Search Results (59)

Precise 3D reconstruction of non-cooperative space targets is a prerequisite for active debris removal and on-orbit servicing. However, this task is impeded by severe environmental challenges. Specifically, the limited dynamic range of visible light cameras leads to frequent overexposure or underexposure under extreme space lighting. Compounded by sparse textures and strong specular reflections, these factors significantly constrain reconstruction accuracy. While existing general-purpose feed-forward models such as MapAnything offer efficient inference, their geometric recovery capabilities degrade sharply when facing significant domain shifts. To address these issues, this paper proposes an enhanced 3D reconstruction framework tailored for the space environment named In-Orbit MapAnything. First, to mitigate data scarcity, we construct a high-quality space target dataset incorporating extreme illumination characteristics, which provides comprehensive auxiliary modalities including accurate camera poses and dense point clouds. Second, we propose the SatMap-Adapter module to mitigate feature degradation caused by severe specular reflections. This architecture employs a hierarchical cascade sampling strategy to align multi-level backbone features and utilizes a lightweight adaptive fusion module to dynamically integrate shallow photometric cues, intermediate structural information, and deep semantic features. Finally, we employ a weight-decomposed low-rank adaptation strategy to achieve parameter-efficient fine-tuning while strictly freezing the pre-trained backbone. Experimental results demonstrate that the proposed method decreases the absolute relative error and Chamfer distance by 15.23% and 20.02% respectively compared to the baseline MapAnything model, while maintaining a rapid inference speed. The proposed approach effectively suppresses reconstruction noise on metallic surfaces and recovers fine geometric structures, validating the effectiveness of our feature-enhanced framework in extreme space environments. Full article

To investigate the dynamic instability mechanism of surrounding rock in deep, rockburst-prone coal seams, a Split Hopkinson Pressure Bar (SHPB) system was utilized to carry out dynamic impact compression tests on Rock–Coal–Rock (RCR) composites featuring four different seam dip angles, namely 0°, 15°, 30°, and 45°. We systematically analyze incorporating high-speed imaging, the mechanical properties, energy evolution, and progressive failure characteristics of the composites under various strain rates. The results indicate that the dynamic compressive strength and elastic modulus of the composites exhibit a significant strain-rate hardening effect. With the increase in the dip angle of the coal seam, the compressive strength of the specimen decreases accordingly. Specifically, the range of 15–30° is identified as a critical transition zone where the failure mode shifts from matrix-dominated bearing to interfacial slip instability. At an impact pressure of 0.12 MPa, the compressive strength drops by 36.9% within this interval. Furthermore, the energy distribution is profoundly modulated by the geometric characteristics of the interface. As the dip angle increases, the degree of wave impedance mismatch at the coal–rock interface intensifies, leading to a sharp rise in the reflected energy ratio (up to 80.7%) and a pronounced attenuation of transmitted energy. Notably, the dissipation energy per unit volume increases with the dip angle, revealing that interfacial sliding and frictional work become the primary energy dissipation pathways under large-inclination conditions. High-speed camera monitoring confirms that the instability mechanism shifts from axial splitting/tension to an interfacial shear-slip mode as the dip angle increases. These findings provide a scientific reference for the stability evaluation of roadway surrounding rock and the prevention of dynamic disasters. Full article

The unpredictable nature of outdoor settings introduces numerous safety concerns, making hazard detection crucial for safe navigation. To address this issue, this paper proposes a sidewalk hazard detection system that combines a Variational Autoencoder (VAE) with a One-Class Support Vector Machine (OCSVM), using a wearable RGB camera as the primary sensing modality to enable low-cost, portable deployment and provide visual detail for detecting surface irregularities and unexpected objects. The VAE is trained exclusively on clean, obstruction-free sidewalk data to learn normal appearance patterns. At inference time, the reconstruction error produced by the VAE is used to identify spatial anomalies within each frame. These flagged anomalies are passed to an OCSVM, which determines whether they constitute a non-hazardous anomaly or a true hazardous anomaly that may impede navigation. To support this approach, we introduce a custom dataset consisting of over 20,000 training images of normal sidewalk scenes and 8000 testing frames containing both hazardous and non-hazardous anomalies. Experimental results demonstrate that the proposed VAE + OCSVM model achieves an AUC of 0.92 and an F1 score of 0.85, outperforming baseline anomaly detection models for outdoor sidewalk navigation. These findings indicate that the hybrid method offers a robust solution for sidewalk hazard detection in real-world outdoor environments. Full article

Conventional respiratory monitoring is often invasive, while most non-contact technologies like radar or cameras are limited to estimating respiratory rate, failing to reconstruct the detailed waveform of the respiratory flow itself. This gap limits their clinical utility for advanced diagnostics. We introduce a novel system that bridges this gap by combining a contactless, impedance-based sensor (the Thoraxmonitor) with a dedicated machine learning framework to directly reconstruct the full respiratory flow signal. Operating at 433 $\mathrm{M}$$\mathrm{Hz}$, the system’s antenna array detects subtle changes in thoracic impedance, which are then translated into a quantitative flow signal by a Multilayer Perceptron Regressor. Based on data from 17 subjects benchmarked against a gold-standard flowmeter, our system accurately detected 98% of respiratory cycles. It achieved remarkable precision in timing respiratory events, with mean deviations of + 60 $\mathrm{m}$$\mathrm{s}$ (±79 $\mathrm{m}$$\mathrm{s}$) for inspiration and + 50 $\mathrm{m}$$\mathrm{s}$ (±63 $\mathrm{m}$$\mathrm{s}$) for expiration, making it suitable for time-critical applications. While a systematic bias in absolute tidal volume prediction currently limits inter-subject comparisons, the system excels at tracking relative intra-subject changes. Crucially, our model quantifies its own reliability, providing an intrinsic self-assessment mechanism. This work demonstrates a significant step beyond simple rate detection towards comprehensive, comfortable, and reliable respiratory analysis in clinical and everyday settings. Full article

Determining sow (Sus scrofa domesticus Erxleben) estrus status requires considerable labor investment, and continuous real-time monitoring is impractical. Workers typically identify estrus at scheduled intervals and determine artificial insemination timing based on experience. However, experience-based methods are subjective, vary with operator expertise, and impede standardized management in large-scale farms. This study employs cameras and deep learning to detect sows and analyze postural changes, enabling estrus detection and optimal insemination timing prediction. Experimental results indicate that the proposed method achieved an accuracy of 70% (42/60), where the recommended insemination timing differed by less than 24 h from human decisions. This approach facilitates data-driven estrus detection and insemination scheduling, potentially reducing labor intensity and improving reproductive outcomes, particularly beneficial for labor-intensive and large-scale swine production systems. Full article

This paper presents a dual-band antenna designed for integration into eyewear. The antenna is intended for a system supporting visually impaired individuals, where a wearable camera integrated into glasses transmits data to a remote receiver. To enhance system reliability within indoor environments, the proposed design supports both fifth-generation (5G) wireless communication and Wi-Fi networks. The compact antenna is specifically dimensioned for integration within eyeglass temples and operates in the 3.5 GHz and 5.8 GHz frequency bands. Prototype measurements, conducted using a human head phantom, validate the antenna’s performance. The results demonstrate good impedance matching across the desired frequency bands and a maximum gain of at least 4 dBi in both bands. Full article

This study proposes a method for estimating the junction temperature of power semiconductors, particularly IGBTs (Insulated Gate Bipolar Transistors) and diodes. Traditional temperature measurement methods using NTC (Negative Temperature Coefficient) sensors have limitations in reflecting dynamic conditions in real time, as temperature changes take time to reach the sensors. To address this, this study proposes a junction temperature estimation method using RC curve fitting and a thermal impedance model. This model represents the thermal behavior of IGBTs and diodes using a Foster thermal network that considers the resistance and capacitance of the heat transfer path. In particular, transient temperature estimation considering thermal coupling enables the prediction of temperature changes in IGBTs and diodes. To verify the proposed temperature estimation method, experiments were conducted to build the model based on data measured with an infrared thermal camera and NTC sensors. The model’s estimated results were compared with actual values across 25 operating regions, achieving a maximum MAE (Mean Absolute Error) of 2.26 °C. A comparative analysis of first-, second-, third-, and fourth-order Foster networks revealed that, while higher orders improve accuracy, gains beyond the second order are minimal relative to computational demands. This study contributes to enhancing not only the reliability of power semiconductor modules but also minimizing the temperature margin for inverters by estimating the junction temperature with better dynamic performance than that achieved by NTC sensors. Full article

The tracking pose of heliostats directly affects the stability and working efficiency of concentrated solar power (CSP) plants. Due to occlusion, over-exposure, and uneven illumination caused by mirror reflection, traditional image processing algorithms showed poor performances on the detection and segmentation of heliostats, which impede vision-based 3D measurement of tracking poses of heliostats. To tackle this issue, object detection using deep learning neural networks are exploited. An improved neural network based on YOLO-v5 framework has been designed to solve the on-line detection problem of heliostats. The model achieves a recognition accuracy of 99.7% for the test set, outperforming traditional methods significantly. Based on segmented results, the corner points of each heliostat are found out using Hough Transform and line intersection methods. The 3D poses of each heliostat are then solved out based on the image coordinates of specific feature points and the camera model. Experimental and field test results demonstrate the feasibility of this hybrid approach, which provides a low-cost solution for the monitoring and measurement of tracking poses of the heliostats in CSP. Full article

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers’ ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R² = 0.9566) with a reference system and performed well in field tests, providing a Lin’s concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection. Full article

Bolt loosening can significantly impact the accuracy, stability, and safety of equipment. The detection of bolt loosening in a timely manner is crucial for ensuring the safety, reliability, performance, and service life of equipment, structures, and systems. Various methods exist for detecting bolt loosening, such as strain gauges and ultrasonic waves. However, these technologies have some limitations that impede their widespread application. In this paper, for the high-pressure pipe manifolds that may experience leakage accidents due to the loosening of bolts, an early bolt loosening detection method based on digital image correlation is proposed. Initially, a model is established through tensile tests to relate the average strain on the side of the bolt head to the axial force. Subsequently, an industrial camera captures images of bolts with random speckles under operational conditions. Using digital image correlation technology, the average strain in a specific region on the side of the bolt head is calculated. By integrating the average strain into the established relationship model between the average strain and axial force, the axial force of the bolt under operational conditions can be predicted, enabling the early assessment of bolt loosening. The findings show that the average strain on the side of the bolt head increases proportionally with the axial force, indicating a strong linear relationship. This method enables accurate prediction of the bolt’s axial force, offering a new approach for identifying the early loosening of bolts in high-pressure manifolds and monitoring structural health. Full article

The Great Wall, a pivotal part of Chinese cultural heritage listed on the World Heritage List since 1987, confronts challenges stemming from both natural deterioration and anthropogenic damage. Traditional conservation strategies are impeded by the Wall’s vast geographical spread, substantial costs, and the inefficiencies associated with conventional surveying techniques such as manual surveying, laser scanning, and low-altitude aerial photography. These methods often struggle to capture the Wall’s intricate details, resulting in limitations in field operations and practical applications. In this paper, we propose a novel framework utilizing depth cameras for the efficient real-time 3D reconstruction of the Great Wall. To overcome the challenge of the high complexity of reconstruction, we generate multi-level geometric features from raw depth images for hierarchical computation guidance. On one hand, the local set of sparse features serve as basic cues for multi-view-based reconstruction. On the other hand, the global set of dense features are employed for optimization guidance during reconstruction. The proposed framework facilitates the real-time, precise 3D reconstruction of the Great Wall in the wild, thereby significantly enhancing the capabilities of traditional surveying methods for the Great Wall. This framework offers a novel and efficient digital approach for the conservation and restoration of the Great Wall’s cultural heritage. Full article

In the past, comprehensive information was imperative for image processing, prompting a preference for high-depth cameras. However, in our research, we discovered that the abundance of image details may impede phase retrieval. Consequently, this paper presents an iterative phase retrieval method based on a low bit depth. Through simulations and experiments, this approach has proven effective in evidently enhancing phase retrieval outcomes. Furthermore, the concept of low bit depth holds promise for broader application across diverse domains within the field of image retrieval. Full article

The study of the hydrophysical properties of rocks is indispensable for the development of hydraulic engineering, especially for blasting operations in water. Reasonable matching between explosives and rocks increases the utilization of explosive energy and improves the blasting performances. Based on the energy law in the rock blasting process, the matching relationship between explosives and rock is studied by combining experimental and theoretical methods for the hydrophysical properties of the rock itself. Firstly, the theoretical solutions for crushing-zone energy, fragmentation energy and fragment-throwing energy are derived. Subsequently, concrete blocks are prepared with four types of cement–sand ratios, and four types of emulsion explosives are used to carry out single-hole blasting tests in which a high-speed camera is used to capture the trajectory of the blasting fragments that are later collected. Finally, the crushing energy, fracturing energy and fragment-throwing energy are calculated according to the test results and the basic parameters of the used explosives and concrete models. The results show that the size and distribution pattern of blasting blocks are significantly affected by the hydrophysical properties of concrete and explosive properties; the higher the energy consumption in the rupture zone, the smaller the size of the fragments and the more uniform the distribution. Moreover, the median utilization efficiency of explosive energy on rock breaking is 26.4%, the energy consumption in the crushing zone is approximately 8.4%, that in the rupture zone is approximately 10.9%, and that in the throwing energy of fragments accounts for approximately 7.1%. It is also found that the traditional wave impedance matching theory fails to obtain the best explosive energy utilization. On the contrary, the concrete specimen had the best fracturing effect and the highest energy utilization of 30.77% when the impedance ratio of concrete to explosives is 1.479. Full article

Trunk compensatory movements frequently manifest during robotic-assisted arm reaching exercises for upper limb rehabilitation following a stroke, potentially impeding functional recovery. These aberrant movements are prevalent among stroke survivors and can hinder their progress in rehabilitation, making it crucial to address this issue. This study evaluated the efficacy of visual feedback, facilitated by an RGB-D camera, in reducing trunk compensation. In total, 17 able-bodied individuals and 18 stroke survivors performed reaching tasks under unrestricted trunk conditions and visual feedback conditions. In the visual feedback modalities, the target position was synchronized with trunk movement at ratios where the target moved at the same speed, double, and triple the trunk’s motion speed, providing real-time feedback to the participants. Notably, trunk compensatory movements were significantly diminished when the target moved at the same speed and double the trunk’s motion speed. Furthermore, these conditions exhibited an increase in the task completion time and perceived exertion among stroke survivors. This outcome suggests that visual feedback effectively heightened the task difficulty, thereby discouraging unnecessary trunk motion. The findings underscore the pivotal role of customized visual feedback in correcting aberrant upper limb movements among stroke survivors, potentially contributing to the advancement of robotic-assisted rehabilitation strategies. These insights advocate for the integration of visual feedback into rehabilitation exercises, highlighting its potential to foster more effective recovery pathways for post-stroke individuals by minimizing undesired compensatory motions. Full article

Stones are one of the primary objects that impede the normal activity of underground pipelines. As human intervention is difficult inside a narrow underground pipe, a robot with a machine vision system is required. In order to remove the stones during regular robotic inspections, precise stone detection, segmentation, and measurement of their distance from the robot are needed. We applied Mask R-CNN to perform an instant segmentation of stones. The distance between the robot and the segmented stones was calculated using spatial information obtained from a lidar camera. Artificial light was used for both image acquisition and testing, as natural light is not available inside the underground pipe. ResNet101 was chosen as the foundation of the Mask R-CNN, and transfer learning was utilized to shorten the training time. The experimental results of our model showed that the average detection precision rate reached 92.0; the recall rate was 90.0%; and the F1 score rate reached 91.0%. The distance values were calculated efficiently with an error margin of 11.36 mm. Moreover, the Mask R-CNN-based stone detection model can detect asymmetrically shaped stones in complex background and lighting conditions. Full article