One-way hash function construction based on 2D coupled map lattices

An algorithm for constructing one-way hash function based on spatiotemporal chaos is proposed. A two-dimensional coupled map lattices (2D CML) with parameters leading to the largest Lyapunov exponent is employed. The state of the 2D CML is dynamically determined by its previous state and the message bit at the corresponding positions. The hash value is obtained by a linear transform on the final state of the 2D CML. Theoretical analysis and computer simulation indicate that our algorithm has good statistical properties, strong collision resistance and high flexibility. It is practical and reliable, with high potential to be adopted as a strong hash function for providing data integrity.     

A hardware design of a massive-parallel, modular NN-based vector quantizer for real-time video coding

This report describes the design of a modular, massive-parallel, neural-network (NN)-based vector quantizer for real-time video coding. The NN is a self-organizing map (SOM) that works only in the training phase for codebook generation, only at the recall phase for real-time image coding, or in both phases for adaptive applications. The neural net can be learned using batch or adaptive training and is controlled by an inside circuit, finite-state machine-based hard controller. The SOM is described in VHDL and implemented on electrically (FPGA) and mask (standard-cell) programmable devices.     

Standardising vulnerability categories

Each vulnerability scanner (VS) represents, identifies and classifies vulnerabilities in its own way, thus making the different scanners difficult to study and compare. Despite numerous efforts by researchers and organisations to solve the disparity in vulnerability names used in the different VSs, vulnerability categories have still not been standardised. This paper highlights the importance of having a standard vulnerability category set. It also outlines an approach towards achieving this goal by generating a standard set of vulnerability categories. A data-clustering algorithm that employs artificial intelligence is used for this purpose. The significance of this research results from having an intelligent technique that aids in the generation of standardised vulnerability categories in a relatively fast way. In addition, the technique is generic in the sense that it allows one to accommodate any VS currently known on the market to create such vulnerability categories. Another benefit is that the approach followed in this paper allows one to also compare various VSs currently available on the market. A prototype is presented to verify the concept.     

Mining e-Learning domain concept map from academic articles

Recent researches have demonstrated the importance of concept map and its versatile applications especially in e-Learning. For example, while designing adaptive learning materials, designers need to refer to the concept map of a subject domain. Moreover, concept maps can show the whole picture and core knowledge about a subject domain. Research from literature also suggests that graphical representation of domain knowledge can reduce the problems of information overload and learning disorientation for learners. However, construction of concept maps typically relied upon domain experts in the past; it is a time consuming and high cost task. Concept maps creation for emerging new domains such as e-Learning is even more challenging due to its ongoing development nature. The aim of this paper is to construct e-Learning domain concept maps from academic articles. We adopt some relevant journal articles and conference papers in e-Learning domain as data sources, and apply text-mining techniques to automatically construct concept maps for e-Learning domain. The constructed concept maps can provide a useful reference for researchers, who are new to the e-Leaning field, to study related issues, for teachers to design adaptive learning materials, and for learners to understand the whole picture of e-Learning domain knowledge.     

Detailed Real-Time Urban 3D Reconstruction from Video

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.     

A neural network-based approach for dynamic quality prediction in a plastic injection molding process

This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A self-organizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated to training and testing the proposed system. In addition, Taguchi’s parameter design method was also applied to enhance the neural network performance. For comparison, an additional back-propagation neural network (BPNN) model was constructed for which six process parameters were used for training and testing. The training and testing data for the two models respectively consisted of 120 and 40 samples. Experimental results showed that such a SOM-BPNN-based model can accurately predict the product quality (weight) and can likely be used for various practical applications.     

一种新的非线性分形压缩算法

提出一种基于Julia-CK集和Logistic映射的非线性分形压缩算法。用Carotid-Kundalini函数生成Julia-CK集,并用Logistic映射生成伪随机数填充量化表。将量化后的Julia-CK集分割成4 4的小图像块,再变换成圆盘。圆盘经过旋转后重新变换为正方形,对Julia-CK集进行适当的分类。编码时在同类中寻找匹配的图像块,扩充了原有的仿射变换,得到一个丰富且可通用的压缩字典,有效地打破图像和数据字典之间的一一对应关系。实验表明,相比于Barnsley提出的经典分形压缩方法,新算法使压缩比提高约36%,重建图像的峰值信噪比提高约27%,具有良好的压缩比,获得了高质量的解码图像。     

虚拟三维场景与二维电子地图交互系统研究

提出并建立了一个虚拟三维场景与二维电子地图交互系统。介绍了系统的组成和各部分实现的功能。对虚拟场景的数据组织、场景数据读入、观察者位置的确定及交互功能的实现等给出解决方案,给出了实现的代码及画面。该系统利用二维电子地图简洁性、宏观性的特点,通过与虚拟三维场景间的互响应减少观察者在虚拟三维场景中漫游所产生的迷失感,在实际应用中取得了较好的效果。     

游戏程序设计中若干问题的探讨

游戏编程是当今程序设计的一个热点,开发过程会遇到各种各样的问题。通过限制构造函数和拷贝函数,设计一个单体全局管理类,可以避免不合适的调用。采用分块技术加栽地图,可以在时空性能上优化游戏。阐述了D3D的空间坐标变换。分析了万向节死锁问题发生的原因,并提出了解决办法。     

Authorware课件网上发布解决方案研究

随着多媒体技术及其相关技术的发展,多媒体制作得到了广泛的重视,应用范围越来越广。现今网络技术的飞速发展及其广泛的应用,Authorware针对网络用户提供了一系列多媒体信息编辑、传输、网上发布的解决方案,为CAI教学在网上实现提供了良好的条件。