Search Results (387)

Unlike traditional control systems that rely on physical input devices, facial gesture-based interaction offers a contactless and intuitive method for operating autonomous systems. Recent advances in computer vision and deep learning have enabled the use of facial expressions and movements for command recognition in human–robot interaction. In this work, we propose a lightweight, real-time facial gesture recognition method, GhostNet-BiLSTM-Attention (GBA), which integrates GhostNet and BiLSTM with an attention mechanism, is trained on the FaceGest dataset, and is integrated with a 3D robot simulation in Unity. The system is designed to recognize predefined facial gestures such as head tilts, eye blinks, and mouth movements with high accuracy and low inference latency. Recognized gestures are mapped to specific robot commands and transmitted to a Unity-based simulation environment via socket communication across machines. This framework enables smooth and immersive robot control without the need for conventional controllers or sensors. Real-time evaluation demonstrates the system’s robustness and responsiveness under varied user and lighting conditions, achieving a classification accuracy of 99.13% on the FaceGest dataset. The GBA holds strong potential for applications in assistive robotics, contactless teleoperation, and immersive human–robot interfaces. Full article

Aiming at the real-time object detection requirements of the intelligent control system for laboratory item transportation in mobile embedded unmanned vehicles, this paper proposes a lightweight hybrid detection system based on the OpenMV vision module. The system adopts a two-stage detection mechanism: in long-distance scenarios (>32 cm), fast target positioning is achieved through red threshold segmentation based on the HSV(Hue, Saturation, Value) color space; when in close range (≤32 cm), it switches to a lightweight deep learning model for fine-grained recognition to reduce invalid computations. By integrating the MobileNetV2 backbone network with the FOMO (Fast Object Matching and Occlusion) object detection algorithm, the FOMO MobileNetV2 model is constructed, achieving an average classification accuracy of 94.1% on a self-built multi-dimensional dataset (including two variables of light intensity and object distance, with 820 samples), which is a 26.5% improvement over the baseline MobileNetV2. In terms of hardware, multiple functional components are integrated: OLED display, Bluetooth communication unit, ultrasonic sensor, OpenMV H7 Plus camera, and servo pan-tilt. Target tracking is realized through the PID control algorithm, and finally, the embedded terminal achieves a real-time processing performance of 55 fps. Experimental results show that the system can effectively and in real-time identify and track the detection targets set in the laboratory. The designed unmanned vehicle system provides a practical solution for the automated and low-power transportation of small items in the laboratory environment. Full article

Bifacial Photovoltaic (bPV) technology is rapidly becoming the standard in the solar photovoltaic (PV) industry due to its ability to capture reflected radiation and generate additional energy. This experimental study analyses the electrical performance of bPV modules under specific installation conditions, including varying heights, module tilt angles (MTA), and surface reflectivity. The methodology combines controlled indoor testing with outdoor experiments that replicate real-world operating environments. The outdoor test setup was carefully designed and included dual data acquisition systems: one with independent sensors and another with wireless telemetry for data transfer from the inverter. A thermal performance model was used to estimate energy output and was benchmarked against experimental measurements. All electrical parameters were obtained in accordance with international standards, including current-voltage characteristic (I–V curve) corrections, using calibrated instruments to monitor irradiance and temperature. Indoor measurements under Standard Test Conditions yielded at bifaciality coefficient $\phi =0.732$, a rear bifacial power gain $BiFi=0.285$, and a relative bifacial gain $BiF{i}_{rel}=9.4\%$. The outdoor configuration employed volcanic red stone (Tezontle) as a reflective surface, simulating a typical mid-latitude installation with modules mounted 1.5 m above ground, tilted from 0° to 90° regarding floor and oriented true south. The study was conducted at a site located at 18.8° N latitude during the early summer season. Results revealed significant non-uniformity in rear-side irradiance, with a 32% variation between the lower edge and the centre of the bPV module. The thermal model used to determine electrical performance provides power values higher than those measured in the time interval between 10 a.m. and 3 p.m. Maximum energy output was observed at a MTA of $0°$, which closely aligns with the optimal summer tilt angle for the site’s latitude. Bifacial energy gain decreased as the MTA increased from $0°$ to $90°$. These findings offer practical, data-driven insights for optimizing bPV installations, particularly in regions between $15°$ and $30°$ north latitude, and emphasize the importance of tailored surface designs to maximize performance. Full article

Labor shortages and uneven terrain in orchards present significant challenges to autonomous navigation. This study proposes a navigation system that integrates Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) data to enhance localization accuracy and map stability through Simultaneous Localization and Mapping (SLAM). To minimize distortions in LiDAR scans caused by ground irregularities, real-time tilt correction was implemented based on IMU feedback. Furthermore, the path planning module was improved by modifying the Rapidly-Exploring Random Tree (RRT) algorithm. The enhanced RRT generated smoother and more efficient trajectories with quantifiable improvements: the average shortest path length was 2.26 m, compared to 2.59 m with conventional RRT and 2.71 m with A* algorithm. Tracking performance also improved, achieving a root mean square error of 0.890 m and a maximum lateral deviation of 0.423 m. In addition, yaw stability was strengthened, as heading fluctuations decreased by approximately 7% relative to the standard RRT. Field results validated the robustness and adaptability of the proposed system under real-world agricultural conditions. These findings highlight the potential of LiDAR–IMU sensor fusion and optimized path planning to enable scalable and reliable autonomous navigation for precision agriculture. Full article

This paper presents a comprehensive study of an ESP32 microcontroller-based self-balancing mobile robot system designed in conjunction with an Android app for Bluetooth control. The robot employs an MPU6050 accelerometer/gyroscope to execute dynamic equilibrium control for robotic balance. This study explores the design of a system composed of an ESP32-based dual-platform architecture. The firmware for the ESP32 executes real-time motor control and sensor processing, while the Android application provides the user interface, data visualization, and command transmission. The system achieves stable operation with tilt angle variations of ±2.5° ($\sigma =0.8°$, n = 50 trials) during normal operation with a PID controller tuned to KP = 6.0, KI = 0.1, and KD = 1.5. In experimental tests, control latency was measured at 38–72 ms (mean = 55 ms, $\sigma =12$ ms) over distances of 1–10 m with a robust Bluetooth connection. Extended operational tests indicated the reliability of both autonomous obstacle avoidance mode and manual control exceeding 95%. Key contributions include gyro drift compensation using a progressive calibration scheme, intelligent battery management for operational efficiency, and a dual-mode control interface to facilitate seamless transition between manual and autonomous operation. Processing of real-time telemetry on the Android application allows visualization of important parameters like tilt angle, motor speeds, and sensor readings. This work contributes to a cost-effective mobile robotics platform (total cost: USD 127) through the provision of detailed design specifications, implementation strategies, and performance characteristics. Full article

Achieving energy-efficient aerial coverage remains a significant challenge for UAV-based missions, especially over hilly terrain where consistent ground resolution is needed. Traditional solutions use changes in altitude to compensate for elevation changes, which requires a significant amount of energy. This paper presents a new way to plan coverage paths (CPP) that uses real-time zoom control of a pan–tilt–zoom (PTZ) camera to keep the ground sampling distance (GSD)—the distance between two consecutive pixel centers projected onto the ground—constant without changing the UAV’s altitude. The proposed algorithm changes the camera’s focal length based on the height of the terrain. It only changes the altitude when the zoom limits are reached. Simulation results on a variety of terrain profiles show that the zoom-based CPP substantially reduces flight duration and path length compared to traditional altitude-based strategies. The framework can also be used with low-cost camera systems with limited zoom capability, thereby improving operational feasibility. These findings establish a basis for further development and field validation in upcoming research phases. Full article

To address the demands for wide-range and high-precision current measurement, this paper proposes a novel current sensor design that integrates spin sensing technology, magnetic shunt effect, and a multi-sensor data fusion algorithm. The spin valve sensors accurately detect the magnetic field generated by the signal current, while the soft magnetic shunt structure attenuates the magnetic field to a level suitable for the spin valve sensors. Consequently, the detection current range can be extended by 6.8 times. Using four spin valve sensors and data fusion with an averaging algorithm, the system can calibrate the errors caused by the displacement or tilt of the current-carrying wire. Experimental results demonstrate that the current sensor achieves a sensitivity of 61.6 mV/V/A, an excellent linearity of 0.55%, and robust measurement performance, as well as strong anti-interference capability. Our study offers a novel solution for high-precision, wide-range current measurement in applications such as those in new energy vehicle electronics and precision electric energy metering. Full article

The error motion of the precision rotary stage, particularly the tilt error motion, significantly influences the accuracy of machining and measuring equipment. Nonetheless, reliable and effective in situ measurement methods for tilt error motion are still limited. Based on the analysis of the conventional three-probe measurement method, this paper proposes a multi-probe measurement method using an ultra-precision reference plate with high-resolution displacement sensors. This method employs principles and methods to avoid harmonic suppression issues through optimal probe designs, enabling simultaneous quantification of tilt and axial error motions via error separation. Error separation techniques can effectively decouple motion errors from artifact form error, making them widely applicable in precision measurement data processing. Experimental validation confirmed that the synchronous measurement error is not greater than 4.69%, consequently affirming the metrological efficacy and reliability of the method. This study provides an effective method for real-time error characterization of rotary stages. Full article

Monitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus lowering the use of moment and tilt sensors that are currently being used for obtaining foundation stiffness. First, a convolutional neural network model is applied to map acceleration and wind speed data within a moving window to corresponding moment and tilt values. Rotational stiffness of the foundation is then estimated by fitting a line in the moment-tilt plane. The results obtained indicate that such a mapping model can provide stiffness values that are within 7% of ground truth stiffness values on average. Second, the developed mapping model is re-trained by using synthetic acceleration and wind speed data that are generated by an autoencoder generative AI network. The results obtained indicate that although the exact amount of stiffness drop cannot be determined, the drops themselves can be detected. This mapping model can be used not only to lower the cost associated with obtaining foundation rotational stiffness but also to sound an alarm when a foundation starts deteriorating. Full article

Research on image sensor (IS)-based visible light positioning systems has attracted widespread attention. However, when the receiver is tilted or under a single LED, the positioning system can only achieve two-dimensional (2D) positioning and requires the assistance of inertial measurement units (IMU). When the LED is not captured or decoding fails, the system’s positioning error increases further. Thus, we propose a novel three-dimensional (3D) visible light positioning system based on image sensors for various environments. Specifically, (1) we use IMU to obtain the receiver’s state and calculate the receiver’s 2D position. Then, we fit the height–size curve to calculate the receiver’s height, avoiding the coordinate iteration error in traditional 3D positioning methods. (2) When no LED or decoding fails, we propose a firefly-assisted unscented particle filter (FA-UPF) algorithm to predict the receiver’s position, achieving high-precision dynamic positioning. The experimental results show that the system positioning error under a single LED is within 10 cm, and the average positioning error through FA-UPF under no light source is 6.45 cm. Full article

Background: Gait impairments are common in individuals with knee osteoarthritis awaiting total knee arthroplasty, affecting their mobility and quality of life. This study aimed to assess and compare biomechanical gait features between individuals awaiting total knee arthroplasty and healthy, non-arthritic controls, focusing on less-explored variables using sensor-based measurements. Methods: A cross-sectional observational study was conducted with 60 participants: 21 individuals awaiting total knee arthroplasty and 39 nonarthritic controls aged 64–85 years. Participants completed a standardized 14 m walk, and 17 biomechanical gait parameters were measured using the BTS G-Walk inertial sensor. Key variables, such as stride duration, cadence, symmetry indices, and pelvic angles, were analyzed for group differences. Results: The pre-total knee arthroplasty group exhibited significantly longer gait cycles and stride durations (p < 0.001), reduced cadence (p < 0.001), and lower gait cycle symmetry index (p < 0.001) than the control group. The pelvic angle symmetry indices for tilt (p = 0.014), rotation (p = 0.002), and obliquity (p < 0.001) were also lower. Additionally, the pre-total knee arthroplasty group had lower propulsion indices for both legs (p < 0.001) and a lower walking quality index on the right leg (p = 0.005). The number of elaborated steps was significantly greater in the pre-total knee arthroplasty group (left, p < 0.001, right: p < 0.001). No significant differences were observed in any other gait parameters. Conclusions: This study revealed significant gait impairment in individuals awaiting total knee arthroplasty. Although direct evidence for prehabilitation is lacking, future research should explore whether targeted approaches, such as strengthening exercises or gait retraining, can improve gait and functional outcomes before surgery. Full article

Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device’s movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device’s tilt or heading can change the vector values. To avoid complications, applications using these sensors often use only the magnitude of the vector, as in geomagnetic-based indoor positioning, or assume fixed device holding postures such as holding a smartphone in portrait mode only. However, using only the magnitude of the vector loses the directional information, while ad hoc posture assumptions work under controlled laboratory conditions but often fail in real-world scenarios. To resolve these problems, we propose a universal vector calibration algorithm that enables consistent three-dimensional vector measurements for the same physical activity, regardless of device orientation. The algorithm works in two stages. First, it transforms vector values in local coordinates to those in global coordinates by calibrating device tilting using pitch and roll angles computed from the initial vector values. Second, it additionally transforms vector values from the global coordinate to a reference coordinate when the target coordinate is different from the global coordinate by correcting yaw rotation to align with application-specific reference coordinate systems. We evaluated our algorithm on geomagnetic field-based indoor positioning and bidirectional step detection. For indoor positioning, our vector calibration achieved an 83.6% reduction in mismatches between sampled magnetic vectors and magnetic field map vectors and reduced the LSTM-based positioning error from 31.14 m to 0.66 m. For bidirectional step detection, the proposed algorithm with vector calibration improved step detection accuracy from 67.63% to 99.25% and forward/backward classification from 65.54% to 100% across various device orientations. Full article

The use of aerial robots for inspection and maintenance in industrial settings demands high maneuverability, precise control, and reliable measurements. This study explores the development of a fully customized unmanned aerial manipulator (UAM), composed of a tilting drone and an articulated robotic arm, designed to perform non-destructive in-contact inspections of iron structures. The system is intended to operate in complex and potentially hazardous environments, where autonomous execution is supported by shared-control strategies that include human supervision. A parallel force–impedance control framework is implemented to enable smooth and repeatable contact between a sensor for ultrasonic testing (UT) and the inspected surface. During interaction, the arm applies a controlled push to create a vacuum seal, allowing accurate thickness measurements. The control strategy is validated through repeated trials in both indoor and outdoor scenarios, demonstrating consistency and robustness. The paper also addresses the mechanical and control integration of the complex robotic system, highlighting the challenges and solutions in achieving a responsive and reliable aerial platform. The combination of semi-autonomous control and human-in-the-loop operation significantly improves the effectiveness of inspection tasks in hard-to-reach environments, enhancing both human safety and task performance. Full article

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO₂, CO, H₂S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies. Full article

Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. Starting from 27 local and plant-level variables recorded at 15 min resolution for a 1 MW array in Çanakkale region, Türkiye (1 August 2022–3 August 2024), we apply a three-stage feature-selection pipeline: (i) variance filtering, (ii) hierarchical correlation clustering with Ward linkage, and (iii) a meta-heuristic optimizer that maximizes a neural-network R² while penalizing poor or redundant inputs. The resulting subset, dominated by apparent temperature and diffuse, direct, global-tilted, and terrestrial irradiance, reduces dimensionality without significantly degrading accuracy. Feature importance is then quantified through two complementary aspects: (a) tree-based permutation scores extracted from a set of ensemble models and (b) information gain computed over random feature combinations. Both views converge on shortwave, direct, and global-tilted irradiance as the primary drivers of active power. Using only the selected features, the best model attains an average R² ≅ 0.91 on unseen data. By utilizing transparent feature-reduction techniques and explainable importance metrics, the proposed approach delivers compact, more generalized, and reliable PV forecasts that generalize to sites lacking embedded sensor networks, and it provides actionable insights for plant siting, sensor prioritization, and grid-operation strategies. Full article