乒乓球男子团体赛选阵的策略软件

一、引言乒乓球男子团体赛不仅是双方队员比技术战术、比身体素质、比拼搏精神的竞赛,也是双方教练员的一场斗智战。众所周知,比赛规定,各队可以报五名正式队员,每次比赛双方只能各出三名运动员,按各自排定的次序对阵,比赛采用九场五胜制.因此,在双方队员交锋以前,教练员必须进行两次重要的决策:

电容式微机械超声换能器阵列的发射电路设计

针对以电容式微机械超声换能器(CMUT)为探头的超声成像系统,设计了基于FPGA的前端发射电路。由FPGA控制高压脉冲芯片产生8路激励脉冲,并通过高压模拟开关芯片分组激励16阵元换能器;设计收发隔离电路限制高压脉冲进入接收部分。搭建测试平台对电路功能进行验证,测试了每一阵元对应的激励脉冲。测试结果表明,实现了脉冲的发射及阵元选通的目的,实现线性扫查;并且收发隔离电路将高压脉冲限制在2.08 V。该电路具有控制方便,灵活性高的优点,为CMUT阵列成像应用提供硬件支持。

Design and Simulation of Routing-switching Protocol Based on Optical Switch Array

An optical routing-switching technology based on optical switch array is proposed.The characteristics of the blocking and nonblocking networks are analyzed and compared,odd-even sorting network is used to realize optical routing-switching,relative routing-switching protocol is designed.Simulation test under load shows that it can reduce a blocking effectively and enhance an efficiency of switching.Further,it can transfer the processing and switching within parallel computer from electric domain to optical domain. It can make parallel computer coordinating computing and processing at much more higher speed, storing and transmitting even more efficiently.

JOINT SPACE-FREQUENCY MULTIUSER SYMBOL DETECTOR FOR MC-CDMA SYSTEM WITH UNIFORM LINEAR ARRAY

The MultiCarrier Code Division Multiple Access (MC-CDMA) scheme is promising for relieving capacity limit problems of Direct Sequence (DS-) CDMA systems due to serious InterChip Interference (ICI) and MultiUser Interference (MUI) in high-data-rate wireless communication systems. In this paper, the Uniform Linear Array (ULA) is applied to the base station of macrocellular MC-CDMA systems in a frequency-selective fading channel environment. A joint space-frequency multiuser symbol sequence detector is developed for all active users within one macrocell without space-frequency channel estimation. Simultaneously, Directions-Of-Arrivals (DOAs) of all active users can also be estimated. By dividing the ULA into two identical overlapping subarrays, a specific auxiliary matrix is constructed, which includes both symbol sequence and DOA information of all active users. Then, based on the subspace method, performing the eigen decomposition on such auxiliary matrix, the closed-form solution of symbol sequences and DOAs for all active users can be obtained. In comparison with schemes based on channel estimation, our algorithm need not explicitly estimate the space-frequency channel for each active user,so it has lower computation complexity. Extensive computer simulations demonstrate the overall performance of this novel scheme.

Determination of Transmission Routing of OXC Nodes Based on AWG Multiplexer by Matrix Transformation

The structures of the space switching and the wavelength switching optical cross connect (OXC) nodes which are based on the arrayed waveguide grating (AWG) multiplexer are analyzed.By the matrix transformation relation between the input and output wavelengths of the AWG multiplexer, the wavelength transmission routings of the space switching and wavelength switching OXC nodes are determined.

BLIND CHANNEL ESTIMATION IN DELAY DIVERSITY FOR FREQUENCY SELECTIVE CHANNELS

Delay diversity is an effective transmit diversity technique to combat adverse effects of fading. Thus far, previous work in delay diversity assumed that perfect estimates of current channel fading conditions are available at the receiver and training symbols are required to estimate the channel from the transmitter to the receiver. However, increasing the number of the antennas increases the required training interval and reduces the available time with in whichdata may be transmitted. Learning the channel coefficients becomes increasingly difficult for the frequency selective channels. In this paper, with the subspace method and the delay character of delay diversity, a channel estimation method is proposed, which does not use training symbols. It addresses the transmit diversity for a frequency selective channel from a single carrier perspective in the form of a simple equivalent flat fading model. Monte Carlo simulations give the performance of channel estimation and the performance comparison of our channel-estimation-based detector with decision feedback equalization, which uses the perfect channel information.

Multi-Domain Collaborative Recommendation with Feature Selection

Collaborative f iltering, as one of the most popular techniques, plays an important role in recommendation systems. However,when the user-item rating matrix is sparse,its performance will be degenerate. Recently,domain-specific recommendation approaches have been developed to address this problem.The basic idea is to partition the users and items into overlapping domains, and then perform recommendation in each domain independently. Here, a domain means a group of users having similar preference to a group of products. However, these domain-specific methods consisting of two sequential steps ignore the mutual benefi t of domain segmentation and recommendation. Hence, a unified framework is presented to simultaneously realize recommendation and make use of the domain information underlying the rating matrix in this paper. Based on matrix factorization,the proposed model learns both user preferences of multiple domains and preference selection vectors to select relevant features for each group of products. Besides, local context information is utilized from the user-item rating matrix to enhance the new framework.Experimental results on two widely used datasets, e.g., Ciao and Epinions, demonstrate the effectiveness of our proposed model.

The P-Median Problem： A Tabu Search Approximation Proposal Applied to Districts

P-median is one of the most important Location-Allocation problems. This problem determines the location of facilities and assigns demand points to them. The p-median problem can be established as a discrete problem in graph terms as： Let G = （V, E） be an undirected graph where V is the set of n vertices and E is the set of edges with an associated weight that can be the distance between the vertices dij= d（vi, Vj） for every i, j =1,...,n in accordance to the determined metric, with the distances a symmetric matrix is formed, finding Vp∈ V such that ｜ Vp｜∈ = p, where p can be either variable or fixed, and the sum of the shortest distances from the vertices in {V-Vp} to their closet vertex in Vp is reduced to the minimum. Under these conditions the P-median problem is a combinatory optimization problem that belongs to the NP-hard class and the approximation methods have been of great aid in recent years because of this. In this point, we have chosen data from OR-Library [1] and we have tested three algorithms that have given good results for geographical data （Simulated Annealing, Variable Neighborhood Search, Bioinspired Variable Neighborhood Search and a Tabu Search-VNS Hybrid （TS-VNS）. However, the partitioning method PAM （Partitioning Around Medoids）, that is modeled like the P-median, attained similar results along with TS-VNS but better results than the other metaheuristics for the OR-Library instances, in a favorable computing time, however for bigger instances that represent real states in Mexico, TS-VNS has surpassed PAM in time and quality in all instances. In this work we expose the behavior of these five different algorithms for the test matrices from OR-Library and real geographical data from Mexico. Furthermore, we made an analysis with the goal of explaining the quality of the results obtained to conclude that PAM behaves with efficiency for the OR-Library instances but is overcome by the hybrid when applied to real instances. On the other hand we have tested the 2 best algorithms （PAM and TS-VNS） with geographic data geographic from Jalisco, Queretaro and Nuevo Leon. In this point, as we said before, their performance was different than the OR-Library tests. The algorithm that attains the best results is TS-VNS.

SENSOR SELECTION FOR RANDOM FIELD ESTIMATION IN WIRELESS SENSOR NETWORKS

This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal criterion is NP-complete. The authors propose an iterative algorithm to pursue a suboptimal solution. Furthermore, in order to improve the bandwidth and energy efficiency of the wireless sensor networks, the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem. In the case of unknown covariance matrix, the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator. Simulation results show the good performance of the proposed algorithms.

Selecting pinning nodes to control complex networked systems

One of the fundamental problems in pinning control of complex networks is selecting appropriate pinning nodes, such that the whole system is controlled. This is particularly useful for complex networks with huge numbers of nodes. Recent research has yielded several pinning node selection strategies, which may be efficient. However, selecting a set of pinning nodes and identifying the nodes that should be selected first remain challenging problems. In this paper, we present a network control strategy based on left Perron vector. For directed networks where nodes have the same in-and out-degrees, there has so far been no effective pinning node selection strategy, but our method can find suitable nodes. Likewise, our method also performs well for undirected networks where the nodes have the same degree. In addition, we can derive the minimum set of pinning nodes and the order in which they should be selected for given coupling strengths. Our proofs of these results depend on the properties of non-negative matrices and M-matrices. Several examples show that this strategy can effectively select appropriate pinning nodes, and that it can achieve better results for both directed and undirected networks.

Subspace choices for the Celis-Dennis-Tapia problem

Grapiglia et al.(2013) proved subspace properties for the Celis-Dennis-Tapia(CDT) problem. If a subspace with lower dimension is appropriately chosen to satisfy subspace properties, then one can solve the CDT problem in that subspace so that the computational cost can be reduced. We show how to find subspaces that satisfy subspace properties for the CDT problem, by using the eigendecomposition of the Hessian matrix of the objection function. The dimensions of the subspaces are investigated. We also apply the subspace technologies to the trust region subproblem and the quadratic optimization with two quadratic constraints.

VARIABLE SELECTION FOR RECURRENT EVENT DATA WITH INFORMATIVE CENSORING

Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.

Efficient hierarchical identity based encryption scheme in the standard model over lattices

Using lattice basis delegation in a fixed dimension, we propose an efficient lattice-based hierarchical identity based encryption（HIBE） scheme in the standard model whose public key size is only（dm^2＋ mn） log q bits and whose message-ciphertext expansion factor is only log q, where d is the maximum hierarchical depth and（n, m, q）are public parameters. In our construction, a novel public key assignment rule is used to averagely assign one random and public matrix to two identity bits, which implies that d random public matrices are enough to build the proposed HIBE scheme in the standard model, compared with the case in which 2d such public matrices are needed in the scheme proposed at Crypto 2010 whose public key size is（2dm^2＋ mn ＋ m） log q. To reduce the message-ciphertext expansion factor of the proposed scheme to log q, the encryption algorithm of this scheme is built based on Gentry＇s encryption scheme, by which m^2 bits of plaintext are encrypted into m^2 log q bits of ciphertext by a one time encryption operation. Hence, the presented scheme has some advantages with respect to not only the public key size but also the message-ciphertext expansion factor. Based on the hardness of the learning with errors problem, we demonstrate that the scheme is secure under selective identity and chosen plaintext attacks.

临沂银雀山汉墓出土《王兵》篇释文

一九七二年临沂银雀山一号汉墓出土大量竹简(概况见《临沂银雀山汉墓发掘简报》,《文物》1974年第2期),墓中还出了一些记篇题的木牍,其中一块抄列《守法》、《要言》、《库法》、《王兵》、《市法》、《守令》、《李法》、《王法》、《委法》、《田法》、《兵今》、《上篇》、《下篇》等十三个篇题。除《委法》、《上篇》、《下篇》外,其余十篇均可根据篇题含义在一号墓竹简中分别理出相应的简文。这里发表的《王兵》篇的文字错见于《管子》的《参患》、《七法》、《地图》、《兵法》诸篇。我们根据简文文义并参照上举《管子》各篇相应的文字系联为一篇。(图版肆)此篇竹简的形制及字体,与篇名见于篇题木牍的其它各篇属于同一类型,篇末又有"王兵者必三具"一段文字,可以确定其为木牍所记之《王兵》篇无疑。《王兵》篇在当时不一定被看成是《管子》书,但它与《管子》的《参患》、《七法》、《地图》等篇关系密切,对我们研究《管子》的源流和沿革提供了重要线索,同时这篇简文也是校勘《管子》相关各篇的重要资料。《王兵》篇竹简大部分残断,经过拼合后,整理出的简文占二十三根整简的地位(篇题简不计在內)。与《管子》相关各篇参互比较,并据本篇文义推勘,中间似无缺简。第一简"主所以卑尊贵贱,国所以存亡安危者,莫凿于兵……",很象是本篇的开头,其前可能亦无缺简。最后一简残断,其后是否有缺简无法确知。但从文义看,全文已近接尾,即使有缺简,也不过一两根而已。下录释文中无法辨识的字用□号表示;因竹简残断而缺去的字也用□号表示,但加〔〕号以与前一种情况相区别,缺字数系参照邻简字数推断。简文中的假借字、古字都随文用( )号注出。为了把《王兵》篇与《管子》的《参患》、《七法》、《地图》等篇的关系显示出来,释文之后另附《王兵》与《管子》相关各篇对照表。我们在对照表中把《王兵》篇分割成八段。这种划分只是为了便于和《管子》各篇相应的文字对照,不是从内容上着眼的。表中《管子》各篇只列与《王兵》篇对应的文字。不过有些片段虽为《王兵》篇所无,但能由此看出《王兵》篇与《管子》各篇的不同或能反映出《管子》各篇经过后人改动的痕迹,这些部分就不作删节,一并录入。所录《管子》原文据《四部丛刊》影宋本。

Degrees of freedom in low rank matrix estimation

The objective of this paper is to quantify the complexity of rank and nuclear norm constrained methods for low rank matrix estimation problems. Specifically, we derive analytic forms of the degrees of freedom for these types of estimators in several common settings. These results provide efficient ways of comparing different estimators and eliciting tuning parameters. Moreover, our analyses reveal new insights on the behavior of these low rank matrix estimators. These observations are of great theoretical and practical importance. In particular, they suggest that, contrary to conventional wisdom, for rank constrained estimators the total number of free parameters underestimates the degrees of freedom, whereas for nuclear norm penalization, it overestimates the degrees of freedom. In addition, when using most model selection criteria to choose the tuning parameter for nuclear norm penalization, it oftentimes suffices to entertain a finite number of candidates as opposed to a continuum of choices. Numerical examples are also presented to illustrate the practical implications of our results.