Multiple-point statistics(MPS)is a useful approach to reconstruct three-dimensional models in the macroscopic or microscopic field.Extracting spatial features for three-dimensional reconstruction from two-dimensional ...Multiple-point statistics(MPS)is a useful approach to reconstruct three-dimensional models in the macroscopic or microscopic field.Extracting spatial features for three-dimensional reconstruction from two-dimensional training images(TIs),and characterizing non-stationary features with directional ductility are two key issues in MPS simulation.This study presents a step-wise MPS-based three-dimensional structures reconstruction algorithm with the sequential process and hierarchical strategy based on two-dimensional images.An extension method is proposed to construct three-dimensional TIs.With a sequential simulation process,an initial guess at the coarsest scale is simulated,in which hierarchical strategy is used according to the characteristics of TIs.To obtain a more refined realization,an expectation-maximization like iterative process with global optimization is implemented.A concrete example of chondrite micro-structure simulation,in which one scanning electron microscopy(SEM)image of the Heyetang meteorite is used as TI,shows that the presented algorithm can simulate complex non-stationary structures.展开更多
Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regio...Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.展开更多
Eddy-current (EC) testing is an effective electromagnetic non-destructive testing (NDT) technique.Planar eddy-current sensor arrays have several advantages such as good coherence,fast response speed,and high sensitivi...Eddy-current (EC) testing is an effective electromagnetic non-destructive testing (NDT) technique.Planar eddy-current sensor arrays have several advantages such as good coherence,fast response speed,and high sensitivity,which can be used for micro-damage inspection of crucial parts in mechanical equipments and aerospace aviation.The main purpose of this research is to detect the defect in a metallic material surface and identify the length of a crack using planar eddy-current sensor arrays in different directions.The principle and characteristics of planar eddy-current sensor arrays are introduced,and a crack length quantification algorithm in different directions is investigated.A damage quantitative detection system is established based on a field programmable gate array and ARM processor.The system is utilized to inspect the micro defect in a metallic material,which is carved to micro crack with size of 7mm(length)×0.1mm(width)×1mm(depth).The experimental data show that the sensor arrays can be used for the length measurement repeatedly,and that the uncertainty of the length measurement is below ±0.2mm.展开更多
In this paper, an improved two-dimensional convolution neural network(2DCNN) is proposed to monitor and analyze elevator health, based on the distribution characteristics of elevator time series data in two-dimensiona...In this paper, an improved two-dimensional convolution neural network(2DCNN) is proposed to monitor and analyze elevator health, based on the distribution characteristics of elevator time series data in two-dimensional images. The current and effective power signals from an elevator traction machine are collected to generate gray-scale binary images. The improved two-dimensional convolution neural network is used to extract deep features from the images for classification, so as to recognize the elevator working conditions. Furthermore, the oscillation criterion is proposed to describe and analyze the active power oscillations. The current and active power are used to synchronously describe the working condition of the elevator, which can explain the co-occurrence state and potential relationship of elevator data. Based on the improved integration of local features of the time series, the recognition accuracy of the proposed 2DCNN is 97.78%, which is better than that of a one-dimensional convolution neural network. This research can improve the real-time monitoring and visual analysis performance of the elevator maintenance personnel, as well as improve their work efficiency.展开更多
Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematica...Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematical and computer modeling of a novel two-dimensional(2D)chaotic system for secure key generation in quantum image encryption(QIE).The proposed map employs trigonometric perturbations in conjunction with rational-saturation functions and hence,named as Trigonometric-Rational-Saturation(TRS)map.Through rigorous mathematical analysis and computational simulations,the map is extensively evaluated for bifurcation behaviour,chaotic trajectories,and Lyapunov exponents.The security evaluation validates the map’s non-linearity,unpredictability,and sensitive dependence on initial conditions.In addition,the proposed TRS map has further been tested by integrating it in a QIE scheme.The QIE scheme first quantum-encodes the classic image using the Novel Enhanced Quantum Representation(NEQR)technique,the TRS map is used for the generation of secure diffusion key,which is XOR-ed with the quantum-ready image to obtain the encrypted images.The security evaluation of the QIE scheme demonstrates superior security of the encrypted images in terms of statistical security attacks and also against Differential attacks.The encrypted images exhibit zero correlation and maximum entropy with demonstrating strong resilience due to 99.62%and 33.47%results for Number of Pixels Change Rate(NPCR)and Unified Average Changing Intensity(UACI).The results validate the effectiveness of TRS-based quantum encryption scheme in securing digital images against emerging quantum threats,making it suitable for secure image encryption in IoT and edge-based applications.展开更多
This paper proposes a subpixel transformation method to correct Keystone and Smile distortions in fiber spectral images from the Fiber Arrayed Solar Optical Telescope.These distortions affect the spatial and spectral ...This paper proposes a subpixel transformation method to correct Keystone and Smile distortions in fiber spectral images from the Fiber Arrayed Solar Optical Telescope.These distortions affect the spatial and spectral positions,degrading resolution and accuracy.To correct Keystone distortion,we use a local summation and peak-finding method to locate central horizontal coordinates,calculate shifting values,and straighten the curves.For Smile distortion,we use quartic polynomial fitting based on absorption lines at different wavelengths.This technique preserves subpixel components,redistributes pixel values,and interpolates non-fiber portions,rectifying the spectra for accurate analysis.The method can also be applied to other astronomical projects like Large Sky Area Multi-Object Fiber Spectroscopic Telescope,enhancing the accuracy and reliability of spectral data in various astronomical studies.展开更多
Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of...Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.展开更多
The two-dimensional grating serves as a critical component in plane grating interferometers for achieving high-precision multidimensional displacement measurements.The calibration of grating groove density and orthogo...The two-dimensional grating serves as a critical component in plane grating interferometers for achieving high-precision multidimensional displacement measurements.The calibration of grating groove density and orthogonality error of grating grooves not only improves the positioning accuracy of grating interferometers but also provides essential feedback for optimizing two-dimensional grating fabrication.This study proposes a method for simultaneous calibration of these parameters using orthogonal heterodyne laser interferometry.A two-dimensional grating interferometer is built with the grating to be measured,and a biaxial laser interferometer provides a displacement reference for it.The phase mapping relationship between grating interference and laser interference is established.The interference phase information obtained by any two displacements can simultaneously solve the above three parameters and obtain the grating installation error.The feasibility of the proposed method is verified by using a 1200 gr/mm two-dimensional grating.The standard deviation of the grating groove density in the X and Y directions is 0.012 gr/mm and 0.014 gr/mm,respectively.The standard deviation of the orthogonality error of grating grooves is 0.004°,and the standard deviation of the installation error is 0.002°.Compared with the atomic force microscope method,the consistency of the grating groove density in the X and Y directions is better than 0.03 gr/mm and 0.06 gr/mm,and the orthogonality error of grating grooves is better than 0.008°.The experimental results show that the proposed method can be simply and efficiently applied to the calibration of the grating line parameters of the two-dimensional grating.展开更多
Although Transformer-based image restoration methods have demonstrated impressive performance,existing Transformers still insufficiently exploit multiscale information.Previous non-Transformer-based studies have shown...Although Transformer-based image restoration methods have demonstrated impressive performance,existing Transformers still insufficiently exploit multiscale information.Previous non-Transformer-based studies have shown that incorporating multiscale features is crucial for improving restoration results.In this paper,we propose a multiscale Transformer(MST)that captures cross-scale attention among tokens,thereby effectively leveraging the multiscale patch recurrence prior of natural images.Furthermore,we introduce a channel-gate feed-forward network(CGFN)to enhance inter-channel information aggregation and reduce channel redundancy.To simultaneously utilise global,local and multiscale features,we design a multitype feature integration block(MFIB).Extensive experiments on both image super-resolution and HEVC compressed video artefact reduction demonstrate that the proposed MST achieves state-of-the-art performance.Ablation studies further verify the effectiveness of each proposed module.展开更多
The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adver...The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.展开更多
Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon...Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon-based readout circuits in a single step.Based on this,we propose a photodiode based on an n-i-p structure,which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors,thus reducing manufacturing costs.Additionally,for the noise complexity in quantum dot image sensors when capturing images,traditional denoising and non-uniformity methods often do not achieve optimal denoising re⁃sults.For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector imag⁃es,a network architecture has been developed that incorporates multiple key modules.This network combines channel attention and spatial attention mechanisms,dynamically adjusting the importance of feature maps to en⁃hance the ability to distinguish between noise and details.Meanwhile,the residual dense feature fusion module further improves the network's ability to process complex image structures through hierarchical feature extraction and fusion.Furthermore,the pyramid pooling module effectively captures information at different scales,improv⁃ing the network's multi-scale feature representation ability.Through the collaborative effect of these modules,the network can better handle various mixed noise and image non-uniformity issues.Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.展开更多
As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and el...As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and electrochemical characteristics,MXenes have shown great potential in brain-inspired neuromorphic computing electronics,including neuromorphic gas sensors,pressure sensors and photodetectors.This paper provides a forward-looking review of the research progress regarding MXenes in the neuromorphic sensing domain and discussed the critical challenges that need to be resolved.Key bottlenecks such as insufficient long-term stability under environmental exposure,high costs,scalability limitations in large-scale production,and mechanical mismatch in wearable integration hinder their practical deployment.Furthermore,unresolved issues like interfacial compatibility in heterostructures and energy inefficiency in neu-romorphic signal conversion demand urgent attention.The review offers insights into future research directions enhance the fundamental understanding of MXene properties and promote further integration into neuromorphic computing applications through the convergence with various emerging technologies.展开更多
In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,eff...In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMI_(pixel)),and modified Visual Information Fidelity(VIF_(m)).展开更多
Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bac...Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bacterial structures,facilitating precise measurement of morphological variations and population behaviors at single-cell resolution.This paper reviews advancements in bacterial image segmentation,emphasizing the shift from traditional thresholding and watershed methods to deep learning-driven approaches.Convolutional neural networks(CNNs),U-Net architectures,and three-dimensional(3D)frameworks excel at segmenting dense biofilms and resolving antibiotic-induced morphological changes.These methods combine automated feature extraction with physics-informed postprocessing.Despite progress,challenges persist in computational efficiency,cross-species generalizability,and integration with multimodal experimental workflows.Future progress will depend on improving model robustness across species and imaging modalities,integrating multimodal data for phenotype-function mapping,and developing standard pipelines that link computational tools with clinical diagnostics.These innovations will expand microbial phenotyping beyond structural analysis,enabling deeper insights into bacterial physiology and ecological interactions.展开更多
Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation with...Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon(second/minute).To achieve the precise forecasting of cloud cover,an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue.An optimal threshold estimation method is further designed to achieve higher cloud identification precision.Considering the cloud's meteorological properties,a random hypersurface model(RHM)based on the Gaussian mixture probability hypothesis density(GM-PHD)filter is applied to track the cloud.The GM-PHD can track the rotation and diffusion of clouds,which helps to estimate sun-cloud collision.Furthermore,a hybrid autoregressive integrated moving average(ARIMA)and backpropagation(BP)neural network-based model is applied for intra-hour PV power forecasting.The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power,improving forecasting precision.展开更多
Two-dimensional(2D)multilayer kagome materials hold significant research value for regulating kagome-related physical properties and exploring quantum effects.However,their development is hindered by the scarcity of a...Two-dimensional(2D)multilayer kagome materials hold significant research value for regulating kagome-related physical properties and exploring quantum effects.However,their development is hindered by the scarcity of available material systems,making the identification of novel 2D multilayer kagome candidates particularly important.In this work,three types of 2D materials with trilayer kagome lattices,namely Sc_(6)S_(5)X_(6)(X=Cl,Br,I),are predicted based on first-principles calculations.These 2D materials feature two kagome lattices composed of Sc atoms and one kagome lattice composed of S atoms.Stability analysis indicates that these materials can exist as free-standing 2D materials.Electronic structure calculations reveal that Sc_(6)S_(5)X_(6)are narrow-bandgap semiconductors(0.76–0.95 e V),with their band structures exhibiting flat bands contributed by Sc-based kagome lattices and Dirac band gaps resulting from symmetry breaking.The sulfur-based kagome lattice in the central layer contributes an independent flat band below the Fermi level.Additionally,Sc_(6)S_(5)X_(6)exhibit high carrier mobility,with hole and electron mobilities reaching up to 10^(3)cm^(2)·V^(-1)·s^(-1),indicating potential applications in low-dimensional electronic devices.This work provides an excellent example for the development of novel multilayer 2D kagome materials.展开更多
Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility...Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility of estimating brain volume through retinal fundus imaging integrated with clinical metadata,and to offer a cost-effective approach for assessing brain health.Methods:Based on clinical information,retinal fundus images,and neuroimaging data derived from a multicenter,population-based cohort study,the Kai Luan Study,we proposed a cross-modal correlation representation(CMCR)network to elucidate the intricate co-degenerative relationships between the eyes and brain for 755 subjects.Specifically,individual clinical information,which has been followed up for as long as 12 years,was encoded as a prompt to enhance the accuracy of brain volume estimation.Independent internal validation and external validation were performed to assess the robustness of the proposed model.Root mean square error(RMSE),peak signal-tonoise ratio(PSNR),and structural similarity index measure(SSIM)metrics were employed to quantitatively evaluate the quality of synthetic brain images derived from retinal imaging data.Results:The proposed framework yielded average RMSE,PSNR,and SSIM values of 98.23,35.78 d B,and 0.64,respectively,which significantly outperformed 5 other methods:multi-channel Variational Autoencoder(mcVAE),Pixelto-Pixel(Pixel2pixel),transformer-based U-Net(Trans UNet),multi-scale transformer network(MT-Net),and residual vision transformer(ResViT).The two-(2D)and three-dimensional(3D)visualization results showed that the shape and texture of the synthetic brain images generated by the proposed method most closely resembled those of actual brain images.Thus,the CMCR framework accurately captured the latent structural correlations between the fundus and the brain.The average difference between predicted and actual brain volumes was 61.36 cm~3,with a relative error of 4.54%.When all of the clinical information(including age and sex,daily habits,cardiovascular factors,metabolic factors,and inflammatory factors)was encoded,the difference was decreased to 53.89 cm~3,with a relative error of 3.98%.Based on the synthesized brain magnetic resonance images from retinal fundus images,the volumes of brain tissues could be estimated with high accuracy.Conclusion:This study provides an innovative,accurate,and cost-effective approach to characterize brain health status through readily accessible retinal fundus images.展开更多
Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual infor...Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation.展开更多
基金substantially supported by the National Natural Science Foundation of China(NSFC)Program(Nos.41972302,41772345)。
文摘Multiple-point statistics(MPS)is a useful approach to reconstruct three-dimensional models in the macroscopic or microscopic field.Extracting spatial features for three-dimensional reconstruction from two-dimensional training images(TIs),and characterizing non-stationary features with directional ductility are two key issues in MPS simulation.This study presents a step-wise MPS-based three-dimensional structures reconstruction algorithm with the sequential process and hierarchical strategy based on two-dimensional images.An extension method is proposed to construct three-dimensional TIs.With a sequential simulation process,an initial guess at the coarsest scale is simulated,in which hierarchical strategy is used according to the characteristics of TIs.To obtain a more refined realization,an expectation-maximization like iterative process with global optimization is implemented.A concrete example of chondrite micro-structure simulation,in which one scanning electron microscopy(SEM)image of the Heyetang meteorite is used as TI,shows that the presented algorithm can simulate complex non-stationary structures.
文摘Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.
基金supported by the National Natural Science Foundation of China (No.61171460)
文摘Eddy-current (EC) testing is an effective electromagnetic non-destructive testing (NDT) technique.Planar eddy-current sensor arrays have several advantages such as good coherence,fast response speed,and high sensitivity,which can be used for micro-damage inspection of crucial parts in mechanical equipments and aerospace aviation.The main purpose of this research is to detect the defect in a metallic material surface and identify the length of a crack using planar eddy-current sensor arrays in different directions.The principle and characteristics of planar eddy-current sensor arrays are introduced,and a crack length quantification algorithm in different directions is investigated.A damage quantitative detection system is established based on a field programmable gate array and ARM processor.The system is utilized to inspect the micro defect in a metallic material,which is carved to micro crack with size of 7mm(length)×0.1mm(width)×1mm(depth).The experimental data show that the sensor arrays can be used for the length measurement repeatedly,and that the uncertainty of the length measurement is below ±0.2mm.
基金Sponsored by the National Natural Science Foundation of China (Grant No.61771223)the Key Research and Development Program of Jiangsu Province(Grant No.SBE2018334)。
文摘In this paper, an improved two-dimensional convolution neural network(2DCNN) is proposed to monitor and analyze elevator health, based on the distribution characteristics of elevator time series data in two-dimensional images. The current and effective power signals from an elevator traction machine are collected to generate gray-scale binary images. The improved two-dimensional convolution neural network is used to extract deep features from the images for classification, so as to recognize the elevator working conditions. Furthermore, the oscillation criterion is proposed to describe and analyze the active power oscillations. The current and active power are used to synchronously describe the working condition of the elevator, which can explain the co-occurrence state and potential relationship of elevator data. Based on the improved integration of local features of the time series, the recognition accuracy of the proposed 2DCNN is 97.78%, which is better than that of a one-dimensional convolution neural network. This research can improve the real-time monitoring and visual analysis performance of the elevator maintenance personnel, as well as improve their work efficiency.
基金funded by Deanship of Research and Graduate Studies at King Khalid University.The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP.2/556/45).
文摘Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematical and computer modeling of a novel two-dimensional(2D)chaotic system for secure key generation in quantum image encryption(QIE).The proposed map employs trigonometric perturbations in conjunction with rational-saturation functions and hence,named as Trigonometric-Rational-Saturation(TRS)map.Through rigorous mathematical analysis and computational simulations,the map is extensively evaluated for bifurcation behaviour,chaotic trajectories,and Lyapunov exponents.The security evaluation validates the map’s non-linearity,unpredictability,and sensitive dependence on initial conditions.In addition,the proposed TRS map has further been tested by integrating it in a QIE scheme.The QIE scheme first quantum-encodes the classic image using the Novel Enhanced Quantum Representation(NEQR)technique,the TRS map is used for the generation of secure diffusion key,which is XOR-ed with the quantum-ready image to obtain the encrypted images.The security evaluation of the QIE scheme demonstrates superior security of the encrypted images in terms of statistical security attacks and also against Differential attacks.The encrypted images exhibit zero correlation and maximum entropy with demonstrating strong resilience due to 99.62%and 33.47%results for Number of Pixels Change Rate(NPCR)and Unified Average Changing Intensity(UACI).The results validate the effectiveness of TRS-based quantum encryption scheme in securing digital images against emerging quantum threats,making it suitable for secure image encryption in IoT and edge-based applications.
基金Astronomy Joint Research Fund supported this work under cooperative agreements between the National Natural Science Foundation of China(NSFC)and the Chinese Academy of Sciences(CAS)(project numbers:U2031132 and U1931206).
文摘This paper proposes a subpixel transformation method to correct Keystone and Smile distortions in fiber spectral images from the Fiber Arrayed Solar Optical Telescope.These distortions affect the spatial and spectral positions,degrading resolution and accuracy.To correct Keystone distortion,we use a local summation and peak-finding method to locate central horizontal coordinates,calculate shifting values,and straighten the curves.For Smile distortion,we use quartic polynomial fitting based on absorption lines at different wavelengths.This technique preserves subpixel components,redistributes pixel values,and interpolates non-fiber portions,rectifying the spectra for accurate analysis.The method can also be applied to other astronomical projects like Large Sky Area Multi-Object Fiber Spectroscopic Telescope,enhancing the accuracy and reliability of spectral data in various astronomical studies.
基金supported by the National Key R&D Program of China(No.2022YFC2504403)the National Natural Science Foundation of China(No.62172202)+1 种基金the Experiment Project of China Manned Space Program(No.HYZHXM01019)the Fundamental Research Funds for the Central Universities from Southeast University(No.3207032101C3)。
文摘Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.
文摘The two-dimensional grating serves as a critical component in plane grating interferometers for achieving high-precision multidimensional displacement measurements.The calibration of grating groove density and orthogonality error of grating grooves not only improves the positioning accuracy of grating interferometers but also provides essential feedback for optimizing two-dimensional grating fabrication.This study proposes a method for simultaneous calibration of these parameters using orthogonal heterodyne laser interferometry.A two-dimensional grating interferometer is built with the grating to be measured,and a biaxial laser interferometer provides a displacement reference for it.The phase mapping relationship between grating interference and laser interference is established.The interference phase information obtained by any two displacements can simultaneously solve the above three parameters and obtain the grating installation error.The feasibility of the proposed method is verified by using a 1200 gr/mm two-dimensional grating.The standard deviation of the grating groove density in the X and Y directions is 0.012 gr/mm and 0.014 gr/mm,respectively.The standard deviation of the orthogonality error of grating grooves is 0.004°,and the standard deviation of the installation error is 0.002°.Compared with the atomic force microscope method,the consistency of the grating groove density in the X and Y directions is better than 0.03 gr/mm and 0.06 gr/mm,and the orthogonality error of grating grooves is better than 0.008°.The experimental results show that the proposed method can be simply and efficiently applied to the calibration of the grating line parameters of the two-dimensional grating.
基金supported in part by the National Natural Science Foundation of China under Grants 62101346 and 62301330the Guangdong Basic and Applied Basic Research Foundation under Grants 2021A1515011702 and 2022A1515110101+1 种基金the Shenzhen Science and Technology Programme under Grants JCYJ20240813141358076 and 20231121103807001the Guangdong Provincial Key Laboratory under Grant 2023B1212060076.
文摘Although Transformer-based image restoration methods have demonstrated impressive performance,existing Transformers still insufficiently exploit multiscale information.Previous non-Transformer-based studies have shown that incorporating multiscale features is crucial for improving restoration results.In this paper,we propose a multiscale Transformer(MST)that captures cross-scale attention among tokens,thereby effectively leveraging the multiscale patch recurrence prior of natural images.Furthermore,we introduce a channel-gate feed-forward network(CGFN)to enhance inter-channel information aggregation and reduce channel redundancy.To simultaneously utilise global,local and multiscale features,we design a multitype feature integration block(MFIB).Extensive experiments on both image super-resolution and HEVC compressed video artefact reduction demonstrate that the proposed MST achieves state-of-the-art performance.Ablation studies further verify the effectiveness of each proposed module.
文摘The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.
基金Supported by the National key research and development program in the 14th five year plan 2021YFA1200700)the National Natural Science Foundation of China(62535018,62431025,62561160113)the Natural Science Foundation of Shanghai(23ZR1473400).
文摘Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon-based readout circuits in a single step.Based on this,we propose a photodiode based on an n-i-p structure,which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors,thus reducing manufacturing costs.Additionally,for the noise complexity in quantum dot image sensors when capturing images,traditional denoising and non-uniformity methods often do not achieve optimal denoising re⁃sults.For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector imag⁃es,a network architecture has been developed that incorporates multiple key modules.This network combines channel attention and spatial attention mechanisms,dynamically adjusting the importance of feature maps to en⁃hance the ability to distinguish between noise and details.Meanwhile,the residual dense feature fusion module further improves the network's ability to process complex image structures through hierarchical feature extraction and fusion.Furthermore,the pyramid pooling module effectively captures information at different scales,improv⁃ing the network's multi-scale feature representation ability.Through the collaborative effect of these modules,the network can better handle various mixed noise and image non-uniformity issues.Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051,ZR2025QB50)+6 种基金Guangdong Basic and Applied Basic Research Foundation(2025A1515011191)the Shanghai Sailing Program(23YF1402200,23YF1402400)funded by Basic Research Program of Jiangsu(BK20240424)Open Research Fund of State Key Laboratory of Crystal Materials(KF2406)Taishan Scholar Foundation of Shandong Province(tsqn202408006,tsqn202507058)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University。
文摘As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and electrochemical characteristics,MXenes have shown great potential in brain-inspired neuromorphic computing electronics,including neuromorphic gas sensors,pressure sensors and photodetectors.This paper provides a forward-looking review of the research progress regarding MXenes in the neuromorphic sensing domain and discussed the critical challenges that need to be resolved.Key bottlenecks such as insufficient long-term stability under environmental exposure,high costs,scalability limitations in large-scale production,and mechanical mismatch in wearable integration hinder their practical deployment.Furthermore,unresolved issues like interfacial compatibility in heterostructures and energy inefficiency in neu-romorphic signal conversion demand urgent attention.The review offers insights into future research directions enhance the fundamental understanding of MXene properties and promote further integration into neuromorphic computing applications through the convergence with various emerging technologies.
基金funded by Anhui Province University Key Science and Technology Project(2024AH053415)Anhui Province University Major Science and Technology Project(2024AH040229)+3 种基金Talent Research Initiation Fund Project of Tongling University(2024tlxyrc019)Tongling University School-Level Scientific Research Project(2024tlxyptZD07)TheUniversity Synergy Innovation Programof Anhui Province(GXXT-2023-050)Tongling City Science and Technology Major Special Project(Unveiling and Commanding Model)(200401JB004).
文摘In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMI_(pixel)),and modified Visual Information Fidelity(VIF_(m)).
基金financially supported by the Open Project Program of Wuhan National Laboratory for Optoelectronics(No.2022WNLOKF009)the National Natural Science Foundation of China(No.62475216)+2 种基金the Key Research and Development Program of Shaanxi(No.2024GH-ZDXM-37)the Fujian Provincial Natural Science Foundation of China(No.2024J01060)the Startup Program of XMU,and the Fundamental Research Funds for the Central Universities.
文摘Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bacterial structures,facilitating precise measurement of morphological variations and population behaviors at single-cell resolution.This paper reviews advancements in bacterial image segmentation,emphasizing the shift from traditional thresholding and watershed methods to deep learning-driven approaches.Convolutional neural networks(CNNs),U-Net architectures,and three-dimensional(3D)frameworks excel at segmenting dense biofilms and resolving antibiotic-induced morphological changes.These methods combine automated feature extraction with physics-informed postprocessing.Despite progress,challenges persist in computational efficiency,cross-species generalizability,and integration with multimodal experimental workflows.Future progress will depend on improving model robustness across species and imaging modalities,integrating multimodal data for phenotype-function mapping,and developing standard pipelines that link computational tools with clinical diagnostics.These innovations will expand microbial phenotyping beyond structural analysis,enabling deeper insights into bacterial physiology and ecological interactions.
基金supported by National Natural Science Foundation of China(U1909201,62206062).
文摘Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon(second/minute).To achieve the precise forecasting of cloud cover,an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue.An optimal threshold estimation method is further designed to achieve higher cloud identification precision.Considering the cloud's meteorological properties,a random hypersurface model(RHM)based on the Gaussian mixture probability hypothesis density(GM-PHD)filter is applied to track the cloud.The GM-PHD can track the rotation and diffusion of clouds,which helps to estimate sun-cloud collision.Furthermore,a hybrid autoregressive integrated moving average(ARIMA)and backpropagation(BP)neural network-based model is applied for intra-hour PV power forecasting.The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power,improving forecasting precision.
基金supported by the Fundamental Research Funds for the Central Universities(WUT:2024IVA052 and Grant No.104972025KFYjc0089)。
文摘Two-dimensional(2D)multilayer kagome materials hold significant research value for regulating kagome-related physical properties and exploring quantum effects.However,their development is hindered by the scarcity of available material systems,making the identification of novel 2D multilayer kagome candidates particularly important.In this work,three types of 2D materials with trilayer kagome lattices,namely Sc_(6)S_(5)X_(6)(X=Cl,Br,I),are predicted based on first-principles calculations.These 2D materials feature two kagome lattices composed of Sc atoms and one kagome lattice composed of S atoms.Stability analysis indicates that these materials can exist as free-standing 2D materials.Electronic structure calculations reveal that Sc_(6)S_(5)X_(6)are narrow-bandgap semiconductors(0.76–0.95 e V),with their band structures exhibiting flat bands contributed by Sc-based kagome lattices and Dirac band gaps resulting from symmetry breaking.The sulfur-based kagome lattice in the central layer contributes an independent flat band below the Fermi level.Additionally,Sc_(6)S_(5)X_(6)exhibit high carrier mobility,with hole and electron mobilities reaching up to 10^(3)cm^(2)·V^(-1)·s^(-1),indicating potential applications in low-dimensional electronic devices.This work provides an excellent example for the development of novel multilayer 2D kagome materials.
基金supported by the National Natural Science Foundation of China(62522119 and 62372358)the Beijing Natural Science Foundation(7242267)+2 种基金the Beijing Scholars Program([2015]160)the Natural Science Basic Research Program of Shaanxi(2023-JC-QN-0719)the Guangdong Basic and Applied Basic Research Foundation(2022A1515110453)。
文摘Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility of estimating brain volume through retinal fundus imaging integrated with clinical metadata,and to offer a cost-effective approach for assessing brain health.Methods:Based on clinical information,retinal fundus images,and neuroimaging data derived from a multicenter,population-based cohort study,the Kai Luan Study,we proposed a cross-modal correlation representation(CMCR)network to elucidate the intricate co-degenerative relationships between the eyes and brain for 755 subjects.Specifically,individual clinical information,which has been followed up for as long as 12 years,was encoded as a prompt to enhance the accuracy of brain volume estimation.Independent internal validation and external validation were performed to assess the robustness of the proposed model.Root mean square error(RMSE),peak signal-tonoise ratio(PSNR),and structural similarity index measure(SSIM)metrics were employed to quantitatively evaluate the quality of synthetic brain images derived from retinal imaging data.Results:The proposed framework yielded average RMSE,PSNR,and SSIM values of 98.23,35.78 d B,and 0.64,respectively,which significantly outperformed 5 other methods:multi-channel Variational Autoencoder(mcVAE),Pixelto-Pixel(Pixel2pixel),transformer-based U-Net(Trans UNet),multi-scale transformer network(MT-Net),and residual vision transformer(ResViT).The two-(2D)and three-dimensional(3D)visualization results showed that the shape and texture of the synthetic brain images generated by the proposed method most closely resembled those of actual brain images.Thus,the CMCR framework accurately captured the latent structural correlations between the fundus and the brain.The average difference between predicted and actual brain volumes was 61.36 cm~3,with a relative error of 4.54%.When all of the clinical information(including age and sex,daily habits,cardiovascular factors,metabolic factors,and inflammatory factors)was encoded,the difference was decreased to 53.89 cm~3,with a relative error of 3.98%.Based on the synthesized brain magnetic resonance images from retinal fundus images,the volumes of brain tissues could be estimated with high accuracy.Conclusion:This study provides an innovative,accurate,and cost-effective approach to characterize brain health status through readily accessible retinal fundus images.
文摘Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation.
