A General Low-Cost Indirect Branch Prediction Using Target Address Pointers

Nowadays energy-efficiency becomes the first design metric in chip development. To pursue higher energy efficiency, the processor architects should reduce or eliminate those unnecessary energy dissipations. Indirect-branch pre- diction has become a performance bottleneck, especially for the applications written in object-oriented languages. Previous hardware-based indirect-branch predictors are generally inefficient, for they either require significant hardware storage or predict indirect-branch targets slowly. In this paper, we propose an energy-efficient indirect-branch prediction technique called TAP （target address pointer） prediction. Its key idea includes two parts： utilizing specific hardware pointers to accelerate the indirect branch prediction flow and reusing the existing processor components to reduce additional hardware costs and power consumption. When fetching an indirect branch, TAP prediction first gets the specific pointers called target address pointers from the conditional branch predictor, and then uses such pointers to generate virtual addresses which index the indirect-branch targets. This technique spends similar time compared to the dedicated storage techniques without requiring additional large amounts of storage. Our evaluation shows that TAP prediction with some representative state-of-the-art branch predictors can improve performance significantly over the baseline processor. Compared with those hardware-based indirect-branch predictors, the TAP-Perceptron scheme achieves performance improvement equivalent to that provided by an 8 K-entry TTC predictor, and also outperforms the VPC predictor.     

Multiparty quantum key agreement with single particles

Two conditions must be satisfied in a secure quantum key agreement (QKA) protocol: (1) outside eavesdroppers cannot gain the generated key without introducing any error; (2) the generated key cannot be determined by any non-trivial subset of the participants. That is, a secure QKA protocol can not only prevent the outside attackers from stealing the key, but also resist the attack from inside participants, i.e. some dishonest participants determine the key alone by illegal means. How to resist participant attack is an aporia in the design of QKA protocols, especially the multi-party ones. In this paper we present the first secure multiparty QKA protocol against both outside and participant attacks. Further more, we have proved its security in detail.     

一种基于带宽的文件分块动态下载模型

讨论在并行下载期间服务器的输出带宽和客户机的输入带宽对并行下载效率的影响,以及一个客户机实施并行下载时能同时使用的服务器数量.提出一种文件分块动态并行下载模型,并在网格结点上进行模拟实验.实验结果表明,在对较大目标文件进行下载时,所提模型的并行下载性能优于一般的静态和动态并行下载模型.     

全相位偶对称频率采样法设计FIR滤波器

提出FIR滤波器全相位偶对称频率采样设计方法,此方法可以根据截止频率的要求设置移位因子,将两个子滤波器分别进行相反方向的移位,并构造补偿滤波器,最后将子滤波器和补偿滤波器组合而成所需的低通、高通、带通、陷波等滤波器,这种设计方法概念明确,简单易行,解决了传统FIR滤波器设计方法不能精确控制截止频率的难题.     

Geometry and Convergence Analysis of Algorithms for Registration of 3D Shapes

The computation of a rigid body transformation which optimally aligns a set of measurement points with a surface and related registration problems are studied from the viewpoint of geometry and optimization. We provide a convergence analysis for widely used registration algorithms such as ICP, using either closest points (Besl and McKay, 1992) or tangent planes at closest points (Chen and Medioni, 1991) and for a recently developed approach based on quadratic approximants of the squared distance function (Pottmann et al., 2004). ICP based on closest points exhibits local linear convergence only. Its counterpart which minimizes squared distances to the tangent planes at closest points is a Gauss–Newton iteration; it achieves local quadratic convergence for a zero residual problem and—if enhanced by regularization and step size control—comes close to quadratic convergence in many realistic scenarios. Quadratically convergent algorithms are based on the approach in (Pottmann et al., 2004). The theoretical results are supported by a number of experiments; there, we also compare the algorithms with respect to global convergence behavior, stability and running time.     

回转轴运动精度的干涉测量与误差补偿分析

采用激光干涉仪检测混联数控机床回转轴的运动精度,达到对其综合评价的目的,为机床的运动精度误差补偿做准备.对雷尼绍激光干涉仪的结构和角度测量原理进行了详细探讨,分析了干涉仪在回转轴运动精度测量中的主要影响因素和误差诱因;作出了角度测量中正弦近似误差特性曲线,并提出了该测量误差的数学模型;讨论了激光波长补偿的机理,提出了一种通用、简便易行的回转轴角位移精度检测方法.     

A hierarchical test methodology for systems on chip

We present a hierarchical test methodology for testing a SOC with heterogeneous cores, including the 1149.1-wrapped, P1500-wrapped, and BIST memory cores. We propose an 1149.1-based hierarchical test manager that also provides P1500 test control signals. This scheme includes a memory BIST interface, providing both serial and parallel access ports for BIST circuits. Our approach offers low area and pin overhead, and high flexibility     

Kernelized multi-view subspace clustering via auto-weighted graph learning

Multi-view subspace clustering has been an important and powerful tool for partitioning multi-view data, especially multi-view high-dimensional data. Despite great success, most of the existing multi-view subspace clustering methods still suffer from three limitations. First, they often recover the subspace structure in the original space, which can not guarantee the robustness when handling multi-view data with nonlinear structure. Second, these methods mostly regard subspace clustering and affinity matrix learning as two independent steps, which may not well discover the latent relationships among data samples. Third, many of them ignore the different importance of multiple views, whose performance may be badly affected by the low-quality views in multi-view data. To overcome these three limitations, this paper develops a novel subspace clustering method for multi-view data, termed Kernelized Multi-view Subspace Clustering via Auto-weighted Graph Learning (KMSC-AGL). Specifically, the proposed method implicitly maps the multi-view data from linear space into nonlinear space via kernel-induced functions, so as to exploit the nonlinear structure hidden in data. Furthermore, our method aims to enhance the clustering performance by learning a set of view-specific representations and their affinity matrix in a general framework. By integrating the view weighting strategy into this framework, our method can automatically assign the weights to different views, while learning an optimal affinity matrix that is well-adapted to the subsequent spectral clustering. Extensive experiments are conducted on a variety of multi-view data sets, which have demonstrated the superiority of the proposed method.

     

Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors

This study aimed to evaluate the value of using 3-D breast MRI morphologic features to differentiate benign and malignant breast lesions. The 3-D morphological features extracted from breast MRI were used to analyze the malignant likelihood of tumor from ninety-five solid breast masses (44 benign and 51 malignant) of 82 patients. Each mass-like lesion was examined with regards to three categories of morphologic features, including texture-based gray-level co-occurrence matrix (GLCM) feature, shape, and ellipsoid fitting features. For obtaining a robust combination of features from different categories, the biserial correlation coefficient (|r_pb|) ≧ 0.4 was used as the feature selection criterion. Receiver operating characteristic (ROC) curve was used to evaluate performance and Student's t-test to verify the classification accuracy. The combination of the selected 3-D morphological features, including conventional compactness, radius, spiculation, surface ratio, volume covering ratio, number of inside angular regions, sum of number of inside and outside angular regions, showed an accuracy of 88.42% (84/95), sensitivity of 88.24% (45/51), and specificity of 88.64% (39/44), respectively. The A_Z value was 0.8926 for these seven combined morphological features. In conclusion, 3-D MR morphological features specified by GLCM, tumor shape and ellipsoid fitting were useful for differentiating benign and malignant breast masses.     

PCLAgenda:一个基于过程的CASE环境

本文描述了ＰＣＬＡｇｅｎｄａ（ｐｒｏｃｅｓｓｃｏｎｔｒｏｌｉｎｇｌａｎｇｕａｇｅａｇｅｎｄａ）的设计与实现．ＰＣＬＡｇｅｎ－ｄａ是ＳＣＯＰ组合框架软件工具系统中提供的以过程为核心的软件工程环境，其主要功能是定义软件开发过程，指导、控制和自动化软件开发工作，支持开发人员的合作和协调．ＰＣＬＡｇｅｎｄａ的核心是基于ＳＣＯＰ软件开发方法学的、面向活动的软件过程模型.