Search Results (42)

The emergence of generative AI presents complex challenges to existing copyright regimes, particularly concerning the large-scale use of copyrighted materials in model training. Legal disputes across jurisdictions highlight the urgent need for a balanced, principle-based framework that protects the rights of creators while fostering innovation. In China, a regulatory approach of “moderate leniency” has emerged—emphasizing control over downstream AI-generated content (AIGC) while adopting a more permissive stance toward upstream training. This model upholds the idea–expression dichotomy, rejecting theories such as “retained expression” or “retained style”, which improperly equate ideas with expressions. A critical legal distinction lies between real-time training, which is ephemeral and economically insignificant, and non-real-time training, which involves data retention and should be assessed under fair use test. A fair use exception specific to AI training is both timely and justified, provided it ensures equitable sharing of technological benefits and addresses AIGC’s potential substitutive impact on original works. Furthermore, technical processes like format conversion and machine translation do not infringe derivative rights, as they lack human creativity and expressive content. Even when training involves broader use, legitimacy may be established through the principle of technical necessity within the reproduction right framework. Full article

The advent of Industry 4.0 brought about digitalisation and the integration of advanced technologies into industrial processes, with wireless networks emerging as a key enabler in the interconnection of smart devices, cyber–physical systems, and data analytics platforms. With the development of Industry 5.0 and its emphasis on human–machine collaboration, Wi-Fi has positioned itself as a viable alternative for industrial wireless connectivity, supporting seamless communication between robots, automation systems, and human operators. However, its adoption in critical applications remains limited due to persistent concerns over latency, reliability, and interference in shared-spectrum environments. This study evaluates the practical performance of Wi-Fi standards from 802.11n (Wi-Fi 4) to 802.11be (Wi-Fi 7) across three representative environments: residential, laboratory, and industrial. Six configurations were tested under consistent conditions, covering various frequency bands, channel widths, and traffic types. Results prove that Wi-Fi 6/6E delivers the best overall performance, particularly in low-interference 6 GHz scenarios. Wi-Fi 5 performs well in medium-range settings but is more sensitive to congestion, while Wi-Fi 4 consistently underperforms. Early Wi-Fi 7 hardware does not yet surpass Wi-Fi 6/6E consistently, reflecting its ongoing development. Despite these variations, the progression observed across generations clearly demonstrates incremental gains in throughput stability and latency control. While these improvements already provide tangible benefits for many industrial communication scenarios, the most significant leap in industrial applicability is expected to come from the effective implementation of high-efficiency mechanisms. These include OFDMA, TWT, scheduled uplink access, and enhanced QoS features. These capabilities, already embedded in the Wi-Fi 6 and 7 standards, represent the necessary foundation to move beyond conventional best-effort connectivity and toward supporting critical, latency-sensitive industrial applications. Full article

In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. This paper proposes a method for predicting driver steering intention with a CNN-GRU hybrid machine learning model. The convolutional neural network (CNN) layer extracts features from the stochastic driver behavior, which is input to the gated-recurrent-unit (GRU) layer. And the driver’s steering intention is forecasted based on the GRU model. Our study was conducted using a driving simulator to observe the lateral control behaviors of 18 participants in four different driving circumstances. Finally, the efficiency of the suggested prediction approach was evaluated employing long-short-term-memory, GRU, CNN, Transformer, and back propagation networks. Experimental results demonstrated that the proposed CNN-GRU model performs significantly better than baseline models. Compared with the GRU network, the CNN-GRU network reduced the RMSE, MAE, and MAPE of the driver’s input torque by 33.22%, 32.33%, and 35.86%, respectively. The proposed prediction method also possesses adaptability to different driver behaviors. Full article

Supernumerary Robotic Limbs (SRLs) are designed to collaborate with the wearer, enhancing operational capabilities. When human limbs are occupied with primary tasks, controlling SRLs flexibly and naturally becomes a challenge. Existing methods such as electromyography (EMG) control and redundant limb control partially address SRL control issues. However, they still face limitations like restricted degrees of freedom and complex data requirements, which hinder their applicability in real-world scenarios. Additionally, fully autonomous control methods, while efficient, often lack the flexibility needed for complex tasks, as they do not allow for real-time user adjustments. In contrast, shared control combines machine autonomy with human input, enabling finer control and more intuitive task completion. Building on our previous work with the mouth-and-tongue interface, this paper integrates a mixed reality (MR) device to form an interactive system that enables shared control of the SRL. The system allows users to dynamically switch between voluntary and autonomous control, providing both flexibility and efficiency. A random forest model classifies 14 distinct tongue and mouth operations, mapping them to six-degree-of-freedom SRL control. In comparative experiments involving ten healthy subjects performing assembly tasks under three control modes (shared control, autonomous control, and voluntary control), shared control demonstrates a balance between machine autonomy and human input. While autonomous control offers higher task efficiency, shared control achieves greater task success rates and improves user experience by combining the advantages of both autonomous operation and voluntary control. This study validates the feasibility of shared control and highlights its advantages in providing flexible switching between autonomy and user intervention, offering new insights into SRL control. Full article

The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers’ driving capabilities and styles is the key to improving system performance, in particular, the acceptance by and adaption of shared control vehicles to human drivers. In this research, personalized shared control considering drivers’ main human factors is proposed. A simulated scenario generation method for human factors was established. Drivers’ driving capabilities were defined and evaluated to improve the rationality of the driving authority allocation. Drivers’ driving styles were analyzed, characterized, and evaluated in a field test for the intention-aware personalized automated subsystem. A personalized shared control framework is proposed based on the driving capabilities and styles, and its evaluation criteria were established, including driving safety, comfort, and workload. The personalized shared control system was evaluated in a human-in-the-loop simulation platform and a field test based on an automated vehicle. The results show that the proposed system could achieve better performances in terms of different driving capabilities, styles, and complex scenarios than those only driven by human drivers or automated systems. Full article

The Tactile Internet (TI) characterises the transformative paradigm that aims to support real-time control and haptic communication between humans and machines, heavily relying on a dense network of sensors and actuators. Non-Orthogonal Multiple Access (NOMA) is a promising enabler of TI that enhances interactions between sensors and actuators, which are collectively considered as users, and thus supports multiple users simultaneously in sharing the same Resource Block (RB), consequently offering remarkable improvements in spectral efficiency and latency. This article proposes a novel downlink power domain Single-Input Single-Output (SISO) NOMA communication scenario for TI by considering multiple users and a base station. The Signal-to-Interference Noise Ratio (SINR), sum rate and fair Power Allocation (PA) coefficients are mathematically derived in the SISO-NOMA system model. The simulations are performed with two-user and three-user scenarios to evaluate the system performance in terms of Bit Error Rate (BER), sum rate and latency between SISO-NOMA and traditional Orthogonal Multiple Access (OMA) schemes. Moreover, outage probability is analysed with varying fixed Power Allocation (PA) coefficients in the SISO-NOMA scheme. In addition, we present the outage probability, sum rate and latency analyses for fixed and derived fair PA coefficients, thus promoting dynamic PA and user fairness by efficiently utilising the available spectrum. Finally, the performance of 4 × 4 Multiple-Input Multiple-Output (MIMO) NOMA incorporating zero forcing-based beamforming and a round-robin scheduling process is compared and analysed with SISO-NOMA in terms of achievable sum rate and latency. Full article

Hemorrhage leading to life-threatening shock is a common and critical problem in both civilian and military medicine. Due to complex physiological compensatory mechanisms, traditional vital signs may fail to detect patients’ impending hemorrhagic shock in a timely manner when life-saving interventions are still viable. To address this shortcoming of traditional vital signs in detecting hemorrhagic shock, we have attempted to identify metrics that can predict blood loss. We have previously combined feature extraction and machine learning methodologies applied to arterial waveform analysis to develop advanced metrics that have enabled the early and accurate detection of impending shock in a canine model of hemorrhage, including metrics that estimate blood loss such as the Blood Loss Volume Metric, the Percent Estimated Blood Loss metric, and the Hemorrhage Area metric. Importantly, these metrics were able to identify impending shock well before traditional vital signs, such as blood pressure, were altered enough to identify shock. Here, we apply these advanced metrics developed using data from a canine model to data collected from a swine model of controlled hemorrhage as an interim step towards showing their relevance to human medicine. Based on the performance of these advanced metrics, we conclude that the framework for developing these metrics in the previous canine model remains applicable when applied to a swine model and results in accurate performance in these advanced metrics. The success of these advanced metrics in swine, which share physiological similarities to humans, shows promise in developing advanced blood loss metrics for humans, which would result in increased positive casualty outcomes due to hemorrhage in civilian and military medicine. Full article

Digital twins (DTs) consist of various technologies and therefore require a wide range of data. However, many businesses often face challenges in providing sufficient data due to technical limitations or business constraints. This can result in inadequate data for training or calibrating the models used within a digital twin. This paper aims to explore how knowledge can be generated from federated digital twins—an approach that lies between digital twin networks and collaborative manufacturing—and how this can be used to enhance understanding for both AI systems and humans. Inspired by the concept of federated machine learning, where data and algorithms are shared across different stakeholders, this idea involves different companies collaborating through their respective DTs, a situation which can be referred to as federated twinning. As a result, the models within these DTs can be enriched with more-detailed information, leading to the creation of verified, high-fidelity models. Human involvement is also emphasized, particularly in the transfer of knowledge. This can be applied to the modeling process itself, which is the primary focus here, or to any control design aspect. Specifically, the paradigm of thermal process modeling is used to illustrate how federated digital twins can help refine underlying models. Two sequential cases are considered: the first one is used to study the type of knowledge that is required from modeling and federation; while the second one investigates the creation of a more suitable form of modeling. Full article

When personal information is shared across the Internet, we have limited confidence that the designated second party will safeguard it as we would prefer. Privacy policies offer insight into the best practices and intent of the organization, yet most are written so loosely that sharing with undefined third parties is to be anticipated. Tracking these sharing behaviors and identifying the source of unwanted content is exceedingly difficult when personal information is shared with multiple such second parties. This paper formulates a model for realistic fake identities, constructs a robust fake identity generator, and outlines management methods targeted towards online transactions (email, phone, text) that pass both cursory machine and human examination for use in personal privacy experimentation. This fake ID generator, combined with a custom account signup engine, are the core front-end components of our larger Use and Abuse of Personal Information system that performs one-time transactions that, similar to a cryptographic one-time pad, ensure that we can attribute the sharing back to the single one-time transaction and/or specific second party. The flexibility and richness of the fake IDs also serve as a foundational set of control variables for a wide range of social science research questions revolving around personal information. Collectively, these fake identity models address multiple inter-disciplinary areas of common interest and serve as a foundation for eliciting and quantifying personal information-sharing behaviors. Full article

Highly integrated information sharing among people, vehicles, roads, and cloud systems, along with the rapid development of autonomous driving technologies, has spurred the evolution of automobiles from simple “transportation tools” to interconnected “intelligent systems”. The intelligent cockpit is a comprehensive application space for various new technologies in intelligent vehicles, encompassing the domains of driving control, riding comfort, and infotainment. It provides drivers and passengers with safety, comfort, and pleasant driving experiences, serving as the gateway for traditional automobile manufacturing to upgrade towards an intelligent automotive industry ecosystem. This is the optimal convergence point for the intelligence, connectivity, electrification, and sharing of automobiles. Currently, the form, functions, and interaction methods of the intelligent cockpit are gradually changing, transitioning from the traditional “human adapts to the vehicle” viewpoint to the “vehicle adapts to human”, and evolving towards a future of natural interactive services where “humans and vehicles mutually adapt”. This article reviews the definitions, intelligence levels, functional domains, and technical frameworks of intelligent automotive cockpits. Additionally, combining the core mechanisms of human–machine interactions in intelligent cockpits, this article proposes an intelligent-cockpit human–machine interaction process and summarizes the current state of key technologies in intelligent-cockpit human–machine interactions. Lastly, this article analyzes the current challenges faced in the field of intelligent cockpits and forecasts future trends in intelligent cockpit technologies. Full article

The increasing volume of personal motorized vehicles (PMVs) in cities has become a serious issue leading to congestion, noise, air pollution and high land consumption. To ensure the sustainability of urban transportation, it is imperative to transition the current transportation paradigm toward a more sustainable state. Transitions within socio-technical systems often arise from niche innovation. Therefore, this paper pursues the technical optimization of such a niche innovation by applying a technical sustainability perspective on an innovative mobility and logistics concept within a case study. This case study is based on a centrally managed connected, automated, shared and electric (CASE) vehicle fleet which might replace PMV use in urban city centers of the future. The key technical system components of the envisioned mobility and logistics concept are analyzed and optimized with regard to economic, ecological and social sustainability dimensions to maximize the overall sustainability of the ecosystem. Specifically, this paper identifies key challenges and proposes possible solutions across the vehicle components as well as the orchestration of the vehicles’ operations within the envisioned mobility and logistics concept. Thereby, the case study gives an example of how different engineering disciplines can contribute to different sustainability dimensions, highlighting the interdependences. Finally, the discussion concludes that the early integration of sustainability considerations in the technical optimization efforts of innovative transportation systems can provide an important building block for the transition of the current transportation paradigm to a more sustainable state. Full article

In recent years, the Internet of medical things (IoMT) has become a significant technological advancement in the healthcare sector. This systematic review aims to identify and summarize the various applications, key challenges, and proposed technical solutions within this domain, based on a comprehensive analysis of the existing literature. This review highlights diverse applications of the IoMT, including mobile health (mHealth) applications, remote biomarker detection, hybrid RFID-IoT solutions for scrub distribution in operating rooms, IoT-based disease prediction using machine learning, and the efficient sharing of personal health records through searchable symmetric encryption, blockchain, and IPFS. Other notable applications include remote healthcare management systems, non-invasive real-time blood glucose measurement devices, distributed ledger technology (DLT) platforms, ultra-wideband (UWB) radar systems, IoT-based pulse oximeters, accident and emergency informatics (A&EI), and integrated wearable smart patches. The key challenges identified include privacy protection, sustainable power sources, sensor intelligence, human adaptation to sensors, data speed, device reliability, and storage efficiency. The proposed mitigations encompass network control, cryptography, edge-fog computing, and blockchain, alongside rigorous risk planning. The review also identifies trends and advancements in the IoMT architecture, remote monitoring innovations, the integration of machine learning and AI, and enhanced security measures. This review makes several novel contributions compared to the existing literature, including (1) a comprehensive categorization of IoMT applications, extending beyond the traditional use cases to include emerging technologies such as UWB radar systems and DLT platforms; (2) an in-depth analysis of the integration of machine learning and AI in IoMT, highlighting innovative approaches in disease prediction and remote monitoring; (3) a detailed examination of privacy and security measures, proposing advanced cryptographic solutions and blockchain implementations to enhance data protection; and (4) the identification of future research directions, providing a roadmap for addressing current limitations and advancing the scientific understanding of IoMT in healthcare. By addressing current limitations and suggesting future research directions, this work aims to advance scientific understanding of the IoMT in healthcare. Full article

To address the safety and efficient driving issues of human–machine shared control vehicles (HSCVs) in future complex traffic environments, this paper proposes a game theory-based interactive control method between HSCVs and surrounding autonomous vehicles (SVs) and involves considering different driving behaviors. In HSCV, a comprehensive driver model integrating steering control and speed control is designed based on the brain emotional learning circuit model (BELCM), and the control authority between the driver and the automation system is dynamically allocated through the evaluation of the driving safety field. Factors such as driving safety and travel efficiency that reflect personalized driving style are considered for modeling the uncertain behavior of SVs. In the interaction between HSCVs and SVs, a method based on game theory and distributed model predictive control (DMPC) that considers the uncertainty of SVs’ driving behavior is established and is finally integrated into a multi-objective constraint problem. The driver control input in an HSCV will also be introduced into the solution process. To demonstrate the feasibility of the proposed method, two test scenarios considering the driving characteristics of different SVs are established. The test results show that automation control systems can promptly stop the human driver’s dangerous operations in an HSCV and safely interact with multiple AVs with different driving characteristics. Full article

During the Ottoman period, wood had replaced marble in Orthodox churches. Between the 16th and 19th century, common, unknown craftsmen created wonderful wood-carved ecclesiastical works by hand, though many of them have been partially or completely destroyed, usually without any of their images being saved. Advances in technology now make it possible to develop a detailed digital copy of an object of our cultural heritage, creating an unaltered prototype material to be studied by all, while offering in parallel the ease of reproducing precise copies, ensuring even museum quality standards. In the present study, a corresponding attempt to record, scan and then reconstruct a 17th century “Bema Door” (located in the Holy Monastery of Hilandar in Mount Athos) is implemented, presented step-by-step and discussed, in order to highlight the challenges and potential of the applied methods. The process included on-site recording of a 3D digital scan, and then the creation of the Bema door copy, first applying Computerized Numerical Control (CNC) machining and then, with elaborate handwork by experienced wood-carvers who successfully completed the process, imparting the necessary and unique human artistic character. The current work transfers the experience on this reproduction technique applied in a Bema door element, shares significant information and details on tools, materials, process steps, etc., in order to be an inspiration and stimulus for the preservation or reproduction of other cultural heritage monuments of high importance. Full article

In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in “naked streets”. This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles’ real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors. Full article