Search Results (59)

Traditional machine learning approaches for 5G network management relieve data from operational networks, which are often noisy and confounded, making it difficult to identify key influencing factors. This research addresses the critical gap between correlation-based prediction and interpretable, data-driven explanation. To this end, a software-defined standalone 5G architecture was developed using srsRAN and Open5GS to support multi-user scenarios. A multi-user environment was then simulated with GNU Radio, from which the initial dataset was collected. This dataset was further generated using a Conditional Tabular Generative Adversarial Network (CTGAN) to improve diversity and balance. Several machine learning models, including Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost, were trained and evaluated for predicting network performance. Among them, XGBoost achieved the best results, with an R² score of 0.998. To interpret the model, we conducted a SHAP (SHapley Additive exPlanations) analysis, which revealed that the download-to-upload bitrate ratio (dl_ul_ratio) and upload bitrate (brate_ul) were the most influential features. By leveraging a controlled experimental 5G environment, this study demonstrates how machine learning can move beyond predictive accuracy to uncover the fundamental principles governing 5G system performance, providing a robust foundation for future network optimization. Full article

Achieving consensus in group decision-making often involves overcoming significant challenges, particularly reconciling diverse perspectives and mitigating biases hindering agreement. Traditional methods relying on human facilitators are usually constrained by scalability and efficiency, especially in large-scale, fast-paced discussions. To address these challenges, this study proposes a novel real-time facilitation framework, employing large language models (LLMs) as automated facilitators within a custom-built multi-user chat system. This framework is distinguished by its real-time adaptive system architecture, which enables dynamic adjustments to facilitation strategies based on ongoing discussion dynamics. Leveraging cosine similarity as a core metric, this approach evaluates the ability of three state-of-the-art LLMs—ChatGPT 4.0, Mistral Large 2, and AI21 Jamba-Instruct—to synthesize consensus proposals that align with participants’ viewpoints. Unlike conventional techniques, the system integrates adaptive facilitation strategies, including clarifying misunderstandings, summarizing discussions, and proposing compromises, enabling the LLMs to refine consensus proposals based on user feedback iteratively. Experimental results indicate that ChatGPT 4.0 achieved the highest alignment with participant opinions and required fewer iterations to reach consensus. A one-way ANOVA confirmed that differences in performance between models were statistically significant. Moreover, descriptive analyses revealed nuanced differences in model behavior across various sustainability-focused discussion topics, including climate action, quality education, good health and well-being, and access to clean water and sanitation. These findings highlight the promise of LLM-driven facilitation for improving collective decision-making processes and underscore the need for further research into robust evaluation metrics, ethical considerations, and cross-cultural adaptability. Full article

This study addresses the impact of pandemics, economic limitations, and physical constraints on physical pilgrimage by proposing and evaluating a culturally sensitive, ritual-oriented virtual Boudhanath Stupa environment. Using user-centered design (UCD) and Behavior-Driven Development (BDD), the project created interactive ritual nodes on a Minecraft–VR platform, combining spatial configuration, symbolic elements, and exploratory freedom to move beyond static representation toward participatory engagement. A mixed-methods evaluation with 50 participants from diverse backgrounds and 2 Tibetan Buddhist experts showed positive feedback for aesthetic experience (M = 4.36) and user control (M = 4.62). Despite its non-photorealistic style, the environment was able to evoke a strong sense of presence and was recognized by experts as a “digital Dharma gate” suitable for younger audiences and those unable to travel to sacred sites. Limitations include a small sample size, a short evaluation period, and a lack of social interaction features. Future development will enhance guidance and feedback, expand narratives, support community co-creation, and introduce multi-user functions, providing a scalable framework for virtual religious cultural heritage. Full article

Detecting volatile organic compounds (VOCs) is essential for health, environmental protection, and industrial safety. VOCs contribute to air pollution, pose health risks, and can indicate leaks or contamination in industries. Applications include air quality monitoring, disease diagnosis, and food safety. This paper focuses on polymer-based hybrid sensor arrays (HSAs) utilizing interdigitated electrode (IDE) geometries for VOC detection. Achieving high selectivity and sensitivity in gas sensing remains a challenge, particularly in complex environments. To address this, we propose HSAs as an innovative solution to enhance sensor performance. IDE-based sensors are designed and fabricated using the Polysilicon Multi-User MEMS process (PolyMUMPs). Experimental evaluations are performed by exposing sensors to VOCs under controlled conditions. Traditional multi-sensor arrays (MSAs) achieve 82% prediction accuracy, while virtual sensor arrays (VSAs) leveraging frequency dependence improve performance: PMMA-VSA and PVP-VSA predict compounds with 100% and 98% accuracy, respectively. The proposed HSA, integrating these VSAs, consistently achieves 100% accuracy in compound identification and concentration estimation, surpassing MSA and VSA performance. These findings demonstrate that proposed polymer-based HSAs and VSAs, particularly with advanced IDE geometries, significantly enhance selectivity and sensitivity, advancing e-Nose technology for more accurate and reliable VOC detection across diverse applications. Full article

Massive multi-input multi-output (massive MIMO) technology offers significant multiplexing gains and enhances transmission rates by efficiently utilizing available airspace resources. However, it requires each antenna to be paired with a separate radio frequency (RF) chain, which leads to the need for numerous RF chains in the system, resulting in high hardware costs, increased computational complexity, and elevated power consumption. To address this, antenna selection technology reduces the number of RF chains required, activating only the antennas that correspond to the available RF chains. Moreover, user scheduling provides multi-user diversity in multi-user massive MIMO systems. Therefore, this paper introduces a joint antenna selection and orthogonality-based user scheduling (JAS-OUS) algorithm aimed at maximizing the system sum rate. Furthermore, to tackle the issue of fairness, which is often overlooked by traditional user scheduling algorithms, a proportional fairness user scheduling (PFUS) approach is proposed. In this scheme, user weights are updated based on proportional fairness, ensuring a fair selection of users for communication in each time slot. Simulation results demonstrate that the JAS-OUS algorithm achieves robust performance across various configurations of transmitting antennas and users. Additionally, when combined with PFUS, the joint algorithm ensures more equitable user participation in communication without compromising the system sum rate. Full article

Guayule (Parthenium argentatum A. Gray) is a valuable domestic source for rubber and resin. At its center of origin in the Northern Mexico and Southern Texas deserts, guayule, a perennial shrub, is hybridized with its relative species mariola (Parthenium incanum Kunth). As rubber and resin are the main products derived from guayule, there is interest in using guayule bagasse as a bioenergy feedstock to meet the growing bioenergy and biofuel demands. This study aimed to explore and characterize phenotypic diversity in cell wall constituents (lignin, cellulose, and hemicellulose) and their yields among 51 guayule and mariola genotypes under two irrigation regimes (well-watered and water-stressed). Significant genotypic and environmental effects were observed for lignin, cellulose and hemicellulose concentrations, and yields, indicating the wide genetic variability of the collection for bioenergy-related traits. Moderate to high entry-mean heritability values for lignin, cellulose, and hemicellulose suggest that selection is feasible to enhance genetic gain. Significant positive correlations were found among cellulose and hemicellulose concentrations and yields, indicating the possibility to select multiple traits together during breeding cycles. High positive correlations between rubber and resin and lignin, cellulose, and hemicellulose yields highlight the opportunity to develop guayule germplasm with enhanced multi-use traits for industrial applications. Wide variations in drought stress indices (stress tolerance index, yield index, and yield stability index) underscore the environmental impact on the lignocellulosic traits. Several genotypes were identified with high stress index scores and could be parental candidates for improving guayule for arid and semi-arid sustainable agricultural systems. The current study is the first to characterize the phenotypic diversities in guayule and mariola for lignocellulosic components and yield, providing the foundation for future breeding efforts aimed at enhancing guayule’s value for diverse production goals and environmental conditions. Full article

Mobile virtual reality (VR) is considered a killer application for future mobile broadband networks. However, for cloud VR, the long content delivery path and time-varying transmission rate from the content provider’s cloud VR server to the users make the quality-of-service (QoS) provisioning for VR users very challenging. To this end, in this paper, we design a 360° VR video service system that leverages proactive caching and mobile edge computing (MEC) technologies. Furthermore, we propose a multiuser access control algorithm tailored to the system, based on analytical results of the delay violation probability, which is derived considering the impact of both the multi-hop wired network from the cloud VR server to the MEC server and the wireless network from the MEC server-connected base station (BS) to the users. The proposed access control algorithm aims to maximize the number of served users by exploiting real-time and dynamic network resources, while ensuring that the end-to-end delay violation probability for each accessed user remains within an acceptable limit. Simulation results are presented to analyze the impact of diverse system parameters on both the user access probability and the delay violation probability of the accessed users, demonstrating the effectiveness of the proposed multiuser access control algorithm. It is observed in the simulation that increasing the computing capacity of the MEC server or the communication bandwidth of the BS is one of the most effective methods to accommodate more users for the system. In the tested scenarios, when the MEC server’s computing capacity (the BS’s bandwidth) increases from 0.8 Tbps (50 MHz) to 3.2 Tbps (150 MHz), the user access probability improves on average by 92.53% (85.49%). Full article

Non-orthogonal multiple access (NOMA) has emerged as a key enabler of massive connectivity in next-generation wireless networks. However, conventional NOMA studies predominantly focus on two-user scenarios, limiting their scalability in practical multi-user environments. A critical challenge in these systems is error propagation in successive interference cancellation (SIC), which is further exacerbated by hardware distortions (HWDs). Hybrid NOMA (HNOMA) mitigates SIC errors and reduces system complexity, yet cell-edge users (CEUs) continue to experience degraded sum spectral efficiency (SSE) and throughput. Cooperative NOMA (C-NOMA) enhances CEU performance through retransmissions but incurs higher energy consumption. To address these limitations, this study integrates intelligent omni-surfaces (IOSs) into a cooperative hybrid NOMA (C-HNOMA) framework to enhance retransmission efficiency and extend network coverage. The closed-form expressions for average outage probability and throughput are derived, and a power allocation (PA) optimization framework is proposed to maximize SSE, with validation through Monte Carlo simulations. The introduction of a novel strong–weak strong–weak (SW-SW) user pairing strategy capitalizes on channel diversity, achieving an SSE improvement of ∼0.48% to ∼3.81% over conventional pairing schemes. Moreover, the proposed system demonstrates significant performance gains as the number of IOS elements increases, even under imperfect SIC (iSIC) and HWD conditions. By optimizing PA values, SSE is further enhanced by at least 2.24%, even with an SIC error of 0.01 and an HWD level of 8%. These results underscore the potential of an IOS-assisted C-HNOMA system with SW-SW pairing as a viable solution for improving multi-user connectivity, SSE, and system robustness in future wireless communication networks. Full article

In the digital age, sharing moments through photos has become a daily habit. However, every face captured in these photos is vulnerable to unauthorized identification and potential misuse through AI-powered synthetic content generation. Previously, we introduced SnapSafe, a secure system for enabling selective image privacy focusing on facial regions for single-party scenarios. Recognizing that group photos with multiple subjects are a more common scenario, we extend SnapSafe to support multi-user facial privacy protection with dynamic access control designed for online photo platforms. Our approach introduces key splitting for access control, an owner-centric permission system for granting and revoking access to facial regions, and a request-based mechanism allowing subjects to initiate access permissions. These features ensure that facial regions remain protected while maintaining the visibility of non-facial content for general viewing. To ensure reproducibility and isolation, we implemented our solution using Docker containers. Our experimental assessment covered diverse scenarios, categorized as “Single”, “Small”, “Medium”, and “Large”, based on the number of faces in the photos. The results demonstrate the system’s effectiveness across all test scenarios, consistently performing face encryption operations in under 350 ms and achieving average face decryption times below 286 ms across various group sizes. The key-splitting operations maintained a $100\%$ success rate across all group configurations, while revocation operations were executed efficiently with server processing times remaining under 16 ms. These results validate the system’s capability in managing facial privacy while maintaining practical usability in online photo sharing contexts. Full article

Given the burgeoning necessity for high-speed, efficient, and secure wireless communication in 6G, visible light communication (VLC) has emerged as a fervent subject of discourse within academic and industrial circles alike. Among these considerations, it is imperative to construct scalable multi-user VLC systems, meticulously addressing pivotal issues such as power dissipation, alignment errors, and the safeguarding of user privacy. However, traditional methods like multiplexing holography (MPH) and multiple focal (MF) phase plates have shown limitations in meeting these diverse requirements. Here, we propose a novel spatial multiplexing holography (SMH) theory, a comprehensive solution that overcomes existing hurdles by enabling precise power allocation, self-designed power coverage, and secure communication through orbital angular momentum (OAM). The transformative potential of SMH is demonstrated through simulations and experimental studies, showcasing its effectiveness in power distribution within systems of VR glasses users, computer users, and smartphone users; enhancing power coverage with an 11.6 dB improvement at coverage edges; and securing data transmission, evidenced by error-free 1080P video playback under correct OAM keys. Our findings illustrate the superior performance of SMH in facilitating seamless multi-user communication, thereby establishing a new benchmark for future VLC systems in the 6G landscape. Full article

The Internet of Things (IoT) is experiencing rapid growth, with an increasing number of devices connected to the Internet. By 2020, approximately 54% of the 21.7 billion active internet-connected devices worldwide were IoT devices. This number is projected to reach 30 billion by 2025, with an average of four IoT devices per person globally. IoT devices use communication protocols, such as Bluetooth, Wi-Fi, and RFID, to facilitate data exchange. However, the absence of standardized communication protocols and reprogrammable architectures presents significant challenges for IoT applications. Smart buildings, which heavily depend on IoT technology, are particularly affected by the diversity of protocols and standards used by different devices. The Web of Things (WoT) framework has been introduced to address these challenges, enabling interoperability among devices with heterogeneous communication protocols and enhancing system programmability. The increasing adoption of IoT devices necessitates more efficient communication protocols and integrated architectures to meet the demands of modern innovative building systems. This study presents a WoT-based modular architecture designed to ensure compatibility among devices and protocols while providing scalable, flexible, and secure solutions tailored to the current IoT trends. In this study, an Application Programming Interface (API) and a Worker Service were developed using .NET Core technology and the WoT framework for modular intelligent building automation. This system integrates various subsystems, leveraging hardware and communication protocols for seamless functionality. The API facilitates device monitoring and control, while the Worker Service manages scheduling and database operations. The system supports asynchronous communication by employing the HTTP and WebSocket protocols and provides multi-user access with role-based authorization. The proposed automation system was implemented and evaluated, demonstrating its practical applicability and effectiveness in managing complex, innovative building environments. Full article

The field of additive manufacturing increasingly demands innovative solutions to optimize material processing, improve equipment efficiency, and address maintenance challenges in high-utilization environments. This study investigates the operation and management of an FFF 3D printing production line comprising eight remotely controlled printers. The system supports custom manufacturing and educational activities, focusing on processing a range of thermoplastics and composite materials. A key contribution of this work lies in addressing the impact of frequent hardware servicing caused by shared use among users. Augmented reality (AR)-guided assembly and disassembly workflows were developed to ensure uninterrupted operations. These workflows are accessible via smart devices and provide step-by-step guidance tailored to specific material and equipment requirements. The research evaluates the effectiveness of AR-enhanced maintenance in minimizing downtime, extending equipment lifespans, and ensuring consistent material performance during manufacturing processes. Furthermore, it explores the role of AR in maintaining the mechanical, thermal, and chemical properties of processed materials, ensuring high-quality outputs across diverse applications. This paper highlights the integration of advanced material processing methodologies with emerging technologies like AR, aligning with the focus on enhancing manufacturing schemes. The findings contribute to improving process efficiency and adaptability in additive manufacturing, offering insights into scalable solutions for remote-controlled and multi-user production systems. Full article

The application of DFT precoding in mitigating peak-to-average power ratio (PAPR) issues in optical wireless communication systems under power constraints is well established. However, the channel spatial diversity for multiusers needs to be considered. This paper proposes a joint DFT and spacetime precoding technique to support multiple users and mitigate the PAPR for optical wireless communication systems when OFDM modulation is used. In our study, we introduce the weighted Bussgang theorem as an alternative method for evaluating the communication performance of hard-clipped systems due to the transmitted power constraint. Our numerical results demonstrate the effectiveness of this approach in analyzing SNR and BER performance. Full article

Searchable encryption (SE) allows users to efficiently retrieve data from encrypted cloud data, but most of the existing SE solutions only support precise keyword search. Fuzzy searchable encryption agrees with practical situations well in the cloud environment, as search keywords that are misspelled to some extent can still generate search trapdoors that are as effective as correct keywords. In scenarios where multiple users can search for ciphertext, most fuzzy searchable encryption schemes ignore the security issues associated with malicious cloud services and are inflexible in multi-user scenarios. For example, in medical application scenarios where malicious cloud servers may exist, diverse types of files need to correspond to doctors in the corresponding departments, and there is a lack of fine-grained access control for sharing decryption keys for different types of files. In the application of medical cloud storage, malicious cloud servers may return incorrect ciphertext files. Since diverse types of files need to be guaranteed to be accessible by doctors in the corresponding departments, sharing decryption keys with the corresponding doctors for different types of files is an issue. To solve these problems, a verifiable fuzzy searchable encryption with blockchain-assisted multi-user scenarios is proposed. Locality-sensitive hashing and bloom filters are used to realize multi-keyword fuzzy search, and the bigram segmentation algorithm is optimized for keyword conversion to improve search accuracy. To realize fine-grained access control in multi-user scenarios, ciphertext-policy attribute-based encryption (CP-ABE) is used to distribute the shared keys. In response to the possibility of malicious servers tampering with or falsifying users’ search results, the scheme leverages the blockchain’s technical features of decentralization, non-tamperability, and traceability, and uses smart contracts as a trusted third party to carry out the search work, which not only prevents keyword-guessing attacks within the cloud server, but also solves the verification work of search results. The security analysis leads to the conclusion that the scheme is secure under the adaptively chosen-keyword attack. Full article

The emergence of Beyond 5G (B5G) networks introduces novel challenges related to interference management, particularly within the context of Multiple-Input, Multiple-Output (MIMO) and Code Division Multiple Access (CDMA) technologies. In this comprehensive review paper, we delve into the intricacies of interference mitigation techniques within the B5G framework, with a specific focus on MIMO and CDMA systems. Firstly, we provide a brief overview of MIMO and CDMA principles, emphasizing their significance in B5G networks. MIMO leverages spatial diversity by employing multiple antennas in both the transmitter and the receiver, thereby enhancing capacity and reliability. CDMA, on the other hand, enables multiple users to share the same frequency band by assigning unique codes to each user. Next, we categorize the various types of interference encountered in MIMO and CDMA systems. These include co-channel interference, adjacent-channel interference, and multiuser interference. Understanding these interference sources is crucial for designing effective mitigation strategies. Our exploration of interference mitigation techniques covers state-of-the-art approaches tailored for MIMO and CDMA scenarios. Lastly, we discuss future research directions in interference mitigation for B5G networks. This review paper provides valuable insights for researchers, practitioners, and network designers seeking to enhance the robustness and efficiency of B5G communication systems by effectively mitigating interference in MIMO and CDMA contexts. Full article