一种优化GCC抽象语法树的方法

由于GCC抽象语法树包含许多有助于编译的细节信息,提出一种优化抽象语法树结构关系的方法,消除冗余结点。通过实验证明了算法的正确性和适用性。     

基于多项式基的Camellia算法S盒硬件优化

该文提出一种基于不可约多项式的Camellia算法S盒的代数表达式，并给出了该表达式8种不同的同构形式。然后，结合Camellia算法S盒的特点，基于理论证明给出一种基于多项式基的S盒优化方案，此方法省去了表达式中的部分线性操作。相对于同一种限定门的方案，在中芯国际(SMIC)130 nm工艺库中，该文方案减少了9.12%的电路面积；在SMIC 65 nm工艺库中，该文方案减少了8.31%的电路面积。最后，根据Camellia算法S盒设计中的计算冗余，给出了2类完全等价的有限域的表述形式，此等价形式将对Camellia算法S盒的优化产生积极影响。     

基于连通域标记的目标检测算法设计与实现

在图像目标识别和跟踪任务中,连通域标记算法的设计优化主要体现在执行速度、存储空间和逻辑判断次数三个方面,因此提出并实现了一种基于一次逐像素扫描连通域标记的单目标检测算法。算法结合包围盒和单目标图像检测的特点,只需单行的图像缓存空间,同时简化复杂的等价标号替换操作,目标的判断准则为目标图像连通区域面积最大化,最终以包围盒形式给出目标位置。FPGA仿真结果表明:该方法资源占用率小,检测一幅图像的总时钟周期数为M×N×4(M,N分别为图像行列数),适用于单目标图像的实时识别与跟踪。     

消除XML亚强多值依赖引起冗余的一种分解方法

数据约束是数据库规范化理论的基础,不完全信息引入XML文档后,需要重新定义数据约束.本文基于子树信息等价和子树信息相容的概念给出了XML亚强多值依赖的定义;提出了弱键路径和XML亚强多值依赖弱范式的定义,通过实例分析XML亚强多值依赖引起数据冗余的原因,提出分解不完全XML文档满足XML亚强多值依赖弱范式的定理.研究成果可较好的处理不完全XML文档中存在XML亚强多值依赖引起的数据冗余问题,避免了数据冗余所带来的操作异常.     

可靠性基础、质量管理与控制

0217142基于模拟退火算法的可靠性优化[刊]/高尚//上海航天.—2002.19(2).—21～23(L)建立了可靠性冗余优化模型,分析了各种优化方法的优缺点。采用模拟退火算法解决了此问题,通过实例给出了算法,并与启发式算法作了比较,结果表明该方法比较有效。参6     

基于粒子群优化的神经网络训练算法研究

本文提出了基于连接结构优化的粒子群优化算法(SPSO)用于神经网络训练,该算法在训练神经网络权值的同时优化其连接结构,删除冗余连接,使神经网络获得与模式分类问题匹配的信息处理能力.经SPSO训练的神经网络应用于Iris,Ionosphere以及Breast cancer模式分类问题,能够部分消除冗余分类参数及冗余连接结构对分类性能的影响.与BP算法及遗传算法比较,该算法在提高分类误差精度的同时可加快训练收敛的速度.仿真结果表明,SPSO是有效的神经网络训练算法.     

XML不完全信息树下的完全函数依赖弱保持

在DTD模型下,在XML信息树中引入了不完全信息,形成一棵不完全信息树。文中以不完全信息树为基础定义了树中节点之间的关系：节点值等价和节点值相容。并在此基础上定义了函数依赖的强保持条件、完全函数依赖弱保持。给出了给定函数依赖集判断完全函数依赖弱保持的算法及满足完全函数依赖弱保持时,当树确定化后成为完全信息树下函数依赖的可能性的证明。     

双树法—有源网络拓扑分析的一种新方法

本文提出了有源网络拓扑的一种新方法,即双树法。它采用封闭网络模型,给出了有效树和有效双树的定义及用它们的值网络行列式值的拓扑公式,并给出相关算法和算例。该方法可用于包括四种受控源在内的线性有源网络,可直接求得各种不同类型的符号网络函数且拓上同,运算过程不出现冗余项。     

动态多目标优化的进化算法及其收敛性分析

给出了动态多目标优化问题的一种新解法.首先对时间变量进行了等区间离散化,在得到的子区间(称为环境)上定义了种群的静态序值方差和静态密度方差.然后把动态多目标优化问题近似地转化成了若干个两个目标的静态优化问题.在给出的一种能自动检测环境变化的应答算子下,提出了一种动态多目标进化算法,同时证明了算法的收敛性.计算机仿真表明新算法对动态多目标优化问题是有效的.     

基于蚁群算法的区域覆盖卫星星座优化设计

首先建立了以区域覆盖性能为目标的卫星星座参数优化模型,通过在连续域定义新的蚂蚁分类与转移策略改进了蚁群算法,使算法能够兼顾多目标优化时目标不兼容的情况,给出了基于该算法实现星座参数优化的框架。优化仿真实验验证了该算法的有效性,可为星座方案决策提供有力的支持。     

A run-based two-scan labeling algorithm.

We present an efficient run-based two-scan algorithm for labeling connected components in a binary image. Unlike conventional label-equivalence-based algorithms, which resolve label equivalences between provisional labels, our algorithm resolves label equivalences between provisional label sets. At any time, all provisional labels that are assigned to a connected component are combined in a set, and the smallest label is used as the representative label. The corresponding relation of a provisional label and its representative label is recorded in a table. Whenever different connected components are found to be connected, all provisional label sets concerned with these connected components are merged together, and the smallest provisional label is taken as the representative label. When the first scan is finished, all provisional labels that were assigned to each connected component in the given image will have a unique representative label. During the second scan, we need only to replace each provisional label by its representative label. Experimental results on various types of images demonstrate that our algorithm outperforms all conventional labeling algorithms.     

Power Optimization in Fault-Tolerant Topology Control Algorithms for Wireless Multi-hop Networks

In ad hoc wireless networks, it is crucial to minimize power consumption while maintaining key network properties. This work studies power assignments of wireless devices that minimize power while maintaining k-fault tolerance. Specifically, we require all links established by this power setting be symmetric and form a k-vertex connected subgraph of the network graph. This problem is known to be NP-hard. We show current heuristic approaches can use arbitrarily more power than the optimal solution. Hence, we seek approximation algorithms for this problem. We present three approximation algorithms. The first algorithm gives an O(kalpha)-approximation where is the best approximation factor for the related problem in wired networks (the best alpha so far is O(log k)). With a more careful analysis, we show our second (slightly more complicated) algorithm is an O(k)-approximation. Our third algorithm assumes that the edge lengths of the network graph form a metric. In this case, we present simple and practical distributed algorithms for the cases of 2- and 3-connectivity with constant approximation factors. We generalize this algorithm to obtain an O(k^2c+2)-approximation for general k-connectivity (2 les c les 4 is the power attenuation exponent). Finally, we show that these approximation algorithms compare favorably with existing heuristics. We note that all algorithms presented in this paper can be used to minimize power while maintaining -edge connectivity with guaranteed approximation factors. Recently, different set of authors used the notion of k-connectivity and the results of this paper to deal with the fault-tolerance issues for static wireless network settings.     

A library of static and dynamic communication algorithms for parallel computation

Communication efficiency is one of the keys to the broad success of parallel computation, as one can see by looking at the successes of parallel computation, which are currently limited to applications that have small communication requirements, or applications that use a small number of processors. In order to use fine grain parallel computation for a broader range of applications, efficient algorithms to execute the underlying interprocessor communications have to be developed. In this paper we survey several generic static and dynamic communication problems that are important for parallel computation, and present some general methodologies for addressing these problems. Our objective is to obtain a collection of communication algorithms to execute certain prototype communication tasks that arise often in applications. These algorithms can be called as communication primitives by the programmer or the compiler of a multiprocessor computer, in the same way that subroutines implementing standard functions are called from a library of functions in a conventional computer. We discuss both algorithms to execute static (deterministic) primitive communication tasks, as well as schemes that are appropriate for dynamic (stochastic) environments. Our emphasis is on algorithms that apply to many similar problems and can be used in various network topologies. This revised version was published online in June 2006 with corrections to the Cover Date.     

Fiber Impairment Compensation Using Coherent Detection and Digital Signal Processing

Next-generation optical fiber systems will employ coherent detection to improve power and spectral efficiency, and to facilitate flexible impairment compensation using digital signal processors (DSPs). In a fully digital coherent system, the electric fields at the input and the output of the channel are available to DSPs at the transmitter and the receiver, enabling the use of arbitrary impairment precompensation and postcompensation algorithms. Linear time-invariant (LTI) impairments such as chromatic dispersion and polarization-mode dispersion can be compensated by adaptive linear equalizers. Non-LTI impairments, such as laser phase noise and Kerr nonlinearity, can be compensated by channel inversion. All existing impairment compensation techniques ultimately approximate channel inversion for a subset of the channel effects. We provide a unified multiblock nonlinear model for the joint compensation of the impairments in fiber transmission. We show that commonly used techniques for overcoming different impairments, despite their different appearance, are often based on the same principles such as feedback and feedforward control, and time-versus-frequency-domain representations. We highlight equivalences between techniques, and show that the choice of algorithm depends on making tradeoffs.      

On the application of the blocking island paradigm in all-optical networks

We investigate the problem of routing and wavelength assignment as well as the problem of the placement of wavelength converters in all-optical networks. In particular, we present a general framework, based on the blocking island (BI) paradigm, to illustrate how it can be used to solve these problems in a unified way. We first give a brief introduction about the BI graph network model, and then use this model to derive simple and general algorithms that can be used in various applications in optical networks. We discuss the implementation issues of our algorithms and present simulation results to evaluate and compare our solutions with other heuristic algorithms under both static and dynamic traffic assumptions.     

Simultaneous power supply, threshold voltage, and transistor sizeoptimization for low-power operation of CMOS circuits

This paper demonstrates a new approach for minimizing the total of the static and the dynamic power dissipation components in a complementary metal-oxide-semiconductor (CMOS) logic network required to operate at a specified clock frequency. The algorithms presented can be used to design ultralow-power CMOS logic circuits by joint optimization of supply voltage, threshold voltage and device widths. The static, dynamic and short-circuit energy components are considered and an efficient heuristic is developed that delivers over an order of magnitude savings in power over conventional optimization methods     

On turbo decoding of nonbinary codes

Symbol-by-symbol maximum a posteriori (MAP) decoding algorithms for nonbinary block and convolutional codes over an extension field GF(p ^a) are presented. Equivalent MAP decoding rules employing the dual code are given which are computationally more efficient for high-rate codes. It is shown that these algorithms meet all requirements needed for iterative decoding as the output of the decoder can be split into three independent estimates: soft channel value, a priori term and extrinsic value. The discussed algorithms are then applied to a parallel concatenated coding scheme with nonbinary component codes in conjunction with orthogonal signaling     

Rate-optimal DSP synthesis by pipeline and minimum unfolding

This paper presents a rate-optimal scheduling for real-time DSP algorithms. By using pipelining and unfolding techniques, the parallel characteristics of recursive DSP algorithms can be exploited. A novel unfolding technique is developed to unravel all concurrency in the recursive data flow graph. A perfect rate unfolded data flow graph is also introduced, which can cause a fully static rate optimal functional pipeline schedule. Experimental results have shown that the proposed method can always yield rate-optimal designs with a smaller unfolding factor compared to previous studies     

Tree‐based channel assignment schemes for multi‐channel wireless sensor networks

Many sensor node platforms used for establishing wireless sensor networks (WSNs) can support multiple radio channels for wireless communication. Therefore, rather than using a single radio channel for whole network, multiple channels can be utilized in a sensor network simultaneously to decrease overall network interference, which may help increase the aggregate network throughput and decrease packet collisions and delays. This method, however, requires appropriate schemes to be used for assigning channels to nodes for multi‐channel communication in the network. Because data generated by sensor nodes are usually delivered to the sink node using routing trees, a tree‐based channel assignment scheme is a natural approach for assigning channels in a WSN. We present two fast tree‐based channel assignment schemes (called bottom up channel assignment and neighbor count‐based channel assignment) for multi‐channel WSNs. We also propose a new interference metric that is used by our algorithms in making decisions. We validated and evaluated our proposed schemes via extensive simulation experiments. Our simulation results show that our algorithms can decrease interference in a network, thereby increasing performance, and that our algorithms are good alternatives for static channel assignment in WSNs. Copyright © 2015 John Wiley & Sons, Ltd.     

Efficiently serving dynamic data at highly accessed web sites

We present architectures and algorithms for efficiently serving dynamic data at highly accessed Web sites together with the results of an analysis motivating our design and quantifying its performance benefits. This includes algorithms for keeping cached data consistent so that dynamic pages can be cached at the Web server and dynamic content can be served at the performance level of static content. We show that our system design is able to achieve cache hit ratios close to 100% for cached data which is almost never obsolete by more than a few seconds, if at all. Our architectures and algorithms provide more than an order of magnitude improvement in performance using an order of magnitude fewer servers over that obtained under conventional methods.