在不增加设备体积重量以及提高发射功率的前提下,为满足高码率遥测数据传输的需求,现有的脉冲编码调制-频调(Pulse Code Modulation Frequency Modulation, PCM-FM)遥测系统广泛使用多符号检测(Multiple Symbol Detection, MSD)算法和Tu...在不增加设备体积重量以及提高发射功率的前提下,为满足高码率遥测数据传输的需求,现有的脉冲编码调制-频调(Pulse Code Modulation Frequency Modulation, PCM-FM)遥测系统广泛使用多符号检测(Multiple Symbol Detection, MSD)算法和Turbo乘积码(TPC)技术。调制指数是PCM-FM遥测系统中的重要参数之一,对系统带宽和误码率有重要影响。本文分析了MSD多符号检测算法原理,通过计算互相关系数及Matlab仿真,分析了调制指数大小对带宽和误码率的影响,指出将调制指数设置为0.715较为合适;分析了调制指数偏差对信噪比损耗的影响,利用MSD算法的相位网格图分析了调制指数偏差对误码和码同步的影响,指出随机数据比连“0”或连“1”数据具有更好的容忍能力。展开更多
Digital data have become a torrent engulfing every area of business, science and engineering disciplines, gushing into every economy, every organization and every user of digital technology. In the age of big data, de...Digital data have become a torrent engulfing every area of business, science and engineering disciplines, gushing into every economy, every organization and every user of digital technology. In the age of big data, deriving values and insights from big data using rich analytics becomes important for achieving competitiveness, success and leadership in every field. The Internet of Things (IoT) is causing the number and types of products to emit data at an unprecedented rate. Heterogeneity, scale, timeliness, complexity, and privacy problems with large data impede progress at all phases of the pipeline that can create value from data issues. With the push of such massive data, we are entering a new era of computing driven by novel and ground breaking research innovation on elastic parallelism, partitioning and scalability. Designing a scalable system for analysing, processing and mining huge real world datasets has become one of the challenging problems facing both systems researchers and data management researchers. In this paper, we will give an overview of computing infrastructure for IoT data processing, focusing on architectural and major challenges of massive data. We will briefly discuss about emerging computing infrastructure and technologies that are promising for improving massive data management.展开更多
文摘在不增加设备体积重量以及提高发射功率的前提下,为满足高码率遥测数据传输的需求,现有的脉冲编码调制-频调(Pulse Code Modulation Frequency Modulation, PCM-FM)遥测系统广泛使用多符号检测(Multiple Symbol Detection, MSD)算法和Turbo乘积码(TPC)技术。调制指数是PCM-FM遥测系统中的重要参数之一,对系统带宽和误码率有重要影响。本文分析了MSD多符号检测算法原理,通过计算互相关系数及Matlab仿真,分析了调制指数大小对带宽和误码率的影响,指出将调制指数设置为0.715较为合适;分析了调制指数偏差对信噪比损耗的影响,利用MSD算法的相位网格图分析了调制指数偏差对误码和码同步的影响,指出随机数据比连“0”或连“1”数据具有更好的容忍能力。
文摘Digital data have become a torrent engulfing every area of business, science and engineering disciplines, gushing into every economy, every organization and every user of digital technology. In the age of big data, deriving values and insights from big data using rich analytics becomes important for achieving competitiveness, success and leadership in every field. The Internet of Things (IoT) is causing the number and types of products to emit data at an unprecedented rate. Heterogeneity, scale, timeliness, complexity, and privacy problems with large data impede progress at all phases of the pipeline that can create value from data issues. With the push of such massive data, we are entering a new era of computing driven by novel and ground breaking research innovation on elastic parallelism, partitioning and scalability. Designing a scalable system for analysing, processing and mining huge real world datasets has become one of the challenging problems facing both systems researchers and data management researchers. In this paper, we will give an overview of computing infrastructure for IoT data processing, focusing on architectural and major challenges of massive data. We will briefly discuss about emerging computing infrastructure and technologies that are promising for improving massive data management.
