一种基于禁忌搜索的多用户检测方法

本文提出一种实现码分多址(ＣＤＭＡ)系统上多用户检测(ＭＵＤ)的禁忌搜索(ｔａｂｕｓｅａｒｃｈ)的方法。 该方法利用传统检测方法的输出作为初始解,直接应用禁忌搜索算法来解决最佳多用户检测的非线性优化组合问题。通过分析以及对同步和异步情况的仿真表明,该方法简单易于实现,具有多项式的计算复杂度,对远近问题不敏感,并且能够得到与最佳检测方法(ＯＤ)非常接近的误码率性能和抗多址干扰性能．     

禁忌搜索算法在系统可靠性分配中的应用

关于可靠性分配的研究已取得了一定的进展,但对于复杂系统的可靠性分配问题,应用传统的方法,很难准确、科学地进行可靠性分配。鉴于此情况,提出应用禁忌搜索算法对复杂系统的可靠性进行分配,以便科学、合理地分配系统可靠性。首先简要介绍了禁忌搜索算法的概念、思想及求解过程,并结合可靠性分配问题,验证了算法在可靠性分配中的应用。     

Channel Assinment Using Tabu Search in Wireless Mesh Networks

Wireless Personal Communications - Wireless networks pave a way for faster and quicker transmission. The most important terminology in mesh networking is assigning proper channel for faster...     

Routing Protocol Based on Tabu Search for Wireless Sensor Networks

This paper investigates the proposition of a new routing protocol for wireless sensor networks called TSRP (Tabu Search based Routing Protocol). In this protocol, we use the mechanism of the meta-heuristic Tabu search to route the data from the sensor (that has sense the events) to the sink. This mechanism is used to select the next sensor that will route the data based on a cost function (considering the energy and the visibility of this sensor compared to the sink). Simulation results, on a car parking application, show that TSRP prolongs the network lifetime than the existing protocols.     

求解二次分配问题的改进禁忌搜索算法

针对二次分配问题,提出了一种改进禁忌搜索算法ITS。ITS基于"集中和分散"策略,由局部搜索和精英重组两个步骤经过多次迭代完成。局部搜索采用RTS(Robust Tabu Search)。在精英重组步骤,对局部优化解中的优良个体采用MPX交叉操作,得到新的可行解。在QAPLIB典型实例上的实验结果表明,与RTS相比,改进后的禁忌搜索算法具有更优的性能。     

A Tabu Search Algorithm for Cluster Building in Wireless Sensor Networks

The main challenge in wireless sensor network deployment pertains to optimizing energy consumption when collecting data from sensor nodes. This paper proposes a new centralized clustering method for a data collection mechanism in wireless sensor networks, which is based on network energy maps and Quality-of-Service (QoS) requirements. The clustering problem is modeled as a hypergraph partitioning and its resolution is based on a tabu search heuristic. Our approach defines moves using largest size cliques in a feasibility cluster graph. Compared to other methods (CPLEX-based method, distributed method, simulated annealing-based method), the results show that our tabu search-based approach returns high-quality solutions in terms of cluster cost and execution time. As a result, this approach is suitable for handling network extensibility in a satisfactory manner.     

Cloud Dynamic Scheduling for Multimedia Data Encryption Using Tabu Search Algorithm

The cloud computing is interlinked with recent and out-dated technology. The cloud data storage industry is earning billion and millions of money through this technology. The cloud remote server storage is on-demand technology. The cloud users are expecting higher quality in minimal cost. The quality of service is playing a vital role in any latest technology. The cloud user always depends on thirty party service providers. This service provider is facing higher competition. The customer is choosing a service based on two parameters one is security and another one is cost. The reason behind this is all our personal data is stored on some third party server. The customer is expecting higher security level. The service provider is choosing many techniques for data security, best one is encryption mechanism. This encryption method is having many algorithms. Then again one problem is raised, that is which algorithm is best for encryption. The prediction of algorithm is one of major task. Each and every algorithm is having unique advantage. The algorithm performance is varying depends on file type. The proposed method of this article is to solve this encryption algorithm selection problem by using tabu search concept. The proposed method is to ensure best encryption method to reducing the average encode and decode time in multimedia data. The local search scheduling concept is to schedule the encryption algorithm and store that data in local memory table. The quality of service is improved by using proposed scheduling technique.

     

基于PRI熵的雷达信号聚类方法研究

针对脉冲分选聚类中存在的问题,引入熵的概念,定义了 PRI(脉冲重复间隔)熵,给出了雷达脉冲序列之间合并的 PRI 熵判据.并在此基础上提出了新的雷达信号聚类方法.采用先分选后合并的思路,通过计算合并前后 PRI 熵是否减小来判断是否对序列合并.通过仿真验证该方法解决增批问题的有效性.     

一种改进的FCM检索结果聚类算法研究

针对检索引擎返回的查询结果数量巨大,用户难以在较短的时间获取有用的信息,传统FCM聚类算法的距离无法完全准确描述文本间的相似程度,本文提出了一种新的文本间的距离定义,改进了模糊C均值聚类(FCM)聚类算法,利用新改进的算法对搜索引擎返回的结果进行聚类,方便了用户查询。实验证明了算法的可行性与有效性。     

Hopfield Neural Network Approach to Clustering in Mobile Radio Networks

ＨｏｐｆｉｅｌｄＮｅｕｒａｌＮｅｔｗｏｒｋＡｐｐｒｏａｃｈｔｏＣｌｕｓｔｅｒｉｎｇｉｎＭｏｂｉｌｅＲａｄｉｏＮｅｔｗｏｒｋｓＪｉａｎｇＹａｎ；ＬｉＣｈｅｎｇｓｈｕ（ＮｏｒｔｈｅｒｎＪｉａｏｔｏｎｇＵｎｉｖｅｒｓｉｔｙ，Ｂｅｉｊｉｎｇ１０００４４）Ａｂ...     

System Level Hardware/Software Partitioning Based on Simulated Annealing and Tabu Search

This paper presents two heuristics for automatic hardware/software partitioning of system level specifications. Partitioning is performed at the granularity of blocks, loops, subprograms, and processes with the objective of performance optimization with a limited hardware and software cost. We define the metric values for partitioning and develop a cost function that guides partitioning towards the desired objective. We consider minimization of communication cost and improvement of the overall parallelism as essential criteria during partitioning. Two heuristics for hardware/software partitioning, formulated as a graph partitioning problem, are presented: one based on simulated annealing and the other on tabu search. Results of extensive experiments, including real-life examples, show the clear superiority of the tabu search based algorithm.     

结群法的多目标优化

提出了结群法的多目标函数。作为例子，针对带宏单元的门阵列给出了相应的算法。理论分析和实验结果均表明，通过多目标优化，可以为评价和设计结群法提供定量依据。     

Enhancing Clustering Stability in VANET: A Spectral Clustering Based Approach

Vehicles can establish a collaborative environment cognition through sharing the original or processed sensor data from the vehicular sensors and status map. Clustering in the vehicular ad-hoc network(VANET) is crucial for enhancing the stability of the collaborative environment. In this paper, the problem for clustering is innovatively transformed into a cutting graph problem. A novel clustering algorithm based on the Spectral Clustering algorithm and the improved force-directed algorithm is designed. It takes the average lifetime of all clusters as an optimization goal so that the stability of the entire system can be enhanced. A series of close-to-practical scenarios are generated by the Simulation of Urban Mobility(SUMO). The numerical results indicate that our approach has superior performance in maintaining whole cluster stability.     

A Principal Component Clustering Approach to Object-Oriented Motion Segmentation and Estimation

This paper presents a framework for object-oriented scene segmentation in video, which uses motion as the major characteristic to distinguish different moving objects and then to segment the scene into object regions. From the feature block (FB) correspondences through at least two frames obtained via a tracking algorithm, the reference feature measurement matrix and feature displacement matrix are formed. We propose a technique for initial motion clustering of the FBs, in which the principal components (PC) of the two matrices are adopted as the motion features. The motion features have several advantages: (1) They are low-dimensional (2-dim). (2) They preserve well both the spatial closeness and the motion similarity of their corresponding FBs. (3) They tend to form distinctive clusters in the feature space, thus allowing simple clustering schemes to be applied. The Expectation-Maximization (EM) algorithm is applied for clustering the motion features. For those scenes involving mainly the camera motion, the PC-based motion features will exhibit nearly parallel lines in the feature space. This facilitates a simple and yet effective layer extraction scheme. The final motion-based segmentation involves labeling of all the blocks in the frame. The EM algorithm is again applied to minimize an energy function which takes motion consistency and neighborhood-sensitivity into account. The proposed algorithm has been applied to several test sequences and the simulation results suggest a promising potential for video applications.     

A Hybrid Clustering Approach Based Q-Leach in TDMA to Optimize QOS-Parameters

An Orthogonal frequency part multiplexing suffers from a considerable challenge due to a high peak to average power ratio (PAPR). Hence, an effective method such as partial transmits sequence (PTS) can avert this defiance by limit the design of PAPR. Therefore, an improving PAPR reduction performance via a novel approach is proposed by detaching each subblock into two parts furthermore exchanges the first sample with the final selection in each portion of the subblock to generate a new partitioning scheme. Several typical traditional segmentation schemes are used to analyze and apply the presented algorithm, such as adjacent, interleaving, and pseudo-random schemes. Besides, two scenarios are adopted based on simulation software in which the number of subcarriers is set to 128 and 256. Based on the results, a superior PAPR reduction performance is achieved based on the improved segmentation schemes regarding traditional strategies in both systems. Moreover, the enhanced adjusted PTS scheme poses a low computational complexity compared with that of the conventional schemes.

     

一种构建CORBA Trader服务集群的方法

OMG的Trader服务是分布对象的“中介”服务,在大规模分布环境下,对Trader服务的需求日益显得迫切．单一节点的Trader服务其本身会变成分布应用的性能瓶颈．本文提出了一种基于一致性Hash方法的Cache节点定位算法和Trader Cache集群框架,并对该框架的负载均衡能力和请求响应能力进行了测试。     

一种改进的基于遗传算法的聚类分析方法

C－均值聚类收敛速度快，但是它容易陷入局部最优，且对初始解很敏感。遗传算法是一种全局搜索方法，但是它收敛速度慢。为了在搜索能力和收敛速度两方面都取得较好的效果，本文提出了一种改进的基于遗传算法的聚类分析方法。实验结果表明：本文提出的算法在聚类分析中搜索到全局最优解（或近似全局最优解）的能力要优于经典遗传算法及C－均值聚类算法；且通过对变异概率的巧妙设置，提高了算法的自适应能力。     

应用说话人聚类技术改善语言辨识系统识别率

说话人差异是影响语言辨识系统性能的一个重要方面.采用说话人聚类技术对训练数据进行了预分类,以此为基础训练各种语言模型,得到聚类后的语言辨识系统.高斯混合模型、高斯混合模型-全局背景模型和遍历隐马尔可夫模型3种语言模型的实验证明,这种方法是有效的,它普遍地减小了说话人差异对语言辨识的影响,提高了语言辨识率.     

Automatic Artefact Removal from Event-related Potentials via Clustering

This paper outlines a method for automatic artefact removal from multichannel recordings of event-related potentials (ERPs). The proposed method is based on, firstly, separation of the ERP recordings into independent components using the method of temporal decorrelation source separation (TDSEP). Secondly, the novel lagged auto-mutual information clustering (LAMIC) algorithm is used to cluster the estimated components, together with ocular reference signals, into clusters corresponding to cerebral and non-cerebral activity. Thirdly, the components in the cluster which contains the ocular reference signals are discarded. The remaining components are then recombined to reconstruct the clean ERPs.