Search Results (144)

Interferometric scattering microscopy (iSCAT) is widely used for label-free tracking of nanoparticles and single molecules. However, its ability to identify small molecules is limited by low imaging contrast blurred with noise. Frame-averaging methods are widely used for reducing background noise but require hundreds of frames to produce a single frame as a trade-off. To address this, we applied a spatial–frequency domain deconvolution algorithm to suppress background noise and amplify the signal for each frame, achieving an improvement of ∼ 3-fold without hardware modification. This enhancement is achieved by compensating for missing information within the optical transfer function (OTF) boundary, while high-frequency components (noise) beyond this boundary are filtered. The resulting deconvolution process provides linear signal amplification, making it ideal for quantitative analysis in mass photometry. Additionally, the localization error is reduced by 20%. Comparisons with traditional denoising algorithms revealed that these methods often extract the side lobes. In contrast, our deconvolution approach preserves signal integrity while enhancing sensitivity. This work highlights the potential of image processing techniques to significantly improve the detection sensitivity of iSCAT for small molecule analysis. Full article

Stress fields imparted with an ultrafast laser can correct low spatial frequency surface figure error of mirrors through ultrafast laser stress figuring (ULSF): the formation of nanograting structures within the bulk substrate generates localized stress, creating bending moments that equilibrize via wafer deformation. For ULSF to be used as an optical figuring process, the ultrafast laser generated stress must be effectively permanent or risk unwanted figure drift. Two isochronal annealing experiments were performed to measure ultrafast laser-generated stress stability in fused silica and Corning ultra-low expansion (ULE) wafers. The first experiment tracked changes to induced astigmatism up to 1000 °C on 25.4 mm-diameter wafers. Only small changes were measured after each thermal cycle up to 500 °C for both materials, but significant changes were observed at higher temperatures. The second experiment tracked stress changes in fused silica and ULE up to 500 °C but with 4 to 16× higher signal-to-noise ratio. Change in trefoil on 100 mm-diameter wafers was measured, and the induced stress in fused silica and ULE was found to be stable after thermal cycling up to 300 °C and 200 °C, respectively, with larger changes at higher temperatures. Full article

This study proposes a Fabry–Perot interferometric system and an associated evaluation method for measuring the weight of moving trains. An optical fiber sensor, comprising a sensing fiber and a supporting structure, is securely bonded to the rail foot. As a train traverses the track, the resulting localized bending induces a change in the sensing fiber’s length, which manifests as a quantifiable phase shift in the interference signal. We developed a physical–mathematical model, based on three Gaussian functions, to describe the temporal change in sensing fiber length caused by the passage of a single bogie. This model enables the determination of a proportionality constant to accurately convert the measured phase change into train weight. Model validation was performed using a train set, including a locomotive and four variably loaded wagons, traveling at 15.47 km/h. This system offers a novel and effective approach for real-time train weight monitoring. Full article

Accurate real-time tracking of forest fires using UAV platforms is crucial for timely early warning, reliable spread prediction, and effective autonomous suppression. Existing detection-based multi-object tracking methods face challenges in accurately associating targets and maintaining smooth tracking trajectories in complex forest environments. These difficulties stem from the highly nonlinear movement of flames relative to the observing UAV and the lack of robust fire-specific feature modeling. To address these challenges, we introduce AO-OCSORT, an association-optimized observation-centric tracking framework designed to enhance robustness in dynamic fire scenarios. AO-OCSORT builds on the YOLOX detector. To associate detection results across frames and form smooth trajectories, we propose a temporal–physical similarity metric that utilizes temporal information from the short-term motion of targets and incorporates physical flame characteristics derived from optical flow and contours. Subsequently, scene classification and low-score filtering are employed to develop a hierarchical association strategy, reducing the impact of false detections and interfering objects. Additionally, a virtual trajectory generation module is proposed, employing a kinematic model to maintain trajectory continuity during flame occlusion. Locally evaluated on the 1080P-resolution FireMOT UAV wildfire dataset, AO-OCSORT achieves a 5.4% improvement in MOTA over advanced baselines at 28.1 FPS, meeting real-time requirements. This improvement enhances the reliability of fire front localization, which is crucial for forest fire management. Furthermore, AO-OCSORT demonstrates strong generalization, achieving 41.4% MOTA on VisDrone, 80.9% on MOT17, and 92.2% MOTA on DanceTrack. Full article

This paper presents SE2-LET-VINS, an enhanced Visual–Inertial Simultaneous Localization and Mapping (VI-SLAM) system built upon the classic Visual–Inertial Navigation System for Monocular Cameras (VINS-Mono) framework, designed to improve localization accuracy and robustness in complex environments. By integrating Lightweight Neural Network (LET-NET) for high-quality feature extraction and Special Euclidean Group in 2D (SE2) optical flow tracking, the system achieves superior performance in challenging scenarios such as low lighting and rapid motion. The proposed method processes Inertial Measurement Unit (IMU) data and camera data, utilizing pre-integration and RANdom SAmple Consensus (RANSAC) for precise feature matching. Experimental results on the European Robotics Challenges (EuRoc) dataset demonstrate that the proposed hybrid method improves localization accuracy by up to 43.89% compared to the classic VINS-Mono model in sequences with loop closure detection. In no-loop scenarios, the method also achieves error reductions of 29.7%, 21.8%, and 24.1% on the MH_04, MH_05, and V2_03 sequences, respectively. Trajectory visualization and Gaussian fitting analysis further confirm the system’s good robustness and accuracy. SE2-LET-VINS offers a robust solution for visual–inertial navigation, particularly in demanding environments, and paves the way for future real-time applications and extended capabilities. Full article

Distributed optical fiber sensing (DOFS) is an advanced tool for structural health monitoring (SHM), offering high precision, wide measurement range, and real-time as well as long-term monitoring capabilities. It enables real-time monitoring of both temperature and strain information along the entire optical fiber line, providing a novel approach for safety monitoring and structural health assessment in transportation engineering. This paper first introduces the fundamental principles and classifications of DOFS technology and then systematically reviews the current research progress on Brillouin scattering-based DOFS. By analyzing the monitoring requirements of various types of transportation infrastructure, this paper discusses the applications and challenges of this technology in SHM and damage detection for roads, bridges, tunnels, and other infrastructure, particularly in identifying and tracking cracks, deformations, and localized damage. This review highlights the significant potential and promising prospects of Brillouin scattering technology in transportation engineering. Nevertheless, further research is needed to optimize sensing system performance and promote its widespread application in this field. These findings provide valuable references for future research and technological development. Full article

In this work, an adaptive robust control method based on Radial Basis Function Neural Network (RBFNN) is proposed. Inspired by the local response characteristics of biological neurons, this method can reduce the influence of nonlinear errors and unknown perturbations in the extreme working conditions of the aircraft, such as high dynamics and strong vibration, so as to achieve high tracking accuracy. In this method, the dynamic model of the nonlinear error of the fiber optic gyroscope is proposed, and then the unknown external interference observer is designed for the system to realize the estimation of the unknown disturbances. The controller design method combines the design of the adaptive law outside the finite approximation domain of the achievable condition design of the sliding mode surface, and adjusts the controller parameters online according to the conditions satisfied by the real-time error state, breaking through the limitation of the finite approximation domain of the traditional neural network. In the finite approximation domain, an online adaptive controller is constructed by using the universal approximation ability of RBFNN, so as to enhance the robustness to nonlinear errors and external disturbances. By designing the output constraint mechanism, the dynamic stability of the system is further guaranteed under the constraints, and finally its effectiveness is verified by simulation analysis, which provides a new solution for high-precision inertial navigation. Full article

The progression of research in concentration photovoltaic systems parallels the advancement of high-efficiency multi-junction solar cells. To translate the theoretical optical framework into practical experimentation, a modular and structurally validated mechanical configuration for a high-concentration photovoltaic (HCPV) system was developed, incorporating boundary conditions and ensuring full system integration. The system incorporates a modular mechanical architecture, allowing flexible integration and interchangeability of optical components for experimental configurations. The architecture offers a high degree of mechanical flexibility, providing each optical stage with multiple linear and angular adjustment capabilities to support precision alignment. To ensure tracking precision, the system was coupled with a three-dimensional sun tracker capable of withstanding torques up to 60 Nm and supporting a combined payload of 80 kg, including counterbalance. The integration necessitated implementation of a counterbalance mechanism along with comprehensive static load analysis to ensure alignment stability and mechanical resilience. A reinforced triangular support structure, fabricated from stainless steel, was validated through simulation to maintain deformation below 0.1 mm under stress levels reaching 5 MN/m², confirming its mechanical robustness and reliability. Windage analysis confirmed that the tracker could safely operate at 15 m/s wind speed for tilt angles of 35° (counter-clockwise) and −5° (clockwise), while operation at a 80° (counter-clockwise) tilt is safe up to 12 m/s, ensuring compliance with local environmental conditions. Overall, the validated system demonstrates structural resilience and modularity, supporting experimental deployment and future scalability. Full article

In recent years, the realization of a society in which humans and robots coexist has become highly anticipated. As a result, robots are expected to exhibit versatility regardless of their operating environments, along with high responsiveness, to ensure safety and enable dynamic task execution. To meet these demands, we design a comprehensive system composed of two primary components: high-speed skeleton tracking and path planning. For tracking, we implement a high-speed skeleton tracking method that combines deep learning-based detection with optical flow-based motion extraction. In addition, we introduce a dynamic search area adjustment technique that focuses on the target joint to extract the desired motion more accurately. For path planning, we propose a high-speed, geometry-informed potential field model that addresses four key challenges: (P1) avoiding local minima, (P2) suppressing oscillations, (P3) ensuring adaptability to dynamic environments, and (P4) handling obstacles with arbitrary 3D shapes. We validated the effectiveness of our high-frequency feedback control and the proposed system through a series of simulations and real-world collision-free path planning experiments. Our high-speed skeleton tracking operates at 250 Hz, which is eight times faster than conventional deep learning-based methods, and our path planning method runs at over 10,000 Hz. The proposed system offers both versatility across different working environments and low latencies. Therefore, we hope that it will contribute to a foundational motion generation framework for human–robot collaboration (HRC), applicable to a wide range of downstream tasks while ensuring safety in dynamic environments. Full article

Monocular visual–inertial odometry based on the MSCKF algorithm has demonstrated computational efficiency even with limited resources. Moreover, the MSCKF-VIO is primarily designed for localization tasks, where environmental features such as points, lines, and planes are tracked across consecutive images. These tracked features are subsequently triangulated using the historical IMU/camera poses in the state vector to perform measurement updates. Although feature points can be extracted and tracked using traditional techniques followed by the MSCKF feature point triangulation algorithm, the number of feature points in the image is often insufficient to capture the depth of the entire environment. This limitation arises from traditional feature point extraction and tracking techniques in environments with textureless planes. To address this problem, we propose an algorithm for extracting and tracking pixel points to estimate the depth of each grid in the image, which is segmented into numerous grids. When feature points cannot be extracted from a grid, any arbitrary pixel without features, preferably on the contour, can be selected as a candidate point. The combination of feature-rich and featureless pixel points is initially tracked using traditional techniques such as optical flow. When these traditional methods fail to track a given point, the proposed method utilizes the geometry of triangulated features in adjacent images as a reference for tracking. After successful tracking and triangulation, this approach results in a more detailed depth map of the environment. The proposed method has been implemented within the OpenVINS environment and tested on various open-source datasets supported by OpenVINS to validate the findings. Tracking arbitrary featureless pixel points alongside traditional features ensures a real-time depth map of the surroundings, which can be applied to various applications, including obstacle detection, collision avoidance, and path planning. Full article

The neural implicit SLAM system performs excellently in static environments, offering higher-quality rendering and scene reconstruction capabilities compared to traditional dense SLAM. However, in dynamic real-world scenes, these systems often experience tracking drift and mapping errors. To address these problems, we suggest DIN-SLAM, a dynamic scene neural implicit SLAM system based on optical flow and depth gradient verification. DIN-SLAM combines depth gradients, optical flow, and motion consistency to achieve robust filtering of dynamic pixels, while optimizing dynamic feature points through optical flow registration to enhance tracking accuracy. The system also introduces a dynamic scene optimization strategy that utilizes photometric consistency loss, depth gradient loss, motion consistency constraints, and edge matching constraints to improve geometric consistency and reconstruction performance in dynamic environments. To reduce the interference of dynamic objects on scene reconstruction and eliminate artifacts in scene updates, we propose a targeted rendering and ray sampling strategy based on local feature counts, effectively mitigating the impact of dynamic object occlusions on reconstruction. Our method supports multiple sensor inputs, including pure RGB and RGB-D. The experimental results demonstrate that our approach consistently outperforms state-of-the-art baseline methods, achieving an 83.4% improvement in Absolute Trajectory Error Root Mean Square Error (ATE RMSE), a 91.7% enhancement in Peak Signal-to-Noise Ratio (PSNR), and the elimination of artifacts caused by dynamic interference. These enhancements significantly boost the performance of tracking and mapping in dynamic scenes. Full article

To achieve rapid distance estimation and tracking of moving targets in a large field of view, this paper proposes an innovative simulation method. Using a low-cost approach, the imaging and distance measurement performance of the designed cooling-type mid-wave infrared compound-eye camera (CM-CECam) is experimentally evaluated. The compound-eye camera consists of a small-lens array with a spherical shell, a relay optical system, and a cooling-type mid-wave infrared detector. Based on the spatial arrangement of the small-lens array, a precise simulation imaging model for the compound-eye camera is developed, constructing a virtual imaging space. Distance estimation and error analysis for virtual targets are performed using the principle of stereo disparity. This universal simulation method provides a foundation for spatial design and image-plane adjustments for compound-eye cameras with specialized structures. Using the raw images captured by the compound-eye camera, a scene-specific piecewise linear mapping method is applied. This method significantly reduces the brightness contrast differences between sub-images during wide-field observations, enhancing image details. For the fast detection of moving targets, ommatidia clusters are defined as the minimal spatial constraint units. Local information at the centers of these constraint units is prioritized for processing. This approach replaces traditional global detection methods, improving the efficiency of subsequent processing. Finally, the simulated distance measurement results are validated using real-world scene data. Full article

Visual odometry (VO), including keypoint detection, correspondence establishment, and pose estimation, is a crucial technique for determining motion in machine vision, with significant applications in augmented reality (AR), autonomous driving, and visual simultaneous localization and mapping (SLAM). For feature-based VO, the repeatability of keypoints affects the pose estimation. The convolutional neural network (CNN)-based detectors extract high-level features from images, thereby exhibiting robustness to viewpoint and illumination changes. Compared with descriptor matching, optical flow tracking exhibits better real-time performance. However, mainstream CNN-based detectors rely on the “joint detection and descriptor” framework to realize matching, making them incompatible with optical flow tracking. To obtain keypoints suitable for optical flow tracking, we propose a self-supervised detector based on transfer learning named OFPoint, which jointly calculates pixel-level positions and confidences. We use the descriptor-based detector simple learned keypoints (SiLK) as the pre-trained model and fine-tune it to avoid training from scratch. To achieve multi-scale feature fusion in detection, we integrate the multi-scale attention mechanism. Furthermore, we introduce the maximum discriminative probability loss term, ensuring the grayscale consistency and local stability of keypoints. OFPoint achieves a balance between accuracy and real-time performance when establishing correspondences on HPatches. Additionally, we demonstrate its effectiveness in VO and its potential for graphics applications such as AR. Full article

β-galactosidase (β-Gal) has emerged as a pivotal biomarker for the comprehensive investigation of diseases associated with cellular senescence. The development of a fluorescent sensor is of considerable importance for precisely detecting the activity and spatial distribution of β-Gal. In this study, we developed two excited-state-altering responsive fluorescent sensors (TF1 and TF2) for ratiometric detection of β-Gal. Two TCF dyes, composed of tricyanofuran (TCF) and naphthol units, feature electron “pull–push” systems and are quenched fluorescence by β-Gal. Upon β-Gal hydrolysis, a significant ratiometric shift in absorption from ca. 475 nm to 630 nm is observed, accompanied by the emergence of a fluorescence signal at ca. 660 nm. The enzyme-responsive optical red-shifts are attributed to the excited-state transition from intramolecular charge transfer (ICT) state to local excited (LE) state, which was confirmed by density functional theory (DFT) calculations. Both fluorescent sensors display exceptional sensitivity and selectivity for the response of β-Gal in PBS solution and are capable of tracking β-Gal within senescent A549 cells. This study introduces a framework for developing multimodal optical probes by systematically modulating excited-state properties, demonstrating their utility in senescence studies, diagnostic assay design, and therapeutic assessment. Full article

In the laser powder bed fusion (LPBF) process, the surface quality of intermediate layers impacts interlayer bonding and part forming quality. Due to the complex dynamic process inherent in LPBF, current monitoring methods struggle to achieve high-quality in situ online monitoring, which limits the in-depth understanding of the evolution mechanisms of the surface morphology of LPBF intermediate layers. This paper employs an optimized coaxial optical imaging method to monitor key LPBF processes and analyzes the intermediate layer surface morphology evolution mechanism considering heat, force, and mass transfer. Results indicate that LPBF intermediate layer surfaces are influenced by energy density, melt pool behavior, and previous layer morphology, forming complex topological structures. At a low energy density, insufficient powder melting causes balling, extended by subsequent melt pools to form a reticulated structure and local large-scale protrusions. Heat accumulation at a high energy density promotes melt pool expansion, reduces melt track overlap, and effectively eliminates defects from previous layers via remelting, with spatter becoming the main defect. Additionally, the melt pool wettability on the part contours captures external powder, forming unique, overhanging contour protrusions. This paper enhances understanding of LPBF intermediate layer surface morphology formation mechanisms and provides a theoretical basis for optimizing surface quality. Full article