Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.     

明代盔甲种类及制作工艺研究

本文通过盔甲的制作及材料,区分了非金属材料的皮甲、瓠子作甲、藤甲、绵甲、纸甲以及金属材料组成的齐腰甲、齐腰明甲、缉甲、布甲、铁甲、钢甲、铜甲、锡甲等类型,并对其制作方式等进行分析。结合不同材质盔甲的使用地区、战争方式进行研究,以对明代盔甲有一个整体性的认知。     

Development of low-loss lead-germanate glass for mid-infrared fiber optics: II. preform extrusion and fiber fabrication

For lead-germanate glass fibers, reducing the content of hydroxyl (OH) groups and the formation of metallic Pb species is essential to pave the way for their applications as low-loss mid-IR fiber optics since OH and metallic Pb species cause intense absorption and scattering loss, respectively, in the mid-IR spectral range. The first part of this study reported the optimization of the glass melting procedure to obtain low amount of OH while preventing formation of metallic Pb species in lead-germanate glass. Here, the second part of this study reports the investigation of the process conditions to fabricate low-loss lead-germanate glass fiber through further understanding of the co-effects of glass melting and heat treatment atmospheres on the formation of nano- and micron-scale metallic Pb species in both the as-produced and heat treated lead-germanate glasses. Finally, using this advance in knowledge, we successfully fabricated low-loss lead-germanate glass fibers with no presence of reduced metallic Pb particles by optimizing dehydration agent, glass melting, preform extrusion and fiber drawing conditions. The optimized fabrication conditions reduced the unstructured fiber loss by almost one order to <0.3 dB/m at 1.55 μm.     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

硼中子俘获治疗加速器ECR质子源电磁场仿真设计

电子回旋共振(ECR)质子源具有可给流强高、亮度高、可靠性高、使用频率高、易维护、小型化等优点，因而被硼中子俘获治疗(BNCT)装置的直线加速器所采用。本文利用CST软件对2.45 GHz ECR质子源进行优化设计。优化后ECR质子源的等离子体发生器腔体的尺寸为101.12 mm×45.00 mm，给出了脊波导耦合器的最优尺寸参数，使等离子体发生器腔体内电场强度提高为普通波导的4.5倍。通过Opera-3D对ECR质子源的引出电极结构进行了仿真计算，并给出了优化参数。另外，初步设计了质子源的线圈磁铁系统，优化了磁场分布。本文结果为质子源的研制提供了数据。     

On the application of remote sensing towards the estimation of cultivated land lost to urbanization

In this research work, a 40-km² SPOT-5 High-Resolution Imagery (HRI) of the Warsak locality in district Peshawar, Pakistan, was utilized to approximate the quantity of cultivated land lost to urbanization, due to the construction of new homes and buildings. The imagery from a period of 2005 to 2015 for wheat crop was taken, specifically during the months of March and June when the crop is rich green and golden ripe respectively. eCognition ® program’s Object-Oriented Classification Method (OOCM) was employed for recognition of land versus buildings. Nearest Neighbour (NN), Support Vector Machine (SVM), Decision Trees (DT) and Random Forests (RF) were utilized for the classification process. The results demonstrated that the urbanized area had increased by approximately 28 per cent in the area considered. Moreover, the efficacy of the proposed method is depicted by an accuracy of 97.9 per cent and a Kappa Statistics of 0.975 for the SVM classifier.     

DCNN深度特征与交替方向乘子的相关滤波跟踪

针对采用相关滤波的判别式目标跟踪遇到的瓶颈问题：由于目标快速移动引起边界效应，使得相关滤波器在学习与更新过程中可能会引入错误，最终错误的累积将导致跟踪失败。在采集深度学习特征与样本相似性度量的基础上，提出一种引入交替方向乘子方法的改进相关滤波目标跟踪算法，选择DCNN深度特征有效地表征待跟踪目标的初始状态，通过在线分类过程中样本相似性比对与半监督学习，辅助解决相关滤波器在学习过程中存在的自学习问题。所提目标跟踪算法特别适合训练样本为持续获得的、同时存储空间较小的机器学习过程，提高目标在快速运动与部分遮挡等复杂情况下的跟踪成功率，针对VOT2016标准测试视频的实验表明：当目标面临快速运动时，对比CN、SAMF、STC算法，所提DA-CFT跟踪算法将跟踪成功率分别由60.4%～73.4%、67.2%～82.9%、80.9%～88.1%提升至85.6%～91.0%。     

可控中子及X射线源测井技术发展现状及趋势

随着核仪器技术的进步以及安全健康的工业发展需求，具有人工可控性的中子源及X射线源逐步在测井领域得到推广应用，为油气等矿产资源的勘探开发提供了关键技术手段。可控中子及X射线源测井技术是以中子发生器或X射线管产生的中子或X射线与地层物质作用，通过探测中子、伽马或X射线从而进行地层孔隙度、密度、油气饱和度和元素含量的测井技术。本文概述了可控中子及X射线源测井技术，回顾了其发展历程；介绍了可控中子及X射线测井技术在数值模拟、仪器研制及数据处理方法方面的研究现状及应用，并展望了可控中子及X射线源测井技术的发展前景，认为未来可控中子及X射线源测井技术可从以下三个方面开展研究：分析不同射线在能量、时间及空间的分布规律，开展探测理论基础研究；联合不同学科优势，开展多类型多模式的新型仪器研制；增强谱数据校正及解析方法研究，开展谱信息综合分析及应用。     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.