移动台车栏板辅助浇筑系统的设计与应用

蒲石河工程下水库大坝顶部混凝土护栏位于坝顶临空边沿,钢筋绑扎和模板支立作业难度大,施工时存在安全隐患。为此,蒲石河工程设计了移动台车栏板辅助浇筑系统并成功应用。实践表明,作业台车设计可靠、安全稳定、操作简便、定位准确、移动方便,保障了施工人员人身安全,节约了成本,加快了工期,提高了模板支立质量,值得类似工程借鉴并推广使用。     

用于高速列车移动网络的资源分配实时算法

误码率高、频繁中断的高速列车移动场景宜采用多宿主容迟网络。但其资源分配计算复杂,实时性较差。基于效用差分法设计初始化过程资源分配算法,以保证过程中每一步的效用单调不减,算法时间复杂度降至O(n)。     

RETRACTED ARTICLE: Biometric identification,financial inclusion and economic growth in India: does mobile penetration matter?

ABSTRACT

Using data on major Indian states during 2001–2014, the paper analyzes the impact of AIM (Aadhaar, Inclusion and Mobile) trinity on economic growth. Using advanced panel data techniques, the evidence suggests that mobile penetration exerts a positive and statistically significant impact on economic growth. In addition, there exist strong complementarities between mobile telephony and financial inclusion. The magnitude of the complementarity is significantly higher in case of financial use as compared with financial access. In addition, the evidence points to the fact that biometric identification process (Aadhaar) is making its impact felt on financial inclusion, primarily by improving financial access.     

中国电信移动网络HACCG的建设方案

文章对MIP及内容计费进行了详细的总结,并提出了中国电信移动网络HA/CCG的建设方案和策略,以及HA/CCG与周边系统的接口要求,能较好地指导电信运营商的准备工作和HA/CCG的网络建设.     

移动无线传感网络节点协同避障的改进方法

传统蜂拥控制模型在协同避障跟踪方面,目前有Reynolds和Tanner的蜂拥模型。笔者曾对其做出了改进,提出了与Steer to Avoid法则相结合的避障模型,该模型在跟踪过程中对凸形障碍有较高的避障效率。由于在Steer to Avoid的方向判断中,目标对节点具有引力,使节点群陷入凹形区域无法绕出。将协同避障模型引入凹形障碍环境中,对模型进一步改进,在Steer to Avoid转向判断时暂时取消目标对节点群的引力,让节点群在进入凹形后自行做出环境的判断并沿着障碍边缘不断搜索路径,最终绕出障碍到达目标。仿真实验结果表明：与传统两个模型相比,该模型在避障的平均速率和时间效率上有显著提高,适用于避开未知的凹形障碍。     

A TDOA location algorithm based on data fusion

A new positioning method in mobile networks is presented. Based on the data fusion technology, it processes multi-layer information fusion for the location estimates achieved by the Chan algorithm, which increases mobile positioning accuracy effectively by only using measured difference of arriving (TDOA) signals. The method is simple and practical, especially when the location estimates are corrupted by the non-line-of-sight (NLOS) error. It not only has high positioning accuracy, but also reduces the location failure probability. Results from computer simulation show that the proposed method is effective in various environments. __________ Translated from Journal of Dalian University of Technology, 2005, 45(1): 138–141 (in Chinese)     

GSM手机音频设计和测试

详细叙述了终端的音频指标要求和测试调试方法，之后基于Infineon平台的一个实际项目中，论述了如何测试和配置控制数据全过程，使读者能够全面了解GSM手机音频方面的技术标准和电路调试过程．     

室内移动机器人超声传感器导航网络分布研究

对于室内广域移动机器人,超声波传感器网络是能够保证给定导航准确度的一种有效方法。合理的超声波传感器网络分布是实现超声波网络导航的重要条件。论述了超声波网络合理分布的原则,据此提出并分析了获得合理超声波传感器网络分布的四个步骤,仿真试验结果验证了它的有效性。     

Rough Terrain Autonomous Mobility—Part 1: A Theoretical Analysis of Requirements

A basic requirement of autonomous vehicles is that of guaranteeing the safety of the vehicle by avoiding hazardous situations. This paper analyses this requirement in general terms of real-time response, throughput, and the resolution and accuracy of sensors and computations. Several nondimensional expressions emerge which characterize requirements in canonical form.The automatic generation of dense geometric models for autonomously navigating vehicles is a computationally expensive process. Using first principles, it is possible to quantify the relationship between the raw throughput required of the perception system and the maximum safely achievable speed of the vehicle. We derive several useful expressions for the complexity of terrain mapping perception under various assumptions. All of them can be reduced to polynomials in the response distance.The significant time consumed by geometric perception degrades real-time response characteristics. Using our results, several strategies of active geometric perception arise which are practical for autonomous vehicles and increasingly important at higher speeds.     

Bayesian Landmark Learning for Mobile Robot Localization

To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (e.g., what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL outperforms two other recent approaches to mobile robot localization.