A self-recovery fragile watermarking scheme for image authentication with superior localization

To address the problems of the inferior localization and high probability of false rejection in existing self-recovery fragile watermarking algorithms, this paper proposes a new self-recovery fragile watermarking scheme with superior localization, and further discusses the probability of false rejection （PFR） and the probability of false acceptance （PFA） of the proposed scheme. Moreover, four measurements are defined to evaluate the quality of a recovered image. In the proposed algorithm, the original image is divided into 2×2 blocks to improve localization precision and decrease PFR under occurrence of random tampering. The PFR under occurrence of region tampering can be effectively decreased by randomly embedding the watermark of each block in conjunction with a novel method of tamper detection. Compared with the current self-recovery fragile watermarking algorithms, the proposed scheme not only resolves the tamper detection problem of self-embedding watermarking, but also improves the robustness against the random tampering of self-embedding watermarking. In addition, the subjective measurements are provided to evaluate the performance of the self-recovery watermarking schemes for image authentication.     

嵌入式应用系统的可靠性设计初探

嵌入式应用系统是一个有计算机内核的智能化电子系统。集成电路的超长寿命、计算机的软件介入与智能化的控制能力,决定了嵌入式应用系统全新的可靠性设计观念。这些新观念是：基于出错概率的多值可靠性、确定应用系统的可靠性等级、建立应用系统的可靠性模型、应用系统的本质可靠性设计与可靠性控制设计。     

Learning a Maximum Margin Subspace for Image Retrieval

One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow down this gap, relevance feedback is introduced into image retrieval. With the user-provided information, a classifier can be learned to distinguish between positive and negative examples. However, in real-world applications, the number of user feedbacks is usually too small compared to the dimensionality of the image space. In order to cope with the high dimensionality, we propose a novel semisupervised method for dimensionality reduction called Maximum Margin Projection (MMP). MMP aims at maximizing the margin between positive and negative examples at each local neighborhood. Different from traditional dimensionality reduction algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which effectively see only the global euclidean structure, MMP is designed for discovering the local manifold structure. Therefore, MMP is likely to be more suitable for image retrieval, where nearest neighbor search is usually involved. After projecting the images into a lower dimensional subspace, the relevant images get closer to the query image; thus, the retrieval performance can be enhanced. The experimental results on Corel image database demonstrate the effectiveness of our proposed algorithm.     

Subband Synthesis for Color Filter Array Demosaicking

This paper presents a new algorithm for demosaicking images captured through a color filter array (CFA). The objective of the CFA demosaicking is to render a full color image from the mosaicked image. This is commonly achieved by estimating the missing color information from the surrounding observed pixels. In this paper, we integrate the observation that color images have a strong intrachannel spatial correlation in low-frequency components and a dominant interchannel correlation in the high- frequency components. A new framework is proposed to utilize this information where the missing pixels in each color channel are estimated from the wavelet subbands. A modified median-filtering operation is then applied in the subband domain. The algorithm is adaptive and produces superior full-resolution images when compared with other methods.     

MCLP-based methods for improving “Bad” catching rate in credit cardholder behavior analysis

Cardholders’ behavior prediction is an important issue in credit card portfolio management. As a promising data mining approach, multiple criteria programming (MCLP) has been successfully applied to classify credit cardholders’ behavior into two groups. In order to better control credit risk for financial institutes, this paper proposes three methods based on MCLP to improve the “Bad” catching accuracy rate. One is called MCLP with unbalanced training set selection, the second is called fuzzy linear programming (FLP) method with moving boundary, and the third is called penalized multi criteria linear programming (PMCLP). The experimental examples demonstrate the promising performance of these methods.     

可信计算及其对Ad Hoc网络安全的启发

该文借鉴可信计算思想,对可信网络进行了初步介绍,在此基础上根据AdHoc自身特点进行了通信安全、动态拓扑、漫游、有限资源等方面的分析,并就如何在AdHoc中应用可信计算提出自己的见解和下一步研究方向。     

小波变换在交通参数检测中的应用

针对视频交通图像的特点,提出了一种将灰度判断与边缘检测相结合来检测交通参数的方法;综合了灰度检测和边缘检测的优点,采用帧平均法来处理视频流,可以减小由于摄像头抖动或背景微小变化而产生的误差;分析了小波变换的特点,应用小波变换对交通图像进行车辆边缘检测,对不同尺度小波变换后的水平方向、垂直方向和对角线方向的高频信息,用互能量交叉的方法进行噪声抑制,突出主要边缘,最后,在车辆边缘识别基础上获得车流量、车速等相关交通参数;实验结果表明,此方法简单、有效,获得了较满意的检测结果。     

基于案例教学的“人工智能”教学的实践与探索

本文结合教学实践,从基于案例的教学及教学内容、教学手段、实践教学等几个方面探讨了“人工智能”课程的教学。     

IPV6防火墙研究

下一代互联网的核心协议IPv6将逐步取代IPv4,但目前适用于IPv6防火墙并不多,在分析了IPSec6的原理和防火墙的原理后,提出了在IPSec6基础上创建IPv6防火墙的方法,给出实现防火墙的实例.     

Java CoG Kits在GridFFP开发中的应用

通过对Java CoG Kits软件包的研究,介绍了Java CoG Kits的结构和GridFTP的特性,讨论了Java CoG Kits中用于开发GridFrP客户端的FTP包的组件层次和类库,描述了GridFTP客户端类库用于编写GridFlP应用的函数,研究了GridFTP第三方控制数据传输模型,给出了实现的过程和步骤,最后基于Java CoG Kits设计实现了GridFTP第三方控制数据传输.