**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**1056

# Search results for: covariance matrix

##### 1056 Exploiting Non Circularity for Angle Estimation in Bistatic MIMO Radar Systems

**Authors:**
Ebregbe David,
Deng Weibo

**Abstract:**

The traditional second order statistics approach of using only the hermitian covariance for non circular signals, does not take advantage of the information contained in the complementary covariance of these signals. Radar systems often use non circular signals such as Binary Phase Shift Keying (BPSK) signals. Their noncicular property can be exploited together with the dual centrosymmetry of the bistatic MIMO radar system to improve angle estimation performance. We construct an augmented matrix from the received data vectors using both the positive definite hermitian covariance matrix and the complementary covariance matrix. The Unitary ESPRIT technique is then applied to the signal subspace of the augmented covariance matrix for automatically paired Direction-of-arrival (DOA) and Direction-of-Departure (DOD) angle estimates. The number of targets that can be detected is twice that obtainable with the conventional ESPRIT approach. Simulation results show the effectiveness of this method in terms of increase in resolution and the number of targets that can be detected.

**Keywords:**
Bistatic MIMO Radar,
Unitary Esprit,
Non circular signals.

##### 1055 Principle Components Updates via Matrix Perturbations

**Authors:**
Aiman Elragig,
Hanan Dreiwi,
Dung Ly,
Idriss Elmabrook

**Abstract:**

**Keywords:**
Online data updates,
covariance matrix,
online
principle component analysis (OPCA),
matrix perturbation.

##### 1054 A Robust Visual Tracking Algorithm with Low-Rank Region Covariance

**Authors:**
Songtao Wu,
Yuesheng Zhu,
Ziqiang Sun

**Abstract:**

**Keywords:**
Visual tracking,
region covariance descriptor,
lowrankregion covariance

##### 1053 Human Face Detection and Segmentation using Eigenvalues of Covariance Matrix, Hough Transform and Raster Scan Algorithms

**Authors:**
J. Prakash,
K. Rajesh

**Abstract:**

**Keywords:**
Circular Hough Transform,
Covariance matrix,
Eigenvalues,
Elliptical Hough Transform,
Face segmentation,
Raster
Scan Algorithm.

##### 1052 A Novel Approach for Coin Identification using Eigenvalues of Covariance Matrix, Hough Transform and Raster Scan Algorithms

**Authors:**
J. Prakash,
K. Rajesh

**Abstract:**

**Keywords:**
Circular Hough Transform,
Coin detection,
Covariance matrix,
Eigenvalues,
Raster scan Algorithm,
Texton.

##### 1051 Elliptical Features Extraction Using Eigen Values of Covariance Matrices, Hough Transform and Raster Scan Algorithms

**Authors:**
J. Prakash,
K. Rajesh

**Abstract:**

**Keywords:**
Circular Hough transform,
covariance matrix,
Eigen
values,
ellipse detection,
raster scan algorithm.

##### 1050 Adaptive Kernel Principal Analysis for Online Feature Extraction

**Authors:**
Mingtao Ding,
Zheng Tian,
Haixia Xu

**Abstract:**

The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated to adapt to the changing characteristics of data. Second, KPC are recursively formulated to overcome the batch nature of standard KPCA.This formulation is derived from the recursive eigen-decomposition of kernel covariance matrix and indicates the KPC variation caused by the new data. The proposed method not only alleviates sub-optimality of the KPCA method for non-stationary data, but also maintains constant update speed and memory usage as the data-size increases. Experiments for simulation data and real applications demonstrate that our approach yields improvements in terms of both computational speed and approximation accuracy.

**Keywords:**
adaptive method,
kernel principal component analysis,
online extraction,
recursive algorithm

##### 1049 Comparative Study on Recent Integer DCTs

**Authors:**
Sakol Udomsiri,
Masahiro Iwahashi

**Abstract:**

**Keywords:**
DCT,
sensitivity,
lossless,
wordlength.

##### 1048 On Generalized New Class of Matrix Polynomial Set

**Authors:**
Ghazi S. Kahmmash

**Abstract:**

New generalization of the new class matrix polynomial set have been obtained. An explicit representation and an expansion of the matrix exponential in a series of these matrix are given for these matrix polynomials.

**Keywords:**
Generating functions,
Recurrences relation and Generalization of the new class matrix polynomial set.

##### 1047 UD Covariance Factorization for Unscented Kalman Filter using Sequential Measurements Update

**Authors:**
H. Ghanbarpour Asl,
S. H. Pourtakdoust

**Abstract:**

**Keywords:**
Unscented Kalman filter,
Square-root unscentedKalman filter,
UD covariance factorization,
Target tracking.

##### 1046 The Robust Clustering with Reduction Dimension

**Authors:**
Dyah E. Herwindiati

**Abstract:**

**Keywords:**
Breakdown point,
Consistency,
2DPCA,
PCA,
Outlier,
Vector Variance

##### 1045 Entropy Based Spatial Design: A Genetic Algorithm Approach (Case Study)

**Authors:**
Abbas Siefi,
Mohammad Javad Karimifar

**Abstract:**

We study the spatial design of experiment and we want to select a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology. In these applications, observations can be collected at different locations and possibly at different times. In spatial design, when the design region and the set of interest are discrete then the covariance matrix completely describe any objective function and our goal is to choose a feasible design that minimizes the resulting uncertainty. The problem is recast as that of maximizing the determinant of the covariance matrix of the chosen subset. This problem is NP-hard. For using these designs in computer experiments, in many cases, the design space is very large and it's not possible to calculate the exact optimal solution. Heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective and exact solution not possible. We developed a GA algorithm to take advantage of the exploratory power of this algorithm. The successful application of this method is demonstrated in large design space. We consider a real case of design of experiment. In our problem, design space is very large and for solving the problem, we used proposed GA algorithm.

**Keywords:**
Spatial design of experiments,
maximum entropy sampling,
computer experiments,
genetic algorithm.

##### 1044 An Improved Adaptive Dot-Shape Beamforming Algorithm Research on Frequency Diverse Array

**Authors:**
Yanping Liao,
Zenan Wu,
Ruigang Zhao

**Abstract:**

Frequency diverse array (FDA) beamforming is a technology developed in recent years, and its antenna pattern has a unique angle-distance-dependent characteristic. However, the beam is always required to have strong concentration, high resolution and low sidelobe level to form the point-to-point interference in the concentrated set. In order to eliminate the angle-distance coupling of the traditional FDA and to make the beam energy more concentrated, this paper adopts a multi-carrier FDA structure based on proposed power exponential frequency offset to improve the array structure and frequency offset of the traditional FDA. The simulation results show that the beam pattern of the array can form a dot-shape beam with more concentrated energy, and its resolution and sidelobe level performance are improved. However, the covariance matrix of the signal in the traditional adaptive beamforming algorithm is estimated by the finite-time snapshot data. When the number of snapshots is limited, the algorithm has an underestimation problem, which leads to the estimation error of the covariance matrix to cause beam distortion, so that the output pattern cannot form a dot-shape beam. And it also has main lobe deviation and high sidelobe level problems in the case of limited snapshot. Aiming at these problems, an adaptive beamforming technique based on exponential correction for multi-carrier FDA is proposed to improve beamforming robustness. The steps are as follows: first, the beamforming of the multi-carrier FDA is formed under linear constrained minimum variance (LCMV) criteria. Then the eigenvalue decomposition of the covariance matrix is performed to obtain the diagonal matrix composed of the interference subspace, the noise subspace and the corresponding eigenvalues. Finally, the correction index is introduced to exponentially correct the small eigenvalues of the noise subspace, improve the divergence of small eigenvalues in the noise subspace, and improve the performance of beamforming. The theoretical analysis and simulation results show that the proposed algorithm can make the multi-carrier FDA form a dot-shape beam at limited snapshots, reduce the sidelobe level, improve the robustness of beamforming, and have better performance.

**Keywords:**
Multi-carrier frequency diverse array,
adaptive beamforming,
correction index,
limited snapshot,
robust.

##### 1043 A Martingale Residual Diagnostic for Logistic Regression Model

**Authors:**
Entisar A. Elgmati

**Abstract:**

Martingale model diagnostic for assessing the fit of logistic regression model to recurrent events data are studied. One way of assessing the fit is by plotting the empirical standard deviation of the standardized martingale residual processes. Here we used another diagnostic plot based on martingale residual covariance. We investigated the plot performance under several types of model misspecification. Clearly the method has correctly picked up the wrong model. Also we present a test statistic that supplement the inspection of the two diagnostic. The test statistic power agrees with what we have seen in the plots of the estimated martingale covariance.

**Keywords:**
Covariance,
logistic model,
misspecification,
recurrent events.

##### 1042 The Partial Non-combinatorially Symmetric N10 -Matrix Completion Problem

**Authors:**
Gu-Fang Mou,
Ting-Zhu Huang

**Abstract:**

An n×n matrix is called an N1 0 -matrix if all principal minors are non-positive and each entry is non-positive. In this paper, we study the partial non-combinatorially symmetric N1 0 -matrix completion problems if the graph of its specified entries is a transitive tournament or a double cycle. In general, these digraphs do not have N1 0 -completion. Therefore, we have given sufficient conditions that guarantee the existence of the N1 0 -completion for these digraphs.

**Keywords:**
Matrix completion,
matrix completion,
N10 -matrix,
non-combinatorially symmetric,
cycle,
digraph.

##### 1041 Fuzzy Adjacency Matrix in Graphs

**Authors:**
Mahdi Taheri,
Mehrana Niroumand

**Abstract:**

**Keywords:**
Graph,
adjacency matrix,
fuzzy numbers

##### 1040 Inverse Matrix in the Theory of Dynamic Systems

**Authors:**
R. Masarova,
M. Juhas,
B. Juhasova,
Z. Sutova

**Abstract:**

**Keywords:**
Dynamic system,
transfer matrix,
inverse matrix,
modeling.

##### 1039 Numerical Treatment of Matrix Differential Models Using Matrix Splines

**Authors:**
Kholod M. Abualnaja

**Abstract:**

This paper consider the solution of the matrix differential models using quadratic, cubic, quartic, and quintic splines. Also using the Taylor’s and Picard’s matrix methods, one illustrative example is included.

**Keywords:**
Matrix Splines,
Cubic Splines,
Quartic Splines.

##### 1038 The Relationship of Eigenvalues between Backward MPSD and Jacobi Iterative Matrices

**Authors:**
Zhuan-de Wang,
Hou-biao Li,
Zhong-xi Gao

**Abstract:**

In this paper, the backward MPSD (Modified Preconditioned Simultaneous Displacement) iterative matrix is firstly proposed. The relationship of eigenvalues between the backward MPSD iterative matrix and backward Jacobi iterative matrix for block p-cyclic case is obtained, which improves and refines the results in the corresponding references.

**Keywords:**
Backward MPSD iterative matrix,
Jacobi iterative matrix,
eigenvalue,
p-cyclic matrix.

##### 1037 On Positive Definite Solutions of Quaternionic Matrix Equations

**Authors:**
Minghui Wang

**Abstract:**

**Keywords:**
Matrix equation,
Quaternionic matrix,
Real representation,
positive (semi)definite solutions.

##### 1036 Connectivity Estimation from the Inverse Coherence Matrix in a Complex Chaotic Oscillator Network

**Authors:**
Won Sup Kim,
Xue-Mei Cui,
Seung Kee Han

**Abstract:**

We present on the method of inverse coherence matrix for the estimation of network connectivity from multivariate time series of a complex system. In a model system of coupled chaotic oscillators, it is shown that the inverse coherence matrix defined as the inverse of cross coherence matrix is proportional to the network connectivity. Therefore the inverse coherence matrix could be used for the distinction between the directly connected links from indirectly connected links in a complex network. We compare the result of network estimation using the method of the inverse coherence matrix with the results obtained from the coherence matrix and the partial coherence matrix.

**Keywords:**
Chaotic oscillator,
complex network,
inverse coherence matrix,
network estimation.

##### 1035 Solving Linear Matrix Equations by Matrix Decompositions

**Authors:**
Yongxin Yuan,
Kezheng Zuo

**Abstract:**

In this paper, a system of linear matrix equations is considered. A new necessary and sufficient condition for the consistency of the equations is derived by means of the generalized singular-value decomposition, and the explicit representation of the general solution is provided.

**Keywords:**
Matrix equation,
Generalized inverse,
Generalized
singular-value decomposition.

##### 1034 The Convergence Results between Backward USSOR and Jacobi Iterative Matrices

**Authors:**
Zuan-De Wang,
Hou-biao Li,
Zhong-xi Gao

**Abstract:**

In this paper, the backward Ussor iterative matrix is proposed. The relationship of convergence between the backward Ussor iterative matrix and Jacobi iterative matrix is obtained, which makes the results in the corresponding references be improved and refined.Moreover,numerical examples also illustrate the effectiveness of these conclusions.

**Keywords:**
Backward USSOR iterative matrix,
Jacobi iterative matrix,
convergence,
spectral radius

##### 1033 A Quantitative Tool for Analyze Process Design

**Authors:**
Andrés Carrión García,
Aura López de Murillo,
José Jabaloyes Vivas,
Angela Grisales del Río

**Abstract:**

Some quality control tools use non metric subjective information coming from experts, who qualify the intensity of relations existing inside processes, but without quantifying them. In this paper we have developed a quality control analytic tool, measuring the impact or strength of the relationship between process operations and product characteristics. The tool includes two models: a qualitative model, allowing relationships description and analysis; and a formal quantitative model, by means of which relationship quantification is achieved. In the first one, concepts from the Graphs Theory were applied to identify those process elements which can be sources of variation, that is, those quality characteristics or operations that have some sort of prelacy over the others and that should become control items. Also the most dependent elements can be identified, that is those elements receiving the effects of elements identified as variation sources. If controls are focused in those dependent elements, efficiency of control is compromised by the fact that we are controlling effects, not causes. The second model applied adapts the multivariate statistical technique of Covariance Structural Analysis. This approach allowed us to quantify the relationships. The computer package LISREL was used to obtain statistics and to validate the model.

**Keywords:**
Characteristics matrix,
covariance structure analysis,
LISREL.

##### 1032 Distribution Sampling of Vector Variance without Duplications

**Authors:**
Erna T. Herdiani,
Maman A. Djauhari

**Abstract:**

**Keywords:**
Asymptotic distribution,
covariance matrix,
likelihood ratio test,
vector variance.

##### 1031 An Algorithm of Ordered Schur Factorization For Real Nonsymmetric Matrix

**Authors:**
Lokendra K. Balyan

**Abstract:**

**Keywords:**
Schur Factorization,
Eigenvalues of nonsymmetric matrix,
Orthoganal matrix.

##### 1030 Tree Sign Patterns of Small Order that Allow an Eventually Positive Matrix

**Authors:**
Ber-Lin Yu,
Jie Cui,
Hong Cheng,
Zhengfeng Yu

**Abstract:**

**Keywords:**
Eventually positive matrix,
sign pattern,
tree.

##### 1029 Numerical Simulation of Effect of Various Rib Configurations on Enhancing Heat Transfer of Matrix Cooling Channel

**Authors:**
Seok Min Choi,
Minho Bang,
Seuong Yun Kim,
Hyungmin Lee,
Won-Gu Joo,
Hyung Hee Cho

**Abstract:**

**Keywords:**
Matrix cooling,
rib,
heat transfer,
gas turbine.

##### 1028 Bounds on the Second Stage Spectral Radius of Graphs

**Authors:**
S.K.Ayyaswamy,
S.Balachandran,
K.Kannan

**Abstract:**

Let G be a graph of order n. The second stage adjacency matrix of G is the symmetric n × n matrix for which the ijth entry is 1 if the vertices vi and vj are of distance two; otherwise 0. The sum of the absolute values of this second stage adjacency matrix is called the second stage energy of G. In this paper we investigate a few properties and determine some upper bounds for the largest eigenvalue.

**Keywords:**
Second stage spectral radius,
Irreducible matrix,
Derived graph

##### 1027 Fusion Filters Weighted by Scalars and Matrices for Linear Systems

**Authors:**
Seok Hyoung Lee,
Vladimir Shin

**Abstract:**

**Keywords:**
Kalman filtering,
fusion formula,
multi-sensor,
mean-square error.