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Maize (Zea mays) is a critical crop for global food security and economic stability. However, it is highly vulnerable to various diseases such as northern leaf blight, common rust, and maize lethal necrosis, which can lead to significant crop losses if not detected early. Traditional CNN-based models, while effective in extracting spatial features, often fail to capture subtle multi-scale variations necessary for distinguishing between disease symptoms. These models also suffer from high computational complexity when deeper layers are introduced to handle fine-grained details. Transformer-based models, on the other hand, provide long-range dependencies but come with significant computational overhead, limiting their use in real-time agricultural applications. To overcome these challenges, we propose MSCPNet, a novel architecture that combines a truncated MobileNetV2 backbone with a Multi-Scale Convolutional PoolFormer block. The truncated backbone ensures that only essential layers for general feature extraction are retained, enhancing the model’s adaptability across domains. The Multi-Scale Convolutional PoolFormer block captures both local and global dependencies through parallel convolutional branches of varying kernel sizes, while the PoolFormer module efficiently handles feature aggregation without the heavy computational cost associated with traditional attention mechanisms. This design allows the model to balance computational efficiency and high accuracy, making it highly suitable for real-time maize disease detection. Extensive evaluations on the maize leaf disease classification task yielded outstanding results, with the proposed MSCPNet achieving an accuracy of 97.44%, precision of 96.76%, recall of 97.37%, F1-score of 97.04%, and an MCC of 0.9653, with a model size of 998,084 parameters and 315,258,752 FLOPs. Furthermore, the model was evaluated on the PlantVillage dataset for tomato leaf disease classification, where it achieved an accuracy of 99.32%, precision of 99.32%, recall of 99.33%, F1-score of 99.32%, and an MCC of 0.9925. These results demonstrate the effectiveness and efficiency of MSCPNet in disease classification across different domains.

Infectious diseases are a major global public health concern. Precise modeling and prediction methods are essential to develop effective strategies for disease control. However, data imbalance and the presence of noise and intensity inhomogeneity make disease detection more challenging. Goal: In this article, a novel infectious disease pattern prediction system is proposed by integrating deterministic and stochastic model benefits with the benefits of the deep learning model. Results: The combined benefits yield improvement in the performance of solution prediction. Moreover, the objective is also to investigate the influence of time delay on infection rates and rates associated with vaccination. Conclusions: In this proposed framework, at first, the global stability at disease free equilibrium is effectively analysed using Routh-Haurwitz criteria and Lyapunov method, and the endemic equilibrium is analysed using non-linear Volterra integral equations in the infectious disease model. Unlike the existing model, emphasis is given to suggesting a model that is capable of investigating stability while considering the effect of vaccination and migration rate. Next, the influence of vaccination on the rate of infection is effectively predicted using an efficient deep learning model by employing the long-term dependencies in sequential data. Thus making the prediction more accurate.

The increasing demand for indoor IoT services has heightened the need for precise indoor localization techniques. This paper presents the Mobile Access Point-based Model (MAPM), which uses the Received Signal Strength Indicator (RSSI) to accurately estimate positions and address the challenges posed by non-line-of-sight (NLOS) conditions in indoor environments, where signal reflections and multipath effects hinder localization accuracy. Key contributions include an innovative strategy that integrates telecommunication processes with data processing for efficient system communication, a hardware implementation using the Universal Software Radio Peripheral (USRP) platform and MATLAB Simulink for software development, and a novel evaluation model with a frequency switch algorithm that enables communication between a single user-side USRP and two access points, resulting in highly accurate RSSI measurements with errors of less than 10 cm in most NLOS scenarios. The MAPM’s performance in mitigating NLOS effects is demonstrated through both simulations and real-world experiments, validating its effectiveness in improving indoor positioning accuracy.

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.

The fifth-generation (5G) wireless communications have been deployed in many countries with the following features: wireless networks at 20 Gbps as peak data rate, a latency of 1-ms, reliability of 99.999%, maximum mobility of 500 km/h, a bandwidth of 1-GHz, and a capacity of 106 up to Mbps/m2. Nonetheless, the rapid growth of applications, such as extended/virtual reality (XR/VR), online gaming, telemedicine, cloud computing, smart cities, the Internet of Everything (IoE), and others, demand lower latency, higher data rates, ubiquitous coverage, and better reliability. These higher requirements are the main problems that have challenged 5G while concurrently encouraging researchers and practitioners to introduce viable solutions. In this review paper, the sixth-generation (6G) technology could solve the 5G limitations, achieve higher requirements, and support future applications. The integration of multiple access techniques, terahertz (THz), visible light communications (VLC), ultra-massive multiple-input multiple-output ( $\mu {\mathrm{ m}}$ -MIMO), hybrid networks, cell-free massive MIMO, and artificial intelligence (AI)/machine learning (ML) have been proposed for 6G. The main contributions of this paper are a comprehensive review of the 6G vision, KPIs (key performance indicators), and advanced potential technologies proposed with operation principles. Besides, this paper reviewed multiple access and modulation techniques, concentrating on Filter-Bank Multicarrier (FBMC) as a potential technology for 6G. This paper ends by discussing potential applications with challenges and lessons identified from prior studies to pave the path for future research.

The most recent ultra-lightweight mutual authentication protocol (UMAP) adopts a mechanism for key updating to keep the security forward and backward. The back-end server updates the secret keys immediately after sending the final message. Certainly, an adversary can intercept the final messages so that the back-end server updates the secret keys while the RFID tag does not. The replay attack can send the last messages by continuing to render the back-end server and RFID tags unauthorized. The major problem faced by existing protocols, when using high cryptographic operations on low-cost RFID tags, requires more memory, and computational power makes them unusable for these types of tags. In addition, there are weaknesses in the design of the existing protocols that lead to the emergence of desynchronization, secret disclosure, and replay attacks. Therefore, this paper proposes the design and development of an ultra-lightweight mutual authentication protocol that overcomes the weaknesses in earlier protocols and builds by implementing the rotation operator, mechanism of the secret keys, T-function, and timestamp technique. The informal and formal analysis results using security protocol verification official tools (Scythe and AVISPA) show that the proposed protocol has the ability to prevent attacks, especially replay attacks. Experimental analyses of our protocol to measure the performance shows that the proposed protocol involves a lower overhead than previous protocols. The total execution time in milliseconds of the processes of the proposed protocol with the existing protocols is 0.031ms, and it achieves an average of 78.51% over other protocols. In addition, this paper provides a comparison with the most recent protocols on performance, privacy and security requirements. Our proposed protocol storage space requirement is lowest than the existing protocols, the cryptographic requirement is ultra-lightweight and efficient and shows that the communication messages for authentication in our protocol are lowest than existing protocols and our protocol fulfills the privacy and security requirements.

With the successful deployment of fifth-generation (5G) systems, both industry and academia anticipate the evolution of 6G systems to meet future data-intensive needs. 6G aims to enhance coverage, capacity, and spectral efficiency, with extremely large-input multiple-output (XL-MIMO) base stations (BS) being central to this vision. In addition, the mid-band spectrum (7-15 GHz) becomes a strategic choice, providing sufficient bandwidth and reasonable path loss characteristics. However, the implementation of XL-MIMO in the mid-band will bring fundamental changes to the electromagnetic signal propagation characteristics, especially due to the increase in Fraunhofer distance. Therefore, it is necessary to model the waves as spherical for near-field scenarios. Furthermore, the efficient operation of communication systems depends on accurate knowledge of channel state information (CSI). However, existing CSI feedback schemes (Type-1 and Type-2) were originally designed based on far-field assumptions and exhibit obvious limitations when applied to XL-MIMO systems operating within the mid-band spectrum. To address this issue, this paper proposes two innovative CSI feedback frameworks: Type-3 for single-user MIMO (SU-MIMO) scenarios and Type-4 for multi-user MIMO (MU-MIMO), providing comparable uplink overhead compared to Type-1 and Type-2 respectively. Furthermore, the performance of the proposed Type-3 and Type-4 feedback schemes in terms of spectral efficiency and overhead is also evaluated and compared with existing Type-1 and Type-2 feedback schemes. Results demonstrate the superiority of the proposed CSI feedback schemes over existing feedback schemes, remaining resilient to performance degradation with an increase in the oversampling factor while maintaining similar uplink overhead. Notably, Type-4 demonstrates significant performance improvements compared to existing Type-2 feedback, and the gain amplifies as the number of UEs increases in MU-MIMO scenarios. The results show that the proposed CSI feedback scheme alleviates the limitations of existing feedback schemes and significantly enhances the system performance in near-field XL-MIMO environments.

In the realm of computer vision, image segmentation has become a crucial task with widespread applications, particularly in medical imaging. Although there have been significant advancements in image segmentation methods, challenges persist in accurately delineating intricate structures within noisy and varied medical images. In this study, we have developed a novel segmentation model that combines the distance regularized level set evolution (DRLSE) model with a local gradient flow-based image (LGFI) and saliency maps. This innovative fusion addresses the limitations of existing methods and offers robust and precise solutions for medical image segmentation. We provide comprehensive mathematical formulations and demonstrate the effectiveness of the proposed model across diverse medical images. Through quantitative and qualitative analyses of the brain tumor segmentation (BraTS) 2019 dataset, we have demonstrated the superior accuracy, robustness, and computational efficiency of the proposed model in comparison with the state-of-the-art methods. This research marks a significant step toward enhancing medical image analysis, with potential applications in diagnostics and healthcare practices.

The emergence of Industrial Internet of Things (IIoT) networks has sparked a demand for reliable and efficient communication systems. One promising strategy to overcome interference and coverage issues in IIoT setups is leveraging massive multiple-input multiple-output (MIMO) technology in a cell-free configuration. However, implementing large antenna arrays for such systems can be impractical and expensive, especially in remote or dynamic environments. Unmanned aerial vehicles (UAVs) have emerged as a versatile solution, including for communication relays. This study investigates the integration of UAV swarms into cell-free massive MIMO systems for IIoT networks. The proposed approach involves deploying a swarm of UAVs equipped with multiple antennas as aerial base stations to serve ground-based IIoT devices. It examines the benefits of employing UAV swarms, such as expanded coverage, better interference management, and increased flexibility. Additionally, it addresses challenges like UAV mobility, resource allocation, and power consumption. To address these challenges, an algorithm for resource allocation is proposed. This algorithm considers factors like UAV mobility, quality-of-service requirements for IIoT devices, and interference among UAVs. It aims to optimize transmit power and antenna selection for each UAV, ensuring QoS satisfaction and enhancing overall system capacity. Simulation results demonstrate the effectiveness of the algorithm, indicating improvements in system capacity, coverage, and interference management. Moreover, the findings highlight the trade-offs between system performance and power consumption within the UAV swarm. In essence, this study contributes to understanding the potential of utilizing UAV swarms in cell-free massive MIMO systems for IIoT networks. The proposed resource allocation algorithm offers insights for planning and enhancing such nodes, paving the way for further research and development in this promising field.

This paper investigates the performance of hovering unmanned aerial vehicles (UAVs) using free space optical (FSO) communications with decode-and-forward (DF) relaying. Multi-hop UAV relays are considered. The channel is modeled considering the atmospheric attenuation due to the sandstorm and relative humidity (RH), in addition to the atmospheric turbulence, angle-of-arrival (AOA) fluctuations, and pointing errors (PE). We first obtain a simplified expression for end-to-end (E2E) outage probability considering atmospheric attenuation conditions, then the impact of various parameters such as transmitted power, field of view (FOV) and optical beamwidth on E2E outage probability is incorporated. We also derive a closed form expression for the ergodic capacity and characterize the effect of different system parameters. Monte Carlo simulation is utilized to prove the analytical results. The asymptotic bound for E2E outage probability is validated to offer insights into the effects of different channel coefficients. Analysis of the results indicates that the impact of low visibility due to sandstorms is comparable to that of high RH on FSO links.

Symmetry is everywhere, found in objects around us, whether artificial or natural, and acts as a mid-level cue for both human and machine perception of the chaotic real world. From real-world symmetries, humans take advantage of various tasks, but their computational treatment remains elusive. This study proposes a novel approach for detecting rotational symmetry and the order of rotation within a single object digital image. The proposed method relies on the extraction of Scale Invariant Features Transform (SIFT) features and the robust centroid point. The centroid is computed on the basis of extracted features, to be drawn in xy-plan so that the centroid is on the origin. Later on, converted to the polar domain to facilitate the extraction of rotationally symmetric pairs and the order of rotation. The symmetry exhibited by each pair in the transform domain is the function of the features’ location, orientation, magnitude, and descriptor vector. Experimental results show that the approach correctly identifies the rotational symmetry if enough features are detected and the centroid is robust one.

In image segmentation, noise and nonuniform intensity can lead to performance degradation in existing models, particularly when dealing with shadow artifacts. This study proposes a hierarchical saliency-driven segmentation model with local correntropy (SalCor) to address this problem, incorporating saliency information with local correntropy-based K-means clustering to formulate an energy function. This approach enables it to extract objects with complex backgrounds effectively regardless of noise and intensity inhomogeneity. An adaptive weight function is introduced to dynamically adjust the intensities of the energy functions (external and internal) based on the image information, resulting in enhanced model resilience to contour initialization and improved robustness. The SalCor model can handle noise robustly by leveraging the local correntropy-based K-means clustering. The proposed approach is evaluated on synthetic and real images, including medical images, such as brain and mammogram magnetic resonance imaging (MRI) and coronavirus disease 2019 (COVID-19) computed tomography images, and is compared with state-of-the-art models. The statistical analysis confirms the SalCor model’s exceptional precision and efficiency. These outcomes indicate that SalCor holds great potential for detecting brain tumors and mammogram tumors in MRIs and early diagnosis of COVID-19.

Agriculture plays the vigorous role in economy as well as it is contemplated to be the mainstay of economic system in emerging countries. Furthermore, it influences the society in huge ways including ancillary livelihoods, anyhow occurring of plant diseases impacts many farmers. Henceforth, erroneous leaf disease identification inclines to menacing effects on plants thereby curtailing product quality, productivity or quantity. To overcome these issues, we have designed Multi-agent DRL and EfficientNet assisted 3D plant leaf disease identification and severity estimation (MULTINET) framework. The main aim of this study is to effectively identify plant leaf diseases and quatify the severity of the disease. The proposed work comprehends of following four consecutive processes. At first, we have performed image pre-treatment for data cleansing using Adaptable LoW Pass Weiner (AWW) filter and then EMbellished Manta-Ray Optimization Algorithm (EMMARO) based data augmentation for enhancing image quality and balance the classes. After that, we have adopted Block Divider Model (BDM) for transfiguring 2D to 3D image which sustenance to navigate the information from multiple perceptive and bargains superior viewpoints from miscellaneous angles. Following that, we have executed dual effective segmentation on 3D image by utilizing Enhanced Deep Q-Network (EnDeep) that embrace of Haar-U-Net (HUNT) Algorithm for appropriate feature extraction and Convolution Feature Attention (CFA) Mechanism for dimensionality reduction. Here, the Multiple Deep Reinforcement Learning (DRL) agents are employed for segmentation and ensuring its accuracy through segmentation reliability checker which accommodates rewards entrenched on its actions. Based on those segmented regions, we have accomplished species detection and disease classification by the proposed Lightweight EfficientNet and Di-attention aided Bilinear-VGG (EBBI) Algorithm. At last, the severity enumeration is performed by contemplating lesions counts and its density. The proposed MULTINET framework is conducted in MATLAB R2020a simulation tool and the performance of proposed work is evaluated in terms of accuracy, precision, sensitivity, F1-score and ROC curve where the proposed work outperforms existing works. The accuracy of the proposed work increases from 19%-29%, precision increases from 19%-28%, sensitivity increases from 31%-38%, F1-score increases from 31%-38%, and ROC curve increases about 0.15–0.22 when compared with the existing works.

Vehicular ad-hoc network (VANET) is an advanced mobile wireless network. The escalation of equipped vehicles on the road grabs the attention of researchers and is constantly striving to take it further. This type of wireless network infrastructure helps to establish communication between vehicles. Roadside units, sensors, and vehicular nodes are the critical components of our work to protect the vehicular network. This research is focused on detecting and preventing fake vehicular nodes and their messages by applying fake node detection and prevention algorithms and counterfeit message detection and prevention algorithms. In our proposed approach, the fake node detection and prevention algorithms check the node profile after establishing the mesh structure. If the profile attribute named “Pen/Rew” satisfies the condition that should be less than or equal to a threshold value (zero), the fake message detection and prevention process starts. The message will be accepted once the situation is satisfied; otherwise, it is declared fake. We utilized ONE (opportunistic network environment) simulator to generate node movement models, route messages between nodes, and visualize mobility and messaging passing in real time. The results indicated that our proposed work perfectly detects and prevents fake nodes and messages.

The aim of this study is to conduct a systematic review of the academic literature regarding applications of drones in road accident management. It is an attempt to address key research questions regarding its status, applications, solutions, challenges, and opportunities. It is the first scholarly study specifically devoted to this topic that we are aware of. A systematic search of the Web of Science and Scopus databases yielded 26 relevant articles out of 147 initially identified records. The findings indicate that the emphasis in earlier studies has predominantly been on accident investigation and analysis, with a specific focus on 3D accident scene reconstruction. This review also shows a growing trend towards adopting emerging technologies like drones and integrating them with Industry 4.0 technologies for efficient road accident management. The advantages of drones for each component of road accident management were explored, and their primary challenges were identified, which are linked to flight (permission or operation) and data (collection or processing). Despite the existence of different solution methodologies and frameworks, there remains a dearth of studies examining their suitability for complex accident cases, real-life scenarios, and large-scale accidents.

The security and reliability of cellular-based wireless IoT networks are often hindered by eavesdropping attacks. Existing approaches have limitations such as ineffective relay selection, untrusted relay nodes, and improper UAV positioning. This article discusses secure and reliable communication in blockchain-enabled cellular-based IoT networks. In this article, we propose a novel approach that ensures the initial level of security to the UAV relays by enrolling the UAV relay nodes to the edge-assisted base stations for initial security and enhances the optimal UAV relay selection using dual-phase optimal relay selection mechanisms. This article performs authentication of UAV relay nodes to enhance security against active eavesdroppers and mitigates the effects of passive eavesdroppers by jointly optimizing power and position based on their actions. This enables eavesdropping to resist secure communication. The proposed approach beats the state-of-the-art algorithm in terms of decreased deployment time and gradually rising efficiency with increasing UEs. The deployment time for the previously presented algorithm is fast expanding, while the proposed integrated deployment algorithm is progressively increasing. The proposed eavesdropping resisted integration technique harvests an overlay complexity tendency due to its hybrid solution, enhancing system stability and minimizing system complexity. We further integrate the blockchain with UAVs in the system and perform authentication and appropriate relay selection in the networks. We will also demonstrate secure and reliable communication in healthcare, transportation, and industry. This paper emphasizes the significance of safe and dependable IoT connectivity and how blockchain technology and appropriate relay selection techniques might be helpful. The results show that the recommended algorithm is superior in terms of both reliability and complexity. When iteration count is minimal, the proposed method and the completely distributed have a comparable but lowest complexity as they outperform one another in different scenarios. Our proposed approach provides a possible solution to existing issues and contributes to the progress of secure and reliable communication in cellular-based wireless IoT networks.

With the advent of 5G communication networks, many novel areas of research have emerged and the spectrum of communicating objects has been diversified. Network Function Virtualization (NFV), and Software Defined Networking (SDN), are the two broader areas that are tremendously being explored to optimize the network performance parameters. Cellular Vehicle-to-Everything (C-V2X) is one such example of where end-to-end communication is developed with the aid of intervening network slices. Adoption of these technologies enables a shift towards Ultra-Reliable Low-Latency Communication (URLLC) across various domains including autonomous vehicles that demand a hundred percent Quality of Service (QoS) and extremely low latency rates. Due to the limitation of resources to ensure such communication requirements, telecom operators are profoundly researching software solutions for network resource allocation optimally. The concept of Network Slicing (NS) emerged from such end-to-end network resource allocation where connecting devices are routed toward the suitable resources to meet their requirements. Nevertheless, the bias, in terms of finding the best slice, observed in the network slices renders a non-optimal distribution of resources. To cater to such issues, a Deep Learning approach has been developed in this paper. The incoming traffic has been allocated network slices based on data-driven decisions as well as predictive network analysis for the future. A Long Short Term Memory (LSTM) time series prediction approach has been adopted that renders optimal resource utilization, lower latency rates, and high reliability across the network. The model will further ensure packet prioritization and will retain resource margin for crucial ones.

The growing penetration of wind generation comes with implicit challenges for power system operators, one of which is the effect that additional shares have on the sufficient magnitude of reserve capacity needed. The determination of adequate reserves would enhance the efficiency and flexibility of the power system. The probabilistic methodology suggested in this paper estimates the reserve requirements as a function of historical data and estimates the incremental increase in reserve requirements as the penetration level increases. Also, the paper estimates the optimal mix of spinning reserve and standing reserve to decrease the number of part-loaded power plants and accommodate more wind power, where a synchronized plant operates less efficiently when partially loaded to provide a reserve. The adopted methodology is applied to real data collected from aggregated wind farms in the period from 2015 to 2020.

Over the past decade, epistemic network analysis (ENA) has emerged as a quantitative ethnography tool for modeling discourse in different types of human behaviors. This article offers a comprehensive systematic review of ENA educational applications in empirical studies ( $\text{n}=76$ ) published between 2010 and 2021. We review the ENA methods that research has relied on, the use of educational theories, their method of application, comparisons across groups and the main findings. Our results show that ENA has helped visually model the coded interactions and illustrate the connection strength among elements of network models. The applications of ENA have expanded beyond discourse analysis to several new areas of inquiry such as modeling surveys, log files or game play. Most of the reviewed articles used ENA based on educational theories and frameworks ( $\text{n}=53$ , 69.7%), with one or more theories per article, while 23 articles (30.3%) did not report theoretical grounding. The implementation of ENA has enabled comparisons across groups and helped augment the insights of other methods such as process mining, however there is little evidence that studies have exploited the quantitative potential of ENA. Most of the reviewed studies used ENA on small sample size with manually coded interactions with few examples of large samples and automated coding.

The need for organized computing education efforts dates back to the 1950s. Since then, computing education research (CER) has evolved and matured from its early initiatives and separation from mathematics education into a respectable research specialization of its own. In recent years, a number of meta-research papers, reviews, and scientometric studies have built overviews of CER from various perspectives. This paper continues that approach by offering new perspectives on the past and present state of CER: analyses of influential papers throughout the years, of the theoretical backgrounds of CER, of the institutions and authors who create CER, and finally of the top publication venues and their citation practices. The results reveal influential contributions from early curriculum guidelines to rigorous empirical research of today, the prominence of computer programming as a topic of research, evolving patterns of learning-theory usage, the dominance of high-income countries and a cluster of 52 elite institutions, and issues regarding citation practices within the central venues of dissemination.

For over five decades, researchers have used network analysis to understand educational contexts, spanning diverse disciplines and thematic areas. The wealth of traditions and insights accumulated through these interdisciplinary efforts is a challenge to synthesize with a traditional systematic review. To overcome this difficulty in reviewing 1791 articles researching the intersection of networks and education, this study combined a scientometric approach with a more qualitative analysis of metadata, such as keywords and authors. Our analysis shows rapidly growing research that employs network analysis in educational contexts. This research output is produced by researchers in a small number of developed countries. The field has grown more recently, through the surge in the popularity of data-driven methods, the adoption of social media, and themes as teacher professional development and the now-declining MOOC research. Our analysis suggests that research combining networks and educational phenomena continues to lack an academic home, as well as remains dominated by descriptive network methods that depict phenomena such as interpersonal friendship or patterns of discourse-based collaboration. We discuss the gaps in existing research, the methodological shortcomings, the possible future directions and most importantly how network research could help advance our knowledge of learning, learners, and contribute to our knowledge and to learning theories.

Automatic subsea operations using real-time underwater optical wireless sensor networks are mooted as a new paradigm of underwater Internet of Things in this paper. To this end, we develop an underwater optical wireless sensor network prototype called AquaE-net, which consists of an optical hub and two sensor nodes with temperature, salinity, conductivity, and pH sensing capabilities. Clock synchronization is realized in AquaE-net, which paves the way for future underwater positioning and navigation. Moreover, real-time and bi-directional network communication with 100% packet success rates has been realized by AquaE-net in free space and turbid water in the harbor of the Red Sea at King Abdullah University of Science and Technology. This is the first step toward future effective marine environmental monitoring and marine resources exploration.

The Internet of Things (IoT) has established itself as a multibillion-dollar business in recent years. Despite its obvious advantages, the widespread nature of IoT renders it insecure and a potential target for cyber-attacks. Furthermore, these devices broad connectivity and dynamic heterogeneous nature can open up a new surface of attack for refined malware attacks. There is a critical need to protect the IoT environment from such attacks and malware. Therefore this research aims to propose an intelligent, SDN-enabled hybrid framework leveraging Cuda Long Short Term Memory Gated Recurrent Unit (cuLSTMGRU) for efficient threat detection in IoT environments. To properly assess the proposed system, a state-of-the-art IoT-based dataset and standard evaluation metrics were used. The proposed model achieved 99.23 % detection accuracy with a low false-positive rate. For further verification, we compare the proposed model results with two of our constructed models (i.e., cuBLSTM and cuGRUDNN) and current benchmark algorithms. The proposed model outclassed the other models regarding speed efficiency, detection accuracy, precision, and other standard evaluation metrics. Finally, the proposed work employed 10-fold cross-validation to ensure that the results were completely unbiased.

Device-to-device (D2D) communication is one of the most promising technologies in wireless cellular networks that can be employed to improve spectral and energy efficiency, increase data rates, and reduce links latency. This paper investigates fifth generation and beyond (5GB) networks, the basics of D2D communication, applications, and classification. Herein, D2D in in-band (IBD) and out-band (OBD) modes are discussed. This paper also presents the integration of D2D communication with other prominent technologies and demonstrates the importance of integration with possible solutions in improving network performance. We further investigate the challenges of D2D communication, opportunities, and future research directions of D2D in 5GB networks. In addition, D2D communication in 6G network challenges and open research areas are introduced.

Indoor tracking is one of the most attractive topics in communication and information technology. Indoor localization mechanisms can help people for navigating in complex environments. Those with complex structures such as airports, shopping malls, hospitals, and others have a massive population (Crowdsourcing) that blocks the visibility (NLOS) between the access points and the users. Accordingly, we present the mobile access point model MAPM as a new algorithm for positioning users indoors based on the mathematical model of received signal strength RSS using UWB-OFDM. MAPM participates in crowdsourcing indoors by using users as mobile access points (MAP). That may decrease the use of several static access points (SAP) indoors and increase the localization precision. First, we use ultra-wideband UWB with orthogonal frequency division multiplexing OFDM as a communication gadget between the users and the system. Those have good quality in terms of accuracy, time response, energy consumption, and efficiency against interference. Second, we measure the received signal strength then, we estimate the distance between users and the access points using the mathematical model of RSS. These can be more adapted to changing environments and device heterogeneity than RSSI fingerprints. Third, we calculate the position information by using the Euclidean distance formula. In this way, we can reduce the effect of most of the problems on the indoor positioning system. We simulate the proposed algorithm in a platform based on three tools, MATLAB for system information processing, MYSQL as a system database, and a control interface coded in Java. In the given simulation results, we find that the average MAPM error in the case of 30 users is 10.19 cm, and the detection rate has a value of 86.66%.

Drones, or unmanned aerial vehicles (UAVs), are among the most beneficial and emerging technologies, with a wide range of applications that can support the sustainability concerns of smart cities and ultimately improve citizens’ quality of life. The goals of this systematic review were to explore the involvement of surveillance drones in smart cities in terms of application status, application areas, proposed models, and characteristics of drones. We conducted this systematic review based on the preferred reporting items for systematic reviews and meta-analyzes (PRISMA) guidelines. We systematically searched the Web of Science and Scopus for journal articles and conference papers written in English and published up to August 2021. Of the 323 records identified, 43 met the inclusion criteria. Findings showed that surveillance drones were used in seven distinct research fields (transportation, environment, infrastructure, object or people detection, disaster management, data collection, and other applications). Air pollution and traffic monitoring were the dominant application areas. The majority of reviewed models were based on the application of rotary-wing single-drones with the camera as the aerial sensor. Reviewed models showed that the adoption of a single or multiple UAVs, either as a stand-alone technology or integrated with other technologies (e.g., internet of things, wireless sensor networks, convolutional neural networks, artificial intelligence, machine learning, computer vision, cloud computing, web applications), can offer efficient and sustainable solutions compared to conventional surveillance methods. This review can benefit academic researchers and practitioners.

Computerisation and digitalisation are shaping the world in fundamental and unpredictable ways, which highlights the importance of computing education research (CER). As part of understanding the roots of CER, it is crucial to investigate the evolution of CER as a research discipline. In this paper we present a case study of a Finnish CER conference called Koli Calling, which was launched in 2001, and which has become a central publication venue of CER. We use data from 2001 to 2020, and investigate the evolution of Koli Calling’s scholarly communities and zoom in on it’s publication habits and internalisation process. We explore the narrative of the development and scholarly agenda behind changes in the conference submission categories from the perspective of some of the conference chairs over the years. We then take a qualitative perspective, analysing the conference publications based on a comprehensive bibliometric analysis. The outcomes include classification of important research clusters of authors in the community of conference contributors. Interestingly, we find traces of important events in the historical development of CER. In particular, we find clusters emerging from specific research capacity building initiatives and we can trace how these connect research spanning the world CER community from Finland to Sweden and then further to the USA, Australia and New Zealand. This paper makes a strategic contribution to the evolution of CER as a research discipline, from the perspective of one central event and publication venue, providing a broad perspective on the role of the conference in connecting research clusters and establishing an international research community. This work contributes insights to researchers in one specific CER community and how they shape the future of computing education

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOMs). Therefore, finding a fast PET classification method that accurately classify image pattern is crucial. To this end, this paper proposes a new scheme for accurate and fast image pattern classification using an efficient DOM. To reduce the computational complexity of feature extraction, an election mechanism is proposed to reduce the number of processed block patterns. In addition, support vector machine is used to classify the extracted features for different block patterns. The proposed scheme is evaluated by comparing the accuracy of the proposed method with the accuracy achieved by state-of-the-art methods. In addition, we compare the performance of the proposed method based on different DOMs to get the robust one. The results show that the proposed method achieves the highest classification accuracy compared with the existing methods in all the scenarios considered.

Internet of Things (IoT) is an instantly exacerbated communication technology that is manifesting miraculous effectuation to revolutionize conventional means of network communication. The applications of IoT are compendiously encompassing our prevalent lifestyle and the integration of IoT with other technologies makes this application spectrum even more latitudinous. However, this admissibility also introduces IoT with a pervasive array of imperative security hazards that demands noteworthy solutions to be swamped. In this scientific study, we proposed Deep Learning (DL) driven Software Defined Networking (SDN) enabled Intrusion Detection System (IDS) to combat emerging cyber threats in IoT. Our proposed model (DNNLSTM) is capable to encounter a tremendous class of common as well as less frequently occurring cyber threats in IoT communications. The proposed model is trained on CICIDS 2018 dataset, and its performance is evaluated on several decisive parameters i.e Accuracy, Precision, Recall, and F1-Score. Furthermore, the designed framework is analytically compared with relevant classifiers, i.e., DNNGRU, and BLSTM for appropriate validation. An exhaustive performance comparison is also conducted between the proposed system and a few preeminent solutions from the literature. The proposed design has circumvented the existing literature with unprecedented performance repercussions such as 99.55% accuracy, 99.36% precision, 99.44% recall, and 99.42% F1-score.

In this study we provide a new viewpoint on the body of literature regarding rewards in serious and educational games. The study includes a quantitative bibliometric analysis of literature in this context from 1969 to 2020. The dataset from the Scopus abstract and citation database was analyzed with the Bibliometrix R library. The data set was manually cleaned to contain only the relevant articles and conference papers. The data was then categorized to match the common themes. From the remaining documents, the amount of annual numbers of publications is presented and the most contributing countries are shown. The most frequent terms from the abstracts and keywords set by the authors are presented, and a co-occurrence network is drawn from the same data. The results of this study reveal that the most occurring topics in this dataset are gamification, physical activity, health, game design, and game-based learning. New directions for research are provided as the most commonly used media appear to be video games and mobile devices in addition to the literature being mostly focused on theory and not practical application.

Teachers’ work is increasingly augmented with intelligent tools that extend their pedagogical abilities. While these tools may have positive effects, they require use of students’ personal data, and more research into student preferences regarding these tools is needed. In this study, we investigated how learning strategies and study engagement are related to students’ willingness to share data with learning analytics (LA) applications and whether these factors predict students’ opt-in for LA-based formative feedback. Students (N = 158) on a self-paced online course set their personal completion goals for the course and chose to opt in for or opt out of personalized feedback based on their progress toward their goal. We collected self-reported measures regarding learning strategies, study engagement, and willingness to share data for learning analytics through a survey (N = 73). Using a regularized partial correlation network, we found that although willingness to share data was weakly connected to different aspects of learning strategies and study engagement, students with lower self-efficacy were more hesitant to share data about their performance. Furthermore, we could not sufficiently predict students’ opt-in decisions based on their learning strategies, study engagement, or willingness to share data using logistic regression. Our findings underline the privacy paradox in online privacy behavior: theoretical unwillingness to share personal data does not necessarily lead to opting out of interventions that require the disclosure of personal data. Future research should look into why students opt in for or opt out of learning analytics interventions.

Real-time, dynamic, and visual monitoring of underwater scenes would become an important research topic with the development of underwater wireless optical communication (UWOC), autonomous underwater vehicle, and video fusion technologies. To this end, we developed a UWOC-based 2K real-time digital video surveillance prototype named AquaF-seer. Using a light-emitting diode and an avalanche photodetector based diffused line-of-sight UWOC system, real-time video transmission with a high resolution of 1920 × 1080 pixels is achieved over various channels, i.e., 1.5-m free space channel, pure water channel, pure water channel with 1.53-mL/s bubble-induced turbulence, pure water channel with 42.40-mL/s bubble-induced turbulence, simulated pure sea water channel, and coastal ocean water channel. Moreover, 46-m and 5-m video monitoring are implemented in free space and an outdoor diving pool. It indicates the reliability of the prototype, which is the first step to realize underwater visual monitoring in future human-robot interaction applications.

Employing circular aperture probes for microwave imaging in the near-field has been proven to render high resolution images of a variety of samples under test (SUT) when compared to the conventionally utilized rectangular aperture probes. In this paper, it is proposed to further enhance the near-field imaging resolution and sensitivity by loading the circular aperture (operating in the K-band frequency range) with a resonant iris. It is shown that the probe exhibits a confined and localized near-field distribution close to the iris opening, thereby improving its resolution and sensitivity. The design and analysis of the probe presented herein are aided by 3D electromagnetic simulations and validated experimentally. The spatial resolution of the probe is established through line scans of the probe over a pair of targets interspaced at known distances. To further demonstrate the utility of the probe in critical non-destructive testing (NDT) applications, a manufactured prototype is devised to detect cracks on metal structures and map corrosion areas hidden under paint layers. It is shown here that the proposed probe provides significant resolution and sensitivity improvement over the un-loaded circular aperture probe. Specifically, for crack detection, the proposed probe provides two-fold and more than three-fold improvement in the resolution and sensitivity, respectively. For corrosion mapping, the proposed probe yields 2 to 5 times higher image signal-to-noise ratio compared to the unloaded aperture probe.

Supporting teaching and learning programming with learning analytics is an active area of inquiry. Most data used for learning analytics research comes from learning management systems. However, such systems were not developed to support learning programming. Therefore, educators have to resort to other systems that support the programming process, which can pose a challenge when it comes to understanding students’ learning since it takes place in different contexts. Methods that support the combination of different data sources are needed. Such methods would ideally account for the time-ordered sequence of students’ learning actions. In this article, we use a novel method (multi-channel sequence mining with Hidden Markov Models, HMMs) that allows the combination of multiple data sources, accounts for the temporal nature of students’ learning actions, and maps the transitions between different learning tactics. Our study included 291 students enrolled in a higher education programming course. Students’ trace-log data were collected from the learning management system and from a programming automated assessment tool. Data were analyzed using multi-channel sequence mining and HMM. The results reveal different patterns of students’ approaches to learning programming. High achievers start earlier to work on the programming assignments, use more independent strategies and consume learning resources more frequently, while the low achievers procrastinate early in the course and rely on help forums. Our findings demonstrate the potentials of multi-channel sequence mining and how this method can be analyzed using HMM. Furthermore, the results obtained can be of use for educators to understand students’ strategies when learning programming.

A novel fully quadrature subcarrier-index shift keying orthogonal frequency division multiplexing (FQSISK-OFDM) modulation scheme for underwater acoustic communications (UWAC) is proposed in this paper. In the FQSISK, the data is conveyed by active and non-active OFDM-subcarriers indices. Moreover, the active odd and even subcarriers are marked with separable predefined dummy variables (DV). Herein, the DV $\pm \delta $ (i.e., it can take any predefined value), and its quadrature $\pm j\delta $ are exploited to distinguish between active even and odd subcarriers. Hence, the possibility of power leakage from active to non-active subcarriers is reduced, which minimizes the effect of severe UWAC inter-carrier-interface (ICI). Moreover, the spectral efficiency of the FQSISK hardly depends on the subcarriers grouping size, which reduces the proposed FQSISK decoder complexity significantly. The simulation results demonstrate the proposed FQSISK superiority, as it achieves higher spectral and energy efficiencies than the plain OFDM and the recently introduced OFDM index modulation (OFDM-IM) techniques.

In order to meet strict Quality-of-Service (QoS) constraints imposed by some industrial applications, the configuration of industrial networks must address the requirements of traffic flows with different priorities such as minimum delay and packet loss. The performance is affected significantly if the end-to-end delay and packet loss surpasses a specific limit, and may become unbefitting for the destination. In this paper, we select Software-Defined Networking (SDN) technology to manage centralized devices and design an optimal path to guarantee the QoS requirements by taking into consideration two types of traffic flows: the first is low-delay while the second is both low-delay and low-loss. By using this optimal path, the Reactive Flow Installation (RFI) method increases the time delay in the forwarding of packets. So as to solve this issue, we propose a Mixed Flow Installation (MFI) method based on caching the flow rules which correspond to the optimal paths in the hash table deployed in the SDN controller memory in order to reduce the computation time of the forwarding path and the load at the SDN controller. Alternatively, the pre-configured flow rules in switches by the Proactive Flow Installation (PFI) method achieves the delay-sensitive. However, the PFI is not modified when the network status or the traffic type changes till the timeout value expires. This can affect the QoS requirements for industrial applications. To handle this challenge, we propose a PFI Re-routing (PFIR) method that redefined faster a new optimal path according to change without waiting the SDN controller for new flow rules. With the care of wireless and wired networks, we have built a simulation network via the OpenDayLight SDN controller and conducted an experimental testbed. The framework results are demonstrated by the performances through reduction of end-to-end delay, packet loss rate, and packet violation ratio.

This paper proposes a downlink synchronization and cell identity (CID) estimation technique for cellular systems based on orthogonal time-frequency space (OTFS). In the proposed technique, each base station (BS) transmits one detection preamble (DP) and two OTFS symbols (pilot and secondary-CID (SCID) signal (SS)). The DP based on a linear frequency-modulated (LFM) waveform, called proposed-LFM (pLFM), carries the primary-CID (PCID) for symbol timing (ST) synchronization, whereas the SS based on the Zadoff-Chu (ZC) sequence mapped in the delay-Doppler domain carries the SCID. The pLFM is obtained by discretizing the LFM waveform, increasing the frequency sweeping parameter beyond the limit of the operational bandwidth, and incorporating the PCID into the LFM waveform. The autocorrelation and cross-correlation functions of the pLFM are analyzed to examine its correlation properties under the influence of a high Doppler shift. To reduce the undesirable correlation properties (side peak and time ambiguity), a receiver processing (RP) scheme is developed for the pLFM. It is found that the pLFM with RP is suitable for the DP design because it can provide an accurate ST in high-mobility scenarios. In addition, the influence of OTFS modulation on the ZC sequence is derived and a low-complexity detection algorithm based on message passing is applied to detect SCID at the user equipment. The simulation results demonstrate that the proposed downlink synchronization and CID estimation techniques are suitable for OTFS-based cellular systems in high-Doppler environments.

This paper presents an adaptive control system using model predictive control for a biogas-fueled power system. The control scheme is derived from an anaerobic digestion model that includes the concentration of biodegradable volatile solid in the reactor, the concentration of volatile solid in influent, the concentration of acidogens, and the concentration of methanogens. All these concentrations are the state variables of model predictive control. The whole biogas-fueled power system has been modeled, implemented, and tested in MATLAB/Simulink environment. To validate the performance of the proposed controller, different operation conditions are studied and analyzed. The simulation results prove the effectiveness and the applicability of the proposed control system under different operating conditions.

The non-line-of-sight (NLOS) underwater communication can offer a viable route in signal propagation and coverage, thus mitigating the pointing, acquisition, and tracking difficulties in line-of-sight optical communication. However, implementing the NLOS link is non-trivial. While the NLOS technique relies on light scattering, i.e., channel turbulence can facilitate NLOS communication, the associated path-loss (PL) can be significant. Signal fading can degrade link robustness, which arises due to ocean water temperature and salinity fluctuation and gradients. To evaluate the robustness of NLOS in natural waters, we systematically measure the link metrics, such as the bit error ratio, PL, and signal-to-noise ratio (SNR), of water bodies of uniform and nonuniform vertical salinity ranging from 30–40‰ (part-per-thousand). We found that salinity-induced turbulence can establish NLOS communication with PL reduction of 2.35 dB/m and SNR increase by 32.5$\%$ for dynamic water. Furthermore, a strong correlation was obtained between the strength of signal fluctuations and the received SNR. Finally, we obtained a Gaussian distribution of the statistical scintillation behavior. These results demonstrated the benefit of using the NLOS regime for underwater wireless sensor networks for aiding designers and engineers.

VANETs clustering is an emerging research topic that serves in the intelligent transportation systems of today's technology. It aims at segmenting the moving vehicles in the road environment into sub-groups named clusters, with cluster heads for enabling effective and stable routing. Most of the VANETs clustering approaches are based on distributed models which make the decision of clusters creation lacking the global view of the vehicle's distribution and mobility in the environment. However, the availability of the LTE and long ranges of base station motivated researchers recently to provide center-based approaches. Unlike existing center-based clustering approaches of VANETs, this article uses the road segmenting phase named grid partitioning before providing summarized information to the clustering center. Furthermore, it presents an integrated approach as a combination of all the clustering tasks including assigning, cluster head selection, removing, and merging. Evaluation of the proposed approach named center-based evolving clustering based on grid partitioning (CEC-GP) is proven superior from the perspective of efficiency, stability, and consistency. An improvement percentage of the efficiency in (CEC-GP) over the benchmarks Center based stable clustering (CBSC) and evolving data clustering algorithm (EDCA) is 65% and 394% respectively.

The misalignment of mobile underwater wireless optical communication (UWOC) systems, compounded by turbulence in underwater scenarios, is a practical problem that can be resolved through various means. This work describes a pulse-position-modulation-based (PPM-based) diffused-line-of-sight UWOC system that offers a solution to this issue. PPM is found to be power-efficient and, in terms of bit-error-ratio performance, outperforms on-off keying modulation and orthogonal frequency-division multiplexing modulation in complex dynamic underwater channels. Through indoor experiments and outdoor deployment, we validated the robustness of the proposed PPM-based mobile UWOC system. This work sheds light on the practical implementation of UWOC networks for relieving the strict pointing-acquisition-and-tracking requirements when an underwater apparatus is transmitting or receiving signals on the fly.

Various near-field microwave imaging probes were developed previously for nondestructive testing of material structures. Sensitivity and resolution are key parameters for quantifying the efficacy of a given imaging probe. For polarized targets like cracks, the sensitivity of conventional aperture probes depends on the relative orientation between the probe's electric-field vector and the target. In practice, target orientation is typically unknown. Furthermore, the imaging resolution of conventional aperture probes is limited to aperture dimensions. Obtaining imaging resolution in the order of few millimeters using conventional aperture probes requires operation at high frequencies, (above 30 GHz). In contrast, planar probes consisting of loops loaded with spiral resonators could provide resolution in the order of few millimeters while working at relatively low frequencies (below 1 GHz). Unlike conventional aperture probes, the radiated near-fields of these probes are characterized by two significant components oriented along orthogonal axes. Therefore, the probe intrinsically provides a polarization-independent response. In this article, the near-field imaging resolution of the planar resonator probe is further enhanced, and its polarization-independent response is demonstrated for the first time. The response of three planar probe prototypes of different dimensions is analyzed based on numerical simulation and experimental results. The performance of the probes is extensively evaluated by acquiring line-scans and 2D-images of metal and dielectric samples. The near-field images of the metal sample obtained using the planar probes working below 1 GHz are compared to the images obtained using aperture probes working at 24 GHz, 33 GHz, and 70 GHz. Furthermore, the probe imaging performance for a polarized target (i.e., narrow cuts) is compared to dual-polarized circular probe working at 24 GHz and open-ended coaxial probe working at 10 GHz. Overall, the proposed probe yielded more than twofold increase in the image signal-to-noise ratio (SNR) compared to all the probes utilized herein.

The Industrial Internet of Things (IIoT) platform consists of purpose-driven communication controllers, enterprise-grade modems (routers and gateways), and edge computing systems that require integrated software and sensing capability in mission-critical environments. Extensible purpose-built industrial supervisory control and data acquisition networks are prone to numerous cybersecurity threats. In this paper, the historical databased qualitative threat assessment was part of the comprehensive risk breakdown (i.e., to quantify assessment and remediation) of the practicing industry (i.e., systems that rely on robotics, big data & analytics). Furthermore, a risk and operability (HAZOP & convolution neural-network) evaluation was proved to be the paramount study for autonomous vulnerability assessment. Through autonomous network management, continuous software monitoring, data-driven device insights, and integrated content filtering, the proposed endpoint protection scheme shows significant improvement in preventing data breaches, denial of service (DoS), and malware detection. A distinctive computational methodology to determine the cyber risk for industrial structures with IoT-explicit control factors has been programmed and elucidated in the perspective of IIoT systems. Firmware driven emulation (integrated and optimized) outcome aided to reduce breach ratio, better incident detection, and enhanced protection of confidential data.

Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of state-of-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in the existence of noise and intensity inhomogeneity. Herein, a novel adaptive level-set evolution protocol based on the internal and external functions is designed to eliminate the initialization sensitivity, thereby making the proposed SRIS model robust to contour initialization. In the level-set energy function, an adaptive weight function is formulated to adaptively alter the intensities of the internal and external energy functions based on image information. In addition, the sign of energy function is modulated depending on the internal and external regions to eliminate the effects of noise in an image. Finally, the performance of the proposed SRIS model is illustrated on complex real and synthetic images and compared with that of the previously reported state-of-the-art models. Moreover, statistical analysis has been performed on coronavirus disease (COVID-19) computed tomography images and THUS10000 real image datasets to confirm the superior performance of the SRIS model from the viewpoint of both segmentation accuracy and time efficiency. Results suggest that SRIS is a promising approach for early screening of COVID-19.

The connectivity of undersea sensors and airborne nodes across the water-air interface has been long sought. This study designs a free-space wireless laser communications system that yields a high net data rate of 850 Mbit/s when perfectly aligned. This system can also be used for an extended coverage of 1963 cm2 at the receiver while sustaining a net data rate of 9 Mbit/s over 10 m. The utility of this system was verified for direct communications across the water-air interface in a canal of the Red Sea based on a pre-aligned link as well as a diving pool under a mobile signal-searching mode. The canal deployment measured a real-time data rate of 87 Mbit/s when pre-aligned in turbid water over 50 min, which confirms the system robustness in harsh water environments. In the pool deployment, a drone configured with a photodetector flew over the surface of the water and recorded the underwater signals without a structure-assisted alignment. Using a four-quadrature amplitude-modulated orthogonal frequency-division multiplexing (4-QAM-OFDM) modulation scheme provided a net data rate of 44 Mbit/s over a 2.3-m underwater and 3.5-m air link. The results validated the link stability and mitigated problems that arise from misalignment and mobility in harsh environments, which paves the way for future field applications.

The beam search protocol in cellular systems requires a significant amount of search time and network resources in order to select a serving base station with the optimal beam pair. In 3D beamforming, the processing time increases significantly because the azimuth and elevation angles need to be considered during beam search. In this paper, we propose a fast 3D beamforming technique for millimeter wave (mmWave) cellular systems with a uniform planar array (UPA). In the proposed technique, the beam search time is reduced significantly by decomposing a UPA into a set of horizontal/vertical uniform linear arrays (ULAs), from which the optimal beam direction in azimuth/elevation angle is obtained. Two types of signals, Zadoff-Chu sequence and linear frequency modulated waveform are used for designing the beam search preamble (BSP), which allows a mobile user to distinguish beams in multi-cell multi-beam environments. The strengths and weaknesses of the two proposed BSPs are analyzed after performing simulation using a simple mmWave cellular system model with UPA. Furthermore, it is demonstrated that the proposed technique significantly reduces the beam search time, i.e., number of beam scans, as compared with the conventional technique.

Our goal is to develop an energy-autonomous solar cell receiver that can be integrated with a variety of smart devices to implement the Internet of Things in next-generation applications. This paper details efforts to develop such a prototype, called AquaE-lite. Owing to the capability of detecting low-intensity optical signals, 20-m and 30-m long-distance lighting and optical wireless communication with data rates of 1.6 Mbit/s and 1.2 Mbit/s have been achieved on a laboratory testbed, respectively. Moreover, field trials on an outdoor solar cell testbed and in the turbid water of a harbor by the Red Sea have been conducted. Under bright sunlight, energy autonomy and 1.2-Mbit/s optical wireless communication over a transmission distance of 15 m have been implemented, which demonstrated that AquaE-lite with an elaborate receiver circuit has excellent performance in energy harvesting and resistance to background noise. In a more challenging underwater environment, 1.2-Mbit/s signals were successfully received over a transmission distance of 2 m. It indicates that energy-autonomous AquaE-lite with large detection area has promising prospects in future underwater mobile sensor networks to significantly relieve the requirement of pointing, acquisition and tracking while resolving the energy issues.

Epilepsy is one of the most prevalent neurological issues faced by a large population around the globe. Epilepsy is marked by intermittent seizures, the detection of which can be a challenging problem. Therefore, reliably detecting the onset of seizures has evoked the interest of researchers over the last few years. Major leaps in the domain of machine learning, signal processing methods, and computational capabilities have made it a tractable task. In this paper, we apply multi-resolution dynamic mode decomposition (MRDMD), which is a data-driven dimensionality reduction technique, on the problem of epileptic seizure detection. This method can effectively separate a complex non-linear system into a collection of time-scale components at different resolutions. We have applied this algorithm on two different scalp EEG datasets, i.e., CHB-MIT and KU Leaven datasets. We have applied necessary post-processing steps to reduce the false alarm rate and boost the sensitivity and specificity. A detailed analysis of the results has been presented for the proposed method applied to both the datasets. The algorithm achieves a sensitivity of 0.937 and 0.96, a specificity of 0.99 and 0.99, a false alarm rate of 0.587 and 0.413 per hour, and a latency of 3.12 and 2.75 s for CHB-MIT and KU Leuven, respectively. The results indicate that the multi-resolution analysis yields a significant improvement in sensitivity compared with the basic dynamic mode decomposition.

In this paper, a design technique of random access preamble (RAP) for high-velocity users (HVUs) in millimeter-wave (mmWave) cellular networks is proposed. After demonstrating the inefficiency of the conventional long-term evolution (LTE)-based RAP for HVUs in mmWave networks, linear-frequency-modulation-based RAP (LFM RAP) is proposed for the detection of HVUs. The correlation of LTE-based RAP, and ambiguity and extended-ambiguity functions of LFM RAP are analyzed to examine their properties affected by the high-velocity of the user. The performance of RAPs is evaluated through detection and false alarm probabilities in a high-velocity and multiuser environment. The results show that the LFM RAP is appropriate for HVUs in mmWave cellular networks because of its high-detection probability in a high-speed environment.