Length Error Research Articles

Advanced Image Steganography Using a U-Net-Based Architecture with Multi-Scale Fusion and Perceptual Loss

Image-to-image steganography refers to the practice of hiding secret image within a cover image, serving as a crucial technique for secure communication and data protection. Existing image-to-image generative adversarial network-based steganographic methods for image hiding demonstrate a high embedding capacity. However, there is still significant room for improvement in terms of the quality of the stego images and the extracted secret images. In this study, we propose an architecture for inconspicuously hiding an image within the Y channel of another image, leveraging a U-Net network and a multi-scale fusion ExtractionBlock. The network is jointly trained using a loss function combining Perceptual Path Length (PPL) and Mean Square Error (MSE). The proposed network is trained and tested on two datasets, Labeled Faces in the Wild and Pascal visual object classes. Experimental results demonstrate that the model not only achieves high invisibility and significant hiding capacity (8 bits per pixel) without altering the color information of the cover image but also exhibits strong generalization ability. Additionally, we introduce the Modified Multi-Image Similarity Metric (MMISM), which integrates the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) values of images, to comprehensively evaluate the network’s hiding and extraction capabilities.

Online Estimation of Ship Dimensions by Combining Images with AIS Reports

Ship dimensions are an important component of static AIS information, and are a key factor in identifying the risks of ship collisions. We describe a method of extracting and correcting ship contour information using inland waterway surveillance video combined with AIS information that does not depend on ship dimension data. A lightweight object detection model was used to determine the ship’s position in an image. Dynamic AIS information was included to produce multigroup control points, solve the optimal homography matrix, and create a transformation model to map image coordinates onto water surface coordinates. A semantic segmentation DeepLabV3+ model was used to determine ship contours from the images, and the actual dimensions of the ship contours were calculated using homography matrix transformation. The mAP of the proposed object detection model and the MIoU of the semantic segmentation model were 86.73% and 91.07%, respectively. The calculation error of the ship length and width were 5.8% and 7.4%, respectively. These statistics indicate that the proposed method rapidly and accurately detected target ships in images, and that the model estimated ship dimensions within a reasonable range.

Numerical study of length and angle gauging for subsurface-inclined cracks based on reflected surface waves with laser ultrasonic technique

ABSTRACT To achieve quantitative detection of subsurface-inclined cracks, the reflected surface waves from subsurface cracks with different lengths and angles were obtained by the finite element method (FEM). The relationship between the time difference of each feature wave in the reflected waves and the length and angle of the subsurface inclined crack was analysed. The arrival time difference between the lowest trough and the first trough of the reflected wave is independent of the angle of the subsurface crack and approximately a linear function of the crack length. Furthermore, when the crack lengths remain unchanged, the arrival time difference between the highest peak and the second trough of the reflected wave is a quadratic function with the angle of the subsurface crack. Thus, a quantitative measurement method for gauging the length and angle of subsurface inclined cracks is proposed based on the above phenomena. The relationships between the length and angle of the subsurface inclined crack and the arrival time difference of the reflected surface waves were obtained by curve fitting. The proposed method was verified by simulation data. The maximum relative error of the crack length obtained by the method was 7.76%, and the maximum relative error of the obtained inclination angle was 11.61% when the crack angle was large. The proposed approach will open the way for simultaneous measurement of the length and angle of subsurface inclined cracks and structures.

Improving autonomous detection in dynamic environments with robust monocular thermal SLAM system

Thermal SLAM outperforms visual SLAM under conditions of nighttime, low visibility (e.g., fog, smoke, and dust), and direct glare. However, the unique non-uniformity correction for thermal imaging will cause data interruption, and the weak texture of the thermal image itself will lead to poor localization accuracy. To overcome these limitations, this paper presents a monocular thermal camera-based simultaneous localization and mapping (SLAM) system that can be used for high-precision and robust localization in challenging dynamic environments with visual degradation. We also conducted extensive experiments on small-scale indoor and outdoor sequences and on large-scale driving sequences, totaling over 180,000 real-world thermal images. Our results show that the developed MonoThermal-SLAM system can achieve camera localization and sparse structured map reconstruction in the face of visual degradation and moving objects in dynamic environments without the need for other sensors. Compared with state-of-the-art monocular SLAM systems, MonoThermal-SLAM was the only system that successfully tracked all sequences to offer unparalleled robustness and higher positioning accuracy. Relative position error (RPE) was less than 0.1 m of the ground-truth trajectory in both the indoor and outdoor sequences. Absolute trajectory error (ATE) improves averagely over 68.57% compared to other best-performing SLAM systems in driving sequences. The average relative error of tracking path length to the ground-truth was less than 5.19%. The superior performance of MonoThermal-SLAM is due to the introduction of several key innovations. First, the developed real-time scene-based denoising chain not only avoids thermal camera data interruptions but also significantly improves the thermal image quality and widens the boundaries of the application of visual SLAM systems for thermal images. Second, the combination of epipolar constraints and semantic segmentation reduces the interference of dynamic objects and improves the robustness of thermal SLAM in dynamic scenes. Third, the implemented SLAM system based on point and line features consists of well-designed initialization, tracking, local mapping, and loop closing to overcome the drawbacks of poor spatial texture distribution of thermal images. Therefore, the system can be used as a novel positioning solution to replace expensive commercial navigation systems, especially in challenging urban dynamic environments with changing light and low visibility in the air.

Absolute measurement of focusing properties of a large-aperture diffractive lens.

Diffractive lenses are popular in large optical systems owing to their lightweight and multifunctional design. However, they are difficult to calibrate accurately due to the cross talk between the first-order diffraction and the background light. Here, a quadriwave lateral shearing interferometry (QWLSI) with spherical wave illumination was proposed to absolutely measure the focusing properties of diffractive lenses by means of the reference background light, in which the corresponding theoretical modeling was first derived, and then the single-shot experiment on a 210 mm-diameter beam was carried out. The results showed that the measurement error of the focal length was 0.59%, and the consistency error was 0.008%.

SpindlesTracker: An Automatic and Low-cost Labeled Workflow for Spindle Analysis.

Quantitative analysis of spindle dynamics in mitosis through fluorescence microscopy requires tracking spindle elongation in noisy image sequences. Deterministic methods, which use typical microtubule detection and tracking methods, perform poorly in the sophisticated background of spindles. In addition, the expensive data labeling cost also limits the application of machine learning in this field. Here we present a fully automatic and low-cost labeled workflow that efficiently analyzes the dynamic spindle mechanism of time-lapse images, called SpindlesTracker. In this workflow, we design a network named YOLOX-SP which can accurately detect the location and endpoint of each spindle under box-level data supervision. We then optimize the algorithm SORT and MCP for spindle's tracking and skeletonization. As there was no publicly available dataset, we annotated a S.pombe dataset that was entirely acquired from the real world for both training and evaluation. Extensive experiments demonstrate that SpindlesTracker achieves excellent performance in all aspects, while reducing label costs by 60%. Specifically, it achieves 84.1% mAP in spindle detection and over 90% accuracy in endpoint detection. Furthermore, the improved algorithm enhances tracking accuracy by 1.3% and tracking precision by 6.5%. Statistical results also indicate that the mean error of spindle length is within 1 μm. In summary, SpindlesTracker holds significant implications for the study of mitotic dynamic mechanisms and can be readily extended to the analysis of other filamentous objects. The code and the dataset are both released on https://github.com/lizhogn/SpindlesTrackerGitHub.

Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN.

Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.

Errors in Pronouncing English in the English Department Students

The aim of this study was to find out students’ errors in pronouncing vowel sounds which consisted of twelve sounds. The researcher used fourth-semester students of English Department at the University of Bengkulu. The method used in this research was descriptive qualitative because the researcher described about the students’ errors. Four students taken from three classes of English Department in academic year 2021/2022 WERE the sample used in this study. To collect the data, the researcher used a pronunciation test as the instrument. In analyzing the data, the researcher transcribed the recording in the form of phonetic transcription. As a result, the researcher found that students made errors in all of the vowel sounds. All of the students made error in pronouncing vocabularies with vowel sound /ɔ:/ and /ʊ/. Those errors are classified into three kinds of errors namely vowel length error, different English vowel sound system from Indonesian Vowel and students’ Wrong Pronunciation Acquisition. First, the students cannot differentiate the vowel length such as vowel /iː/ into vowel /ɪ/ and vice versa, vowel /u:/ to vowel /ʊ/, vowel /ɜː/ into vowel /e/ and vowel sound /Ʌ/ into /ɑ:/. Second, the students substituted the vowel /æ/ into vowel /e/ and vowel sound /ә/ into /e/ or vice versa because there is no vowel sounds such as /æ/ and /ә/ in Indonesian. Last, the students substituted the vowel /ɔː/ into /ɑ:/ and vowel /ʊ/ into vowel /ə/ where they were actually different but the students considered that it was true.

Predicting the postoperative intraocular lens position based on IOL Master 700 biometry, compared with results from the anterior segment analysis system.

Predict intraocular lens position after cataract surgery using the IOL Master 700 and explore the associated ocular parameters compared with the results obtained from the anterior segment analysis system (Sirius, CSO Inc, Florence, Italy). A total of 98 patients (106 eyes) were included in the retrospective study. The postoperative intraocular lens position was obtained using the IOL Master 700 and measured using Adobe Illustrator software. Correlation analysis and linear regression analysis were applied to study the correlation between the actual position of the postoperative intraocular lens (ALP) and the ocular parameters. In addition, Bland-Altman consistency analysis was used to compare the consistency between any two among the predicted intraocular lens position (ALPi) obtained using IOL Master 700 biometry, the predicted artificial lens position (ALPs) calculated using the anterior segment analysis system, or the ALP. Ocular parameters, including preoperative anterior chamber depth, lens thickness, axial length, white-to-white, and postoperative refractive error were all correlated with ALP after cataract surgery (P < 0.05) using univariate analysis. However, in multivariate linear regression, only the first three variables were correlated with ALP. Compared with the equation obtained by the anterior segment analysis, the equation from IOL Master 700 biometry provided a better fit. The results of the consistency analysis showed that ALP, ALPi, and ALPs were in good agreement. IOL Master 700 biometry can help predict intraocular lens position after surgery, and its accuracy is better than that provided by the anterior segment analysis system.

Automatic estimation of RQD based on deep ensemble learning and fracture fitting

In the geological survey of hydraulic engineering, borehole drilling is an effective method to detect the fractures and faults of the rock mass, which is significant in rock mass evaluation. After the borehole drilling, the measurement of the cores is conducted for Rock Quality Designation (RQD) calculation. Meanwhile, the photos of the borehole cores are saved as engineering documents. However, the borehole cores' measurement and RQD calculation is laborious work for geological engineers. Therefore, we have proposed an RQD estimation method using the deep ensemble model. First, we have applied the deep ensemble model to realize the preliminary segmentation of the borehole cores using the deep architecture of UNet. The pre-trained models include ResNeXt, SEResNeXt, SENet154, InceptionResNetv2, and EfficientNetb5. Then, the edges of the binary image of the cores are extracted to build the waveform of edge pixels. Moreover, the areas of the fractures, namely Region of Interest (ROI), can be determined using the waveform and the fractures were fitted in ROI using the least squares method. Finally, the borehole cores in images are segmented completely and the RQD estimation can be carried out. We also have compared the calculation result of the cores’ length and the RQD. The errors of most cores length calculated automatically are under 4 cm, and the errors of most RQD calculated automatically are under 3, which proves the effectiveness of the proposed method. In the discussion, we have pointed out the factor which can decrease the accuracy of the calculation. The work contributes to the RQD automatic calculation and can decrease the work for the engineers.

Approximate NFA universality and related problems motivated by information theory

In coding and information theory, it is desirable to construct maximal codes that can be either variable length codes or error control codes of fixed length. However deciding code maximality boils down to deciding whether a given NFA is universal, and this is a hard problem (including the case of whether the NFA accepts all words of a fixed length). On the other hand, it is acceptable to know whether a code is ‘approximately’ maximal, which then boils down to whether a given NFA is ‘approximately’ universal. Here we introduce the notion of a (1−ε)-universal automaton and present polynomial randomized approximation algorithms to test NFA universality and related hard automata problems, for certain natural probability distributions on the set of words. We also conclude that the randomization aspect is necessary, as approximate universality remains hard for any fixed polynomially computable ε.

Adaptive Hybrid Iterative Linearization Algorithms for IM/DD Optical Transmission Systems

For optical field recovery and linear dispersion compensation, we propose a performance-enhanced linearization algorithm, termed adaptive hybrid multi-constraint iteration algorithm (MCIA), which does not require any physical modifications to standard configurations of intensity-modulation and direct-detection (IM/DD) transmission systems. To improve the sensitivity to the residual inter-symbol interference (ISI) effect, we introduce, after fiber backward-propagation, a linear feed-forward equalizer (FFE) pair into the proposed algorithm. To improve the sensitivity to fiber dispersion estimation errors, we utilize a two-stage dispersion estimator coupled with the G-S iteration. After 100-Gb/s PAM-4 signal transmissions over 400-km fibers, the simulation results show that the MCIA offers a 1.5-dB optical signal-to-noise ratio (OSNR) gain and a 1-dB optical power budget improvement compared with the decision-directed data-aided iterative algorithm (DD-DIA), for highly dispersive IM/DD transmissions. By performing adaptive dispersion estimation, the MCIA has higher tolerance to estimation errors in fiber length. Moreover, for cases subject to large dispersion, the usage of the embedded FFE pair not only desensitizes the MCIA on the limited bandwidth effect, but also accelerates the convergence performance for reaching lower BERs. We experimentally demonstrate that the proposed algorithm can support 150-Gb/s PAM-4 transmissions over 25-km standard single mode fibers (SSMF), where just a 7-tap FFE-pair is required. For 150 Gb/s transmissions, the tolerance to fiber length estimation error is increased from 0.9 km to 20 km.

Analysis of the correlation between peripapillary retinal nerve fiber layer cross-sectional area and myopia in individuals aged 50 years and above

Objective: To measure the peripapillary retinal nerve fiber layer (RNFL) cross-sectional area in individuals aged 50 years and above with different refractive errors, and analyze its correlation with axial length and refractive error. Methods: This was a cross-sectional study conducted as part of the "Beijing Eye Study". The study was population-based and longitudinally designed. In 2001, a cohort of individuals aged 40 years and above from five urban communities in Haidian District and three rural communities in Daxing District, Beijing, were surveyed. Follow-up examinations were conducted in 2011. In this study, the follow-up data from 2011 were collected and analyzed. One eye of each participant was randomly selected, and the participants were categorized into four groups based on their spherical equivalent: emmetropia group (-0.50 D≤spherical equivalent≤0.50 D), low myopia group (-3.00 D≤spherical equivalent<-0.50 D), moderate myopia group (-6.00 D≤spherical equivalent<-3.00 D), and high myopia group (spherical equivalent<-6.00 D). Spectral-domain optical coherence tomography (OCT) was used to perform circular scans with a diameter of 12° centered on the optic disc. ImageJ software and Heidelberg Eye Explorer software were used to calculate the RNFL cross-sectional area. One-way analysis of variance was used to compare the differences in RNFL thickness and RNFL cross-sectional area among different groups. Linear regression analysis was performed to analyze the correlation between RNFL thickness and axial length and spherical equivalent, as well as the correlation between RNFL cross-sectional area and axial length and spherical equivalent. Results: A total of 184 participants (184 eyes) were included in the study, including 88 males and 96 females. The median age was 59 (54, 66) years, with 87 right eyes and 97 left eyes. There were 50 participants (50 eyes) in the emmetropia group, low myopia group, and moderate myopia group, and 34 participants (34 eyes) in the high myopia group. There were no significant differences in age, gender, and eye laterality among the groups (all P>0.05). The RNFL cross-sectional areas in the emmetropia, low myopia, moderate myopia, and high myopia groups were (1.115±0.106), (1.122±0.136), (1.105±0.105), and (1.096±0.106) mm2, respectively, with no significant differences observed (F=0.43, P=0.730). The RNFL thickness in the emmetropia, low myopia, moderate myopia, and high myopia groups were (102.5±9.5), (102.5±12.1), (94.2±8.3), and (90.2±8.9) μm, respectively, with a significant difference observed (F=16.42, P<0.001). Univariate linear regression analysis was performed with spherical equivalent as the independent variable and peripapillary RNFL thickness as the dependent variable, yielding the regression equation: peripapillary RNFL thickness=102.651+1.634 × spherical equivalent (R2=0.21, P<0.001). Similarly, when axial length was used as the independent variable and peripapillary RNFL thickness as the dependent variable, the regression equation was: peripapillary RNFL thickness=174.161-3.147 × axial length (R2=0.18, P<0.001). There was no significant correlation between RNFL cross-sectional area and spherical equivalent (P=0.065) or axial length (P=0.846). Conclusions: There were no significant differences in peripapillary RNFL cross-sectional area among individuals aged 50 years and above with different axial lengths or refractive errors.

Aerial hyperspectral remote sensing detection for maritime search and surveillance of floating small objects

Over the past decades, maritime accidents have been increasing due to the rise in maritime transportation and ship traffic. While detecting accident-prone vessels is crucial, it is equally important to identify individuals in distress and small floating objects. Real-time monitoring and wide-area high-resolution observations enabled by aerial remote sensing have proven effective in maritime detection. In this study, we developed a technology for detecting small objects by conducting two aerial experiments targeting various objects, including ships, mannequins (human-shaped objects), and maritime safety equipment floating in coastal areas, thereby acquiring hyperspectral image data. By utilizing the hyperspectral data, we detected the pixels corresponding to the edges of ships and employed an ellipse fitting approach to identify the vessels, achieving a length error of 0.44 m. Additionally, we detected small floating objects based on a spectral database using spectral matching. The N-finder algorithm (N-FINDR) spectral unmixing technique was applied to detect lifebuoys, buoyant apparatus, and mannequins, resulting in relatively small length errors ranging from 0.08 to 0.17 m. As satellite hyperspectral sensors continue to advance significantly, it is expected that this study will contribute to future research in the field of detecting small objects and maritime surveillance.

An End-to-End Physics-Informed Neural Network for Defect Identification and 3-D Reconstruction Using Rotating Alternating Current Field Measurement

Alternating current field measurement (ACFM) technique has been widely used in defect detection of metal structures. However, the identification and reconstruction of defects depend on human experience or simple empirical formulas, which leads to misjudgment of defects and large quantization errors. This paper proposes an end-to-end physics-informed neural network for defect identification and three-dimensional (3-D) reconstruction. The high precision automatic detection system with a specially designed probe is established to detect defects in any direction. The Faster-RCNN network is used to identify and classify defects. The physics-informed Pix2Pix network is constructed to realize 3-D reconstruction of defects with different types. The results show that the established end-to-end physics-informed neural network can realize the identification and 3-D reconstruction of defects, in which the mean average precision is 0.9982, the average length error of cracks is 0.9249 mm, the average depth error of cracks is 0.3402 mm, the average volume error of corrosion is 0.0667, and the average maximum depth error of corrosion is 0.3464 mm.

A two-dimensional video based quantification method and clinical application research of motion disorders

The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson's disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson's disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.

Investigation of the efficacy and safety of retinal inactivation as a treatment for amblyopia in cats.

Deprivation of normal vision early in postnatal development elicits modifications of neural circuitry within the primary visual pathway that can cause a severe and intractable vision impairment (amblyopia). In cats, amblyopia is often modeled with monocular deprivation (MD), a procedure that involves temporarily closing the lids of one eye. Following long-term MD, brief inactivation of the dominant eye's retina can promote recovery from the anatomical and physiological effects of MD. In consideration of retinal inactivation as a viable treatment for amblyopia it is imperative to compare its efficacy against conventional therapy, as well as assess the safety of its administration. In the current study we compared the respective efficacies of retinal inactivation and occlusion of the dominant eye (reverse occlusion) to elicit physiological recovery from a prior long-term MD in cats. Because deprivation of form vision has been associated with development of myopia, we also examined whether ocular axial length or refractive error were altered by a period of retinal inactivation. The results of this study demonstrate that after a period of MD, inactivation of the dominant eye for up to 10 days elicited significant recovery of visually-evoked potentials that was superior to the recovery measured after a comparable duration of reverse occlusion. After monocular retinal inactivation, measurements of ocular axial length and refractive error were not significantly altered from their pre-inactivation values. The rate of body weight gain also was not changed during the period of inactivation, indicating that general well-being was not affected. These results provide evidence that inactivation of the dominant eye after a period of amblyogenic rearing promotes better recovery than eye occlusion, and this recovery was achieved without development of form-deprivation myopia.

Design of Linear Functional Noncircular Gear with High Contact Ratio Used in Continuously Variable Transmission

Continuously variable transmission (CVT) of noncircular gear has the technical advantages of large bearing capacity and high transmission efficiency. The key technology of CVT with noncircular gear has been broken through some countries, and is in the stage of deep application research. Although the characteristics and design methods of noncircular gear pairs have been continuously studied in China, the noncircular gear CVT is still in the preliminary exploration and research stage. The linear functional noncircular gear pair, whose transmission ratio is a linear function in the working section, to realize continuously variable transmission was the research object in this paper. According to the required transmission ratio in the working section, the transmission ratio function in the non-working section was constructed by using a polynomial. And then the influence of pitch curve parameters in the working section on which in the non-working section was also analyzed to obtain the pitch curve suitable for transmission of this gear pair. In addition, for improving the stability and bearing capacity of gear transmission, the noncircular gear pair transmission with high contact ratio was designed. Furthermore, the accurate value of the contact tooth length was calculated based on the gear principle and the characteristics of the involute tooth profile, from this the contact tooth length error was calculated by comparing the accurate value with its actual value obtained by the rolling experiment. Finally, an indirect method to verify the contact ratio by detecting the contact length error of the tooth profile was proposed.

Laser-generated surface waves for quantitative detection of inclined surface cracks based on finite element analysis

ABSTRACT In this paper, transmitted surface waves are used to quantitatively detect the length and angle of inclined surface cracks based on laser ultrasonic technique. The interaction between laser-generated surface waves and inclined surface cracks is analysed, and the propagation path and mode conversion process of the transmitted surface waves are considered. Then, the propagation paths of some peaks and troughs of the transmitted surface waves are obtained. Based on the obtained path and modal conversion information, a quantitative detection method for the length and angle of inclined surface cracks is proposed. The finite element method is used to verify the propagation paths and proposed method, and the results show that the arrival time of the theoretical path is consistent with that of the simulation. The relative measurement errors of the surface crack length and angle are less than 15% and 10%, respectively, which verifies the rationality and correctness of the proposed method.

Hybrid achromatic microlenses with high numerical apertures and focusing efficiencies across the visible

Compact visible wavelength achromats are essential for miniaturized and lightweight optics. However, fabrication of such achromats has proved to be exceptionally challenging. Here, using subsurface 3D printing inside mesoporous hosts we densely integrate aligned refractive and diffractive elements, forming thin high performance hybrid achromatic imaging micro-optics. Focusing efficiencies of 51–70% are achieved for 15μm thick, 90μm diameter, 0.3 numerical aperture microlenses. Chromatic focal length errors of less than 3% allow these microlenses to form high-quality images under broadband illumination (400–700 nm). Numerical apertures upwards of 0.47 are also achieved at the cost of some focusing efficiency, demonstrating the flexibility of this approach. Furthermore, larger area images are reconstructed from an array of hybrid achromatic microlenses, laying the groundwork for achromatic light-field imagers and displays. The presented approach precisely combines optical components within 3D space to achieve thin lens systems with high focusing efficiencies, high numerical apertures, and low chromatic focusing errors, providing a pathway towards achromatic micro-optical systems.