Search Results (199)

Silicon photomultipliers (SiPMs) have become widely used as photodetectors in high-energy physics, nuclear physics, medical imaging, and space applications. In many of these fields, SiPMs are required to operate in high-radiation environments, which are notoriously problematic for silicon sensors. For this reason, it is essential to study the changes in their performance characteristics after exposure to radiation. In this study, a number of SiPM samples were exposed to non-uniform radiation at the CHARM facility at CERN. Half of the samples were operated above breakdown during the test, while others remained off. Intermittent measurements allowed for tracking the changes in I-V curves and signal shapes during the irradiation itself. The focus was on detecting differences in irradiation damage between the operational and non-operational SiPM samples. The I-V curves and signal shapes in both cases for three different types of SiPM are presented, and a comparison is made. Full article

Perception-oriented evaluation of automotive headlamps still relies mainly on human-vision photometric criteria, although forward-facing cameras are increasingly safety-critical sensing elements for night driving. This paper benchmarks 16 measured production headlamp light distributions with a simulation chain that combines headlamp spectra and beam patterns, diffuse scene reflection, an imaging-transfer model, and an EMVA-based camera model. The quantitative chain maps scene radiance to sensor-domain signal-to-noise ratio, derives task-specific required signal-to-noise curves from a six-network object-recognition ensemble, and aggregates local threshold satisfaction as region-of-interest coverage across three target reflectances and five driving speeds using WLTP moving-time weights. For the baseline RGB camera, WLTP-weighted coverage ranges from 18.95% to 53.48% across the evaluated light distributions, corresponding to a factor of 2.82 between the weakest and strongest distribution. The camera-parameter sweeps show that favorable beam placement can deliver comparable benchmark coverage with roughly 60% smaller pixel pitch than the weakest distribution, corresponding to an 84% reduction in pixel area, or at materially shorter exposure times. The WLTP-weighted coverage score correlates positively with the established Headlamp Safety Performance Rating, with Pearson $r=0.68$ for the RGB configuration, indicating partial alignment between human-centric and camera-centric illumination needs while confirming that the metrics are not interchangeable. The results identify headlamp light distribution as a primary design parameter for nighttime camera perception and provide a quantitative basis for co-design of automotive lighting and camera-based systems. Full article

Product defect inspection is critical in industrial applications; however, it remains increasingly challenging in mass production environments, particularly for glossy or curved surface products. Conventional inspection of such surfaces typically relies on manual visual examination using gauges and operator judgment, which is time consuming and prone to inconsistency. This study proposes a robust defect detection framework for curved and reflective surfaces using a KEYENCE displacement laser sensor. The system integrates the Dijkstra algorithm, the Nearest Neighbor Algorithm, and a Genetic Algorithm to optimize the laser scanning path for structured image data generation. To validate the proposed framework, datasets were generated from both healthy and defective samples and used to train multiple deep learning models. A comparative analysis was conducted using YOLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures. Experimental results demonstrate that YOLOv11 achieved the best overall performance, attaining an mAP50 score of 0.844 while also exhibiting lower computational complexity and faster inference. Full article

Chimeric antigen receptor (CAR) T-cell therapy is an effective treatment for hematologic malignancies. However, it is limited by high costs, risk of severe toxicities such as cytokine release syndrome and neurotoxicity, and heterogeneous patient responses. The current therapy monitoring depends largely on subjective symptom assessment, routine laboratory tests, and basic vital signs, without real-time, quantitative evaluation of CAR T-cell expansion or activation in clinical practice. This lack of timely immune monitoring hampers individualized care and contributes to increased treatment costs. To address this need, we present a proof-of-concept, label-free rapid optical imaging (ROI) biosensor with automated machine learning analysis for direct quantification of CAR T-cells from whole blood. This microfluidic platform integrates red blood cell (RBC) removal, CAR T-cell capture, and imaging-based quantification on a single chip, eliminating the need for centrifugation, staining, and operator-dependent interpretation. For validation, 50 μL whole blood samples spiked with Jurkat cells expressing CD19 CARs underwent RBC depletion by agglutination and microfiltration. The remaining blood components were then incubated on a sensor chip functionalized with recombinant CD19 protein. Captured CAR T-cells were imaged by brightfield microscopy and automatically enumerated using a machine learning algorithm trained on fluorescence-validated cells. The CD-19 cells’ capture performance was validated by flow cytometry and fluorescence imaging. The trained machine learning model validated at 88% sensitivity and 96% specificity. Buffer and whole blood calibration curves were established across clinically relevant concentrations (1–1000 cells/µL) with triple replicates. The results showed high correlation (0.975 and 0.990 R²) between the spiked concentration and the detected CAR T-cells, with a 95% certainty limit of detection (LOD) and quantification (LOQ) of 0.6 and 1.1 cells/µL for spiked buffer, and 14 and 67 cells/µL for spiked whole-blood, respectively. Full article

Background: Myocardial ischemia (MI) is a major cause of morbidity and mortality worldwide and requires timely and reliable detection. Although Spin-Exchange Relaxation-Free (SERF) magnetocardiography (MCG) provides femtotesla-level sensitivity for identifying non-linear cardiac repolarization anomalies, its clinical deployment is currently impeded by the computational bottlenecks inherent to portable edge platforms. Methods: We propose a “Sensor-to-Image” Edge-AI framework that links quantum sensing with computer vision. Single-channel SERF-MCG signals from a large cohort of 2118 subjects (1135 Healthy, 983 Ischemia) were transformed into phase-space images using three distinct encoding modalities: Recurrence Plots (RP), Gramian Angular Summation Fields (GASF), and Markov Transition Fields (MTF). These visual representations were subsequently analyzed by a streamlined MobileNetV3-Small architecture, optimized for low-latency inference. To maximize diagnostic precision, an adaptive weighted fusion mechanism was engineered to combine the chaotic specificity captured by RP with the morphological sensitivity of GASF through a validation-optimized fixed global weighting strategy. Results: In our experiments, the fusion model achieved an Area Under the Curve (AUC) of 0.865, which was higher than the 1D-CNN baseline (AUC 0.857) and the single-modality models. Notably, the fusion strategy significantly elevated sensitivity to 88.3% while maintaining a specificity of 66.5%. Although specificity is moderate, this trade-off prioritizes high sensitivity to minimize false negatives in pre-hospital screening scenarios. The average inference time was 4.7 ms per sample on a standard CPU, suggesting suitability for real-time Point-of-Care (PoC) scenarios under further on-device validation. Conclusions: The results suggest that multi-view phase-space fusion can capture subtle spatio-temporal changes associated with ischemia. The proposed lightweight framework may support the development of portable SERF-MCG systems with embedded AI screening. Full article

X-ray imaging of dense breasts and breast implants often suffers from reduced lesion visibility because strong attenuation lowers contrast, while conventional rhodium (Rh) K-edge filtering suppresses part of the high-energy spectral tail. This study presents a Monte Carlo framework for spectroscopic mammography using a voxelated 1 mm thick cadmium telluride (CdTe) sensor and a first-order detector interaction model to evaluate energy-dependent image quality. The model reproduces fluorescence and inter-voxel energy redistribution in CdTe, but not the full detector chain, and remains idealized with respect to charge transport, carrier collection, threshold dispersion, and pile-up. Energy-resolved simulations in the 10–50 keV range were used to compute spectroscopic contrast-to-noise ratio (CNR) curves and to form integrated-spectrum (IS) images for four tested spectra. For the dense-breast calcium hydroxyapatite (HA) speck detection task considered here, and under the present simulation assumptions, replacing the standard 28 kVp + 50 μm Rh spectrum with 28 kVp + 1 mm Al increased the simulated IS image CNR by 23.11%, with an approximately 5% increase in estimated primary-incident air kerma at the phantom entrance plane. Preliminary experimental implant-phantom images were included as a qualitative feasibility check, showing a trend consistent with simulations. Within the limits of this task-specific simulation, the results suggest that preserving the transmitted high-energy tail can improve HA speck visibility for the present 1 mm CdTe photon-counting detector, with the 28 kVp + 1 mm Al spectrum outperforming the other tested cases. Full article

Background: Exacerbations of chronic obstructive pulmonary disease (COPD) are a leading cause of morbidity, mortality, and healthcare burden worldwide. Early detection and timely intervention remain important challenges in COPD management, given the unpredictable nature of acute deterioration and limitations of traditional spirometry-based risk assessment. Methods: This narrative review synthesizes artificial intelligence (AI)-driven approaches for predicting and detecting chronic obstructive pulmonary disease (COPD) exacerbations across electronic health records, wearable sensors, imaging, environmental data, and patient-reported outcomes, emphasizing novel discoveries and emerging relationships rather than predictive performance. Results: Three major discoveries have been made. First, measurable physiological and behavioral deterioration may precede symptom recognition by approximately 7–14 days, thereby establishing a potential intervention window for anticipatory care. Second, machine learning (ML) models integrating pollutant exposure, medication adherence, and clinical characteristics have identified phenotypes with differential environmental sensitivity, including unexpected exposure–adherence interactions. Third, deep neural network analysis of full spirometry curves has revealed structural phenotypes beyond traditional Forced Expiratory Volume (FEV₁)-based measures and novel imaging biomarkers. The predictive performance ranges from the Area Under the Curve (AUC) 0.72–0.95, with a pooled meta-analytic AUC of approximately 0.77. Conclusions: AI has uncovered hidden patterns in the progression of COPD, supporting a shift from reactive to anticipatory management. Translation to routine care requires prospective validation, improved interpretability, workflow integration, and generalizability and equity. Full article

Cylinders are common in both industrial and daily settings. Accurate geometric fitting of their parameters, including position, orientation, and radius, is important in real-world perception tasks and industrial applications. At present, consumer-level RGB-D cameras provide three-dimensional (3D) point cloud data with acceptable accuracy and are widely adopted in various sensing applications. Consequently, this task is typically formulated as a geometric fitting problem based on point cloud data. However, point cloud data acquired from such sensors often contain noise, particularly when scanning curved surfaces, which directly degrades the performance of point cloud-based fitting methods. In this paper, we propose an edge–point cloud fusion approach for the geometric fitting of cylinder parameters from single-view RGB-D data. Our approach leverages two-dimensional (2D) image-domain edge constraints together with point cloud data, then fuses them in a unified formulation to jointly optimize cylinder parameters. By explicitly incorporating reliable edge information, our method effectively mitigates the effects of noise in point cloud data. We evaluate the proposed method using real-world RGB-D data, and the experimental results show that our approach achieves significant improvements in both accuracy and robustness. Full article

To address the challenge of identifying damage in the hangers and bridge deck systems of long-span suspension bridges, this paper proposes a non-contact monitoring method based on video image recognition. This method extracts structural vibration displacement responses through video acquisition and image analysis, and combined with the strain mode change rate index, it achieves damage localization, type identification, and severity assessment. The principle of extracting displacement time-history data from video images is first elaborated, and MATLAB-based computational code is developed, including pixel tracking and time-history curve generation methods. The eigensystem realization algorithm is used to identify displacement mode shapes, which are then converted into strain mode shapes via the central difference method. The strain mode change rate and its deviation rate are proposed as damage indicators: under undamaged conditions, the curve is smooth; at damage locations, peaks appear; the distribution range of peaks can distinguish between hanger damage and bridge deck cracks; the deviation rate quantifies damage severity. The feasibility of the method is validated through finite element simulations and physical model experiments. The results show that hanger damage causes broad peaks, while bridge deck cracks present narrow peaks; the deviation rate increases monotonically with damage severity. Applied to an in-service suspension bridge, the method successfully identified hanger bending and weld cracking, with assessment results consistent with on-site inspections. This study demonstrates that the strain mode change rate analysis based on video images enables damage identification without prior knowledge of the structural health state, relying solely on the damaged state response. Offering advantages such as non-contact measurement, full-field monitoring, and no need for sensor deployment, it provides a new technical approach for the long-term monitoring of suspension bridge hanger systems. Full article

Multi-wavelength lensless microscopy enables high-speed, wide-field, and high-throughput imaging, making it highly attractive for modern biomedical applications. However, its practical performance is often limited by unreliable autofocusing and wavelength-dependent phase inconsistencies, which together degrade reconstruction fidelity in complex environments. To explicitly address these two limitations, we present a fully scanning-free computational microscopy framework using a static four-wavelength Light-Emitting Diode (LED) illumination module that sequentially switches between wavelengths to provide strong spectral constraints. For robust geometric parameter estimation, we develop an Adaptive-Weighted Multi-wavelength Autofocus (A-WMAF) scheme that exploits the differential defocus sensitivities of multiple wavelengths to yield a single, sharply peaked autofocus curve and thereby reliably determines the sample–sensor distance. To mitigate chromatic phase inconsistencies, we further introduce an iterative optical-path-difference (OPD)–domain adaptive fusion strategy that fuses multi-wavelength phase estimates in a physically consistent OPD space, suppressing wavelength-dependent artifacts and reconstruction noise. With only four raw holograms acquired within seconds, the proposed method achieves high-fidelity quantitative phase reconstruction with a Phase Structural Similarity Index Measure (SSIM) of 0.9942 and a quantitative OPD accuracy of 95.0%, as well as a measured lateral resolution of 1.23 µm, surpassing the Nyquist–Shannon sampling limit. Experimental demonstrations on fixed biological samples and long-term live-cell monitoring validate that the proposed framework simultaneously achieves reliable autofocusing and chromaticity-robust phase fusion, highlighting its potential for high-throughput biomedical imaging and clinical diagnostics. Full article

This work presents a biomimetic curved compound-eye imaging system (BCCEIS) engineered for extended-range depth mapping. The system is designed to emulate an apposition-type compound eye and comprises three key components: a hemispherical array of lenslets forming a curved multi-aperture imaging surface, an optical relay subsystem that transforms the curved focal plane into a flat image plane compatible with a commercial CMOS sensor, and a high-resolution CMOS detector. Comprehensive optical analysis confirms effective aberration correction, with the root-mean-square (RMS) spot radii across the field of view (FOV) remaining smaller than the radius of the Airy disk. The fabricated prototype achieves an angular resolution of 2.5 mrad within an ultra-wide 97.4° FOV. Furthermore, the system demonstrates accurate depth reconstruction within the entire FOV at distances up to approximately 2 m, exhibiting errors below 2%. Owing to its compact form, wide FOV, and robust depth-sensing performance, the BCCEIS shows strong potential as a payload for unmanned aerial vehicles in applications such as security surveillance and obstacle avoidance. Full article

Endoscopes are widely used in medicine, making objective evaluation of imaging performance essential for device development and quality assurance. Image resolution is commonly characterized by the modulation transfer function (MTF); however, its interpretation depends critically on how spatial frequency is defined and reported. Because spatial frequency is directly tied to sampling, it can be expressed in different units across the imaging chain, including the object plane, image sensor plane, and digital image domain. Inconsistent conversion between these spaces and domains can mislead comparisons and even alter the apparent ranking of regions of interest (ROIs) or imaging systems. This work presents a systematic analysis of spatial-frequency relationships along the endoscopic imaging chain and provides a practical conversion and interpretation workflow for MTF analysis. The framework accounts for sensor sampling, in-camera processing, resampling or scaling, and geometric distortion. Because geometric distortion introduces position-dependent sampling across the field of view, ROI-specific local-magnification measurements are incorporated to convert measured MTFs to a consistent object space spatial-frequency axis. Two case studies illustrate the implications. First, an off-axis ROI may appear to outperform the image center when MTF is expressed in digital image domain cycles per pixel, but this conclusion reverses after conversion to object space cycles per millimeter using local magnification. Second, resampled image outputs can yield inflated MTF curves unless scaling differences between formats are explicitly incorporated into the spatial-frequency axis. Overall, the proposed conversion and reporting workflow enables consistent and physically meaningful MTF comparison across devices, ROIs, and acquisition configurations when geometric distortion, sampling, or resampling differs, clarifying how optics, sensor characteristics, and image processing jointly determine reported MTF results. Full article

This work presents a compact refractometric system based on In-Line Digital Holography (ILDH) for the non-invasive characterization of transparent media, encompassing both liquids and high-refractive-index optical glasses. The core of the system is a cost-effective, lensless setup in which a 532 nm laser source and a microscope objective generate a divergent spherical wavefront that illuminates a 10 μm aluminum particle. The resulting diffraction pattern, modulated by samples in the optical path, is recorded by a CMOS sensor. The refractive index of the sample is determined by numerically locating the axial position of the particle-reconstructed image, which directly corresponds to the optical path difference introduced by the test medium. The optimal reconstruction plane is objectively located using an autofocus algorithm based on the Kurtosis metric, which identifies the sharpest image. The system successfully characterizes media across a broad refractive index range from 1.33 to 1.78, yielding linear calibration curves for both liquid and solid samples. The instrument achieves an axial reconstruction resolution of 30 μm and a refractive index precision of ±0.01 RIU. This ILDH approach offers a highly portable, cost-effective, and non-contact solution for refractive index measurement, demonstrating significant potential for industrial quality control and high-throughput point-of-care applications. Full article

This study presents an optical and thermal design for a compact 10× periscope zoom lens suitable for smartphones, employing a hybrid thermal compensation scheme to ensure stable imaging performance over a wide range of temperatures. Our proposed zoom optics system integrates passive and active compensation mechanisms, further enhancing thermal stability through the use of a curved image sensor. Passive compensation is achieved through the selection of low-G optical materials and an optimized structural configuration. In contrast, active compensation dynamically adjusts the zoom group position in response to changes in ambient temperature. Optical simulations confirm that this 10× periscope zoom lens, composed of a prism, eight aspherical lenses, and two parallel plates, maintains diffraction-limited resolution and less than 2% distortion at all zoom positions (Zoom 1 to Zoom 6), achieving a total depth of 4.96 mm. Thermal analysis at temperatures ranging from −20 °C to 60 °C demonstrates that the optimized design, utilizing a curved sensor (Design type 3), achieves an average MTF of 0.58 and an average degradation rate of only 12.8%, exhibiting excellent non-thermal performance. These results highlight the effectiveness of the proposed novel hybrid thermal compensation strategy and surface sensor integration in realizing high-magnification, thermally stable periscope optics for next-generation smartphone imaging systems. Full article

This paper presents a federated learning (FL) architecture tailored for anomaly detection in wire arc additive manufacturing (WAAM) that preserves data privacy while enabling secure and distributed model training across heterogeneous process units. WAAM’s inherent process complexity, characterized by high-dimensional and asynchronous sensor streams, including current, voltage, travel speed, and visual bead profiles, necessitates a decentralized learning paradigm capable of handling non-identical client distributions without raw data pooling. To this end, the proposed framework integrates reversible data hiding in the encrypted domain (RDHE) for the secure embedding of sensor-derived features into weld images, enabling confidential parameter transmission and tamper-evident federation. Each client node employs a domain-specific long short-term memory (LSTM)-based classifier trained on locally curated time-series or vision-derived features, with model updates embedded and transmitted securely to a central aggregator. Three FL strategies, FedAvg, FedProx, and FedPer, are systematically evaluated against four robust aggregation techniques, including KRUM, Multi KRUM, and Trimmed Mean, across 100 communication rounds using eight non-independent and identically distributed (non-IID) WAAM clients. Experimental results reveal that FedPer coupled with Trimmed Mean delivers the optimal configuration, achieving maximum F1-score (0.912), area under the curve (AUC) (0.939), and client-wise generalization stability under both geometric and temporal noise. The proposed approach demonstrates near-lossless RDHE encoding (PSNR > 90 dB) and robust convergence across adversarial conditions. By embedding encrypted intelligence within weld imagery and tailoring FL to WAAM-specific signal variability, this study introduces a scalable, secure, and generalizable framework for process monitoring. These findings establish a baseline for federated anomaly detection in metal additive manufacturing, with implications for deploying privacy-preserving intelligence across smart manufacturing (SM) networks. The federated pipeline is backbone-agnostic. We instantiate LSTM clients because the sequences are short (five steps) and edge compute is limited in WAAM. The same pipeline can host Transformer/TCN encoders for longer horizons without changing the FL or security flow. Full article