WAVELET BASED SPECTRAL CORRELATION METHOD FOR DPSK CHIP RATE ESTIMATION

A wavelet-based spectral correlation algorithm to detect and estimate BPSK signal chip rate is proposed. Simulation results show that the proposed method can correctly estimate the BPSK signal chip rate, which may be corrupted by the quadratic characteristics of the spectral correlation function, in a low SNR environment.

Study on Evaluation of the Rainstorm Disaster in Fujian Province Based on Spectral Clustering Model with Grey Correlation Analysis

[ Objective] The research aimed to study assessment index system of the rainstorm disaster in Fujian Province based on spectral cluste- ring model with grey correlation analysis. [Method] According to meteorological disaster yearbook in Fujian Province, by comprehensively consider- ing disaster-inducing factor, disaster-inducing environment, disaster-sustaining body and regional disaster-prevention level, evaluation index system of the regional rainstorm disaster in Fujian was established. By spectral clustering model based on grey correlation analysis, dsk zoning of the rain- storm disaster was conducted in each area of Fujian. Finally, effect and application of the clustering model were analyzed by case research. [ Re- sult] In order to dig immanent connection among regional characteristics and improve disaster-preventing linkage performance of the evaluation unit, a spectral clustering model based on grey correlation analysis was used to conduct risk zoning of the rainstorm disaster in Fujian Province. Moreo- ver, combined weight was introduced to judge each evaluation index, so as to adjust clustering model. By case study, rainstorm disaster levels in 67 counties were obtained. Internal characteristics of each type were analyzed, and main correlation factors of each type were extracted. It was compared with statistical result of the rainstorm disaster, verifying validity and feasibility of the model. [ Conclusion] The method was feasible, and its evaluated result had better differentiation and decision accuracv.

Systematic error suppression scheme of the weak equivalence principle test by dual atom interferometers in space based on spectral correlation

Systematic error suppression and test data processing are very important in improving the accuracy and sensitivity of the atom interferometer(AI)-based weak-equivalence-principle(WEP) test in space. Here we present a spectrum correlation method to investigate the test data of the AI-based WEP test in space by analyzing the characteristics of systematic errors and noises. The power spectrum of the Eotvos coefficient η, systematic errors, and noises in AI-based WEP test in space are analyzed and calculated in detail. By using the method, the WEP violation signal is modulated from direct current(DC) frequency band to alternating current(AC) frequency band. We find that the signal can be effectively extracted and the influence of systematic errors can be greatly suppressed by analyzing the power spectrum of the test data when the spacecraft is in an inertial pointing mode. Furthermore, the relation between the Eotvos coefficient η and the number of measurements is obtained under certain simulated parameters. This method will be useful for both isotopic and nonisotopic AI-based WEP tests in space.

Walsh Spectral Characteristics and the Auto-Correlation Function Characteristics of Forming Orthomorphic Permutations of Multi-Output Functions

Orthomorphic permutations have good characteristics in cryptosystems. In this paper, by using of knowledge about relation between orthomorphic permutations and multi-output functions, and conceptions of the generalized Walsh spectrum of multi-output functions and the auto-correlation function of multi-output functions to investigate the Walsh spectral characteristics and the auto-correlation function characteristics of orthormophic permutations, several results are obtained.

A blind modulation recognition algorithm based on cyclic spectral correlation

Cyclic spectral correlation above the bifrequency plane for the received signal was calculated by the strip spectral correlation algorithm （SSCA）and then was normalized. The result was expressed by matrix. The sum of error-square was computed between corresponding elements for the theoretical sampling matrix of all kinds of modulated signals and calculated matrix. The modulation type was recognized by exploiting the minimum value of the sum of error-square. No extracted characteristic parameter and prior information are needed for identifying the modulation type compared to the conventional methods. In addition, the new method extends the recognition scope and has high recognition probability at low SNR. The simulation results obtained by means of Monter-Carlo method proved the presented algorithm.

SPECTRAL CORRELATION METHOD IN THE IDENTIFICATION OF FREQUENCY-SELECTIVE FADING RADIO ENVIRONMENT

Many modulated communication signals exhibit a cyclostationarity （or periodic correlation） property. To exploit the underlying periodicity of the modulated signal in the real-time recognition schcme of radio eilvironments, this letter introduces a spectral correlation method to identify the number and the direction of multi-path in the environment of frequency-selective fading channel. By simulation, the graphs of spatial spectra corresponding to a certain cycle frequency is presented to show the accuracy of spectral correlation method in multi-path environment recognition.

Second-Order Correlation Function for Asymmetric-to-Symmetric Transitions due to Spectrally Indistinguishable Biexciton Cascade Emission

We report the observed photon bunching statistics of biexciton cascade emission at zero time delay in single quantum dots by second-order correlation function g（2） （T） measurements under continuous wave excitation. It is found that the bunching phenomenon is independent of the biexciton binding energy when it varies from 0.59 meV to nearly zero. The photon bunching takes place when the exeiton photon is not spectrally distinguishable from the biexciton photon, and either of them can trigger the %tart＇ in a Hanbury-Brown and Twiss setup. However, if the exciton energy is spectrally distinguishable from the biexciton, the photon statistics will become asymmetric and a cross-bunching lineshape can be obtained. The theoretical calculations based on a model of three-level rate-equation analysis are consistent with the result of g（2）（τ） correlation function measurements.

Time series analysis of mantle cycles Part Ⅰ:Periodicities and correlations among seven global isotopic databases

In this study,seven isotopic databases are presented and analyzed to identify mantle and crustal episodes on a global scale by focusing on periodicity ranging from 70 to 200 million years(Myr).The databases are the largest,or among the largest,compiled for each type of data-with an objective of finding some samples from every region of every continent,to make each database as global as conceivably possible.The databases contain zircon Lu/Hf isotopic data,whole-rock Sm/Nd isotopic data,U/Pb detrital zircon ages,U/Pb igneous zircon ages,U/Pb non-zircon ages,whole-rock Re/Os isotopic data,and large igneous province ages.Part I of this study focuses on the periodicities of age histograms and geochemical averages developed from the seven databases,via spectral and cross-correlation analyses.Natural physical cycles often propagate in exact integer multiples of a fundamental cycle,referred to as harmonics.The tests show that harmonic geological cycles of^93.5 and^187 Myr have persisted throughout terrestrial history,and the cyclicities are statistically significant for U/Pb igneous zircon ages,U/Pb detrital zircon ages,U/Pb zircon-rim ages,large igneous province ages,meanεHf(t)for all samples,meanεHf(t)values for igneous-only samples,and relative abundance of mafic rocks.Equally important,cross-correlation analyses show these seven time-series are nearly synchronous(±7 Myr)with a model consisting of periodicities of 93.5 and 187 Myr.Additionally,the similarities between peaks in the 93.5 and 187 Myr mantle cycles and terminal ages of established and suspected superchrons provide a framework for predicting and testing superchron periodicity.

Quantum correlation dynamics of three non-coupled two-level atoms in different reservoirs

Time evolution dynamics of three non-coupled two-level atoms independently interacting with their reservoirs is solved exactly by considering a damping Lorentzian spectral density.For three atoms initially prepared in Greenberger-Horne-Zeilinger-type state,quantum correlation dynamics in a Markovian reservoir is compared with that in a nonMarkovian reservoir.By increasing detuning quantity in the non-Markovian reservoir,three-atom correlation dynamics measured by negative eigenvalue presents a trapping phenomenon which provides long-time quantum entanglement.Then we compare the correlation dynamics of three atoms with that of two atoms,measured by quantum entanglement and quantum discord for an initial robuster-entangled type state.The result further confirms that quantum discord is indeed different from quantum entanglement in identifying quantum correlation of many bodies.

Correlation between Chaotic Dynamics and Level Spacings： the Lyapunov and Dixon Dip Approaches to Highly Excited Vibration of Deuterium Cyanide

Chaotic dynamics of highly excited vibration of deuterium cyanide is explored by two independent approaches： （1） the Lyapunov analysis, based on the classical phase space for the levels, and （2） the Dixon dip analysis based on the concepts of pendulum dynamics and quantized levels. The results show that there is evident correlation between these two algorithms. We also propose that the reciprocal of energy difference between two nearby Dixon dips can be taken as a qualitative measure for the degree of dynamical chaos.

Area-Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization

To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix factorization. In the proposed me-thod, the spatial correlation property between two adjacent areas is expressed by a priori probability density function, and the endmembers extracted from one of the adjacent areas are used to estimate the priori probability density func-tions of the endmembers in the current area, which works as a type of constraint in the iterative spectral unmixing process. Experimental results demonstrate the effectivity and efficiency of the proposed method both for synthetic and real hyperspectral images, and it can provide a useful tool for spatial correlation and comparation analysis between ad-jacent or similar areas.

SPEECH ENHANCEMENT BASED ON DYNAMIC NOISE ESTIMATION WITHIN AUTO-CORRELATION DOMAIN

Most noise suppression algorithms of single channel use the mean of noisy segments to estimate the characteristics of noise spectrum, ignoring the estimation of noise in speech segments. Therefore, when the energy level of noise varies with the time, the performance of removing noise will be degraded. To solve this problem, a speech enhancement approach based on dynamic noise estimation within correlation domain was proposed. This method exploits the characteristics that noise energy mainly concentrates on 0 th order correlation coefficients, signal is auto correlated but signal and noise, noise and noise are uncorrelated, then estimates and decomposes the noise, thus helps to solve the above mentioned problem. The results of recognition experiments on speech signals of 15 Chinese cities’ names corrupted by noise of exhibition hall shows, this approach is better than SS (Spectral Subtraction) method, adapts better to the variances of energy levels of speech signal corrupted by noise, has some practicability to improve the robustness of recognition systems under noisy environment.

Correlation Analysis of the Fluorescence Data of Some Benzaldehyde Derivatives by the Dual-Parameter Equation

The fluorescence emission wavelength [lambda(max(em))] values of three types of benzaldehyde derivatives, namely, ethylene acetals (1-Ys), 4-nitrophenylhydrazones (2-Ys) and phenylhydrazones (3-Ys), have been measured. Correlation analyses by the dual-parameter equation show that the lambda(max(em)) values of 1-Ys are mainly affected by the spin-delocalization effects of the substituents, while those of 2-Ys are mainly affected by the polar effects. However, those of 3-Ys are independent of the substituents.

基于OFMD和FSC的滚动轴承复合故障诊断

针对滚动轴承的复合故障诊断问题,深入研究了一种基于优化特征模态分解和快速谱相关的复合故障诊断方法。首先,通过理论分析,提出脉冲能量因子指标来实现特征模态分解的参数选择以及最优分量的选取;然后,基于快速谱相关原理设计谱相关相对强度曲线和改进快速谱相关图,用于确定不同故障调制后对应的最优载波,对最优载波进行包络处理,从而分离轴承的复合故障特征,最终实现复合故障的准确性诊断。通过模拟故障试验和工程案例分析结果表明,该文所提方法相比于经验模态分解能够有效滤除噪声干扰,具有良好的鲁棒性,同时,避免了解卷积方法设定参数的缺陷,且与Autogram方法相比,能够有效分离复合故障特征,避免复合故障特征成分耦合。

基于PSD特征的FBCCA脑电信号识别方法

当前基于稳态视觉诱发电位(steady-state visual evoked potential,SSVEP)的脑机接口(brain-computer interfaces,BCIs)使用的都是单一识别算法,针对不同时间长度的识别准确率较低。提出了一种基于滤波器组的典型相关分析(filter bank canonical correlation analysis,FBCCA)与功率谱密度(power spectral density,PSD)分析相结合的SSVEP识别算法,可以提高SSVEP识别的普适性与准确率。该方法使用FBCCA寻找高相似度的参考频率信号,再通过多组PSD分析来锁定最终的响应频率,完成频率识别。该方法无需经过训练就能得到较高的识别准确率。实验结果表明:在刺激时长为1 s时,该方法能达到86.61%的准确率,比PSD分析方法提升了5.44%,比典型相关性分析方法(canonical correlation analysis,CCA)提升了10.38%的准确率,比FBCCA提升了8.86%的准确率。

脑小血管病认知障碍患者脑电功率谱变化与中性粒细胞/淋巴细胞比值相关性及临床价值研究

目的:探索脑小血管病认知障碍(CSVCI)患者脑电功率谱变化与中性粒细胞/淋巴细胞比值(NLR)的相关性,并分析其临床价值。方法:收集脑小血管病(CSVD)患者61例,依据蒙特利尔认知评估量表(MoCA)评分,将其分为伴有认知障碍组(CSVCI组,29例)及无认知功能障碍组(CSVD组,32例),同期招募体检健康者20例为对照组(HC组)。对三组进行脑电图检查,比较全脑定量脑电图功率谱密度(PSD),并分析其与NLR的相关性。结果:CSVCI组年龄高于HC组,受教育年限低于CSVD组和HC组(均P<0.05)。CSVCI组和CSVD组高血压占比高于HC组(均P<0.05)。CSVCI组脑室旁及总脑白质Fazekas评分高于CSVD组和HC组,且CSVD组高于HC组(均P<0.05)。CSVCI组和CSVD组深部脑白质Fazekas评分高于HC组(均P<0.05)。CSVCI组δ、θ频段PSD值高于CSVD组,α频段PSD值低于CSVD组和HC组,CSVD组δ频段PSD值低于HC组(均P<0.05)。多因素Logistic回归表明α频段全脑PSD减低及受教育年限低为CSVCI发生的独立危险因素(均P<0.05)。CSVD患者α频段PSD值与MoCA评分呈正相关,δ频段PSD值与MoCA评分呈负相关,且脑室旁Fazekas评分、深部脑白质Fazekas评分及总脑白质Fazekas评分与MoCA评分呈负相关(均P<0.05)。NLR与θ频段全脑PSD呈正相关(P<0.05)。结论:CSVCI患者脑电快波活动减低而慢波活动增加。α频段全脑PSD值独立于Fazekas评分外影响CSVD患者认知障碍的发生,可能成为评估认知障碍严重程度、预测认知障碍发生的早期客观指标。NLR可能为导致脑电慢化的机制,有可能成为早期预测脑电慢化、认知障碍的客观指标。

磐安地震序列的震源参数和谱振幅相关系数研究

基于浙江测震台网记录到的2017年2—9月磐安地区发生的地震序列资料,采用Brune震源模型理论和波谱分析方法,得到磐安震群序列的应力降、视应力等震源参数。利用台站的零频幅值,计算谱振幅相关系数。采用聚类分组,并结合Cut and Paste(CAP)反演方法得到ML4.0主地震事件的震源机制解,系统分析小地震的震源机制的一致性程度。结果显示:震源区应力降值在0.00~0.80 MPa之间,整体构造应力较低;谱振幅相关系数较低,在0.86~0.95之间,震源机制整体相似程度不高;应力积累没有形成一个优势方向,可能指示磐安序列为低摩擦应力的断层作用。研究结果表明:磐安地震序列为普通的小震序列,其震源机制类型与构造应力场基本一致;谱振幅相关分析法可为小震序列的震后快速判定提供重要依据。

晋城市农村黑臭水体成因及污染因子相关性研究

农村黑臭水体治理是农业农村污染治理攻坚战的重要内容,但黄河流域农村黑臭水体污染成因及污染特征研究还鲜有报道。研究选取晋城市3个典型县(区)的农村黑臭水体为研究对象,通过三维荧光光谱及相关性分析,探究水体溶解性有机质(DOM)组成及来源,解析黑臭水体污染成因。结果表明:A、B、C县(区)黑臭水体的主要污染因子分别为生活污水、畜禽养殖及内源污染;基于荧光光谱分析发现,3种不同类型污染源的黑臭水体中DOM主要由类溶解性微生物代谢产物(280/335,275/345,255/345)、低激发类酪氨酸类(220/310)及类色氨酸(225/340,245/380,225/340)组成;不同污染类型常规指标(DO、TN、NH_(3)-N)与荧光指数(FI、BIX、HIX)呈现良好相关性,其中,生活源DO与HIX呈现正相关,相关性系数最高为0.994(p<0.01),内源污源TN与FI总体呈现负相关,相关性系数最高为-0.954(p<0.05),畜禽养殖源NH3-N与BIX呈现负相关,相关性系数最高为-0.998(p<0.05)。

基于谱残差方法的工业互联网时间序列异常检测

谱残差算法是一种针对图像显著性检测的算法,也可用于无监督时间序列的异常检测。从频域变换、平滑算法、去除季节性影响、阈值自适应调节等多个环节研究针对谱残差算法的改进,提出一种基于谱残差算法的多变量时间序列异常检测方法。实验证明,所提出的改进可以提高异常检测的准确率,去除环境因素造成的季节性影响,且检测异常用时优于已有算法。另外,所提算法可以根据实际需要,自适应调节异常判定阈值。为了适应工业系统常出现的多变量时间序列数据,在谱残差算法的基础上结合用于处理多变量数据的独立成分分析算法,使算法适用于多变量时间序列。实验表明,谱残差算法与独立成分分析结合的算法能够应用于工业系统的异常自动检测,并且可以保证算法所需的准确性和实时性。

多维度评价及优化黄芪瞬时高温灭菌工艺

为优化黄芪瞬时高温灭菌(high temperature short time,HTST)工艺参数并多维度考察其对黄芪质量的影响,基于指标相关性的权重系数(criteria importance though intercrieria correlation,CRITIC)法采用正交设计优化黄芪灭菌工艺参数,以灭菌温度、灭菌时间、药材粉碎粒度为考察因素,以灭菌率、5种化合物含量及1,1-二苯基-2-苦基肼自由基(1,1-diphenyl-2-picrylhydrazyl,DPPH·)清除率为考察指标,通过直观及方差分析评价灭菌对3个考察指标的影响;并运用液质联用技术(liquid chromatography-mass spectrometry,LC-MS)确认指纹图谱共有峰的结构,以偏最小二乘回归分析法分析共有峰与抗氧化活性的谱效关系。结果显示,灭菌温度是具有显著影响的因素(P<0.05),最佳灭菌工艺参数为灭菌温度(170±2)℃,灭菌时间5 s,粉碎粒度50目;按优化工艺灭菌后的3批样品微生物水平均符合药典规定,5种化合物含量及DPPH·清除率与灭菌前比较无明显变化;灭菌前后指纹图谱相似度均大于0.900;谱效学分析相关性结果与化合物单体抗氧化的试验结果及其结构特点基本一致。综上所述,瞬时高温灭菌对黄芪中微生物具有明显杀灭作用且对其质量无明显影响,表明该方法适用于黄芪药材灭菌。