Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

分布式光纤振动传感系统的多级零频点定位研究

为了减少直线结构Sagnac分布式光纤传感系统的定位误差,提出一种多级零频点的优化方法,通过分析直线结构Sagnac分布式光纤传感定位原理,确定固定的采样速率下离散化是造成定位误差大的原因.并分析得出振动信号频域曲线会周期出现多个零频点,进而对振动定位的多级零频点优化方法进行了理论推导,阐明高倍零频点下的定位误差会有效减小.通过实验验证表明,在总长为11.769 km的传感光纤上,优化后的定位方法在不改变采样速率下可保证定位误差提升到±20 m范围内,可以基本满足系统对外界振动信号进行精确定位的要求.     

重点实验室科研信息管理规划与设计

根据目前重点实验室科研信息管理工作的需要,采用ASP技术和Access网络数据库,开发了科研信息管理系统。对科研管理工作进行了系统的研究,初步实现了科研信息管理的无纸化和网络化办公。系统的开发对减少科研信息管理工作的重复工作,实现信息共享,提高工作效率有十分重要的意义。     

五年制高职动漫专业毕业设计考核评价探究

提升职业教育教学质量是高职院校的核心任务。毕业设计是专业教学的重要环节,它不仅关系到毕业生的质量,也关系到专业的未来发展。从考核评价的角度,对五年制高职动漫专业学生的毕业设计进行可操作性的探讨。     

用于半自动视频对象提取的自适应网格图像分割

随着MPEG－4标准的发展和基于内容的视频处理研究,视频对象平面（VOP）的有效产生成为一个关键问题。本文提出一种基于区域的自适应网格彩色图像分割方法,可用于获得半自动视频对象跟踪和提取所需的初始VOP。该方法利用CIE L＊a*b＊色彩空间的特征量,对视频序列的第一帧进行三角形网格的分裂与合并。对MPEG－4标准测试序列的分割实验取得了较好的结果。     

Source number estimation and separation algorithms of underdetermined blind separation

Recently, sparse component analysis （SCA） has become a hot spot in BSS research. Instead of independent component analysis （ICA）, SCA can be used to solve underdetermined mixture efficiently. Two-step approach （TSA） is one of the typical methods to solve SCA based BSS problems. It estimates the mixing matrix before the separation of the sources. K-means clustering is often used to estimate the mixing matrix. It relies on the prior knowledge of the source number strongly. However, the estimation of the source number is an obstacle. In this paper, a fuzzy clustering method is proposed to estimate the source number and mixing matrix simultaneously. After that, the sources are recovered by the shortest path method （SPM）. Simulations show the availability and robustness of the proposed method.     

“和欣”手机的数据完整性技术研究

"和欣"嵌入式操作系统是基于CAR构件技术、支持构件化应用的操作系统,是国家863支持的TD-SCDMA的操作系统标准。本文简要介绍了"和欣"操作系统上的数据库系统SQLite,并着重分析了SQLite的数据库原子性在单文件数据库与多文件数据库中的具体实现过程,并且介绍了错误发生时执行数据恢复的过程。     

A discriminative learning framework with pairwise constraints for video object classification

To deal with the problem of insufficient labeled data in video object classification, one solution is to utilize additional pairwise constraints that indicate the relationship between two examples, i.e., whether these examples belong to the same class or not. In this paper, we propose a discriminative learning approach which can incorporate pairwise constraints into a conventional margin-based learning framework. Different from previous work that usually attempts to learn better distance metrics or estimate the underlying data distribution, the proposed approach can directly model the decision boundary and, thus, require fewer model assumptions. Moreover, the proposed approach can handle both labeled data and pairwise constraints in a unified framework. In this work, we investigate two families of pairwise loss functions, namely, convex and nonconvex pairwise loss functions, and then derive three pairwise learning algorithms by plugging in the hinge loss and the logistic loss functions. The proposed learning algorithms were evaluated using a people identification task on two surveillance video data sets. The experiments demonstrated that the proposed pairwise learning algorithms considerably outperform the baseline classifiers using only labeled data and two other pairwise learning algorithms with the same amount of pairwise constraints.     

电脑黑客与网络安全

叙述了黑客对网络安全的破坏作用,说明了黑客对网络进行病毒攻击的手段和信息轰炸的攻击方法,并利用敌方网络系统进行情报窃取、情报欺骗。对网络安全的防护技术进行了探讨。