Search Results (297)

As global aquaculture continues to expand, there is increasing interest in sustainable and non-invasive tools for monitoring fish growth. Nile tilapia (Oreochromis niloticus) is one of the most farmed species worldwide. Its biomass estimation often relies on manual sampling or stereo-camera systems limited by water turbidity. This study establishes a robust relationship between lateral target strength (TS) and the total length (TL) and weight (W) of Nile tilapia using a cost-effective 201 kHz single-beam echosounder. Measurements were conducted with free-swimming fish in a controlled pond environment (TL range, 13–44 cm). The results show a strong linear correlation between acoustic and biometric data. Specifically, the relationship for mean TS was defined as TS_mean = 20.4log(TL) − 68.8 (R² = 0.93) and TS_mean = 6.3log(W) − 55.4 (R² = 0.96), proving the system’s accuracy for biomass estimation. Furthermore, the Method of Fundamental Solutions (MFS) was employed for numerical validation based on X-ray morphometry of the swim bladder. Very good agreement was observed between experimental data and numerical simulations, reinforcing the validity of the acoustic models despite the inherent complexity of biological targets. These findings demonstrate that calibrated single-beam acoustic systems provide a viable, non-intrusive tool for real-time monitoring in aquaculture ponds. Full article

In urban environments, autonomous vehicles face critical challenges in localization and perception under extreme lighting conditions, including rapid illumination changes, high contrast, and nighttime low-light scenarios. To address the performance degradation of traditional LiDAR-inertial odometry systems under such conditions, this study proposes a high-precision FAST-LIO2-EC algorithm that fuses event cameras into the FAST-LIO2 framework. Event cameras, with their microsecond temporal resolution and 140 dB dynamic range, provide asynchronous edge information that complements LiDAR point clouds and IMU measurements. We validate the proposed system through real-world road tests conducted on public roads and closed test tracks, covering three typical extreme lighting scenarios: tunnel entrance/exit transitions, high-contrast shadow boundaries, and nighttime sparse-lighting conditions. The experimental platform is equipped with a 32-beam LiDAR, a 6-axis IMU, a DVS event camera, and an RTK-GNSS system for ground truth trajectory acquisition. Real-world results demonstrate that the FAST-LIO2-EC system achieves significant improvements in localization accuracy and robustness. In illumination change scenarios, the Absolute Trajectory Error (ATE) is reduced by 32.5% compared to the baseline FAST-LIO2 system, with zero tracking loss events. The point cloud quality is substantially enhanced, with more uniform distribution and clearer obstacle boundaries. In high-contrast scenarios, both systems maintain comparable performance with ATE below 0.15 m. However, in nighttime scenarios, the fusion system shows moderate improvement (15.3% ATE reduction) but reveals sensitivity to event camera noise, indicating the need for adaptive thresholding strategies. Supplementary simulation experiments validate the system’s robustness under varying speeds and sensor noise levels. This work provides a practical solution for autonomous vehicle deployment in complex urban lighting environments, with a comprehensive analysis of real-world performance boundaries and deployment considerations. Full article

Photothermal therapy (PTT) is an emerging minimally invasive approach for cancer treatment that relies on photothermal agents capable of efficiently converting near-infrared (NIR) light into localized heat. In this work, silica–gold nanostructures (SGNs) were synthesized and systematically evaluated to investigate how silica core size influences the photothermal response of the SGNs and optimize their performance as a photothermal agent. SGNs were synthesized with silica cores ranging from 54 to 244 nm in diameter and coated with gold nanoparticles of 4–10 nm in size, enabling controlled tuning of their localized surface plasmon resonance within the NIR region. The morphology and composition were characterized by SEM, TEM, and EDS; optical properties were analyzed by UV-Vis spectroscopy. The SGNs photothermal response low-power laser irradiation at 852 nm and 1310 nm and temperature changes were monitored using a thermographic camera. A maximum temperature increase of 7.1 °C was observed for SGNs with a silica core diameter of approximately 77 nm under the 852 nm laser irradiation. Numerical simulations of the absorption efficiency showed good agreement with experimental UV–Vis spectra and thermal measurements, revealing a size-dependent shift of the absorption toward longer wavelengths for larger nanostructures. These results demonstrate that the photothermal response of silica–gold nanostructures can be rationally tuned through the control of core size and gold growth parameters, providing a framework for the design of wavelength-matched photothermal agents for PTT applications. Full article

Head injuries remain one of the major health concerns in contact sports such as boxing. Despite the widespread use of protective gloves and helmets, their biomechanical effectiveness in mitigating head acceleration and reducing brain injury risk remains uncertain. This study aims to biomechanically assess available boxing equipment solutions and identify the brain–skull system’s response to physical forces from a boxing punch. A dedicated experimental setup was developed using mini triaxial accelerometers and a high-speed camera to measure head accelerations in a Primus unbreakable dummy. Tests were performed using gloves of different masses (0 oz, 10 oz, and 16 oz) and three head protection configurations: no helmet, rugby helmet, and boxing helmet. The resultant accelerations were analyzed and compared across test conditions. Peak wrist accelerations ranged from 195.00 to 271.77 m/s², while head accelerations did not exceed biomechanical injury thresholds. The boxing helmet, composed of multilayer polyurethane foam, did not consistently decrease acceleration; in some cases, it produced higher overloads due to increased head mass and moment of inertia. A rugby helmet made of open-cell EVA (ethylene vinyl acetate) foam with lower density exhibited more favorable energy-dissipation characteristics under low-impact conditions. Glove mass also influenced acceleration differently between male and female participants, likely due to variations in punch velocity and force generation. This work is a pilot study using two trained adult volunteers to validate the combined IMU–video measurement framework. The results serve as hypothesis-generating mechanistic observations rather than population-level effect estimates. Protective effectiveness in boxing depends on a complex interaction between material properties, geometry, and user biomechanics. Optimal equipment design should balance energy absorption and mass to minimize both linear and rotational accelerations. Future studies should integrate advanced material modeling and finite element simulations to support the development of adaptive, lightweight protective systems. Full article

Visual orthopedic gait assessment in dogs is recognized as subjective and is limited by interobserver variability. Objective detection of lameness is offered by biomechanical analysis, where asymmetry between limbs is quantified through kinematic parameters and symmetry indices. However, the minimum number of trials (full stride cycles) required to reliably discriminate lameness has remained a challenge. In this study, six healthy adult dogs were used. Mild, reversible lameness was induced in one forelimb using a cotton pad. Dogs were walked along a straight runway, and kinematic data were captured with a high-speed video camera. Stride length (SLE), support time (ST), and elbow range of motion (ROM) were measured. Symmetry indices (for linear and temporal parameters) and the symmetry angle (for angular parameters) were computed. The asymptotic distribution of these indices was derived using the delta method, which allowed for the construction of confidence intervals (CIs) and hypothesis tests for an asymmetry threshold of 3%. The number of trials required to achieve reliable detection was estimated through statistical simulations. Results indicated that the required number of trials was highly dependent on both the kinematic parameter and the magnitude of asymmetry. While detecting subtle asymmetries (≈4%) required a high number of trials (up to 347 for stride length), the requirements decreased substantially for more pronounced lameness. For a true asymmetry of 6%, 11–39 trials per limb were sufficient to achieve 80–90% power. It is concluded that the collection of only five trials is insufficient for detecting mild asymmetries. A statistical framework and practical recommendations for kinematic gait studies in dogs are provided. Full article

Robust global point-cloud registration remains a key challenge in robotic neurosurgery, particularly for markerless patient registration, where anatomical surface acquisition can be incomplete and noisy. This paper proposes practical pre-processing steps, defines performance criteria, and evaluates the keypoint-based 4-Points Congruent Set (K4PCS) and Super4PCS algorithms for global registration. Experiments are conducted on surface point clouds segmented from real patient head CT scans, while all measurement errors are synthetically simulated by applying clinically relevant perturbations, including large initial misalignment, Gaussian (CT-like) and non-Gaussian (camera-like) noise injection, and partial scans, across 30 different poses. Registration performance is quantified using pose errors and noise-aware surface-distance/overlap measures, while run-time is assessed under a newly developed selective downsampling strategy and compared to standard voxel downsampling. Results show that both algorithms reliably converge from substantial misalignment and remain robust after noise injection, with computation times ranging from 0.1 s to >15 min. Partial-to-whole registration achieves accuracy comparable to whole-to-whole registration (errors $<{10}^{-3}$ mm), but typically exceeds real-time run-times. Selective downsampling provides a clear improvement in precision and, in most cases, also improves speed compared to the voxel-based downsampling method. Overall, the results indicate that robust and real-time markerless head registration is feasible under clinical conditions. Full article

Live weight is widely used as a reference indicator for growth performance and for evaluating the accuracy of weight measurement technologies in pig production. However, live weight is not a fixed physiological quantity, and finishing pigs naturally experience substantial short-term mass fluctuations due to normal behaviors such as drinking, feeding, urination, and defecation. In this study, we integrated published physiological and behavioral parameters into a stochastic simulation model to quantify within-day live-weight dynamics in finishing pigs weighing approximately 100 kg. The simulation was conducted with 1-min temporal resolution over a 24-h period. The model demonstrated that short-term weight fluctuations of approximately ±3–5 kg can occur within a single day, even when measurement error is minimal. Across 1000 simulated pigs, the mean daily fluctuation range was 4.2 kg, confirming that kilogram-scale variation is physiologically expected under normal conditions. These results provide a plausible physiological basis for understanding the frequently reported discrepancies between camera-based weight estimates and instantaneous floor-scale measurements. Camera systems primarily reflect body mass derived from external morphology, whereas floor scales measure instantaneous total mass that includes transient contributions from gastrointestinal contents, ingested water, and retained waste. Consequently, direct comparisons based on instantaneous scale readings can be misleading when used as ground truth. Our findings indicate that commonly cited accuracy claims of ±2–3 kg for camera weighing systems should be interpreted with caution, as normal physiological weight variation often exceeds this range. Recognizing live weight as a dynamic physiological variable is essential for developing biologically meaningful evaluation frameworks and for the appropriate interpretation and comparison of weight measurement technologies in precision livestock farming. Full article

Slag entrainment within the mold is a significant cause of surface defects in continuously cast slabs. As a key component for controlling molten steel flow, the structure of the submerged entry nozzle directly influences the flow field characteristics and slag entrainment behavior within the mold. This paper employs a 1:4-scale water–oil physical model combined with numerical simulation to investigate the effects of elliptical and circular submerged entry nozzles on slag entrainment behavior in a wide slab mold under different casting speeds and immersion depths. High-speed cameras were used to visualize meniscus fluctuations and oil droplet entrainment processes. An alternating control variable method was employed to quantitatively delineate a slag-free “safe zone” and a “slag entrainment zone” where oil droplets fall, determining the critical casting speed and critical immersion depth under different operating conditions. The results show that, given the nozzle immersion depth and slag viscosity, the maximum permissible casting speed range without slag entrainment can be obtained, providing a reference for industrial production parameter control. The root mean square (RMS) of surface fluctuations was introduced to characterize the activity of the meniscus flow. It was found that the RMS value decreases with increasing nozzle immersion depth and increases with increasing casting speed, showing a good correlation with the frequency of slag entrainment. Numerical simulation results show that compared with elliptical nozzles, circular nozzles form a more symmetrical flow field structure in the upper recirculation zone, with a left–right vortex center deviation of less than 5%, resulting in higher flow stability near the meniscus and thus reducing the risk of slag entrainment. Full article

Unmanned aerial vehicles (UAVs) increasingly share low-altitude airspace with birds, making early distinguishing between drones and biological targets critical for safety and security. This work addresses long-range scenarios where objects occupy only a few pixels and appearance-based recognition becomes unreliable. We develop a model-driven simulation pipeline that generates synthetic data with a controlled camera model, atmospheric background and realistic motion of three aerial target types: multicopter, fixed-wing UAV and bird. From these sequences, each track is encoded as a time series of image-plane coordinates and apparent size, and a bidirectional long short-term memory (LSTM) network is trained to classify trajectories as drone-like or bird-like. The model learns characteristic differences in smoothness, turning behavior and velocity fluctuations, and to achieve reliable separation between drone and bird motion patterns on synthetic test data. Motion-trajectory cues alone can support early distinguishing of drones from birds when visual details are scarce, providing a complementary signal to conventional image-based detection. The proposed synthetic data and sequence classification pipeline forms a reproducible testbed that can be extended with real trajectories from radar or video tracking systems and used to prototype and benchmark trajectory-based recognizers for integrated surveillance solutions. The proposed method is designed to generalize naturally to real surveillance systems, as it relies on trajectory-level motion patterns rather than appearance-based features that are sensitive to sensor quality, illumination, or weather conditions. Full article

Accurate Unmanned Aerial Vehicle (UAV) positioning is vital for swarm cooperation. However, this remains challenging in situations where Global Navigation Satellite System (GNSS) and other external infrastructures are unavailable. To address this challenge, we propose to use only the onboard Microelectromechanical System Inertial Measurement Unit (MIMU), Magnetic sensor, Monocular camera and Ultra-Wideband (UWB) device to construct a distributed and anchor-free cooperative localization system by tightly fusing the measurements. As the onboard UWB measurements under dynamic motion conditions are noisy and discontinuous, we propose an adaptive adjustment method based on chi-squared detection to effectively filter out inconsistent and false ranging information. Moreover, we introduce the pose-only theory to model the visual measurement, which improves the efficiency and accuracy for visual-inertial processing. A sliding window Extended Kalman Filter (EKF) is constructed to tightly fuse all the measurements, which is capable of working under UWB or visual deprived conditions. Additionally, a novel Multidimensional Scaling-MAP (MDS-MAP) initialization method fuses ranging, MIMU, and geomagnetic data to solve the non-convex optimization problem in ranging-aided Simultaneous Localization and Mapping (SLAM), ensuring fast and accurate swarm absolute pose initialization. To overcome the state consistency challenge inherent in the distributed cooperative structure, we model not only the UWB noisy uncertainty but also the neighbor agent’s position uncertainty in the measurement model. Furthermore, we incorporate the Covariance Intersection (CI) method into our UWB measurement fusion process to address the challenge of unknown correlations between state estimates from different UAVs, ensuring consistent and robust state estimation. To validate the effectiveness of the proposed methods, we have established both simulation and hardware test platforms. The proposed method is compared with state-of-the-art (SOTA) UAV localization approaches designed for GNSS-challenged environments. Extensive experiments demonstrate that our algorithm achieves superior positioning accuracy, higher computing efficiency and better robustness. Moreover, even when vision loss causes other methods to fail, our proposed method continues to operate effectively. Full article

This study focuses on the prediction and analysis of temperature distribution during end milling of AISI 4340 steel. The influence of cutting parameters—cutting speed, feed per tooth, and depth of cut—on temperature generation in the cutting zone was investigated using a CCD experimental plan. Temperature was measured with a thermal imaging camera, while the milling process was simulated using Third Wave AdvantEdge 7.1 FEM software. The obtained temperatures ranged from 74 °C to 200 °C, depending on the cutting conditions. A second-order regression model with three factors was developed and showed an average prediction error of 8.62%, while the alternative fitted model had an average error of 10.91%. FEM simulations using AdvantEdge 7.1 demonstrated a somewhat higher deviation, with an average error of 14.75% relative to experiments. The highest deviations for all approaches occurred at extreme cutting parameters (very low or very high depth of cut). The study demonstrates that FEM simulations are an effective tool for predicting thermal behavior in milling and optimizing cutting parameters. Accurate prediction of cutting zone temperatures can improve tool life, enhance process efficiency, and support the selection of optimal machining conditions, which is very important from an industry point of view. Full article

Event camera vision systems have recently been gaining traction as swift and agile sensing devices in the field of unmanned aerial vehicles (UAVs). Despite their inherent superior capabilities covering high dynamic range, microsecond-level temporary resolution, and robustness to motion distortion which allow them to capture fast and subtle scene changes that conventional frame-based cameras often miss, their utilization has yet to be widespread. This is due to challenges like insufficient real-world validation, unstandardized simulation platforms, limited hardware integration and a lack of ground truth datasets. This systematic review paper presents an investigation that seeks to explore the dynamic vision sensor christened event camera and its integration to (UAVs). The review synthesized peer-reviewed articles between 2015 and 2025 across five thematic domains, datasets, simulation tools, algorithmic paradigms, application areas and future directions, using the Scopus and Web of Science databases. This review reveals that event cameras outperformed traditional frame-based systems in terms of latency and robustness to motion blur and lighting conditions, enabling reactive and precise UAV control. However, challenges remain in standardizing evaluation metrics, improving hardware integration, and expanding annotated datasets, which are vital for adopting event cameras as reliable components in autonomous UAV systems. Full article

This paper presents an intelligent robotic system for in-pipe inspection that integrates a novel mechanical design, deep learning-based defect detection, and high-fidelity simulation for real-time validation. Unlike existing solutions, the proposed system combines a Mecanum wheel-based mobile platform with a modular arm and advanced pan-tilt camera, enabling navigation and inspection of pipes ranging from 100 mm to 500 mm in diameter. A comprehensive dataset of 53,486 images, including 27,000 annotated defect instances across six critical classes, was used to train a YOLOv11-based detection framework. The model achieved high accuracy with a precision of 0.9, recall of 0.8, mAP@0.5 of 0.9, and mAP@0.5:0.95 of 0.6, outperforming previous YOLO versions, SSD, RCNN, and DinoV2 by 26% in mAP. Real-time testing on a Raspberry Pi Camera 3 Wide IR module validated the robust detection under realistic conditions. This work contributes a mechanically adaptable robot, an optimized deep learning inspection framework, and an integrated simulation-to-deployment workflow, providing a scalable and autonomous solution for industrial pipeline inspection. Full article

Understanding drop dispersion behavior is significant to the optimization of liquid dispersion devices. In this work, the drop dispersion behavior in the combined trapezoid spray tray was directly observed and analyzed with a high-speed camera. It was found that the fracture of the liquid neck is the main mode for the liquid column to generate drops. The dispersion behavior of the drops was simulated by CFD, and it was found that the liquid neck is caused by the surrounding vortex field and the uneven pressure distribution inside the liquid column. At the same time, the dispersion time of the drops was counted, and it was found that the drop dispersion time ranges from 5 to 60 ms, depending on the drop diameter and the gas kinetic energy factor in plate hole F₀. Full article

This study investigates the melt-flow behavior of the AlSi₉Cu₃ alloy during high-pressure die casting using a combined experimental and numerical approach. A transparent die and a high-speed camera were used to capture the transient motion of the melt front, while a validated computational model reproduced the filling dynamics under identical boundary conditions. The influence of the gating-system geometry—particularly the gate thickness, flow-path length, and inlet cross-section—was analyzed with respect to filling velocity, filling time, and flow stability. To quantify hydraulic losses that arise in practical die-casting conditions, an empirical correction coefficient k₂ was introduced. Its value was obtained by regression analysis based on ten repeated measurements of filling time for each configuration. The deviation between the simulated and experimental velocities did not exceed 5%, demonstrating the reliability of the numerical model within the tested parameter range. The results show that the optimized gating design reduces flow instability, suppresses air entrapment zones, and yields a more uniform velocity distribution across the cavity. The empirical relations derived involving k₂ provide a practical tool for preliminary design of gating systems, enabling faster optimization without extensive trial-and-error procedures. The methodology presented in this work offers a validated basis for improving gating-system performance in high-pressure die casting of aluminum alloys. Full article