F-OFDM仿真系统设计与时频同步算法

针对新一代5G波形的F-OFDM技术开展了研究,通过把一个宽带分为若干个子带,对每个子带进行滤波处理以实现更好的通信效能。基于Simulink平台进行了F-OFDM仿真系统的搭建,重点对F-OFDM符号同步方法和频偏估计方法进行了研究与仿真分析。实验结果表明,基于Chu序列相比采用PN序列可以获得更好的同步效果,基于导频的Classen频偏估计算法相比基于CP的CFO估计算法和基于训练序列的Moose估计方法可以获得更好的频偏估计效果。     

自适应SA-PSO优化的威布尔混合分布参数估计方法及应用

针对采用传统参数估计方法得到的模型拟合误差较大的问题，建立多重威布尔混合分布参数估计的非线性最小二乘模型，并提出基于模拟退火（SA）思想的自适应粒子群（PSO）算法进行求解。在PSO算法优化过程中，采用自适应方法调整惯性权重和加速因子，加快其收敛速度；引入模拟退火机制，根据Metropolis准则确定最优粒子的取舍，改善其全局搜索能力。将该方法应用到某型柴油机喷油器失效分布的参数估计中，并与图解法、基于Levenberg-Marquardt的非线性最小二乘法、标准PSO算法、自适应PSO算法求解的结果进行比较，分析所提方法的优化性能及精度。结果表明，该方法能够有效提高多重威布尔混合分布模型参数估计的精度和效率。     

基于广义二次互相关的低信噪比信号时延估计

针对时延估计精度受噪声影响,导致时延估计不准确的问题,在现有广义二次相关算法的基础上,提出了一种改进的广义二次相关算法,通过将二次相关函数先做指数运算,降低噪声干扰,再将傅里叶逆变换得到的相关函数做高次方运算,达到锐化峰值提高时延正确率的目的。仿真结果表明,信噪比(SNR)在0~10 dB时,改进算法的均方根误差明显优于广义二次相关算法,正确率相比于广义二次相关算法也显著提高,且在更低SNR的情况下仍然具有一定优势。     

Tailoring cotton fabric with wettability gradient and anisotropic penetration of liquid by spray coating

Abstract

Comfort has been one of the most important features of clothing, particularly for sportswear, which requires an effective transport of heat and moisture from the inner to the outer side of clothing. We herein report the use of a simple technique of spray coating and commercially available water repellents for a one-sided hydrophobic and self-initiated one-way water transport cotton fabric. The highlight of this work is the simplicity of the process and choice of chemicals that can be adopted easily by any textile finishing industry. On this fabric, water was able to diffuse and penetrate the fabric structure in one direction from the hydrophobic to the hydrophilic side but was unable to go the opposite direction. The directional transport improved with smaller droplets and lower add-on achievable by higher air pressure and longer distance of spray coating. From moisture management tests, the best result was obtained with the spray coating of Phobol NB-NH at an air pressure of 3.0?kgf/cm² and a distance of 120?cm. Phobol NB-NH gave better result of transport and overall comfort properties than Phobotex RSY.     

Backtesting portfolio value-at-risk with estimated portfolio weights

This article theoretically and empirically analyzes backtesting portfolio value-at-risk (VaR) with estimation risk in an intrinsically multi-variate framework. It particularly takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating portfolio weights and that from estimating the multi-variate dynamic model make the existing methods in a univariate framework inapplicable. It proposes a general theory to quantify estimation risk applicable to the present problem and suggests practitioners a simple but effective way to implement valid inference to overcome the effect of estimation risk in backtesting portfolio VaR. In particular, we apply our theory to the efficient mean-variance-skewness portfolio for a multi-variate generalized autoregressive conditional heteroscedasticity model with multi-variate general hyperbolic distributed innovations. Some Monte Carlo simulations and an empirical application demonstrate the merits of our method.     

Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO₂ solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO₂ solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.     

Maximum likelihood-based adaptive differential evolution identification algorithm for multivariable systems in the state-space form

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.     

Shape from focus based on 3D structure tensor using optical microscopy

Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.     

A multi-tone central divided difference frequency tracker with adaptive process noise covariance tuning

The problem of real-time frequency estimation of nonstationary multi-harmonic signals is important in many applications. In this paper, we propose a novel multi-frequency tracker based on a state-space representation of the signal with Cartesian filters and the second-order central divided difference filter (CDDF), which improves the performance of the extended Kalman filter (EKF) by using Stirling's interpolation method to approximate the mean and covariance of the state vector. A crucial element of the method is the adaptive scaling of the process noise covariance matrix appearing in the filter equations, as a function of the innovation sequence, which tunes the accuracy-reactivity trade-off of the filter. The proposed solution is evaluated against two approaches from the literature, namely the factorized adaptive notch filter (FANF) and the extended Kalman filter frequency tracker (EKFFT). Several experiments emphasize the estimation accuracy of the proposed method as well as the improved robustness with respect to initial errors and input signal complexity. The presented method appears to be particularly efficient with rapidly varying frequencies, thanks to the update mechanism that adjusts the filter parameters based on the amplitude of the estimation error.     

In-line characterization of dispersion uniformity evolution during a twin-screw blending extrusion based on near-infrared spectroscopy

Properties of an immiscible polymer blend have been proved to be closely related to dispersion uniformity of the minor phase. At present, dispersion uniformity is difficult to evaluate during the blending process, resulting in hysteretic feedback. Aiming at this problem, this work utilized near-infrared (NIR) spectroscopy to in-line characterize dispersion uniformity evolution during a twin-screw extrusion. A multichannel NIR measurement system was set up and applied to evaluate the blending process of polypropylene and polyolefin elastomer (POE). Based on the NIR spectra collected at different positions of the extruder, five prediction models of POE content were established using the light gradient boosting machine algorithm. Dispersion uniformity was characterized through the fluctuation of the predicted content. The evolution of dispersion under such processing parameters was consistent with scanning electron microscopy.