Capacity optimization through sensing threshold adaptation for cognitive radio networks

We propose capacity optimization through sensing threshold adaptation for sensing-based cognitive radio networks. The objective function of the proposed optimization is the maximization of the capacity at the secondary user subject to transmit power and sensing threshold constraints for protecting the primary user. After proving the concavity of capacity on sensing threshold, the problem is solved using the Lagrange duality decomposition method in conjunction with a subgradient iterative algorithm. The numerical results show that the proposed optimization can lead to significant capacity maximization for the secondary user as long as this is affordable to the primary user.     

A primal-dual interior point method for optimal zero-forcing beamformer design under per-antenna power constraints

In this paper, we consider an optimal zero-forcing beamformer design problem in multi-user multiple-input multiple-output broadcast channel. The minimum user rate is maximized subject to zero-forcing constraints and power constraint on each base station antenna array element. The natural formulation leads to a nonconvex optimization problem. This problem is shown to be equivalent to a convex optimization problem with linear objective function, linear equality and inequality constraints and quadratic inequality constraints. Here, the indirect elimination method is applied to reduce the convex optimization problem into an equivalent convex optimization problem of lower dimension with only inequality constraints. The primal-dual interior point method is utilized to develop an effective algorithm (in terms of computational efficiency) via solving the modified KKT equations with Newton method. Numerical simulations are carried out. Compared to algorithms based on a trust region interior point method and sequential quadratic programming method, it is observed that the method proposed is much superior in terms of computational efficiency.     

Linear multiplicative programming

An algorithm for solving a linear multiplicative programming problem (referred to as LMP) is proposed. LMP minimizes the product of two linear functions subject to general linear constraints. The product of two linear functions is a typical non-convex function, so that it can have multiple local minima. It is shown, however, that LMP can be solved efficiently by the combination of the parametric simplex method and any standard convex minimization procedure. The computational results indicate that the amount of computation is not much different from that of solving linear programs of the same size. In addition, the method proposed for LMP can be extended to a convex multiplicative programming problem (CMP), which minimizes the product of two convex functions under convex constraints.     

Optimal infrastructure design and expansion of broadband wireless access networks

This paper investigates the important infrastructure design and expansion problem for broadband wireless access networks subject to user demand constraints and system capacity constraints. For the problem, an integer program is derived and a heuristic solution procedure is proposed based on Lagrangean relaxation. In the computational experiments, our Lagrangean relaxation based algorithm can solve this complex design and expansion problem quickly and near optimally. Based on the test results, it is suggested that the proposed algorithm may be practically used for the infrastructure design and expansion problem for broadband wireless access networks.     

High order generalized upwind schemes and numerical solution of singular perturbation problems

High even order generalizations of the traditional upwind method are introduced to solve second order ODE-BVPs without recasting the problem as a first order system. Both theoretical analysis and numerical comparison with central difference schemes of the same order show that these new methods may avoid typical oscillations and achieve high accuracy. Singular perturbation problems are taken into account to emphasize the main features of the proposed methods. AMS subject classification (2000)  65L10, 65L12, 65L50     

An optimization approach for ambulance location and the districting of the response segments on highways

In this paper we present a method to optimize the configuration and operation of emergency medical systems on highways. Different from the approaches studied in the previous papers, the present method can support two combined configuration decisions: the location of ambulance bases along the highway and the districting of the response segments. For example, this method can be used to make decisions regarding the optimal location and coverage areas of ambulances in order to minimize mean user response time or remedy an imbalance in ambulance workloads within the system. The approach is based on embedding a well-known spatially distributed queueing model (hypercube model) into a hybrid genetic algorithm to optimize the decisions involved. To illustrate the application of the proposed method, we utilize two case studies on Brazilian highways and validate the findings via a discrete event simulation model.     

Call Admission: A New Approach to Quality of Service

Quality of service guarantees are an important and much discussed aspect of ATM network design. However, there is no standard definition of quality of service. Moreover, some often-used criteria seem quite crude. We consider call admission to a bufferless ATM multiplexer with on/off sources. A new criterion for a guarantee on average cell loss is proposed. This criterion represents the quality of service from the point of view of the user, and is thus more reliable. We calculate the optimal policy that minimizes blocking subject to the guarantee, when there is only one type of user.The measure of cell-loss we propose is applicable to a wide range of models. It gives rise to a mathematical programming formulation, which we derive explicitly for our case.     

汽车后市场App评价指标体系研究

科学合理的识别汽车后市场App评价的关键指标、判明各指标之间的关系并建立评价指标体系,对提高汽车后市场App市场竞争力和改善用户体验具有重要意义.基于用户体验评价方法,通过网络和问卷调查获取数据,采用因子分析法,构建了包含基本服务达成、推广宣传、安装决策、用户体验、用户粘度、下载量等6个一级指标和相对应的22个二级指标的汽车后市场App评价指标体系.选取10款典型的汽车后市场App进行测评,研究结果表明该评价指标体系可为汽车后市场App的设计、应用、推广与评价提供有益的指导,具有较强的应用价值.     

Bifurcation problems for discrete variational inequalities

The buckling of a beam or a plate which is subject to obstacles is typical for the variational inequalities that are considered here. Birfurcation is known to occur from the first eigenvalue of the linearized problem. For a discretization the bifurcation point and the bifurcating branches may be obtained by solving a constrained optimization problem. An algorithm is proposed and its convergence is proved. The buckling of a clamped beam subject to point obstacles is considered in the continuous case and some numerical results for this problem are presented.     

基于非结构自适应网格的复合有限体积法

利用文献[1]中将Lax-Wendroff格式和Lax-Friedrichs格式整体复合作用构成二维无结构网格上的复合型有限体积法,同时利用Delaunay方法,根据流场流动特性变化的梯度值为指示器对网格进行加密和粗化,实现自适应,并将此方法应用到二维浅水波方程的求解上,进行了二维部分溃坝,倾斜水跃的数值实验.结果表明,该方法是一个计算稳定、能适应复杂的求解域、能很好地捕捉激波、且计算速度快的算法.     

Decentralized Algorithm for Centralized Variational Inequalities in Network Resource Allocation

Decentralized algorithms would be useful for making network resource allocations in large-scale and complex system networks because such networks tend to lack centralized operators and are subject to continuous infrastructure improvements. In this paper, we consider a variational inequality for network resource allocation and devise a decentralized allocation algorithm for it. The proposed algorithm enables each user in the network to decide its own optimal resource allocation in cooperation with other users without using other users’ private information such as their utility functions. Moreover, we present a convergence analysis on the algorithm and apply it to the network resource allocation problem.     

The convergence conditions of diagonalization and projection methods for fixed demand asymmetric network equilibrium problems

We have considered algorithms proposed for solving the fixed demand user optimized network equilibrium problems with asymmetric user costs. Making use of the property that the problem can be expressed in the form of a variational inequality, there are many methods which are proposed in the literature. For example, the projection methods and the diagonalization method are developed. But the convergence of these variational inequality methods often requires restrictive conditions. We have assessed the relevance of these conditions with respect to the asymmetry level of the cost function.     

Preemptive benchmarking problem: An approach for official statistics in small areas

National Statistical Agencies and Autonomous Institutions are extremely interested in using information from those areas that are actually smaller than the actual areas for which a survey is initially designed. As such, small area estimation and its application are valuable when conducting research on Official Statistics. A wide range of different methods are available which provide estimations to small area levels, being reasonable to guarantee that they add up to the published design-based estimations in a large area that includes these small areas. This requirement is known as benchmarking. Different algorithms, all based on distances between original data and modified data, are introduced in this paper, with the intention of satisfying the benchmarking property. We provide rules to apply these proposed calibrated methods according to user criteria. Goal programming with priorities methodology is used to represent user preferences. The result is a collection of different interdependent network flow problems. Some of these problems require the development of ad hoc methods. The introduced methods are assessed by a Monte Carlo simulation study using the Spanish Labour Force Survey in the Canary Islands. The results also show that the consistency of the estimator is independent of the used calibrated methods, but it does depend on the benchmarking weights.     

Hypothesis Tests of Convergence in Markov Chain Monte Carlo

Abstract

Deciding when a Markov chain has reached its stationary distribution is a major problem in applications of Markov Chain Monte Carlo methods. Many methods have been proposed ranging from simple graphical methods to complicated numerical methods. Most such methods require a lot of user interaction with the chain which can be very tedious and time-consuming for a slowly mixing chain. This article describes a system to reduce the burden on the user in assessing convergence. The method uses simple nonparametric hypothesis testing techniques to examine the output of several independent chains and so determines whether there is any evidence against the hypothesis of convergence. We illustrate the proposed method on some examples from the literature.     

融入用户社交关系与信任关系的应用推荐方法研究

随着近年来互联网技术的快速发展,应用获取平台都面临着信息过载的问题.面对大量应用,解决用户不能快速准确地找到满足其偏好的应用的问题迫在眉睫.已有的如Cosine、Pearson等协同过滤方法普遍存在稀疏性、冷启动和可扩展性等问题,从而对推荐结果产生影响.文章在考虑用户社交关系、偏好及信任关系的基础上,提出了融合用户社交...     

Criterion function for accessibility,uncertainty problems in regional land-use planning

One objective in regional planning is the creation of communities with great accessibility. Thus we should plan the locations of inhabitants and the activities of the region so that the total accessibility will be maximized subject to some restrictions. This is a quadratic programming problem, which can be solved by quadratic programming techniques, but we cannot then take into account the uncertainties of the problem.In this paper a new criterion function is proposed for accessibility, uncertainty problems in regional land-use planning. It is derived from Hurwicz's generalized maximin principle. Many advantages are gained, for the planning problem is separated into linear programming problems, the uncertainties are taken into consideration as in game theory and the methods of parametric programming are available.A simplified problem of the populations of three town areas is studied and the method is generalized for problems of many activities and areas.     

An Ergodic Algorithm for the Power-Control Games for CDMA Data Networks

In this paper, we consider power control for the uplink of a direct-sequence code-division multiple-access data network. In the uplink, the purpose of power control is for each user to transmit enough power so that it can achieve the required quality of service without causing unnecessary interference to other users in the system. One method that has been very successful in solving this purpose for power control is the game-theoretic approach. The problem for power control is modified as a Nash equilibrium problem in which each user can choose its transmit power in order to maximize its own utility, and a Nash equilibrium is an ideal solution of the power-control game. We present a noncooperative power-control game in which each user can choose the transmit power in a way that it gets the sufficient signal-to-interference-plus-noise ratio and maximizes its own utility. To ensure the existence of a solution, we also propose the variational inequality problem which is connected with the proposed game. On a linear receiver, we deal with the matched filter receiver. Next we present a new ergodic algorithm for the proposed power control because the existing iterative algorithms can not be applied effectively to the proposed power control. We also present convergence analysis for the proposed algorithm. In addition, applying the proposed algorithm to the proposed power control, we provide numerical examples for the transmit power, the signal-to-interference-plus-noise ratio and so on. Numerical results for the proposed algorithm shall show that as compared with the existing power-control game and its method, all users in the network can enjoy the sufficient signal-to-interference-plus-noise ratio and achieve the required quality of service.      

Process-oriented knowledge management: a case study

Most knowledge repositories store documents organized by subject areas. In process-oriented knowledge management, each knowledge management project is developed around an organizational process and the mission, rationale and objectives of the process define the scope of the project. At the heart of the process-oriented knowledge management strategy is a knowledge management system called KMS. KMS has a workflow management subsystem that enables it to capture knowledge in context as it is created and present knowledge to the user at the right step of the process. Its repository contains not only knowledge created and manipulated in an organizational process, but also the knowledge of the process itself in the form of process designs, case histories and lessons learned from past experiences. This paper describes the process-oriented knowledge management strategy and its implementation in the real estate organization of a real-life global company.     

The Downs-Thomson Paradox: Existence, Uniqueness and Stability of User Equilibria

Consider a network where two routes are available for users wishing to travel from a source to a destination. On one route (which could be viewed as private transport) service slows as traffic increases. On the other (which could be viewed as public transport) the service frequency increases with demand. The Downs-Thomson paradox occurs when improvements in service produce an overall decline in performance as user equilibria adjust. Using the model proposed by Calvert [10], with a ⋅|M|1 queue corresponding to the private transport route, and a bulk-service infinite server queue modelling the public transport route, we give a complete analysis of this system in the setting of probabilistic routing. We obtain the user equilibria (which are not always unique), and determine their stability.AMS subject classification: 60K30, 90B15, 90B20, 91A10, 91A13This revised version was published online in June 2005 with corrected coverdate     

A key-lock-pair oriented access control scheme for the growth of users and files

A new access control scheme for the growth of users and files in file protection systems is proposed. Our scheme associates each user with a user key and each file with a file key. For each key, there are some corresponding locks, that can be extracted from a nonsingular matrix. Through simple operations on keys and locks, privacy decisions of the protection system can easily be revealed. Furthermore, by employing our method, whenever a new user or file is joined, the corresponding key values and lock values will be determined immediately without changing any previously defined keys and locks.