A Reduced Search Soft-Output Detection Algorithm and Its Application to Turbo-Equalization

To decrease the complexity of MAP algorithm, reduced state or reduced search techniques can be applied. In this paper we propose a reduced search soft output detection algorithm fully based on the principle of M algorithm for turbo equalization, which is a suboptimum version of the Lee algorithm. This algorithm is called soft output M algorithm (denoted as SO M algorithm), which applies the M strategy to both the forward recursion and the extended forward recursion of the Lee algorithm. Computer simulation results show that, by properly selecting and adjusting the breadth parameter and depth parameter during the iteration of turbo equalization, this algorithm can obtain good performance and complexity trade off.

BP神经网络在肝硬化治疗预测中的应用

本文利用BP神经网络建立起66例肝硬化治疗结果数据预测模型,并基于matlab得出预测结果。实验证明利用BP神经网络可有效地预测肝病治疗效果。

An improved potential field method for mobile robot navigation

In order to overcome the inherent oscillation problem of potential field methods(PFMs) for autonomous mobile robots in the presence of obstacles and in narrow passages,an enhanced potential field method that integrates Levenberg-Marquardt(L-M) algorithm and k-trajectory algorithm into the basic PFMs is proposed and simulated.At first,the mobile robot navigation function based on the basic PFMs is established by choosing Gaussian model.Then,the oscillation problem of the navigation function is investigated when a mobile robot nears obstacles and passes through a long and narrow passage,which can cause large computation cost and system instability.At last,the L-M algorithm is adopted to modify the search direction of the navigation function for alleviating the oscillation,while the k-trajectory algorithm is applied to further smooth trajectories.By a series of comparative experiments,the use of the L-M algorithm and k-trajectory algorithm can greatly improve the system performance with the advantages of reducing task completion time and achieving smooth trajectories.

A Robust Collaborative Recommendation Algorithm Based on k-distance and Tukey M-estimator

The existing collaborative recommendation algorithms have lower robustness against shilling attacks.With this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator.Firstly,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor model.The influence of attack profiles on the recommendation results is reduced through adjusting similarities among users.Then,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature matrix.Finally,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization model.Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.

Multiple phase detector of M-ary phase shift keying symbols in code division multiple access systems

A novel iterative technique, the phase descent search detection was proposed. This technique constrained the solution （PDS） algorithm, for M-ary phase shift keying （M-PSK） symbols to have a unit magnitude and it was based on coordinate descent iterations where coordinates were the unknown symbol phases. The PDS algorithm, together with a descent local search （also implemented as a version of the PDS algorithm）, was used multiple times with different initializations in a proposed multiple phase detector; the solution with the minimum cost was then chosen as the final solution. The simulation results show that for highly loaded multiuser scenarios, the proposed technique has a detection performance that is close to the single-user bound. The results also show that the multiple phase detector allows detection in highly overloaded scenarios and it exhibits near-far resistance. In particular, the detector has a performance that is significantly better, and complexity that is significantly lower, than that of the detector based on semi-definite relaxation.

A nearest neighbor search algorithm of high-dimensional data based on sequential NPsim matrix

Problems existin similarity measurement and index tree construction which affect the performance of nearest neighbor search of high-dimensional data. The equidistance problem is solved using NPsim function to calculate similarity. And a sequential NPsim matrix is built to improve indexing performance. To sum up the above innovations,a nearest neighbor search algorithm of high-dimensional data based on sequential NPsim matrix is proposed in comparison with the nearest neighbor search algorithms based on KD-tree or SR-tree on Munsell spectral data set. Experimental results show that the proposed algorithm similarity is better than that of other algorithms and searching speed is more than thousands times of others. In addition,the slow construction speed of sequential NPsim matrix can be increased by using parallel computing.

多波长BOTDR系统中布里渊散射谱的特征提取

根据布里渊散射谱的传输特性和高精度特征提取的要求,理论分析了布里渊频移的波长依赖性和多波长探测光传感时的外差检测布里渊散射谱特征,提出了利用Pseudo-Voigt基函数和莱文伯格-马夸尔特(L-M)优化算法对布里渊散射叠加谱进行特征提取。通过与洛伦兹、高斯和5次多项式曲线拟合法进行预测比较,在中心频移为11.122 903GHz的单波长和多波长传感的仿真散射谱模型中,本文所提方法的频移测量误差最小,对应的温度测量误差仅为0.047、0.000和0.112℃,且拟合度最好。在采用多模法布里-珀罗激光器的布里渊散射谱检测系统中,Pseudo-Voigt曲线拟合的综合评价指标优于其他3种拟合方法。仿真分析和实验结果表明,Pseudo-Voigt曲线拟合适用于多波长传感时布里渊散射叠加谱的特征提取,可有效地提高预测精度。

A Group Testing Problem on a n- Item Set Containing Two Defective Ones

In this paper we study a group testing model ФX, Y,, XY. In other words, we consider a n-item set containing exactly two defective ones. The purpose of this paper is to find out the two defective items with a worst-case minimum number of tests, each of which will indicates whether the subset being tested contains all good （normal） items or not and in the latter case, it is not sure that the tested subset is of one defective （bad） item or two. Based on the M-sharp algorithm obtained in the previous paper and some combinatorial skills, we derive an almost optimal algorithm for the so called n-problem.

SELECTING AN ADAPTIVE SEQUENCE FOR COMPUTING RECURSIVE M-ESTIMATORS IN MULTIVARIATE LINEAR REGRESSION MODELS

In this paper, the authors consider an adaptive recursive algorithm by selecting an adaptive sequence for computing M-estimators in multivariate linear regression models. Its asymptotic property is investigated. The recursive algorithm given by Miao and Wu （1996） is modified accordingly. Simu- lation studies of the Mgorithm is also provided. In addition, the Newton-Raphson iterative algorithm is considered for the purpose of comparison.

ANALYSIS AND COMPUTATIONAL ALGORITHM FOR QUEUES WITH STATE-DEPENDENT VACATIONS I:G/M(n)/1/K

In this paper we study a queueing system with state-dependent services and state-dependent vacations, or simply G/M（n）/1/K. Since the service rate is state-dependent, this system includes G/M/c and G/M/c/K queues with various types of station vacations as special cases. We provide a recursive algorithm using the supplementary variable technique to numerically compute the stationary queue length distribution of the system. The only input requirement is the Laplace-Stieltjes transform of the interarrival distribution as well as the state-dependent service rate and state-dependent vacation rate. In a subsequent companion paper, we study its dual system M（n）/G/1/K queue with statedependent vacations.