HEVC的高效分像素运动补偿

现有的视频编码方法单独针对不同的编码技术进行优化,虽然简化了算法的实现,但却忽略了各者之间的内在关系而局限了性能的进一步提升.本文尝试针对HEVC中的分像素插值和分像素运动估计建立一种整合的优化算法,同时提升两者的计算速度.首先,我们通过细分不同分像素的插值方法,把每个分像素的插值代价融合在分像素运动估计的搜索中,并构建一种性价比优先的分像素运动估计方法.该运动估计方法的每一步搜索都按照最小化计算代价和最大化率失真收益的原则进行,保证以最小的计算消耗获得最优的性能.其次,为了配合该方法我们针对HEVC的特性构建了一个分区域分像素集的分像素插值算法.该插值算法在分像素运动估计过程中仅动态地计算所用到的分像素,解决了现有插值算法无法同时降低重复计算和冗余计算的问题.实验结果表明,我们的整合算法可以在几乎不产生率失真性能损失的情况下,大幅地加快分像素运动估计和插值的速度.     

Fractional Modeling and SOC Estimation of Lithium-ion Battery

This paper proposes a state of charge (SOC) estimator of Lithium-ion battery based on a fractional order impedance spectra model. Firstly, a battery fractional order impedance model is derived on the grounds of the characteristics of Warburg element and constant phase element (CPE) over a wide range of frequency domain. Secondly, a frequency fitting method and parameter identification algorithm based on output error are presented to identify parameters of the fractional order model of Lithium-ion battery. Finally, the fractional order Kalman filter approach is introduced to estimate the SOC of the lithium-ion battery based on the fractional order model. The simulation results show that the fractional-order model can ensure an acceptable accuracy of the SOC estimation, and the error of estimation reaches maximally up to 0.5% SOC.      

线性分式扰动下奇异系统鲁棒滤波递推算法

研究了线性分式扰动下线性奇异系统的状态估计问题, 给出了一种 Kalman 形式的递推滤波算法. 研究表明, 线性分式不确定性可以表示为一系列加性不确定性的交集. 本文讨论了如何寻找保守性最弱的加性不确定性来近似该交集, 并证明了该问题在鲁棒滤波过程中可以转化为凸优化问题. 数值仿真验证了上述算法的有效性. 对于具有结构约束的线性分式不确定性, 该算法的性能优于现有算法.     

基于H.264的1/4像素精度的快速搜索算法*

新的视频标准 H.264为了获得更精确的运动向量和更高的压缩比,引入了分数像素运动补偿技术, 但同时也增加了运动补偿过程的复杂度。为了克服这一局限,根据相邻分数像素点之间高度相关的运动估计匹配误差和运动矢量方向,以及运动向量具有中心偏置的特性提出了一种新的1/4像素精度快速搜索算法(QAFSA)。实验表明：该算法在保持图像质量和码率基本不变的情况下大大减少了搜索点的数目,有效地提高了分数像素运动估计的速度。     

分数阶Fourier变换域极值搜索的混沌优化算法研究

正如傅里叶变换采用正弦基,单频信号能够在频域形成峰值,分数阶Fourier变换采用线性调频基,线性调频(LFM)信号能够在分数阶Fourier域上实现聚焦,利用此聚焦性通过搜索峰值可实现LFM信号检测和参数估计.通常采用步进式搜索方法,效率低下.为了克服该缺点,通过对分数阶Fourier域优化问题本质的研究,将混沌优化算法引入到分数阶Fourier域极值搜索中.仿真结果表明:本文的方法优于传统的步进式搜索法.     

Robust fractional order singular Kalman filter

In this article, the state estimation problem of linear fractional order singular (FOS) systems subject to matrix uncertainties is investigated where a recursive robust algorithm is derived. Considering an uncertain discrete-time linear FOS system with added process and measurement noises, we aim to design a robust Kalman-type state estimation algorithm based on an optimal data fitting approach with a given sequence of observations. As a substitute for the stochastic formulation, this general filter is obtained by minimizing a completely deterministic regularized residual norm in its worst-possible form at each step over admissible uncertainties. Analysis of the algorithm shows that not only does the proposed robust filter cover the traditional robust Kalman filters (KFs), but it also represents an extension of the nominal fractional singular KF (FSKF) when the system is not subject to uncertainties. Furthermore, besides giving a sufficient condition for the existence of the robust filter, we derive conditions for the asymptotic properties of the filter, where we demonstrate that the filter and the Riccati equation are stable and converge when an equivalent system is detectable and stabilizable. A numerical example is included to demonstrate the performance of the introduced filter.     

基于改进的布谷鸟搜索算法对分数阶生物系统的参数估计

近年来,非线性分数阶系统的参数估计问题已经在许多科学和工程领域特别是计算生物学中,引起了广泛的兴趣.本文针对分数阶生物系统的参数估计问题,将系统参数和分数阶导数同时作为独立的未知参数来进行估计,并提出了一种改进的布谷鸟搜索(improved cuckoo search, ICS)算法来求解该问题.在ICS算法中,通过引入一个自适应参数控制机制,同时结合反向学习方法,从而达到提高算法收敛速度和估计值精度的目的.最后,以三种经典的分数阶生物动力系统模型为例进行了数值仿真,其中还考虑了有测量误差和噪声数据的情形.仿真结果表明ICS算法具有良好的适应性、较高的收敛可靠性及精度,为求解非线性分数阶系统参数估计问题提供了一种有效工具.     

语音编码器中的分数基音估计算法

在保证同等音质的前提下,为降低语音编码器中分数基音估计的复杂度,提出一种基于多项式拟合的分数基音估计算法。以整数点相关度序列为基础,在其最大值点附近进行多项式拟合,解析给出分数基音估计值。与现有的基于采样函数插值的分数基音估计算法相比,不使用常数插值表,无插值、遍历和比较操作,实现运算量与插值因子无关,典型情况下的运算复杂度仅为现有算法的1/25。在实际语音编码器中,所提算法对各类语音的客观及主观测试结果表明,其长时预测增益和编码音质都与现有算法相当。     

时变分数时滞系统最优预报自校正极点配置PID控制算法*

本文在广义模型描述被控过程的基础上,利用广义预测理论克服时滞,增强控制系统的鲁棒性,提出一种最优预报自校正极点配置PID控制新算法,仿真结果表明这种控制策略对具有未知或时变分数时滞系统是很有效的。     

脉冲噪声环境下宽带循环平稳信号DOA估计算法

针对传统二阶循环相关算法在脉冲噪声环境中的显著退化问题,本文以α稳定分布为噪声模型,提出基于分数低阶循环相关的波达方向(Direction of arrival,DOA)估计算法。利用分数低阶循环相关的相移特性,将宽带循环平稳信号的DOA估计问题转化为"中心频率"为ε的窄带问题,解决了宽带情况下DOA估计困难的问题。计算机仿真结果进一步验证了此算法的有效性,且性能优于传统SC-SSF(Spectral correlation signal subspacefitting)算法。     

一种适用于H.264的分数像素快速运动估计算法

新的视频标准H.264为了获得更精确的运动向量和更高的压缩比,引入了分数像素运动补偿技术,但同时也增加了运动补偿过程的复杂度.为了克服这一局限,充分利用搜索点失真度随着与全局最小点之间距离的增加而增大的特点,以及运动向量具有中心偏置的特性提出了一种新的快速分数像素运动估计算法.实验表明：该算法在保持图像质量和码率基本不变的情况下大大减少了搜索点的数目,提高了分数像素运动估计的速度,有效地减少了其复杂度.     

Detection and parameter estimation of multicomponent LFM signal based on the fractional fourier transform

Copyright by Science in China Press 2Linear frequency modulation (LFM or chirp) signals are widely used in information systems such as radar, sonar, and communications. In these systems, to detect and estimate LFM signals is an important problem. For a long time, various methods based on maximum likelihood estimator are the predominant solutions to this task. Most of these methods can be ascribed to a multivariable optimization algorithm and are usually computationally demanding in impleme…     

一种简化的分数阶傅里叶变换及其应用

首先在分数阶傅里叶计算分解的基础上,引入了一种简化分数阶傅里叶算法(Reduced fractional Fourier transform,RFRFT),然后推导了时限LFM信号的RFRFT模平方,给出了其归一化前后信号参数的变换关系,同时借助角度变换提高了RFRFT估计参数的分辨率和读数精度;最后结合一次相位差分法将其推广至多分量高阶PSS的参数估计,并通过仿真验证了算法的有效性.     

低码率下残差值预测的快速分数运动估计

提出了一种在低码率下的快速分数运动估计算法。该算法利用相邻运动补偿块时域的相关性,预测当前最优分数匹配位置的残差值,继而判断是否终止当前分数点搜索。根据实验结果,所提出的方法在不损失任何率失真性能的情况下,对于常用的几个QIF和CIF标准测试序列,其平均的分数搜索点数可减少到6以下。与全分数运动估计方法（FFPS）相比,所耗费的搜索点数减少了近一倍;而与目前常用的快速算法相比,所耗费的搜索点数也减少了50%～80%。     

分数低阶时频滑动平均模型参数估计

针对稳定分布环境下非平稳过程分析方法时频滑动平均（TFMA）模型算法的退化,引入分数低阶统计量共变,提出了一种改进的分数低阶时频时频滑动平均（FLO-TFMA）模型算法。推导了FLO-TFMA模型的参数求解过程,给出了基于FLO-TFMA模型的时频谱估计。通过在稳定分布环境下对TFMA模型算法和所提出的FLO-TFMA模型算法的参数估计均方误差（MSE）比较和时频谱估计比较,仿真结果表明,FLO-TFMA模型算法的参数估计精度优于TFMA模型算法,TFMA模型时频谱估计完全失效,而FLO-TFMA模型时频谱算法能较好地进行时频谱估计。     

基于FRFT的线性调频信号映射方法的快速算法

针对基于分数傅立叶变换(Fractional Fourier transform，FRFT)的线性调频信号(LFM)的参数估计方法，提出了基于Radon变换的判决准则，并建立了相应的快速算法。该映射方法将具有同样调频斜率、同样多普勒中心频率、不同初始相位的信号映射到同一个点。该快速算法的计算量等同于FRFT的计算量，计算结果的精确度取决于连续两次的FRFT运算。     

Simultaneous state estimation and parameter identification in linear fractional order systems using coloured measurement noise

In the present paper, the identification and estimation problem of a single-input–single-output (SISO) fractional order state-space system will be addressed. A SISO state-space model is considered in which parameters and also state variables should be estimated. The canonical fractional order state-space system will be transformed into a regression equation by using a linear transformation and a shift operator that are appropriate for identification. The identification method provided in this paper is based on a recursive identification algorithm that has the capability of identifying the parameters of fractional order state-space system recursively. Another subject that will be addressed in this paper is a novel fractional order Kalman filter suitable for the systems with coloured measurement noise. The promising performance of the proposed methods is verified using two stable fractional order systems.     

基于运动矢量空间相关性的H.264分像素运动估计

随着整像素运动估计快速算法的发展,分像素运动估计的计算量在运动估计中所占比重越发明显。为了减少分像素运动估计的计算量,提出了一种利用运动矢量空间相关性来预测整像素运动块,对整像素运动块进行分像素搜索过程跳过的分像素运动估计方法。实验结果表明,该算法与全分像素搜索算法结合使用,在基本保持搜索精度不变的情况下,比单纯的全分像素搜索算法减少60%左右的分像素搜索点。该算法可与其他快速分像素搜索算法结合使用,以获得更好的编码性能。     

基于最优阶次分数阶神经网络的交通流预测

建立基于最优阶次的分数阶神经网络的动态预测模型,给出数据预处理、最优阶次优化和预测算法流程步骤,给定模型预测精确度的性能指标。分数阶神经网络是从时频两方面分析数据,比BP神经网络具有更灵活有效的函数逼近能力;针对短时数据分析,分数阶神经网络局部性与小波神经网络一致具有多分辨力,且有更强的自适应能力、更快的收敛速度和更高的预测精度。以短时交通流量数据为例进行仿真,与基于小波神经网络和BP神经网络模型的短时交通流量预测仿真比较,分析评价性能指标,结果表明分数阶神经网络最优阶次下可实现灵活快速有效的交通流量动态预测。     

Nonlinear modeling of PEMFC based on fractional order subspace identification

Aiming at the multivariable, nonlinear and fractional‐order characteristics of proton exchange membrane fuel cell (PEMFC), this paper presents a nonlinear state space model based on a novel fractional Hammerstein model subspace identification theory. To reduce the complexity of modeling and choose the suitable input variables, canonical correlation analysis (CCA) method is used to select the most influential factors as the model input variables, and correlation analysis (CA) method is employed to remove the redundant input variables. To guarantee that the input‐output data are derivable at different fractional order, a Poisson moment function (PMF) is employed to construct the fractional order Hammerstein model with a six‐order polynomial as the front static nonlinear unit. To improve computing speed, a fractional differential short memory method (SMM) is proposed to reduce the computation cost of the identification algorithm. Meanwhile, a fuzzy genetic algorithm is adopted to acquire the best fractional order. Simulation results show that the fractional subspace identifying method can avoid fuel cell's internal complexity and PEMFC identification model can describe the working process of PEMFC accurately and quickly, which will provide an ideal control model for some advanced controller.