查询词典 algorithm

The main work and innovation1 In general , a image compression method has different effect to different image . Some are fit for image which has much high frequency . Some are fit for image which has much low frequency .This paper analysis the applicability of fractal algorithm . In this dissertation , an improved algorithum is given . The basic algorithum without eight change and the joint use of quantitative coding , The compression ratio and PSNR can be adjusted , and it can get more quickly speed of coding .2 To a group of exact fractal code , the decoding image can be any resolution .With this characteristic ,choose the proper domain block and range block,this paper get an algorithum which can get higher decoding PSNR and speed than the basic fractal algorithum.3 Give a new method to calculate the scale factor . The calculation of basic algorithum is complex and enormous . The factor of scale is an important variable of the basic algorithm. Now there are two methods to calculate the factor of scale .A new method was given to calculate the factor of scale. The calculation load and complexity are reduced by the new method. Compared with the basic algorithm , the method can reduce coding time.4 The basic fractal algorithum is based on the Collage theorem and contraction mapping theorem . A mathematic model based on basic fractal algorithum is given .

本文的主要工作及创新点主要包括以下几点：1一般情况下，一种图像压缩的方法对不同特点的图像，压缩效果会有所不同，本文利用分形压缩编码对不同特点的图像进行编码，分析了分形编码对不同图像的编码适用性，利用去掉8种变换的分形编码算法与简单的均匀量化编码方案相结合，得到了一种可以调节压缩比与解码质量的分形编码方案，该提高编码速度6倍以上。2分形编码的特点之一是分形解码的分辨率无关性，即对于一组分形编码可以将其解码到任意分辨率下，本文利用分形编码的这一特点，结合不同的量化编码方案，选择合适的定义域块与值域块及适当的编解码分辨率，得到了一种提高解码质量，编码速度提高12倍以上的加速算法。3比例因子是基本分形算法中的一个重要变量，目前有两种计算比例因子的方法，文中给出了一种新的求比例因子的方法，该方法简化了求比例因子的计算，从整体上减小了基本分形算法的计算量，简化了算法4分形压缩的数学基础是不动点定理与拼贴定理，本文根据基本的自动分形编码方法，建立数学模型对其进行推导，得到了用普通数学的极限理论解释分形编码的数学表达式，从而对由基本分形编码得到的解码图像中的像素点的值给出了确定的表达式。

The study of heterogeneous mapping algorithm based on the human eyes space-variant system. According to the "one-one mapping ring" and mapping direction we classify the mapping transformation into two sorts: forward algorithm and reverse algorithm. Based on the general rules of constructing this mapping model linear model and arctangent model are studied and verified.

本文基于人眼的空间分辨率可变视觉理念，研究了非均匀采样变换实现算法和非均匀采样变换的尺度轴转换与角度轴转换具有的"一一映射环"以及非均匀采样实现的两类转换：正向转换与逆向转换；在非均匀采样转换模型的基本构造规则基础上，讨论了区别于对数极坐标转换的线性非均匀采样模型及反正切非均匀采样模型的建立。

Genetic algorithm with matrix encoding, different mutate operator, crossover operator and selection operator based on order rank selection algorithm was applied to solve the problem. Genetic algorithm was approved to be convergence.

本文第三章提出了基于矩阵编码的遗传算法的最低费用的下一跳路由，解决了下一跳路由中不考虑网络负载和时延可能产生拥塞的问题，优化了网络的性能，使得网络中的带宽得到合理分配。

Experiment in this paper validates the convergence of the improved Mean Shift algorithm, contrast convergence rate of improved Mean Shift algorithm with convergence rate of traditional Mean Shift algorithm.

本文的实验也进一步验证了改进的Mean Shift算法的收敛性，并对比了改进前后的Mean Shift算法的收敛速度。

So the priority that combines packet delay and dynamic queue length is obtained. A packet scheduling algorithm based on this priority scheme is proposed for the input memory switch. The scheduling algorithm supports both packet delay and packet queue length. The application of this scheduling algorithm in Crossbar and Clos networks is presented.

在此优先级的基础上，提出了输入缓存交换结构中能够兼顾分组时延与分组队长的分组调度算法，将此调度算法用于单Crossbar交换单元和Clos网络，并提出了在Clos网络中按照优先级均衡负载的路由算法。

Association rules mining is a very active field in data mining and there are many algorithms today. In this field, the most classical algorithm is Apriori put forward by Agrawal et. al and it is used widely. The paper analyzes the advantages an disadvantages of Apriori, AprioriTid and AprioriHybrid algorithm in mining association rules. To solve the bottle of AprioriHybrid algorithm, a Support-Matrix method to rapidly verify the 2-frequent itemsets is put forward. To accelerate the speed of verify the k-frequent itemsets, a simple and highly efficient method of minimizing the trade database is given. All these two methods improve the efficiency of AprioriHybrid method.

关联规则挖掘是数据挖掘中一个非常活跃的领域，当前有许多种算法，最经典并被广泛使用的是Agrawal等人提出的Apriori算法及其衍生算法，本文分析了Apriori，AprioriTid和AprioriHybrid算法的优缺点，针对AprioriHybrid算法的瓶颈提出一种使用支持度矩阵对频繁2项集快速验证的方法，并给出一种简单易行，而又高效的逐步缩减交易数据库的方法，加快对候选频繁k项集的验证速度，从而提高了AprioriHybrid算法的效率。

In the algorithm, radial-basis function neural network was utilized as forward model, and genetic algorithm was used to solve the optimization problem in the inverse problem, which can avoid the local minimum solution possibly encountered in the iterative inverse algorithm based on gradient descent and consequently achieve the global optimized solution of the inverse problem.

在该算法中，径向基函数神经网络用作前向模型，遗传算法用于求解逆问题中的优化问题，其优点是能够避免基于梯度下降法的迭代逆算法中可能遇到的局部最小问题，并能得到逆问题的全局最优解。

We present a SNMP-based topology discovery algorithm using direct connection theorem and simple connection theorem for judging relationship of switches in subnets. In order to in discover the map of network without SNMP, several key problems such as subnet guessing and multi-address routers are discussed in details. And then a topology discovery algorithm based on primitive protocols is presented. We also deeply study some routing protocols such as OSPF and BGP and propose a routing-protocols-based algorithm for topology discovery.

基于判断交换机互联的直接连接定理和间接连接定理，提出了一种完整的基于SNMP的拓扑发现算法；详细讨论了子网获取、多址路由器处理等关键问题，针对不同的网络环境给出了相应的解决方案，并以此为基础提出了一种基于通用协议的拓扑发现算法；在对OSPF，BGP等网络路由协议进行分析的基础上给出了一种基于路由协议的拓扑发现算法，并设计了一个自上而下的拓扑发现框架结构。

This paper proposes a method, which use genetic algorithm and particle swarm optimization algorithm of evolutionary algorithm, to solve the problems of multiple sequence alignments.

本文提出使用遗传算法和粒子群优化算法来解决多序列比对问题。

The convergence speed of forward algorithm is as fast as ten times of backward algorithm.Additionally,the decrypted image quality and noiseproof ability for the forward algorithm are also higher than the backward algorithms obviously.

不仅如此，整个算法的收敛速度比后向算法快一个数量级，而且无论迭代结果的解密图象质量还是抑制噪声能力也都明显优于后向算法。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。