Robust Multidimensional Matched Subspace Classifiers Based on Weighted Least-Squares

We propose and design two classes of robust subspace classifiers for classification of multidimensional signals. Our classifiers are based on robust M-estimators and the least-median-of-squares principle, and we show that they may be unified as iterated reweighted oblique subspace classifiers. The performance of the proposed classifiers are demonstrated by two examples: noncoherent detection of space-time frequency-shift keying signals, and shape classification of partially occluded two-dimensional (2-D)_ objects. In both cases, the proposed robust subspace classifiers outperform the conventional subspace classifiers     

Estimation of Constrained Parameters With Guaranteed MSE Improvement

We address the problem of estimating an unknown parameter vector x in a linear model y=Cx+v subject to the a priori information that the true parameter vector x belongs to a known convex polytope X. The proposed estimator has the parametrized structure of the maximum a posteriori probability (MAP) estimator with prior Gaussian distribution, whose mean and covariance parameters are suitably designed via a linear matrix inequality approach so as to guarantee, for any xisinX, an improvement of the mean-squared error (MSE) matrix over the least-squares (LS) estimator. It is shown that this approach outperforms existing "superefficient" estimators for constrained parameters based on different parametrized structures and/or shapes of the parameter membership region X     

基于全变差的加权最小二乘法PET图像重建

传统的加权最小二乘法、惩罚项加权最小二乘法虽然能够重建得到较好质量的图像,但在欠采样的条件下不能很好的拟制噪声.全变差作为正则项已广泛用于图像重建中,利用图像稀疏的先验知识能够在欠采样的条件下很好的重建图像.本文结合加权最小二乘法和全变差的优点,构造了基于全变差正则项的加权最小二乘法目标函数,运用交替求解的方法,将目标函数分解为求解二次优化和全变差正则化的优化问题,并分别用超松弛迭代方法和梯度下降法求解这两个优化问题.采用Zubal模型对该算法与传统算法进行仿真验证比较,并用相关系数、方差、信噪比等参数描述图像重建质量.结果表明在欠采样条件下,该算法能够更好的拟制噪声,重构效果比传统的有明显地提高.     

基于线性最小二乘估计与中心距离加权的相关跟踪算法研究

相关跟踪算法以其理论简单、直观、对图像质量要求不高等特点,一直是人们在跟踪算法方面的研究重点.针对相关跟踪抗遮挡能力差、容易受目标姿态变化和相似目标干扰的问题,使用了线性最小二乘估计对目标运动进行预测,同时采用高斯+1型函数和椭球面函数作为中心距离加权系数来改善跟踪模板使得模板更加突出中心位置信息.改进后的相关跟踪算法...     

Tracking Performance of an Adaptive MIMO-OFDM LS Estimator via MSE Criterion Over a Wireless Time Correlated Fading Channel

In this contribution, the performance of an adaptive least squares channel estimator for a time correlated MIMO-OFDM channel is presented in a closed form relation. As a criterion for performance evaluation, the well-known mean square error (MSE) is employed. The pilot aided channel estimation is used to extract the channel coefficients. To minimize the MSE and arrange phase shift orthogonally, the pilots are constructed with equal power and space. The model of the channel is developed by implementing a first order Markov model, and it is proved that the performance of the system is dependent upon the defined forgetting factor, the number of transmit antennas, the number of multipath channel taps, transmitted power, and Doppler shift. The effect of each parameter on the performance of the system is discussed separately. The computer simulation results follow with the analytical results comprehensively.     

Near Field Source Separation Improvement Through Unconditional Maximum Likelihood Estimator

In this work, we are focused in the improvement of the near field source separation through the approach of the Unconditional Maximum Likelihood (UML) estimator. Four aspects are considered: separation sources, SNR variation, snapshots and multiple sources, in order to evaluate their influence in the capacity separation for the case of closely spaced sources. In this way, we can establish the minimum conditions for the sources separation. In addition, we investigated the effects of snapshots and the increasing number of sources in their spatial position estimation. Using Monte Carlo simulation, we obtained the Root Mean Square (RMS) error of the source’s direction of arrival. For evaluation purposes we include also MUSIC simulations. Our results show that the UML estimator improves source’s separation performance under low SNR and snapshot values as well as increasing number of sources.     

基于加权最小二乘的合成孔径高度计重跟踪估计器设计与验证

重跟踪是提取高度计回波信号精确参数估计的重要步骤,现有的合成孔径雷达(SAR)高度计重跟踪估计器主要基于最小二乘法。该方法未考虑高度计各距离单元的统计特性不同对参数估计精度的影响,同时现有的加权最小二乘重跟踪估计器使用半解析模型,计算效率低,不利于应用于业务化。对此,该文利用合成孔径高度计解析化的合成孔径高度计研究与应用(SAMOSA)回波模型设计了新的加权方法,通过加权处理使高度计各距离单元的统计特性达到一致,进而提高了重跟踪估计器的测距和有效波高估计精度。该文利用Sentinel-3A卫星数据进行了验证,验证结果也表明了测距和有效波高这两个参数的估计精度均有提高：在2 m有效波高下,测距精度提高9%,有效波高精度提高了11%;在4 m有效波高下,测距精度提高13%,有效波高精度提高了20%。     

An Efficient Universal Addition Scheme for All Hybrid-Redundant Representations with Weighted Bit-Set Encoding

Redundant and hybrid-redundant number representations are used extensively to speed up arithmetic operations within general-purpose and special-purpose digital systems, with the latter (containing both redundant and nonredundant digits) offering cost advantages over fully redundant systems. We use weighted bit-set (WBS) encoding as a paradigm for uniform treatment of five previously studied variants of hybrid-redundant systems. We then extend the class of hybrid-redundant numbers to coincide with the entire set of canonical WBS numbers by allowing an arbitrary nonredundant position, heretofore restricted to ordinary bits (posibits), to hold a negatively weighted bit (negabit). This flexibility leads to interesting and useful symmetric variants of hybrid-redundant representations. We provide a high-level circuit design, based solely on binary full-adders, for a constant-time universal hybrid-redundant adder capable of producing a canonical WBS-encoded sum of two canonical WBS (or extended hybrid) numbers. This is made possible by the use of conventional binary full-adders for reducing any collection of three posibits and negabits, where negabits use an inverted encoding. We compare our adder to previous designs, showing advantages in speed, cost, and regularity. Furthermore we explore representationally closed addition schemes, holding the benefit of greater regularity and reusability, and provide high-level representationally closed designs for all the previously studied variants of hybrid redundancy and for the new symmetric variants introduced here. Finally, we present a new functionality for a conventional (4; 2) compressor in combining any collection of five equally weighted negabits and posibits, and show its utility in the design of multipliers for extended hybrid-redundant numbers. Ghassem Jaberipur received BS in electrical engineering and PhD in computer engineering from Sharif University of Technology in 1974 and 2004, respectively, MS in engineering (majoring in computer hardware) from University of California, Los Angeles, in 1976, and MS in computer science from University of Wisconsin, Madison, in 1979. Since 1979, he has been with the Department of Electrical and Computer Engineering, Shahid Beheshti University, in Tehran, Iran, teaching courses in compiler construction, automata theory, design and implementation of programming languages, and computer arithmetic. Behrooz Parhami (PhD, University of California, Los Angeles, 1973) is Professor of Electrical and Computer Engineering at University of California, Santa Barbara. He has research interests in computer arithmetic, parallel processing, and dependable computing. In his previous position with Sharif University of Technology in Tehran, Iran (1974--88), he was also involved in educational planning, curriculum development, standardization efforts, technology transfer, and various editorial responsibilities, including a five-year term as Editor of Computer Report, a Persian-language computing periodical. His technical publications include over 200 papers in peer-reviewed journals and international conferences, a Persian-language textbook, and an English/Persian glossary of computing terms. Among his publications are three textbooks on parallel processing (Plenum, 1999), computer arithmetic (Oxford, 2000), and computer architecture (Oxford, 2005). Dr. Parhami is a Fellow of both the IEEE and the British Computer Society, a member of the Association for Computing Machinery, and a Distinguished Member of the Informatics Society of Iran for which he served as a founding member and President during 1979-84. He also served as Chairman of IEEE Iran Section (1977-86) and received the IEEE Centennial Medal in 1984. Mohammad Ghodsi Mohammad Ghodsi received BS in electrical engineering from Sharif University of Technology (SUT, Tehran, Iran) in 1975, MS in electrical engineering and computer science from University of California at Berkeley in 1978, and PhD in computer science from the Pennsylvania State University in 1989. He has been affiliated with SUT as a faculty member since 1979. Presently, he is a Professor in SUT's Computer Engineering Department. His research interests include design of efficient algorithms, parallel and systolic algorithms, and computational geometry.     

一种混沌伪随机序列均匀化普适算法的改进

为了分析盛利元等所述算法的安全性与普适性,从信息论的角度提出了单轮迭代信息损失量和动力学系统平均信息损失速度的概念,分析结果表明,第二类比特位变换的单轮迭代信息损失量为12比特,标准第二类比特位变换的单轮迭代信息损失量与指数e有关,存在信息损失量较小的可能性,将1023-e作为移位位数,使得标准第二类比特位变换无法遍历[-1,1]区间内的所有浮点数.进一步提出了暂态数据和第一类暂态变换的概念,并对文献[14]中所述算法进行了改进,改进后算法能够将任意混沌输出序列转换为至[0,1]区间内的浮点数,转换过程的信息损失量为L-1比特,接近有限计算精度为L时的最大信息损失速度I_max=L,且通过χ检验可证明转换后的混沌输出序列服从均匀分布.     

Bounds for the MSE performance of constant modulus estimators

The constant modulus (CM) criterion has become popular in the design of blind linear estimators of sub-Gaussian independent and identically distributed (i.i.d.) processes transmitted through unknown linear channels in the presence of unknown additive interference. In this paper, we present an upper bound for the conditionally unbiased mean-squared error (UMSE) of CM-minimizing estimators that depends only on the source kurtoses and the UMSE of Wiener estimators. Further analysis reveals that the extra UMSE of CM estimators can be upper-bounded by approximately the square of the Wiener (i.e., minimum) UMSE. Since our results hold for vector-valued finite-impulse response/infinite-impulse response (FIR/IIR) linear channels, vector-valued FIR/IIR estimators with a possibly constrained number of adjustable parameters, and multiple interferers with arbitrary distribution, they confirm the longstanding conjecture regarding the general mean-square error (MSE) robustness of CM estimators     

An Improvement of UMP‐BP Decoding Algorithm Using the Minimum Mean Square Error Linear Estimator

In this paper, we propose the modified uniformly most powerful (UMP) belief‐propagation (BP)‐based decoding algorithm which utilizes multiplicative and additive factors to diminish the errors introduced by the approximation of the soft values given by a previously proposed UMP BP‐based algorithm. This modified UMP BP‐based algorithm shows better performance than that of the normalized UMP BP‐based algorithm, i.e., it has an error performance closer to BP than that of the normalized UMP BP‐based algorithm on the additive white Gaussian noise channel for low density parity check codes. Also, this algorithm has the same complexity in its implementation as the normalized UMP BP‐based algorithm.     

Steady-State MSE Performance of the Set-Membership Affine Projection Algorithm

The set-membership affine projection (SM-AP) algorithm has many desirable characteristics such as fast convergence speed, low power consumption due to data-selective updates, and low misadjustment. The main reason hindering the widespread use of the SM-AP algorithm is the lack of analytical results related to its steady-state performance. In order to bridge this gap, this paper presents an analysis of the steady-state mean square error (MSE) of a general form of the SM-AP algorithm. The proposed analysis results in closed-form expressions for the excess MSE and misadjustment of the SM-AP algorithm, which are also applicable to many other algorithms. This work also provides guidelines for the analysis of the whole family of SM-AP algorithms. The analysis relies on the energy conservation method and has the attractive feature of not assuming a specific model for the input signal. In addition, the choice of the upper bound for the error of the SM-AP algorithm is addressed for the first time. Simulation results corroborate the accuracy of the proposed analysis.     

A Universal Weighted Powed Function of Television Noise and Its Application to High-Definition Tv System Design

The author proposes a new weighted function of noise power affecting television picture defined in a two-dimensional picture frequency domain, including the vertical frequency domain. This function is of universal use and applicable to television standards with any aspect ratio and differing number of scanning lines.     

Weighted 3D GS Algorithm for Image-Qquality Improvement of Multi-Plane Holographic Display

Theoretically,three-dimensional(3D) GS algorithm can realize 3D displays;however,correlation of the output image is restricted because of the interaction among multiple planes,thus failing to meet the image-quality requirements in practical applications.We introduce the weight factors and propose the weighted 3D GS algorithm,which can realize selective control of the correlation of multi-plane display based on the traditional 3D GS algorithm.Improvement in image quality is accomplished by the selection of appropriate weight factors.     

Novel MSE adaptive control of optical PMD compensators

We describe a new effective technique for the adaptive adjustment of the control parameters of any structure of polarization-mode dispersion compensator, such as, e.g., those based on a cascade of polarization controllers and polarization-maintaining fibers. This technique is based on the mean square error between the photodetected signal and the decided symbol and allows us to obtain fast convergence and a lower outage probability with a very limited complexity.     

自适应LMS算法均方误差收敛的充分条件

本文给出了保证自适应最小均方算法均方误差(或平均功率)收敛的充分条件,并分析了在该条件下均方误差的收敛性能。     

MSE系统对抗中最佳干扰参数的仿真研究

首先介绍了MSE系统的最佳干扰技术，提出对该系统的最佳干扰样式。并在此基础上分析不同的干扰源参数对MSE系统干扰效果的影响，通过仿真得出了对MSE系统的最佳干扰参数，为MSE干扰系统的研制提供了理论依据。     

InSAR相位解缠最小二乘算法的研究

相位解缠是干涉合成孔径雷达（InSAR）测量地形高程的重要步骤之一。由噪声和欠采样导致的相位不一致性问题是相位解缠的难点。本文分析了相位解缠最小二乘算法的原理和性质,总结了算法的优缺点。最后采用加拿大RadarSatl数据进行实验,验证了分析的结果。     

一种野战地域通信网网络节点和链路重要性的评估方法

评估网络节点和链路的重要性,是研究野战地域通信网网络可靠性的重要内容。在采用节点收缩法判断网络节点重要性的基础上,结合实例,提出了利用层次分析法确定连接于核心节点的各条链路权重值的方法,进而得出野战地域通信网网络节点和链路重要性的评估方法,为野战地域通信网的可靠性研究和电子对抗作战提供参考。