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A remote healthcare system allows patients to be monitored and diagnosed at home; however, a reliable wireless link is essential to make it possible. This study proposes a miniaturized implantable antenna resonating at 401–406 MHz, medical implant communication service band. The proposed implantable antenna has a compact 12 mm $\times$ 12 mm $\times$ 0.64 mm (92.16 mm3) footprint and is fabricated on a bio-compatible Roger R03010 material. To improve the safety, a superstrate and silicone coating around the antenna are employed to completely insulate the antenna from the surrounding tissues. The finite difference time domain and finite-element methods were utilized for numerical analysis and optimization of the proposed antenna, followed by fabrication and testing. For better validation, simulations of the proposed implantable antenna, including reflection coefficient and gain were performed within a three-layer body phantom while the measurements were conducted inside minced pork. The developed prototype of the implantable antenna exhibits 74 MHz measured bandwidth and −31.6 dBi measured gain at 402 MHz. Further, the specific absorption rate analyses were performed for safety concerns and found to be within the standard limits. Both the simulated and measured results exhibit good agreements, with the overall performance reaching the state-of-the-art.

The Internet of Things (IoT) has dramatically changed human context with the environment, ensuring productivity, comfort, and quality of life through a variety of services and applications. Nevertheless, the rapid growth of IoT devices has introduced significant security concerns like device vulnerabilities, unauthorized access, and potential data breaches.This article deals with an immediate call to empower IoT resilience against a wide range of sophisticated and prevalent cybersecurity threats. We developed two novel hybrid deep learning mechanisms, CNN-GRU (Convolutional Gated Recurrent Neural Networks) and CNN-LSTM (Convolutional Long Short-Term Memory Neural Networks), and extensively evaluated their performance on the state-of-the-art Kitsune and TON-IoT publicly available datasets. These benchmark datasets contain a variety of multivariate IoT attacks. The aim is to demonstrate the robustness of the proposed algorithms in effectively identifying telnet, password, distributed denial of service (DDoS), injection, and backdoor vulnerabilities in IoT ecosystems. We achieved approximately 99.6% accuracy in correctly distinguishing between malevolent and non-malicious activities on the Kitsune dataset. Additionally, the TON-IoT dataset demonstrated a remarkable accuracy rate of 99.00%, with minimal drops and low false alert rates. The time efficiency of both proposed algorithms renders them well-suited for deployment in IoT ecosystems. We evaluated and cross validated the proposed techniques with current benchmarks. Consequently, the proposed hybrid deep learning anomaly detection approaches not only enhance IoT security but also provide a robust control system for addressing emerging multivariate cyber threats.

Fractional order calculus (FOC) uses arbitrary order operations in differentiation and integration. Previously, the absence of methods to solve fractional differential equations limited the application of fractional order calculus. However, with the development of new methods, FOC is now applicable in various fields and has been utilized to enhance control system performance in traditional techniques. FOC is used in this study to develop a new fractional structure for the sliding mode control approach that offers improved performance and more robustness to external disturbances. The fractional order sliding mode control (FOSMC) strategy is designed using a Lyapunov-based sliding condition to ensure system stability. To enhance performance, genetic algorithms are used to adjust the fractional orders and controller parameters. The proposed fractional sliding mode control system is applied on an unmanned aerial vehicle system affected by external disturbances and then it is compared with a conventional integer order sliding mode control (IOSMC) system. Simulation results have proved the efficiency of the proposed fractional order controller where it outperforms the conventional sliding mode controller in terms of better transient dynamics and more robustness to external disturbances.

New Radio Unlicensed (NR-U) is the key representative access technology beyond 5G implementation to alleviate the spectrum crunch. NR-U shares a 5 GHz unlicensed band with WiFi, which has contention challenges for the coexisting systems due to physical and link layer protocols disparity. Being a scheduled access system, NR-U transmissions can only start at strict periodic time slots, which requires introducing a synchronization gap period in the listen-before-talk (LBT) approach. In this paper, we address these issues and analyze the impact of various gap-based NR-U approaches to the fair and efficient coexistence of the two networks. The dependency of successful spectrum access of the two systems on the gap period is also investigated. We also present a machine learning data-driven approach to unlicensed channel selection for spectrum sharing by NR-U. The results based on actual data collected from real-life WiFi deployment scenarios indicate significant improvement in coexistence performance and spectrum utilization of the unlicensed band with the proposed approach. It is shown through simulation results that the gap period before the backoff procedure provides better coexistence performance compared to the gap-based approach, where the synchronization gap is introduced after the LBT backoff. Further, the results indicate that if the gap interval exceeds a certain threshold value for each coexistence scenario, the WiFi network starts dominating the unlicensed channel, completely blocking the NR-U transmissions.

In this contribution the authors propose a hybrid Boundary Element Method – Physics Informed Neural Networks (BEM – PINN) approach, to be used for the resolution of partial differential equations arising when formulating boundary-value problems in electromagnetism. The approach retains both the advantages of integral methods (compact representation and no need to mesh large domains) and differential methods, where the term “differential” refers here to the Automatic Differentiation carried out during the training phase of the PINN. The method is easy to implement and adds an additional flexibility to purely PINN based solution methods.

Multi-agent systems (MAS) are composed of multiple autonomous agents that interact and collaborate to achieve common objectives. These systems are characterized by their decentralization, scalability, robustness, and the local perspectives of individual agents. In the realm of quadrotor applications, MAS can perform complex tasks such as formation flying, surveillance, search and rescue, and cooperative payload transport. The control of such systems often involves techniques like trajectory planning, consensus algorithms, formation control, and obstacle avoidance. This study introduces a multi-agent quadrotor system controlled using sliding mode backstepping control (SMBC), which combines the advantages of backstepping and sliding mode control to ensure stability and robustness. The novel aspect of this research is the transformation of the leader quadrotor into a fractional-order system (FOS) while maintaining the followers as integer-order systems, all controlled using the same SMBC method. MATLAB simulations validate that the fractional-order leader (FOL) achieves superior trajectory tracking compared to the integer-order leader. Furthermore, the followers tracking the fractional-order leader exhibit enhanced performance, with reduced tracking errors and quicker response times. These findings demonstrate the potential advantages of incorporating fractional-order dynamics into multi-agent control systems.

The increasing integration of cyber-physical systems (CPS) in critical infrastructures has heightened the importance of ensuring their security against various cyber threats. Game theory has emerged as a powerful analytical tool to model and analyze the strategic interactions between attackers and defenders in CPS. This survey provides a comprehensive review of the state-of-the-art research on the application of game theory in the security of CPS, with a specific focus on attack models and security analysis. We employ machine learning algorithms to classify existing research papers based on the attack target and the types of attacks they address. Our classification reveals significant trends and gaps in the current literature, offering insights into the effectiveness of different game-theoretic approaches and the prevalence of various attack models. By synthesizing the findings from over 800 research papers, we highlight the strengths and limitations of existing methodologies and propose directions for future research. This survey aims to serve as a valuable resource for researchers and practitioners seeking to enhance the security of CPS through game-theoretic frameworks and machine learning techniques.

Accents, or changes in how different people speak the same word/sentence in the same language, pose substantial communication issues in most spoken languages. This is a well-known fact, but how does the accent of one language affect learning/speaking another? In this paper, we look at how Arab accents influence the English language. To that end, we built a deep machine-learning system for Arabic accent recognition that was learned from an in-house English speech database of four Arabic accents collected from Jordan, Iraq, Saudi Arabia, and Tunisia. The proposed system employs Mel spectrograms of an English-spoken paragraph to train an LSTM neural network to recognize the accent in each sound signal. Although the collected data was extremely difficult to learn due to the presence of both males and females and fluent speakers in each class, the proposed system could recognize speakers with various accents by up to 79%. This answers the study’s main question, demonstrating that speakers with an Arabic accent have their way of speaking English, which varies by country. As a result, if trained on appropriate and adequate data, the proposed system can also be used to recognize accents in any language.