The cyclic prefix (CP) is a fundamental feature that enables orthogonal frequency division multiplexing (OFDM) to achieve efficient frequency-domain equalization and resilience against delay spread (DS). However, the trade-off is a reduction in spectral efficiency (SE), prompting the need for methods to minimize CP length without compromising performance. This paper introduces an innovative CP reduction approach leveraging array non-stationarity in extra-large MIMO (XL-MIMO) systems, particularly through the concept of visibility regions (VRs). Unlike conventional methods that adopt a worstcase scenario for CP length, our approach customizes the CP length based on the maximum excess delay of each VRs, thereby reducing the CP overhead. To enhance diversity gain, maximal ratio combining (MRC) is used to optimize the contributions from antenna elements. Simulation results validate our method's ability to improve SE while reducing latency and maintaining low computational complexity. The proposed approach demonstrates practical gains with standard CP configurations, marking a significant step toward more efficient and robust XL-MIMO OFDM systems.

Vehicle-to-vehicle dynamic wireless charging (V2V-DWC) is a contemporary electrified transportation technology in which a dedicated charging vehicle supplies power to a user vehicle. This technology is gaining popularity due to the slow progress in energy storage technology and the limited availability of plug-in charging stations. The concept of V2V wireless power transfer offers a means for EVs to replenish their batteries during a journey. Literature shows that the concept has been empirically validated through analysis and experimentation, however, the issue of misalignment has not been addressed from this perspective yet. For optimal power transfer in V2V, the coils need to be in perfect alignment. Lateral Misalignment (LTM) arises when these coils are not properly aligned which results in undesirable energy loss. Furthermore, there is still a deficiency in the development of appropriate controllers for V2V misalignment problem solution. This paper aims to develop Neural Network based Adaptive Fuzzy Logic Controller (ANFIS), to mitigate the misalignment problem of V2V-DWC system. The paper delves into a comparative analysis of the proposed controller with the existing Fuzzy Logic Controller (FLC) to assess their effectiveness for different degrees of LTMs. The effectiveness of the proposed ANFIS controller has been assessed using simulations in MATLAB/Simulink alongside experimental tests. Results show that proposed ANFIS outperforms the FLC in both simulated and experimental scenarios in solving the V2V misalignment issue.

The double slit experiment is one of basic experiments in Physical optics, where two slit are parallel. Recently, Non-parallel double slit experiments have been performed, we observe the hybrid patterns, the partial interference pattern embedded in diffraction patterns. Furthermore, Curved double slit experiments have been performed, we observe Arc-shape interference patterns and Point-symmetry interference patterns. In this article, we study Non-Parallel Curved Double Slit, and observe Butterfly-shape interference patterns.

In the data-driven era, graph data is widely distributed in multiple fields, and its complex network relationships pose higher challenges to data analysis models. Although graph neural networks (GNNs) perform well in processing non-Euclidean spatial data, traditional methods are usually limited to capturing local information through message passing or relying on graph diffusion to obtain global information. This single perspective makes it difficult to achieve a balance between global and local information, especially in heterogeneous graphs, which can easily lead to information loss or over-smoothing. To this end, we propose Constrained Diffusion Graph Neural Networks (CDnet), which optimizes the graph diffusion range by adding dual constraints: Constrained Diffusion (CD) and Feature Information Flow Routing (FIFR). The CD module reduces the diffusion depth by adaptively reducing the number of diffusion steps, efficiently captures global information and reduces invalid propagation. The FIFR mechanism dynamically regulates the direction and strength of information flow between nodes to ensure precise control of information transmission and retention of local features. Extensive experimental results on public homogeneous and heterogeneous datasets show that CDnet outperforms state-of-the-art methods, demonstrating its excellent generalization and robustness in complex graph analysis. Code is publicly available at: https://github.com/FangjingLi/CDnet.

The broad adaptation of cloud computing in healthcare has deeply aroused serious concerns about privacy and security, particularly in managing sensitive patient data. This paper proposes a state-ofthe-art architecture that integrates cloud infrastructure with edge computing for secure healthcare monitoring using Wireless Body Area Networks. The proposed architecture based on an edge node ensures the preservation of privacy through anonymization techniques, which is achieved through the integrated ARX toolkit with advanced techniques including l-diversity, t-closeness, and δ presence. In our framework, a matrix-based access control system was introduced to manage permissions precisely. It also employed the Attribute-Based Signcryption for secure data transmission. The system will process the vital medical parameters through specialized functions at the edge nodes. It adopts a computation approach that balances efficiency in processing with privacy requirements in a very effective way. It integrates fuzzy logic into the architecture for diagnosis in medicine while considering compliance to healthcare regulations. These include a multi-layered mechanism for the preservation of privacy, a lean edge-based processing system for the parameters of medicine, and an integrated access control strategy. This work has contributed to the field of secure healthcare systems by showing how edge computing can improve both privacy and efficiency in the processing of medical data while imposing strict security standards at the cloud.

The paper proposes a new approach to the fast solution of matrix equations resulting from boundary element discretization of integral equations. By hybridizing fast iterative H-matrix solvers with a fast direct H-matrix preconditioner factorization, we create a framework that can be tuned between the extremes of a direct solver and a unpreconditioned iterative solver. This tuning is largely achieved using a single numerical parameter representing the preconditioner tolerance. A more complicated scheme involving two different tolerances is also briefly considered. The proposed framework is demonstrated on a high-order accurate Locally Corrected Nyström solution of surface integral equations for PEC targets. Examples consider various scattering problems including those featuring strong physical resonances. We show that appropriately choosing the preconditioner tolerance achieves the prescribed solution accuracy with minimal CPU time. Expanding from one to two tolerance parameters further enhances the framework by providing the flexibility to dynamically adjust tolerance, enabling higher compression while maintaining accuracy and fast convergence. This adaptive strategy offers significant potential for optimizing the balance between memory usage and CPU time in the future.

We investigate cooperative impersonation jamming on angle-based physical layer authentication (PLA) within 6G systems using hybrid antenna arrays. PLA leverages angleof-arrival (AoA) information to authenticate user equipment, however, it remains vulnerable to sophisticated jamming where multiple adversaries cooperate. We extend previous research by formulating a comprehensive model of PLA that integrates hybrid arrays and by developing optimized jamming strategies that consider energy and information constraints. Our results demonstrate that angle-based authentication in analog arrays are vulnerable to cooperative jamming, compared with hybrid arrays. Additionally, the combiner design in the hybrid array, along with the energy and information constraints on jamming strategies, significantly influences the success of jamming. By identifying vulnerabilities in PLA and studying effective countermeasures, this work contributes to advancing physical layer authentication in 6G systems.

The significance of collaboration and coordination in these systems cannot be overstated. They are fundamental to ensuring that individual agents, each with their unique capabilities and roles, can function together effectively. This paper delves into the theoretical foundations and practical mechanisms of collaboration and coordination within MAS, exploring their evolution, key concepts, and real-world applications.

In this paper, we propose a novel Canon Q-Transformation method for signal quantization that differs from traditional vector quantization (Q-Transforms) approaches. Our method focuses on generalizations on canonic expected signals, providing a more accurate representation of expectable signals. Unlike traditional methods, which often overlook the classification of expectable signals via fundamental measurement limitations compared to the affirmation of expected signal as detected signal via practical filter quantization or determining the expected signal by theoretical idealization, our approach integrates this key element to Q-Transform module in order to enhance signal fidelity.

Vehicular Ad-Hoc Networks (VANETs) encounter various security threats, such as greedy behaviour attacks, with most existing research focusing on the CSMA/CD protocol. This paper investigates the TDMA protocol, specifically Distributed Time Division Multiple Access (DTMAC). In this paper, we focused on identifying and addressing four novel types of greedy actions that attackers can take advantage of, revealing vulnerabilities that have not been investigated before. To detect these behaviours, we propose a watchdog model designed to analyse network traffic, extract relevant features, and generate datasets at varying levels of network density. We use Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel to identify attackers in the network, employing Grid Search Cross-Validation (GSCV) for optimal results. The effectiveness of our proposed solution is evaluated through indepth simulations using the NS2 simulator and Python. The results show that the proposed detection method can achieve a high detection rate with an accuracy attaining 95% in low density scenario and 80% in high density scenario.

The Si insulated-gate bipolar transistor (IGBT) and SiC MOSFET hybrid switch offers an effective approach to achieve both high efficiency and cost-effectiveness. Given the varying operational conditions of VVVF (Variable Voltage Variable Frequency) inverters, adaptive control of delay-time and frequency division is crucial to optimizing performance. However, traditional optimization methods are computationally expensive which requires too long time to complete optimization, making them impractical for use in VVVF inverters in vehicles such as trains or cars. This paper introduces a novel approach that divides the optimization process into offline and online stages, employing a Deep Learning approach utilizing Neural Net (NN) algorithm in both stages. This method significantly reduces the time required for optimization compared to conventional adaptive techniques, enabling real-time applicability in VVVF inverters for vehicles. The proposed method is validated using simulation data to train the NN model and test its accuracy, which can be further evaluated with real-world data in future studies.

As wireless communication networks evolve to support more devices and higher data rates, ensuring secure communication has become increasingly critical. Physical Layer Security (PLS) offers a promising approach to safeguarding data transmission by exploiting the physical properties of the communication channel. However, implementing PLS techniques often requires significant energy resources, which can be a limiting factor, particularly in resource-constrained environments like IoT networks. Energy harvesting (EH) technology presents a viable solution by enabling devices to harness ambient energy, thereby supporting the continuous operation of PLS mechanisms. This paper explores the role of energy harvesting in enhancing physical layer security, discussing various EH techniques, their integration with PLS strategies, and the challenges and future directions in this emerging area of research.

Generative Adversarial Networks (GANs) are increasingly becoming essential tools for improving the perception of autonomous vehicles in adverse environmental conditions, like low light, fog, rain, and snow. This survey discusses recent trends in applying GANs to tackle these problems and classifies the available work based on environmental conditions and GAN architectures. Studies showcase the impact of different GAN models such as CycleGAN, Super-Resolution GANs, and DeblurGAN in enhancing image quality, visibility, and detection accuracy of objects under challenging conditions. For instance, CycleGAN has been remarkably effective in nighttime image enhancement; DeblurGANs significantly reduce motion blur at capture. Furthermore, multi-modal fusion approaches have been applied very successfully with RGB and infrared (IR) data creating strong perception systems that work well in diverse operational conditions. However, practically deployed driving solutions must process information quickly while remaining computationally efficient. This survey emphasizes how GAN technology has made autonomous vehicle research more adaptable and safer; it also points out potential graduate study directions involving lighter-weight models of GANs or domain-adaptive architectures to make them more applicable. We hope our comprehensive review will help researchers utilize GAN technology to enhance perception for autonomous vehicles.

In the era of digital healthcare, the exchange of medical data is crucial for enhancing patient care and advancing medical research. However, traditional data exchange systems often face challenges such as security breaches, data silos, and lack of interoperability. This paper proposes a blockchainbased solution for revolutionizing medical data exchange by creating a secure and decentralized marketplace. Leveraging blockchain technology ensures data integrity, enhances privacy, and facilitates seamless data sharing among stakeholders. Our approach includes smart contracts for automating transactions and ensuring compliance with regulatory standards. The proposed marketplace fosters trust, reduces costs, and promotes innovation in healthcare by enabling efficient and secure data exchange. This paper also discusses the implementation details, potential challenges, and future directions for blockchain in medical data management.

Endoscopic Lumbar Discectomy (ELD) is a minimally invasive surgical procedure to remove the herniated material of a disc in the lumbar spine [1]. In the traditional approach, the surgeon holds the endoscope and inserts the instruments into its lumen to reach the surgical window and perform the discectomy. Some steps, such as opening of the ligamentum flavum, require the help of an assistant to hold the endoscope while the surgeon is occupied handling other tools: this operation results in an inevitable waste of time. To overcome this and other drawbacks of the traditional procedure, a robotic approach has been investigated, which involves the use of a collaborative robot to hold the endoscope throughout the procedure. In this context, the gripper and its actuation system must be safe, reliable and lightweight. The following paper describes a novel mechanism for manual actuation of the gripper responsible for holding the endoscope, specifically designed to allow the surgeon to have full control over the gripping force applied.

Stress tracing is an important research domain that supports many applications, such as health care and stress management; and its closest related works are derived from stress detection. However, these existing works cannot well address two important challenges facing stress detection. First, most of these studies involve asking users to wear physiological sensors to detect their stress states, which has a negative impact on the user experience. Second, these studies have failed to effectively utilize multimodal physiological signals, which results in less satisfactory detection results. This paper formally defines the stress tracing problem, which emphasizes the continuous detection of human stress states. A novel deep stress tracing method, named DST, is presented. Note that DST proposes tracing human stress based on physiological signals collected by a noncontact ultrawideband radar, which is more friendly to users when collecting their physiological signals. In DST, a signal extraction module is carefully designed at first to robustly extract multimodal physiological signals from the raw RF data of the radar, even in the presence of body movement. Afterward, a multimodal fusion module is proposed in DST to ensure that the extracted multimodal physiological signals can be effectively fused and utilized. Extensive experiments are conducted on three real-world datasets, including one self-collected dataset and two publicity datasets. Experimental results show that the proposed DST method significantly outperforms all the baselines in terms of tracing human stress states. On average, DST averagely provides a 6.31% increase in detection accuracy on all datasets, compared with the best baselines.

Long-term stability of output characteristics of piezoresistive pressure sensor chips in the form of microelectromechanical system (MEMS) is one of the most important parameters. The research analyzes the variation of useful signal of high sensitive pressure sensor chips and its errors in mechanical and temperature characteristics, which are also time-dependent. The main feature of this study is the application of the previously developed construction of mechanical part, which allows to achieve a balance between extremely high sensitivity and low error rates for ultra-low pressure range. The research demonstrates the changes after 5 years of aging for 24 pressure sensor chip samples (chip dimension 6.15x6.15 mm 2) for pressure range of 0.5 kPa with sensitivity S0,5 after = (34.5 ± 6.0) mV/V/kPa, nonlinearity 2KNL 0.5 after = (0.71 ± 0.47) %/FS and temperature hysteresis up to 0.5% in the temperature range from-30 ⁰C to +60 ⁰C. The results of long-term stability of useful signal shows that the errors for 10 out of 24 samples does not exceed the values of 1.5%, which mostly connects with influence of residual mechanical stresses (RMS) from the assembly design of pressure sensor.

Mutual coupling model is crucial in designing multiple-input-multiple-output (MIMO) antennas since mutual coupling will degrade overall MIMO performance from distorted radiation patterns and reduced antenna efficiencies. Typically, full-wave simulations have to be employed, which is often timeconsuming. Here, the artificial neural network (ANN) method is developed to reduce the modelling time. Compared to previous ANN methods that directly use model parameters as input, a novel physical preprocessing approach is proposed to incorporate antenna correlation information before the network training. As a proof of concept, the mutual coupling model of a nonuniform strongly-coupled array is realized. Furthermore, we use the trained networks for capacity estimation and power allocation with the water-filling algorithm, showing favourable model performance. The proposed physically preprocessed ANN model significantly outperforms traditional analytical solutions and direct modelling networks in terms of prediction accuracy, dataset construction costs, and network convergence, which could facilitate the fast optimization and design of advanced antenna arrays for MIMO communications.