A robust adaptive weighted constant modulus algorithm for blind equalization of wireless communications systems under impulsive noise environment

A robust adaptive weighted constant modulus algorithm is proposed for blind equalization of wireless communication systems under impulsive noise environment. The influence of the impulsive noise is analyzed based on numerical analysis method. Then an adaptive weighted constant modulus algorithm is constructed to adaptively suppress impulsive noise. Theoretical analysis is provided to illustrate that the proposed algorithm has a robust equalization performance since the impulsive noise is adaptively suppressed. Moreover, the proposed algorithm has stable and quick convergence due to avoidance of large misadjuntment and adoption of large step size. Simulation results are presented to show the robust equalization performance and the fast convergence speed of the proposed algorithm under both impulsive noise and Gaussian noise environments.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

一种基于自适应退避值的ZigBee网络CSMA/CA接入机制

目前,IEEE 802.15.4/ZigBee网络CSMA/CA接入机制中退避值的初始值采取了固定设置模式,难以适应不同的网络应用环境,从而对ZigBee网络的时延、能耗、吞吐量等性能造成了一定的负面影响。文章提出一种基于自适应退避值的Zigbee网络CSMA/CA接入机制,旨在通过动态调整退避指数和竞争窗口长度,自适应节点数据传输情况,达到提高网络性能的目的。仿真结果表明,文章所提出的自适应接入机制在提高ZigBee网络吞吐量、降低能耗等方面优势较为明显。     

基于Hammerstein模型的非线性信道广义线性盲均衡算法

为了提高非线性信道盲均衡的性能、降低运算复杂度,本文以Hammerstein模型代替传统的Volterra级数模型来模拟非线性信道,利用非线性信道接收信号呈现非圆性的特点,构造了一种新的基于Wiener非线性模型的广义线性盲均衡器,并在常模准则的基础上提出了NCWL-CMA和NCWL-CMA Newton-like两种非线性信道广义线性盲均衡器抽头系数更新算法.理论分析和仿真实验结果表明,与传统盲均衡算法相比,新算法显著地降低了剩余码间干扰,提高了收敛速度.     

基于兴趣点方向梯度直方图的图像检索方法

提出了一种基于图像兴趣点方向梯度直方图的检索方法.为了提高检索准确度,首先采用直方图均衡化增强图像对比度,然后利用SURF(Speeded Up Robust Features)检测子检测图像中的兴趣点,以兴趣点为中心,对兴趣点邻域内分块方向梯度直方图进行图像特征描述,最后进行相似性度量.该算法通过直方图均衡化,提取到图像中更丰富的细节信息尤其对于颜色单一与颜色较深的图像,而且算法中充分利用了图像中包含信息量较多的图像兴趣点.实验证明,该算法提高了图像检索的准确度,相比其他算法取得了更好的检索结果.     

叠加训练序列设计及其高效传输技术研究

采用训练序列与信息数据叠加的传输方案由于消除训练序列占用的频带开销而受到广泛关注。然而,如何高效的分离叠加信号是实现高效信道估计和可靠检测的基础。通过叠加周期训练序列,研究了基于数据依赖的叠加训练(DDST)方案的高效信道估计和检测方案。并结合信道编码技术,研究了编码条件下DDST方案与传统时分复用(TDM)方案的性能。仿真结果表明,在消除训练带宽开销的情况下,获得DDST方案与TDM的误码率基本保持一致。     

基于隐马尔科夫模型的CPM信号盲Turbo均衡算法

针对高阶连续相位调制信号(CPM)盲均衡中存在的均衡性能较差以及不容易收敛等问题,从隐马尔科夫模型(HMM)的角度出发,建立一种新的EM-SOVA信道盲均衡方法,并结合Turbo均衡思想,提出了一种适用于高阶CPM信号的盲Turbo均衡算法。该算法通过将SOVA内嵌到EM算法的迭代过程中,有效改善了信道均衡效果,同时使用Turbo均衡的软信息迭代处理来进一步提高低信噪比条件下系统性能。理论分析和仿真结果表明,所提算法具有良好的盲均衡性能以及收敛性。     

基于MIMO的可见光通信中的信道均衡

针对基于多输入多输出(MIMO)的室内可见光通信(Visible-Light Communication,VLC)系统中存在的码间串扰(ISI)问题进行了分析,并提出了适用于MIMO可见光通信系统的信道均衡方案及实现方法.仿真实验结果表明,该方法可以有效降低MIMO可见光通信系统中的码间串扰的影响.     

基于Zynq-7000的宽带自适应噪声抵消系统设计

由于干扰噪声常常是随机变化和非平稳的,传统的滤波方法很难消除.针对这一问题,选用自适应格型联合算法(GAL-NLMS).在Zynq-7000芯片PL部分实现了一个22阶的GAL-NLMS算法,在芯片的PS部分实现了驱动程序的设计和基于Qt的界面设计.处理速度达到175 Mbit/s,并在ZedBoard开发平台实现了宽带自适应噪声抵消系统.用被噪声干扰过的QPSK和16QAM调制信号对系统进行测试,结果表明系统有效.