基于Multi-WHFPN与SimAM注意力机制的版面分割

作为OCR的预处理工作,版面分割技术越来越受到学术界和工业界重视。针对版面分割中遇到的检测速度慢、目标区域边界不准确以及细小目标易遗漏等问题,提出了YOLOv7-MSY模型。此模型首先借鉴残差连接思想,提出了Multi-WHFPN网络结构。它采用可训练的权重参数,突出特征融合过程中特征重要性,并添加了小目标检测头,从而提升对小目标的检测性能;其次,引入SimAM注意力机制,可以在不增加额外参数的基础上在3D维度评估特征权重,以增强重要特征,抑制无效特征;最后,使用YEIOU来代替原模型中的定位损失函数,提升了模型的收敛速度与回归精度。在江苏省档案馆提供的数据集上进行实验对比,YOLOv7-MSY对目标区域边界检测更加敏感,对细小目标的检测效果更好。YOLOv7-MSY的mAP@.5达到了0.871,相较于原YOLOv7模型提高了7.84%。该模型的版面分割的效果优于其他类型的版面分割算法,具有良好的泛化性能,并且版面分割速度处于较高水平。

A novel multi-pin rectangular waveguide slow-wave structure based backward wave amplifier at 340 GHz

A backward wave amplifier（BWA） in a terahertz regime with a novel slow-wave structure（SWS） composed of multi parallel grating pins inside a rectangular waveguide is analyzed. The multi-pin rectangular waveguide SWS possesses good performance and is compatible with micro-fabrication technologies. The dispersion and interaction impedance of the multipin SWS are presented. The stopbands of the modes cling together in a Brillouim zone. The SWS has a high interaction impedance that is suitable for the interaction of multi cylindrical beams. The design, which is based on three parallel pins supporting the wave–beam interaction with four cylindrical beams, is verified by three-dimensional particle-in-cell simulations. A BWA with the central frequency at 340 GHz is demonstrated, and the output power is more than 100 mW.A tuning frequency range of 15 GHz（333–348 GHz） is obtained with a gain of more than 20 dB.

A 330-W single-frequency retrievable multi-tone monolithic fiber amplifier

A single-frequency retrievable phase modulated multi-tone fiber amplifier is presented in theory and demonstrated in experiment. A multi-tone seed laser generated by a sine wave phase modulated single-frequency laser is employed for stimulated Brillouin scattering suppression in an all-fiber amplifier. A demodulation signal which is π phase shifted with respect to the modulation signal is used to retrieve the single-frequency laser from the multi-tone laser. In experiment, we first optimize the all-fiber master-oscillator power-amplifier. With this amplifier, we demonstrate a single-frequency retrievable multi-tone laser with 330-W output when driven by the multi-tone seed, while the ultimate output power is only 130 W when driven by the single-frequency laser. Then, we carry out an experiment for retrieving the single-frequency laser from the amplified multi-tone laser. Results indicate that the single-frequency laser can be retrieved with a sideband suppression of more than 20 dB. Retrieving an even higher power single-frequency laser is possible if a high power demodulator is available.

Investigation on the Gain of Multi wavelength Pumping Broadband Raman Amplifier

The Stokes spectrum in the stimulated Raman scattering (SRS) is very complicated. In this article, we use both Gaussian and Lorentz approximation to investigate the gain properties of multi pumping broadband Raman amplifier, and present some schemes for flattening the gain. All the results show that the flatness of the gain spectrum is closely related to the pumping frequency grid. By researching the gain properties of Raman amplifiers of such ITU-T fibers as G.652, G.653, G.655 and large effective area non zero dispersion flattening fiber, we find that the Raman gain is associated with the fiber type and its transmission characteristics, especially it decreases with the increase of the effective core area.

Multi-resolution image segmentation based on Gaussian mixture model

Mixture model based image segmentation method, which assumes that image pixels are independent and do not consider the position relationship between pixels, is not robust to noise and usually leads to misclassification. A new segmentation method, called multi-resolution Ganssian mixture model method, is proposed. First, an image pyramid is constructed and son-father link relationship is built between each level of pyramid. Then the mixture model segmentation method is applied to the top level. The segmentation result on the top level is passed top-down to the bottom level according to the son-father link relationship between levels. The proposed method considers not only local but also global information of image, it overcomes the effect of noise and can obtain better segmentation result. Experimental result demonstrates its effectiveness.

An algorithm for segmentation of lung ROI by mean-shift clustering combined with multi-scale HESSIAN matrix dot filtering

A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.

A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation

To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.

Multi-segment and Multi-ply Overlapping Process of Multi Coupled Activities Based on Valid Information Evolution

Complex product development will inevitably face the design planning of the multi-coupled activities, and overlapping these activities could potentially reduce product development time, but there is a risk of the additional cost. Although the downstream task information dependence to the upstream task is already considered in the current researches, but the design process overall iteration caused by the information interdependence between activities is hardly discussed; especially the impact on the design process＇ overall iteration from the valid information accumulation process. Secondly, most studies only focus on the single overlapping process of two activities, rarely take multi-segment and multi-ply overlapping process of multi coupled activities into account; especially the inherent link between product development time and cost which originates from the overlapping process of multi coupled activities. For the purpose of solving the above problems, as to the insufficiency of the accumulated valid information in overlapping process, the function of the valid information evolution （VIE） degree is constructed. Stochastic process theory is used to describe the design information exchange and the valid information accumulation in the overlapping segment, and then the planning models of the single overlapping segment are built. On these bases, by analyzing overlapping processes and overlapping features of multi-coupling activities, multi-segment and multi-ply overlapping planning models are built; by sorting overlapping processes and analyzing the construction of these planning models, two conclusions are obtained： （1） As to multi-segment and multi-ply overlapping of multi coupled activities, the total decrement of the task set development time is the sum of the time decrement caused by basic overlapping segments, and minus the sum of the time increment caused by multiple overlapping segments; （2） the total increment of development cost is the sum of the cost increment caused by all overlapping process. And then, based on overlapping degree analysis of these planning models, by the V1E degree function, the four lemmas theory proofs are represented, and two propositions are finally proved： （1） The multi-ply overlapping of the multi coupled activities will weaken the basic overlapping effect on the development cycle time reduction （2） Overlapping the multi coupled activities will decrease product development cycle, but increase product development cost. And there is trade-off between development time and cost. And so, two methods are given to slacken and eliminate multi-ply overlapping effects. At last, an example about a vehicle upper subsystem design illustrates the application of the proposed models; compared with a sequential execution pattern, the decreasing of development cycle （22%） and the increasing of development cost （3%） show the validity of the method in the example The proposed research not only lays a theoretical foundation for correctly planning complex product development process, but also provides specific and effective operation methods for overlapping multi coupled activities.

MULTI-REGION SEGMENTATION OF SAR IMAGE BY A MULTIPHASE LEVEL SET APPROACH

In this letter,a multiphase level set approach unifying region and boundary-based infor-mation for multi-region segmentation of Synthetic Aperture Radar(SAR)image is presented.Anenergy functional that is applicable for SAR image segmentation is defined.It consists of two termsdescribing the local statistic characteristics and the gradient characteristics of SAR image respectively.A multiphase level set model that explicitly describes the different regions in one image is proposed.The purpose of such a multiphase model is not only to simplify the way of denoting multi-region by levelset but also to guarantee the accuracy of segmentation.According to the presented multiphase model,the curve evolution equations with respect to edge curves are deduced.The multi-region segmentationis implemented by the numeric solution of the partial differential equations.The performance of theapproach is verified by both simulation and real SAR images.The experiments show that the proposedalgorithm reduces the speckle effect on segmentation and increases the boundary alignment accuracy,thus correctly divides the multi-region SAR image into different homogenous regions.

Creation of Multiple Subwavelength Focal Spot Segments Using Phase Modulated Radially Polarized Multi Gaussian Beam

Based on the vector diffraction theory, the effect of complex phase filters on intensity distribution of a radially polarized multi Gaussian beam in the focal region of high NA lens is theoretically investigated. It is observed that a properly designed multi belt complex phase filter can generate subwavelength novel focal patterns including splitting of focal spots and generation of multiple focal spot segments such as eight, six and four focal spots along the optical axis are obtained. We expect that such an investigation is useful for optical manipulation and material processing, multiple high refractive index particle trapping technologies.

DDoS Defense Algorithm Based on Multi-Segment Timeout Technology

Through the analysis to the DDoS（distributed denial of service） attack, it will conclude that at different time segments, the arrive rate of normal SYN （Synchronization） package are similar, while the abnormal packages are different with the normal ones. Toward this situation a DDoS defense algorithm based on multi-segment timeout technology is presented, more than one timeout segment are set to control the net flow. Experiment results show that in the case of little flow, multi-segment timeout has the ability dynamic defense, so the system performance is improved and the system has high response rate.

A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram

In this work, we propose an original approach of semi-vectorial hybrid morphological segmentation for multicomponent images or multidimensional data by analyzing compact multidimensional histograms based on different orders. Its principle consists first of segment marginally each component of the multicomponent image into different numbers of classes fixed at K. The segmentation of each component of the image uses a scalar segmentation strategy by histogram analysis;we mainly count the methods by searching for peaks or modes of the histogram and those based on a multi-thresholding of the histogram. It is the latter that we have used in this paper, it relies particularly on the multi-thresholding method of OTSU. Then, in the case where i) each component of the image admits exactly K classes, K vector thresholds are constructed by an optimal pairing of which each component of the vector thresholds are those resulting from the marginal segmentations. In addition, the multidimensional compact histogram of the multicomponent image is computed and the attribute tuples or ‘colors’ of the histogram are ordered relative to the threshold vectors to produce (K + 1) intervals in the partial order giving rise to a segmentation of the multidimensional histogram into K classes. The remaining colors of the histogram are assigned to the closest class relative to their center of gravity. ii) In the contrary case, a vectorial spatial matching between the classes of the scalar components of the image is produced to obtain an over-segmentation, then an interclass fusion is performed to obtain a maximum of K classes. Indeed, the relevance of our segmentation method has been highlighted in relation to other methods, such as K-means, using unsupervised and supervised quantitative segmentation evaluation criteria. So the robustness of our method relatively to noise has been tested.

A Multi-Agent Approach to Arabic Handwritten Text Segmentation

The segmentation of individual words into characters is a vital process in handwritten character recognition systems. In this paper, a novel approach is proposed to segment handwritten Arabic text (words). We consider the “Naskh” font style. The segmentation algorithm employs seven agents in order to detect regions where segmentation is illegal. Feature points (end points) are extracted from the remaining regions of the word-image. Initially, the middle of every two successive end points is considered as a candidate segmentation point based on a set of rules. The experimental results are very promising as we achieved a success rate of 86%.

High-power and high optical conversion efficiency diode-end-pumped laser with multi-segmented Nd：YAG/Nd：YVO-4

A novel flat-flat resonator consisting of two crystals（Nd：YAG ＋ Nd：YVO4） is established for power scaling in a diode-end-pumped solid-state laser. We systematically compare laser characteristics between multi-segmented（Nd：YAG ＋ Nd：YVO4） and conventional composite（Nd：YAG ＋ Nd：YAG） crystals to demonstrate the feasibility of spectral line matching for output power scale-up in end-pumped lasers. A maximum continuous-wave output power of 79.2 W is reported at 1064 nm, with Mx2= 4.82, My2= 5.48, and a pumping power of 136 W in the multi-segmented crystals（Nd：YAG ＋ Nd：YVO4）. Compared to conventional composite crystals（Nd：YAG ＋ Nd：YAG）, the optical-optical conversion efficiency of multi-segmented crystals（Nd：YAG ＋ Nd：YVO4） from 808 nm to 1064 nm is enhanced from 30% to 58.8%,while the laser output sensitivity as affected by the diode-laser temperature is reduced from 55% to 9%.

Building Detection and Counting in Convoluted Areas Using Multiclass Datasets with Unmanned Aerial Vehicles (UAVs) Imagery

This paper studies the effect of breaking single-class building data into multi-class building data for semantic segmentation under end-to-end architecture such as UNet, UNet++, DeepLabV3, and DeepLabv3+. Although, the already existing semantic segmentation methods for building detection work on the imagery of developed world, where the buildings are highly structured and there is a clearly distinguishable space present between the building instances, the same methods do not work as effectively on the developing world where there is often no clear differentiable spaces between instances of building thus reducing the number of detected instances. Hence as a noble approach, we have added building contours as new class along with building segmentation data, and detected the building contours and the inner building regions, hence giving the precise number of buildings existing in the input imagery especially in the convoluted areas where the boundary between the buildings are often hard to determine even for human eyes. Breaking down the building data into multi-class data increased the building detection precision and recall. This is useful in building detection where building instances are convoluted and are difficult for bare instance segmentation to detect all the instances.

自走式马铃薯捡拾装袋机设计与试验

针对马铃薯分段收获中,人工捡拾劳动强度大、效率低、成本高的问题,设计了一种自走式马铃薯捡拾装袋机。该自走式马铃薯捡拾装袋机能一次性完成马铃薯捡拾、薯土分离、除秧和装袋的工作。阐述了自走式马铃薯捡拾装袋机整体结构,并对捡拾装置、升运链装置、三级输送链装置、分拣台以及卸料装置等关键部件进行详细设计;运用DEM-MBD耦合的方法对马铃薯在两级输送链交接处的运动过程及受力情况进行分析;运用Box-Behnken试验方法,以漏薯率和伤薯率为评价指标,以整机前进速度、捡拾装置输送链线速度、升运链线速度、三级输送链线速度为试验因素,对该机工作参数进行四因素三水平试验,使用Design-Expert软件建立二次多项式回归模型。对回归模型进行优化后,绘制响应面,并得出该机最优工作参数。田间试验表明:当前进速度为0.70 m/s、捡拾装置输送链线速度为1.10 m/s、升运链线速度为1.20 m/s、三级输送链线速度为1.30 m/s时,漏薯率为2.82%,伤薯率为3.61%,满足马铃薯捡拾收获作业要求。

MCFNet:融合上下文信息的多尺度视网膜动静脉分类网络

针对由于血管类间具有强相似性造成的动静脉错误分类问题,提出了一种新的融合上下文信息的多尺度视网膜动静脉分类网络(multi-scale retinal artery and vein classification network,MCFNet),该网络使用多尺度特征(multi-scale feature,MSF)提取模块及高效的全局上下文信息融合(efficient global contextual information aggregation,EGCA)模块结合U型分割网络进行动静脉分类,抑制了倾向于背景的特征并增强了血管的边缘、交点和末端特征,解决了段内动静脉错误分类问题。此外,在U型网络的解码器部分加入3层深度监督,使浅层信息得到充分训练,避免梯度消失,优化训练过程。在2个公开的眼底图像数据集(DRIVE-AV,LES-AV)上,与3种现有网络进行方法对比,该模型的F1评分分别提高了2.86、1.92、0.81个百分点,灵敏度分别提高了4.27、2.43、1.21个百分点,结果表明所提出的模型能够很好地解决动静脉分类错误的问题。

融合多维特征的街景图像语义分割方法

为进一步提升深度学习语义分割方法在复杂街景图像上的分割精度,本文基于PointRend网络提出了一种融合多维特征(Multi-Dimensional Features,MDF)的街景图像语义分割网络(MDFNet)。首先,通过构建目标区域增强模块优化特征提取子网络,在深度网络的每个卷积块自适应地细化中间特征图,从而强化对复杂街景图像多维特征信息的精细提取;接着,在特征融合时引入特征金字塔网格,使用不同的卷积核处理不同尺度的街景图像,从而更加全面地获取复杂街景图像各类目标的不同分辨率特征;最后,使用双解码头对图像细节进行更细致的恢复,得到逐像素分类的结果。实验结果表明,本文网络与DeepLabV3、SegFormer等其他优秀分割网络相比,在Cityscapes复杂街景数据集上分割精度更高,平均交并比达到了80.11%,相比于其他网络提升了3.51%以上,对复杂街景图像的理解力更强。