Over the past few decades, elderly care has become an increasingly concerning problem across the world. As humans age, health can deteriorate severely, leaving the elderly population prone to suffering from various diseases. The lack of proper resources and effective services has further exacerbated this crisis. Existing solutions to this problem are not sustainable or practical. This research paper examines the growing need for effective elderly care solutions due to an aging global population. It analyzes the current market, highlighting the shortcomings of existing robotic assistive devices like Robear and HUG. The paper proposes improvements for future robotic designs, emphasizing autonomous operation, AI integration, enhanced mechanical design, and cost reduction to create more effective and widely accessible solutions. The substantial market potential for these improved robotic systems is also discussed, citing projected market growth and the increasing number of elderly individuals worldwide. Finally, the paper concludes by stressing the need for safer, more adaptable, and affordable robotic aids to enhance the quality of life for both elderly individuals and their caregivers.  

The Location Service ETSI standard has evolved to closing a critical gap between the standards for positioning mechanism of 5G mobile networks and the Multi-access Edge Computing (MEC) standard framework. Location Service (LS) generally allows, among other functions, to optimize services according to users' geographical location. Although LS already offers features typical of the next generation of mobile networks, such as 3D positioning of user equipment, the LS standardization framework is still evolving to meet the demands in support of next-generation mobile networks, most notably the sixth generation paradigm. Some of the features in the current standards, for instance, the concepts of zones, carry the promise of possibly transformative LS applications in future mobile networks. For instance, LS can help the building of a "6G network of (sub-)networks", with various technology solutions, towards the fully decentralized network operation. These and the related developments are driving further evolution of MEC and LS standards to support such new features. This article addresses the evolution of current Location API standards towards 6G mobile networks. We propose extensions and improvements to the current Location API data model to include data fields to characterize novel features and the envisioned novel use cases in the next-generation mobile networks. We show that the proposed modifications allow us to abstract the user's location into a tuple of physical and network locations, allowing MEC applications to perform complex data flow optimization.

Physical plasma is a quasi-neutral gaseous electronics that exhibit collective behavior. It has been known that it develops chaotic behavior of the type that leads to various instabilities. This is the main problem in confining physical plasma to achieve energy production for example. However, it is found that even a quiescent physical plasma system, which consists of a multi – dipole thermionic physical plasma source and a Langmuir electric probe as a diagnostic instrument, can reveal the system’s chaotic behavior at the physical plasma charge population collection level. By inspection, the classic Verhulst population curve was shown to resemble a typical physical plasma system Langmuir probe I - V (voltage – current) trace, thereby indicating such chaotic behavior. Further, an experiment was conducted by mixing of two ion species in the same physical plasma system which led to the occurrence of unstable modes, with their onset being sensitive to the percentage of density by pressure of each species, and the distance from the physical plasma vessel boundary region.

This paper investigates network slicing in radio access networks (RANs) that support multiple virtual operators, focusing on maximizing operator utility and ensuring service isolation on a shared physical platform. Network slicing enables efficient resource management and allows for flexible scaling of services based on diverse operator requirements. We evaluate RAN resource slicing for virtual operators by addressing two key challenges: resource reservation and allocation. Operators predict future resource demands using artificial neural networks and reserve a portion of physical resources accordingly. Following reservation, the allocation process is modeled as a Markov decision process aimed at maximizing revenue, which is solved using reinforcement learning algorithms. This dual optimization approach ensures utility maximization and efficient service delivery for virtual operators while balancing network load and enhancing system robustness in dynamic environments. Additionally, our approach accounts for fluctuating user traffic patterns and varying operator priorities, demonstrating significant potential for improving resource utilization, maintaining service-level agreements, and fostering scalable, resilient network operations in highly virtualized environments. Through comprehensive simulation, we establish that the proposed method leads to a significant improvement in resource utilization and operator utility across various traffic loads and operator priority scenarios, clearly demonstrating its competitive advantage in the context of resource reservation and allocation for network slicing in virtualized environments.

Gradient-based flux observers offer promise in permanent magnet synchronous motor (PMSM) encoderless control due to their computational efficiency and adaptability but face challenges in gain tuning and noise sensitivity. This paper proposes a robust optimized flux observer combining the huber loss function (HLF) and root mean square propagation (RMSProp) algorithm to overcome these limitations. Firstly, a robust nonlinear flux observer is developed with the HLF to enhance the steady state performance of the encoderless control by leveraging its smoothness and noise suppression capabilities. Secondly, the RMSProp algorithm is employed to optimize the gradient search process, dynamically adjusting the step size in real-time based on historical gradient information to ensure improved dynamic response. Additionally, the Lyapunov stability criterion is utilized to analyze the stability of the proposed controller and prove the bounds of its observation error. Finally, experimental results confirm the proposed control strategy, achieving high-precision position estimation. Angular estimation errors under no-load and load conditions are reduced to below-0.09 rad and-0.08 rad, respectively, compared to-0.22 rad and-0.15 rad with conventional methods.

Diabetes is a persistent metabolic disorder that impacts millions globally, presenting a significant health challenge worldwide with increasing prevalence and significant healthcare implications. Early and accurate prediction of diabetes can aid in timely intervention and disease management. This study investigates the efficacy of machine learning algorithms in predicting diabetes using the Sylhet Diabetes Hospital dataset, which consists of clinical records from 520 patients. Various feature selection methodologies, including Pearson correlation analysis, Genetic Algorithm, Chi-Square test, and Recursive Feature Elimination (RFE), were employed to identify the most biologically significant predictors associated with diabetes onset. Five machine learning models-Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree (DT), Random Forest (RF), and Logistic Regression (LR)-were trained and validated through cross-validation techniques. Among these techniques, the Random Forest algorithm exhibited the highest predictive performance, achieving an accuracy of 94.70% and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) score of 98.22%, indicating its superior ability to differentiate between diabetic and non-diabetic cases. Making it the most Dependable for diabetes prediction. These outcomes highlight the potential of machine learning in enhancing diabetes diagnosis and risk assessment. Future work includes integrating deep learning techniques and expanding datasets to improve generalizability and predictive performance.

This paper introduces a reinforcement learning framework designed to optimize energy distribution in smart grids. By using Proximal Policy Optimization (PPO) and Graph Neural Networks (GNNs), the framework effectively manages the grid's complex topology to enhance decision-making. It addresses critical challenges, including minimizing energy loss, improving reliability, and reducing operational costs. The model was trained and evaluated using the Grid2Op simulation environment and its associated L2RPN dataset, which encompasses realistic scenarios such as load fluctuations, renewable energy variability, and grid faults. The proposed method demonstrated significant advancements, achieving a reduction in energy loss to 6.8%, a reliability improvement with a SAIDI score of 3.2, and a 32% decrease in operational costs. These outcomes outperform recent methods such as GCN-SAC and rule-based systems. Furthermore, the model exhibited faster computation times, making it highly suitable for real-time grid applications. The integration of curriculum learning enabled the agent to adapt to complex and unseen scenarios effectively. This study presents a robust and scalable solution for smart grid optimization, offering significant potential for managing modern power systems and integrating renewable energy sources efficiently.

Autonomous vehicles must be safe. A safe vehicle is a vehicle that can accurately and robustly understand the environment and sensibly decide what to do next. Driverless systems rely on several sensors to perceive their surroundings, combining their characteristics and differences in a mutually beneficial way. Audio has been long overlooked in this context, even though it can provide information which is crucial for safety (e.g., the siren of an emergency vehicle approaching from far and that cannot be seen). The consequence is that only a few sound-based datasets are available, which, in return, further limits investigations in the area. This paper aims to demonstrate that, even when dealing with unbalanced and/or not too-big datasets, there might be hidden information to be used and which could greatly enhance the power of those datasets. In particular, we aim to prove that contextual information, meant as the type of place where and the time of the day when a sound is heard, can significantly ease its identification. By relying on a simple Convolutional Neural Network, we show that contextual information, even when coarse, can increase the accuracy of an acoustic object detector up to 85%.

Event-Related Potentials are crucial for analyzing brain activity in EEG recordings. However, latency variability distorts averaged signals, necessitating advanced alignment strategies. Traditional cross-correlation methods use constant displacements but lack subsample accuracy and fail to capture non-linear temporal shifts. This study evaluates different pre-processing strategies for ERP single-trial matrices before alignment, including a novel iterative and robust ERP reference selection method and optimized filtering strategies such as non-linear, anisotropic diffusion. Variational 1D alignment, which allows non-flat displacements with subsample accuracy, is applied to synthetic and real EEG datasets, including P300 oddball experiments (visual and auditory). Variational alignment with optimized filtering and reference selection preserves signal morphology and enhances signal gain to match the ground truth in synthetic data. The structure of non-linear displacements contains peak-to-peak variability, allowing signal reconstruction without loss of critical temporal features. Applying this method to real EEG data reduces amplitude differences caused by jitter while maintaining key ERP components. This method significantly improves ERP alignment, potentially enhancing BCI applications, resolution, and pattern recognition, particularly in later-stage ERPs. Additionally, explicit 1D displacement estimation enables time-resolved jitter variance analysis. This approach may reduce auditory brainstem response measurement times and extend to EEG-like data, including OPM and MEG evoked potentials, aiding in ERP-based diagnostics.

Rehabilitation is a critical component of healthcare services that often requires prolonged monitoring to achieve optimal outcomes. One of the major challenges in rehabilitation tracking is the necessity for patients to visit medical centers, which can be costly and time-consuming, especially for individuals in remote areas. Although telehealth solutions such as video conferencing have been introduced, they are predominantly qualitative and do not provide precise rehabilitation tracking. Consequently, there is a need for a robust, accessible, and accurate method for rehabilitation monitoring. To address this need, we introduce a quantified, easily accessible framework for extracting and analyzing human finger joint torques to enable virtual hand interaction. Hand landmarks from MediaPipe are integrated with the MANO hand model in PyBullet. The hand positions, rotations, and joint angles extracted from the landmarks are used as controller inputs to mirror human hand movements in a virtual environment. A PID controller was designed to accurately track joint angle trajectories, with its parameters optimized using genetic algorithms. The torques extracted from the metacarpophalangeal and proximal interphalangeal joints were validated, achieving mean squared errors of 4.346 × 10 −4 (N.m) 2 and 1.506 × 10 −5 (N.m) 2 , respectively. This quantification and accessibility of human joint dynamics information is highly desirable for tracking rehabilitation progress using exoskeleton robotics.

This paper presents a comprehensive cardiovascular modeling framework that integrates partial differential equations (PDEs), computational fluid dynamics (CFD), and advanced optimization techniques to simulate blood circulation with high accuracy. Unlike traditional models, which rely on simplified assumptions, this approach incorporates pulsatile flow dynamics, non-Newtonian blood viscosity effects, and patient-specific data to enhance predictive capabilities. The governing equations account for arterial compliance, shear stress variations, and complex boundary conditions, providing a more physiologically relevant representation of blood flow. Convergence analysis demonstrates that the numerical solutions approach the exact solution with an error function given by E h = ||u h − u|| L 2 + ||p h − p|| L 2 , confirming a significantly improved convergence rate over classical methods. Stability analysis ensures perturbation decay over time, satisfying d dt ||u ′ || 2 ≤ 0, which guarantees the dissipation of disturbances and validates numerical robustness. A major advancement of this work is the integration of patient-specific parameters, enabling tailored simulations for cardiovascular interventions. The model has clinical applications in optimizing ventricular assist devices (VADs), refining surgical techniques, and improving drug delivery strategies. Future work will focus on real-time validation using clinical data and further refinement of computational efficiency to enhance applicability in medical practice.

This work presents the development of an intelligent and portable system for identifying foliar diseases in soybean crops. The system uses Artificial Intelligence (AI) and Computer Vision techniques to assist farmers in diagnosis and decisionmaking for efficient crop management, contributing to increased productivity and reduced losses. A convolutional neural network (CNN) model was developed on the edge impulse platform, achieving an accuracy of 87.7% in classifying five prevalent diseases in Brazil: Target Spot, Asian Rust, Brown Spot, Frog Eye Leaf Spot, Potassium Deficiency, and the Healthy class. The prototype integrates the ESP32-CAM microcontroller for image capture of leaves and inferences, an OLED display to visualize diagnostic information, and the Ublox NEO-6M GPS module to record geographical location, allowing the creation of disease incidence maps. In addition, an interactive dashboard was developed on the Power BI platform, along with an automated report, to provide farmers with detailed information on disease incidence in the crop, including a practical guide with a checklist to help in decision-making and phytosanitary management of the crop.

The emergence of unexpected incidents in electrical power systems (EPSs) has led to partial disruptions of the national electricity grid, necessitating the deployment of emergency diesel generators (EDGs) to restore power supply to affected areas. This study aims to evaluate the operational effectiveness of three distinct EDG power plants associated with an oil facility and a nearby gas platform during power outages, specifically focusing on scenarios that involve starting power transformers (PTs) and induction motors (IMs). Through the application of technical specifications provided by Stamford Company and the utilization of the EMTP-RV software tool, the EDGs, featuring identical excitation systems and governor parameters, were systematically modeled and analyzed for comparative purposes. The results of this comparative investigation are anticipated to yield significant cost reductions for power plant operations while enhancing overall system safety.

Non-contact manipulation at the air-liquid interface holds significant potential for applications in microrobotics, non-invasive assembly, and biochemistry analysis. However, achieving simultaneous position and orientation (pose) control of floating objects remains a considerable challenge, particularly for adaptive control without prior modeling of the objects. Here, we introduce the Vision-based Adaptive Laser Gripper (VALG) system addressing these challenges. By leveraging the distributed thermocapillary flow induced by patterned laser scanning, a pose control strategy based on the equidistant contour scanning laser is proposed and validated. The proposed system relies solely on visual recognition to generate adaptive laser grippers, which achieve static equilibrium to simultaneously constrain the position and orientation of the floating objects. Experimental validation demonstrates the effectiveness of the VALG system in independent position and orientation control, coupled pose control, and path following. The VALG system facilitates smooth, precise, fast, and adaptive pose control of generalized floating objects, establishing it as a universal and versatile platform for non-contact manipulation at the air-liquid interface.

Traffic congestion affects the country's conveyance system, especially when emergencies occur at traffic lights. To solve this problem, we developed a light control system that responds to emergency vehicle signals via radio frequency (RF) transmission. This system uses the PIC 16F877A microcontroller to update the transmission light in emergency situations and return to normal when the situation is resolved. The system aims to reduce accidents at intersections by providing a clear path for emergency vehicles. The project enables wireless communication via radio frequency transmission at a frequency of 434 MHz, allowing lights to be changed in response to emergency vehicles. Future improvements may include integration with in-flight flight controls to enhance the current lighting system.

Stimuli-responsive 3D microsensors/actuators hold vast potential for applications in wearable electronics, biomedical scaffolds, MEMS arrays, and swarm microrobots. However, the integrated design and fabrication of 3D microsensors/actuators with geometric programmability remains a significant challenge. In this work, we present Al/Nitinol thermal bimorph microstructures, along with their integrated design and fabrication methods based on photolithography and film deposition techniques. These 3D structures are both flexible and geometrically programmable, enabling a wide range of complex 3D configurations to be achieved through planar design and allowing initial deformation amplitudes to be defined by mold-free heat treatment. The resulting microstructure exhibits excellent photothermal and electrothermal properties, demonstrating a linear and fast response performance under both tethered electrical actuation and untethered optical actuation. The proposed fabrication method supports compatible, miniaturized, precise, parallel, and high-throughput production, enabling thousands of microstructures with various configurations to be manufactured simultaneously on a single 4-inch wafer. Our approach provides a novel design and fabrication paradigm for creating 3D microsensors/actuators with outstanding stimuli-responsive and geometric programming capabilities at the submillimeter scale.

A document by G K Wardhana. Click on the document to view its contents.

Multi-beam systems are a key technology for the highspeed links of the next-generation communication standards. Due to the stringent space constraints for allocating antennas on a platform, it is of paramount importance to assess-with respect to the physical sizethe multi-beam performance of the antenna in terms of the maximum number of simultaneous orthogonal beams. This is done by resorting to the concept of the observable field, which is here extended to arbitrary geometries, with a particular focus on planar domains. Then, this concept is used to assess the multi-beam performance of a wideband phased array prototype, developed for mobile communications. The Signal-to-Interference Ratio (SIR), computed from the measured radiation patterns of the prototype, is analyzed versus the frequency and the number of beams, and compared to the benchmark case of an ideal antenna radiating the observable field.