Search Results (1,131)

Efficient BCIs (Brain-Computer Interfaces) harnessing EEG (Electroencephalography) have shown potential in controlling mobile robots, also presenting new possibilities for assistive technologies. This study explores the integration of advanced deep learning models—ASTGCN, EEGNetv4, and a combined CNN-LSTM architecture—with ROS (Robot Operating System) to control a two-wheeled mobile robot. The models were trained using a published EEG dataset, which includes signals from subjects performing thought-based tasks. Each model was evaluated based on its accuracy, F1-score, and latency. The CNN-LSTM architecture model exhibited the best performance on the cross-subject strategy with an accuracy of 88.5%, demonstrating significant potential for real-time applications. Integration with ROS was facilitated through a custom middleware, enabling seamless translation of neural commands into robot movements. The findings indicate that the CNN-LSTM model not only outperforms existing EEG-based systems in terms of accuracy but also underscores the practical feasibility of implementing such systems in real-world scenarios. Considering its efficacy, CNN-LSTM shows a great potential for assistive technology in the future. This research contributes to the development of a more intuitive and accessible robotic control system, potentially enhancing the quality of life for individuals with mobility impairments. Full article

Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person’s pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photographs. This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM. A physiotherapist evaluated the degrees of ROM in shoulders (flexion, extension, and abduction) and elbows (flexion and extension) for fifty-two participants using both Universal Goniometer (UG) and five HPE models. Participants were instructed to repeat each movement three times to obtain measurements with the UG, then positioned while photos were captured using the NLMeasurer mobile application. The paired t-test, bias, and error measures were employed to evaluate the difference and agreement between measurement methods. Results indicated that the MoveNet Thunder INT16 model exhibited superior performance. Root Mean Square Errors obtained through this model were <10° in 8 of 10 analyzed movements. HPE models demonstrated better performance in shoulder flexion and abduction movements while exhibiting unsatisfactory performance in elbow flexion. Challenges such as image perspective distortion, environmental lighting conditions, images in monocular view, and complications in the pose may influence the models’ performance. Nevertheless, HPE models show promise in identifying KJPs and facilitating ROM measurements, potentially enhancing convenience and efficiency in assessments. However, their current accuracy for this application is unsatisfactory, highlighting the need for caution when considering automated upper limb ROM measurement with them. The implementation of these models in clinical practice does not diminish the crucial role of examiners in carefully inspecting images and making adjustments to ensure measurement reliability. Full article

Perovskite solar cells (PSCs) have emerged as promising candidates for next-generation photovoltaic technology due to their remarkable power-conversion efficiencies (PCEs). Since their introduction, the PCE of PSCs has advanced from 3.8% to over 26%. Nonetheless, challenges pertaining to stability and reliability continue to impede their commercial viability. Recent progress in interface engineering and materials science has underscored the potential of two-dimensional (2D) materials, particularly MXenes, in mitigating these challenges. MXenes represent a class of two-dimensional materials with significant potential for application in PSCs, attributed to their exceptional electrical conductivity, high carrier mobility, remarkable optical transparency, chemical stability, and tunable surface chemical properties. When employed as electron transport layers, MXenes enhance charge transfer and extraction efficiency, leading to substantial improvements in PCEs. Furthermore, their integration into hole transport layers and use as interfacial modifiers contribute to the mitigation of degradation pathways, thereby enhancing device longevity. The unique structural and electronic characteristics of MXenes facilitate their application as transparent electrodes, presenting opportunities for cost reduction and improved optical properties. This review provides a comprehensive overview of the current advancements in MXene-based PSCs, emphasizing significant accomplishments and exploring future research directions aimed at enhancing the efficiency and stability of these devices. Full article

The trapping mechanism at the AlGaN/GaN interface in the p-GaN high electron mobility transistors (HEMTs) and its impact on the turn-on characteristics of direct-coupled FET logic (DCFL) inverters were investigated across various supply voltages (V_DD) and test frequencies (f_m). The frequency-conductance method identified two trap states at the AlGaN/GaN interface (trap activation energy E_c-E_T ranges from 0.345 eV to 0.363 eV and 0.438 eV to 0.47 eV). As V_DD increased from 1.5 V to 5 V, the interface traps captured more electrons, increasing the channel resistance (R_channel) and drift-region resistance (R_drift) of the p-GaN HEMTs and raising the low-level voltage (V_OL) from 0.56 V to 1.01 V. At f_m = 1 kHz, sufficient trapping and de-trapping led to a delay of 220 µs and a V_OL instability of 320 mV. Additionally, as f_m increased from 1 kHz to 200 kHz, a positive shift in the threshold voltage of p-GaN HEMTs occurred due to the dominance of trapping. This shift caused V_OL to rise from 1.02 V to 1.40 V and extended the fall time (t_fall) from 153 ns to 1 µs. This investigation enhances the understanding of DCFL GaN inverters’ behaviors from the perspective of device physics on power switching applications. Full article

Programming has gained significant importance worldwide as societies increasingly rely on computer application systems. To support novices in learning various programming languages, we have developed the Programming Learning Assistant System (PLAS). It offers several types of exercise problems with different learning goals and levels for step-by-step self-study. As a personal answer platform in PLAS, we have implemented a web application using Node.js and EJS for Java and Python programming. Recently, the Flutter framework with Dart programming has become popular, enabling developers to build applications for mobile, web, and desktop environments from a single codebase. Thus, we have extended PLAS by implementing the Flutter environment with Visual Studio Code to support it. Additionally, we have developed an image-based user interface (UI) testing tool to verify student source code by comparing its generated UI image with the standard one using the ORB and SIFT algorithms in OpenCV. For efficient distribution to students, we have generated Docker images of the answer platform, Flutter environment, and image-based UI testing tool. In this paper, we present the implementation of a web-based answer platform for the Flutter Programming Learning Assistant System (FPLAS) by integrating three Docker images using Docker Compose. Additionally, to capture UI images automatically, an Nginx web application server is adopted with its Docker image. For evaluations, we asked 10 graduate students at Okayama University, Japan, to install the answer platform on their PCs and solve five exercise problems. All the students successfully completed the problems, which confirms the validity and effectiveness of the proposed system. Full article

Road surface information, encompassing aspects like road surface damages and facility distributions, is vital for maintaining and updating roads in smart cities. The proposed mobile sensing framework uses multi-modal sensors, including a GPS, gyroscope, accelerometer, camera, and Wi-Fi, integrated into a Jetson Nano to collect comprehensive road surface information. The collected data are processed, stored, and analyzed on the server side, with results accessible via RESTful APIs. This system enables the detection of road conditions, which are visualized through the web mapping technique. Based on this concept, the Mobile Sensor Framework for Road Surface analysis (MSF4RS) is designed, and its use significantly enhances road surface data acquisition and analysis. Key contributions include (1) the integration of multi-modal IoT sensors to capture comprehensive road surface data; (2) the development of a software environment that facilitates robust data processing; and (3) the execution of experiments using the MSF4RS, which synergistically combines hardware and software components. The framework leverages advanced sensor technologies and server-based computational methods and offers a user-friendly web interface for the dynamic visualization and interactive exploration of road surface conditions. Experiments confirm the framework’s effectiveness in capturing and visualizing road surface data, demonstrating significant potential for smart city applications. Full article

In the context of climate change, there is a growing need for accurate, real-time data on water quality in river waterways. This results in the development of advanced monitoring systems. This article presents a remote water quality monitoring system designed specifically for use in inland waterways, the basic elements of which are placed in a buoy with an IoT unit. The proposed system uses a network of sensors strategically placed along the waterway to continuously measure critical parameters: temperature, pH, dissolved oxygen, and conductivity. Various compatibility, efficiency, and ease-of-use tests have been conducted to verify each aspect of the monitoring system. It has been shown that the sensors operate within the intended accuracy ranges. The central unit equipped with a GSM (Global System for Mobile Communications) module can wirelessly transmit data to a main server, enabling remote access and analysis via a user-friendly interface of the developed application. The paper details the technical architecture of the system, the integration of GSM technology to ensure reliable data transmission, and the results of the monitoring studies of the proposed parameters. The remote monitoring system offers significant benefits in terms of early detection of pollution events, ensuring the safety of aquatic life, and supporting sustainable navigation practices. The research results highlight the potential of GSM-based remote monitoring systems to revolutionize water quality management in waterways in various regions. Full article

Electroencephalography (EEG) is a non-invasive technique with high temporal resolution and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data classification is one of the key applications within brain–computer interface (BCI) systems, utilizing EEG signals from motor imagery tasks. BCI is very useful for people with severe mobility issues like quadriplegics, spinal cord injury patients, stroke patients, etc., giving them the freedom to a certain extent to perform activities without the need for a caretaker, like driving a wheelchair. However, motion artifacts can significantly affect the quality of EEG recordings. The conventional EEG enhancement algorithms are effective in removing ocular and muscle artifacts for a stationary subject but not as effective when the subject is in motion, e.g., a wheelchair user. In this research study, we propose an empirical error model-based artifact removal approach for the cross-subject classification of motor imagery (MI) EEG data using a modified CNN-based deep learning algorithm, designed to assist wheelchair users with severe mobility issues. The classification method applies to real tasks with measured EEG data, focusing on accurately interpreting motor imagery signals for practical application. The empirical error model evolved from the inertial sensor-based acceleration data of the subject in motion, the weight of the wheelchair, the weight of the subject, and the surface friction of the terrain under the wheelchair. Three different wheelchairs and five different terrains, including road, brick, concrete, carpet, and marble, are used for artifact data recording. After evaluating and benchmarking the proposed CNN and empirical model, the classification accuracy achieved is 94.04% for distinguishing between four specific classes: left, right, front, and back. This accuracy demonstrates the model’s effectiveness compared to other state-of-the-art techniques. The comparative results show that the proposed approach is a potentially effective way to raise the decoding efficiency of motor imagery BCI. Full article

The increasing number of individuals with disabilities—over 61 million adults in the United States alone—underscores the urgent need for technologies that enhance autonomy and independence. Among these individuals, millions rely on wheelchairs and often require assistance from another person with activities of daily living (ADLs), such as eating, grooming, and dressing. Wheelchair-mounted assistive robotic arms offer a promising solution to enhance independence, but their complex control interfaces can be challenging for users. Automating control through deep learning-based object detection models presents a viable pathway to simplify operation, yet progress is impeded by the absence of specialized datasets tailored for ADL objects suitable for robotic manipulation in home environments. To bridge this gap, we present a novel ADL object dataset explicitly designed for training deep learning models in assistive robotic applications. We curated over 112,000 high-quality images from four major open-source datasets—COCO, Open Images, LVIS, and Roboflow Universe—focusing on objects pertinent to daily living tasks. Annotations were standardized to the YOLO Darknet format, and data quality was enhanced through a rigorous filtering process involving a pre-trained YOLOv5x model and manual validation. Our dataset provides a valuable resource that facilitates the development of more effective and user-friendly semi-autonomous control systems for assistive robots. By offering a focused collection of ADL-related objects, we aim to advance assistive technologies that empower individuals with mobility impairments, addressing a pressing societal need and laying the foundation for future innovations in human–robot interaction within home settings. Full article

The performance of organic field-effect transistors (OFETs) is highly dependent on the dielectric–semiconductor interface, especially in ion-gel-gated OFETs, where a significantly high carrier density is induced at the interface at a low gate voltage. This study investigates how altering the alkyl side chain length of donor–acceptor (D-A) copolymers impacts the electrical performance of ion-gel-gated OFETs. Two difluorobenzothiadiazole-based D-A copolymers, PffBT4T-2OD and PffBT4T-2DT, are compared, where the latter features longer alkyl side chains. Although PffBT4T-2DT shows a 2.4-fold enhancement of charge mobility in the SiO₂-gated OFETs compared to its counterpart due to higher crystallinity in the film, PffBT4T-2OD outperforms PffBT4T-2DT in the ion-gel-gated OFETs, manifested by an extraordinarily high mobility of 17.7 cm²/V s. The smoother surface morphology, as well as stronger interfacial interaction between the ion-gel dielectric and PffBT4T-2OD, enhances interfacial charge accumulation, which leads to higher mobility. Furthermore, PffBT4T-2OD is blended with a polymeric elastomer SEBS to achieve ion-gel-gated flexible OFETs. The blend devices exhibit high mobility of 8.6 cm²/V s and high stretchability, retaining 45% of initial mobility under 100% tensile strain. This study demonstrates the importance of optimizing the chain structure of polymer semiconductors and the semiconductor–dielectric interface to develop low-voltage and high-performance flexible OFETs for wearable electronics applications. Full article

This paper introduces a model that predicts four common smartphone-holding postures, aiming to enhance user interface adaptability. It is unique in being completely independent of platform and hardware, utilizing the inertial measurement unit (IMU) for real-time posture detection based on sensor data collected around tap gestures. The model identifies whether the user is holding and operating the smartphone with one hand or using both hands in different configurations. For model training and validation, sensor time series data undergo extensive feature extraction, including statistical, frequency, magnitude, and wavelet analyses. These features are incorporated into 74 distinct sets, tested across various machine learning frameworks—k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF)—and evaluated for their effectiveness using metrics such as cross-validation scores, test accuracy, Kappa statistics, confusion matrices, and ROC curves. The optimized model demonstrates a high degree of accuracy, successfully predicting the holding hand with a 95.7% success rate. This approach highlights the potential of leveraging sensor data to improve mobile user experiences by adapting interfaces to natural user interactions. Full article

This article reviews the latest advancements in perovskite solar cell (PSC) components for innovative photovoltaic applications. Perovskite materials have emerged as promising candidates for next-generation solar cells due to their exceptional light-absorbing capabilities and facile fabrication processes. However, limitations in their stability, scalability, and efficiency have hindered their widespread adoption. This review systematically explores recent breakthroughs in PSC components, focusing on absorbed layer engineering, electron and hole transport layers, and interface materials. In particular, it discusses novel perovskite compositions, crystal structures, and manufacturing techniques that enhance stability and scalability. Additionally, the review evaluates strategies to improve charge carrier mobility, reduce recombination, and address environmental considerations. Emphasis is placed on scalable manufacturing methods suitable for large-scale integration into existing infrastructure. This comprehensive review thus provides researchers, engineers, and policymakers with the key information needed to motivate the further advancements required for the transformative integration of PSCs into global energy production. Full article

As the interface between frozen and unfrozen soil, the ice front is not only a spatial location concept, but also a potentially dangerous interface where the mechanical properties of soil could change abruptly. Accurately identifying its spatial position is essential for the safe and efficient execution of large-scale frozen soil engineering projects. Electrical capacitance tomography (ECT) is a promising method for the visualization of frozen soil due to its non-invasive nature, low cast, and rapid response. This paper presents the design and optimization of a mobile circular capacitance sensor (MCCS). The MCCS was used to measure frozen soil samples along the depth direction to obtain moisture distribution and three-dimensional images of the ice front. Finally, the experimental results were compared with the simulation results from COMSOL Multiphysics to analyze the deviations. It was found that the fuzzy optimization design based on multi-criteria orthogonal experiments makes the MCCS meet various performance requirements. The average permittivity distribution was proposed to reflect moisture distribution along the depth direction and showed good correlation. Three-dimensional reconstructed images could provide the precise position of the ice front. The simulation results indicate that the MCCS has a low deviation margin in identifying the position of the ice front. Full article

The particle size distribution (PSD) is a crucial parameter used to characterize the material composition of debris-flow deposits which determines their hydraulic permeability, affecting the mobility of debris flows and, hence, the sustainable development of debris-flow fans. Three types of graded bedding structures—normal, reverse, and mixed graded bedding structures—are characterized by discontinuous gradation within a specific deposit thickness. A series of permeability tests were conducted to study the effects of bed sediment composition, particularly coarse grain sizes and fine particle contents, on the permeability and migration of fine particles in discontinuous debris-flow deposits. An increase in fine particles within the discontinuously graded bed sediment led to a power-law decrease in the average permeability coefficient. With fine particle contents of 10% and 15% in the bed sediments, the final permeability coefficient consistently exceeded the initial value. However, this trend reversed when the fine particle contents were increased to 20%, 25%, and 30%. Lower fine particle contents indicated enhanced permeability efficiency due to more interconnected voids within the coarse particle skeleton. Conversely, an increase in fine particle content reduced the permeability efficiency, as fine particles tended to aggregate at the lower section of the seepage channel. An increase in coarse particle size decreased the formation of flow channels at the coarse–fine particle interface, causing fine particles to move slowly along adjacent or clustered slow flow channels formed by fine particles, resulting in decreased permeability efficiency. Three formulae are proposed to calculate the permeability coefficients of discontinuously graded bed sediments, which may aid in understanding the initiation mechanism of channel deposits. Based on experimental studies and field investigations, it is proposed that achieving sustainable development of debris-flow fans requires a practical approach that integrates three key components: spatial land-use planning, in situ monitoring of debris flows and the environment, and land-use adjustment and management. This comprehensive and integrated approach is essential for effectively managing and mitigating the risks associated with debris flows, ensuring sustainable development in vulnerable areas. Full article

One of the critical challenges in mobile robotics is obstacle avoidance, ensuring safe navigation in dynamic environments. In this sense, this work presents a comparative study of two intelligent control approaches for mobile robot obstacle avoidance based on a fuzzy architecture. The first approach is a neuro-fuzzy interface that combines neural networks’ learning capabilities with fuzzy logic’s rule-based reasoning, offering a flexible and adaptable control strategy. The second is a classic Mamdani fuzzy system that relies on human-defined fuzzy rules, providing an intuitive approach to control. A key contribution of this work is the development of a fast comprehensive, model-based dataset for neural network training generated without the need for real sensor data. The results show the evaluation of these two systems’ performance, robustness, and computational efficiency using low-cost ultrasonic sensors on a Pioneer 3DX robot within the Coppelia Sim environment. Full article