结合自适应稀疏表示和全变分约束的图像重建

In view of the limitation of fixed complete orthogonal transformation, represented by two-dimensional wavelet transform and discrete cosine transform in compressed sensing high-resolution image reconstruction, this paper proposes a new method for high-resolution image reconstruction based on adaptive redundant dictionary sparse representation with the total variation constraint.The algorithm takes the intermediate image in the process of iteration as the training sample to get a redundant dictionary suitable for sample characteristics by adaptive learning. It makes full use of the correlation between dictionary atoms and the image to get an ideal complete sparse representation, thus reducing the sampling rate and improving the quality of image reconstruction. Finally, the algorithm takes the total variation as a constraint and uses the split Bregman iterative method to solve the sparse optimization problem. Simulation shows that the proposed method can reconstruct high quality images under a low sampling rate.     

高选择性陷波超宽带天线设计

The resonant properties of the interdigital capacitance loading loop resonator (IDCLLR) and its dual counterpart complimentary interdigital capacitance loading loop resonator (CIDCLLR) are studied. Due to the high capacitance/inductance of the IDCLLR/CIDCLLR, the resonators can generate lower resonant frequencies and narrower resonant bandwidths than many other structures. A compact ultra-wideband (UWB) antenna with quadruple sharp band-notching characteristics is presented by utilizing IDCLLRs and CIDCLLRs. Measured results show that the proposed antenna has a wide bandwidth from 3 to 11GHz(VSWR<2), with four sharp notched bands centered on 3.5, 5.2, 5.8 and 7.5GHz, respectively, avoiding the elimination of many useful frequencies. Moreover, the presence of IDCLLRs and CIDCLLRs scarcely affects the performance of the UWB antenna at its operating frequencies.     

ISAR imaging algorithm for targets with the complex motion based on the high-order ambiguity function-Lv's distribution

In ISAR imaging for targets with the complex motion, since the azimuth high order phase terms caused by the time-varying rotation vector will deteriorate the azimuth focusing quality, the traditional RD algorithm and LFM model are not appropriate. Thereby, in the case when the received signal can be modeled as cubic phase signals (CPSs), this paper proposes an ISAR imaging algorithm based on HAF-LVD (high-order ambiguity function-Lv's distribution). First, this algorithm defines a novel double lag parametric instantaneous autocorrelation function, and then applies the scaling operator to remove the coupling and utilizes FFT to achieve the energy accumulation. Finally, the non-searching estimation of the moving parameter and the ISAR images for targets are accomplished by the obtained HAF-LVD. Because of the introduction of the scaling factor, this algorithm can flexibly deal with more changeful and hostile ISAR environment without loss of the anti-noise performance and computational efficiency. Simulation results validate the effectiveness of the ISAR imaging approach.     

一种情感极性感知的信任模型

针对在线评论中文字评论和等级评价不一致的问题,提出基于情感极性分析的综合评价生成算法,并依据隐马尔可夫模型(Hidden Markov Model,HMM)构建了信任计算模型,将按时间排序的综合评价作为观测序列用于寻找最优的信任状态序列,把实体处于最可信状态的概率作为其可信度.该模型不仅提高了信任计算的准确性,还体现了信任的动态性.仿真实验验证了所提模型的有效性.     

Segmentation of the image with multi-visual features for a traffic scene

Scene segmentations based on the pixel classifying calculation are complicated, and they use insufficient features, thus resulting in a low accuracy, so a new model is proposed to overcome these shortcomings,which is to learn these geometric classes based on multi-visual features of super-pixels. First, various features are extracted from the super-pixels of an input image. These features are used for classifying the super-pixels. Then the difference between the adjacent super-pixels is calculated to predict their consistency. The initial classification result and the consistency are synthesized to the Markov Random Field energy function, which is then minimized based on the graph-cuts algorithm to get the final labels of the super-pixels. Experimental results prove the effectiveness of the multi-visual features and the optimization method proposed, with superior performance achieved for traffic scenes.     

Probability-based location anonymity algorithm

As one of the most effective location privacy preservation technologies, the k-anonymity model provides safeguards for location privacy of the mobile client against vulnerabilities for abuse by constructing an anonymous area of k users including the protected one. However, most existing k-anonymity models only utilize the users who are sending requests at recent time. If there are not enough requesting users, the generated anonymous area of the k-anonymity model will be larger than expected. In this paper, a Probability-based Location Anonymity (PLA) algorithm is proposed for protecting location privacy of the mobile users in a road network. The PLA model takes advantage of the historical path track of the users who are not sending the request currently, and then computes the probability into the anonymous section so that it can greatly reduce the size of the anonymous area. Experimental results show that the PLA algorithm is superior to the k-anonymity and it increases its anonymous efficiency enormously.     

ID-based trapdoor mercurial commitment under one-way functions assumptions

The notion of identity-based trapdoor mercurial commitment incorporates the advantages of both identity-based trapdoor commitment and trapdoor mercurial commitment. So far, the existing identity-based trapdoor mercurial commitment schemes are based on the strong Diffie-Hellman assumption or computational Diffie-Hellman assumption. One-way function existence is one of the fundamental assumptions in the cryptography. In this paper, we first propose a general construction for identity-based trapdoor mercurial commitment based on the one-way function. Meanwhile, we give a concrete instantiation based on the Boneh-Boyen signature scheme.     

椭圆曲线密码的优化设计方法

椭圆曲线密码算法依赖于离散对数问题的困难性,具有安全强度高、计算复杂度小的特点.椭圆曲线密码系统的主要操作为点乘运算,是加解密过程中最为耗时的部分.文中对点乘运算进行优化,提出了椭圆曲线密码算法实现的硬件体系结构,设计了基于FPGA/ASIC的加解密系统.通过对有限二进制域的乘法优化、平方优化和除法优化,提高了加解密算法的实现效率.分析和测试表明,所设计的硬件体系结构具有硬件资源消耗小、模块接口复杂度低和可扩展性强的特点,且支持113、163、193等多种密钥长度,相对于椭圆曲线密码算法的软件实现,文中的椭圆曲线密码处理器加速比最高可达到上千倍.     

两方量子密钥协商协议的改进

针对Hsueh和Chen的基于最大纠缠态两方量子密钥协商协议存在安全漏洞,即发送方可单方控制共享密钥的问题,通过增加接收方的幺正操作给出了一个改进方案.利用幺正操作来代替对发送方的安全检测,这从根本上满足了量子密钥协商中各参与者都贡献于共享密钥的生成和分配这一基本要求,从而使其抗发送方攻击的安全性依赖于基本物理原理而非检测光子技术.安全分析表明,该方案可有效抵抗外部攻击和参与者攻击.与另一改进方案相比,该方案以更小的代价解决了原协议的漏洞,提高了协议效率.