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In this paper, a novel polarization beam splitter (PBS) based on dual-core photonic crystal fiber (DC-PCF) is proposed. The proposed DC-PCF PBS contains two kinds of lattices and three kinds of air holes to form the asymmetrical elliptic dual-core structure. By using the full-vector finite element method, the propagation characteristics of the proposed DC-PCF PBS are investigated. The simulation results show that the bandwidth of the proposed DC-PCF PBS can be up to 340 nm, which covers the S + C + L + U communication bands, the shortest splitting length is 1.97 mm, and the maximum extinction ratio appears near wavelength 1550 nm. Moreover, the insertion loss of the proposed DC-PCF PBS is very low. It is believed that the proposed DC-PCF PBS has important applications in the field of all-optical communication and network.

Accurate and robust anatomical landmark localization is a mandatory and crucial step in deformation diagnosis and treatment planning for patients with craniomaxillofacial (CMF) malformations. In this paper, we propose a trainable end-to-end cephalometric landmark localization framework on Cone-beam computed tomography (CBCT) scans, referred to as CMF-Net, which combines the appearance with transformers, geometric constraint, and adaptive wing (AWing) loss. More precisely: (1) we decompose the localization task into two branches: the appearance branch integrates transformers for identifying the exact positions of candidates, while the geometric constraint branch at low resolution allows the implicit spatial relationships to be effectively learned on the reduced training data. (2) We use the AWing loss to leverage the difference between the pixel values of the target heatmaps and the automatic prediction heatmaps. We verify our CMF-Net by identifying the 24 most relevant clinical landmarks on 150 dental CBCT scans with complicated scenarios collected from real-world clinics. Comprehensive experiments show that it performs better than the state-of-the-art deep learning methods, with an average localization error of 1.108 mm (the clinically acceptable precision range being 1.5 mm) and a correct landmark detection rate equal to 79.28%. Our CMF-Net is time-efficient and able to locate skull landmarks with high accuracy and significant robustness. This approach could be applied in 3D cephalometric measurement, analysis, and surgical planning.

Spectral CT has the ability of quantitative material analysis by exploring the difference in the attenuation properties under different X-ray energies, and has become an important direction for the development of CT technology. However, due to the non-linearity and inconsistency of the observation, there is noise boosting in material decomposition and serious decrease of basis material images. In this paper, we design a U-Net convolutional neural network structure with double-entry and double-out to realize the effective material decomposition of high- and low-energy images. When designing the network, we introduce the attention mechanism, and add CBAM modules combining channel attention and spatial attention to improve the overall performance of the network. We also design an ablation experiment to test the module from a vertical perspective, and compare the material decomposition performance of each network from a horizontal perspective. The results show that the proposed Attention U-Net (AT-U-Net) has advantages in reducing noise boosting and improving the quality of basis material images.

Hollow-core negative curvature fibers (HC-NCFs) have become one of the research hotspots in the field of optical fiber because of their potential applications in the data and energy transmissions. In this work, a new kind of single-polarization single-mode HC-NCF with nested U-type cladding elements is proposed. To achieve the single-polarization single-mode transmission, we use two different silica tubes in thickness, which satisfy the resonance and anti-resonance conditions on the U-type cladding elements and the cladding tubes, respectively. Besides, the elliptical elements are introduced to achieve good single-mode performance. By studying the influences of the structure parameters on the propagation characteristics, the optimized structure parameters are obtained. The simulation results show that when the wavelength is fixed at 1550 nm, the single-polarization single-mode transmission is achieved, with the polarization extinction ratio of 25749 and minimum high-order mode extinction ratio of 174. Furthermore, the confinement loss is only 0.0015 dB/m.

A scattering plate consisting of multiple lead strips is an effective tool for scatter correction and dose reduction in cone-beam CT(CBCT). Existing scattering plate correction methods take advantage of data redundancy by partially blocking cone beam data from a single scan to make up and perform scattering correction. However, for geometric objects with large cone angles during CBCT scans, existing backprojection-filteration(BPF) algorithms cannot accurately reconstruct the object due to data inconsistency and truncation, and may produce additional image artifacts. In order to improve the image quality at large cone angles, this paper proposes a hybrid scatter correction method for filteration-backprojection (FBP) image reconstruction from partially blocked cone beam data using data rearrangement. The proposed method uses a blocker consisting of multiple lead strips that are placed between the light source and the object to provide scatter correction and image reconstruction with beam-blocked data. In combination with a data rearrangement algorithm, the cone-beam projection data is rearranged into a tilted parallel projection data, followed by weighted filtering and inverse projection reconstruction. The experimental results of CBCT show that the proposed method effectively corrects the scattering in each projection and reconstructs the scatter-corrected image from a single scan.

Deep neural networks (DNNs) can be attacked by adversarial examples that are undetectable by humans. Generation-based approaches have recently gained popularity because they directly translate the input distribution to the distribution of adversarial instances, making them more effective and efficient. However, existing techniques are susceptible to overfitting on the substitute model, limiting the transferability of adversarial examples. In this paper, we introduce data augmentation into AdvGAN, called AdvGAN-DE, which can dramatically improve the transferability of adversarial examples. Experiments demonstrate that we enhance the success rate by 31.09% for defense models and 10.23% for typically trained models on average when compared to AdvGAN.

The influence of curing temperature on the bond strength of ITZ was studied in this paper. It is found that, Increasing curing temperature can improve the bond strength of ITZ in the early age, but it will significantly decreases the growth trend of the bond strength. The micro-morphology characteristics were examined by the method of scanning electron microscopy, It was found that, the higher the curing temperature is, the more uneven the hydration products distribution of ITZ is, and also the looser the ITZ structure is.

Most scanning schemes of multi-energy computed tomography (MECT) require multiple sets of full-scan measurements under different x-ray spectra, which limits the application of MECT with incomplete scan. To handle this problem, a flexible MECT scanning strategy is proposed in this paper, which divides one half scan into three curves. Also, a novel MECT reconstruction algorithm is developed to relax the requirement of data acquisition of MECT. For MECT, gradient images of CT images at different energies ideally share the same position of zero-value set (Pos-OS) for the same object. Based on this observation, the characteristics of limited-angle artifacts is first explored, and it is found that the limited-angle artifacts in the image domain are closely related to the angle trajectory of the scan. Inspired by this discovery, the Pos-OS of the gradient image from the fusion CT image is extracted, and it is incorporated as prior knowledge into the TV minimization model in the form of equality constraints. The alternating direction method is exploited to solve the improved optimization model iteratively. Based on this, the proposed algorithm is derived to eliminate the limited angle artifacts in the image domain.The experimental results show that the proposed method achieves higher reconstruction quality under the designed scanning configuration than other methods in the literature.

In order to find out how temperature history affect leaching resistance, the leaching depths and compressive strength loss rate of pastes under three different temperature histories have been studied, the result shows that, with the progress of leaching, the compressive strength of samples continue to decrease, but the rate of decrease gradually slows down, and the leaching resistance of temperature match curing cured sample is better than that of standard curing cured, while the leaching resistance of circadian cycle curing cured sample is worse than that of standard curing cured.

In this study, the redox mediator accelerated the degradation of dyes and improved the efficiency of dye wastewater treatment by immobilizing quinone mediators on the membrane. Specifically, polyvinylidene fluoride, quinone, and lithium chloride were used as the membrane substrate, redox mediator modifier, and porogen, respectively. By changing the type of quinone, the concentration of quinone, and the concentration of crossing-linker, the effect of quinone mediator-functionalized polyvinylidene fluoride membrane on the decolorization of dye wastewater was investigated. This study has shown that the cross-linking agent could effectively improve the compatibility of the quinone mediator casting solution system, and the increase in the addition amount of quinone mediator increased the decolorization rate of the membrane. The increase of the quinone content could also improve the decolorization effect of the dye. The decolorization rate of the original membrane without adding quinone was only 36%, whereas the decolorization rate of the blend membrane reached 75%, which shows the PVDF film with immobilized quinone mediator exhibited a significant dye degradation effect.

Using resting-state functional connectivity based on regions of interest to explore connectivity changes between specific brain regions and the whole brain in insomnia patients after real-time functional magnetic resonance neurofeedback (rt-fMRI NF) therapy has the limitation that the brain information used is not comprehensive enough. While the use of functional network connectivity (FNC) can more systematically and effectively explore the effect of neurofeedback intervention on brain activity in patients with insomnia. In this paper, we used independent component analysis (ICA) method to reveal the spatial properties of brain networks in the whole brain of insomnia patients, then examined and compared the FNC before and after rt-fMRI neurofeedback. The whole-brain FNC results show that the functional connectivity between the executive control network (ECN) and salience network (SAN), default mode network (DMN) were significantly decreased, while the connectivity between the DMN and the SAN was increased. In summary, rt-fMRI neurofeedback training for insomnia patients alters intrinsic functional network connectivity, which may provide objective neuroimage evidence for emotion and sleep improvement in insomnia patients.

Geometrical accuracy of an X-ray Computed Tomography (CT) system is crucial to achieve high quality tomographic reconstructions. Methods to correct the resulting geometric artifacts have been comprehensively described in the past few years. Deep convolution neural network is increasingly used in CT imaging, which has a great potential in image feature learning and processing tasks. In this work, a geometric artifact correction method exploiting generative adversarial networks (GAN) is developed. The U-Net structure is employed as the generator of the network to extract the CT image features with geometric artifacts. The convolutional neural network (CNN) based on image block perception acts as a discriminator for the network, further constraining the optimization of the generator. The proposed method has been shown experimentally feasible for geometric artifacts correction in circular cone beam CT by performing more accurate feature extraction. The peak signal to noise ratio (PSNR) of the corrected phantom images increased by 11.812 on average, and the root mean square error (RMSE) decreased by 9.982 on average.

In geometric calibration of cone-beam computed tomography (CBCT), sphere-like objects such as balls are widely imaged, the positioning information of which is obtained to determine the unknown geometric parameters. In this process, the accuracy of the detector location of CB projection of the center of the ball, which we call the center projection, is very important, since geometric calibration is sensitive to errors in the positioning information. Currently in almost all the geometric calibration using balls, the center projection is invariably estimated by the center of the support of the projection or the centroid of the intensity values inside the support approximately. Clackdoyle's work indicates that the center projection is not always at the center of the support or the centroid of the intensity values inside, and has given a quantitative analysis of the maximum errors in evaluating the center projection by the centroid. In this paper, an exact method is proposed to calculate the center projection, utilizing both the detector location of the ellipse center and the two axis lengths of the ellipse. Numerical simulation results have demonstrated the precision and the robustness of the proposed method. Finally there are some comments on this work with non-uniform density balls, as well as the effect by the error occurred in the evaluation for the location of the orthogonal projection of the cone vertex onto the detector.

Dual-energy computed tomography (DECT) has capability to improve material differentiation, but most scanning schemes require two sets of full-scan measurements at different x-ray spectra, limiting its application to imaging system with incomplete scan. In this study, using one half-scan and a second limited-angle scan, we propose a DECT reconstruction method by exploiting the consistent information of gradient images at high- and low-energy spectra, which relaxes the requirement of data acquisition of DECT. Based on the theory of sampling condition analysis, the complementary support set of gradient images plays an important role in image reconstruction because it constitutes the sufficient and necessary condition for accurate CT reconstruction. For DECT, the gradient images of high- and low-energy CT images ideally share the same complementary support set for the same object. Inspired by this idea, we extract the prior knowledge of complementary support set (Pri-CSS) from the gradient image of the first half-scan CT image to promote the second limited-angle CT reconstruction. Pri-CSS will be incorporated into total variation regularization model in the form of constrains. Alternative direction method is applied to iteratively solve the modified optimization model, thereby deriving the proposed algorithm to recover low-energy CT image from limited-angle measurements. The qualitative and quantitative experiments on digital and real data are performed to validate the proposed method. The results show that the proposed method outperforms its counterparts and achieve high reconstruction quality for the designed scanning configuration.

With the acceleration of urbanization in China, urban flood disasters have gradually become a prominent type of urban disease, seriously affected the normal production and life of urban residents, and brought great challenges to urban flood control and disaster reduction work. Based on the current situation of urban flood control in Pinghu City and the latest Guidelines for the Compilation of Urban Flood Control Emergency Preparedness Plan, this paper analyses the scope of application, key defense objects and key points of urban flood control in Pinghu City, and puts forward the organizational system and responsibilities of urban flood control, preventive and early warning measures, emergency response actions, public defense guidelines, personnel transfer and placement, etc. Specific non-engineering measures for urban flood control are put forward for Pinghu City.

X-ray film has been applied widely in the elementary diagnosis and screening of thorax diseases. The rapid development of deep learning has improved automatic diagnosis of thorax disease. But different thorax disease in database inevitably exist the problem of unbalanced quantity because of the different morbidity in real life. So, the restricted factor is not just the architecture of the models but the unbalanced problem of database. What's more, different from the same problem of natural images, this unbalanced problem is not just the unbalanced quantity but also the unbalanced complexity of diagnosis between samples. In this study, we introduced the Focal Loss and proposed the W-FL (weight Focal Loss) function which makes the model pay more attention to the difficult samples and those kinds of diseases which have small quantity and protect the loss and computed gradients from overwhelming by easy negatives. We evaluate the efficiency of W-FL function on the public Chest X-ray14 database using prevailing per-trained models including AlexNet, Inception-v3 and ResNet. We exceed at least 2% average AUC value on 14 different thorax diseases than the W-CEL function which is commonly proposed to solve the unbalanced problem in this database.

To investigate the predictive ability of Radiomics signature for preoperative pathological grading of Hepatocellular Carcinomas (HCC), the no contrast MRI images were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiomics features. Variable selection via LASSO and logistic regression were used to select the most-predictive Radiomics features for the pathological grading. Cross-Validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Overall, the prediction performances by Radiomics features showed statistically significant correlations with pathological grading, however, improvement on the prediction performance by combining T1WI and T2WI data, classification performance obtained the AUC 0.829 in training dataset and the AUC 0.742 in validation dataset. This study consisted of 170 consecutive patients (training dataset: n=125; validation dataset, n=45). The results showed Radiomics signature was developed and validated to be a significant predictor for discrimination HCC pathological grading, which may serve as a complementary tool for the preoperative tumour grading in HCC.

We report on a novel luminescent phenomenon in Gd2O2S : Er,Yb prepared by a solid-state reaction. After irradiation at 980 nm, the afterglow emission of Er 3+ is observed. X-ray diffraction, photoluminescent emission, long-lasting phosphorescence emission and decay curves were used to characterize the phosphor. It is found that the afterglow is improved with the co-doping of Ti 4+ and Mg 2+ . Also the upconversion luminescent spectrum of Er 3+ is changed. We ascribe this phenomenon to the change in the environment of activator ions. The phosphorescence properties of the sample are researched in detail. Due to the dependence of afterglow intensities on different irradiative powers, a possible two-step-absorption upconversion long-lasting phosphorescence mechanism is proposed. This kind of mechanism may overcome the shortcomings of the long persistent phosphors which are used for UV irradiation.

The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography (CT) reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts. To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated. The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation.

Sparse-view x-ray computed tomography (CT) imaging is an interesting topic in CT field and can efficiently decrease radiation dose. Compared with spatial reconstruction, a Fourier-based algorithm has advantages in reconstruction speed and memory usage. A novel Fourier-based iterative reconstruction technique that utilizes non-uniform fast Fourier transform (NUFFT) is presented in this work along with advanced total variation (TV) regularization for a fan sparse-view CT. The proposition of a selective matrix contributes to improve reconstruction quality. The new method employs the NUFFT and its adjoin to iterate back and forth between the Fourier and image space. The performance of the proposed algorithm is demonstrated through a series of digital simulations and experimental phantom studies. Results of the proposed algorithm are compared with those of existing TV-regularized techniques based on compressed sensing method, as well as basic algebraic reconstruction technique. Compared with the existing TV-regularized techniques, the proposed Fourier-based technique significantly improves convergence rate and reduces memory allocation, respectively.

The application of wavy structures in stretchable electrochemical energy storage devices is reviewed. First, the mechanical analysis of wavy structures, specific to flexible electronics, is introduced. Second, stretchable electrochemical energy storage devices with wavy structures are discussed. Finally, the present problems and challenges are reviewed, and possible directions for future research are outlined.

The realization of the all-optical router with multiple input ports and output ports will have direct application to achieve the quantum network. We present a scheme of all-optical routing of coherent-state photons in a waveguide-cavity coupled system. Our router has four input ports and four output ports. The routing of photons is based on the interference. The outcomes show that the transport of the coherent-state photons injected through any input port can be controlled by the phases of the coherent-state photons injected through other input ports. This control can be realized at the single-photon level and requires no additional control fields. Therefore, the all-optical routing at single-photon level can be achieved.

The projection matrix model is used to describe the physical relationship between reconstructed object and projection. Such a model has a strong influence on projection and backprojection, two vital operations in iterative computed tomographic reconstruction. The distance-driven model (DDM) is a state-of-the-art technology that simulates forward and back projections. This model has a low computational complexity and a relatively high spatial resolution; however, it includes only a few methods in a parallel operation with a matched model scheme. This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations. Our proposed model has been implemented on a GPU (graphic processing unit) platform and has achieved satisfactory computational efficiency with no approximation. The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop, respectively, with an image size of 256×256×256 and 360 projections with a size of 512×512. We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation. The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction.

Its powerful nondestructive characteristics are attracting more and more research into the study of computed tomography (CT) for dimensional metrology, which offers a practical alternative to the common measurement methods. However, the inaccuracy and uncertainty severely limit the further utilization of CT for dimensional metrology due to many factors, among which the beam hardening (BH) effect plays a vital role. This paper mainly focuses on eliminating the influence of the BH effect in the accuracy of CT dimensional metrology. To correct the BH effect, a novel exponential correction model is proposed. The parameters of the model are determined by minimizing the gray entropy of the reconstructed volume. In order to maintain the consistency and contrast of the corrected volume, a punishment term is added to the cost function, enabling more accurate measurement results to be obtained by the simple global threshold method. The proposed method is efficient, and especially suited to the case where there is a large difference in gray value between material and background. Different spheres with known diameters are used to verify the accuracy of dimensional measurement. Both simulation and real experimental results demonstrate the improvement in measurement precision. Moreover, a more complex workpiece is also tested to show that the proposed method is of general feasibility.

We study the entanglement trapping of two entangled qubits, each of which is in its own photonic band gap, based on the weak measurement and quantum measurement reversal. An almost maximal entanglement of the two-qubit system can be trapped by using a certain weak measurement strength. Furthermore, we find that the optimal entanglement enhancing is not only dependent on the weak measurement strength but also on the different initial states. The outcomes in our scheme are completely different from that without any measurement on the studied system.

The high radiation dose in computed tomography (CT) scans increases the lifetime risk of cancer, which becomes a major clinical concern. The backprojection-filtration (BPF) algorithm could reduce the radiation dose by reconstructing the images from truncated data in a short scan. In a dental CT, it could reduce the radiation dose for the teeth by using the projection acquired in a short scan, and could avoid irradiation to the other part by using truncated projection. However, the limit of integration for backprojection varies per PI-line, resulting in low calculation efficiency and poor parallel performance. Recently, a tent BPF has been proposed to improve the calculation efficiency by rearranging the projection. However, the memory-consuming data rebinning process is included. Accordingly, the selective BPF (S-BPF) algorithm is proposed in this paper. In this algorithm, the derivative of the projection is backprojected to the points whose x coordinate is less than that of the source focal spot to obtain the differentiated backprojection. The finite Hilbert inverse is then applied to each PI-line segment. S-BPF avoids the influence of the variable limit of integration by selective backprojection without additional time cost or memory cost. The simulation experiment and the real experiment demonstrated the higher reconstruction efficiency of S-BPF.

The interior tomography is commonly met in practice, whereas the self-calibration method for geometric parameters remains far from explored. To determine the geometry of interior tomography, a modified interval subdividing based method, which was originally developed by Tan et al.,[11] was presented in this paper. For the self-calibration method, it is necessary to obtain the reconstructed image with only geometric artifacts. Therefore, truncation artifacts reduction is a key problem for the self-calibration method of an interior tomography. In the method, an interior reconstruction algorithm instead of the Feldkamp?Davis?Kress (FDK) algorithm was employed for truncation artifact reduction. Moreover, the concept of a minimum interval was defined as the stop criterion of subdividing to ensure the geometric parameters are determined nicely. The results of numerical simulation demonstrated that our method could provide a solution to the self-calibration for interior tomography while the original interval subdividing based method could not. Furthermore, real data experiment results showed that our method could significantly suppress geometric artifacts and obtain high quality images for interior tomography with less imaging cost and faster speed compared with the traditional geometric calibration method with a dedicated calibration phantom.

Linear scan computed tomography (LCT) is of great benefit to online industrial scanning and security inspection due to its characteristics of straight-line source trajectory and high scanning speed. However, in practical applications of LCT, there are challenges to image reconstruction due to limited-angle and insufficient data. In this paper, a new reconstruction algorithm based on total-variation (TV) minimization is developed to reconstruct images from limited-angle and insufficient data in LCT. The main idea of our approach is to reformulate a TV problem as a linear equality constrained problem where the objective function is separable, and then minimize its augmented Lagrangian function by using alternating direction method (ADM) to solve subproblems. The proposed method is robust and efficient in the task of reconstruction by showing the convergence of ADM. The numerical simulations and real data reconstructions show that the proposed reconstruction method brings reasonable performance and outperforms some previous ones when applied to an LCT imaging problem.

Compton scattering imaging is a novel radiation imaging method using scattered photons. Its main characteristics are detectors that do not have to be on the opposite side of the source, so avoiding the rotation process. The reconstruction problem of Compton scattering imaging is the inverse problem to solve electron densities from nonlinear equations, which is ill-posed. This means the solution exhibits instability and sensitivity to noise or erroneous measurements. Using the theory for reconstruction of sparse images, a reconstruction algorithm based on total variation minimization is proposed. The reconstruction problem is described as an optimization problem with nonlinear data-consistency constraint. The simulated results show that the proposed algorithm could reduce reconstruction error and improve image quality, especially when there are not enough measurements.