Multiphoton fluorescent images with a spatially varying background signal: a ML deconvolution method

By means of multiphoton laser scanning microscopy, neuroscientists can look inside the brain deeper than has ever been possible before. Multiphoton fluorescent images, as all optical images, suffer from degradation caused by a variety of sources (e.g. light dispersion and absorption in the tissue, laser fluctuations, spurious photodetection and staining deficiency). From a modelling perspective, such degradations can be considered the sum of stochastic noise and a background signal. Among the methods proposed in the literature to perform image deconvolution in either confocal or multiphoton fluorescent microscopy, Vicidomini et al. (2009) were the first to incorporate models for noise (a Poisson process) and background signal (spatially constant) in the context of regularized inverse problems. Unfortunately, the so-called split-gradient deconvolution method (SGM) they used did not consider possible spatial variations in the background signal. In this paper, we extend the SGM by adding a maximum-likelihood estimation step for the determination of a spatially varying background signal. We demonstrate that the assumption of a constant background is not always valid in multiphoton laser microscopy and by using synthetic and actual multiphoton fluorescent images, we evaluate the face of validity of the proposed method, and compare its accuracy with the previously introduced SGM algorithm.     

A Numerical Study of Diagonally Split Runge–Kutta Methods for PDEs with Discontinuities

Diagonally split Runge–Kutta (DSRK) time discretization methods are a class of implicit time-stepping schemes which offer both high-order convergence and a form of nonlinear stability known as unconditional contractivity. This combination is not possible within the classes of Runge–Kutta or linear multistep methods and therefore appears promising for the strong stability preserving (SSP) time-stepping community which is generally concerned with computing oscillation-free numerical solutions of PDEs. Using a variety of numerical test problems, we show that although second- and third-order unconditionally contractive DSRK methods do preserve the strong stability property for all time step-sizes, they suffer from order reduction at large step-sizes. Indeed, for time-steps larger than those typically chosen for explicit methods, these DSRK methods behave like first-order implicit methods. This is unfortunate, because it is precisely to allow a large time-step that we choose to use implicit methods. These results suggest that unconditionally contractive DSRK methods are limited in usefulness as they are unable to compete with either the first-order backward Euler method for large step-sizes or with Crank–Nicolson or high-order explicit SSP Runge–Kutta methods for smaller step-sizes. We also present stage order conditions for DSRK methods and show that the observed order reduction is associated with the necessarily low stage order of the unconditionally contractive DSRK methods. The work of C.B. Macdonald was partially supported by an NSERC Canada PGS-D scholarship, a grant from NSERC Canada, and a scholarship from the Pacific Institute for the Mathematical Sciences (PIMS). The work of S. Gottlieb was supported by AFOSR grant number FA9550-06-1-0255. The work of S.J. Ruuth was partially supported by a grant from NSERC Canada.     

基于全文检索的Segmenter分词算法改进

流行的Segmenter分词算法是开放的java源代码,它是一个很好的分词工具。但是,在全文检索系统中对分词有特殊的要求,例如歧义包客、专业词典等。所以在这里将Segmenter词典和算法都进行了改选,建立了树状词典,在匹配算法中使用了歧义包容法则、分支处理、动态规划和词库预载入。以适应电子商务案例全文检索系统。     

用改进的竞争Hopfield神经网络求解多边形近似问题

多边形近似是提取曲线特征点和简化曲线描述的一种重要方法.提出一种改进的Hopfield神经网络多边形近似算法,该算法利用选择拐点策略减少了搜索空间,重新定义了神经网络的能量函数,使其更能反映优化目标;引?入合并拆分搜索策略,有效帮助神经网络脱离局部最小值.实验结果表明,提出的改进算法是有效的,比其它算法如关键点检测法、竞争Hopfield神经网络、混沌Hopfield神经网络、遗传算法等具有更优的性能.     

一种带有空槽分瓣定子结构的直驱永磁电机

随着风电市场的发展,风力发电机功率逐渐提高,电机体积越来越大,体积增大为直驱电机运输带来困难。当电机外径大于5 m时,运输成本会随着电机直径增加而迅速增加。为了解决这一问题,需要考虑将电机沿着径向分为若干个模块,运输到项目现场后再进行电机总装配。电机分瓣后同一个线圈无法同时存在于不同的两瓣电机上,采用空槽结构可以有效解决这个问题。首先提出采用空槽结构实现直驱电机的分瓣技术。其次通过对比空槽与不空槽电机的电磁性能分析空槽的可行性。最后对比不同极槽配合下空槽对电机的电磁性能影响,得出空槽方案的可行性。     

基于多尺度分割的图像识别残差网络研究

深度卷积神经网络能够解决复杂的计算机视觉问题,被广泛应用于图像识别任务中。在基于深度卷积神经网络的图像识别过程中,增加网络的深度和宽度能够产生丰富的特征信息,使用多尺度分割方法能够有效减少冗余的特征信息。然而,增加网络的深度和进行多尺度分割都会影响识别速度。如何在保证精度的同时提高识别速度,成为设计高效网络的关键问题。通过增加网络宽度的方法对ResNet残差网络进行改进,在保证精度的基础上提升识别速度。使用ResNet-D中的残差结构并减少网络长度,得到长度只有7层的残差网络,同时对HS-ResNet中的多尺度分割方法进行优化,只保留最后一次连接合并操作,得到图像识别残差网络SSRNet。在CIFAR 10和CIFAR 100数据集上的实验结果显示,SSRNet速度最高较ResNet网络提升7倍多,同时错误率最高下降8.81%,表明缩短网络长度可大幅加快图像识别速度,同时结合多尺度分割方法能够有效提升识别精度。     

短切碳纤维增强碳化硅陶瓷基复合材料的高温冲击压缩力学性能

采用先驱体浸渍裂解法(即PIP法)制备出3种不同短切碳纤维(C_(sf))体积分数的圆柱形短切碳纤维增强陶瓷基复合材料(C_(sf)/SiC复合材料)试件,通过高温加热装置和自组装功能的霍普金森压杆装置对试件进行高温和动态荷载耦合作用下的冲击压缩试验,并通过扫描电子显微镜(SEM)观察C_(sf)/SiC复合材料断口形貌,对试件的破坏形态进行分析。试验结果表明,采用PIP法制备的C_(sf)/SiC复合材料试件中C_(sf)分布均匀,在外应力作用下C_(sf)/SiC复合材料试件发生破坏,碳纤维和碳纤维束与SiC基体脱粘被不断拔出。试件的抗压强度随C_(sf)体积含量的增加而呈现先增加后减小的变化趋势,C_(sf)体积含量为21%的试件抗压强度最高,为96.55 MPa。与常温相比,在高温压缩试验中随着复合材料试件平均温度的升高,C_(sf)/SiC复合材料试件破碎后的块度越来越大,整体性越来越好,当温度达到300℃时,C_(sf)体积含量对C_(sf)/SiC复合材料试件抗压强度的影响较小。     

产品细节形态设计探究

目的探究产品细节形态设计中的设计原则和方法。方法结合现有产品的调研汇总、基本模型的建立分析以及问卷调查的结果,对影响产品外观细节形态的两个基本要素——圆角和分割线的属性对产品视觉美感的影响进行研究,并给出相关实例验证。结论分析了产品细节形态中圆角和分割线两个视觉元素对产品视觉感受的影响,提出了在产品细节形态设计中合理设计圆角和分割线的原则和方法,给出了从圆角和分割线两个方面提升整体产品视觉美感的优化方案。     

灰岩和白云岩动态压缩力学性能的SHPB实验

为了得到爆破荷载作用下岩石的破坏特性及爆破振动波的传播特性,利用RMT-150C多功能实验机和改进后的ф50mm的分离式Hopkinson压杆装置,分别研究灰岩和白云岩试件的静态力学特性和在5种不同应变率等级下的动态力学性能。实验结果表明,随着平均应变率的增加,灰岩和白云岩试件的动态抗压强度、峰值应变、吸收能、比能量吸收值以及破碎程度都明显增加,表现出显著的应变率效应,而初始弹性模量对应变率的相关性不敏感。从岩石的动态抗压强度和能量吸收两个方面,对比分析灰岩和白云岩动态力学性能的共性和差异性,更合理地解释了岩石在动态冲击荷载下的破坏本质。     

道路网络中的连续最近邻查询

为了减少连续最近邻查询中计算K个最近邻的次数和减小算法需要的存储空间,提出一种道路网络中求连续最近邻的方法。给出分点的计算方法及连续最近邻查询算法,对算法的正确性、可终止性进行证明,并分析算法复杂度。与相关算法进行实验比较,得出该算法更适合于对象频繁发生变化的实际网络。