A unified approach to three eigendecomposition methods forfrequency estimation

The authors present a unified approach to three eigendecomposition-based methods for frequency estimation in the presence of noise. These are the Tufts-Kumaresan (TK) method, the minimum-norm (MN) method, and the total least squares (TLS) method. It is shown that: (1) the MN method is a modified version of the TK method; (2) the TLS method is a generalization of the MN method; (3) the TLS solution vector can be expressed in matrix form, and an alternative way of computing it is presented; (4) the MN and the TLS methods exhibit some improvement over the TK method     

Use of inertial sensors for ambulatory assessment of center-of-mass displacements during walking

Current methods for center-of-mass (CoM) estimation are restricted to gait laboratories. The aim of this study was to estimate CoM displacement under ambulatory conditions with inertial sensors. A sacral inertial sensor (SIS method) was used to estimate the CoM displacement by double integration of the acceleration. Overestimation of the displacement caused by pelvic rotations was compensated (CSIS method). The CoM displacement estimations using the (C)SIS method were compared to the conventional methods of the segmental analysis (SA) method and the sacral marker (SM) method by the intraclass correlations and the root-mean-square (RMS) differences between the CoM curves. Accurate ambulatory measurement of the CoM displacement using inertial sensors was possible. Estimations of the sacrum position using the SIS method and the SM method were similar with mean (SD) RMS differences of 3.23 (0.87), 2.96 (0.42), and 3.22 (0.78) mm for, respectively, the x-, y- and z-directions. The CoM estimation of the SIS method has RMS differences of 5.67 (1.20), 7.16 (3.28), and 3.49 (1.29) mm compared the SA method. The CSIS method shows a clear improvement in these estimations of the CoM with RMS differences of 5.52 (1.29), 4.44 (1.89), and 3.17 (1.41) mm and is generally applicable for healthy subjects.     

一种求解三维抛物方程的迭代方法

为了高效且高精度地求解三维抛物方程(parabolic equation,PE),提出了一种求解三维PE的迭代方法.该方法采用有限差分（finite difference,FD）近似方法,保留交替方向隐式（alternating direction implicit, ADI）方法的计算模式.推导了该方法对应的相对误差关系式,在推导过程中考虑计算式分裂项丢失带来的误差影响.对比现有的求解三维PE的ADI方法 (记为ADI-PE)和克兰克·尼科森(Crank-Nicolson)求解方法 (记为CN-PE),该方法能够提高计算精准度,且可通过迭代达到一定计算精度.该方法保留了ADI的计算高效特性,是一种求解三维PE的高效高精准度的方法.     

以改进的混合方法预测室外到室内的电波传播

采用了理想匹配吸收层技术改进了了基于衍射几何射线法(GTD)与时域有限差分法(FDTD)的混合方法，改进后的方法使计算结果比基于Mur吸收边界条件的混合法更精确更具有稳定性。该方法可作为一种移动通信系统设计方法的新选择；利用该方法研究了由室外到室内的电波传播预测，给出了计算结果和传播规律性。     

新型无条件稳定SSCN-FDTD算法

提出了一种新型的基于split-step方案和Crank-Nicolson方案的时域有限差分法(finite-difference timedomain method FDTD),并且证明了此种算法的无条件稳定性.所提出的算法采用新的矩阵分解形式,沿着x、y、z三个方向进行分解,将三维问题转化为一维问题,与alternating direction implicit(ADI)-FDTD算法、split-step(SS)-FDTD(1,2)算法和SS-FDTD(2,2)算法相比,减少了计算复杂度,提高了计算效率;同时所提出的算法具有二阶时间精度和二阶空间精度.新型算法的推导程序比基于指数因子分解的无条件FDTD算法更简单.将新型算法用于计算谐振腔结构,在计算相对误差一致的情况下,计算时间比ADI-FDTD算法节省约31%,比SS-FDTD(1,2)算法节省约13.5%.     

Equivalence between voltage-processing methods and discreteorthogonal Legendre polynomial (DOLP) approach

There are three methods for solving the least-squares estimation (LSE) problem. (1) the power method; (2) the voltage-processing method (square-root method); and (3) the discrete orthogonal Legendre polynomial (DOLP) method. The first involves a matrix inversion and is sensitive to computer round-off errors. The second and third do not require a matrix inversion and are not as sensitive to computer round-off errors. It is shown that the voltage-processing LSE methods (Givens, Householder, and Gram-Schmidt) become the discrete orthogonal Legendre polynomial (DOLP) LSE method when the data can be modeled by a polynomial function and the times between measurements are equal. Furthermore, when the data can be modeled by a polynomial function and the time between measurements are equal, the DOLP is the preferred method because it does not require an orthonormal transformation and it does not require the back-substitution method     

柔性结构拓扑优化中几种求解方法的比较

通过对两种柔性结构的拓扑优化,比较了拓扑优化中三种主要求解方法的优缺点。分别用优化准则法(OC)、序列线性规划法(SLP)和移动渐近线法(MMA)对悬臂结构和力反相机构进行了拓扑设计。OC法通用性差,但效率高;SLP法通用性好,但收敛速度慢,对初值敏感;MMA法计算效率低,但对初值不敏感,比较稳定。     

A new 2-D FDTD method applied to scattering by infinite objects with oblique incidence

A new two-dimensional (2-D) finite-difference time domain (FDTD) method applied to scattering by infinite objects with oblique incidence is proposed. 2-D Maxwell's equations, differential equations, and perfectly matched layer (PML) absorbing boundary conditions (ABC) are derived. The incident wave, computed by the 1-D FDTD method, is set on the connecting boundary. The accuracy and the efficiency of the proposed method have been verified by comparing the results of the split-field periodic FDTD method, the sine-cosine method, and the transmission line theory method with the proposed method.     

Multispectral code excited linear prediction coding and itsapplication in magnetic resonance images

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.     

一种互谱相位在时延估计中的应用方法

时延估计(TDE)是水声领域的重要研究课题,基于时延估计的水声目标被动测向、被动定位技术是水声目标无源定位的重要分支。目前,较为常用的时延估计方法有基本互相关法(NCC)、广义互相关法(GCC)、自适应时延估计(LMS)等。不同于以上方法的思路,该文利用两路信号的互谱相位(CSP)设计了一种时延估计的新方法,该方法将互谱相位进行傅里叶变换,利用其变换域估算相位斜率以求取时延,能够实现多目标分辨、消除时延估计模糊与背景起伏。仿真实验证明,该方法效果要优于基本互相关,广义互相关等方法。     

基于约束总体最小二乘方法的到达时差到达频差无源定位算法

两步加权最小二乘方法(two-stage WLS)是求解TDOA/FDOA无源定位问题的经典线性方法,但也存在着定位偏差和均方误差对测量噪声的适应能力较差的缺点。该文根据TDOA/FDOA的伪线性定位方程组特点,将其建立为一种带约束条件的约束总体最小二乘(CTLS)模型,并采用拉格朗日乘子法求解带约束条件的CTLS问题,建立了几种最小二乘类定位方法的统一解,从而将约束加权最小二乘(CWLS)定位解和约束最小二乘(CLS)定位解变为该文CTLS定位解的特例。仿真表明,该文方法比两步加权最小二乘方法具有更低的均方误差,并能够有效减小定位偏差,因而具有更好的测量噪声适应能力。     

Group decorrelation enhanced subspace method for identifying FIR MIMO channels driven by unknown uncorrelated colored sources

Identification of finite-impulse-response (FIR) and multiple-input multiple-output (MIMO) channels driven by unknown uncorrelated colored sources is a challenging problem. In this paper, a group decorrelation enhanced subspace (GDES) method is presented. The GDES method uses the idea of subspace decomposition and signal decorrelation more effectively than the joint diagonalization enhanced subspace (JDES) method previously reported in the literature. The GDES method has a much better performance than the JDES method. The correctness of the GDES method is proved assuming that 1) the channel matrix is irreducible and column reduced and 2) the source spectral matrix has distinct diagonal functions. However, the GDES method has an inherent ability to trade off between the required condition on the channel matrix and that on the source spectral matrix. Simulations show that the GDES method yields good results even when the channel matrix is not irreducible, which is not possible at all for the JDES method.     

Gabor feature based classification using the enhanced fisher lineardiscriminant model for face recognition

This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from (1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; (2) the development of a Gabor-Fisher classifier for multi-class problems; and (3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.     

部分集成局部特征尺度分解:一种新的基于噪声辅助数据分析方法

 局部特征尺度分解(Local Characteristic-Scale Decomposition,LCD)是最近提出的一种类似于经验模态分解(Empirical Mode Decomposition,EMD)的非平稳信号分析方法.为解决LCD方法的模态混淆问题,论文首先提出了基于噪声辅助分析的集成局部特征尺度分解方法(Ensemble LCD,ELCD).然而,ELCD有类似于总体平均经验模态分解(Ensemble EMD, EEMD)和互补总体平均经验模态分解(Complementary,CEEMD)的固有缺陷,在此基础上,同时结合最近提出的随机性检测方法——排列熵(Permutation Entropy,PE),论文提出了部分集成局部特征尺度分解(Partly Ensemble LCD,PELCD)方法.仿真数据分析表明,论文提出的PELCD方法不仅能够有效地抑制LCD分解的模态混淆,而且在抑制伪分量的产生以及分量精确性等方面要优于CEEMD和ELCD方法.     

降低正交频分复用系统峰均功率比的部分压扩算法

针对现有压扩变换法系统性能差的缺点,提出了一种降低系统峰均功率比的部分压扩算法。该方法根据正交频分复用(OFDM)系统信号幅度服从瑞利分布的统计特性,仅压缩大幅度信号保持了系统信号幅度的分布特征,弥补了现有压扩变换的不足,且具有带外功率小的优点。在M阶调制方式下的系统仿真结果表明,部分压扩方法与选择性映射和部分传输序列等方法相比,可获得相近的峰均功率比压缩效果并且在同样的系统误码率条件下比指数压扩法获得约log2(M)dB信噪比增益。     

载流子迁移率测量方法研究

给出了载流子迁移率的各种测量方法,渡越时间（TOP）法、霍尔效应法、电压衰减法、辐射诱发导电率（SIC）法、表面波传输法、外加电场极性反转法和电流-电压特性法,并给出了这些测量方法的使用范围。     

光通信中基于SCG(4,k)码的一类改进LDPC码构造方法

针对SCG(4,k)码构造方法的分析与研究,提出一种适用于光通信系统中改进的新颖低密度奇偶校验(LDPC)码构造方法.该方法在保留SCG(4,k)构造方法的基础上,采用类似于准循环的构造形式,使生成的码字不受短环干扰,并具有良好的围长特性;并且与改进前的SCG(4,k)码相比,该构造方法具有硬件实现方面节省存储空间和降低计算复杂度等优点.采用该方法构造了码率为93.7％的LDPC(3969,3720)码.仿真结果表明:这种码的净编码增益(NCG)比广泛用于光通信系统中的经典RS(255,239)码提高了1.99 dB.     

Fast maximum likelihood for blind identification of multiple FIRchannels

This paper develops a fast maximum likelihood method for estimating the impulse responses of multiple FIR channels driven by an arbitrary unknown input. The resulting method consists of two iterative steps, where each step minimizes a quadratic function. The two-step maximum likelihood (TSML) method is shown to be high-SNR efficient, i.e., attaining the Cramer-Rao lower bound (CRB) at high SNR. The TSML method exploits a novel orthogonal complement matrix of the generalized Sylvester matrix. Simulations show that the TSML, method significantly outperforms the cross-relation (CR) method and the subspace (SS) method and attains the CRB over a wide range of SNR. This paper also studies a Fisher information (FI) matrix to reveal the identifiability of the M-channel system. A strong connection between the FI-based identifiability and the CR-based identifiability is established     

基于子空间中主成分最优线性预测的高光谱波段选择

针对高光谱遥感图像的异常检测问题,为了使高光谱降维数据能更完整地保留其光谱信息,提出了基于子空间中主成分最优线性预测的波段选择方法.采用改进相关性度量的谱聚类方法将高光谱波段划分为不同的子空间,并对各子空间中的波段进行主成分分析(PCA),选择主要分量作为重构目标;以子空间追踪法为搜索策略,从各子空间中选择数个波段对其重构目标进行联合最优线性预测;合并各子空间中的所选波段得到最佳波段子集.实验结果表明,该方法选择的波段子集可以较完整地重构原始数据,与原始数据以及自适应波段选择(ABS)方法、线性预测(LP)方法、最大方差主成分分析(MVPCA)方法、自相关矩阵波段选择(ACMBS)方法、组合因子最优波段选择(OCFBS)方法得到的波段子集相比,其波段子集具有更好的异常检测性能.     

Gabor feature-based face recognition using supervised locality preserving projection

This paper introduces a novel Gabor-based supervised locality preserving projection (GSLPP) method for face recognition. Locality preserving projection (LPP) is a recently proposed method for unsupervised linear dimensionality reduction. LPP seeks to preserve the local structure which is usually more significant than the global structure preserved by principal component analysis (PCA) and linear discriminant analysis (LDA). In this paper, we investigate its extension, called supervised locality preserving projection (SLPP), using class labels of data points to enhance its discriminant power in their mapping into a low-dimensional space. The GSLPP method, which is robust to variations of illumination and facial expression, applies the SLPP to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. We performed comparative experiments of various face recognition schemes, including the proposed GSLPP method, PCA method, LDA method, LPP method, the combination of Gabor and PCA method (GPCA) and the combination of Gabor and LDA method (GLDA). Experimental results on AR database and CMU PIE database show superior of the novel GSLPP method.