A Probing Model of Secret Key Generation Based on Channel Autocorrelation Function

Secret key generation(SKG)is a promising solution to the problem of wireless communications security.As the first step of SKG,channel probing affects it significantly.Although there have been some probing schemes,there is a lack of research on the optimization of the probing process.This study investigates how to optimize correlated parameters to maximize the SKG rate(SKGR)in the time-division duplex(TDD)mode.First,we build a probing model which includes the effects of transmitting power,the probing period,and the dimension of sample vectors.Based on the model,the analytical expression of the SKGR is given.Next,we formulate an optimization problem for maximizing the SKGR and give an algorithm to solve it.We conclude the SKGR monotonically increases as the transmitting power increases.Relevant mathematical proofs are given in this study.From the simulation results,increasing appropriately the probing period and the dimension of the sample vector could increase the SKGR dramatically compared to a yardstick,which indicates the importance of optimizing the parameters related to the channel probing phase.

Application of Non-Linear Cobb-Douglas Production Function with Autocorrelation Problem to Selected Manufacturing Industries in Bangladesh

In developing counties, efficiency of economic development has been determined by the analysis of industrial production. An examination of the characteristic of industrial sector is an essential aspect of growth studies. The growth of a country can be measured by Gross Domestic Product (GDP). GDP is substantially affected by the industrial output. Industrial gross output is mainly a function of capital and labor input. If the effect of labor and capital input to output is at a satisfactory level in an industry or in a group of industries, then industrial investment will increase. As a result, the number of industries will increase, which will directly affect GDP and also will decrease the unemployment rate. This is why, industrial input-output relationship is so important for any industry as well as for the overall industrial sector of a country. To forecast the production of a firm is necessary to identify the appropriate model. MD. M. Hossain et al. [1] have shown that Cobb-Douglas production function with additive errors was more suitable for some selected manufacturing industries in Bangladesh. The main purpose of this paper is to detect the autocorrelation problem of Cobb-Douglas production model with additive errors. The result shows that autocorrelation is presented in some manufacturing industries. Finally, this paper removes the autocorrelation problem and re-estimates the parameters of the Cobb- Douglas production function with additive errors.

The Partial Autocorrelation Function of an ARMA (1.1) Process

In this paper, we obtain an explicit expression for the partial autocorrelation of an ARMA (1.1) process and discuss its asymptotic behaviour briefly.

Measuring Dynamic Correlations of Words in Written Texts with an Autocorrelation Function

In this study, we regard written texts as time series data and try to investigate dynamic correlations of word occurrences by utilizing an autocorrelation function (ACF). After defining appropriate formula for the ACF that is suitable for expressing the dynamic correlations of words, we use the formula to calculate ACFs for frequent words in 12 books. The ACFs obtained can be classified into two groups: One group of ACFs shows dynamic correlations, with these ACFs well described by a modified Kohlrausch-Williams-Watts (KWW) function;the other group of ACFs shows no correlations, with these ACFs fitted by a simple stepdown function. A word having the former ACF is called a Type-I word and a word with the latter ACF is called a Type-II word. It is also shown that the ACFs of Type-II words can be derived theoretically by assuming that the stochastic process governing word occurrence is a homogeneous Poisson point process. Based on the fitting of the ACFs by KWW and stepdown functions, we propose a measure of word importance which expresses the extent to which a word is important in a particular text. The validity of the measure is confirmed by using the Kleinburg’s burst detection algorithm.

Autocorrelation of Sequences Generated by Single Cycle T-Functions

Cryptographic properties of the single cycle T-function's output sequences are investigated.Bounds of autocorrelation functions of the kth coordinate sequence and bounds of state output sequence are calculated respectively.The Maximum Sidelobe Ratio(MSR) of the kth coordinate sequence and the MSR of state output sequence are given respectively.The bounds of autocorrelation functions show that the values of autocorrelation functions are large when shifts are small.Comparisons of the autocorrelations between the state output sequence and coordinate output sequence are illustrated.The autocorrelation properties demonstrate that T-functions have cryptographic weaknesses and the illustration result shows coordinate output sequences have better autocorrelation than that of state output sequences.

LPI radar signal detection based on the combination of FFT and segmented autocorrelation plus PAHT

This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.

Vertex degree autocorrelation topological index and its application in QSAR study of organic chemicals

Considering the calculated result and higher degeneracy existing in the calculation of autocorrelation topological index totally depend on experimental parameters, a new group of autocorrelation topological index as A t, B t, C t and D t was designed and developed based on the vertex degree of molecular topology and autocorrelation function of mathematics. Autocorrelation function f(i) was calculated from the square root of the vertex degree, revised vertex degree and their combination, and they are (δ i) 1/2 , (δ V i) 1/2 ,(δ V i+δ i) 1/2  and (δ E i-δ i) 1/2 / N. With the matrix description method achieved, and the unit input in matrix of unsaturated bond and heteroatoms revised based on the adjacency matrix and distance matrix of organic molecular graph, the corresponding computer software has also been designed and developed. Better results have been obtained for the application of these indexes in QSAR study of organic chemicals.

An Efficient Arrhythmia Detection Using Autocorrelation and Statistical Approach

Computational electrocardiogram (ECG) analysis is one of the most crucial topics in cardiovascular research domain especially in identifying abnormalities of heart condition through cardiac arrhythmia symptom. There are many existing works focusing on recognizing the abnormalities condition through arrhythmia symptom, however, the detection rate is still unsatisfied. Arrhythmia consists of more than 14 various types of symptoms. Therefore, most of the existing research found it difficult to classify the entire symptom and maintain the overall accuracy especially in long hour data. In this study, a new mechanism to overcome this issue is proposed: A combination between Autocorrelation methods with K-Nearest Neighbor (KNN) classifier method is introduced to accurately and robustly detect 14 types of Arrhythmia symptom regardless of the origin of the symptom in a long hour data. Moreover, variability analysis based on periodic autocorrelation result is proposed and used for classification procedure. 1 minute and 12 hours duration data was chosen to compare and signify the most suitable time duration to detect Arrhythmia symptom. In addition, an analytical result and discussion is done to provide justification behind each tendency of Arrhythmia and Normal Sinus symptom in autocorrelation result. As the result of proposed method performance evaluation, it was revealed that the accuracy of 95.5% in discriminating Arrhythmia from Normal Sinus data is achieved. Furthermore, it was confirmed that utilizing autocorrelation result in long hour data can help to generalize abnormalities characteristic of heart condition like Arrhythmia symptom. It is concluded that the proposed method can be useful to diagnose abnormalities of heart condition at any stage.

海浪谱法模拟海面场景仿真参数选取准则

为提升海面场景模拟的物理真实感,本文探讨了仿真参数对海洋粗糙面模拟精确性的影响,并建立了海表模拟中仿真参数的选取准则。依据海浪谱和随机过程统计理论,采用自相关函数研究了海面相关长度随风速变化规律。通过建立海浪蒙特卡罗仿真模型,结合随机粗糙面误差分析,提出了不同风速条件下海面场景仿真参数的选取准则。研究表明:将海表模拟的采样间隔限定在顺风向相关长度的0.1~0.5倍,可显著提高海面场景模拟的精确度;而当海面仿真参数超出上述范围时,可能导致海面场景模拟失真,进而影响其视觉真实感。本文所提出的仿真参数选取标准可为开发航海模拟器提供技术支撑。

中国城市多中心空间结构的发育程度、基本模式及影响因素——基于297个地级及以上城市的实证研究

利用高精度活动人口数据,整合空间自相关分析与多中心模型,识别2019年297个地级及以上城市的所有中心地,从中心数量、距离、均衡性评价多中心程度,基于前述3大指标归纳出10类城市空间结构模式。研究表明,除少量分散型城市,中心数越多,相应城市越少;山地海岛、与邻省交界或发达城市更易远离主中心形成副中心;组团式布局的大城市和强县弱区型中小城市具有相对均衡的多中心,圈层扩张型城市则多呈极化格局。经济社会发展水平提高是形塑多中心结构的首要因素,自然地形起伏助推多中心发育,但政府搬迁、兴建开发区等外迁政策对多中心程度并无显著影响,修建道路也难以壮大新中心。可见多中心结构是经济社会高水平发展的产物,而非跨越式打造而成的。

使用接收函数自相关约束渤海湾北部沉积层结构

地震灾后调查表明盆地对地震动有显著的放大效应.低速、松散的沉积层和下伏基岩之间的物性差异,通常被认为是导致地震破坏增加的直接原因,它会显著增加盆地内地震动的持续时间、放大地震动幅度.可靠的沉积层结构和基阶共振频率有利于估计沉积层的放大作用并评估该区地震危险性.本研究利用渤海湾地区43个流动地震台站的远震波形资料提取了P波接收函数,进一步通过接收函数自相关得到了台站下方沉积层的共振频率,并依照钻孔资料推导出的共振频率和沉积层厚度之间的经验关系,给出了研究区的沉积层厚度.研究区共振频率存在由东北向西南减小的趋势,其中研究区东北部共振频率主要介于0.30~0.77 Hz之间,对应的沉积层厚度介于120~500 m;研究区西南平原地区绝大部分台站的共振频率低于0.30 Hz,沉积层厚度介于500~1252 m.本文结果与前人采用HVSR方法给出的共振频率具有较好的一致性,通过与已有反射和钻孔资料对比研究,推测本文所给出的沉积厚度为晚第三纪-第四纪(N+Q)的沉积盖层厚度.

自相关加权融合的高精度频率估计算法

为保证雷达对抗侦察系统在复杂电磁环境下的工作性能,提出了一种基于自相关加权融合的多段信号频率估计算法。对各段含噪信号进行自相关处理得到初相位为零、频率与原信号一致的高信噪比正弦信号,利用反余切算子构建支持度矩阵对自相关信号进行实时加权融合,在粗估计基础上建立参考信号,通过最小化误差函数求得高精度频率估计结果。仿真结果表明,相较于现有算法,本文算法在满足较低计算量的同时,不但精度明显提高,且在不同信噪比、信号长度以及信号异常等条件下均具备稳定的估计性能,为基于多传感器的雷达对抗情报侦察提供了参考。

基于街道单元的北京中心城区碳排放时空变化研究

以北京市第六次和第七次人口普查数据为基础,按照街道人口分布,估算中心城区121个街道(乡镇)的碳排放,通过运用空间自相关法,并结合街道单元的主导用地功能,分析了2010—2020年这些街道单元的碳排放时空变化特征。结果显示:1)中心城区街道的碳排放总体呈下降趋势,但不同街道的降碳效果差异显著;2)街道单元的主导用地功能被划分为5种单一功能单元和10种混合功能单元,它们的降碳效果存在差异;3)在降碳效果方面,单一功能单元转变为以景观休闲为主导时,降碳效果最为显著,当混合功能单元转变为以商业、居住、产业或公共服务为主导的单一功能时,降碳效果受到一定的抑制;4)以公共—居住和商业—居住混合功能为主导的高碳排放街道单元在空间上有聚集效应,景观休闲功能对于维持街道单元低碳排放状态有一定的积极作用。

农业工程科技创新、土地利用功能转变与乡村振兴耦合协调分析

论文构建农业工程科技创新、土地利用功能转变与乡村振兴三系统评价指标体系,利用我国30个省区市2011-2022年的面板数据开展实证分析。结果表明:2011-2022年,三系统均处于稳步发展状态,农业工程科技创新发展速度最快且东部水平最高,土地利用功能转型速度最慢且西部稍好,乡村振兴发展程度最高且东部水平最高;三系统协调度值稳步上升,但协调水平不高,全国协调度呈现两头小、中间大的“椭球形”特征和“东高-中西低”的空间布局;三系统协调度呈空间正相关,存在空间集聚特征但集聚程度不高,H-H集聚主要分布于东部,L-L集聚主要分布在西部,L-H集聚分布在安徽、江西等地区。应持续推动农业工程科技创新,优化土地利用形态,推进乡村振兴,实现三系统更高水平协调发展。

基于音频匹配的藏语驱动视觉语音合成算法研究

为解决唇部轮廓检测精度较低、视觉语音合成效果不好的问题,提出了基于音频匹配的藏语驱动视觉语音合成算法。该算法从藏语驱动视觉语音信号中提取短时能量和过零率,并建立语音信号的短时自相关函数。首先,提取语音信号中的特征信息,以此获得藏语语音信号的基音轨迹,即音频特征;其次,建立了唇部时空分析模型,分析唇部轮廓在发音过程中变化趋势,采用主成分分析法提取唇部轮廓特征;最后,通过输入输出隐马尔可夫模型获取音频特征与唇部轮廓特征之间的关联,在音频匹配的基础上合成藏语驱动视觉语音。实验结果表明,该方法具有较高的唇部轮廓检测精度,视觉语音合成效果较好。

珠三角城市群城市活力空间分布特征研究

城市活力是城市发展的内生动力,对城市高质量发展具有重要意义。基于夜间灯光数据、微博地理位置服务(LBS)签到数据和兴趣点(POI)数据,运用核密度分析、香农—维纳多样性指数和双变量空间自相关等研究方法对珠三角城市群经济、社会、文化、功能方面的活力空间分布特征进行研究。研究发现:(1)珠三角城市群经济活力呈现东高西低、沿西北—东南轴带延展的空间分布特征,社会活力呈零散分布,文化活力高值区集聚在广佛中心区和深圳,功能活力高值区在中部或呈散点状分布。(2)珠三角城市群综合活力以中低活力水平为主,总体空间格局呈现中间高、四周低的特征。(3)综合活力与经济活力为显著正相关,经济发展对城市活力提升有积极作用。(4)在珠三角城市群核心区外围,城市综合活力与城市经济、社会、文化的活力水平存在不匹配的状况,局部地区社会经济发展水平落后于城市开发建设,存在公共设施闲置和利用率不足等问题。通过对城市活力空间分布特征的研究,可以聚焦城市发展中的薄弱环节,对提高城市品质具有现实意义。

基于多源数据的苏州市区职住关系状况研究

首先基于百度热力图数据分时刻的热力平均值区分工作与休息时间段,对各社区(村)的职住关系指数进行测算,得到职弱住强、职住平衡、职强住弱三类区域并分析其空间分布。接着运用空间自相关对职住关系的空间集聚特征进行测度。最后,基于POI数据通过三区各类POI数量占比和香农多样性指数分析城市功能对职住关系的影响。结果表明:苏州市区总体上职住均衡而空间分布较为分散,呈现出职住分离由中心向外围逐渐加剧的情况。职住关系状况不仅受到苏州城市定位、城市建设历程与城市经济发展模式等因素影响,同时也很大程度上受到土地利用和城市功能的影响。适度的功能混合能够促进职住平衡,而单一的功能分区容易导致职住分离。

Stem taper functions for Betula platyphylla in the Daxing’an Mountains,northeast China

Accurate prediction of stem diameter is an important prerequisite of forest management.In this study,an appropriate stem taper function was developed for upper stem diameter estimation of white birch(Betula platyphylla Sukaczev)in ten sub-regions of the Daxing’an Mountains,northeast China.Three commonly used taper functions were assessed using a diameter and height dataset comprising 1344 trees.A first-order continuous-time error structure accounted for the inherent autocorrelation.The segmented model of Max and Burkhart(For Sci 22:283–289,1976.https://doi.org/10.1093/fores tscie nce/22.3.283)and the variable exponent taper function of Kozak(For Chron 80:507–515,2004.https://doi.org/10.5558/tfc80507-4)described the data accurately.Owing to its lower multicollinearity,the Max and Burkhart(1976)model is recommended for diameter estimation at specific heights along the stem for the ten sub-regions.After comparison,the Max and Burkhart(1976)model was refitted using nonlinear mixed-effects techniques.Mixed-effects models would be used only when additional upper stem diameter measurements are available for calibration.Differences in region-specific taper functions were indicated by the method of the non-linear extra sum of squares.Therefore,the particular taper function should be adjusted accordingly for each sub-region in the Daxing’an Mountains.

Effect of the Illuminating Point Spread Function and Roughness on the Speckle Correlation Field

Under conditions differing from those subjected for central limit theorem, the spatial autocorrelation function of speckle pattern resulting from illuminated rough surface is investigated. Its dependence on different illuminating apertures and the average of the roughness heights is presented theoretically and experimentally. The experiments were carried out using a set of circular and square apertures having different sizes. The results indicate that, increasing the size of the illuminating aperture leads to a decrease in the width of the main lobe of the spatial autocorrelation function.