Search Results (97)

Precise and real-time estimation of the robotic arm’s position on the patient’s side is essential for the success of remote robotic surgery in Tactile Internet (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation, combined with a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The proposed approach addresses challenges such as network delays, jitter, and packet loss to ensure reliable and precise operation in remote surgical applications. The method integrates the optimization problem into the Informer model by embedding constraints such as energy efficiency, smoothness, and robustness into its training process using a differentiable optimization layer. The Informer framework uses features such as ProbSparse attention, attention distilling, and a generative-style decoder to focus on position-critical features while maintaining a low computational complexity of $\mathcal{O}(LlogL)$. The method is evaluated using the JIGSAWS dataset, achieving a prediction accuracy of over 90% under various network scenarios. A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework’s superior performance in handling position prediction and meeting real-time requirements, making it suitable for Tactile Internet-enabled robotic surgery. Full article

The increasing global demand for seafood underscores the necessity for sustainable aquaculture practices. However, several challenges, including rising operational costs, variable environmental conditions, and the threat of disease outbreaks, impede progress in this field. This review explores the transformative role of the Artificial Intelligence of Things (AIoT) in mitigating these challenges. We analyse current research on AIoT applications in aquaculture, with a strong emphasis on the use of IoT sensors for real-time data collection and AI algorithms for effective data analysis. Our focus areas include monitoring water quality, implementing smart feeding strategies, detecting diseases, analysing fish behaviour, and employing automated counting techniques. Nevertheless, several research gaps remain, particularly regarding the integration of AI in broodstock management, the development of multimodal AI systems, and challenges regarding model generalization. Future advancements in AIoT should prioritise real-time adaptability, cost-effectiveness, and sustainability while emphasizing the importance of multimodal systems, advanced biosensing capabilities, and digital twin technologies. In conclusion, while AIoT presents substantial opportunities for enhancing aquaculture practices, successful implementation will depend on overcoming challenges related to scalability, cost, and technical expertise, improving models’ adaptability, and ensuring environmental sustainability. Full article

Soft robots, constructed from deformable materials, offer significant advantages over rigid robots by mimicking biological tissues and providing enhanced adaptability, safety, and functionality across various applications. Central to these robots are electroactive polymer (EAP) actuators, which allow large deformations in response to external stimuli. This review examines various EAP actuators, including dielectric elastomers, liquid crystal elastomers (LCEs), and ionic polymers, focusing on their potential as artificial muscles. EAPs, particularly ionic and electronic varieties, are noted for their high actuation strain, flexibility, lightweight nature, and energy efficiency, making them ideal for applications in mechatronics, robotics, and biomedical engineering. This review also highlights piezoelectric polymers like polyvinylidene fluoride (PVDF), known for their flexibility, biocompatibility, and ease of fabrication, contributing to tactile and pressure sensing in robotic systems. Additionally, conducting polymers, with their fast actuation speeds and high strain capabilities, are explored, alongside magnetic polymer composites (MPCs) with applications in biomedicine and electronics. The integration of machine learning (ML) and the Internet of Things (IoT) is transforming soft robotics, enhancing actuation, control, and design. Finally, the paper discusses future directions in soft robotics, focusing on self-healing composites, bio-inspired designs, sustainability, and the continued integration of IoT and ML for intelligent, adaptive, and responsive robotic systems. Full article

With the emergence of artificial intelligence and advancements in network technologies, the imminent arrival of 6G is not very far away. The 6G technology will introduce unique and innovative applications of the Tactile Internet in the near future. This paper highlights the evolution towards the Tactile Internet enabled by 6G technology, along with the details of 6G capabilities. It emphasizes the stringent requirements for emerging Tactile Internet applications and the critical role of parameters, such as latency, reliability, data rate, and others. The study identifies the important characteristics of future Tactile Internet applications, interprets them into explicit requirements, and then discusses the associated design considerations. The study focuses on the role of application characteristics of various applications, like virtual reality/augmented reality, remote surgery, gaming, smart cities, autonomous vehicles, industrial automation, brain–machine interface, telepresence/holography, and requirements in the design of 6G and the Tactile Internet. Furthermore, we discuss the exclusive parameters and other requirements of Tactile Internet to realize real-time haptic interactions with the help of 6G and artificial intelligence. The study deliberates and examines the important performance parameters for the given applications. It also discusses various types of sensors that are required for Tactile Internet applications. Full article

Liquid crystal elastomers (LCE) are a promising material to achieve reversible actuation while being able to perform work, showing great potential as artificial muscles in soft robotics and medical technology. Here, a wet spinning process to prepare liquid crystal elastomer fibers (LCEF) with reversible actuation capability is presented. Furthermore, we demonstrate the ability to process side-chain liquid crystal (LC) 4-Methoxyphenyl 4-(3-butenyloxy)benzoate (MBB) into a fiber, enlarging the material variance available in this field. The wet spinning process is presented and discussed in terms of spinning parameters and their influence on fiber properties, especially LC orientation. Moderate draw ratios of up to 2.3 enable highly oriented mesogens (f = 0.64), enabling the contractile behavior. The generated MBB-based LCEF show low activation temperature (54.52 °C), temperature-dependent mechanical properties, reversible contraction behavior while lifting up to 140 times their own weight and are able to perform work of up to 3.857 J kg⁻¹. Actuation properties are compared with human skeletal muscle, and possible strategies of further enhancing the LCEF performance are discussed. The generated data show promising features of the LCEF for use as artificial muscle fibers in medical applications, e.g., prosthetics and artificial cardiac tissue. Full article

Cardiovascular diseases pose a significant threat to global health, and cardiac rehabilitation (CR) has become a critical component of patient care. Heart Rate Variability Biofeedback (HRVB) is a non-invasive approach that helps modulate the Autonomic Nervous System (ANS) through Resonance Frequency (RF) breathing, supporting CR for cardiovascular patients. However, traditional HRVB techniques rely heavily on manual RF selection and face-to-face guidance, limiting their widespread application, particularly in home-based CR. To address these limitations, we propose a remote human-computer collaborative HRVB system, “FreeResp”, which features autonomous RF adjustment through a simplified cognitive computational model, eliminating the reliance on therapists. Furthermore, the system integrates wearable technology and the Internet of Things (IoT) to support remote monitoring and personalized interventions. By incorporating tactile guidance technology with an airbag, the system assists patients in performing diaphragmatic breathing more effectively. FreeResp demonstrated high consistency with conventional HRVB methods in determining RF values (22/24) from 24 valid training samples. Moreover, a one-month home-based RF breathing training using FreeResp showed significant improvements in Heart Rate Variability (HRV) (p < 0.05). These findings suggest that FreeResp is a promising solution for home-based CR, offering timely and precise interventions and providing a new approach to long-term cardiovascular health management. Full article

As the telecommunications landscape braces for the post-5G era, this paper embarks on delineating the foundational pillars and pioneering visions that define the trajectory toward 6G wireless communication systems. Recognizing the insatiable demand for higher data rates, enhanced connectivity, and broader network coverage, we unravel the evolution from the existing 5G infrastructure to the nascent 6G framework, setting the stage for transformative advancements anticipated in the 2030s. Our discourse navigates through the intricate architecture of 6G, highlighting the paradigm shifts toward superconvergence, non-IP-based networking protocols, and information-centric networks, all underpinned by a robust 360-degree cybersecurity and privacy-by-engineering design. Delving into the core of 6G, we articulate a systematic exploration of the key technologies earmarked to revolutionize wireless communication including terahertz (THz) waves, optical wireless technology, and dynamic spectrum management while elucidating the intricate trade-offs necessitated by the integration of such innovations. This paper not only lays out a comprehensive 6G vision accentuated by high security, affordability, and intelligence but also charts the course for addressing the pivotal challenges of spectrum efficiency, energy consumption, and the seamless integration of emerging technologies. In this study, our goal is to enrich the existing discussions and research efforts by providing comprehensive insights into the development of 6G technology, ultimately supporting the creation of a thoroughly connected future world that meets evolving demands. Full article

Background: Despite the significant advancements of liver surgery in the last few decades, the survival rate of patients with liver and pancreatic cancers has improved by only 10% in 30 years. Precision medicine offers a patient-centered approach, which, when combined with machine learning, could enhance decision making and treatment outcomes in surgical management of ihCC. This study aims to develop a decision support model to optimize treatment strategies for patients with ihCC, a prevalent primary liver cancer. Methods: The decision support model, named MedMax, was developed using three data sources: studies retrieved through a systematic literature review, expert opinions from HPB surgeons, and data from ihCC patients treated at Heidelberg University Hospital. Expert opinions were collected via surveys, with factors rated on a Likert scale, while patient data were used to validate the model’s accuracy. Results: The model is structured into four decision-making phases, assessing diagnosis, treatment modality, surgical approach, and prognosis. Prospectively, 44 patients with ihCC were included for internal primary validation of the model. MedMax could predict the appropriate treatment considering the resectability of the lesions in 100% of patients. Also, MedMax could predict a decent surgical approach in 77% of the patients. The model proved effective in making decisions regarding surgery and patient management, demonstrating its potential as a clinical decision support tool. Conclusions: MedMax offers a transparent, personalized approach to decision making in HPB surgery, particularly for ihCC patients. Initial results show high accuracy in treatment selection, and the model’s flexibility allows for future expansion to other liver tumors and HPB surgeries. Further validation with larger patient cohorts is required to enhance its clinical utility. Full article

A therapeutic glove, which enables medical non-professionals to perform physiotherapeutic gripping and holding movements on patients, would significantly improve the healthcare situation in physiotherapy. The glove aims to detect the orthogonal pressure load and provide feedback to the user. The use of textile materials for the glove assures comfort and a good fit for the user. This, in turn, implies a textile realization of the sensor system in order to manufacture both the glove and the sensor system in as few process steps as possible, using only one textile manufacturing technique. The flat knitting technology is an obvious choice here. The aim of the study is to develop a textile capacitive pressure sensor that can be integrated into the fingertips of a glove using flat knitting technology and to evaluate its sensor properties with regard to transmission behavior, hysteresis and drift. It was shown that the proposed method of a flat knitting sensor fabrication is suitable for producing both the sensors and the glove in one single process step. In addition, the implementation of an entire glove with integrated pressure sensors, including the necessary electrical connection of the sensor electrodes via knitted conductive paths in three fingers, was successfully demonstrated. 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

Taking advantage of its high-temperature resistance and elongation properties, conductive-coated polyetheretherketone (PEEK) filament yarn can be used as a textile-based electroconductive functional element, in particular as a strain sensor. This study describes the development of electrical conductivity on an inert PEEK filament surface by the deposition of metallic nickel (Ni) layers via an electroless galvanic plating process. To enhance the adhesion properties of the nickel layer, both PEEK multifilament and monofilament yarn surfaces were metalized by plasma torch pretreatment, followed by nickel plating. Electrical characterizations indicate the potential of nickel-coated PEEK for structural monitoring in textile-reinforced composites. In addition, surface energy measurements before and after plasma torch pretreatment, surface morphology, nickel layer thickness, chemical structure changes, and mechanical properties were analyzed and compared with untreated PEEK. The thickness of the Ni layer was measured and showed an average thickness of 1.25 µm for the multifilament yarn and 3.36 µm for the monofilament yarn. FTIR analysis confirmed the presence of new functional groups on the PEEK surface after plasma torch pretreatment, indicating a successful modification of the surface chemistry. Mechanical testing showed an increase in tensile strength after plasma torch pretreatment but a decrease after nickel plating. In conclusion, this study successfully developed conductive PEEK yarns through plasma torch pretreatment and nickel plating. Full article

To guarantee the reliability of Tactile Internet (TI) applications such as telesurgery, which demand extremely high reliability and are experiencing rapid expansion, we propose a novel smart resilience mechanism for Next-Generation Ethernet Passive Optical Networks (NG-EPONs). Our architecture integrates Artificial Intelligence (AI) and Software-Defined Networking (SDN)-Enabled Broadband Access (SEBA) platform to proactively enhance network reliability and performance. By harnessing the AI’s capabilities, our system automatically detects and localizes fiber faults, establishing backup communication links using Radio Frequency over Glass (RFoG) to prevent service disruptions. This empowers NG-EPONs to maintain uninterrupted, high-quality network service even in the face of unexpected failures, meeting the stringent Quality-of-Service (QoS) requirements of critical TI applications. Our AI model, rigorously validated through 5-fold cross-validation, boasts an average accuracy of 81.49%, with a precision of 84.33%, recall of 78.18%, and F1-score of 81.00%, demonstrating its robust performance in fault detection and prediction. The AI model triggers immediate corrective actions through the SDN controller. Simulation results confirm the efficacy of our proposed mechanism in terms of delay, system throughputs and packet drop rate, and bandwidth waste, ultimately ensuring the delivery of high-quality network services. Full article

Every texture in the real world provides us with the essential information to identify the physical characteristics of real objects. In addition to sight, humans use the sense of touch to explore their environment. Through haptic interaction we obtain unique and distinct information about the texture and the shape of objects. In this paper, we enhance X3D 3D graphics files with haptic features to create 3D objects with haptic feedback. We propose haptic attributes such as static and dynamic friction, stiffness, and maximum altitude that provide the optimal user experience in a virtual haptic environment. After numerous optimization attempts on the haptic textures, we propose various haptic geometrical textures for creating a virtual 3D haptic environment for the tactile Internet. These tangible geometrical textures can be attached to any geometric shape, enhancing the haptic sense. We conducted a study of user interaction with a virtual environment consisting of 3D objects enhanced with haptic textures to evaluate performance and user experience. The goal is to evaluate the realism and recognition accuracy of each generated texture. The findings of the study aid visually impaired individuals to better understand their physical environment, using haptic devices in conjunction with the enhanced haptic textures. Full article

Texture recognition is a pivotal task in computer vision, crucial for applications in material sciences, medicine, and agriculture. Leveraging advancements in Deep Neural Networks (DNNs), researchers seek robust methods to discern intricate patterns in images. In the context of the burgeoning Tactile Internet (TI), efficient texture recognition algorithms are essential for real-time applications. This paper introduces a method named Local Binary Convolution Network with Intra-class Normalization (LBCNIN) for texture recognition. Incorporating features from the last layer of the backbone, LBCNIN employs a non-trainable Local Binary Convolution (LBC) layer, inspired by Local Binary Patterns (LBP), without fine-tuning the backbone. The encoded feature vector is fed into a linear Support Vector Machine (SVM) for classification, serving as the only trainable component. In the context of TI, the availability of images from multiple views, such as in 3D object semantic segmentation, allows for more data per object. Consequently, LBCNIN processes batches where each batch contains images from the same material class, with batch normalization employed as an intra-class normalization method, aiming to produce better results than single images. Comprehensive evaluations across texture benchmarks demonstrate LBCNIN’s ability to achieve very good results under different resource constraints, attributed to the variability in backbone architectures. Full article

The integration of material attribute estimation (MAE) within augmented reality, virtual reality, and mixed reality telecommunication systems stands as a pivotal domain, evolving rapidly with the advent of the Tactile Internet. This unifying implementation process has the potential for improvements in the realism and interactivity of immersive environments. The interaction between MAE and the haptic Internet could lead to significant advances in haptic feedback systems, enabling more accurate and responsive user experiences. This systematic review is focused on the intersection of MAE and the Tactile Internet, aiming to find an implementation path between these technologies. Motivated by the potential of the haptic Internet to advance telecommunications, we explore its potential to advance the analysis of material attributes within AR, VR, and MR applications. Through an extensive analysis of current research approaches, including machine learning methods, we explore the possibilities of integrating the TI into MAE. By exploiting haptic and visual properties stored in the materials of 3D objects and using them directly during rendering in remote access scenarios, we propose a conceptual framework that combines data capture, visual representation, processing, and communication in virtual environments. Full article