新的鲁棒模糊C-均值聚类分割算法及其应用

针对Krinidis和公茂果等提出的系列鲁棒模糊局部C-均值聚类算法存在聚类中心迭代公式缺乏严格数学理论基础的不足,于是将其聚类目标函数及其约束条件采用拉格朗日乘子法进行严格数学推导,从而获得最优解逼近的隶属度和聚类中心迭代表达式,并通过多次循环迭代实现图像聚类分割。实验结果表明,本文所建议的鲁棒模糊局部C-均值聚类分割算法是有效的,相比现有鲁棒模糊局部C-均值聚类分割算法更适合复杂遥感等图像的分割需要。     

面向移动自组网的分布式认知MAC协议改进设计

针对移动自组网MAC协议未考虑节点移动性、缺乏功率控制机制、算法开销较大等问题,改进设计基于信道可维持时间的分布式认知MAC协议。新协议建立了节点存储信息结构,根据节点功率与移动速度的关系,构建信道可维持时间模型,并改进信道感知和数据传输算法,增加移动性和信道增益考虑,降低速度和功率对信道感知的影响,提高协议对移动性的适应能力。结果表明,对于中低速网络而言,该协议在不增加控制开销的基础上,丢包率、时延和频谱利用率等性能得到较大改善。     

视频编码技术的若干新进展

对数字视频编码技术的新进展进行了综述。先对传统视频编码方法进行了分析,指出其存在的不足,然后对基于压缩感知理论的视频编码、分布式视频编码、基于视觉特性的视频编码和可分级视频编码这四种视频编码技术的新进展进行了介绍,最后对未来可能的发展方向进行了展望,可为视频编码技术的相关研究提供参考。     

基于相互认证和3DES加密的智能卡远程支付系统认证方案

针对现有基于智能卡支付系统的安全方案存在密码暴露、信息泄露和身份认证等问题,提出一种新的基于相互认证和3DES加密的智能卡远程支付系统认证方案。分析基于二次剩余的支付认证方案的不足,在注册、登录、身份认证和密码更改阶段对其进行改进,避免密码暴露攻击,提高密码更改阶段的安全性,同时结合3DES加密算法对支付信息进行加密处理。性能分析表明,该方案能有效抵御多种攻击,且用户能够自由地修改密码,同时可对用户信息进行匿名保护。与现有智能卡支付认证方案相比,该方案提高了支付系统的安全性能且具有较小的计算复杂度。     

一种联盟博弈频谱感知机制

为了在主用户具有异构性且认知用户处于多路径衰减、阴影效应等低感知性能的认知环境中,使认知用户公平有效地进行频谱感知,提出一种联盟博弈频谱感知机制,将频谱感知问题建模为效用不可转移的联盟博弈。首先,将认知用户划分成多个子联盟,根据头结点选取原则选出各个子联盟的头结点;其次,根据规则进行子联盟合并或分裂;最后,子联盟头结点利用“OR”规则进行数据融合,得到最终感知结果并广播给子联盟内的所有认知用户。仿真实现和性能评价结果表明,该机制具有好的性能。     

Output feedback control of linear fractional transformation systems subject to actuator saturation

In this paper, the control problem for a class of linear parameter varying (LPV) plant subject to actuator saturation is investigated. For the saturated LPV plant depending on the scheduling parameters in linear fractional transformation (LFT) fashion, a gain-scheduled output feedback controller in the LFT form is designed to guarantee the stability of the closed-loop LPV system and provide optimised disturbance/error attenuation performance. By using the congruent transformation, the synthesis condition is formulated as a convex optimisation problem in terms of a finite number of LMIs for which efficient optimisation techniques are available. The nonlinear inverted pendulum problem is employed to demonstrate the effectiveness of the proposed approach. Moreover, the comparison between our LPV saturated approach with an existing linear saturated method reveals the advantage of the LPV controller when handling nonlinear plants.     

A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems

Wireless sensor networks (WSNs) as one of the key technologies for delivering sensor-related data drive the progress of cyber-physical systems (CPSs) in bridging the gap between the cyber world and the physical world. It is thus desirable to explore how to utilize intelligence properly by developing the effective scheme in WSN to support data sensing and fusion of CPS. This paper intends to serve this purpose by proposing a prediction-based data sensing and fusion scheme to reduce the data transmission and maintain the required coverage level of sensors in WSN while guaranteeing the data confidentiality. The proposed scheme is called GM–KRLS, which is featured through the use of grey model (GM), kernel recursive least squares (KRLS), and Blowfish algorithm (BA). During the data sensing and fusion process, GM is responsible for initially predicting the data of next period with a small number of data items, while KRLS is used to make the initial predicted value approximate its true value with high accuracy. The KRLS as an improved kernel machine learning algorithm can adaptively adjust the coefficients with every input, while making the predicted value more close to actual value. And BA is used for data encoding and decoding during the transmission process due to its successful applications across a wide range of domains. Then, the proposed secure data sensing and fusion scheme GM–KRLS can provide high prediction accuracy, low communication, good scalability, and confidentiality. In order to verify the effectiveness and reasonableness of our proposed approach, we conduct simulations on actual data sets that are collected from sensors in the Intel Berkeley research lab. The simulation results have shown that the proposed scheme can significantly reduce redundant transmissions with high prediction accuracy.     

Terrain mapping under extreme light conditions with direct stereo matching method through aggregating matching costs by weight

One of the biggest problems in applying stereo vision techniques in field robotics is how to acquire 3D terrain maps under extreme light conditions. Through multiple exposures, the dynamic range of images can be increased. In this paper, instead of using existing lighting enhancement methods such as exposure fusion to increase the texture of 2D image, we propose that the matching costs of the images grabbed with multiple exposures are directly summed by weight. Compared with the previous methods such as exposure fusion, with the proposed method, it is not necessary to fuse the 2D images captured with multiple exposures, and for each pixel of the matching image, the local information in its local window can be better retained. Since it is possible that the camera is moved between exposures when the images are grabbed, the images captured with multiple exposures are aligned to the image acquired with auto exposure. In order to evaluate the performance of the proposed method, two different stereo matching algorithms were used: a local window-based method and semi-global method. Through experiments in laboratory and outdoors with a stereo vision camera fixed on a tripod and held in the hand, it was verified that the proposed method consistently allowed more valid points to be obtained and the 3D model of terrain can be built more accurately. Especially when the local window-based method was used, the proposed method performed much better.