基于混沌与自编码相融合扩频码的构造

为了提高扩频通信系统数据传输的有效性和安全性,提出了一种混沌映射与自编码非线性融合的思想构造扩频码方法。该方法首先将Logistic混沌序列通过改进产生的复合符号混沌序列与自编码算法输出反馈进行逻辑异或,然后与自编码序列经非线性Henon映射相融合产生高质量的复合扩频码。以混沌和自编码为基础分析了该方法的有效性,并与现有方法进行比较,提高了序列的相关性和复杂度。在DS-CDMA系统中进行仿真验证,结果表明该方法产生的扩频序列抗多径干扰能力更好,并能获得较低的扩频误码率,有利于扩频通信的需要。     

基于m序列的宽间隔跳频序列的生成

为了克服对偶法生成宽间隔跳频随机序列的缺点,基于m序列,提出非线性模d法,利用非连续抽头模型,生成宽间隔跳频随机序列.比较了对偶法生成的跳频序列和非线性模d法生成的跳频序列的自相关性能和互相关性能等.计算结果表明,非线性模d法是对偶法的明显改进,其生成序列的随机性、抗干扰性都有明显改善,且相关性能接近于最佳跳频序列的相关性能.     

一种3GPP LTE伪随机序列生成优化算法

伪随机序列(Pseudo-random Sequence)被广泛应用在通信系统中信道估计、信号加扰和频率跳转等方面.3GPP LTE标准采用Gold序列作为伪随机序列,针对Gold序列生成公式中存在较大状态偏移量以及常规算法生成序列耗时较多等问题,提出了利用状态转移矩阵快速生成序列的优化算法.实验表明,基于TI公司TMS3206455开发测试平台,在生成相同长度序列时,优化算法比常规算法能减少大约17 000多个系统时钟周期,至少节省49%系统资源,对提高系统整体性能有重要意义.     

基于改进M序列的数字噪声源设计及其FPGA实现

常规模拟方法产生的噪声功率谱不平坦且很难控制。为产生高随机、高速度数字噪声,利用多位M序列的长周期伪随机性,截取高位M序列的部分位求和来产生新的M序列,证明其具有高位M序列的性质,并将其用于数字噪声源的设计应用。运用qurtusⅡ软件进行硬件编程仿真,并用matlab对仿真结果进行功率谱分析,证明新方法产生的随机噪声具有高随机、高速度的良好性能,且方法简单实用,具有一定应用价值。     

直接序列扩频系统中强窄带干扰抑制的一种非线性自适应抑制快速算法

本文将一种改进的非线性自适应抑制快速算法应用于直接序列扩频通信的强窄带干扰抑制中,并将其与其它时域自适应抑制算法进行仿真比较,在收敛速度和收敛精度相当的情况下,该算法结构简单、计算量适中且误码率小,特别是相对于其它算法,其对窄带干扰的抑制深度具有明显的优势。     

基于APEX20KE系列FPGA的超长M序列限幅脉冲压缩实现

首先介绍了高占空比、长M序列搜索雷达信号处理中超长M序列脉冲限幅压缩的实现及其特点以及APEX20KE系列FPGA的特点，指出实现超长M序列实时脉冲压缩的困难，在此基础上介绍一种以ALTERA的APEX20KE系列FPGA为处理器的超长M序列实时脉冲压缩算法的实现。     

基于SARIMA-BP神经网络方法的汽车销量预测研究

对汽车销量时间序列的预测方法进行了研究,针对汽车销量影响因素表现出的多样性,应用灰色关联分析法对其进行筛选。针对汽车销量时间序列表现出的线性特征和环境动态变化导致的非线性规律,单一的线性预测方法和非线性预测方法都无法满足时间序列的预测要求。提出了一种SARIMA-BP神经网络预测方法,利用SARIMA方法对时间序列的线性部分进行建模,利用BP神经网络方法对时间序列的非线性部分进行建模。仿真结果表明,SARIMA-BP神经网络方法比单一模型的预测准确率更高。     

TD-SCDMA通信基站架设的分层多目标优化模型

从.TD-SCDMA通信基站架设中所涉及的架设成本、通信容量,以及架设时间三方面的不同要求入手,建立经济效益与时间优化的具有优先级的分层多目标非线性规划模型,并给出求解该问题的宽容分层序列算法.最后进行试验,得到了问题的弱有效解,验证了此模型的可行性.     

几种发射分集OFDM系统信道估计方法性能评估

给出了3种基于发射分集的OFDM系统信道估计方法,分别从训练序列对算法的影响、估计准确度、算法复杂度和算法适用性等方面对各自性能作了分析比较,并进行了仿真.分析比较和仿真结果表明,训练序列对3种算法的影响很大,通过训练序列的正交化设计,LS和EM算法可以得到很大程度的简化;EM算法与LS算法估计精度相当,信噪比较大时,多项式模型算法估计精度要远远高于LS和EM算法;总体上来说,多项式模型算法复杂度最高,EM算法复杂度最低.     

基于快速OPAST算法的直扩信号PN码序列估计

在直接序列扩频（DSSS）通信对抗系统中,伪码（PN）序列估计是一个重要的研究课题。针对在某些情况下权值向量不收敛的问题,提出了一种基于快速正交投影逼近子空间跟踪（OPAST）算法和滑动窗技术的直扩信号PN码序列估计算法,对非同步接收DSSS信号按照宽窗口分段,利用快速OPAST算法提取主特征向量,滑动窗技术实现码同步。该算法迭代权值向量具有正交性以及良好的收敛性,同时解决了常见相位模糊问题。算法具有较低复杂度,数据存储量少,易于硬件实现与实时处理。仿真结果表明,在-10 dB的较低信噪比背景环境中,该快速OPAST算法可以正确有效地估计出PN码序列。     

一种残余频偏算法在OFDM同步跟踪中的应用

在正交频分复用(OFDM)系统中,定时误差、多普勒效应以及本地振荡器不稳定等因素都会引起估计频率产生偏移,致使子载波星座相位发生旋转,并随着时间而不断累积,最终导致解调失效。为此,提出了用均方误差短时反馈算法来矫正旋转相位的新方法。该方法利用在反馈时间段内计算得出的残余相偏来直接矫正后续接收数据的相位。仿真结果表明,该算法很好地满足实时跟踪的要求,具有运算简单、速度快等特点,同时也提高了系统的性能。     

基于旋转不变技术实现多用户交织OFDM频偏估计

本文提出一种针对多用户交织OFDM载频偏移估计的算法。首先介绍上行链路中交织OFDM的信号结构,利用其周期性质,形成特定的数据矩阵,利用旋转不变技术(ESPRIT)实现载频偏移估计。这种估计方法不需要利用任何训练序列或导频信号,进一步提高了系统频带利用率,降低了计算复杂度。仿真表明,该估计算法在信噪比大于零的情况下也能获得较好的频偏估计。     

TD-SCDMA系统中智能天线的重复迭代自适应算法

提出一种用于TD-SCDMA系统的重复迭代自适应智能天线算法。通过在自适应算法中把阵列输入信号和作为参考信号的训练序列重复使用,可以在短训练序列情况下实现较好的收敛,提高通信系统的传输效率。对LMS算法中全训练序列重复迭代和滑动窗口重复迭代算法进行了仿真,结果表明,在有限训练序列时本算法比传统LMS算法更好地逼近最佳解。     

基于Tent映射双向耦合映象格子的宽间隔跳频序列及其性能分析

为实时产生宽间隔跳频序列,提出了一种基于Tent映射双向耦合映象格子的宽间隔 跳频序列实时产生方法。该方法采用多比特量化和比特抽取相结合的量化方法和改进的平 移替代法进行宽间隔处理。仿真结果表明,实时产生的宽间隔跳频序列服从均匀分布,具有 宽的跳频间隔、良好的汉明相关性和复杂的相空间分布。     

Donor Segmentation: When Summary Statistics Don't Tell the Whole Story

Funding pressures amidst the slow economic recovery from the late-2000's recession have forced universities, as well as other not-for-profit organizations, to increase the volume and sophistication of their direct marketing activities. The efficiency of direct marketing strategies is linked to an organization's ability to effectively target individuals. In this paper, we present a finite-mixture model framework to segment the alumni population of a university in the midwestern United States.Much of the research on customer segmentation summarizes response data (e.g., purchase and contribution histories) via recency, frequency and monetary value (RFM) statistics. Individuals sharing similar RFM characteristics are grouped together; the rationale being that the best predictor of future behavior is past behavior. Summary statistics such as RFM, however, introduce aggregation bias that mask the dynamics of purchase/contribution behavior. Accordingly, we implement latent-class segmentation models where alumni are classified based on how an individual's contribution sequence compares to those of other individuals. The framework's capability to process contribution sequences, i.e., longitudinal data, provides fundamental new insights into donor contribution behavior, and provides a rigorous mechanism to infer and segment the population based on unobserved heterogeneities (as well as based on other observable characteristics). Specifically, we analyze Markov mixture models to segment alumni based on contribution-behavior patterns, under the assumption of serially-dependent contribution sequences. We use the expectation–maximization algorithm to obtain parameter estimates for each segment. Through an extensive empirical study, we highlight the substantive insights gained through the processing of the full contribution sequences, and establish the presence of three distinct classes of alumni in the population (each with a discernible contribution pattern). The proposed framework, collectively, provides a basis to tailor direct marketing policies to optimize specific performance criteria (e.g., profits).     

一种基于混沌序列的数字图像加密算法

对Logistic映射混沌序列生成方法作了一些改进，改进后生成的序列不仅克服了一维混沌序列的短周期行为，而且统计性能有了一定程度的改善。提出了一种基于此混沌序列的JPEG格式数字图像加密算法，通过在图像压缩的同时执行加密运算，使密图具有良好的压缩效率，试验表明加密效果是令人掩意的。     

Daily outfit satisfaction: the effects of self and others' evaluation on satisfaction with what I wear today

This study investigated the satisfaction of the outfit which an individual chose to wear for the day by examining how self and others' evaluations influenced the outfit satisfaction and the intention to re‐wear. In addition, moderator variables (body satisfaction and body surveillance) that affected the effect of the evaluative factors upon satisfaction and behavioural intention were also examined. An online‐survey was conducted and the sample size used in the analysis was 349 in total. Using SPSS 18.0 and AMOS 18.0, structural equation modeling analysis and one‐way analysis of variance were implemented for hypothesis testing. The results showed that self‐evaluation as a perception of appropriateness of the outfit for that day and others' positive feedback increases outfit satisfaction. Appropriateness perception also had a significant impact on the intention to re‐wear. However, feedback from others on one's outfit did not have a significant direct effect on behavioural intention. Three‐way ANOVA was performed to verify the impact the feedback from others, body satisfaction, and body surveillance had on the respondent's intention to re‐wear, and a three‐way interaction effect was statistically significant. While individuals with high body satisfaction were likely to be confident about their appearance overall, the feedback from others did not impact their satisfaction. Individuals with low body satisfaction were influenced by others' feedback on their outfit. Individuals with high levels of body surveillance who sensitively monitored others' feedback especially displayed decreased intention to re‐wear when feedback from others on their outfit was negative. This research contributed to the deeper understanding of consumer's clothing behaviour at the post‐purchase stage. Enhanced understanding of lasting satisfaction and benefits pursued throughout the time while using the product would be essential in developing consumer‐centric marketing strategies.     

High‐Frequency Positive Feedback Trading and Market Quality: Evidence from China's Stock Market

This paper managed to measure the positive feedback trading intensity and its asymmetry with high‐frequency transaction data of China's individual stocks. The intraday positive feedback trading is found to be heterogeneous, and buying‐winners effect is significantly stronger than selling‐losers effect. In general, the high‐frequency asymmetric positive feedback trading's impact on market quality is mixed: The intraday positive feedback trades contribute to a liquid and active‐trading market but at the same time slow down the price discovery process and reduce the price efficiency.     

一种新的混沌扩频序列产生方法

本文提出了一种新的混沌扩频序列产生方法。该方法基于神经网络的强大学习能力和副近非线性函数能力,应用具有全局最优的ＢＰ改进算法通过训练学习建立起具有混沌性态的优化神经网络模型,利用网络权值调整的灵活性来产生混沌扩频序列。计算机仿真结果表明,该模型产生的混沌扩频序列调整更容易,比基于单一混沌映射能产生更多符合扩频通信要求的扩频序列。