Search Results (167)

The rapid evolution of virtual reality systems and the broader metaverse landscape has prompted growing research interest in biometric authentication methods for user verification. These solutions offer an additional layer of access control that surpasses traditional password-based approaches by leveraging unique physiological or behavioral traits. Current literature emphasizes analyzing controller position and orientation data, which presents challenges when using convolutional neural networks (CNNs) with non-continuous Euler angles. The novelty of the presented approach is that it addresses this limitation. We propose a modality transformation approach that generates acceleration and angular velocity signals from trajectory and orientation data. Specifically, our work employs algebraic techniques—including quaternion algebra—to model these dynamic signals. Both the original and transformed data were then used to train various CNN architectures, including Vanilla CNNs, attention-enhanced CNNs, and Multi-Input CNNs. The proposed modification yielded significant performance improvements across all datasets. Specifically, F1-score accuracy increased from 0.80 to 0.82 for the Comos subset, from 0.77 to 0.82 for the Quest subset, and notably from 0.83 to 0.92 for the Vive subset. Full article

For organizations, protecting computer networks has always been a very tough and demanding task. In the current technological era digital resources can now be protected without the need for outdated traditional perimeter-based security techniques. Organizations can use the Zero Trust Network Access (ZTNA) approach to safeguard and filter their vital digital assets for the company’s benefit. This platform uses sophisticated logical authentication to test the system’s ability to authenticate users, and network monitoring is used to look for possible security flaws and system vulnerabilities. By evaluating users’ interactions with the system and their handling of assigned digital resources, multi-factor authentication filters out unwanted access attempts. Three fundamental access control styles are provided by network segmentation, giving administrators the option to manage access in a democratic, strict, or flexible manner (least privilege approach). Full article

The rapid evolution of the metaverse is driving the development of new digital design tools that integrate Computer-Aided Design (CAD) and Building Information Modeling (BIM) technologies. Core technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) are increasingly combined with BIM to enhance collaboration and innovation in design and construction workflows. However, current BIM–VR integration often remains limited to isolated tasks, lacking persistent, multi-user environments that support continuous project collaboration. This study proposes a BIM-based Virtual World (VW) framework that addresses these limitations by creating an immersive, real-time collaborative platform for the Architecture, Engineering, and Construction (AEC) industry. The system enables multi-user access to BIM data through avatars, supports direct interaction with 3D models and associated metadata, and maintains a persistent virtual environment that evolves alongside project development. Key functionalities include interactive design controls, real-time decision-making support, and integrated training capabilities. A prototype was developed using Unreal Engine and supporting technologies to validate the approach. The results demonstrate improved interdisciplinary collaboration, reduced information loss during design iteration, and enhanced stakeholder engagement. This research highlights the potential of BIM-based Virtual Worlds to transform AEC collaboration by fostering an open, scalable ecosystem that bridges immersive environments with data-driven design and construction processes. Full article

With the explosive growth of Internet of Things (IoT) devices, massive amounts of heterogeneous data are continuously generated. However, IoT data transactions and sharing face multiple challenges such as limited device resources, untrustworthy network environment, highly sensitive user privacy, and serious data silos. How to achieve fine-grained access control and privacy protection for massive devices while ensuring secure and reliable data circulation has become a key issue that needs to be urgently addressed in the current IoT field. To address the above challenges, this paper proposes a blockchain-based data transaction and privacy protection framework. First, the framework builds a multi-layer security architecture that integrates blockchain and IPFS and adapts to the “end–edge–cloud” collaborative characteristics of IoT. Secondly, a data sharing mechanism that takes into account both access control and interest balance is designed. On the one hand, the mechanism uses attribute-based encryption (ABE) technology to achieve dynamic and fine-grained access control for massive heterogeneous IoT devices; on the other hand, it introduces a game theory-driven dynamic pricing model to effectively balance the interests of both data supply and demand. Finally, in response to the needs of confidential analysis of IoT data, a secure computing scheme based on CKKS fully homomorphic encryption is proposed, which supports efficient statistical analysis of encrypted sensor data without leaking privacy. Security analysis and experimental results show that this scheme is secure under standard cryptographic assumptions and can effectively resist common attacks in the IoT environment. Prototype system testing verifies the functional completeness and performance feasibility of the scheme, providing a complete and effective technical solution to address the challenges of data integrity, verifiable transactions, and fine-grained access control, while mitigating the reliance on a trusted central authority in IoT data sharing. Full article

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

Stroke is one of the leading causes of long-term disability worldwide, often resulting in motor impairments that limit the ability to perform daily activities independently. Conventional rehabilitation exoskeletons, while effective, are typically rigid, bulky, and expensive, limiting their usability outside of clinical settings. In response to these challenges, this work presents the development and validation of a novel soft exosuit designed for elbow flexion rehabilitation, incorporating a multi-wire Shape Memory Alloy (SMA) actuator capable of both position and force control. The proposed system features a lightweight and ergonomic textile-based design, optimized for user comfort, ease of use, and low manufacturing cost. A sequential activation strategy was implemented to improve the dynamic response of the actuator, particularly during the cooling phase, which is typically a major limitation in SMA-based systems. The performance of the multi-bundle actuator was compared with a single-bundle configuration, demonstrating superior trajectory tracking and reduced thermal accumulation. Surface electromyography tests confirmed a decrease in muscular effort during assisted flexion, validating the device’s assistive capabilities. With a total weight of 0.6 kg and a fabrication cost under EUR 500, the proposed exosuit offers a promising solution for accessible and effective home-based rehabilitation. Full article

The accelerating pace of digital life has intensified psychological strain, increasing the demand for accessible and systematized emotional support tools. Relaxing video games—defined as low-pressure, non-competitive games designed to promote calm and emotional relief—offer immersive environments that facilitate affective engagement and sustained user involvement. This study proposes a computational framework that integrates sentiment analysis and topic modeling to investigate the affective mechanisms and behavioral dynamics associated with relaxing gameplay. We analyzed nearly 60,000 user reviews from the Steam platform in both English and Chinese, employing a hybrid methodology that combines sentiment classification, dual-stage Latent Dirichlet Allocation (LDA), and multi-label mechanism tagging. Emotional relief emerged as the dominant sentiment (62.8%), whereas anxiety was less prevalent (10.4%). Topic modeling revealed key affective dimensions such as pastoral immersion and cozy routine. Regression analysis demonstrated that mechanisms like emotional relief (β = 0.0461, p = 0.001) and escapism (β = 0.1820, p < 0.001) were significant predictors of longer playtime, while Anxiety Expression lost statistical significance (p = 0.124) when contextual controls were added. The findings highlight the potential of relaxing video games as scalable emotional regulation tools and demonstrate how sentiment- and topic-driven modeling can support system-level understanding of affective user behavior. This research contributes to affective computing, digital mental health, and the design of emotionally aware interactive systems. Full article

Autonomous drone navigation presents challenges for users unfamiliar with manual flight controls, increasing the risk of collisions. This research addresses this issue by developing a multifunctional drone control system that integrates hand gesture recognition, obstacle avoidance, and 3D mapping to improve accessibility and safety. The system utilizes Google’s MediaPipe Hands software library, which employs machine learning to track 21 key landmarks of the user’s hand, enabling gesture-based control of the drone. Each recognized gesture is mapped to a flight command, eliminating the need for a traditional controller. The obstacle avoidance system, utilizing the Flow Deck V2 and Multi-Ranger Deck, detects objects within a safety threshold and autonomously moves the drone by a predefined avoidance distance away to prevent collisions. A mapping system continuously logs the drone’s flight path and detects obstacles, enabling 3D visualization of drone’s trajectory after the drone landing. Also, an AI-Deck streams live video, enabling navigation beyond the user’s direct line of sight. Experimental validation with the Crazyflie drone demonstrates seamless integration of these systems, providing a beginner-friendly experience where users can fly drones safely without prior expertise. This research enhances human–drone interaction, making drone technology more accessible for education, training, and intuitive navigation. Full article

Ransomware is a group of malware that aims to make computing resources unavailable, demanding a ransom amount to return control back to users. Ransomware can be classified into two types: crypto-ransomware and locker ransomware. Crypto-ransomware employs strong encryption and prevents users’ access to the system. Locker ransomware makes access unavailable to users either by locking the boot sector or the user’s desktop. The proposed solution is an anomaly-based ransomware detection and prevention system consisting of post- and pre-encryption detection stages. The developed IDS is capable of detecting ransomware attacks by monitoring the usage of resources, triggered by anomalous behavior during an active attack. By analyzing the recorded parameters after recovery and logging any adverse effects, we were able to train the system for better detection patterns. The proposed solution allows for detection and intervention against the crypto and locker types of ransomware attacks. In previous work, the authors introduced a novel anti-ransomware tool for Windows platforms, known as R-Locker, which demonstrates high effectiveness and efficiency in countering ransomware attacks. The R-Locker solution employs “honeyfiles”, which serve as decoy files to attract ransomware activities. Upon the detection of any malicious attempts to access or alter these honeyfiles, R-Locker automatically activates countermeasures to thwart the ransomware infection and mitigate its impact. Building on our prior R-Locker framework this work introduces a multi-stage detection architecture with resource–behavioral hybrid analysis, achieving cross-platform efficacy against evolving ransomware families not addressed previously. Full article

Photosynthetically active radiation (PAR) is crucial for plant growth, influencing photosynthesis efficiency and crop yield. The increasing adoption of controlled-environment agriculture (CEA) necessitates precise PAR monitoring. The high cost of commercial PAR sensors, however, limits their accessibility and widespread use, creating a growing need for a low-cost alternative capable of reliable deployment in diverse agricultural environments. Building on recent advancements in PAR sensing using multi-channel spectral sensors such as the AS7341 and AS7265, this study develops the electronics for an AS7341-based, open-source, cost-effective (~USD 50) PAR sensor validated across a broad PPFD range and conditions, ensuring reliability and ease of replication. It uses a relatively simple multi-linear regression that offers real-time applications without energy intensive machine learning. The developed sensor is calibrated against the industry-standard Apogee SQ-500SS PAR sensor in four distinct farming environments: (i) horizontal grow lights, (ii) vertical agrotunnel lighting, (iii) agrivoltaics, and (iv) in greenhouses. A mean error ranging from 1 to 5% indicates its suitability for controlled environment farming and continuous data logging. The open-source hardware design and systematic installation guidelines enable users to replicate, calibrate, and integrate the sensor with minimal background in electronics and optics. Full article

A multitude of factors, including accidents, chronic illnesses, and conflicts, contribute to rising global amputation rates. The World Health Organization (WHO) estimates that 57.7 million people lived with traumatic limb amputations in 2017, with many lacking access to affordable prostheses. This study presents a preliminary framework for a low-cost, electromyography (EMG)-controlled upper limb prosthesis, integrating 3D printing and EMG sensors to enhance accessibility and functionality. Surface electrodes capture bioelectric signals from muscle contractions, processed via an Arduino Uno to actuate a one-degree-of-freedom (1-DoF) prosthetic hand. Preliminary results demonstrate reliable detection of muscle contractions (threshold = 7 ADC units, ~34 mV) and motor actuation with a response time of ~150 ms, offering a cost-effective alternative to commercial systems. While limited to basic movements, this design lays the groundwork for scalable, user-centered prosthetics. Future work will incorporate multi-DoF control, AI-driven signal processing, and wireless connectivity to improve precision and usability, advancing rehabilitation technology for amputees in resource-limited settings. 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

This study presents a multi-user virtual reality (VR) tool designed to enhance hands-on learning in engineering education through real-time sensorized digital twins. The motivation stems from the limitations of traditional laboratory settings, such as time constraints and restricted access to physical equipment, which can hinder practical learning. The developed environment allows multiple students, wearing VR headsets, to interact simultaneously with a real-time synchronized virtual model of an engine, replicating its physical counterpart at the Mechanical Engineering Laboratory of the Pontificia Universidad Católica de Valparaíso, Chile. This novel integration of VR and digital twin technology offers students a unique opportunity to observe engine behavior in operation within a safe, controlled virtual space. By bridging theoretical knowledge with practical experience, this approach deepens understanding of complex mechanical concepts while fostering the development of key technical skills. Additionally, the use of real-time data visualization and digital twins provides a safer, more interactive, and efficient alternative to traditional laboratory practices, overcoming constraints like time limitations and equipment availability. This innovative method introduces students to Industry 4.0 principles, encouraging data-driven analysis and informed decision making. Full article

Aiming to address the issue of multi-user dynamic spectrum access in an opportunistic mode in cognitive radio networks leading to low sum throughput, we propose a multi-user opportunistic spectrum access method based on multi-head self-attention and multi-agent deep reinforcement learning. First, an optimization model for joint channel selection and power control in multi-user systems is constructed based on centralized training with a decentralized execution framework. In the training phase, the decision-making policy is optimized using global information, while in the execution phase, each agent makes decisions according to its observations. Meanwhile, a multi-constraint dynamic proportional reward function is designed to guide the agent in selecting more rational actions by refining the constraints and dynamically adjusting the reward proportion. Furthermore, a multi-head self-attention mechanism is incorporated into the critic network to dynamically allocate attention weights to different users, thereby enhancing the ability of the network to estimate the joint action value. Finally, the proposed method is evaluated in terms of convergence, throughput, and dynamic performance. Simulation results demonstrate that the proposed method significantly improves the sum throughput of secondary users in opportunistic spectrum access. Full article

Future smart cities will consist of a heterogeneous environment, including UGVs (Unmanned Ground Vehicles) and UAVs (Unmanned Aerial Vehicles), used for different applications such as last mile delivery. Considering the vulnerabilities of GNSS (Global Navigation System Satellite) in urban environments, a resilient PNT (Position, Navigation, Timing) solution is needed. A key research question within the PNT community is the capability to deliver a robust and resilient time solution to multiple devices simultaneously. The paper is proposing an innovative time dissemination framework, based on IQuila’s SDN (Software Defined Network) and quantum random key encryption from Quantum Dice to multiple users. The time signal is disseminated using a wireless IEEE 802.11ax, through a wireless AP (Access point) which is received by each user, where a KF (Kalman Filter) is used to enhance the timing resilience of each client into the framework. Each user is equipped with a Jetson Nano board as CC (Companion Computer), a GNSS receiver, an IEEE 802.11ax wireless card, an embedded RTC (Real Time clock) system, and a Pixhawk 2.1 as FCU (Flight Control Unit). The paper is presenting the performance of the fusion framework using the MUEAVI (Multi-user Environment for Autonomous Vehicle Innovation) Cranfield’s University facility. Results showed that an alternative timing source can securely be delivered fulfilling last mile delivery requirements for aerial platforms achieving sub millisecond offset. Full article