Face detection by structural models

Despite the successes in the last two decades, the state-of-the-art face detectors still have problems in dealing with images in the wild due to large appearance variations. Instead of leaving appearance variations directly to statistical learning algorithms, we propose a hierarchical part based structural model to explicitly capture them. The model enables part subtype option to handle local appearance variations such as closed and open month, and part deformation to capture the global appearance variations such as pose and expression. In detection, candidate window is fitted to the structural model to infer the part location and part subtype, and detection score is then computed based on the fitted configuration. In this way, the influence of appearance variation is reduced. Besides the face model, we exploit the co-occurrence between face and body, which helps to handle large variations, such as heavy occlusions, to further boost the face detection performance. We present a phrase based representation for body detection, and propose a structural context model to jointly encode the outputs of face detector and body detector. Benefit from the rich structural face and body information, as well as the discriminative structural learning algorithm, our method achieves state-of-the-art performance on FDDB, AFW and a self-annotated dataset, under wide comparisons with commercial and academic methods.     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.     

一种基于ARM的集中器的硬件实现

介绍了一种以ARM9为核心,以Windows CE5.0为操作系统的集中器。它符合国家电网的Q/GDW 374.2-2009技术标准,可以在国家电网中使用。给出了集中器的系统框图,介绍了集中器的主要实现电路,论述了其主要工作原理。     

Multi-scale patch-based sparse appearance model for robust object tracking

When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others.     

Effective naive Bayes nearest neighbor based image classification on GPU

Non-parametric classifier, Naive Bayes nearest neighbor, is designed with no training phase, and its performance outperforms many well-trained learning-based image classifiers. Unfortunately, despite its high accuracy, it suffers from great computational pressure from distance computations in space of local feature. This paper explores accelerating strategies from perspectives of both algorithm design and software development. Our approach integrates space decomposition capability of Product quantization (PQ) and parallel accelerating capability of underlying computational platform, Graphics processing unit (GPU). PQ is exploited to compress the indexed local features and prune the search space. GPU is used to ease most of computational pressure by processing the tasks in parallel. To achieve good parallel efficiency, a new sequential classification process is first designed and decomposed into independent components with high parallelism. Effective parallelization techniques are then presented to make use of computational resources. Parallel heap array is built to accelerate the process of feature quantization. Distance table lookup is built to speed up the process of feature search. Comparative experiments on UIUC-Sport dataset demonstrate that our integrated solution outperforms other implementations significantly on Core-quad Intel Core i7 950 CPU and GPU of NVIDIA Geforce GTX460. Scalability experiment on 80 million tiny images database shows that our approach still performs well when large-scale image database is explored.     

Water-soluble and amphiphilic polymers

Summary Three PEO-PSt-PEO triblock polymers and their parent ho-nopolystyrene have been studied by means of light scattering technique in chloroform, toluene and ethyl acetate. It has been found that the weight average molecular weights calculated by using the equation Mapp- were unbelievable negative values; and the largest deviation appeared for the sample with PEO mole content of about 50%. Data of PSt-PEO-PSt triblock polymers in literature have been quoted and discussed.     

基于安全性能接口的调度和管理模型研究

以云计算效能提高为目的, 以研究Job调度和资源管理两大决定云计算环境能效的最重要因素为基础, 使用调度和管理对象对作为系统统计信息的参考指标进行采集的方式展开研究。性能代理和服务接口模式(PASI)已经从云环境中采集了原始数据, 通常情况下应用于云的安全技术属于被动防御模式, 如实时动态监控或者深度包检测模式(DPI), 其主要目的是实现动态检测和防御。提出了一个有效的框架以提高云计算中心的综合性能指标, 该架构的建立是基于PASI与DPI相结合的方式, 并且提出了一个基于安全PASI的Job调度和资源管理模型(JDRMSP)。实验结果表明, 该模型有效地提升了云环境的整体运作效能。     

硅压阻式气压高度计的设计与实现

采用硅压阻式压力传感器,以ATmega88PA为控制器设计了气压高度计。内嵌全温度范围的曲面拟合算法进行温度补偿及线性化处理,解决了压力传感器的温漂和非线性问题,并用海拔高度与气压的关系计算高度。测量结果通过RS-485接口发送给上位机。测试结果表明,该气压高度计压力测量误差优于0.50‰,分辨率为0.01 h Pa,线性度为0.999 9;标准大气环境下高度测量误差为0.16 m,线性度为0.999 8。该气压高度计具有质量小、功耗低、精度高、工作可靠等优点。     

基于改进卡方统计的微博特征提取方法

通过对微博文本特征信息的分析与研究,提出一种基于改进卡方统计的微博特征提取方法。扩充微博信息分类特征,在传统的卡方统计量的基础上,引入了频度等因素,改进特征选择方法;在传统的特征项权值计算的基础上,提出了新的改进卡方统计量的方法,改进权重计算效果。对上述方法利用经典KNN和SVM算法进行了测试,实验结果表明该方法提高了微博信息分类的准确率。