Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation

This study presents an image segmentation system that automatically segments and labels T1-weighted brain magnetic resonance (MR) images. The method is based on a combination of unsupervised learning algorithm of the self-organizing maps (SOM) and supervised learning vector quantization (LVQ) methods. Stationary wavelet transform (SWT) is applied to the images to obtain multiresolution information for distinguishing different tissues. Statistical information of the different tissues is extracted by applying spatial filtering to the coefficients of SWT. A multidimensional feature vector is formed by combining SWT coefficients and their statistical features. This feature vector is used as input to the SOM. SOM is used to segment images in a competitive unsupervised approach and an LVQ system is used for fine-tuning. Results are evaluated using Tanimoto similarity index and are compared with manually segmented images. Quantitative comparisons of our system with the other methods on real brain MR images using Tanimoto similarity index demonstrate that our system shows better segmentation performance for the gray matter while it gives average results for white matter.     

Learning the attachment theory with the CM-ED concept map editor

This paper presents a study carried out at the University of the Basque Country UPV/EHU with the aim of evaluating the CM-ED (concept map editor) with social education students. Concept mapping is a widely accepted technique that promotes meaningful learning. Graphically representing concepts of the learning domain and relationships between them helps students integrate new knowledge into their current cognitive structure. Due to the flexibility of computer-aided drawing graphs, several concept mapping tools have been developed and their use has been studied over the last few years. CM-ED is a multilingual and multimedia software program designed for drawing concept maps. Until recently, CM-ED had been mainly used and evaluated in computer science university degree. This paper represents a qualitative step in the evaluation of CM-ED: from technical students to students of more theoretical fields. The main characteristics of the CM-ED editor and the carried out study are presented in this paper.     

Computational intelligence tools for next generation quality of service management

In this paper we explore the interest of computational intelligence tools in the management of heterogeneous communication networks, specifically to predict congestion, failures and other anomalies in the network that may eventually lead to degradation of the quality of offered services. We show two different applications based on neural and neuro-fuzzy systems for quality of service (QoS) management in next generation networks for voice and video service over heterogeneous Internet protocol (V2oIP) services. The two examples explained in this paper attempt to predict the communication network resources for new incoming calls, and visualizing the QoS of a communication network by means of self-organizing maps.     

Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.     

基于Guided Filter的地形图中线要素提取算法

针对彩色扫描地形图中线要素与背景要素难以分离,现有线要素提取算法提取的结果中存在边界不准确的问题,提出基于Guided Filter的地形图中线要素提取算法。利用基于能量密度和Shear变换相结合的线要素提取算法对线要素进行粗提取;引入Guided Filter,以源地形图图像作为Guided Filter的引导图像,以粗提取的线要素图像作为Guided Filter的输入图像,经过滤波处理获得的线要素信息更为显著;利用OTSU算法得到最终的线要素信息。实验结果表明：相对于现有的线要素提取算法,提出的算法能够更为准确地提取出地形图中的线要素,并具有更好的噪声抑制能力。     

多服务器环境下基于扩展混沌映射的认证密钥协商协议

传统的单服务器环境下基于智能卡认证方案,单个服务器对所有的注册远程用户提供服务。如果用户想要从不同的服务器获得网络服务,则必须分别在不同的服务器注册。为解决以上问题,研究者提出了多服务器认证方案。然而,文献中的大部分方案都不能实现强安全特性。受到切比雪夫映射的半群特性和基于扩展混沌映射的密钥协商协议启发,本文提出一种多服务器环境中的认证方案。新方案不需要使用验证表并且允许用户访问不同的服务器而不需要分别注册。新方案不仅可以抵抗各类攻击,还实现了用户的强匿名性。和以前的相关协议相比,新协议具有高效性和安全性,因而适合在实际环境中应用。     

SOM神经网络改进及在遥感图像分类中的应用*

针对自组织特征神经网络自身算法的特点和缺陷,采用遗传算法对网络进行改进,形成了基于遗传算法的自组织特征神经网络,并从输入向量、竞争层神经元数量设置和初始权向量设定三方面,结合遥感图像的特性对自组织特征映射网络遥感图像分类的方法进行了改进。将该方法应用于择西安地区的ETM+卫星遥感图像进行分类试验,结果表明,基于遗传算法的自组织特征映射网络使得遥感图像的分类精度更高,且该算法实现简单,具有一定的工程应用价值。     

低空突防用数字地形数据压缩研究

地形数据是低空突防系统最主要的信息来源之一,由于机载条件对电子设备的重量和体积有很大的限制,因此地形数据必须按低空防的要求进行压缩以减小其存储空间,本文探讨并比较了采用。ＬＰＣ,ＤＣＴ和ＢＴＣ三种压缩算法运行低空防用地形数据压缩的特点,文中首先设计了一个三阶预测器对地形数据进行ＬＰＣ压缩,其次给出了ＤＣＴ编码的恢复误差的估计式,并根据该估计式按低空防要求选取量化矩阵以压缩地形数据,最后提出了一种块间自适应的ＢＴＣ算法,仿真结果表明,块间自适应ＢＴＣ算法的综合压缩的性能较合乎低空突防的要求。     

Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison

This paper compares two Self-Organizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a significant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments.