Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

New insights on survivability in multi-domain optical networks

With the development of intelligent optical networks and the general multi-protocol label switching (GMPLS) technique, the seamless convergence between IP network and optical network is no longer be a dream but a practical reality. Similar to the Internet, current optical networks have been divided into multiple domains each of which has its own network provider and management policy. Therefore, the development of multi-domain optical networks will be the trend of new-generation intelligent optical networks, and GMPLS-based survivability for multi-domain optical networks will become a hot topic of research in the future. This paper provides a comprehensive review of the existing survivable schemes in multi-domain optical networks and analyzes the shortcomings of current research. Based on previous studies, we present possible challenges and propose new ideas to design efficient survivable schemes to guide the future work of researchers in multi-domain optical networks.     

Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

Parallel Lagrange interpolation on <Emphasis Type="Italic">k</Emphasis>-ary <Emphasis Type="Italic">n</Emphasis>-cubes with maximum channel utilization

This paper proposes an efficient parallel algorithm for computing Lagrange interpolation on k-ary n-cube networks. This is done using the fact that a k-ary n-cube can be decomposed into n link-disjoint Hamiltonian cycles. Using these n link-disjoint cycles, we interpolate Lagrange polynomial using full bandwidth of the employed network. Communication in the main phase of the algorithm is based on an all-to-all broadcast algorithm on the n link-disjoint Hamiltonian cycles exploiting all network channels, and thus, resulting in high-efficiency in using network resources. A performance evaluation of the proposed algorithm reveals an optimum speedup for a typical range of system parameters used in current state-of-the-art implementations.

一种多分类器集成的手写体汉字识别方法

多分类器集成是手写体汉字识别领域的新方向。本文提出的多分类器集成方法通过改进的欧氏距离分类器将待识别汉字分类到某个粗分结果集中,然后根据粗分结果集选择1-N(one-against-rest)的SVM分类器对待识别汉字进行细分,最后用贝叶斯集成两级分类器。实验对国标一级汉字中的1034个手写汉字进行识别,证明了方案的有效性。     

基于Gur Game的LEACH改进算法

在LEACH中引入了Gur Game算法,来实现对无线传感网络中工作节点总数的控制。在节点密度较高的网络中,当算法满足它的节点密度要求时,允许部分节点进入低能耗状态同时也减少了簇头节点个数。仿真实验证明改进算法与LEACH相比能有效延长了系统的生存时间。     

一种高效无线传感器网络认证及密钥协商方案

随着无线传感器网络在社会各个领域的渗透,其安全机制的研究一直是无线传感器网络的研究热点。在分析了无线传感器网络中认证及密钥协商方案设计时应满足条件的基础上,基于单向HASH函数和对称密码算法,设计了无线传感器网络中传感器节点间一种高效的认证及密钥协商方案。并从互认证、会话密钥、可抵御攻击等方面对该方案的安全性进行了分析,说明了该方案的安全性能较好,可有效抵御典型攻击行为;详细比较该方案和现有类似方案的运行性能,结果表明,该方案在存储容量需求、计算复杂性和通信带宽需求方面均优于现有方案。     

无线Ad hoc网络MAC层吞吐量分析

MAC层吞吐量分析是无线Ad hoc网络容量分析的基础。对CSMA协议特别是IEEE802.11DCF协议建立了一个Markov链分析模型。分析得出状态间的转移概率,通过建立状态方程得出稳态概率的线性方程。通过数值方法得出稳态解,从而得到无线Ad hoc网络MAC层吞吐量,并与公认的网络容量分析结果做了比较。通过吞吐量与节点数及分组大小的关系曲线,为网络性能的优化提供了理论基础。     

基于非下采样Contourlet和MRF的纹理图像分割

提出了一种基于非下采样Contourlet变换(NSCT)和马尔科夫随机场(MRF)相结合的纹理图像分割算法.算法包括两个步骤,首先通过NSCT实现对图像纹理特征的提取,并使用模糊C-均值完成对图像的初始分割;然后将初始分割结果用 MRF模型表示,通过贝叶斯置信传播得到图像的最终分割结果.实验结果表明,对于纹理图像,该方法在分割错误率、区域一致性以及边缘的准确性方面都比传统小波变换的方法有了明显的改善.     

数据融合技术在废弃弹药分类中的应用

为了对废弃弹药进行分类并安全处理,提出了基于数据融合技术的超声波检测弹体内部成份方法.应用超声检测仪采集大量弹体内部成份样本数据,运用数据融合技术建立神经网络融合模型,对超声波的首波、幅值、频率、增益等特征进行融和处理,从而判断弹药的种类.实验结果表明,该方法准确可靠,具有较高的推广应用价值.