Comparison of approaches based on optimization and algebraic iteration for binary tomography

Binary tomography represents a special category of tomographic problems, in which only two values are possible for the sought image pixels. The binary nature of the problems can potentially lead to a significant reduction in the number of view angles required for a satisfactory reconstruction, thusly enabling many interesting applications. However, the limited view angles result in a severely underdetermined system of equations, which is challenging to solve. Various approaches have been proposed to address such a challenge, and two categories of approaches include those based on optimization and those based on algebraic iteration. However, the relative strengths, limitations, and applicable ranges of these approaches have not been clearly defined in the past. Therefore, it is the main objective of this work to conduct a systematic comparison of approaches from each category. This comparison suggested that the approaches based on algebraic iteration offered both superior reconstruction fidelity and computation efficiency at low (two or three) view angles, and these advantages diminished at high view angles. Meanwhile, this work also investigated the application of regularization techniques, the selection of optimal regularization parameter, and the use of a local search technique for binary problems. We expect the results and conclusions reported in this work to provide valuable guidance for the design and development of algorithms for binary tomography problems.     

A verifiable multi-secret sharing scheme based on cellular automata

In this paper, we propose a verifiable (t, n)-threshold multi-secret sharing scheme, based on one-dimensional cellular automata where the number of secrets is not restricted by n or t. We show that our scheme can be used to solve an open problem proposed recently in Alvarez et al. (2008) [G. Alvarez, L. Hernández Encinas, A. Martín del Rey, A multisecret sharing scheme for color images based on cellular automata, Information Sciences 178 (2008) 4382-4395].     

A high payload frequency-based reversible image hiding method

To provide both a high payload capacity and a good stego-image quality, a high payload frequency-based reversible image hiding (HPFRIH) method was presented in this paper. These performances can be achieved by transforming a cover image from the spatial domain into the frequency domain through Haar digital wavelet transform (HDWT) method followed by an adaptive arithmetic coding method to encode the HDWT coefficients in a high-frequency band. The HPFRIH method was used to conceal both the compressed data and the secret data in the high-frequency band. This simple method can completely reconstruct the cover image only from the stego-image. Moreover, experimental results showed that the HPFRIH method offers both impressive hiding capacity and satisfied stego-image quality.     

不同理性双寡头R&D竞争分析及混沌控制

利用动力系统的分支理论研究具有不同理性层次的双寡头R&D竞争模型.讨论了该模型均衡点的存在性与稳定性,并进行了数值仿真.仿真结果表明:考虑到企业的个人理性要求,现实中的模型稳定点数量町能比理论上的少,而Nash均衡点是模型的唯一稳定点:R&D投入调整速度的变化对模型的稳定性有较明显的影响;运用延迟反馈控制法可使陷入混沌状态的模型晕新稳定在Nash均衡.     

An EDBoost algorithm towards robust face recognition in JPEG compressed domain

In this paper, we describe a novel multiclass boosting algorithm, EDBoost, to achieve robust face recognition directly in JPEG compressed domain. In comparison with existing boosting algorithms, the proposed EDBoost exploits Euclidean distance (ED) to eliminate non-effective weak classifiers in each iteration of the boosted learning, and hence improves both feature selection and classifier learning by using fewer weak classifiers and producing lower error rates. When applied to face recognition, the EDBoost algorithm is capable of selecting the most discriminative DCT features directly in JPEG compressed domain to achieve high recognition performances. In addition, a new DC replacement scheme is also proposed to reduce the effect of illumination changes. In comparison with the existing techniques, the proposed scheme achieves robust face recognition without losing the important information carried by all DC coefficients. Extensive experiments support the conclusion that the proposed algorithm outperforms all representative existing techniques in terms of boosted learning, multiclass classification, lighting effect reduction and face recognition rates.     

凸显计算机专业特色的离散数学教学研究与实践

离散数学是计算机专业的一门重要基础课,对离散数学的教学研究一直是从事计算机教育工作者关注的热点。文章提出在计算机专业离散数学的教学过程中,教师应避免将该课程讲成一门数学理论课,而是对每一部分应结合计算机领域的相关实际应用背景进行阐述与引入,提高教学的针对性,从而调动学生学习积极性,促进教学质量的提高。     

离散数学课程教学改革探索与实践

离散数学是计算机科学的核心基础理论课,该课程不但为学生学好后续课程提供数学理论基础,而且学好该课程有利于培养学生的数学思维。本文结合教学实践,分析了当前离散数学教学中存在的问题,针对该课程的特点,从教学内容、教学手段和教学方法3个方面对离散数学的教学改革进行了探讨,力求使该课程的教学上一个新的台阶。     

离散数学的课堂导入法研究

成功的课堂导入法可以激发学生的求知欲,开启学生的思维,帮助教师顺利完成教学任务。文章针对离散数学定义定理多、内容丰富、理论抽象,学生感觉难学的问题,通过分析离散数学的作用、特点和教学现状,研究几种课堂导入法及其重要作用。提出在实际教学过程中,通过运用课堂导入法,引导学生学习兴趣,调动学生参与课堂的激情,达到提高离散数学的教学质量,圆满完成教学任务的目标。     

新建本科院校数学与应用数学专业程序设计语言课程设置与教学

程序设计语言课程是数学与应用数学专业的基础课,设置合理的课程体系是新建本科院校人才培养模式的一个重要环节,以专业需求和培养学生综合素质为目的,根据目前流行的程序设计语言,从课程选择与设置、教材建设、加强实践教学和改进教学手段等方面做了探讨。     

一种综合空域和时频域的数字水印算法

提出一种空域和时频域相结合的多分辨率灰度图像水印算法。该算法对原始载体图像进行离散小波分解,并对灰度水印图像先进行拉普拉斯金字塔分解再进行位平面分解。将水印图像位平面分解系数嵌入到原始图像的水平和垂直分量上,水印嵌入小波系数的位置由具有混沌特性的Arnold映射确定,从而实现水印的盲提取。实验结果证明,该算法对图像的普通处理,如噪声、滤波、剪切、JPEG压缩和旋转等,均具有较好的鲁棒性。