A Squared-Chebyshev wavelet thresholding based 1D signal compression

In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression;but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.

Low-complexity signal detection for massive MIMO systems via trace iterative method

Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

LTE系统中的Mean-OTDOA定位算法

由于LTE蜂窝网中远近效应的影响,终端测量到的邻近基站信号的定位参数会存在较大的偏差,导致OTDOA定位方法(到达时间差定位法)估计的终端位置存在较大误差。基于此,提出一种改进的Mean-OTDOA定位算法。首先估计终端与各基站的时延,然后对终端与多基站的距离测量值进行平均,作为OTDOA定位方法中的参考距离,最后利用泰勒级数展开法对终端位置进行估计。仿真结果表明,该算法可提高终端的定位精度,在基站数目为5、测量误差标准差为50 m时,本算法的均方根误差比OTDOA算法降低了5.2039 m,且随着基站数目的增加,定位精度的改善程度优于OTDOA算法。

基于RMSE的惯导系统随机误差影响分析

对随机误差在惯导系统的统计传播特性进行了研究,通过基于自相关函数的线性系统随机过程输入的输出响应分析,给出了SINS均方根误差(RMSE)的计算方法。以位置、航向参数误差为例,推导了惯性器件未补偿的逐次启动常值误差和白噪声误差对导航参数RMSE的解析表达式,讨论了其应用方法,并指出逐次启动常值误差主要造成纬度、航向振荡性RMSE和随时间发散的经度RMSE,白噪声误差主要造成随时间发散的纬度、经度和航向RMSE。数字仿真验证了结论的正确性,同时表明RMSE统计分析可以用于指导惯导系统设计和导航系统精度评定。

Estimating van Genuchten Model Parameters of Undisturbed Soils Using an Integral Method

The van Genuchten model is the most widely used soil water retention curve （SWRC） model. Two undisturbed soils （clay and loam） were used to evaluate the accuracy of the integral method to estimate van Genuchten model parameters and to determine SWRCs of undisturbed soils. SWRCs calculated by the integral method were compared with those measured by a high speed centrifuge technique. The accuracy of the calculated results was evaluated graphically, as well as by root mean square error （RMSE）, normalized root mean square error （NRMSE） and Willmott＇s index of agreement （1）. The results obtained from the integral method were quite similar to those by the centrifuge technique. The RMSEs （4.61 ×10^-5 for Eum-Orthic Anthrosol and 2.74 × 10^-4 for Los-Orthic Entisol） and NRMSEs （1.56 × 10^-4 for Eum- Orthic Anthrosol and 1.45 ×10^-3 for Los-Orthic Entisol） were relatively small. The 1 values were 0.973 and 0.943 for Eum-Orthic Anthrosol and Los-Orthic Entisol, respectively, indicating a good agreement between the integral method values and the centrifuge values. Therefore, the integral method could be used to estimate SWRCs of undisturbed clay and loam soils.

A New Method of Embedded Fourth Order with Four Stages to Study Raster CNN Simulation

A new Runge-Kutta （PK） fourth order with four stages embedded method with error control is presentea m this paper for raster simulation in cellular neural network （CNN） environment. Through versatile algorithm, single layer/raster CNN array is implemented by incorporating the proposed technique. Simulation results have been obtained, and comparison has also been carried out to show the efficiency of the proposed numerical integration algorithm. The analytic expressions for local truncation error and global truncation error are derived. It is seen that the RK-embedded root mean square outperforms the RK-embedded Heronian mean and RK-embedded harmonic mean.

A Low-Complexity Signal Detection Utilizing AOR Iterative Method for Massive MIMO Systems

Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error(MMSE) signal detection using the accelerated overrelaxation(AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.

Demodulation method combining virtual reference interferometry and minimum mean square error for fiber-optic Fabry–Perot sensors

We propose a cavity length demodulation method that combines virtual reference interferometry（VRI） and minimum mean square error（MMSE） algorithm for fiber-optic Fabry–Perot（F-P） sensors. In contrast to the conventional demodulating method that uses fast Fourier transform（FFT） for cavity length estimation,our method employs the VRI technique to obtain a raw cavity length, which is further refined by the MMSE algorithm. As an experimental demonstration, a fiber-optic F-P sensor based on a sapphire wafer is fabricated for temperature sensing. The VRI-MMSE method is employed to interrogate cavity lengths of the sensor under different temperatures ranging from 28°C to 1000°C. It eliminates the ＂mode jumping＂ problem in the FFT-MMSE method and obtains a precision of 4.8 nm, corresponding to a temperature resolution of 2.0°C over a range of 1000°C. The experimental results reveal that the proposed method provides a promising, high precision alternative for demodulating fiber-optic F-P sensors.

Forecasting Methods to Reduce Inventory Level in Supply Chain

Based on the two-level supply chain composed of suppliers and retailers, we assume that market demand is subject to an ARIMA(1, 1, 1). The supplier uses the minimum mean square error method (MMSE), the simple moving average method (SMA) and the weighted moving average method (WMA) respectively to forecast the market demand. According to the statistical properties of stationary time series, we calculate the mean square error between supplier forecast demand and market demand. Through the simulation, we compare the forecasting effects of the three methods and analyse the influence of the lead-time L and the moving average parameter N on prediction. The results show that the forecasting effect of the MMSE method is the best, of the WMA method is the second, and of the SMA method is the last. The results also show that reducing the lead-time and increasing the moving average parameter improve the prediction accuracy and reduce the supplier inventory level.

Novel robust S transform based on the clipping method

This paper presents a novel robust S transform algorithm based on the clipping method to process signals corrupted by impulsive noise.The proposed algorithm is introduced to determine the clipping threshold value according to the characteristics of the signal samples.Signals in various impulsive noise models are considered to illustrate that the robust S transform can achieve better performance than the standard S transform.Moreover,mean square errors for instantaneous frequency estimation of the robust S transform are compared with that of the standard S transform,showing that the robust S transform can achieve significantly improved instantaneous frequency estimation for the signals in impulsive noise.

Evaluation of forecasting methods from selected stock market returns

Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making.Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices.However,there have been very few studies of groups of stock markets or indices.The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets.In this context,this study aimed to examine the predictive performance of linear,nonlinear,artificial intelligence,frequency domain,and hybrid models to find an appropriate model to forecast the stock returns of developed,emerging,and frontier markets.We considered the daily stock market returns of selected indices from developed,emerging,and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models.The results showed that no single model out of the five models could be applied uniformly to all markets.However,traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.

On the Application of Bootstrap Method to Stationary Time Series Process

This article introduces a resampling procedure called the truncated geometric bootstrap method for stationary time series process. This procedure is based on resampling blocks of random length, where the length of each blocks has a truncated geometric distribution and capable of determining the probability p and number of block b. Special attention is given to problems with dependent data, and application with real data was carried out. Autoregressive model was fitted and the choice of order determined by Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The normality test was carried out on the residual variance of the fitted model using Jargue-Bera statistics, and the best model was determined based on root mean square error of the forecasting values. The bootstrap method gives a better and a reliable model for predictive purposes. All the models for the different block sizes are good. They preserve and maintain stationary data structure of the process and are reliable for predictive purposes, confirming the efficiency of the proposed method.

Film-Forming Properties of Fullerene Derivatives in Electrospray Deposition Method

Thin films of three types of fullerene derivatives were prepared through the electrospray deposition (ESD) method. The optimized conditions for the fabrication of the thin films were investigated for different types of fullerene derivatives: [6,6]-phenyl-C61-butyric acid methyl ester, [6,6]-phenyl-C71-butyric acid methyl ester, and indene-C60-monoadduct. The spray diameter during the ESD process was observed as a function of the supply rate achieved by changing the applied voltage. In all cases, the spray diameter increased with increasing applied voltage, reaching the maximum diameter (Dmax) in the voltage range 4 to 6 kV. It was clear that Dmax was influenced by the dipole moments of the fullerene derivatives (as calculated by density functional theory methods). Scanning electron microscopy observation of the?fabricated thin films showed that imbricated structures were formed through the stacking of the fullerene-derivative sheets. Atomic force microscopy images revealed that the density of the imbricated structure was dependent on the spray diameter during the ESD process, and the root-mean-square roughness of the film surface decreased with increasing applied voltage. These findings suggest that the ESD method will be effective for the preparation of fullerene-derivative thin films for the production of organic devices.

健听成年人全方向水平声源定位能力初步研究

目的利用360°全方向24和36声源测试设备,初步探讨健听中青年和健听老年前期-老年人水平声源定位特点。方法选取2021年4月至2021年9月中国人民解放军总医院耳鼻喉科收治的43例健听成年受试者为研究对象,其中男性22例,女性21例;根据年龄分为中青年组(21~49岁)20例和老年前期-老年组(50~72岁)23例。两组分别给予纯音听阈测试、全方向24声源(间隔15°)和36声源(间隔10°)水平声源定位(sound localization,SL)能力评估。给声强度60 dB HL,给声刺激为1 kHz啭音,通过计算均方根误差(root mean square,RMS)、平均绝对误差(mean absolutely error,MAE)等评估受试者的声源定位能力。结果24声源老年前期-老年组MAE、RMS均值高于中青年组的MAE、RMS均值,差异有统计学意义(P<0.05);36声源老年前期-老年组MAE、RMS高于中青年组的MAE、RMS,差异无统计学意义(P>0.05)。24声源和36声源前场MAE和RMS均高于后场的MAE和RMS,前后场的MAE和RMS比较,差异有统计学意义(P<0.01);左右场的MAE、RMS比较,差异无统计学意义(P>0.05)。24声源前后混淆比例为7.73%,36声源前后混淆比例为15.42%;24声源和36声源均为正前方的声源定位准确度最差;老年前期-老年组前后混淆的比例高于中青年组,差异无统计学意义(P>0.05)。结论健听老年前期-老年人全方向24声源和36声源水平定位能力,相比健听中青年组有所下降。左右场的定位准确度高,前后场的定位准确度低,正前方定位准确度最低。全方向水平声源定位能力的测试结果与扬声器数量有关,且反应趋势具有一致性。

基于第十三代国际地磁参考场模型在中国区域特征分析与研究

根据最新的第十三代国际地磁参考场模型(IGRF13),计算了2015—2020年中国区域地磁场模型七要素长期变化速率,并在此基础上分析我国区域地磁场长期变化特征。通过分析计算我国28个地磁台的IGRF13模型值与实际地磁场的长期变化速率、差值及均方误差,结果显示:IGRF13模型所显示的地磁场长期变化与我国区域地磁场实际观测变化基本一致,但在局部区域也存在差异,IGRF13模型能够体现中国区域地磁场的特征。应用IGRF13模型数据时需要考虑局部区域与台站实际观测数据的误差。

自适应分数阶偏微分方程修正模型的能量泛函及Euler-Lagrange方程研究

首先对分数阶微分方程进行构建,结合全变分项,提出了修正的自适应分数阶偏微分方程模型。研究首先确定出分数阶偏分去噪模型的最优分数阶数,当分数阶次为1.8时,峰值信噪比和结构相似度达到33.12和0.874,均方根误差降低至5.62。然后将研究提出的模型与全变分模型、分数阶偏分去噪模型等在图像上进行对比实验,研究提出的模型在峰值信噪比、结构相似度上达到最高,分别为29.045与0.839,均方根误差为9.427,表明模型能够抑制阶梯效应,具有优越的去噪性能。

基于激光扫描仪的点云配准方法

针对激光扫描仪实际扫描得到的不完整点云配准困难问题,提出了一种基于对应点对的配准方法。通过激光扫描仪进行实验,得到被测工件的实测点云数据,基于Visual Studio软件配置Point Cloud Library环境,对实测模型与理想模型的点云配准进行研究。首先对实测点云数据进行体素滤波以及均匀下采样的预处理;其次通过对应点对的方式进行对齐为后续精细配准提供较好的变换初值,后基于ICP算法实现点云配准精配准;最终以均方根误差作为点云配准精度评价指标对配准结果进行评价。借助CloudCompare软件对配准结果进行直观展示分析可知,在实测工件本身存在不绝对光滑的情况下,配准的均方根误差可控制在0.62 mm,表明该方法对于不完整点云的配准效果较好。

基于高分辨格点数据东北水稻延迟型冷害风险评估及保险费率厘定

利用5km×5km空间分辨率3h时间分辨率的气象格点数据集,研究东北地区水稻延迟型冷害风险及其保险费率的厘定。基于东北地区1981-2010年5-9月平均温度的格点数据集和99个气象站的站点观测数据,以相关系数和均方根误差评价格点气象数据在东北地区的可用性。以日平均气温稳定通过10℃和18℃的日数作为获取水稻气候安全种植区域的指标,在水稻气候安全种植范围内,分析东北地区水稻延迟型冷害的空间分布特征,确定保险费率。结果表明,东北地区1981-2010年5-9月平均温度气象站点观测数据与格点数据的相关系数高,均方根误差小,表明格点数据在东北地区可用。水稻气候安全种植区域占东北的56.5%,主要分布在辽宁省、吉林省中西部、黑龙江省西南部和东北部、蒙东西部及东部与辽宁和吉林省接壤的区域。在水稻气候安全种植区内,水稻延迟型冷害发生频率呈南低北高,中间低东西高的分布特征,且重度延迟型冷害发生频率最高。低温冷害风险指数空间分布与之相似,内蒙古西部和东北部、黑龙江北部和吉林西部局部地区是风险指数的高值区。东北地区1981-2010年水稻延迟型冷害的天气指数保险费率在空间分布上与东北地区低温冷害风险指数的空间分布相似,呈南部低,北部高,中间低,东西高的特征,整个区域的保险费率在0.010~0.094,可为保险公司制定具体费率提供参考。

CFSv2对浙江省延伸期逐日降水预报性能评估及解释应用初探

利用CFSv2长序列回报资料和近6 a实时预报数据,系统分析了模式对浙江短中期(Short-medium Range,SMR,1~10 d)和延伸期(Extended Range,ER,11~30 d)逐日降水的预测性能,并基于系统误差订正(Systematic Bias Correction,SBC)技术开展了解释应用。结果表明:(1)模式回报技巧在SMR时段快速衰减,而在ER时段的衰减明显趋缓;SMR时段的相关系数远高于ER时段,但这两个时段的均方根误差较为接近;(2)从季节演变来看,模式技巧在秋冬季较高而在春夏季相对较低;(3)模式回报结果表现出显著的系统性偏差,这一偏差在各个起报日(提前1~30 d起报)中稳定存在,采用SBC技术开展解释应用,发现订正后模式的回报技巧在ER时段显著提升;(4)进一步将SBC技术应用于实时预报,发现实时预报的技巧也得到了一定的改善,且ER时段的改进效果更为显著。

双线程滑动窗口多系统GNSS超快精密定轨实现及评估

全球导航卫星系统(GNSS)超快精密定轨为GNSS实时应用提供了高精度空间基准。基于天地协同定位、导航与授时(PNT)网络服务中心实现了四系统GNSS卫星超快精密定轨,并对定轨结果进行精度评价。介绍了天地协同PNT网络的概念内涵以及网络服务中心部署的超快精密定轨软件架构和详细功能,并针对实时应用需求提出了一种双线程滑动窗口超快精密定轨策略。最后利用重叠弧段比较、与外部轨道产品比较以及卫星激光测距(SLR)检核3种方式对定轨结果进行了精度评价。结果表明,与武汉大学分析中心的最终事后精密轨道产品相比,四系统GNSS MEO卫星预报6 h弧段的径向均方根(RMS)误差整体在2~5 cm水平,BDS2 IGSO卫星最小一维RMS误差在10~15 cm水平;GPS和Galileo卫星的SLR检核残差均值在1~3 cm水平,标准差在3~6 cm水平,能够满足后续厘米级实时应用对空间基准的精度需求。