Fast source mask co-optimization method for high-NA EUV lithography

Extreme ultraviolet(EUV)lithography with high numerical aperture(NA)is a future technology to manufacture the integrated circuit in sub-nanometer dimension.Meanwhile,source mask co-optimization(SMO)is an extensively used approach for advanced lithography process beyond 28 nm technology node.This work proposes a novel SMO method to improve the image fidelity of high-NA EUV lithography system.A fast high-NA EUV lithography imaging model is established first,which includes the effects of mask three-dimensional structure and anamorphic magnification.Then,this paper develops an efficient SMO method that combines the gradient-based mask optimization algorithm and the compressivesensing-based source optimization algorithm.A mask rule check(MRC)process is further proposed to simplify the optimized mask pattern.Results illustrate that the proposed SMO method can significantly reduce the lithography patterning error,and maintain high computational efficiency.

Modeling and analysis for the image mapping spectrometer

The snapshot image mapping spectrometer（IMS） has advantages such as high temporal resolution,high throughput,compact structure and simple reconstructed algorithm.In recent years,it has been utilized in biomedicine,remote sensing,etc.However,the system errors and various factors can cause cross talk,image degradation and spectral distortion in the system.In this research,a theoretical model is presented along with the point response function（PRF） for the IMS,and the influence of the mirror tilt angle error of the image mapper and the prism apex angle error are analyzed based on the model.The results indicate that the tilt angle error causes loss of light throughput and the prism apex angle error causes spectral mixing between adjacent sub-images.The light intensity on the image plane is reduced to 95%when the mirror tilt angle error is increased to ±100 ＂（≈ 0.028°）.The prism apex error should be controlled within the range of 0-36＂（0.01°）to ensure the designed number of spectral bands,and avoid spectral mixing between adjacent images.

A Gaussian Noise-Based Algorithm for Enhancing Backdoor Attacks

Deep Neural Networks(DNNs)are integral to various aspects of modern life,enhancing work efficiency.Nonethe-less,their susceptibility to diverse attack methods,including backdoor attacks,raises security concerns.We aim to investigate backdoor attack methods for image categorization tasks,to promote the development of DNN towards higher security.Research on backdoor attacks currently faces significant challenges due to the distinct and abnormal data patterns of malicious samples,and the meticulous data screening by developers,hindering practical attack implementation.To overcome these challenges,this study proposes a Gaussian Noise-Targeted Universal Adversarial Perturbation(GN-TUAP)algorithm.This approach restricts the direction of perturbations and normalizes abnormal pixel values,ensuring that perturbations progress as much as possible in a direction perpendicular to the decision hyperplane in linear problems.This limits anomalies within the perturbations improves their visual stealthiness,and makes them more challenging for defense methods to detect.To verify the effectiveness,stealthiness,and robustness of GN-TUAP,we proposed a comprehensive threat model.Based on this model,extensive experiments were conducted using the CIFAR-10,CIFAR-100,GTSRB,and MNIST datasets,comparing our method with existing state-of-the-art attack methods.We also tested our perturbation triggers using various defense methods and further experimented on the robustness of the triggers against noise filtering techniques.The experimental outcomes demonstrate that backdoor attacks leveraging perturbations generated via our algorithm exhibit cross-model attack effectiveness and superior stealthiness.Furthermore,they possess robust anti-detection capabilities and maintain commendable performance when subjected to noise-filtering methods.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Chained Dual-Generative Adversarial Network:A Generalized Defense Against Adversarial Attacks

Neural networks play a significant role in the field of image classification.When an input image is modified by adversarial attacks,the changes are imperceptible to the human eye,but it still leads to misclassification of the images.Researchers have demonstrated these attacks to make production self-driving cars misclassify StopRoad signs as 45 Miles Per Hour(MPH)road signs and a turtle being misclassified as AK47.Three primary types of defense approaches exist which can safeguard against such attacks i.e.,Gradient Masking,Robust Optimization,and Adversarial Example Detection.Very few approaches use Generative Adversarial Networks(GAN)for Defense against Adversarial Attacks.In this paper,we create a new approach to defend against adversarial attacks,dubbed Chained Dual-Generative Adversarial Network(CD-GAN)that tackles the defense against adversarial attacks by minimizing the perturbations of the adversarial image using iterative oversampling and undersampling using GANs.CD-GAN is created using two GANs,i.e.,CDGAN’s Sub-ResolutionGANandCDGAN’s Super-ResolutionGAN.The first is CDGAN’s Sub-Resolution GAN which takes the original resolution input image and oversamples it to generate a lower resolution neutralized image.The second is CDGAN’s Super-Resolution GAN which takes the output of the CDGAN’s Sub-Resolution and undersamples,it to generate the higher resolution image which removes any remaining perturbations.Chained Dual GAN is formed by chaining these two GANs together.Both of these GANs are trained independently.CDGAN’s Sub-Resolution GAN is trained using higher resolution adversarial images as inputs and lower resolution neutralized images as output image examples.Hence,this GAN downscales the image while removing adversarial attack noise.CDGAN’s Super-Resolution GAN is trained using lower resolution adversarial images as inputs and higher resolution neutralized images as output images.Because of this,it acts as an Upscaling GAN while removing the adversarial attak noise.Furthermore,CD-GAN has a modular design such that it can be prefixed to any existing classifier without any retraining or extra effort,and 2542 CMC,2023,vol.74,no.2 can defend any classifier model against adversarial attack.In this way,it is a Generalized Defense against adversarial attacks,capable of defending any classifier model against any attacks.This enables the user to directly integrate CD-GANwith an existing production deployed classifier smoothly.CD-GAN iteratively removes the adversarial noise using a multi-step approach in a modular approach.It performs comparably to the state of the arts with mean accuracy of 33.67 while using minimal compute resources in training.

Rigorous and integrated self-calibration model for a large-field-of-view camera using a star image

This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclination of the optical axis is thoroughly considered with respect to the image plane,and a rigorous imaging model including 8 unknown intrinsic parameters is built.Second,the basic calibration equation based on star vector observations is presented.Third,the partial derivative expressions of all 11 camera parameters for linearizing the calibration equation are deduced in detail,and an iterative solution using the least squares method is given.Furtherly,simulation experiment is designed,results of which shows the new model has a better performance than the old model.At last,three experiments were conducted at night in central China and 671 valid star images were collected.The results indicate that the new method obtains a mean magnitude of reprojection error of 0.251 pixels at a 120°FoV,which improves the calibration accuracy by 38.6%compared with the old calibration model(not considering the inclination of the optical axis).When the FoV drops below 20°,the mean magnitude of the reprojection error decreases to 0.15 pixels for both the new model and the old model.Since stars instead of manual control points are used,the new method can realize self-calibration,which might be significant for the long-duration navigation of vehicles in some unfamiliar or extreme environments,such as those of Mars or Earth’s moon.

A remote sensing-based agricultural drought indicator and its implementation over a semi-arid region, Jordan

The objective of the study was to develop a remote sensing （i.e., Landsat-8 and MODIS）-based agricultural drought indicator （ADI） at 30-m spatial resolution and 8-day temporal resolution and also to evaluate its performance over a heterogeneous agriculture dominant semi-arid region in Jordan. Firstly, we used principal component analysis （PCA） to evaluate the correlations among six commonly used remote sensing-derived agricultural drought related variables. The variables included normalized difference water index （NDWI）, normalized difference vegetation index （NDVI）, visible and shortwave drought index （VSDI）, normalized multiband drought index （NMDI）, moisture stress index （MSI）, and land surface temperature （LST）. Secondly, we integrated the relatively less correlated variables （that were found to be NDWI, VSDI, and LST） to generate four agricultural drought categories/conditions （i.e., wet, mild drought, moderate drought, and severe drought）. Finally, we evaluated the ADI maps against a set of 8-day ground-based standardized precipitation index values （i.e., SPI-I, SPI-2, ..., SPLS） by use of confusion matrices and observed the best results for SPI-4 （i.e., overall accuracy and Kappa-values were 83% and 76%, respectively） and SPI-5 （i.e., overall accuracy and Kappa-values were 85% and 78%, respectively）. The results demonstrated that the method would be valuable for monitoring agricultural drought conditions in semi-arid regions at both a reasonably high spatial resolution （i.e., 30-m） and a short time period （i.e., 8-day）.

基于物联网感知层的移动机器人自组织网动态视觉图像建模(英文)

The dynamic multichannel binocular visual image modeling is studied based on Internet of Things (IoT) Perception Layer, using mobile robot self-organizing network. By employing multigroup mobile robots with binocular visual system, the real visual images of the object will be obtained. Then through the mobile self-organizing network, a three-dimensional model is rebuilt by synthesizing the returned images. On this basis, we formalize a novel algorithm for multichannel binocular visual three-dimensional images based on fast three-dimensional modeling. Compared with the method based on single binocular visual system, the new algorithm can improve the Integrity and accuracy of the dynamic three-dimensional object modeling. The simulation results show that the new method can effectively accelerate the modeling speed, improve the similarity and not increase the data size.

Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics.To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed.The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

AN EFFICIENT LABELLING ALGORITHM FOR WOODPANELS SURFACE DEFECTS

This paper describes an efficient approach for labeling images using a combination of pipeline (Datacube) and (general purpose computer) processing. The output of the algorithm is coordinate list of labeled object pixels that facilitates further high level operations. It is an efficient labeling algorithm for a automatic classification of surface defects on wood boards.

Monoexponential, biexponential and stretched-exponential models based diffusion weighted imaging: a comparative study in the differential diagnosis of benign and malignant breast lesions

Objective To investgate the value of various parameters obtained from monoexponential,biexponential,and stretched exponential diffusion-weighted imaging models in the differential diagnosis of breast lesions.Methods A retrospective study was performed in 54 patients with pathologically confirmed malignant tumors(n=30),benign lesions(n=34)and normal fibroglandular

Three-dimensional temperature reconstruction of diffusion flame from the light-field convolution imaging by the focused plenoptic camera

The plenoptic imaging technique provides a promising approach to the non-invasive three-dimensional measurement, especially for the high-temperature combustion diagnosis. We establish a light-field convolution imaging model for diffusion flame in this work, considering the radiation transfer process inside the diffusion flame and the light transfer process inside the focused plenoptic camera together. The radiation transfer process is described by the radiation transfer equation and solved by the generalized source multi-flux method. Wave optics theory is adopted to describe the light transfer process, combining Fresnel diffraction and the phase conversion of the lens. The flame light-field image is obtained by the light-field convolution imaging model and adopted as the measurement signal to reconstruct three-dimensional temperature field. The inverse problem of temperature reconstruction is solved by the least square QR decomposition method. The simulative temperature reconstruction work is conducted, including the inverse analysis, the uncertainty analysis, and the measurement noise influence. All the results show that the proposed measurement method is available to reconstruct three-dimensional temperature with satisfactory accuracy and acceptable uncertainty. Both symmetric and asymmetric distributed temperature fields are investigated, and the reconstructed results prove the validity and universality of the measurement method.

Establishment of Multi-Site Infection Model in Zebrafish Larvae for Studying Staphylococcus aureus Infectious Disease

Zebrafish（Danio rerio） is an ideal model for studying the mechanism of infectious disease and the interaction between host and pathogen.As a teleost,zebrafish has developed a complete immune system which is similar to mammals.Moreover,the easy acquirement of large amounts of transparent embryos makes it a good candidate for gene manipulation and drug screening.In a zebrafish infection model,all of the site,timing,and dose of the bacteria microinjection into the embryo are important factors that determine the bacterial infection of host.Here,we established a multi-site infection model in zebrafish larvae of 36 hours post-fertilization（hpf） by micro-injecting wild-type or GFP-expressing Staphylococcus aereus（5.aureus） with gradient burdens into different embryo sites including the pericardial cavity（PC）,eye,the fourth hindbrain ventricle（4V）,yolk circulation valley（YCV）,caudal vein（CV）,yolk body（YB）,and Duct of Cuvier（DC） to resemble human infectious disease.With the combination of GFP-expressing S.aureus and transgenic zebrafish Tg（corola：eGFP;lyz：Dsred） and Tg（lyz：Dsred） lines whose macrophages or neutrophils are fluorescent labeled,we observed the dynamic process of bacterial infection by in vivo multicolored confocal fluorescence imaging.Analyses of zebrafish embryo survival, bacterial proliferation and myeloid cells phagocytosis show that the site- and dose-dependent differences exist in infection of different bacterial entry routes.This work provides a consideration for the future study of pathogenesis and host resistance through selection of multi-site infection model.More interaction mechanisms between pathogenic bacteria virulence factors and the immune responses of zebrafish could be determined through zebrafish multi-site infection model.

Multi-scale magnetic resonance measurements and validation of Discrete Element Model simulations

This short review describes the capabilities of magnetic resonance （MR） to image opaque single- and two-phase granular systems, such as rotating cylinders and gas-fluidized beds operated in different fluidization regimes. The unique capability of MR to not only image the solids＇ distribution （voidage） but also the velocity of the particulate phase is clearly shown. It is demonstrated that MR can provide measurements over different length and time scales. With the MR equipment used for the studies summarized here, temporal and spatial scales range from sub-millisecond to hours and from a few hundred micrometres to a few centimetres, respectively. Besides providing crucial data required for an improved understanding of the underlying physics of granular flows, multi-scale MR measurements were also used to validate numerical simulations of granular systems. It is shown that predictions of time-averaged properties, such as voidage and velocity of the particulate phase, made using the Discrete Element Model agree very well with MR measurements.

A polynomial hybrid reflection model and measurement of its parameters based on images of sample

Reflectance model is a basic concept in computer vision. Some existing models combining the classical diffuse reflectance model and those for surfaces containing specular components can approximately describe real reflectance. But the ratio of diffuse and specular reflection decided manually has no clear meaning. We propose a new polynomial hybrid reflectance model. The reflectance map equation with a known shape （for example cylinder） as a sample is used to estimate parameters of the proposed reflectance model by least square regression algorithm. Then the reflectance parameters for surfaces of the same class of materials can be determined. Experiments are performed for a metal surface. The synthesis images produced by the proposed method and existing ones are compared with the real acquired image, and the results show that the proposed reflectance model is suitable for describing real reflectance.

Discriminatively learning for representing local image features with quadruplet model

Traditional hand-crafted features for representing local image patches are evolving into current data-driven and learning-based image feature, but learning a robust and discriminative descriptor which is capable of controlling various patch-level computer vision tasks is still an open problem. In this work, we propose a novel deep convolutional neural network(CNN) to learn local feature descriptors. We utilize the quadruplets with positive and negative training samples, together with a constraint to restrict the intra-class variance, to learn good discriminative CNN representations. Compared with previous works, our model reduces the overlap in feature space between corresponding and non-corresponding patch pairs, and mitigates margin varying problem caused by commonly used triplet loss. We demonstrate that our method achieves better embedding result than some latest works, like PN-Net and TN-TG, on benchmark dataset.

A Low Spectral Bias Generative Adversarial Model for Image Generation

We propose a systematic analysis of the neglected spectral bias in the frequency domain in this paper.Traditional generative adversarial networks(GANs)try to fulfill the details of images by designing specific network architectures or losses,focusing on generating visually qualitative images.The convolution theorem shows that image processing in the frequency domain is parallelizable and performs better and faster than that in the spatial domain.However,there is little work about discussing the bias of frequency features between the generated images and the real ones.In this paper,we first empirically demonstrate the general distribution bias across datasets and GANs with different sampling methods.Then,we explain the causes of the spectral bias through the deduction that reconsiders the sampling process of the GAN generator.Based on these studies,we provide a low-spectral-bias hybrid generative model to reduce the spectral bias and improve the quality of the generated images.

石窟寺数据库建设中Image Based Modeling照片阵列建模的应用探索——以桂林石窟寺调查为例

石窟寺是文物的重要组成部分,本文以2021年全国石窟寺专项调查工作为基础,讨论了Image Based Modeling建模技术的相关发展以及应用,探寻其应用于石窟寺调查的可能性,及其应用于日后文物保护研究以及建设文物数据库的重要价值。

A Method to Estimate the Steganographic Capacity in DCT Domain Based on MCUU Model

In order to estimate maximum steganographic capacity of discrete cosine transform（DCT） domain in JPEG image, this paper presents a method based on the maximize capacity under undetectable model（MCUU）. We analyze the relation between steganographic capacity and affecting factors（image size, steganography operator, loading band, embedding intensity and image complexity）. Then we design a steganography analyzer architecture and a steganographic algorithm which can dynamically increase the steganographic capacity. Compared with other methods of embedding capacity estimation in DCT domain, the proposed methods utilizes general steganalysis methods rather than one specific steganalysis technique and takes five essential factors into account, which improves the commonality and comprehensiveness of capacity estimation, respectively. The experimental results show that steganographic capacity for quantization index modulation（QIM） is almost twice that of spread spectrum（SS） based on MCUU model.