An operational modal analysis method in frequency and spatial domain

A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.

Improved control for distributed parameter systems with time-dependent spatial domains utilizing mobile sensor-actuator networks

A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial differential equations （PDEs） with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

A convolutional neural network to detect possible hidden data in spatial domain images

Hiding secret data in digital multimedia has been essential to protect the data.Nevertheless,attackers with a steganalysis technique may break them.Existing steganalysis methods have good results with conventional Machine Learning(ML)techniques;however,the introduction of Convolutional Neural Network(CNN),a deep learning paradigm,achieved better performance over the previously proposed ML-based techniques.Though the existing CNN-based approaches yield good results,they present performance issues in classification accuracy and stability in the network training phase.This research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain images.The proposed method comprises three phases:pre-processing,feature extraction,and classification.Firstly,in the pre-processing phase,we use spatial rich model filters to enhance the noise within images altered by data hiding;secondly,in the feature extraction phase,we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features;and finally,in the classification,we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation,followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax function.The results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2%with reduced training time up to 30.81%.

Generation of a continuous-variable quadripartite cluster state multiplexed in the spatial domain

As a highly entangled quantum network, the duster state has the potential for greater information capacity and use in measurement-based quantum computation. Here, we report generating a continuous-variable quadripartite ＂square＂ cluster state of multiplexing orthogonal spatial modes in a single optical parametric amplifier （OPA）, and further improve the quality of entanglement by optimizing the pump profile. We produce multimode en- tanglement of two first-order Hermite-Gauss modes within one beam in a single multimode OPA and transform it into a duster state by phase correction. Furthermore, the pump-profile dependence of the entanglement of this state is explored. Compared with fundamental mode pumping, an enhancement of approximately 33% is achieved using the suitable pump-profile mode. Our approach is potentially scalable to multimode entanglement in the spatial domain. Such spatial duster states may contribute to future schemes in spatial quantum information processing.

Efficient approach and application of the Green's functions in spatial domain in multilayered media

Based on the dipole source method, all components of the Green＇s functions in spectral domain are restructured concisely by four basis functions, and in terms of the two-level discrete complex image method （DCIM） with the high order Sommerfeld identities, an efficient algorithm for closed-form Green＇s functions in spatial domain in multilayered media is presented. This new work enjoys the advantages of the surface wave pole extraction directly carried out by the generalized integral path without troubles of that all components of Green＇s function in spectral domain should be reformed respectively in transmission line network analogy, and then the Green＇s functions for mixed-potential integral equation （MPIE） analysis in both near-field and far-field in multilayered media are obtained. In addition, the curl operator for coupled field in MPIE is avoided conveniently. It is especially applicable and useful to characterize the electromagnetic scattering by, and radiation in the presence of, the electrically large 3-D objects in multilayered media. The numerical results of the S-parameters of a microstrip periodic bandgap （PBG） filter, the radar cross section （RCS） of a large microstrip antenna array, the characteristics of scattering, and radiation from the three-dimensional （3-D） targets in multilayered media are obtained, to demonstrate better effectiveness and accuracy of this technique.

A Spatial Domain Quantum Watermarking Scheme

This paper presents a spatial domain quantum watermarking scheme. For a quantum watermarking scheme,a feasible quantum circuit is a key to achieve it. This paper gives a feasible quantum circuit for the presented scheme. In order to give the quantum circuit, a new quantum multi-control rotation gate, which can be achieved with quantum basic gates, is designed. With this quantum circuit, our scheme can arbitrarily control the embedding position of watermark images on carrier images with the aid of auxiliary qubits. Besides reversely acting the given quantum circuit, the paper gives another watermark extracting algorithm based on quantum measurements. Moreover, this paper also gives a new quantum image scrambling method and its quantum circuit. Differ from other quantum watermarking schemes, all given quantum circuits can be implemented with basic quantum gates. Moreover, the scheme is a spatial domain watermarking scheme, and is not based on any transform algorithm on quantum images. Meanwhile, it can make sure the watermark be secure even though the watermark has been found. With the given quantum circuit, this paper implements simulation experiments for the presented scheme. The experimental result shows that the scheme does well in the visual quality and the embedding capacity.

Compressive imaging based on multi-scale modulation and reconstruction in spatial frequency domain

Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.

Speckle reduction by selective spatial-domain mask in digital holography

An improved method of using a selective spatial-domain mask to reduce speckle noise in digital holography is proposed.The sub-holograms are obtained from the original hologram filtered by the binary masks including a shifting aperture for being reconstructed. Normally, the speckle patterns of these sub-reconstructed images are different. The speckle intensity of the final reconstructed image is suppressed by averaging the favorable sub-reconstructed images which are selected based on the most optimal pixel intensity sub-range in the sub-holograms. Compared with the conventional spatial-domain mask method, the proposed method not only reduces the speckle noise more effectively with fewer sub-reconstructed images,but also reduces the redundant information used in the reconstruction process.

NEW WIND-INDUCED RESPONSES ANALYSIS METHOD OF SPATIAL STRUCTURES IN FREQUENCY DOMAIN WITH MODE COMPENSATION

Large span spatial lattice structures have many natural frequencies in a narrow frequency range, the conventional frequency domain method is difficult to contain all significant contribution modes. Through numerical examples, it is found that some high order modes are likely to be overlooked because of their higher positions of modal order, in spite of their significance to wind response. According to the contributions of modes to strain energy of system, the paper presented an efficient method to compensate the errors owing to missing out some significant high order modes. The effectiveness of the proposed method is verified through a numerical analysis of the wind responses of a spherical dome.

基于DOMAIN模型的干热河谷稀树灌木草丛空间分布

以温度、DEM(数字高程模型)、NDVI(归一化植被指数)3个因子为参数,运用DOMAIN模型对元江县干热河谷稀树灌木草丛空间分布进行模拟,实现了其空间分布的可视化表达。模拟结果表明:稀树灌木草丛群落主要分布于除元江县城外的河谷两侧,其中北部分布较为集中,南部较为分散。在自然条件及人为因素双重干扰下,元江县干热区域的面积呈现出扩大趋势,尤其是元江北部龙潭乡以北、青龙厂镇以南的地区存在更高的演化风险,建议将上述区域及元江国家级自然保护区章巴片区列为重点森林保护区域。

Modeling and analysis of Schistosoma Argonaute protein molecular spatial conformation

Objective:To analyze the amino acid sequence composition,secondary structure,the spatial conformation of its domain and other characteristics of Argonaute protein.Methods:Bioinformatics tools and the internet server were used.Firstly,the amino acid sequence composition features of the Argonaute protein were analyzed,and the phylogenetic tree was constructed.Secondly,Argonaute protein's distribution of secondary structure and its physicochemical properties were predicted.Lastly,the protein functional expression form of the domain group was established through the Phyre-based analysis on the spatial conformation of Argonaute protein domains.Results:593 amino acids were encoded by Argonaute protein,the phylogenetic tree was constructed,and Argonaute protein's distribution of secondary structure and its physicochemical properties were obtained through analysis.In addition,the functional expression form which comprised the N-terminal PAZ domain and C-terminal Piwi domain for the Argonaute protein was obtained with Phyre.Conclusions:The information relationship between the structure and function of the Argonaute protein can be initially established with bioinformatics tools and the internet server,and this provides the theoretical basis for further clarifying the function of Schistosoma Argonaute protein.

跨域数据管理

随着数据成为新的生产要素和数字中国顶层战略的推进,跨域数据共享和流通对于实现数据要素价值最大化变得至关重要。国家通过布局全国一体化大数据中心体系、启动“东数西算”工程等一系列举措,为数据要素的跨域应用提供了基础设施。然而,传统的数据管理局限于单一域内,无法满足跨域场景下的数据管理需求。跨域数据管理面临通信层面的跨空间域挑战、数据建模层面的异构模型融合问题,以及数据访问层面的跨信任域挑战。从跨空间域、跨管辖域和跨信任域3个视角出发,探讨了跨域数据管理的内涵、研究挑战及关键技术,并展望了其未来发展趋势。

基于深度学习和双域融合的红外成像制导系统复杂背景噪声去除方法

红外成像制导系统受到严苛运行环境影响,其成像过程伴随复杂背景噪声干扰,严重影响系统制导跟踪精度。为减少复合噪声对红外成像效果的影响,在分析多种常见噪声的成因和特性的基础上,提出一种基于加性成分和乘性成分的噪声特性先验设定,结合空间域和变换域的双域融合去噪思想,设计了一种基于深度卷积神经网络的多类型噪声去除方法。该方法将富梯度流卷积模块引入UNet++结构以缩减梯度信息冗余并提升多感受野特征提取能力;针对噪声形态特性提出维度注意力机制以实现双域噪声估计;引入高阶双树复小波变换作为域变换方法,提升在不同尺度和方向上对噪声成分的识别能力。通过消融实验验证了噪声先验设定以及双域融合去噪思想的有效性和优越性,通过对比实验证明了所提方法对多种类型噪声均具有优秀的去噪能力。所提方法对高斯噪声的去噪峰值信噪比和结构相似度指标分别达到29.57和0.85,优于其他典型噪声抑制方法;对多类型混合噪声则分别达到27.84和0.82,达到良好的去噪水平。此外,也验证了所提方法对真实图像噪声具有优秀的去噪能力。

Weber Law Based Approach for Multi-Class Image Forgery Detection

Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images.Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries.However,an image can contain one or more types of forgeries.This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain.Firstly,images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing forgery.The proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and healthy.Benchmarked(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and testing.On average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.

Enhancement of Error-Correction Coding of Spatial Watermarks in Gray Code

This paper demonstrates how channel coding can improve the robustness of spatial image watermarks against signal distortion caused by lossy data compression such as the JPEG scheme by taking advantage of the properties of Gray code. Two error-correction coding (ECC) schemes are used here: One scheme, referred to as the vertical ECC (VECC), is to encode information bits in a pixel by error-correction coding where the Gray code is used to improve the performance. The other scheme, referred to as the horizontal ECC (HECC), is to encode information bits in an image plane. In watermarking, HECC generates a codeword representing watermark bits, and each bit of the codeword is encoded by VECC. Simple single-error-correcting block codes are used in VECC and HECC. Several experiments of these schemes were conducted on test images. The result demonstrates that the error-correcting performance of HECC just depends on that of VECC, and accordingly, HECC enhances the capability of VECC. Consequently, HECC with appropriate codes can achieve stronger robustness to JPEG—caused distortions than non-channel-coding watermarking schemes.

Spatial Image Watermarking by Error-Correction Coding in Gray Codes

In this paper, error-correction coding (ECC) in Gray codes is considered and its performance in the protecting of spatial image watermarks against lossy data compression is demonstrated. For this purpose, the differences between bit patterns of two Gray codewords are analyzed in detail. On the basis of the properties, a method for encoding watermark bits in the Gray codewords that represent signal levels by a single-error-correcting (SEC) code is developed, which is referred to as the Gray-ECC method in this paper. The two codewords of the SEC code corresponding to respective watermark bits are determined so as to minimize the expected amount of distortion caused by the watermark embedding. The stochastic analyses show that an error-correcting capacity of the Gray-ECC method is superior to that of the ECC in natural binary codes for changes in signal codewords. Experiments of the Gray-ECC method were conducted on 8-bit monochrome images to evaluate both the features of watermarked images and the performance of robustness for image distortion resulting from the JPEG DCT-baseline coding scheme. The results demonstrate that, compared with a conventional averaging-based method, the Gray-ECC method yields watermarked images with less amount of signal distortion and also makes the watermark comparably robust for lossy data compression.

基于布里渊光时域峰值边沿分析的变压器绕组局部热点检测

变压器绕组的温度分布一直是电网运行人员重点关注的对象。与传统传感器相比,分布式光纤传感具有抗电磁干扰能力强、可实现分布式测量等优势,其中布里渊光时域分析技术(BOTDA)的性能稳定,适用于大多数场景,但BOTDA的空间分辨率为m级,难以分辨绕组运行中的局部热点。该文从布里渊散射增益谱的机理入手,利用双峰拟合算法,并结合对异常峰值曲线的边沿分析,提出了一种在不增加系统硬件复杂度的基础上提升BOTDA空间分辨率的方法,以满足局部热点检测要求。通过对光纤沿线不同位置、不同热点长度及不同热点温度下的模拟试验验证了该方法的有效性,在实验室条件下可将准确传感的热点长度提升至原空间分辨率的一半以内。基于变压器实际绕组的局部升温试验,实现了绕组异常热点的精确定位和长度感知,并准确解调了绕组局部热点的温度,进一步验证了该方法的实际可行性和检测优越性,温度相对误差控制在5%以内,为提高变压器绕组温度在线监测的经济性和可行性提供新的思考,同时也为变压器绕组局部故障的提前感知和预警提供了新的思路。

基于混合神经网络和注意力机制的生物医学事件触发词识别方法

生物医学事件作为生物医学文本挖掘的重要组成部分,在生物医学研究和疾病的预防中发挥着重要作用.触发词识别是生物医学事件抽取的关键和前提步骤,旨在提取描述事件类型的关键词.传统方法在特征提取过程中过分依赖自然语言处理工具,导致耗费人工成本.另外,由于生物医学文献的特殊性—长文本语句多,导致长距离依赖问题比较明显.为了解决这些问题,我们提出了一种混合结构,由残差卷积神经网络和双向长短期神经网络、混合神经网络和多头注意力机制组成.该模型利用残差卷积神经网络提取单词级特征并利用双向长短期神经网络提取上下文语义信息.此外,本文通过空间域滑动窗口将长句划分为等长短句,在不破坏上下文信息的前提下,避免了长距离依赖.实验结果表明,本文提出的方法在生物医学事件抽取通用语料MLEE(Multi-Level Event Extraction)上取得了较好的效果,F值达到81.15%.

Spatially dispersive model of wire medium for scattering

The electromagnetic properties of wire medium are studied by analyzing its transmission coefficient, and a homogeneous uniaxial effective permittivity tensor with one spatially dispersive component is obtained. In order to evaluate the validity of the equivalent model for scattering problems, the bistatic and monostatic radar cross sections （RCS） of a block of wire medium are investigated with the finite-difference time-domain （FDTD） method. The difference of RCS from the physical wire medium and the corresponding equivalent model has been compared and analyzed under va- rious parameters of wire medium, which clearly demonstrates the accuracy of the equivalent model of wire medium.