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Description: 一款国产的8051模拟器（全部源代码）
本软件是一款8051模拟器，他的特点是可以模拟多处理器平台，在简单的多工作区间的界面上可以很方便的模拟8051处理器， 最重要的是这个单片机系统可以完全由你自己订制，你甚至可以随意创建自己的多单片机系统，什么SMP 基于串行总线的分布式系统，集群式系统，只要你能想到的，都可以办到，充分发挥你的想象力。现在1.0版本已经基本体现出了这种思想，在以后的版本中将使这一特性变的 更加强大!
http://vchelp.net/copathway/project_view.asp?prj_id=1343-Simulator 8051 (all source code) of the 8051 is a software simulator, he can simulate the characteristics of a multi-processor platform, the more simple interval work on the interface can be easily simulated 8051 processor, the most important is that the SCM system can be entirely your own custom you can even create your own free multi processor systems, based on what SMP Serial Bus distributed systems, cluster systems, as long as you can think of, it can be done, give full play to your imagination. Version 1.0 now has basically reflected this thinking, in the future version of this feature will become even more powerful! Http://vchelp.net/copathway/project_view.asp prj_id = 1343
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Size: 207583 |
Author: 叶树深 |
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Description: 一个不错的cache，不但能够实现map的所有操作，而却具有内嵌的集群支持能力-a good cache, not only can be achieved in all operations map, but it is embedded cluster support capacity
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Size: 3308173 |
Author: 罗绳礼 |
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Description: 在Delphi中用ComBOX控件显示局域网中所有组中计算机的计算机名，在Delphi6.0下调试通过。-used in Delphi ComBOX LAN Control showed all of the computer cluster computer, and under the Delphi6.0 through debugging.
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Size: 392663 |
Author: 陈 |
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Description: 图像集群（Image Clustering）
(1)图像读入，显示图像所在路径；
(2)采用imgcluster函数进行图像集群，选择集群个数后进行图像集群；
(3)运行后，在原图像上显示集群灰度图；
(4)若要显示各个集群情况，可打开【Show Clustering Image】新窗体，显示各集群类的基于原图的彩绘区域。其中非当前集群范围，则显示灰度为255的黑色。用户可点击按纽上下查看所有集群图。-image cluster (Image Clustering) (1) read into the images, Images show host path; (2) use of imgcluster function for image clusters, After the number of clusters chosen for image clusters; (3) After the operation, in the original image displayed on the gray level clusters; (4) To show that the various clusters, [Show Open Clustering Image -- new windows, showed that the cluster type based on the maximum of regional painting. Clusters of non-current range, it shows that the intensity of 255 black. Users can click on View All button next cluster map.
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Size: 114137 |
Author: mecal |
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Description: The last step in training phase is refinement of the clusters
found above. Although DynamicClustering counters all the
basic k-means disadvantages, setting the intra-cluster similarity
r may require experimentation. Also, a cluster may
have a lot in common with another, i.e., sequences assigned
to it are as close to it as they are to another cluster. There
may also be denser sub-clusters within the larger ones.
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Size: 40101 |
Author: yznushuangyu |
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Description: Recent advances in experimental methods have resulted in the generation
of enormous volumes of data across the life sciences. Hence clustering and
classification techniques that were once predominantly the domain of ecologists
are now being used more widely. This book provides an overview of these
important data analysis methods, from long-established statistical methods
to more recent machine learning techniques. It aims to provide a framework
that will enable the reader to recognise the assumptions and constraints that
are implicit in all such techniques. Important generic issues are discussed first
and then the major families of algorithms are described. Throughout the focus
is on explanation and understanding and readers are directed to other resources
that provide additional mathematical rigour when it is required. Examples
taken from across the whole of biology, including bioinformatics, are provided
throughout the book to illustrate the key concepts and each technique’s
potential.
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Size: 3196888 |
Author: fortunesr |
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Description: 图像集群（Image Clustering）
(1)图像读入，显示图像所在路径；
(2)采用imgcluster函数进行图像集群，选择集群个数后进行图像集群；
(3)运行后，在原图像上显示集群灰度图；
(4)若要显示各个集群情况，可打开【Show Clustering Image】新窗体，显示各集群类的基于原图的彩绘区域。其中非当前集群范围，则显示灰度为255的黑色。用户可点击按纽上下查看所有集群图。-image cluster (Image Clustering) (1) read into the images, Images show host path; (2) use of imgcluster function for image clusters, After the number of clusters chosen for image clusters; (3) After the operation, in the original image displayed on the gray level clusters; (4) To show that the various clusters, [Show Open Clustering Image-- new windows, showed that the cluster type based on the maximum of regional painting. Clusters of non-current range, it shows that the intensity of 255 black. Users can click on View All button next cluster map.
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Size: 113664 |
Author: mecal |
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Description: The last step in training phase is refinement of the clusters
found above. Although DynamicClustering counters all the
basic k-means disadvantages, setting the intra-cluster similarity
r may require experimentation. Also, a cluster may
have a lot in common with another, i.e., sequences assigned
to it are as close to it as they are to another cluster. There
may also be denser sub-clusters within the larger ones. -The last step in training phase is refinement of the clustersfound above. Although DynamicClustering counters all thebasic k-means disadvantages, setting the intra-cluster similarityr may require experimentation. Also, a cluster mayhave a lot in common with another, ie, sequences assignedto it are as close to it as they are to another cluster. Theremay also be denser sub-clusters within the larger ones.
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Size: 39936 |
Author: yznushuangyu |
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Description: 彩色图像分割算法，
首先rgb到hsv，然后量化，分割成许多个小区域，然后用区域合并算法进行合并，一直预先设定的区域数目或者达到满足的聚类条件为止停止合并-Color Image Segmentation Algorithm, first of all, rgb to hsv, and then quantified, separated into many small regions, and then use the region merging algorithm to merge, has been pre-set number of regional or cluster to meet the conditions to reach so far to stop the merger
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Size: 205824 |
Author: gajs |
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Description: Recent advances in experimental methods have resulted in the generation
of enormous volumes of data across the life sciences. Hence clustering and
classification techniques that were once predominantly the domain of ecologists
are now being used more widely. This book provides an overview of these
important data analysis methods, from long-established statistical methods
to more recent machine learning techniques. It aims to provide a framework
that will enable the reader to recognise the assumptions and constraints that
are implicit in all such techniques. Important generic issues are discussed first
and then the major families of algorithms are described. Throughout the focus
is on explanation and understanding and readers are directed to other resources
that provide additional mathematical rigour when it is required. Examples
taken from across the whole of biology, including bioinformatics, are provided
throughout the book to illustrate the key concepts and each technique’s
potential.-Recent advances in experimental methods have resulted in the generationof enormous volumes of data across the life sciences. Hence clustering andclassification techniques that were once predominantly the domain of ecologistsare now being used more widely. This book provides an overview of theseimportant data analysis methods, from long-established statistical methodsto more recent machine learning techniques. It aims to provide a frameworkthat will enable the reader to recognise the assumptions and constraints thatare implicit in all such techniques. Important generic issues are discussed firstand then the major families of algorithms are described. Throughout the focusis on explanation and understanding and readers are directed to other resourcesthat provide additional mathematical rigour when it is required. Examplestaken from across the whole of biology, including bioinformatics, are providedthroughout the book to illustrate the key concepts and each technique spotential.
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Size: 3196928 |
Author: fortunesr |
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Description: 一个多集群的监控系统，可根据文件中定义的入口IP地址获取其机器上的GANGLIA信息，监控其所在网格内所有集群信息和节点机的CPU、内存、硬盘、负载等信息。-More than one cluster of monitoring systems, can be defined in accordance with the entrance of the document to obtain its IP address Ganglia machine information, monitor their grid information and all cluster node machine CPU, memory, hard drive, load and other information.
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Size: 6476800 |
Author: 李毅 |
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Description: Performs hierarchical clustering of data using specified method and
seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al - Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Database (2001)".
Namely, it searches cutoff where groups are independent. The techinque uses an econometric approach of verifying that variables in
multiple regression are linearly independent: if all the diagonal
elements of inverse correlation matrix of data are less than VIF-Performs hierarchical clustering of data using specified method and
seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al- Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Database (2001)".
Namely, it searches cutoff where groups are independent. The techinque uses an econometric approach of verifying that variables in
multiple regression are linearly independent: if all the diagonal
elements of inverse correlation matrix of data are less than VIF
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Size: 2048 |
Author: tra ba huy |
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Description: K means clustering of data implemented for all kinds of data-K means clustering of data implemented for all kinds of data...
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Size: 13312 |
Author: kailash |
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Description: 本算法在vc++6.0中进行实验。分别就分解聚类和C-均值聚类两种方法在IRIS数据集上进行操作。分类前先将数据集中的样本顺序打乱形成混合数据。分解聚类中，采用前100个样本用对分法编制程序将数据分为两类。C-均值聚类采用全部的150个样本，将类别参数K设为3，将数据分为三类。-The algorithm in vc++6.0 in the experiment. Separate cluster and decomposition of two C-means clustering methods operate on the IRIS data set. Classification of samples before the first data set in order disrupt the formation of mixed data. Decomposition clustering using the top 100 on the sub-samples prepared with the program data into two categories. C-means clustering using all 150 samples, the type of parameter K set to 3, the data into three categories.
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Size: 2048 |
Author: 万鹏程 |
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Description: 提出两种方法来实现集群动态多代理系统的同步。在广泛研究了同步行为相比,所有的耦合代理asymptot -收敛到相同的值集群中的同步问题研究
纸,我们要求所有的发展相互联系的代理
成几个集群和每个代理只内同步
它的集群。第一种方法是添加一个常数强迫
每个代理的动力是由积极的
扩散耦合 另一种是正面介绍和消极的代理之间的耦合。一些足够和构造或必要条件保证n-cluster同步行为。给出了仿真结果说明了理论分析的有效性。-This paper presents two approaches to achieving
cluster synchronization in dynamical multi-agent systems. In
contrast to the widely studied synchronization behavior, where
all the coupled agents converge to the same value asymptot-
ically, in the cluster synchronization problem studied in this
paper, we require that all the interconnected agents to evolve
into several clusters and each agent only to synchronize within
its cluster. The fi rst approach is to add a constant forcing to
the dynamics of each agent that are determined by positive
diffusive couplings and the other is to introduce both positive
and negative couplings between the agents. Some suffi cient and/
or necessary conditions are constructed to guarantee n-cluster
synchronization behavior. Simulation results are presented to
illustrate the effectiveness of the theoretical analysis.
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Size: 467968 |
Author: 小松 |
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Description: In this paper, we propose a learning automatabased
weighted cluster formation algorithm calledMCFA in
which the mobility parameters of the hosts are assumed to
be random variables with unknown distributions. In the proposed
clustering algorithm, the expected relative mobility of
each host with respect to all its neighbors is estimated by
sampling its mobility parameters in various epochs. MCFA
is a fully distributed algorithm in which each mobile independently
chooses the neighboring host with the minimum
expected relative mobility as its cluster-head.
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Size: 522240 |
Author: ShAzZ |
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Description: 建立数学模型模拟动物的集群运动。
2. 建立数学模型刻画鱼群躲避黑鳍礁鲨鱼的运动行为。
3. 假定动物群中有一部分个体是信息丰富者(如掌握食物源位置信息，掌握迁徙路线信息)，建模分析它们对于群运动行为的影响，解释群运动方向决策如何达成。-The cluster motion mathematical model is established to simulate the animal.
The motion behavior of 2 established the mathematical model depicting the fish to avoid blacktip reef shark.
3 assumes that the animal group in a part of the individual information is abundant (such as master food source location information, grasp the migration route information), modeling and analysis of their effect on the group of exercise behavior, explaining how to reach the direction of movement of group decision.
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Size: 5120 |
Author: 韩志坚 |
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Description: k-means算法是一种动态聚类算法，基本原理如下[24]：首先预先定义分类数k，并随机或按一定的原则选取k个样品作为初始聚类中心；然后按照就近的原则将其余的样品进行归类，得出一个初始的分类方案，并计算各类别的均值来更新聚类中心；再根据新的聚类中心对样品进行重新分类，反复循环此过程，直到聚类中心收敛为止。-K- means algorithm is a dynamic clustering algorithm, the basic principle of [24] as follows: first of all the predefined class number k, and random or according to certain principle to choose k as a sample as the initial cluster centers Then according to the principle of the nearest to categorize the rest of the samples, it is concluded that an initial classification scheme, and various other calculated mean to update the cluster centers The samples again according to the new clustering center to reclassify, recycling the process, until the clustering center of convergence.
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Size: 1024 |
Author: ouyang |
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Description: In wireless sensor networks (WSNs), the bene ts of exploiting the sink mobility to prolong network lifetime have been well recognized. In physical environments, all kinds of obstacles could exit in the sensing field. Therefore, a research challenge is how to efficiently dispatch the mobile sink to and an obstacle-avoiding shortest route.
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Size: 4288512 |
Author: wadgiad |
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Description: 1) 使用凝聚型层次聚类算法（即最小生成树算法）对所有数据点进行聚类，最后聚成3类。相异度定义方法可选择single linkage、complete linkage、average linkage或者average group linkage中任意一种。
2) 使用C-Means算法对所有数据点进行聚类。C=3。
任务2（必做）：
使用高斯混合模型（GMM）聚类算法对所有数据点进行聚类。C=3。并请给出得到的混合模型参数（包括比例??、均值??和协方差Σ）。
任务3（全做）：
1) 参考数据文件第三列的类标签，使用聚类有效性评价的外部方法Normalized Mutual Information指标，分别计算任务1和任务2聚类结果的有效性。
2) 使用聚类有效性评价的内部方法Xie-Beni指标，分别计算任务1和任务2聚类结果的有效性。(The main results are as follows: 1) the condensed hierarchical clustering algorithm (that is, the minimum spanning tree algorithm) is used to cluster all the data points, and finally it is grouped into three categories. Any of the single linkage,complete linkage,average linkage or average group linkage methods can be selected for the definition of dissimilarity. 2) using C-Means algorithm to cluster all data points. C = 3.)
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Size: 26624 |
Author: 小鹏鹏123 |
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