Dictionary Learning-Based Image Reconstruction for Terahertz Computed Tomography

Terahertz computed tomography (THz CT) demonstrates its advantages in aspects of nonmetallic and nonpolar materials penetration, 3D internal structure visualization, etc. To perform satisfied reconstruction results, it is necessary to obtain complete measurements from many different views. However, this process is time-consuming and we usually obtain incomplete projections for THz CT in practice, which generates artifacts in the final reconstructed images. To address this issue, dictionary learning-based THz CT reconstruction (DLTR) model is proposed in this study. Especially, the image patches are extracted from other state-of-the-art reconstructed images to train the initial dictionary by using the K-SVD algorithm. Then, the dictionary can be adaptively updated during THz CT reconstruction. Finally, the updated dictionary is used for further updating reconstructed images. In order to verify the accuracy and quality of DLTR method, the filtered back-projection (FBP), simultaneous algebraic reconstruction technique (SART), and total variation (TV) reconstruction are chosen as comparisons. The experiment results show that the DLTR method has a good capability for noise suppression and structures preservation.

     

Image segmentation and labeling using the Polya urn model

We propose a segmentation method based on Polya's (1931) urn model for contagious phenomena. A preliminary segmentation yields the initial composition of an urn representing the pixel. The resulting urns are then subjected to a modified urn sampling scheme mimicking the development of an infection to yield a segmentation of the image into homogeneous regions. This process is implemented using contagion urn processes and generalizes Polya's scheme by allowing spatial interactions. The composition of the urns is iteratively updated by assuming a spatial Markovian relationship between neighboring pixel labels. The asymptotic behavior of this process is examined and comparisons with simulated annealing and relaxation labeling are presented. Examples of the application of this scheme to the segmentation of synthetic texture images, ultra-wideband synthetic aperture radar (UWB SAR) images and magnetic resonance images (MRI) are provided.     

多模GNSS伪距组合定位方法设计与验证

在卫星信号受干扰严重的复杂地貌环境中,由于可见卫星数目较少、卫星信号质量差,单模卫星定位导航的精度一直不尽人意,尤其是针对动态导航定位精度问题更为明显。为了提高动态导航定位精度,创新设计了一种多模GNSS的伪距组合定位算法,该算法属于一种有迭代组合定位算法,通过高度角先验定权模型确定初始迭代权矩阵,在加权最小二乘法解算过程中通过方差分量估计后验模型,不断迭代更新权矩阵,进而得到目标的精确位置,该算法需依赖初值和多次迭代,但定位精度要优于Gauss-Newton迭代算法和Helmert方差分量估计法。实验仿真结果表明,相较于Gauss-Newton迭代算法和Helmert方差分量估计法,文中设计的算法定位精度分别提高了45.1%和23%。最后结合实际空投试验分析表明,文中所述算法能够准确的解算出空投物资的落点,可以为飞行器导航系统设计人员提供参考,具有一定的理论意义和实用价值。     

Support vector machine incremental learning triggered by wrongly predicted samples

According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.     

用神经网络实现VBR视频通信量的在线预测

VBR(Varible Bit Rate)视频信号具有时变性、非线性和突发性等特点,实现该信号通信量的高精度预测难度较大.针对以上问题,本文提出了一种用于VBR视频通信量预测的自适应神经网络模型,网络训练采用离线与在线相结合的方式,同时通过删除不重要的权重,以优化网络的拓扑结构,提高网络的推广能力,降低网络在线学习的计算复杂度;对VBR视频通信量预测的模拟结果表明该模型具有高的预测精度,并能满足通信系统对预测实时性的要求.     

基于边缘特征的自动运动对象分割

为了准确分割出视频场景中的运动对象,该文提出了一种基于边缘特征的运动对象分割及跟踪算法。首先对相邻帧进行自适应变化检测,得到相邻帧二值差分图像。结合当前帧Canny算子检测的边缘图像,获得运动对象的初始边缘模板。其次对运动对象的运动分为快变和慢变两部分进行跟踪并更新运动对象的边缘模板。最后对运动对象的边缘模板进行数学形态学处理得到运动对象的外轮廓,使用梯度向量流场作为外力的改进活动轮廓算法收缩获得运动对象准确的闭合轮廓曲线。该算法对运动对象的整体运动和局部形变都有很强的鲁棒性, 能够得到运动对象准确的轮廓,并且对复杂背景有很好的适应性。     

Parameter re-estimation in semicontinuous hidden Markov modellingof speech with feedback to vector quantisation codebook

The parameters of the semicontinuous hidden Markov model (SCHMM) can be re-estimated by allowing the codebook to be updated, thus achieving an optimised codebook/model combination. With the optimised codebook, the SCHMM can offer improved recognition accuracy in comparison to both the continuous and the discrete hidden Markov model     

Identification and compensation of feedthrough in an unstable electrostatic bearing

A technique to identify feedthrough coupling in an unstable one degree-of-freedom electrostatic bearing is described. The feedthrough is caused by the simultaneous use of electrodes for differential capacitive sensing and electrostatic actuation. Cancellation of the feedthrough over a broad frequency band is necessary for the system to be practically stabilizable. A feedforward filter based on open-loop estimates of the feedthrough is adequate for designing a stabilizing controller, however, the resulting stability margins are poor because the closed-loop feedthrough differs from the open-loop estimates which determine the filter. The main contribution of the paper shows how the initial feedforward filter can be updated using feedthrough estimates obtained by testing the closed-loop system at its nominal operating point. It is demonstrated that the improved feedthrough cancellation facilitates the implementation of an updated controller which reduces sensitivity function peaking and increases the closed-loop bandwidth.     

Volumetric intraoperative brain deformation compensation: model development and phantom validation

During neurosurgery, nonrigid brain deformation may affect the reliability of tissue localization based on preoperative images. To provide accurate surgical guidance in these cases, preoperative images must be updated to reflect the intraoperative brain. This can be accomplished by warping these preoperative images using a biomechanical model. Due to the possible complexity of this deformation, intraoperative information is often required to guide the model solution. In this paper, a linear elastic model of the brain is developed to infer volumetric brain deformation associated with measured intraoperative cortical surface displacement. The developed model relies on known material properties of brain tissue, and does not require further knowledge about intraoperative conditions. To provide an initial estimation of volumetric model accuracy, as well as determine the model's sensitivity to the specified material parameters and surface displacements, a realistic brain phantom was developed. Phantom results indicate that the linear elastic model significantly reduced localization error due to brain shift, from > 16 mm to under 5 mm, on average. In addition, though in vivo quantitative validation is necessary, preliminary application of this approach to images acquired during neocortical epilepsy cases confirms the feasibility of applying the developed model to in vivo data.     

Local morphology fitting active contour for automatic vascular segmentation

In this paper, we propose an active contour model using local morphology fitting for automatic vascular segmentation on 2-D angiogram. The vessel and background are fitted to fuzzy morphology maximum and minimum opening, separately, using linear structuring element with adaptive scale and orientation. The minimization of the energy associated with the active contour model is implemented within a level set framework. As in the current local model, fitting the image to local region information makes the model robust against the inhomogeneous background. Moreover, selective local estimations for fitting that are precomputed instead of updated in each contour evolution makes the evolution of level set robust again initial location compared to the current local model. The results on synthetic image and real angiogram compared with other methods are presented. It is shown that the proposed method can achieve automatic and accurate segmentation of vascular angiogram.     

3D model retrieval using weighted bipartite graph matching

In this paper, we propose a view-based 3D model retrieval algorithm, where many-to-many matching method, weighted bipartite graph matching, is employed for comparison between two 3D models. In this work, each 3D model is represented by a set of 2D views. Representative views are first selected from the query model and the corresponding initial weights are provided. These initial weights are further updated based on the relationship among these representative views. The weighted bipartite graph is built with these selected 2D views, and the matching result is used to measure the similarity between two 3D models. Experimental results and comparison with existing methods show the effectiveness of the proposed algorithm.     

基于GEF和EMF的JavaEE快速开发工具设计

讨论了基于GEF和EMF技术的JavaEE应用快速开发工具,即jStudio。该工具是一个桌面程序,采用MDA模型驱动开发。通过可视化、图形化的编辑器建立业务模型对象,生成增、删、改、查等所有代码,并能够自动编译、自动发布和部署及同步更新数据库,能够快速高效地自动生成基于Struts＋Spring＋Hibernate框架的JavaEE应用系统和构件。实践检验表明,jStudio能极大地提高软件开发效率和生产力,加快软件价值的实现。     

一种集成电路测试流程分级动态调整方法

针对集成电路测试过程中测试时间长,影响测试效率的问题,提出了一种集成电路测试流程分级动态调整方法.通过统计样本集成电路中每种测试类型和每条测试向量的测试故障率来建立贝叶斯概率模型,根据其命中故障点的概率高低分级调整它们的加载顺序.随着测试的进行,不断收集测试数据,动态更新测试类型和测试向量的测试故障率,同步调整测试类型以及测试向量的加载顺序.实验表明,使用动态调整后的测试流程可以更早的发现故障电路,显著减少故障电路的测试时间,提高测试效率.本算法是完全基于软件的,不需要增加硬件开销,可以相容于传统的集成电路测试流程.     

Test patterns reordering method based on Gamma distribution

Aiming at the problem that the test time is too long and the test efficiency is affected, an adaptive test patterns reordering method based on Gamma distribution was proposed. And a probability model based on Gamma distribution for the probability of each test pattern hitting fault was established. During the test, the circuit to be tested was added to the sample space, and the parameters of probability model were updated dynamically, and the test patterns were reordered synchronously. The experimental results showed that the reordered test patterns have higher test quality, which can reduce the test time and test cost of the faulty circuit. The algorithm is completely software-based and does not require any additional hardware overhead and is directly compatible with traditional integrated circuit testing process.     

毫米波MIMO系统中迭代最小均方误差混合波束成形算法

混合波束成形结构能有效解决毫米波MIMO系统中射频链路受限的问题,但要设计性能较优的混合波束成形算法仍然存在困难.为了实现更高的频谱利用率,提出了一种性能较优的迭代最小均方误差(Alt-MMSE)混合波束成形算法.该算法利用数字矩阵的正交特性,首先进行初始数字矩阵设计,然后通过最小化发送信号与接收信号的均方误差不断迭代更新数字矩阵,在每一次迭代过程中,通过更新后的数字矩阵得到模拟矩阵的相位信息.仿真结果表明,与OMP混合波束成形算法和基于矩阵分解的混合处理方案相比,该算法具有更优的性能且更接近于纯数字波束成形.     

基于Grassmann流形几何深度网络的图像集识别方法

近年来,深度学习以其强大的非线性计算能力在目标检测和识别任务中取得了巨大的突破。现有的深度学习网络几乎都是以数据的欧氏结构为前提,而在计算机视觉中许多数据都具有严格的流形结构,如图像集可表示为Grassmann流形。基于数据的流形几何结构来设计深度学习网络,将微分几何理论与深度学习理论相结合,提出一种基于Grassmann流形的深度图像集识别网络。同时在模型训练过程中,使用基于矩阵链式法则的反向传播算法来更新模型,并将权值的优化过程转换为Grassmann流形上的黎曼优化问题。实验结果表明:该方法不仅在结果上识别准确率得到了提高,同时在训练和测试速度上也有一个数量级的提升。     

空间目标监视雷达探测低轨卫星效能分析

与光学观测和无线电侦测等手段相比,雷达探测具有全天时、全天候的独特优势。文中详细分析了常用的两种卫星位置计算方法,系统阐述了空间监视雷达模型的性能模型、建模思路与构建过程。以北美防空联合司令部于2008年2月14日更新的在轨运行低轨卫星的轨道参数为基础,从经度和纬度两个方面探讨了探测雷达处于不同位置时其扫描波束的仰角对探测低轨卫星数目和次数的影响,并分析了不同仰角的扫描屏对单颗卫星的探测效能。     

基于MBD三维模型的工艺制造试验

按照MBD信息化建设规划,在结构设计能够实现MBD三维模型设计的基础上,开展了基于MBD三维模型的工艺制造方法试验。以MBD三维数模设计为输入基础,结合生产制造软硬件环境现状,对三维模型的工艺制造各流程步骤进行了分析,包括三维模型的读入、工艺性分析、零件的数控编程以及三维模型零件数控加工等。初步形成了三维设计与工艺制造流程。