M2M小数据业务的IEEE 802.11WLAN分析模型

机器对机器(M2M,machine to machine)通信有着巨大的市场潜力,无线局域网(WLAN)作为3G网络的主要补充必将承载大量M2M业务。针对具有时延容忍、耐性重试等特点的典型的M2M小数据业务,提出了一种大时间尺度退避的IEEE 802.11 DCF网络分析模型,推导了MAC层服务时间的概率分布;建立IPP/G/1/K离散时间排队系统模型,研究了非饱和负载下M2M业务在WLAN非理想信道中传输的QoS性能。算法仿真结果表明,大时间尺度退避机制有效提高了系统的吞吐量,降低了系统阻塞率。     

Strategic Thinking of the Internet of Things and M2M Services

Editor' s Desk: The Internet of Things is an important part of the emerging high-tech industry and has been recognized as one of the important technologies for addressing global financial crisis and revitalizing the economy. Therefore, at the beginning of the new year, we are honored to have invited Mr. Samuel Qi (Qi Qingzhong), a famous Chinese communication technology expert and GM of Shanghai Symphony Telecommunications Co., Ltd., to give his views on the development strategy of the Internet of Things and Machine to Machine (M2M) Communications.     

Optimal Resource allocation in NOMA‐based M2M communication using Hybrid Rider Optimization with FireFly: Power Saving Strategy of IoT

Nowadays, the Orthogonal Multiple Access (OMA) principle has utilized for allocating proper radio resources in wireless networks. However, as the count of users rises, OMA‐based approaches may not satisfy the stringent emerging requirements including very low latency, very high spectral efficiency, and massive device connectivity. Moreover, there are significant challenges in cellular‐enabled Machine‐to‐Machine (M2M) communications due to the unique features of M2M‐based applications. In order to overwhelm these challenges, non‐orthogonal multiple access (NOMA) principles emerge as a solution to enhance the spectral efficiency while allowing some degree of multiple access interference at receivers. Hence, this paper intends to develop an optimal resource allocation mechanism for M2M communication. Here, the nonlinear energy harvesting performed with the aid of an accessing technology termed as NOMA. The key objective of the proposed resource allocation model is the minimization of the total energy consumption of the network. For attaining the minimized power consumption, the time allocation, and transmission power of NOMA is optimally tuned by a hybrid optimization algorithm. The proposed hybrid algorithm merges the beneficial concepts of Rider Optimization Algorithm (ROA) and FireFly (FF) algorithm and implements a new algorithm termed as FireFly Modified Bypass‐based Rider Optimization Algorithm (FMB‐ROA). Finally, the analysis of total energy concerning various constraints validates the performance of the proposed model over conventional models.     

基于M值概率分布的网络视频流分类

为了改善网络视频流的细粒度分类效果,该文分析视频流传输过程中的特征变化与流分类之间的关系。根据不同类型的视频流具有不同的下行传输速率变化模式,提出一种新的基于下行速率传输的视频流分类特征--M值概率分布,并使用支持向量机(SVM)实现网络视频流的分类。实验结果表明,M值概率分布相比较于已有的常见流特征,可以更好地实现6种典型的网络视频流分类。     

Energy‐efficient resource allocation for lifetime maximization in M2M networks

Machine‐to‐machine (M2M) communications is one of the major enabling technologies for the realization of the Internet of Things (IoT). Most machine‐type communication devices (MTCDs) are battery powered, and the battery lifetime of these devices significantly affects the overall performance of the network and the quality of service (QoS) of the M2M applications. This paper proposes a lifetime‐aware resource allocation algorithm as a convex optimization problem for M2M communications in the uplink of a single carrier frequency division multiple access (SC‐FDMA)‐based heterogeneous network. A K‐means clustering is introduced to reduce energy consumption in the network and mitigate interference from MTCDs in neighbouring clusters. The maximum number of clusters is determined using the elbow method. The lifetime maximization problem is formulated as a joint power and resource block maximization problem, which is then solved using Lagrangian dual method. Finally, numerical simulations in MATLAB are performed to evaluate the performance of the proposed algorithm, and the results are compared to existing heuristic algorithm and inbuilt MATLAB optimal algorithm. The simulation results show that the proposed algorithm outperforms the heuristic algorithm and closely model the optimal algorithm with an acceptable level of complexity. The proposed algorithm offers significant improvements in the energy efficiency and network lifetime, as well as a faster convergence and lower computational complexity.     

一种JavaIC卡专用CPU结构研究

本文提出了一种针对Java卡虚拟机的硬件结构.Java卡虚拟机是Java语言与智能卡之间的接口,开发支持Java语言的智能卡对IC卡的发展十分重要.现有的智能卡中实现的Java卡虚拟机性能较差,本文给出一种基于Java卡虚拟机的专用CPU结构,该结构直接执行Java字节码,并采用了两条指令合并机制.文中将详细介绍所提出的硬件结构,包括指令集的选取,模块划分及各功能模块结构,并着重介绍硬件结构中的两条指令同时执行机制的实现.     

基于台达运动控制技术的LED组件检测机

在简单介绍LED组件检测机运动控制要求的基础上,重点讨论基于台达机电一体化技术平台的运动控制系统解决方案。方案用伺服技术替代原检测机液压定位控制系统,较好的满足检测机CCD传感器高速精确定位问题。     

锂电池打包机整形定位和拉胶带结构的设计

锂电池打包机是一种用于锂电池极组外包封的自动化设备,基于客户新生产工艺,对锂电池打包机整形定位和拉胶带结构进行设计,阐述了锂电池打包机的工作原理、工作流程、工艺要求,分析了如何提高锂电池的打包精度和表面平整度。     

基于EHFSM的电控模拟系统软件设计与实现

某电控模拟系统中,采用传统事件驱动思想设计的程序往往存在着高复杂性和低可读性的缺点,且由于代码的可维护性和复用性较差,使得程序编写的工作量非常巨大,为解决这一问题,提出采用有限状态（FSM）建立过程模型的设计方案,鉴于该电控模拟系统层级复杂,而FSM描述能力有限的实际,提出基于分层扩展有限状态机（EHFSM）的改进方法,对电控模拟系统进行了软件设计实现.     

对基于虚拟机机制的智能网SCF程序结构的研究

本文提出了一种基于虚拟机机制的智能网业务控制功能（ Ｓ Ｃ Ｆ）的程序结构。基于这种结构可以实现 Ｓ Ｃ Ｆ功能与智能业务的无关,即智能业务的引入与修改不需要重新编译 Ｓ Ｃ Ｆ软件。通过把一个智能呼叫与一个虚拟机相映射,做到了对智能业务的逻辑描述与具体实现的统一。本文还详细讨论了智能呼叫在 Ｓ Ｃ Ｆ 中的状态转移和所设计的虚拟机的内部结构,以及基于虚拟机加解释器的工作原理和优点     

Lexicon-based approach outperforms Supervised Machine Learning approach for Urdu Sentiment Analysis in multiple domains

Web is facilitating people to express their views and opinions on different topics through reviews and blogs. Effective advantages can be reaped from these reviews and blogs by fusing the sentiment knowledge. In this research, Sentiment Analysis of Urdu blogs from multiple domains is done by using the two widely used approaches i.e. the Lexicon-based approach and the Supervised Machine Learning approach. Three well known classifiers i.e. Support Vector Machine, Decision Tree and K Nearest Neighbor are used in case of Supervised Machine Learning approach whereas a wide coverage Urdu Sentiment Lexicon and an efficient Urdu Sentiment Analyzer are used in Lexicon-based approach. In both the approaches the information are fused from two sources to successfully perform Sentiment Analysis. In case of Lexicon-based approach, the two sources are the wide coverage Urdu Sentiment Lexicon and the efficient Urdu Sentiment Analyzer. In case of Supervised Machine Learning approach, the two sources are the un-annotated data and annotated data along with important attributes. After performing Sentiment Analysis using both the approaches, the results are observed carefully and on the basis of experiments performed in this research, it is concluded that the Lexicon-based approach outperforms Supervised Machine Learning approach not only in terms of Accuracy, Precision, Recall and F-measure but also in terms of economy of time and efforts used.     

基于Gabor小波和支持向量机的人脸识别方法

基于Gabor小波在图像表征方面的优越性，提出了将Gabor小波和支持向量机(SVM)相结合用于人脸识别的方案。用Gabor小波对人脸图像进行特征提取，再利用SVM策略对特征向量进行分类识别，实验的仿真结果验证了本算法的有效性。     

NLOS identification for UWB localization based on import vector machine

Ultra-Wide Band (UWB) based localization is one of the most promising techniques for high accuracy localization. The crucial factor that aggravates the localization precision is None-Line-of-Sight (NLOS) propagation. To address this issue, we propose a novel NLOS identification algorithm with feature selection strategy and a localization algorithm based on Import Vector Machine (IVM) with high accuracy and low complexity. The feature selection strategy further meliorates the classification accuracy. The probability outputs of IVM is employed by the localization algorithm and yields higher positioning accuracy than its counterpart methods – Support Vector Machine (SVM) and Relevance Vector Machine (RVM). Simulation results prove that IVM is a robust and efficient method for NLOS identification and localization.     

Java卡的研究与实现

Ｊａｖａ卡是一种新的智能,它基于Ｊａｖａ语言和虚拟机。Ｊａｖａ卡应用程序（ａｐｐｌｅｔ）可以运行于Ｊａｖａ卡运行环境－ＪＣＲＥ（Ｊａｖａ Ｃａｒｄ Ｒｕｎｔｉｍｅ Ｅｎｖｉｒｏｎｍｅｎｔ）中,ＪＣＲＥ包含Ｊａｖａ卡虚拟机－ＪＣＶＭ（Ｊａｖａ Ｃａｒｄ Ｖｉｒｔｕａｌ Ｍａｃｈｉｎｅ）、核心ＡＰＩ类库和相关的本地方法。ＪＣＶＭ由两部分组成：卡外虚拟机和卡内虚拟机。主要介绍了卡内虚拟机的解释执行、异常     

高压变频器在煤气加压机中的应用

2005年11月份邯郸钢铁厂8万煤气柜外送煤气加压机采用高压变频技术改造。其原因是该加压机外送量波动较大，采用普通阀门调节打回流，将会浪费大量电能，采用变频技术改造后，节能效果非常明显。     

Java虚拟机内存管理分析

讨论了Java关键技术组成，深入介绍了Java虚拟机的体系结构，分析了虚拟机中内存管理的垃圾回收机制。同时，对现有的一些流行垃圾回收算法进行了深入分析和对比以及对某些算法的改进。Java虚拟机中存在多种垃圾回收机制，通过对Java虚拟机中内存管理机制的深入分析，可以使开发者针对不同的内存分配及回收策略选择合适的虚拟机。     

用于表示级特征融合与分类的相关熵融合极限学习机

在极限学习机(ELM)网络结构和训练模式的基础上,该文提出了相关熵融合极限学习机(CF-ELM)。针对多数分类方法中表示级特征融合不充分的问题,该文将核映射与系数加权相结合,提出了能够有效融合表示级特征的融合极限学习机(F-ELM)。在此基础上,用相关熵损失函数替代均方误差(MSE)损失函数,推导出用于训练F-ELM各层权重矩阵的相关熵循环更新公式,以增强其分类能力与鲁棒性。为了检验方法的可行性,该文分别在数据库Caltech 101, MSRC和15 Scene上进行实验。实验结果证明,该文所提CF-ELM能够在原有基础上进一步融合表示级特征,从而提高分类正确率。     

基于虚拟机的兼容微处理器功能验证平台

本文根据验证兼容指令集微处理器的要求，提出了利用虚拟机快速建立微处理器功能验证平台，使用已有的操作系统和应用程序作测试程序验证兼容微处理器的方法，并给出了在验证兼容Intel486指令集微处理器AMEX86中的具体应用。     

基于支持向量机的嵌入拒识代价的手写字符识别研究

支持向量机(SVM-Support Vector Machine)是在统计学习理论基础上发展起来的一种新的机器学习方法.传统的SVM是基于两类问题的,而实际需要解决的一般是多类问题.因此,将SVM应用于多类问题对挖掘SVM的应用潜力将具有非常重要的意义.此研究针对SVM在手写字符中的作用所提出的拒识代价和误分类代价是一...