Wireless channel extraction analyzing based on graph theory

Wireless channels comprise various signal characteristics that correspond to different features. This research applies digital signal processing to first excavate and categorize various features found in the channel data. Then, borrowing from graph theory, fast clustering analysis and decision tree modeling are introduced to identify unique “fingerprint” characteristics. Finally, two scenarios were tested using artificial neural networks to identify and verify their applicability in different geographical locations.     

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.     

A deep learning scheme for mental workload classification based on restricted Boltzmann machines

The mental workload (MWL) classification is a critical problem for quantitative assessment and analysis of operator functional state in many safety-critical situations with indispensable human–machine cooperation. The MWL can be measured by psychophysiological signals. In this work, we propose a novel restricted Boltzmann machine (RBM) architecture for MWL classification. In relation to this architecture, we examine two main issues: the optimal structure of RBM and selection of the most important EEG channels (electrodes) for MWL classification. The trial-and-error and entropy-based pruning methods are compared for the RBM structure identification. The degree of importance of EEG channels is calculated from the weights in a well-trained network in order to select the most relevant channels for classification task. Extensive comparative results showed that the selected EEG channels lead to accurate MWL classification across subjects.     

Linux下连续媒体文件系统研究

Linux是Internet时代最辉煌的成果之一,它已经成为Internet上最受欢迎的网络操作系统。人们通过Linux提供面向Internet的各种服务,范围从WWW到网上直播。但基于Linux的高质量视频点播服务（VOD）鲜见有人研究。文章正是在这种背景下提出了Linux下的连续媒体文件系统（LCMFS）,将其作为基于Linux的高质量视频点播服务系统的底层支撑。文章先介绍了与Linux相关的背景知识,接着对Linux的现有文件系统—Ext2文件系统的结构进行剖析,指出其在处理连续媒体文件时的不足之处,然后据此提出了LCMFS的设计思想和原则,并阐述了LCMFS的设计及实现方案,最后对LCMFS的性能进行了分析和测试。     

Optimization of Industrial Structure Considering the Uncertainty of Water Resources

This paper developed a stochastic linear fractional programming model for industry optimization allocation base on the uncertainty of water resources incorporating chance constrained programming and fractional programming. In this paper, the stochastic linear fractional programming is used in the real word. The development SLFP has the following advantages: (1) The model can compare the two aspects of the targets; (2) The model can reflect the system efficiency intuitively; (3) The model can deal with uncertain issues with probability distribution; (4) The model can give different optimal plans under different risk conditions. The model has a significant value for the industry optimization allocation under uncertainty in local and areas to achieve the maximum economic benefits and the full use of the water resources.     

基于消除地方高校成长上限基模的实证研究——以江西M学校为例

本文基于系统动力学反馈基模分析方法,在分析江西地方高校发展的成长上限基模和对策基模的基础上进行实证研究,引入相关数据,构建消除成长上限基模的顶点赋权模型,并提出综合对策思考。     

云南村镇垃圾收转运系统建设探讨

对虚拟的垃圾收运项目区域,从覆盖范围的确定、收集方式的选择、收运设施的设置、收运线路的规划4个方面做了较为全面的分析和研究,期望村镇垃圾收转运系统的设计和实施能够实用、适用,并对云南省同类型的村镇垃圾收转运系统建设具有借鉴意义。     

水口水电厂设备状态监测与诊断分析系统

结合工程实践,全面地描述了水口水电厂机组、主变的状态监测与诊断分析系统,包括总体目标和设计原则,系统总体结构,监测范围和测点布置,软件系统组成,系统的主要功能及应用。     

基于嵌入式系统开发平台的设计与实现

本文首先介绍了EVB2107评估板、评估板的系统开发工具和集成开发环境,然后对扩展板上各模块都从原理入手结合该模块要实现的功能说明其设计思想和实现原理,并就各模块驱动程序的编程思想及流程进行了论述。