一种多用户MIMO系统干扰对齐优化算法

干扰对齐技术可以获得干扰信道自由度的最佳值,从而有效改善系统的性能。在实际系统中干扰对齐技术通常采用迭代的方法进行预编码矩阵与干扰抑制矩阵的设计,而迭代方法都需要对发送预编码矩阵进行初始化处理。然而,目前大多数已有的研究所采用的初始化处理方法都忽略了干扰的影响。因此,在此基础上提出了一种基于新的初始化方法的优化算法,该方法在初始化预编码矩阵中既考虑了干扰信号也考虑了有用信号。首先,选取均方误差和最小化作为优化目标,然后利用正交三角（QR）分解将信道空间分为有用信号空间与干扰信号空间来进行预编码矩阵的初始化设计,经过反复迭代得到发送预编码矩阵与干扰抑制矩阵的最优解。理论分析和仿真结果表明,所提算法在收敛性、均方误差、和速率等方面都优于其他算法。     

Boosting算法及其在动态视频图像中的应用

Boosting是一种有效的分类器组合方法,它用某个分类算法生成一系列的基分类器,每个基分类器的训练依赖于在其之前产生的分类器的分类结果,基分类器在训练集上的错误率用于调整训练样本的概率分布,最终分类器通过单个基分类器的加权投票建立起来。将Boosting算法应用在动态车型图像检测中,大大提高了对运动过程中车辆的识别能力,对智能交通系统的发展起着推动作用。     

一种基于遗传算法的区域物流网络选优方法

本文利用区域物流的有关理论,探讨了物流网络建设的框架,结合区域物流服务的需要,从第三方物流企业的角度出发,侧重于对区域现有的物流中心进行整合和选择,利用改进的遗传算法对现有物流节点进行最优化选择,从而在根本上提高区域物流网络建模的效率和运行参数的可靠性。     

基于方差约束控制的复杂控制系统故障诊断方法

将方差约束控制用于研究复杂控制系统故障诊断。首先根据系统运行情况提取故障点在各个子系统的状态观测值,定义了故障点的状态观测均值、误差并求得状态协方差。然后通过熵系数法来确定各影响因素的权重,用欧氏范数法和线性加权法确定系统正常运行的最大允许阈值,一旦实际值超过阈值则报警,采取修正措施。最后通过实例,得到了一种新的基于欧氏范数方差约束控制的故障诊断方法,应用于航天产品漏电故障诊断。结果表明,该方法的故障阈值是由系统本身特性得到,故能够客观、真实地确定阈值的量化模型,克服了传统的专家系统需要训练大量历史数据的弊端,实现了故障在线实时诊断。     

基于SHARC处理器的PGA算法实现

合成孔径雷达(SAR)实时处理技术的发展对相位梯度自聚焦(PGA)算法的工程实现提出了要求。基于以多片主流SHARC处理器为核心构成的硬件平台,针对PGA算法数据处理量大、计算复杂度高的特点,设计开发了并行处理的系统拓扑和程序流水结构及其软件程序。机载SAR实测数据的处理结果验证了该方案的有效性。     

非正则LDPC码在DSP上的实现

首先简单介绍了非正则LDPC码的结构,给出了一种基于IEEE802.16e直接编码法生成的(576,288)非正则LDPC码的编译码原理。然后详细论述了其在TI定点DSP(TMS320C5510)上的定点化算法实现方式,并在经过C和部分汇编优化后将算法效率提高了70%以上,达到了实时系统要求。最后给出了该LDPC码与(2,1,7)卷积码在AWGN信道下的性能对比,表明这种中短码长的非正则LDPC码较卷积码有较大的纠错性能优势。     

基于改进BP网络的城市可持续发展水平模糊综合评价

在建立城市可持续发展水平评价指标体系的基础上,提出了一种基于改进BP网络的城市可持续发展水平模糊综合评价方法,该方法不仅可以模拟专家对城市的可持续发展水平进行综合评价,而且有效地改进了人为评价方面的失误,为城市的可持续发展水平评价提供了一种新的评价模型。     

The determinants of technology adoption by UK farmers using Bayesian model averaging: the cases of organic production and computer usage

We review and implement a reversible jump approach to Bayesian model averaging for the Probit model with uncertain regressors. Two applications are investigated. The first is the adoption of organic systems in UK farming, and the second is the influence of farm and farmer characteristics on the use of a computer on the farm. While there is a correspondence between the conclusions we would obtain with and without model averaging results, we find important differences, particularly in smaller samples. Concerning the adoption of an organic system, we find that attitudes to the sustainability of the current system along with the ability of organic farms alone to satisfy society’s needs for food are influential. Additionally, the source of management information used by the farmer has a significant impact. Regarding the adoption of computers, we confirm the findings of previous work that the level of education affects uptake and that age is a factor determining adoption. We also find that dairy and organic farms are more likely to use a computer. The physical size of the farm is positively associated with the probability of computer use while net farm income has a limited impact.     

改进Adaboost算法对县域粮食作物产量的预测

Adaboost算法是一种典型的机器学习算法,但其算法在训练过程中用时较长.针对这个问题,采用改进的机器学习DWTAdaboost算法与Adaboost算法对1950—2012年内蒙古自治区扎兰屯地区的各项经济统计数据进行分析,比较两种算法模型对数据分析的优劣.结果表明DWTAdaboost算法在保证准确率不变的前提下效率更高.     

Citizen-centered big data analysis-driven governance intelligence framework for smart cities

Sensors and systems within rapidly expanding smart cities produce citizen-centered big data which have potential value to support citizen-centered urban governance decision-making. There exists a wealth of extant conceptual studies, however, further operational studies are needed to establish a specific path towards implementation of such data to governance decision-making with analytical algorithms that are appropriate for each step of the path. This paper proposes a framework for the use of citizen-centered big data analysis to drive governance intelligence in smart cities from two perspectives: urban governance issues and data-analysis algorithms. The framework consists of three layers: 1) A data-merging layer, which builds a citizen-centered panoramic data set for each citizen by merging citizen-related big data from multiple sources in collaborative urban governance via similarity calculation and conflict resolution; 2) a knowledge-discovery layer, which plots the citizen profile and citizen persona at both individual and group levels in terms of urban public service delivery and citizen participation via simple statistical analysis techniques, machine learning, and econometrics methods; and 3) a decision-making layer, which uses ontology models to standardize urban governance-related attributes, personas, and associations to support governance decision-making via data mining and Bayesian Net techniques. Finally, the proposed framework is validated in a case study on blood donation governance in China. This research highlights the value of citizen-centered big data, pushes data-to-decision research from conceptual to operational, synthesizes previously published frameworks for citizen-centered big data analysis in smart cities, and enhances the mutual supplement cross multiple disciplinaries.