Search Results (763)

Digital technologies that continuously quantify animal behavior, physiology, and production offer significant potential for the early identification of health and welfare disorders of dairy cows. In this study, a multimodal artificial intelligence (AI) framework is proposed for real-time health monitoring of dairy cows through the integration of physiological, behavioral, production, and thermal imaging data, targeting veterinarian-confirmed udder, leg, and hoof infections. Predictions are generated at the cow-day level by aggregating multimodal measurements collected during daily milking events. The dataset comprised 88 lactating cows, including veterinarian-confirmed udder, leg, and hoof infections grouped under a single ‘sick’ label. To prevent information leakage, model evaluation was performed using a cow-level data split, ensuring that data from the same animal did not appear in both training and testing sets. The system is designed to detect early deviations from normal health trajectories prior to the appearance of overt clinical symptoms. All measurements, with the exception of the intra-ruminal bolus sensor, were obtained non-invasively within a commercial dairy farm equipped with automated milking and monitoring infrastructure. A key novelty of this work is the simultaneous integration of data from three independent sources: an automated milking system, a thermal imaging camera, and an intra-ruminal bolus sensor. A hybrid deep learning architecture is introduced that combines the core components of established models, including U-Net, O-Net, and ResNet, to exploit their complementary strengths for the analysis of dairy cow health states. The proposed multimodal approach achieved an overall accuracy of 91.62% and an AUC of 0.94 and improved classification performance by up to 3% compared with single-modality models, demonstrating enhanced robustness and sensitivity to early-stage disease. Full article

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

Fused deposition modeling (FDM) suffers from unpredictable mechanical properties in nominally identical prints. Current quality assurance relies on destructive testing or expensive post-process inspection, while existing machine learning approaches focus primarily on printing parameters rather than real-time thermal environments. The objective of this proof-of-concept study is to develop a low-cost, non-destructive framework for predicting tensile strength during FDM printing by directly measuring convective thermal gradients surrounding the print. To accomplish this, we introduce thermal fingerprinting: a novel non-destructive technique that combines Background-Oriented Schlieren (BOS) imaging with machine learning to predict tensile strength during printing. We captured thermal gradient fields surrounding PLA specimens (n = 30) under six controlled cooling conditions using consumer-grade equipment (Nikon D750 camera, household hairdryers) to demonstrate low-cost implementation feasibility. BOS imaging was performed at nine critical layers during printing, generating thermal gradient data that was processed into features for analysis. Our initial dual-model ensemble system successfully classified cooling conditions (100%) and showed promising correlations with tensile strength (initial 80/20 train–test validation: R² = 0.808, MAE = 0.279 MPa). However, more rigorous cross-validation revealed the need for larger datasets to achieve robust generalization (five-fold cross-validation R² = 0.301, MAE = 0.509 MPa), highlighting typical challenges in small-sample machine learning applications. This work represents the first successful application of Schlieren imaging to polymer additive manufacturing and establishes a methodological framework for real-time quality prediction. The demonstrated framework is directly applicable to real-time, non-contact quality assurance in FDM systems, enabling on-the-fly identification of mechanically unreliable prints in laboratory, industrial, and distributed manufacturing environments without interrupting production. Full article

Effective monitoring of maize phenology under stress conditions is crucial for optimizing agricultural management and mitigating yield losses. Crop prediction models constructed from Convolutional Neural Network (CNN) have been widely applied. However, CNNs often struggle to capture long-range temporal dependencies in phenological data, which are crucial for modeling seasonal and cyclic patterns. The Transformer model complements this by leveraging self-attention mechanisms to effectively handle global contexts and extended sequences in phenology-related tasks. The Transformer model has the global understanding ability that CNN does not have due to its multi-head attention. This study, proposes a synergistic framework, in combining CNN with Transformer model to realize global-local feature synergy using two models, proposes an innovative phenological monitoring model utilizing near-ground remote sensing technology. High-resolution imagery of maize fields was collected using unmanned aerial vehicles (UAVs) equipped with multispectral and thermal infrared cameras. By integrating this data with CNN and Transformer architectures, the proposed model enables accurate inversion and quantitative analysis of maize phenological traits. In the experiment, a network was constructed adopting multispectral and thermal infrared images from maize fields, and the model was validated using the collected experimental data. The results showed that the integration of multispectral imagery and accumulated temperature achieved an accuracy of 92.9%, while the inclusion of thermal infrared imagery further improved the accuracy to 97.5%. This study highlights the potential of UAV-based remote sensing, combined with CNN and Transformer as a transformative approach for precision agriculture. Full article

Using additive manufacturing for the design of inserts in injection moulding (IM) offers advantages in product development and customization. However, challenges related to operating temperature and mechanical resistance remain. This article presents a systematic screening methodology to evaluate the suitability of materials for specific applications. Ten commercial Material Extrusion (MEX) filaments were selected to produce test samples. Moldex3D simulation software was employed to model the IM process using two thermoplastics and to determine the temperature and pressure conditions that the printed inserts must withstand. Simulation results were critically interpreted and cross-referenced with the experimental material characterisations to evaluate material suitability. Nine of the ten MEX materials were suitable for IM with LDPE, and five with PP. Dimensional assessments revealed that six insert solutions required further post-processing for assembly, while three did not. All of the selected materials successfully survived 10 injection cycles without encountering any significant issues. The simulation results were validated by comparing temperature data from a thermal imaging camera during IM, revealing only minor deviations. The study concludes that combining targeted material characterization with CAE simulation provides an effective and low-cost strategy for selecting MEX filaments for injection moulding inserts, supporting rapid tooling applications in niche production. Full article

Stromboli’s volcanic activity fluctuates in intensity and style, and periods of heightened activity can trigger hazardous events such as crater collapses and lava overflows. This study investigates the volcano’s explosive behavior surrounding the 19 May 2021 crater-rim failure, which primarily affected the N2 crater and partially involved N1, by integrating high-frequency thermal imaging and high-resolution unmanned aerial system (UAS) surveys to quantify eruption parameters and vent morphology. Typically, eruptive periods preceding vent instability are characterized by evident changes in geophysical parameters and by intensified explosive activity. This is quantitatively monitored mainly through explosion frequency, while other eruption parameters are assessed qualitatively and sporadically. Our results show that, in addition to explosion rate, the spattering rate, the predominance of bomb- and gas-rich explosions, and the number of active vents increased prior to the collapse, reflecting near-surface magma pressurization. UAS surveys revealed that the pre-collapse configuration of the northern craters contributed to structural vulnerability, while post-collapse vent realignment reflected magma’s adaptation to evolving stress conditions. The May 2021 events were likely influenced by morphological changes induced by the 2019 paroxysms, which increased collapse frequency and amplified the 2021 failure. These findings highlight the importance of integrating quantitative time series of multiple eruption parameters and high-frequency morphological surveys into monitoring frameworks to improve early detection of system disequilibrium and enhance hazard assessment at Stromboli and similar volcanic systems. Full article

Accurate modelling of reflection, transmission, absorption, and emission at material interfaces is essential for infrared imaging, rendering, and the simulation of optical and sensing systems. This need is particularly pronounced across the short-wave to long-wave infrared (SWIR–LWIR) spectrum, where many materials exhibit dispersion- and wavelength-dependent attenuation described by complex refractive indices. In this work, we introduce a unified formulation of the full Fresnel equations that directly incorporates wavelength-dependent complex refractive-index data and provides physically consistent interface behaviour for both dielectrics and conductors. The approach reformulates the classical Fresnel expressions to eliminate sign ambiguities and numerical instabilities, resulting in a stable evaluation across incidence angles and for strongly absorbing materials. We demonstrate the model through spectral-rendering simulations that illustrate realistic reflectance and transmittance behaviour for materials with different infrared optical properties. To assess its suitability for thermal-infrared applications, we also compare the simulated long-wave emission of a heated glass sphere with measurements from a LWIR camera. The agreement between measured and simulated radiometric trends indicates that the proposed formulation offers a practical and physically grounded tool for wavelength-parametric interface modelling in infrared imaging, supporting applications in spectral rendering, synthetic data generation, and infrared system analysis. Full article

(1) Background: This study presents an adaptive, contactless, and privacy-preserving respiratory-rate monitoring system based on thermal imaging, designed for real-time operation on embedded edge hardware. The system continuously processes temperature data from a compact thermal camera without external computation, enabling practical deployment for home or clinical vital-sign monitoring. (2) Methods: Thermal frames are captured using a $256\times 192$ TOPDON TC001 camera and processed entirely on an NVIDIA Jetson Orin Nano. A YOLO-based detector localizes the nostril region in every even frame (stride = 2) to reduce the computation load, while a Kalman filter predicts the ROI position on skipped frames to maintain spatial continuity and suppress motion jitter. From the stabilized ROI, a temperature-based breathing signal is extracted and analyzed through an adaptive median–MAD hysteresis algorithm that dynamically adjusts to signal amplitude and noise variations for breathing phase detection. Respiratory rate ($RR$) is computed from inter-breath intervals (IBI) validated within physiological constraints. (3) Results: Ten healthy subjects participated in six experimental conditions including resting, paced breathing, speech, off-axis yaw, posture (supine), and distance variations up to 2.0 m. Across these conditions, the system attained a MAE of $0.57\pm 0.36$ BPM and an RMSE of $0.64\pm 0.42$ BPM, demonstrating stable accuracy under motion and thermal drift. Compared with peak-based and FFT spectral baselines, the proposed method reduced errors by a large margin across all conditions. (4) Conclusions: The findings confirm that accurate and robust respiratory-rate estimation can be achieved using a low-resolution thermal sensor running entirely on an embedded edge device. The combination of YOLO-based nostril detector, Kalman ROI prediction, and adaptive MAD–hysteresis phase that self-adjusts to signal variability provides a compact, efficient, and privacy-preserving solution for non-invasive vital-sign monitoring in real-world environments. Full article

This paper investigates pouch-type lithium-ion battery cells with a nominal voltage of 3.7 V and a nominal capacity of 57 Ah. A numerical model of the cell was developed and implemented using the NTGK method, which accurately predicts electrochemical and thermal processes. The results of numerical modeling matched with the experimental results of battery cell temperature measurements—the average deviation was about 4.5%; therefore, it can be considered reliable for further engineering research and construction of battery modules. In the experimental part of the paper, the battery cell was loaded in various C-rates (from 0.5 to 2 C), using heat flux sensors, thermocouples, and a thermal imaging camera. The studies revealed that the highest temperature is in the tabs area of cells. The temperature on the face of the cell surface exceeds 35 °C already from a load of 1.35 C, which accelerates cell degradation and reduces the number of cycles. Thermal imaging revealed uneven temperature distribution, whereby the top of the cell heats up more than the bottom of the cell and the temperature gradient can reach 2–4 °C. It was observed that during faster charge/discharge modes, the temperature rises from the tabs of the cell, and during slower ones, more in the middle face surface of the cell. The studies highlight the need to apply additional cooling solutions, especially cooling of the upper cell face, to ensure durability and uniform heat distribution. 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

To meet the requirements of high resolution, compact size, and ultra-lightweight for micro–nano satellite optoelectronic payloads while ensuring high structural stability during launch and in-orbit operation, mirrors were designed with high surface accuracy. The opto-thermo-mechanical system of the space camera was designed and analyzed accordingly. First, an optical system was designed to achieve high resolution and a compact form factor. A coaxial triple-reflector configuration with multiple refractive paths was adopted, which significantly shortened the optical path and laid the foundation for a lightweight, compact structure. This design also defined the accuracy and tolerance requirements for the primary and secondary mirrors. Subsequently, mathematical models for topology optimization and dimensional optimization were established to optimize the design of the main support structure, primary mirror, and secondary mirror. Two design schemes for the main support structure and primary mirror were compared. Steady-state thermal analysis and thermal control design were carried out for both mirrors. Simulations were then performed on the main system (including the primary/secondary mirror assemblies and the main support structure). Under the combined effects of gravity, a 4 °C temperature increase, and an assembly flatness deviation of 0.01 mm, the surface accuracy of both mirrors, the displacement of the secondary mirror relative to the primary mirror reference, and the tilt angle all met the overall specification requirements. The system’s first-order natural frequency was 156.731 Hz. After precision machining, fabrication, and assembly, wavefront aberration testing was conducted on the main system with the optical axis horizontal. Under gravity, the root mean square (RMS) wavefront error at the center of the field of view was 0.073λ, satisfying the specification of ≤1/14λ. The fundamental frequency measured during vibration testing was 153.09 Hz, which aligned closely with the simulated value and well exceeded the requirement of 100 Hz. Additionally, in-orbit imaging verification was conducted. All results satisfied the technical specifications of the satellite’s overall requirements. Full article

In this study, we present a non-invasive and contactless method for estimating the internal temperature of Cr:LiCAF laser crystals using temperature-dependent shifts in fluorescence emission peaks. A high-resolution calibration dataset was created with 181 spectral points from 10 to 100 °C. Linear regression yielded a temperature estimation model with an R² of 0.73, which was validated under both lasing and non-lasing conditions. To further evaluate the reliability of this optical thermometry method, thermal imaging data from a FLIR E75 infrared camera were incorporated. Surface temperatures measured at various diode current levels closely matched the internal temperature predictions based on fluorescence shifts (MAE = 0.775 °C, R² = 0.993), confirming the robustness of the method. This dual-approach validation enhances confidence in using fluorescence-based diagnostics for real-time thermal monitoring in laser systems. The combined use of spectrometer-based and thermal camera measurements suggests potential for hybrid diagnostics in laser research and development, offering improved thermal feedback for optimizing high-power laser performance. Full article

Unmanned aerial vehicles (UAVs) equipped with thermal cameras are increasingly used in search and rescue (SAR) operations, where low visibility and small human footprints make detection a critical challenge. Existing datasets are mostly limited to urban or open-field scenarios, and our experiments show that models trained on such heterogeneous data achieve poor results. To address this gap, we collected and annotated thermal images in mountainous environments using a DJI M3T drone under clear daytime conditions. This mountain-specific set was integrated with ten existing sources to form an extensive benchmark of over 75,000 images. We then performed a comparative evaluation of object detection models (YOLOv8/9/10, RT-DETR) and semantic segmentation networks (U-Net variants), analyzing accuracy, inference speed, and energy consumption on an NVIDIA Jetson AGX Orin. Results demonstrate that human detection tasks can be accurately solved through both semantic segmentation and object detection, achieving 90% detection accuracy using segmentation models and 85% accuracy using the YOLOv8 X detection model in mountain scenarios. On the Jetson platform, segmentation achieves real-time performance with up to 27 FPS in FP16 mode. Our contributions are as follows: (i) the introduction of a new mountainous thermal image collection extending current benchmarks and (ii) a comprehensive evaluation of detection methods on embedded hardware for SAR applications. Full article

Miniaturized long-wavelength infrared (LWIR) imaging systems are highly desirable for applications such as portable thermal sensing, unmanned surveillance, and medical diagnostics. Conventional refractive optics in the LWIR regime often require multiple lens configurations to extend depth of field (DoF), leading to increased size, weight, and cost. Although existing LWIR metalenses demonstrate competent capabilities, comprehensive approaches to DoF engineering have yet to be explored. Here, we demonstrate a miniature large-DoF camera using a metalens. The designed metalens features a 14 mm diameter aperture and weighs only 0.8 g while maintaining sharp focus over a working distance ranging from 1 m to 22 m. By leveraging subwavelength phase engineering, the metalens achieves high-resolution imaging with low aberration. The integrated camera exhibits an ultra-compact form factor, i.e., 2.3 cm × 2.3 cm × 1.2 cm (length × width × height) and weighs just 25 g. Experimental results confirm the superior DoF performance, enabling clear imaging across varying distances without mechanical refocusing. The advance provides a promising pathway toward ultra-compact, large-DoF LWIR imaging systems for applications ranging from autonomous vehicles to portable medical diagnostics and miniature surveillance devices. Full article

Reliable geolocation of thermal hotspots, such as smoldering embers that can reignite after vegetation fire suppression, deep-seated peat fires, or underground coal seam fires, is critical to prevent fire resurgence, limit prolonged greenhouse gas emissions, and mitigate environmental and health impacts. This study develops and tests an algorithm to estimate the GPS positions of thermal hotspots detected in infrared images acquired by an unmanned aerial vehicle (UAV), designed to operate over flat and mountainous terrain. Its originality lies in a reformulated Bresenham traversal of the digital elevation model (DEM), combined with a lightweight, ray-tracing-inspired strategy that efficiently detects the intersection of the optical ray with the terrain by approximating the ray altitude at the cell level. UAV flight experiments in complex terrain were conducted, with thermal image acquisitions performed at 60 m and 120 m above ground level and simulated hotspots generated using controlled heat sources. The tests were carried out with two thermal cameras: a Zenmuse H20T mounted on a Matrice 300 UAV flown both with and without Real-Time Kinematic (RTK) positioning, and a Matrice 30T UAV without RTK. The implementation supports both real-time and post-processed operation modes. The results demonstrated robust and reliable geolocation performance, with mean positional errors consistently below 4.2 m for all the terrain configurations tested. A successful real-time operation in the test confirmed the suitability of the algorithm for time-critical intervention scenarios. Since July 2024, the post-processed version of the method has been in operational use by the Corsica fire services. Full article