一种针对LSB匹配隐写的负载定位新算法

针对最不重要比特位(Least significant bit, LSB)匹配隐写算法,本文提出了一种新的负载定位算法。将隐写负载定位看作二分类问题,将载密图像每个像素位置看作待分类样本,通过提取载密图像集中每个像素位置在8个方向上的相邻像素差分平方均值特征,利用支持向量机(Support vector machine,SVM)分类器,将每个像素位置划分到正确的类别——负载位置或非负载位置。本文从理论和实验两方面验证了所提分类特征的有效性。针对LSB匹配隐写,本文方法与最大后验概率(Maximum a posteriori, MAP)载体估计方法做出比较,在低嵌入率条件下,本文方法的定位性能有明显提高。     

联合模板先验概率和稀疏表示的目标跟踪

目的 虽然基于稀疏表示的目标跟踪方法表现出了良好的跟踪效果,但仍然无法彻底解决噪声、旋转、遮挡、运动模糊、光照和姿态变化等复杂背景下的目标跟踪问题。针对遮挡、旋转、姿态变化和运动模糊问题,提出一种在粒子滤波框架内,基于稀疏表示和先验概率相结合的目标跟踪方法。方法 通过先验概率衡量目标模板的重要性,并将其引入到正则化模型中,作为模板更新的主要依据,从而获得一种新的候选目标稀疏表示模型。结果 在多个测试视频序列上,与多种流行算法相比,该算法可以达到更好的跟踪性能。在5个经典测试视频下的平均中心误差为6.77像素,平均跟踪成功率为97%,均优于其他算法。结论 实验结果表明,在各种含有遮挡、旋转、姿态变化和运动模糊的视频中,该算法可以稳定可靠地跟踪目标,适用于视频监控复杂场景下的目标跟踪。     

基于关键列分组排序的列存储结构

磁盘作为海量数据的主要存储介质,具有容量大、成本低的优点,但是磁盘IO带宽远远落后于数据增长速度,日益成为大数据管理系统的性能瓶颈。因此,优化存储结构、提高读写效率是大数据时代管理系统面临的重要挑战。提出了一种基于关键列分组排序的混合列存储结构KCGS-Store,根据关键列分组将关系表划分为存储池,确保池内所有记录在关键列上的取值或取值范围相同,然后逐列进行池合并。合并后的关键列,以池为单位有序排列,执行条件查询时能够有效过滤无关列值,减少数据读取量,提升查询性能。同时利用池号索引,以少量时间空间代价完成记录重组。实验数据表明,与ORCFile、Parquet存储结构相比,KCGS-STORE在存储空间、数据加载、SQL查询等方面都有不同程度的优化。     

Exact and approximate flexible aggregate similarity search

Aggregate similarity search, also known as aggregate nearest-neighbor (Ann) query, finds many useful applications in spatial and multimedia databases. Given a group Q of M query objects, it retrieves from a database the objects most similar to Q, where the similarity is an aggregation (e.g., \({{\mathrm{sum}}}\), \(\max \)) of the distances between each retrieved object p and all the objects in Q. In this paper, we propose an added flexibility to the query definition, where the similarity is an aggregation over the distances between p and any subset of \(\phi M\) objects in Q for some support \(0< \phi \le 1\). We call this new definition flexible aggregate similarity search and accordingly refer to a query as a flexible aggregate nearest-neighbor ( Fann ) query. We present algorithms for answering Fann queries exactly and approximately. Our approximation algorithms are especially appealing, which are simple, highly efficient, and work well in both low and high dimensions. They also return near-optimal answers with guaranteed constant-factor approximations in any dimensions. Extensive experiments on large real and synthetic datasets from 2 to 74 dimensions have demonstrated their superior efficiency and high quality.     

Adaptive chatter mitigation control for machining processes with input saturations

Chatter is an unstable nonlinear dynamical phenomenon often encountered in machining operations because of the self‐excitation mechanism, which may lead to overcut or rapid tool wear, and hence, greatly influence the surface quality and productivity in milling operations. Recent years have witnessed an increasing industrial demand of high quality and high efficiency machining. This paper hereby develops a constrained active adaptive control method to mitigate the chatter dynamics with input saturations. To guarantee the feasibility of the proposed approach, moderate stable conditions of the closed‐loop system are afterwards derived by using the LaSalle–Yoshizawa theorem as well. Finally, numerical simulations are conducted to show the substantially enlarged stable region in the Lobe Diagram. Thus, the method can be expected to improve the efficiency of milling processes. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive actuator failure compensation for multivariable feedback linearizable systems

A feedback linearization‐based adaptive control scheme is developed for multivariable nonlinear systems with redundant actuators subject to uncertain failures. Such an adaptive controller contains a direct adaptive actuator failure compensator to compensate the uncertain actuator failure, a nonlinear feedback to linearize the nonlinear dynamics, and a linear feedback to stabilize the linearized system. The key new design feature is the estimation of both the failure patterns and the failure values, for direct adaptive actuator failure compensation, newly developed for multivariable feedback linearizable nonlinear systems. With direct control signal adaptation, the adaptive failure compensation design ensures closed‐loop stability and asymptotic output tracking in the presence of actuator failure uncertainties. Simulation results from an application to attitude control of a near‐space vehicle dynamic model are presented to verify the desired system performance with adaptive actuator failure compensation. Copyright © 2015 John Wiley & Sons, Ltd.     

A projection-based decomposition for the scalability of evolvable hardware

Scalability is a main and urgent problem in evolvable hardware (EHW) field. For the design of large circuits, an EHW method with a decomposition strategy is able to successfully find a solution, but requires a large complexity and evolution time. This study aims to optimize the decomposition on large-scale circuits so that it provides a solution for the EHW method to scalability and improves the efficiency. This paper proposes a projection-based decomposition (PD), together with Cartesian genetic programming (CGP) as an EHW system namely PD-CGP, to design relatively large circuits. PD gradually decomposes a Boolean function by adaptively projecting it onto the property of variables, which makes the complexity and number of sub-logic blocks minimized. CGP employs an evolutionary strategy to search for the simple and compact solutions of these sub-blocks. The benchmark circuits from the MCNC library, \(n\)-parity circuits, and arithmetic circuits are used in the experiment to prove the ability of PD-CGP in solving scalability and efficiency. The results illustrate that PD-CGP is superior to 3SD-ES in evolving large circuits in terms of complexity reduction. PD-CGP also outperforms GDD+GA in evolving relatively large arithmetic circuits. Additionally, PD-CGP successfully evolves larger \(n\)-even-parity and arithmetic circuits, which have not done by other approaches.     

Upper Bounds on Number of Steals in Rooted Trees

Inspired by applications in parallel computing, we analyze the setting of work stealing in multithreaded computations. We obtain tight upper bounds on the number of steals when the computation can be modeled by rooted trees. In particular, we show that if the computation with n processors starts with one processor having a complete k-ary tree of height h (and the remaining n ? 1 processors having nothing), the maximum possible number of steals is \({\sum }_{i=1}^{n}(k-1)^{i}\binom {h}{i}\).     

Set-based particle swarm optimization with status memory for knapsack problem

Set-based particle swarm optimization (S-PSO) operates on discrete space. S-PSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In S-PSO, a velocity is a set with possibility and a position is a candidate solution. In this paper, we present a novel algorithm of set-based particle swarm optimization with status memory (S-PSOSM) to decide the position based on the previous position for solving knapsack problem. Some operators are redefined for S-PSOSM. S-PSOSM is a simple algorithm because the state of probability reduces. In addition, the weight of S-PSOSM is discussed. S-PSOSM shows high qualities in experimental results.