailed case study demonstrates the real-world application of Transfer Learning in industrial networks, showcasing its practical benefits. Thus, this paper contributes to the advancement of Transfer Learning in IIoT-N by providing insights into its potential to enhance industrial processes and address existing challenges. It offers theoretical and practical perspectives on how Transfer Learning can be effectively applied in industrial environments, driving efficiency and adaptability.

Transfer learning models tackle two critical problems in deep learning. First, for small datasets, it reduces the problem of overfitting. Second, for large datasets, it reduces the computational cost as fewer iterations are required to train the model. Standard transfer learning models such as VGGNet, ResNet, and GoogLeNet require significant memory and computational power, limiting their use on devices with limited resources. The research paper contributes to overcoming this problem by compressing the transfer learning model using channel pruning. In current times, computational cost is more significant compared to memory cost. The convolution layer with fewer parameters contributes more to computational cost. Thus, we focus on pruning the convolution layer to reduce computational cost. Total loss is a combination of prediction loss and regularization loss. Regularization loss is the sum of the magnitudes of parameter values. The training process aims to reduce total loss. In order to reduce total loss, the regularization loss also needs to be reduced. Therefore, training not only minimizes prediction error but also manages the magnitude of the model’s weights. Important weights are maintained at higher values to keep the prediction loss low, while unimportant weight values can be reduced to decrease regularization loss. Thus regularization adjusts the magnitudes of parameters at varying rates, depending on their importance. Quantitative pruning methods select parameters based on their magnitude, which improves the effectiveness of the pruning process. Standard $L_{1}$ and $L_{2}$ regularization focus on individual parameters, aiding in unstructured pruning. However, group regularization is required for structured pruning. To address this, we introduce a novel group regularization loss designed specifically for structured channel pruning. This new regularization loss optimizes the pruning process by focusing on entire groups of parameters belonging to the channel rather than just individual ones. This method ensures that structured pruning is more efficient and targeted. Custom Standard Deviation (CSD) is calculated by summing the absolute differences between each parameter value and the mean value. To evaluate the parameters of a given channel, both the $L_{1}$ norm and CSD are computed. The novel regularization loss for a channel in the convolutional layer is defined as the ratio of $L_{1}$ norm to CSD ( $L_{1}Norm/CSD$ ). This approach groups the regularization loss for all parameters within a channel, making the pruning process more structured and efficient. Custom regularization loss further improves pruning efficiency, enabling a 46.14% reduction in parameters and a 61.91% decrease in FLOPs. This paper also employs the K-Means algorithm for similarity-based pruning and evaluates three distance metrics: Manhattan, Euclidean, and Cosine. Results indicate that pruning by K-Means algorithms using Manhattan distance leads to a 35.15% reduction in parameters and a 49.11% decrease in FLOPs, outperforming Euclidean and Cosine distances using the same algorithm.

Heterogenous Wireless Sensor Networks (HWSNs) are cost-effective solutions to monitor the environment. These networks face limited battery as a critical challenge. Traditionally, energy-efficient routing algorithms have been the primary solution. While Artificial Intelligence (AI)-based approaches, particularly metaheuristics, show promise, current methods suffer from a key limitation: problem formulation hinders their success. Existing approaches rely on the array data structure for tree construction, leading to inefficiency. This paper proposes a novel Genetic Algorithm (GA)-based approach for data collection in HWSNs. It introduces a simpler and more intuitive tree data structure specifically designed by GA, leading to improved performance. Additionally, the paper proposes population initialization schemes and GA operators customized for the problem. Finally, an advanced cost function is employed that distributes workloads based on hop count to the Base Station (BS), further optimizing energy consumption. Extensive simulations demonstrate the superiority of the proposed algorithm in data gathering. It increases the number of received bits to the BS by an average of 6.6%, while also almost significantly improves the network lifetime and the number of alive nodes.

A novel five-level (5L) Knight MLI for power electronics applications is proposed in this paper. The proposed inverter is constructed by modifying the conventional 5L neutral point clamped multilevel inverter (NPC MLI). The NPC MLI architecture involves a large number of components for higher level and higher switching frequency operation. In this work paper, the proposed $3\Phi $ 5L-Knight MLI is made up by evading the usage of clamping diodes, series switches, and quadratic switches. Additionally, the phase disposition pulse width modulation (PD-PWM) control technique is also employed for the proposed $3\Phi ~5$ L-Knight MLI. Compared to other traditional 5L topologies, this proposed Knight MLI has fewer components and a high-quality output voltage. It can operate at several different ranges of voltage ratings without requiring power semiconductors to be connected in series. In comparison to the NPC MLI, the proposed MLI reduces the switching stress as two switches are operated per inverter leg instead of four switches. Another feature is that there is a considerable reduction in power losses as the current flows only through fewer elements. The topology attained an efficiency of approximately 92.39% under dynamic loading. Around 75% of the switches encountered one-quarter of the peak output voltage Vdc, 20% of the switches experienced half of the peak output voltage Vdc, and the remaining switches faced the peak output voltage Vdc as voltage stress. The proposed 5L MLI topology is explored thoroughly using the MATLAB simulation model. The evaluation of results is also demonstrated concerning the proposed PD-PWM technique by comparing its performance with the conventional sinusoidal PWM method. The real-time hardware-in-loop (HIL) simulator is also used to validate the simulation outcomes of the suggested model.

Customized bus (CB) played an important role in mitigating the widespread transmission of viruses on public transit during epidemic outbreaks. However, there is limited research on the operational strategies of CB considering the influence of epidemics on passengers’ travel behavior. This paper presents a customized bus route planning and maximum seat occupancy rate setting optimization model under the assumption of elastic demand while considering the in-vehicle infection risk cost of passengers. The constraints are linearized, and the model is transformed into a mixed-integer quadratic programming problem. A hybrid algorithm based on genetic algorithm (GA) combined with simulated annealing (SA) operations and embedded descent local search is proposed to solve the route planning and maximum seat occupancy rate problem. Numerical studies are conducted to examine the model properties and the effectiveness of the hybrid algorithm. Results indicate that jointly optimizing routing and maximum seat occupancy rate of CB can significantly improve the profitability of CB while controlling total infection risk of passengers during epidemic outbreaks. Passengers’ perception on in-vehicle infection risk is helpful in reducing system infection risk. Neglecting this perception may prevent CB operators from achieving optimal operational efficiency and profitability due to overestimation of passenger demand. Notably, tightening the route length limit of CB is not always an effective way to mitigate system infection risk during an epidemic. The M-SAGA proposed in this paper is able to achieve significantly better solution quality especially for large-size networks.

Object detection using drones is a sophisticated technology that employs a camera mounted on a drone in conjunction with a computer vision algorithm to pinpoint the precise location of an object and ascertain its type. Drones are capable of rapidly scanning extensive areas, thereby facilitating efficient data collection and analysis. This capability can yield critical information and bolster swift response efforts. The utilization of object detection technology in drones offers numerous advantages. Nevertheless, despite the benefit of drones’ ability to swiftly scan wide areas, several challenges persist, including image resolution, the detection of small-sized objects, overlapping objects, and concentrated distributions. In this paper, we introduce DCE-YOLOv8, an advanced model based on YOLOv8. DCE-YOLOv8 is engineered to address the low detection rate of small objects in drone imagery. To effectively detect small objects, it is imperative to either enhance the resolution of drone images or efficiently extract the features of small objects. Additionally, the efficient integration of these extracted features is crucial. The ERB(Efficient Residual Bottleneck) and DCE(Divided Context Extraction) modules are incorporated into the Backbone, with the ERB module reducing the number of parameters to render the model more lightweight. The DCE module focuses on extracting features pertinent to small objects. Subsequently, the rate of missed detections is mitigated by comprehensively merging the shallow and deep features extracted from the neck part. The proposed method is trained using the VisDrone, demonstrating superior detection performance compared to other state-of-the-art methods. When comparing the proposed method with the YOLOv8 small version using the VisDrone dataset, the mean Average Precision value improved by approximately 43%, increasing from 22.8mAP to 32.7mAP, while the number of parameters decreased by about 57%, from 11,166,560 to 4,822,382. The Average Inference Time per Image has been optimized to 11.4 ms, which is relatively slower than YOLOv8’s 5.9 ms, yet it still maintains a robust frame rate of 87.71 FPS, emphasizing its potential for real-time detection applications. Furthermore, the proposed method underwent additional experiments using the TT100K and AFO datasets. Compared to YOLOv8 small, it demonstrates superior performance while maintaining a comparable average inference time. This paper holds significant value in balancing object detection rates and real-time operational speed, serving as a guiding reference for in-depth research in related fields.

The synergy between software engineering (SE) and artificial intelligence (AI) catalyzes software development, as numerous recent studies illustrate an intensified intersection between these domains. This systematic literature review examines the integration of AI techniques or methodologies across SE phases and related activities spanning from 2013 to 2023, resulting in the selection of 110 research papers. Investigating the profound influence of AI techniques, including machine learning, deep learning, natural language processing, optimization algorithms, and expert systems, across various SE phases—such as planning, requirement engineering, design, development, testing, deployment, and maintenance—is the focal point of this study. Notably, the extensive adoption of machine learning and deep learning algorithms in the development and testing phases has enhanced software quality through defect prediction, code recommendation, and vulnerability detection initiatives. Furthermore, natural language processing’s role in automating requirements classification and sentiment analysis has streamlined SE practices. Optimization algorithms have also demonstrated efficacy in refining SE activities such as feature location and software repair action predictions, augmenting precision and efficiency in maintenance endeavors. Prospective research emphasizes the imperative of interpretable AI models and the exploration of novel AI paradigms, including explainable AI and reinforcement learning, to promote ethical and efficient software development practices. This paper fills the gap identified in AI techniques dedicated to improving SE phases. The review concludes that AI in SE is revolutionizing the discipline, enhancing software quality, efficiency, and innovation, with ongoing efforts targeting the mitigation of identified limitations and the augmentation of AI capabilities for intelligent and dependable SE.

Shared gradients are extensively utilized for safeguarding the privacy of training data. However, an increasing body of research is uncovering that the gradients or model parameters transmitted in distributed systems may also leak users’ private information. A majority of these studies are predicated on intercepting the data transmitted by individual users to derive their private information. A smaller portion of research can extrapolate a multitude of users’ private data through the average gradients passed by the server. These investigations have analyzed information leakage in image and text domains, yet have not explored the leakage issues inherent within recommendation systems operating in distributed environments. Furthermore, the impact of batch size and model parameters on the degree of leakage has not been sufficiently analyzed. This paper proposes that within recommendation systems, it is feasible to infer the encapsulated user privacy data by receiving average gradients or model parameters provided by the server. Additionally, the paper evaluates the extent to which various parameters within the system impact the leakage and presents corresponding defensive measures while assessing their efficacy.

This paper proposes a narrow-route path planning for a mobile robot using deep deterministic policy gradient (DDPG) considering a drive system. In this paper, a narrow road is defined as a space in which a mobile robot with a non-holonomic constraint cannot move without performing turnabouts. There are various drive systems for mobile robots such as an independent two-driven wheels type, a steering type, and a car-like type. In an independent two-driven wheels type, the mobile robot plans the path including on-the-spot turning. On the other hand, in a steering type and a car-like type, the mobile robot performs the turnabouts on a narrow road. In wheeled robots, differences in drive systems can be expressed as a turning radius limit. The proposed method generates narrow-route path planning considering a turning radius limit due to a drive system. The proposed method is based on machine learning and uses DDPG as reinforcement learning. The trained model determines the translational and angular velocities that include turnabouts / on-the-spot turning according to environmental information in real time. In the simulation and experiments, we confirmed that the proposed method allowed a mobile robot, with or without a turning radius limit, to pass through a narrow road. In addition, the robustness against the trained model was evaluated by several narrow roads that differed from the learning environment. In the case of the drive system with the turning radius limit, the success rate of driving on narrow roads including different learning environments was 94% in simulation and 85% in experiments. Therefore, the effectiveness of the proposed method was confirmed by the simulation and experimental results.

The marine geoid can be determined through the utilization of the marine gravity anomaly model. The accuracy of retrieving marine gravity anomaly model with altimetry data has been improved due to the abundance and high quality of available altimetry data. The SDUST2022 GRA marine gravity anomaly model is the global marine gravity anomaly model constructed by integrating multi-source altimeter satellites, including ICEsat-2 laser altimeter satellite for the first time. The model optimizes the deflection of the vertical accuracy imbalance. The Stokes formula is employed in this study to construct the geoid model, while the two-dimensional fast Fourier transform convolution in planar coordinate is utilized to enhance computational efficiency. Additionally, the remove-computer-restore method is applied during the calculation process. Firstly, the accuracy of the algorithm is verified using the 2190-degree XGM2019e_2159 gravity field model and the 2159-degree EGM2008 gravity field model. Subsequently, the XGM2019e_2159 is removed from the SDUST2022 GRA marine gravity anomaly model, and the residual geoid height is computed utilizing the Stokes formula. Then, the geoid height of the corresponding reference field is restored. Finally, a geoid model for China Sea and its adjacent ocean is constructed. The results showed that the geoid of the China Sea and its adjacent ocean gradually increases from west to east, and the longwave and shortwave characteristics of the seafloor can be clearly seen. The reliability of the model established in this paper was verified by the least-squares collocation method, and the geoid model calculated by the two methods had a good consistency. The mean sea surface model was introduced to calculate the mean dynamic topography. According to the mean dynamic topography, the Kuroshio Extension and the mesoscale eddy phenomenon caused by the instability of background current could be clearly seen. Compared with the mean dynamic topography of DTU22MDT, the difference between the two mean dynamic topography was basically within the range of centimeters. This showed that the mean dynamic topography of the China Sea and its adjacent ocean calculated in this paper was reliable, that is, the geoid of the China Sea and its adjacent ocean constructed in this paper was reliable.

The Mobile Edge Computing (MEC) system is crucial in modern networks, particularly for applications requiring low latency and high energy efficiency. Efficient task scheduling in MEC environments to balance these requirements is challenging. This paper presented a method that improves the Deep Deterministic Policy Gradient (DDPG) algorithm for better performance in MEC systems. Stability is enhanced through a soft update mechanism for target network parameters and an experience replay buffer to store past experiences, reducing sample correlation and improving training efficiency. It also proposed a reward function considering both latency and energy consumption, comparing the effects of linear and nonlinear reward functions on system performance. The task scheduling process includes state representation, action selection, environment feedback, network updates, and target network soft updates. The experimental results indicate that by analyzing the performance of different reward functions, a nonlinear reward function has been determined to achieve faster convergence. The improved DDPG algorithm outperforms traditional task scheduling algorithms in terms of latency and energy consumption under different task loads, and also has significant advantages in robustness. The ablation experiment further verified the positive contribution of each improved part to the overall performance. At Task load 10, the latency of DDPG, DDPG+Soft update, DDPG+Experience reply buffer, and this paper model were 267 ms, 263 ms, 260 ms, and 250 ms, respectively, with energy consumption of 988J, 974 J, 969 J, and 965 J. The improved DDPG algorithm in this paper not only effectively solves the task scheduling problem in MEC systems, but also provides important references for future research.

In order to effectively do the reengineering of process models within managing their lifecycles, it is important to maintain the qualitative levels of control-flow gateway-activity formations of the discovered process models, as highly as possible, in the process mining time. However, almost all the conventional process mining systems are hardly assured the consistent functionality in discovering the higher level of qualitative formations; Setting aside the quality issue, some of them even suffer from discovering inaccurate formations. Therefore, this paper tries to devise an algorithmic approach of detecting a small group of traces in a process log dataset, which may exert strong effects on degrading the qualitative level of control-flow gateway-activity formations of the discovered process model, because these traces negatively affect to form the structural formations during the process mining time. In detail, this paper formulates the new type of exceptional traces, which is named as structural anomaly-trace type, within a process log dataset, and formally defines a series of related conceptual definitions. Next, an algorithmic approach to automatically detect the structural anomaly-traces is described, and its functional correctness is verified through its implemented process mining system operating with an exemplary dataset of the Customer Summary process logs available from the 4TU Center for Research Data. Finally, this paper validates and discusses the feasibility as well the effectiveness of the proposed approach on the process mining time as well as the process reengineering time of the process lifecycle management scheme in process-aware enterprises and organizations.

The pervasive use of AI assistant systems and machine learning-based applications in various fields and everyday life has significantly shifted. However, this shift is not without its challenges. The emergence of security threats, various attacks, and vulnerabilities in this domain has not only questioned their use but also sparked the interest of security experts and researchers, underlining the urgency and importance of this topic. However, a comprehensive and systematic research endeavor is yet to be undertaken on threat modeling based on violating basic tenets of information security on the various components of a machine learning system and evaluating their security risks. This lack of comprehensive threat modeling for each violation of a machine learning system’s confidentiality, integrity, availability, and privacy for various attacks and their risk analysis is a significant gap in the field. This article aims to bridge this gap by proposing a simple, efficient, and time-saving approach to evaluate potential attacks and their security risks by utilizing the attack tree and a risk analysis method in the Adversarial Machine Learning (AML) field. One of the most important steps in determining the overall risk of the attack is evaluating the risk attached to each node in an attack tree. A systematic approach that includes describing the system architecture and identifying its assets under various operational environment scenarios is also outlined in this paper. This approach can also offer crucial insights to security experts, aiding them in understanding and mitigating potential threats and risk analysis in AML systems. To ensure the validity and reliability of our findings, we have conducted a thorough and rigorous review of academic papers, summarizing different threats and attacks and their root cause analysis.

Accurate short-term power forecasting is crucial for the successful commercialization of solar energy, helping to prevent financial losses in energy markets. Federated learning (FL) offers a promising approach for power forecasting with small databases, enabling marketers to collaboratively develop models without exchanging sensitive data. An initial simulation of this FL use case reveals a clear discrepancy between the solar power forecasting performance of the FL model and central learning (CL) models trained on each marketer’s data. While some benefit from participating in FL, others achieve far better results with individually trained forecasting models. This paper demonstrates that the discrepancy is due to heterogeneity between the data of each marketer. As this is a poorly researched area in FL regression, a taxonomy of heterogeneity characteristics (skews) is first proposed. Next the influence of the skews are analyzed conducting a simulation study. The study has shown that differences in autocorrelation and number of data points among time series from FL clients have the greatest impact on the performance of the FL regression model. A first heterogeneity quantification approach based on ARMA-GARCH models is proposed to address this problem in the development of federated solar power forecasting systems and other federated regression problems. In addition, the paper highlights the need for further investigation of heterogeneity in federated regression problems.

FERC rule 881 requires the use of ambient-adjusted ratings (AAR) instead of standard static line ratings (SLR) to ensure more precise assessments of the maximum transfer capability of transmission lines. AAR offers several advantages, resulting in improved transfer capacities. However, the practical details of calculations of AARs require careful and detailed consideration. This paper describes the rationale, procedures and technical references used as a basis for determining AARs for FERC Order 881 compliance. IEEE standards concerning the individual power facilities are reviewed for references to ambient temperature and solar heating. The formulas, references and other relevant information are consolidated in the paper providing the necessary information to perform AAR calculations. Additionally the paper provides recommendations on developing, where applicable, conservative equipment-type rating scaling factors to be used across a utility’s fleet.

The widespread use of unmanned aerial vehicles (UAVs) drone cybersecurity testing is becoming an emerging and evolving research area for cybersecurity and privacy issues avoidance and prevention. This paper contributes to guiding the research activity by systematically surveying the commonly adopted solutions and proposals for cybersecurity testing in the drone research domain. It highlights the research challenges and issues, classifies the current proposal, methodologies, and techniques, and suggests future directions. After gathering a collection of papers using automated inquiry of well-known digital libraries and snowballing techniques, a classification schema has been proposed and applied to the identified research works. Furthermore, research questions have been identified and answered through the performed classification. The paper provides an outlook on cybersecurity testing in drone environments. It also lists current criticalities, challenges, gaps, and future directions useful to improve drone quality and increase cybersecurity. The analysis reveals that the collected results point to a meaningful evolution and innovative approaches in cybersecurity testing within current research activities.

The depletion of fossil fuel reserves and the urgent need to cut greenhouse gas emissions are driving a significant shift in the global energy sector. This transformation necessitates advanced energy management strategies in microgrids to integrate renewable energy sources efficiently. Traditional methods, including classical, metaheuristic, model predictive, stochastic, and robust control techniques, have been extensively studied. However, these approaches often struggle with slow performance and high computational demands, making them less effective for real-time applications due to the need for frequent re-optimization. In contrast, reinforcement learning, a branch of machine learning, excels by continuously learning and optimizing through real-time interactions This approach offers greater flexibility and adaptability in complex and dynamic environments. This paper provides a comparative review of various control strategies for microgrid energy management, examining their strengths and limitations. We place a particular focus on reinforcement learning algorithms and their application in microgrids. This paper conducts a detailed analysis of studies on reinforcement learning-based energy management systems, evaluating their potential to enhance energy efficiency, optimize usage, and contribute to the sustainability of modern energy infrastructures. This review aims to enhance understanding in this field and offer insights for future research on smart energy management systems.

Breast cancer has become the cancer with the highest incidence rate in the world. Robot-assisted puncture is gradually being applied to breast puncture biopsy procedures. In this paper, to address the problem of lesion drift existing in the current work of breast puncture robots, a breast tissue manipulation system based on uncalibrated vision is designed to manipulate breast tissue to move the puncture target to a position that is easy to puncture, optimizing the puncture path to reduce procedure failure rates. Firstly, the Canny operator and SURF algorithm extract the target contour and features from the breast images captured by the camera. An improved algorithm combining stochastic Hough transform and direct least squares is proposed to enhance the accuracy of the target localization in the images. Secondly, uncalibrated vision is introduced into breast tissue manipulation, the image-deformation Jacobi matrix is derived from the system model. Then, the online estimation of the image-deformation Jacobi matrix is carried out using the Unscented Kalman Filter (UKF) algorithm. The robotic arm is controlled by the vision controller to accomplish the manipulation task. Simulations verify the high performance of the control scheme based on the UKF algorithm for online estimation of the image-deformation Jacobian matrix. Finally, breast tissue manipulation experimental results show that the manipulation system successfully shifts the breast tissue deformation target to the desired target position, and the manipulation accuracy meets the clinical requirements. The breast tissue morphology control is achieved, which verifies the effectiveness and feasibility of the design in this paper.

Amidst global efforts to reduce carbon emissions, the significance of bioenergy as a sustainable alternative has become increasingly prominent. To promote the development of the bioenergy industry, corresponding intervention policies are indispensable. This paper explores the role of government intervention policies—the Skill Training Policy (STP) and the Promotion Relationship Policy (PRP)—in advancing the bioenergy sector within a contract farming framework. This paper develops models to analyze the optimal government intervention policies by capturing the strategic interactions between the government, the bioenergy company, and the farmer. The findings reveal that both STP and PRP increase biomass feedstocks supply and the profit for the farmer and the bioenergy company, enhancing overall social welfare. Specially, When the implementation cost of STP is low enough, it can enable the farmer and the bioenergy company to achieve higher profits and assist the government in achieving greater social welfare than PRP. When the green benefit from bioenergy is high, STP can more effectively promote the supply of biomass feedstocks and enhance social welfare. However, when the green benefit is relatively low, PRP is a better choice. This research provides insights for policymakers to tailor government subsidies to promote the bioenergy industry efficiently.

This review paper provides a systematic examination of differential protection schemes in microgrids, particularly focusing on configurations with electronically coupled Distributed Energy Resources (DERs). It consolidates research findings to delineate the challenges and operational complexities introduced by DERs in microgrid protection. The review dissects a variety of approaches ranging from conventional methods to sophisticated data-driven and selective phase-tripping strategies. In doing so, it identifies the gaps in current technologies and underscores the need for evolution in protection schemes to cater to the dynamic nature of microgrids with inverter-based resources (IBR) domination. The paper highlights the shift towards adaptive and intelligent protection mechanisms that promise improved fault detection, isolation, and system resilience. By offering a comprehensive analysis of existing literature, this paper underlines the critical role of differential protection in maintaining microgrid integrity and reliability in the face of growing IBR penetration. Its contribution lies in presenting a cohesive narrative on the progress and prospects of IBR-dominated microgrid protection, serving as a foundational resource for advancing research and practice in this evolving domain.

Currently, the research on virtual power plant (VPP) is mainly divided into internal optimal scheduling and external optimal scheduling, the internal scheduling is mainly carried out from the two sides of the source and load and the application of market mechanism, while the external scheduling is in line with the current diversified power market, and considers the synergistic operation with other VPPs. Therefore, To address the problems faced by VPP in dispatch operation, such as the low economic efficiency of VPPs operating independently and the high impact of grid electricity prices and source-load uncertainty on the safe and stable operation of VPPs, a method for energy sharing operation based on Nash multi-stage robust optimization for multi-VPP is proposed. Firstly, in order to coordinate the economic interests conflict between the operators of the VPP and electric vehicle (EV) users, the Stackelberg game theory is adopted to characterize the interactive behavior between the VPP operator and the EV users. Meanwhile, a multi-stage robust optimization model is used to construct the upper-level entity, taking into account the uncertainty of price-source-load. Unlike traditional models, this paper utilizes a min-maxmin-maxmin construction to describe the internal relationships of the model. Based on this, a multi-VPP energy sharing optimization framework is established using a Nash game. It combines robust optimization with Nash bargaining theory to create a multi-VPP Nash multi-stage robust optimization model. Additionally, nested C&CG algorithms are combined with ADMM algorithms is proposed in order to solve this model effectively. Finally, three multi-energy VPPs were selected for case study analysis in this paper, and the effectiveness of the proposed method was validated through simulation. Finally, through the case study, the results show that the optimization method proposed in this paper is feasible, after the optimization of the Nash three-stage robust model, in the face of the impact of multiple uncertainties, the cooperative operation of each VPP to enhance the revenue of 396.40 yuan, 610.75 yuan, 732.53 yuan, respectively, compared with the independent operation of the operating cost of each VPP have been significantly reduced, and the system load demand of each VPP can be fully satisfied.

Transformers are crucial devices in electrical power systems, playing a key role in voltage regulation, power transmission, and distribution. Leakage impedance is one of the main factors influencing the transformer’s performance. Leakage impedance affects the efficiency and stability of the power transformers. This paper focuses on the numerical estimation for the calculation of leakage impedance with three sections in the primary winding (high-voltage winding) and a single section in the secondary winding (low-voltage winding). This paper aims to comprehensively examine the variations that arise from the different cross-sectional areas of the various sections of the high-voltage winding, as well as how these variations affect the transformer’s overall leakage impedance. This study focuses on examining the effect of different cross-section areas of the high-voltage winding on leakage impedance. Finite element analysis is used to measure and evaluate the leakage impedance of the transformer under different configurations. This study examines the impact of cross-sectional area variations on the transformer’s leakage impedance, highlighting the crucial roles of impedance and inductance.

Plant pest and disease management, especially in the early stages of infestation, is a critical challenge that poses significant threats and has potential to devastate agricultural crops, causing total yield loss and food insecurity. Traditional inspection methods are time-consuming and prone to errors due to limited labor expertise. Therefore, to tackle these challenges, harnessing advanced technologies such as artificial intelligence (AI), Machine Learning/Deep Learning (ML/DL), and Internet of Things (IoT) is essential for managing and mitigating agriculture hazards. This research presents a comprehensive review of the state-of-the-art DL architectures integrated with IoT-based systems applied to plant pest and disease detection (PPDD) by investigating different potential approaches that have been employed using DL and IoT up to the year 2024 to address challenges in agriculture. Convolutional Neural Network (CNN) architectures for image recognition, object detection, and their integration with IoT, embedded into mobile devices and unmanned aerial vehicles (UAV) are explored. Moreover, the research discusses the advantages and limitations of these techniques, emphasizing their architecture design, efficiency and accuracy. The findings demonstrate that there is a tradeoff between robustness and complexity among existing techniques, and authors recommend future trends aimed at creating robust models with fewer parameters that are more accurate and easily implementable on small IoT-based and portable devices suitable for in-field and real-time applications. Furthermore, while existing review papers discuss either DL or IoT separately, this research paper uniquely focuses on their combined models, providing a comprehensive overview of the synergistic potential of leveraging IoT-driven technologies alongside advanced DL algorithms to ease the task of researchers in the field of precision agriculture particularly in PPDD.

In practical engineering, significant noise and amplitude fluctuations in bearing vibration data, hinder the accuracy of fault identification. In order to overcome these difficulties, reduce external interference, and accurately obtain effective bearing fault information in complex environments, a new technique of adaptive SVMD and RCMDSE-IDHT bearing fault diagnosis is proposed. Firstly, the article proposes to adopt the Crested Porcupine Optimizer (CPO) algorithm to optimise the Successive Variational Modal Decomposition (SVMD) parameters to form the adaptive SVMD (ASVMD) algorithm; Subsequently, the original bearing vibration signals are decomposed using the ASVMD to form a number of Intrinsic Mode Function (IMF) signals; Secondly, the Pearson correlation coefficient is used to filter the IMF signal and reconstruct so as to remove the noise from the original signal; In order to further improve the fault identification accuracy, the article proposes to optimise and improve the Differential Symbolic Entropy (DSE) to form the Refined Composite Multi-scale Differential Symbolic Entropy (RCMDSE); and the RCMDSE is used for comprehensive feature extraction of the reconstructed vibration signal; Finally, for the defects of the DHT classifier, the article proposes to adopt the Spearman’s correlation coefficient and the entropy weight method to improve the traditional Mahalanobis Distance (MD) to form the improve Weighted Mahalanobis Distance (IWMD), which uses the advantages of IWMD to improve the Self-organizing Divisive Hierarchical Voronoi Tessellation (DHT) classifier to form improve DHT (IDHT) classifier. In order to test the accuracy as well as the effectiveness of the new method proposed in the article, the bearing data was used for test analysis, and the accuracy of the failure recognition accuracy was as high as 99.237%through test analysis. To verify the superiority of the method mentioned, compared with the six fault recognition models, the results showed that the accuracy of the diagnosis of the new method of faults in this paper increased by 0.72% to 11.75%. At the same time, to validate the extensiveness of the method proposed in this paper, the article utilizes the Southeast University bearing dataset for validation.

The advancement of intelligent transportation systems is crucial for improving road safety and optimizing traffic flow. In this paper, we present SafeSmartDrive, an integrated transportation monitoring system designed to detect and assess critical elements in the driving environment while simultaneously monitoring driver behavior. The system is structured into four key layers: perception, filtering and preparation, detection and classification, and alert. SafeSmartDrive focuses on two primary objectives: (1) detecting and assessing essential traffic elements, including vehicles (buses, cars, motorcycles, trucks, bicycles), traffic signs and lights, pedestrians, animals, infrastructure damage, accident classification, and traffic risk assessment, and (2) evaluating driver behavior across various road types, such as highways, secondary roads, and intersections. Machine learning and deep learning algorithms are employed throughout the system’s components. For traffic element detection, we utilize YOLOv9 in this paper, which outperforms previous versions like YOLOv7 and YOLOv8, achieving a precision of 83.1%. Finally, we present the evaluation of the SafeSmartDrive system’s real-time detection capabilities in a specific scenario in Casablanca. SafeSmartDrive’s comprehensive architecture offers a novel approach to improving road safety through the integration of advanced detection, classification, and risk assessment capabilities.

Sub-Terahertz communication has broad application prospects for realizing ultra-broadband sixth generation (6G) system. One fundamental challenge when moving to new spectrum is to understand the science of radio propagation and propose an accurate and effective channel prediction method. In this paper, we first conduct extensive vector network analyzer-based radio propagation measurements at 140 GHz and 220 GHz in indoor hallway and lobby environments and at 280 GHz in an indoor laboratory environment. Omnidirectional and best directional path loss are modeled by empirical single-band and multi-band path loss models. Numerical results demonstrate that large-scale close-in model in this paper is simpler and more physically-based compared to floating-intercept model. In particular, a path loss prediction method based on environment features is proposed, which can predict path loss directly by utilizing random forest method, and the propagation environment are defined and extracted by scatterer features and related features of the transmitter and receiver. The performance of the proposed method is better than that of empirical path loss models. The measured results not only enrich the datasets of indoor sub-THz channel propagation, also can guide communication systems, network planning and deployment for 6G and beyond.

This paper proposes an analytical method to calculate the switch-on sequences that provides Time Modulated Antenna Arrays (TMAA) with beam scanning in the sideband region. Such time sequences can be calculated to locate the main beam both in the azimuth and elevation planes. A comparison between H. Shanks’ method and the method proposed in this paper is presented. The radiation patterns of uniform linear, square and circular arrays are presented to demonstrate the efficacy of the proposed method. Additionally, a 6-element circular antenna array prototype for 5G applications was constructed. Such array resonates at the frequency of 3.612 GHz. The radiation patterns were measured in the anechoic chamber, demonstrating the effectiveness of the proposed method.

This paper presents a new design proposal for the simplification of linear beam-forming networks by applying a novel phase interpolation technique. This paper proposes the strategic utilization of 1-to-3 division nodes and 2-to-1 recombination nodes to reconstructing the cophasal values necessary for radiation beam scanning in linear phased array systems. This novel technique simplifies the beam-forming network, achieving a reduction of phase shifters by up to 60% and ±18° of scan angle. These benefits are obtained using uniform amplitude excitation and without additional amplitude controls. The complete antenna system considers a broadband V-slot as antenna element for obtaining a bandwidth performance of approximately 1.2 GHz. This results in a 22% of bandwidth. This is illustrated by using a prototype with experimental measurement results and simulations based on electromagnetic solver (CST Microwave Studio). These results reveal an interesting reduction of phase shifters for a good trade-off of beam-steering range and bandwidth behavior. The experimental results are analyzed and compared with other techniques and previous works cited in the literature. This novel methodology provides interesting results of a high benefit for wireless applications of phased antenna systems.

This paper presents a novel and insightful approach to beamforming a $2\times 2$ antenna array using the theory of characteristic modes. The array comprises four rectangular patch antenna elements designed on an FR-4 substrate resonating at 2.4 GHz. The characteristic mode analysis (CMA) presented a fascinating physical insight into the surface current directions and the radiation characteristics of the array structure. The excitation of a specific characteristic mode led to the design of a dual-beam antenna array. After feed excitation, characteristic mode analysis (CMA) was employed to understand multi-beam pattern reconfigurability in the multi-port structure. The information of distinct and orthogonal modal characteristic fields guides the beamforming of multibeam arrays. Modal characteristic fields of specific modes were identified using a modal weighting coefficient that may combine upon port phase change for beamforming at a certain scan angle. The potential of multi-dimensional coverage of a multibeam array is presented by selecting the different phase combinations of feeding ports. The paper discusses the strengths and limitations of beam scanning due to the combination of modes. This physical insight is beneficial for future pattern reconfigurable antenna array design procedures. Measurements on a realized prototype are in a good agreement with simulations, proving the proposed concept.

In this paper, a comprehensive study of wideband dielectric resonator antennas (DRAs) loaded with special dispersive materials is given in detail. The concept and theory for a new class of wideband and compact DRAs are introduced for the first time using the new material whose relative permittivity is inversely proportional to the frequency power of n (i.e., $\varepsilon _{r }$ (f) ${=} {k}$ / $f^{n}$ , k is a constant). Traditional DRAs are normally of limited bandwidth and unstable radiation patterns. The proposed new DRAs exhibit excellent advantages in bandwidth enhancement, size reduction, single-mode purity (predominantly supporting a single mode with minimal interference from other unwanted modes), and stable radiation patterns. The new dielectric resonator theory with wideband resonance characteristics is developed. Based on this, wideband and compact cylindrical and rectangular DRAs are designed and studied. The simulated and measured results demonstrate the merits of the proposed antennas. It is shown that without changing the structures, the percentage bandwidth of the proposed cylindrical DRA made of a dispersive material can be significantly increased to 120%, compared to the DRA using the traditional material with a constant permittivity (4%). The paper provides a new approach for making wideband and compact DRAs with stable radiation patterns for future wideband wireless communications and radar applications.