Search Results (476)

This study presents a U-Net-based automatic segmentation framework for quantitative analysis of surface morphology in a PEEK-based composite following tribological testing. Controlled Pin-on-Disc tests were conducted to characterize tribological performance, worn surfaces were captured by laser scanning microscopy to acquire optical images and height maps, and the model produced pixel-level segmentation masks distinguishing different regions, enabling high-throughput, objective analysis of worn surface morphology. Sixty-three manually annotated image sets—with labels for fiber, third-body patch, and matrix regions—formed the training corpus. A 70-layer U-Net architecture with four-channel input was developed and rigorously evaluated using five-fold cross-validation. To enhance performance on the challenging patch and fiber classes, the top five model instances were ensembled through Bayesian-optimized weighted voting, achieving significant improvements in class-specific F1 metrics. Segmentation outputs on unseen data confirmed the method’s robustness and generalizability across complex surface topographies. This approach establishes a scalable, accurate tool for automated morphological analysis, with potential extensions to real-time monitoring and other composite systems. Full article

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article

Diabetic retinopathy (DR), a leading cause of blindness in working-age adults, arises from chronic hyperglycemia-induced oxidative stress, inflammation, and vascular dysfunction. Current therapies such as laser photocoagulation, intravitreal anti-vascular endothelial growth factor (VEGF) agents, and steroids target advanced stages but fail to prevent early neuronal and microvascular damage. Emerging evidence highlights oxidative stress as a key driver of DR pathogenesis, disrupting the blood-retinal barrier (BRB), promoting neurodegeneration and angiogenesis. Advances in imaging, particularly optical coherence tomography angiography (OCTA), enable earlier detection of neurodegeneration and microvascular changes, underscoring DR as a neurovascular disorder. Polyphenols, such as resveratrol, curcumin, and pterostilbene, exhibit multitarget antioxidant, anti-inflammatory, and anti-angiogenic effects, showing promise in preclinical and limited clinical studies. However, their low bioavailability limits therapeutic efficacy. Nanotechnology-based delivery systems enhance drug stability, tissue targeting, and sustained release, offering potential for early intervention. Future strategies should integrate antioxidant therapies and precision diagnostics to prevent early irreversible retinal damage in diabetic patients. Full article

This study employs a deep learning-based object detection model, GoogleNet, to identify cracks in cultural heritage images. Subsequently, a semantic segmentation model, SegNet, is utilized to determine the location and extent of the cracks. To establish a scale ratio between image pixels and real-world dimensions, a parallel laser-based measurement approach is applied, enabling precise crack length calculations. The results indicate that the percentage error between crack lengths estimated using deep learning and those measured with a caliper is approximately 3%, demonstrating the feasibility and reliability of the proposed method. Additionally, the study examines the impact of iteration count, image quantity, and image category on the performance of GoogleNet and SegNet. While increasing the number of iterations significantly improves the models’ learning performance in the early stages, excessive iterations lead to overfitting. The optimal performance for GoogleNet was achieved at 75 iterations, whereas SegNet reached its best performance after 45,000 iterations. Similarly, while expanding the training dataset enhances model generalization, an excessive number of images may also contribute to overfitting. GoogleNet exhibited optimal performance with a training set of 66 images, while SegNet achieved the best segmentation accuracy when trained with 300 images. Furthermore, the study investigates the effect of different crack image categories by classifying datasets into four groups: general cracks, plain wall cracks, mottled wall cracks, and brick wall cracks. The findings reveal that training GoogleNet and SegNet with general crack images yielded the highest model performance, whereas training with a single crack category substantially reduced generalization capability. Full article

The peening process plays a pivotal role in enhancing the properties of aluminium alloys across various industries, including aerospace, automotive, and construction. Among the critical factors influencing this process, the shot peening time is of paramount importance for studying material characteristics. In the present study, we undertook a comprehensive investigation into the mechanical properties, surface roughness, and damage evolution behaviour of 7075 aluminium alloy subjected to different shot peening durations. This investigation was conducted using a microhardness tester, laser confocal microscope, scanning electron microscope, and other advanced equipment, in conjunction with digital image correlation methods and temperature evolution analysis. Our findings demonstrate that the shot peening time has a profound impact on the mechanical properties of the 7075 alloy. Specifically, the microhardness, tensile strength, and surface roughness of the alloy increased with increasing shot peening time, whereas the elongation rate exhibited a non-monotonic trend, initially decreasing and then increasing. Utilising DIC and temperature evolution analysis, we analysed the influence of shot peening time on the damage evolution behaviour of the alloy and developed tensile damage evolution equations tailored to different shot peening durations. The damage evolution of the 7075 alloy under various shot peening times was observed to proceed through two distinct stages: smooth development and rapid damage. Notably, the damage evolution laws derived from both techniques exhibited good consistency and agreement. The present study serves as a theoretical foundation for exploring the surface peening and damage evolution of 7075 aluminium alloy, which holds significant implications for optimising peening parameters and predicting material life in engineering applications. Full article

Capacitive micromachined ultrasonic transducers (CMUTs) have been developed for air-coupled applications to address key challenges such as noise, prolonged ringing, and side-lobe interference. This study introduces an optimized CMUT design that leverages the squeeze-film damping effect to achieve a low-quality factor, enhancing resolution and temporal precision for imaging as one of the suggested airborne application. The device was fabricated using the PolyMUMPs process, ensuring high structural accuracy and consistency. Finite element analysis (FEA) simulations validated the optimized parameters, demonstrating improved displacement, reduced side-lobe artifacts, and sharper main lobes for superior imaging performance. Experimental validation, including Laser Doppler Vibrometer (LDV) measurements of membrane displacement and mode shapes, along with ring oscillation tests to assess Q-factor and signal decay, confirmed the device’s reliability and consistency across four CMUT arrays. Additionally, this study explores the implementation of multi-frequency CMUT arrays, enhancing imaging versatility across different air-coupled applications. By integrating multiple frequency bands, the proposed CMUTs enable adaptable imaging focus, improving their suitability for diverse diagnostic scenarios. These advancements highlight the potential of the proposed design to deliver a superior performance for airborne applications, paving the way for its integration into advanced diagnostic systems. Full article

Millimeter-wave (mmWave) antennas and antenna arrays have gained significant attention due to their pivotal role in emerging wireless communication, sensing, and imaging technologies. With the rapid deployment of 5G and the transition toward 6G networks, the demand for compact, high-gain, and reconfigurable mmWave antennas has intensified. This article highlights recent advancements in mmWave antenna technologies, including hybrid beamforming using phased arrays, dynamic beam-steering enabled by liquid crystal and MEMS-based structures, and high-capacity MIMO architectures. We also examine the integration of metamaterials and metasurfaces for miniaturization and gain enhancement. Applications covered include wearable antennas with low-SAR textile substrates, conformal antennas for UAV-based mmWave relays, and high-resolution radar arrays for autonomous vehicles. The study further analyzes innovative fabrication methods such as inkjet and aerosol jet printing, micromachining, and laser direct structuring, along with advanced materials like Kapton, PDMS, and graphene. Numerical modeling techniques such as full-wave EM simulation and machine learning-based optimization are discussed alongside experimental validation approaches. Beyond communications, we assess mmWave systems for biomedical imaging, security screening, and industrial sensing. Key challenges addressed include efficiency degradation at high frequencies, interference mitigation in dense environments, and system-level integration. Finally, future directions, including AI-driven design automation, intelligent reconfigurable surfaces, and integration with quantum and terahertz technologies, are outlined. This comprehensive synthesis aims to serve as a valuable reference for advancing next-generation mmWave antenna systems. Full article

The output energy of Laser Active Optical Systems (LAOSs), in which image brightness is amplified within the laser-active medium, is always higher than the input energy. This contrasts with conventional optical systems (OSs). As a result, a LAOS enables the creation of laser beams with tailored energy distribution across the aperture, making them ideal for material processing applications. This concept was first successfully implemented using metal vapor lasers as the gain medium. In these systems, material processing was achieved by using a laser beam that either carried the required energy profile or the image of the object itself. Later, other laser media were utilized for LAOSs, including barium vapor, strontium vapor, excimer XeCl lasers, and solid-state media. Additionally, during the development of these systems, several modifications were introduced. For example, Space-Time Light Modulators (STLMs) and CCD cameras were incorporated, along with the use of multipass amplifiers, disk-shaped or thin-disk (TD) solid-state laser amplifiers, and other advancements. These techniques have significantly expanded the range of power, energy, pulse durations, and operating wavelengths. Currently, TD laser amplifiers and STLMs based on Digital Light Processor (DLP) technology or Digital Micromirror Devices (DMDs) enhance the potential to develop LAOS devices for Subtractive and Additive Technologies (ST, AT), applicable in both macromachining (cutting, welding, drilling) and micro-nano processing. This review presents comparable characteristics and requirements for these various LAOS applications. Full article

Total internal reflection fluorescence–structured illumination microscopy (TIRF-SIM) can enhance the lateral resolution of fluorescence microscopy to twice the diffraction limit, enabling subtler observations of activity in subcellular life. However, the lack of an axial resolution makes it difficult to resolve three-dimensional (3D) subcellular structures. In this paper, we present an alternative TIRF-SIM axial resolution enhancement method by exploiting quantitative information regarding the distance between fluorophores and the surface within the evanescent field. Combining the lateral super-resolution information of TIRF-SIM with reconstructed axial information, a 3D super-resolution image with a 25 nm axial resolution is achieved without attaching special optical components or high-power lasers. The reconstruction results of cell samples demonstrate that the axial resolution enhancement method for TIRF-SIM can effectively resolve the axial depth of densely structured regions. Full article

A flexible and highly sensitive piezoresistive strain sensor was fabricated through the application of CO₂ laser ablation on a composite film composed of multi-walled carbon nanotubes, carbon black, and polydimethylsiloxane (MWCNT/CB/PDMS). The results of scanning electron microscopy (SEM) surface analysis shows that the “bush-like” conductive structure on the PDMS-based composite material membrane post-laser ablation is formed. Transmission electron microscopy (TEM) images and X-ray diffraction (XRD) spectra of the ablation products indicated the formation of an amorphous carbon layer on the surface of carbon nanomaterials due to laser ablation. Experimental findings revealed that the sensitivity (GF) value of the sensor based on CNT0.6CB1.0-P3.0 is up to 584.7 at 5% strain, which is approximately 14% higher than the sensitivity 513 of the sensor previously prepared by the author using CO₂ laser ablation of MWCNT/PDMS composite films. The addition of a very small volume fraction of CB particles significantly enhances the piezoresistive sensitivity of the sensor samples. Combined with the qualitative analysis of microscopic morphology characterization, CB and MWCNT synergistically promote the deposition of amorphous carbon. This phenomenon increases the probability of tunnel effect occurrence in the strain response region of the sensor, which indirectly confirms the synergistic enhancement effect of the combined action of CB and MWCNT on the piezoresistive sensitivity of the sensor. Full article

In this paper, we experimentally investigated the excitation of spoof surface plasmon polaritons (SSPPs) supported by a 1D subwavelength grating with a rectangular profile in the terahertz (THz) frequency range. Using the attenuated total reflection technique and the THz radiation of the Novosibirsk free electron laser, we carried out detailed studies of both angular and gap spectra at several wavelengths. A shallow grating supporting a fundamental mode was fabricated by means of multibeam X-ray lithography and used as a test sample. The results indicated that we achieved 1-THz tunability of resonance in the frequency range from 1.51 to 2.54 THz on a single grating, which cannot be obtained with active tunable metamaterials. The Q factors of the resonances in the angular spectra were within the range of 19.4–37.6, while the resonances of the gap spectra had a Q factor lying within the 1.17–2.03 range. The gap adjustment capability of the setup shown in the work has great potential in modulation of the absorption efficiency, whereas the angular tuning and recording data from each point of the grating will enable real-time monitoring of changes in the surrounding medium. All of this is highly important for enhanced terahertz real-time absorption spectroscopy and imaging. Full article

The increasing pace of climate-driven changes in forest ecosystems calls for reliable remote sensing techniques for quantifying above-ground carbon storage. In this article, we compare the methodology and results of traditional field surveys, mobile laser scanning, optical drone imaging and photogrammetry, and both drone-based and light aircraft-based aerial laser scanning to determine forest stand parameters, which are suitable to estimate carbon stock. Measurements were conducted at four designated sampling points established during a large-scale project in deciduous and coniferous tree stands of the Dudles Forest, Hungary. The results of the surveys were first compared spatially and quantitatively, followed by a summary of the advantages and disadvantages of each method. The mobile laser scanner proved to be the most accurate, while optical surveying—enhanced with a new diameter measurement methodology based on detecting stem positions from the photogrammetric point cloud and measuring the diameter directly on the orthorectified images—also delivered promising results. Aerial laser scanning was the least accurate but provided coverage over large areas. Based on the results, we recommend adapting our carbon stock estimation methodology primarily to mobile laser scanning surveys combined with aerial laser scanned data. Full article

Eggshell quality inspection plays a pivotal role in enhancing the commercial value of poultry eggs and ensuring their safety. It effectively enables the screening of high-quality eggs to meet consumer demand for premium egg products. This paper analyzes the surface characteristics, ultrastructure, and mechanical properties of poultry eggshells. It systematically reviews current advances in eggshell quality inspection technologies and compares the suitability and performance of techniques for key indicators, including shell strength, thickness, spots, color, and cracks. Furthermore, the paper discusses challenges in non-destructive testing, including individual egg variations, species differences, hardware precision limitations, and inherent methodological constraints. It summarizes commercially available portable and online non-destructive testing equipment, analyzing core challenges: the cost–accessibility paradox, speed–accuracy trade-off, algorithm interference impacts, and the technology–practice gap. Additionally, the paper explores the potential application of several emerging technologies—such as tactile sensing, X-ray imaging, laser-induced breakdown spectroscopy, and fluorescence spectroscopy—in eggshell quality inspection. Finally, it provides a comprehensive outlook on future research directions, offering constructive guidance for subsequent studies and practical applications in production. Full article

In high-resolution microscopic imaging, using shorter-wavelength ultraviolet (UV) lasers as illumination sources is a common approach. However, the high spatial coherence of such lasers, combined with the surface roughness of the sample, often introduces disturbances in the received optical field, resulting in strong speckle noise. This paper presents a novel speckle noise suppression method specifically designed for coherent laser-based microscopic imaging. The proposed approach integrates statistical physical modeling and image gradient discrepancy into the training of a Cycle Generative Adversarial Network (CycleGAN), capturing the perturbation mechanism of speckle noise in the optical field. By incorporating these physical constraints, the method effectively enhances the model’s ability to suppress speckle noise without requiring annotated clean data. Experimental results under high-resolution laser microscopy settings demonstrate that the introduced constraints successfully guide network training and significantly outperform traditional filtering methods and unsupervised CNNs in both denoising performance and training efficiency. While this work focuses on microscopic imaging, the underlying framework offers potential extensibility to other laser-based imaging modalities with coherent noise characteristics. Full article

Background: Nasal reconstruction requires a balance between aesthetic and functional restoration. Recent advances in three-dimensional (3D) printing have introduced new approaches to this field, enabling precise, patient-specific interventions. This review explores the applications, benefits, and challenges of integrating 3D printing in nasal reconstruction. Methods: A literature search was conducted using PubMed, Scopus, and Web of Science to identify studies on 3D printing in nasal reconstruction. Peer-reviewed articles and clinical trials were analyzed to assess the impact of 3D-printed models, implants, and bioengineered scaffolds. Results: 3D printing facilitates the creation of anatomical models, surgical guides, and implants, enhancing surgical precision and patient outcomes. Techniques such as stereolithography (SLA) and selective laser sintering (SLS) enable high-resolution, biocompatible constructs using materials like polylactic acid, titanium, and hydroxyapatite. Computational fluid dynamics (CFD) tools improve surgical planning by optimizing nasal airflow. Studies show that 3D-printed guides reduce operative time and improve symmetry. Emerging bioprinting techniques integrating autologous cells offer promise for tissue regeneration. Challenges and Future Directions: Challenges include high costs, imaging limitations, regulatory hurdles, and limited vascularization in bioprinted constructs. Future research should focus on integrating bioactive materials, artificial intelligence-assisted design, and regulatory standardization. Conclusions: 3D printing offers specific advantages in nasal reconstruction, improving precision and outcomes in selected cases. Addressing current limitations through technological and regulatory advancements will further its clinical integration, potentially enhancing reconstructive surgery techniques. Full article