A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

一种高误码(n,k,m)非系统卷积码盲识别算法

针对信息截获等领域中的卷积码盲识别问题，提出一种高误码 (n,k,m)非系统卷积码盲识别算法。首先建立可变的数据矩阵模型，对侦收到的数据进行相关预处理，以减小误码对识别的影响，提高算法的容错性能；再对预处理后的数据矩阵进行统计分析，识别出卷积码的各项参数，并提取各个数据矩阵的校验序列；进而利用校验序列构建线性方程组求解生成多项式矩阵组，通过设定筛选生成多项式矩阵的条件，筛选出非系统卷积码的生成多项式矩阵，最终完成对非系统卷积码的识别。仿真实验表明，该算法可以对高误码(n,k,m)非系统卷积码实现有效的盲识别。     

A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

一种广义不可分支持向量机算法

针对标准的C-SVM(C-support vector machine)算法在处理很多实际分类问题时，对识别错误代价损失差异很大的极端情况表现出的局限性，提出一种通用的 广义支持向量机算法。根据识别错误后所付出的代价，可以把最优分类面向代价损失低的一方进行推移，留给代价损失高的一方更大的空间，提高其识别率，从而减小识别错误后带来的代价损失。该方法进一步提高了标准C SVM的适用性以及样本的正确识别率，将新算法应用到高分辨雷达距离像的识别中，实验证明,广义C-SVM能取得比传统C-SVM更好的识别效果。     

基于机器视觉的运煤车车号识别技术研究

在铁路运煤装车过程中为了快速、准确地识别车号,提出一种基于机器视觉的运煤车车号识别技术。将连通区域提取与投影分割法结合,实现车号的粗定位、细分割,并对图像中的断裂字符进行二次分割,构建了基于BP神经网络的分类模型进行车号识别,提升了煤炭装车的效率和精度。     

海战场栅格节点的接入与迁移管理技术研究*

针对海战场作战平台中设备繁杂、异构的特点，栅格网络架构将各类作战平台设备以节点形式进行管理，但现有研究中尚缺乏具体的节点接入与迁移实现的流程方法与技术。论文针对上述问题，提出了海战场栅格节点接入与迁移的一种标准化流程与技术的实现方法，并利用仿真软件进行了实例的仿真验证，验证了节点接入与迁移标准化流程的可行性，并证明了其在技术上的可实现性。     

Nash equilibrium cooperative transmission strategy considering competition

In cooperative communications, employing the idle relay node for forwarding data can effectively improve the transmission rate and performance of the wireless networks. Therefore, various relay node selection algorithms are proposed. However, most of the existing algorithms just focus on the benefit caused by relay nodes, while ignoring the negative effect of the backoff duration time which does not exist in direct transmission, when more than one flow compete for the same relay node. To this end, considering both advantage and adverse impact caused by the relay node, we adopt the game-theoretic approach and propose a cooperative transmission strategy based on the Nash equilibrium, named the Nash Equilibrium Cooperative Transmission Strategy (NECTS). The NECTS can not only guarantee the gain of each flow, but also maximize the system performance, and improve the system throughput effectively. Simulation results show that the NECTS can take the advantage of cooperative communications. Meanwhile, it can avoid the unnecessary competition, and its performance outperforms that of the existing methods.     

A self-adaptive weighted affinity propagation clustering for key frames extraction on human action recognition

In this paper, we propose a novel approach for key frames extraction on human action recognition from 3D video sequences. To represent human actions, an Energy Feature (EF), combining kinetic energy and potential energy, is extracted from 3D video sequences. A Self-adaptive Weighted Affinity Propagation (SWAP) algorithm is then proposed to extract the key frames. Finally, we employ SVM to recognize human actions on the EFs of selected key frames. The experiments show the information including whole action course can be effectively extracted by our method, and we obtain good recognition performance without losing classification accuracy. Moreover, the recognition speed is greatly improved.