专业健身中心运动减肥效果的研究——以荆州市为例

肥胖,不仅给日常生活带来诸多不便,而且还引发一系列疾病.人们不断地寻求减肥的良方,但终因对肥胖缺乏科学的认识,收效甚微.本文通过对贵阳市健身房运动减肥人群的调查,分析健身房减肥失败的原因,系统的研究了贵阳市健身房运动减肥的的效果,并提出了运动减肥的对策,对选择健身房减肥的人群,减肥计划、运动强度、饮食规律、运动持续性做出重点研究.这研究能提高健身房运动减肥的效果,对人们健康减肥有很大帮助.     

试论电器内部矛盾运动的规律

一、电器的基本矛盾 电器是一种电气的开关控制设备.它的任务就是根据外界指定的讯号对电或非电对象进行控制、调节和保护.为了完成这个任务,电器在特性方面必须具有接受和反应讯号的“感应机能”和实现控制作用的“执行机能”,在结构方面必须具有与之相适应的“感应器官”部分和“执行器官”部分(简称感应竞争和执行部分).感应部分和执行部分间相互联结和相互作用的过程,就是电器实现控制任务的过程.从整个电器来看,感应部分和执行部分完成任务的最后目标是一致的,从结杓上看它们结合在一体中,所以它们是统一的,但从两部分各自的机能来看,…     

基于深度图象Warping的动态图形反走样算法

时间域上的动态图形反走样算法是提高序列画面图象播放质量的关键技术。由于该技术需在时间域上加密采样,因而其计算过程非常耗时。本文首先给出了一种新的深度图象ｗａｒｐ－ｉｎｇ算法,并用它来快速生成由于摄像机参数变化而产生的静态景物画面的细微变化,而运动景物画面则根据其深度值复合到相应时刻的静态景物画面中。利用这一技术,我们可快速地在相邻帧画面中间进行加密采样。而Ｇａｕｓｓ点优化采样策略则极大地提高了反走样算法的效率。理论和实验结果表明,本算法能高效地实现复杂场景图形的动态反走样,它不仅独立于场景复杂性,而且可根据画面质量要求来调整精度。     

基于虚拟现实的载人潜水器操纵运动仿真系统

载人潜水器操纵运动仿真系统可用于性能研究和人员训练.与已有类似仿真系统不同的是,介绍的仿真系统显示的海底视景是基于驾驶人员通过摄像头所观察到的清晰化小场景,更符合潜水器操纵实际情况.介绍了仿真系统的体系结构,建立了空间六自由度运动方程,进行了仿真计算,讨论了两个影响视景显示的关键问题:视景建模和显示的实时性.工程试验结果表明,仿真系统的设计是合理有效的.     

行人运动仿真研究综述

行人运动仿真是近年来仿真领域研究的热点之一。从行人运动仿真的研究意义和研究内容出发,介绍了国内外行人运动仿真研究发展历程和研究现状,论述了行人运动仿真模型的三种类型,分析了成本效益元胞模型、元胞自动机模型、磁场力学模型、社会力学模型、排队网络模型五种典型的行人运动仿真模型的原理和适用条件。最后探讨了目前行人运动仿真研究中存在的问题及未来的发展前景。     

基于对偶四元数的航姿系统姿态更新算法研究

以微小型捷联航姿参考系统为研究对象,建立基于对偶四元数的导航系统姿态更新解算参数模型。该方法区别于描述刚体纯旋转变换的四元数方法,将载体系与地理系之间坐标转换的旋转与平移过程进行统一、融合,通过建立最小方差目标方程,求解最优的姿态更新矩阵。构建Kalman滤波方程,最终确定载体姿态,航向角实时信息。仿真表明,对偶四元数所建姿态四元数误差的均值与均方差均小于0.002。Kalman滤波器构造符合实际系统要求,在传感器测量精度有限的情况下,可使俯仰角、横滚角、航向角误差分别减小到0.1°,0.1°,0.2°。     

光学运动捕捉散乱数据处理的一种方法

阐述了一种适用于被动式光学人体运动捕捉散乱数据处理的方法,该方法基于模版匹配的原理,根据人体标示的特征点的局部相对刚性结构以及整体拓扑结构关系,利用刚性结构匹配、运动轨迹追踪、拓扑结构校验实现散乱数据的处理,从而得到人体运动参数及骨架拓扑结构。该方法解决了追踪算法在特征点重合时的追踪出错问题,处理方法经优化后数据处理过程能够满足运动捕捉的实时性要求,具备错误自动检测和纠正能力,处理过程无须人工干涉。     

Modeling simulation and experiment of micro-Doppler signature of precession

Spatial precession is a special micro-motion of the spinning-directional target, and the micro-Doppler signature of the cone-shaped target with precession is studied. The micro-motion model of precession is built first, and then the micro-Doppler model is developed based on the proposed concept of micro-motion matrix, by which the theoretical formula of micro-Doppler signature of precession is derived. In order to further approach to the actual case, the occlusion effect is firstly considered in micro-Doppler, and the simulated result with occlusion effect is well in accordance with the measured result in microwave anechoic chamber, which suggests that the micro motion model and micro-Doppler model of precession are both valid.     

Moving object detection in framework of compressive sampling

Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for signals. In the image application with limited resources the camera data can be stored and processed in compressed form. An algorithm for moving object and region detection in video using a compressive sampling is developed. The algorithm estimates motion information of the moving object and regions in the video from the compressive measurements of the current image and background scene. The algorithm does not perform inverse compressive operation to obtain the actual pixels of the current image nor the estimated background. This leads to a computationally efficient method and a system compared with the existing motion estimation methods. The experimental results show that the sampling rate can reduce to 25% without sacrificing performance.     

Fast motion estimation algorithm for H.264/AVC based on centered prediction

H.264/AVC video coding standard can achieve roughly half of the bit-savings over MPEG2 and MPEG4 for a given quality. However, this comes at a cost in considerably increased complexity at the encoder and thus increases the difficulty in hardware implementation. The high redundancy that exists between the successive frames of a video sequence makes it possible to achieve a high data compression ratio. Motion estimation (ME) plays an important role in motion compensated video coding. A fast motion estimation algorithm for H.264/AVC is proposed based on centered prediction, called centered prediction based fast mixed search algorithm (CPFMS). It makes use of the spatial and temporal correlation in motion vector (MV) fields and feature of all-zero blocks to accelerate the searching process. With the initialized searching point prediction, adaptive search window changing and searching direction decision, CPFMS is provided to reduce computation in block-matching process. The experimental results show that the speed of CPFMS is nearly 12 times of FS with a negligible peak signal-noise ratio (PSNR) loss. Also, the efficiency of CPFMS outperforms some popular fast algorithms such as hybrid unsymmetrical cross multi-hexagongrid search and a novel multidirectional gradient descent search evidently.