Direction Matrix Research Articles

DOA estimation of noncircular signals with direction-dependent mutual coupling

In this paper, a reweighted sparse recovery algorithm based on the optimal weighted subspace fitting (WSF) for non-circular signals in direction-dependent mutual coupling (MC) is proposed. Firstly, a new augmented model is constructed by leveraging the characteristics of non-circular signals. Next, a new direction matrix without mutual coupling coefficients is obtained by a novel transformation method. Then, two sparse recovery models are constructed by utilizing the WSF technique, and the sparsity of the solution is increased by constructing a weighted matrix. Finally, the direction of arrival (DOA) is achieved by a sparse recovery approach. For both coherent and incoherent signals, the developed approach can achieve precise DOA estimation in the case of direction-dependent MC. The robustness and advantage of the developed approach are testified by various experiments.

Accurate identification of single-cell types via correntropy-based Sparse PCA combining hypergraph and fusion similarity

ABSTRACT The advent of single-cell RNA sequencing (scRNA-seq) technology enables researchers to gain deep insights into cellular heterogeneity. However, the high dimensionality and noise of scRNA-seq data pose significant challenges to clustering. Therefore, we propose a new single-cell type identification method, called CHLSPCA, to address these challenges. In this model, we innovatively combine correntropy with PCA to address the noise and outliers inherent in scRNA-seq data. Meanwhile, we integrate the hypergraph into the model to extract more valuable information from the local structure of the original data. Subsequently, to capture crucial similarity information not considered by the PCA model, we employ the Gaussian kernel function and the Euclidean metric to mine the similarity information between cells, and incorporate this information into the model as the similarity constraint. Furthermore, the principal components (PCs) of PCA are very dense. A new sparse constraint is introduced into the model to gain sparse PCs. Finally, based on the principal direction matrix learned from CHLSPCA, we conduct extensive downstream analyses on real scRNA-seq datasets. The experimental results show that CHLSPCA performs better than many popular clustering methods and is expected to promote the understanding of cellular heterogeneity in scRNA-seq data analysis and support biomedical research.

Construction of complex high order subharmonic vibrations in a nonlinear rotor system I: Eigenvalue dynamics and prediction

In real turbomachinery testing, we always confront strange high order vibrations which scatter a few points or draw some local lines in the experimental bifurcation diagrams. Such vibrations maybe ignored due to the lack of experiences and knowledge while they can be important in design and operation due to the nonsmooth jumping and instability. This paper proposes a new method for predicting the complex high order subharmonic vibrations for a nonlinear rotor system with light rub. The nonlinearity is induced by the contact of the bristles and the rotor. A continuous nonlinear rotor system is established based on the geometric and bending force diagrams. We discretize the complex orbits and built implicit mappings between the adjacent nodes. A directional eigenfunctional matrix is constructed which maps each nodes and passes the perturbation along the orbits for semi-analytical prediction of the high order subharmonic vibrations. Stability criterion is defined. Numerical bifurcation diagrams and harmonic contours are presented for quick glance of the global subahrmonic vibrations. Two local subharmonic vibrations of odd orders are succesfuly predicted. Local high order subharmonic vibrations of “full tree” and “half tree” are predicted and achieved accurately. An experimental platform is established to compare the semi-analytical predictions with experimental results. The research delivers a new perspective for predicting and computing the high order subharmonic vibrations in nonlinear rotor system.

Traffic Target Location Estimation Based on Tensor Decomposition in Intelligent Transportation System

As the safety problems and economic losses caused by traffic accidents are becoming more and more serious, intelligent transportation system (ITS) came into being. After the outbreak of COVID-19, how to achieve effective traffic scheduling and macro command under less contact has attracted more attention. Therefore, the location estimation of traffic objectives is a key issue. In the developed framework, for the target parameter estimation in traffic, frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is introduced into ITS, and tensor decomposition is used to process transportation big data (TBD) to improve the real-time performance of target location estimation. Unfortunately, spatial colored noise and array gain-phase error will affect the performance of FDA-MIMO radar in ITS. An algorithm that can solve the angle-range estimation problem of FDA-MIMO radar in the co-existence of array gain-phase error and spatial colored noise is proposed. Firstly, the four-dimensional tensor is constructed by using the temporal un-correlation of colored noise. Therefore, the influence of colored noise in ITS is removed. Secondly, the direction matrix containing target information is obtained by parallel factor (PARAFAC) decomposition. For the array gain-phase error, the optimization problem is constructed, and the Lagrange multiplier is employed to calculate the optimal solution. The effect of gain-phase error is eliminated by utilizing the optimal solution and the direction matrices. Finally, the location information of motor vehicle is achieved by calculating the solution of least square (LS) fitting. The developed scheme can achieve the location information of motor vehicles in the co-existence of array gain-phase error and spatial colored noise. Comprehensive numerical experiments illustrate that the developed scheme in ITS can efficiently obtain the location information of motor vehicles.

A perfectly matched layer for the absorption of electromagnetic waves using differential forms in three-dimensional domains

As a significant contribution, we present a novel formulation of the complex frequency-shifted perfectly matched layer (CFS-PML) for differential forms, introducing the innovative concept of a directional incidence matrix. This paper explores the application of differential forms in simulating wave propagation problems in three-dimensional domains. To confine the unbounded domain, we utilize a discretization technique based on the CFS-PML. Additionally, we provide an illustrative example that involves a system comprising a GPR (Ground Penetrating Radar) horn antenna, air, lossy soil, and a buried sphere.

αV-Integrin-Dependent Inhibition of Glioblastoma Cell Migration, Invasion and Vasculogenic Mimicry by the uPAcyclin Decapeptide.

Among the deadliest human cancers is glioblastoma (GBM) for which new treatment approaches are urgently needed. Here, the effects of the cyclic decapeptide, uPAcyclin, are investigated using the U87-MG, U251-MG, and U138-MG human GBM and C6 rat cell models. All GBM cells express the αV-integrin subunit, the target of uPAcyclin, and bind specifically to nanomolar concentrations of the decapeptide. Although peptide exposure affects neither viability nor cell proliferation rate, nanomolar concentrations of uPAcyclin markedly inhibit the directional migration and matrix invasion of all GBM cells, in a concentration- and αV-dependent manner. Moreover, wound healing rate closure of U87-MG and C6 rat glioma cells is reduced by 50% and time-lapse videomicroscopy studies show that the formation of vascular-like structures by U87-MG in three-dimensional matrix cultures is markedly inhibited by uPAcyclin. A strong reduction in the branching point numbers of the U87-MG, C6, and U251-MG cell lines undergoing vasculogenic mimicry, in the presence of nanomolar peptide concentrations, was observed. Lysates from matrix-recovered uPAcyclin-exposed cells exhibit a reduced expression of VE-cadherin, a prominent factor in the acquisition of vascular-like structures. In conclusion, these results indicate that uPAcyclin is a promising candidate to counteract the formation of new vessels in novel targeted anti-GBM therapies.

Directional policy matrix and cloud model: Theory and application in the evaluation of city-water interaction

The evolution direction of the relationship between city and water determines the healthy prosperity and sustainable development of a city. Scientific evaluation of the matching relationship between water resources and social economy in different cities is the key to analyze the interaction relationship between city and water. Current research on sustainable evaluation of complex systems is dominated by one-dimensional static evaluation, which cannot help find gaps and predict development. Insufficient analysis of the city-water relationship reduces the reference and foresight of policy making. Moreover, conventional methods cannot overcome the uncertainty of the evaluation process. Therefore, this paper constructs a coordinated evaluation model based on directional policy matrix and cloud model, and categorizes cities in different development stages and water resources conditions into the matrix. Based on the analysis of the sources of contradictions in the relationship between city and water for three types of cities, such as cooperative interaction, competitive interaction and conflict interaction, water resources utilization policy and urban development strategy are adjusted accordingly. According to the evaluation results, the single system of the city, such as water resource carrying capacity or economic society level, has a positive development. However, from the point of view of the matching degree of the composite system, the interaction between city and water in most cities is not friendly. Among the cities, leading cities (Guangzhou, Shanghai, Wuhan) and robust cities (Chongqing, Chengdu) have a healthy match between water resources and social economy, and the relationship between city and water is cooperative. In driving cities (Beijing, Tianjin) and restrictive cities (Zhengzhou, Xi 'an), water resources and social economy do not match, and the relationship between city and water exists competition or even conflict. Based on the contradictions of different types of cities, the system in need of improvement and vulnerability indicators, water resources policy and economic society strategy recommendations are given to ensure sustainable development.

Multi-layered cement-hydrogel composite with high toughness, low thermal conductivity, and self-healing capability

The inherent quasi-brittleness of cement-based materials, due to the disorder of their hydration products and pore structures, present significant challenges for directional matrix toughening. In this work, a rigid layered skeleton of cement slurry was prepared using a simplified ice-template method, and subsequently flexible polyvinyl alcohol hydrogel was introduced into the unidirectional pores between neighboring cement platelets, resulting in the formation of a multi-layered cement-based composite. A toughness improvement of over 175 times is achieved by the implantation of such hard-soft alternatively layered microstructure. The toughening mechanism is the stretching of hydrogels at the nano-scale and deflections of micro-cracks at the interfaces, which avoid stress concentration and dissipate huge energy. Furthermore, this cement-hydrogel composite also exhibits a low thermal conductivity (around 1/10 of normal cement) and density, high specific strength and self-healing properties, which can be used in thermal insulation, seismic high-rise buildings and long-span bridges.

An Efficient 2-D DOA Estimation Method for Unfolded Coprime L-Shaped Array

In this article, a novel two-dimensional direction of arrival (2-D DOA) estimation method using unfolded coprime L-shaped array is presented. First, the signal subspace is divided to establish the relationship between two subarrays. Thus, the 2-D DOA estimation problem is transformed into one-dimensional (1-D) one. Moreover, by introducing the extraction matrix, the nonuniform array can also fit polynomial root finding, from which the initial elevation and azimuth estimates can be obtained. Finally, to further improve the estimation accuracy, we compensate the estimation offsets via least square constructed from the first-order Taylor expansion of the direction matrix. The proposed algorithm outperforms the conventional algorithms in the aspect of computational complexity while achieves preferable estimation performance. Specifically, the complexity of the proposed method is reduced by several orders of magnitude, with RMSE reduced by up to 6 dB.

An adaptive modified three-term conjugate gradient method with global convergence

In this paper, an adaptive three-term conjugate gradient method is proposed based on the Perry family by using a modified secant condition. The new method depends on a positive parameter which has a critical role in the efficiency of the algorithm. We propose an adaptive choice for this parameter to control the condition number of the direction matrix by minimizing the distance between the smallest and largest eigenvalue. Global convergence of the new algorithm is proved for general functions, under standard assumptions. Experimental results verify the expected numerical behavior of our algorithm.

Sparse signal reconstruction via Hager–Zhang-type schemes for constrained system of nonlinear equations

In this article, two Hager–Zhang (HZ) type projection algorithms are presented for large-dimension nonlinear monotone problems and sparse signal recovery in compressed sensing. This goal is attained by conducting singular value analysis of a nonsingular HZ-type search direction matrix as well as applying the idea by Piazza and Politi [J Comput Appl Math. 2002;143(1):141–144] and minimizing the Frobenius norm of an orthornormal matrix. The paper attempts to fill the gap in the work of Hager and Zhang [Pac J Optim. 2006;2(1):35–58], Waziri et al. [Appl Math Comput. 2019;361:645–660], Sabi'u et al. [Appl Numer Math. 2020;153:217–233] and Babaie-Kafaki [4OR-Q J Oper Res. 2014;12:285-292], where the sufficient descent or global convergence condition is not satisfied when the HZ parameter is in the interval . The proposed schemes are also suitable for solving non-smooth nonlinear problems. Also, by employing some mild conditions, global convergence of the schemes are established, while numerical comparison with four effective HZ-type methods show that the new methods are efficient. Furthermore, to illustrate their practical application, both methods are applied to solve the -norm regularization problems to recover a sparse signal in compressive sensing. The experiments conducted in that regard show that the methods are promising and perform better than two other methods in the literature.

A descent family of the spectral Hestenes–Stiefel method by considering the quasi-Newton method

The prominent computational features of the Hestenes–Stiefel parameter as one of the fundamental members of conjugate gradient methods have attracted the attention of many researchers. Yet, as a weak stop, it lacks global convergence for general functions. To overcome this defect, a family of spectral version of Hestenes–Stiefel conjugate gradient methods is introduced. To compute the spectral parameter, in the account of worthy properties of quasi-Newton methods, we minimize the distance between the search direction matrix of the spectral conjugate gradient method and the BFGS (Broyden–Fletcher–Goldfarb–Shanno) update. To achieve sufficient descent property, the search direction is projected in the orthogonal subspace to the gradient of the objective function. The convergence analysis of the proposed method is carried out under standard assumptions for general functions. Finally, the practical merits of the suggested method are investigated by numerical experiments on a set of CUTEr test functions using the Dolan–Moré performance profile. The results show the computational efficiency of the proposed method.

Efficient prediction and classification for cirrhosis disease using LBP, GLCM and SVM from MRI images

To enhance the specificity of Magnetic resonance imaging (MRI) based cirrhosis stage-diagnosis, a method of diagnosis incorporating the scan image texture discovery with classification techniques are suggested. First of all the liver MRI images are preprocessed and Region of Interest (ROI) image field is retrieved from the MRI images and the Lloyd method is compressed and quantified. Then a Local Binary Pattern (LBP) process filtering the ROI image and then extracts the texture features of the LBP images from four directions with a twenty directional Gray-Level Co-occurrence Matrix (GLCM). After that, the Support Vector Machine (SVM) technique has been used for MRI image classification and cirrhosis disease prediction. The experimental findings indicate that the procedure suggested will diagnose hepatic cirrhosis correctly.

SAR image directional context covariance matrix： Construction and its application in terrain classification

SAR image directional context covariance matrix： Construction and its application in terrain classification

Vector methods for determination of rigid body orientation

Vector methods for determining the orientation of a rigid body using information about vectors in the reference and body-related coordinate systems are considered. The analysis is based on the least squares method in vector-matrix form. This approach allows us to consider the application of different methods of solving the problem from a single standpoint: matrix (using the directional cosine matrix), geometric (using the Gibbs vector), quaternion (using the quaternion of rotation). Numerical evaluation of the accuracy of eight orientation determination algorithms obtained on the basis of these methods was performed.

Do we innovate atop giants' shoulders?

PurposeThe main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.Design/methodology/approachThe authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.FindingsInnovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.Originality/valueThis paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.

Modified optimal Perry conjugate gradient method for solving system of monotone equations with applications

This article proposes an optimal value for the scaled Perry conjugate gradient (CG) method, which aims to solve large-scale monotone nonlinear equations. An optimal choice for the scaled parameter is obtained by minimizing the largest and smallest eigenvalues of the search direction matrix. In addition, the corresponding Perry CG parameter is incorporated with the hyperplane approach to propose a robust algorithm for solving monotone equations. The global convergence of the proposed method is established based on monotonicity and Lipschitz continuity assumptions. The robustness of the proposed algorithm is validated by examples involving numerical solving of monotone equations with their application to signal and image restoration problems.

Resolving morphogenesis into quantifiable cell behaviours.

Morphogenesis is extremely diverse, but its systematic quantification to determine the physical mechanisms that produce different phenotypes is possible by quantifying the underlying cell behaviours. These are limited and definable: they consist of cell proliferation, orientation of cell division, cell rearrangement, directional matrix production, cell addition/subtraction and cell size/shape change. Although minor variations in these categories are possible, in sum they capture all possible morphogenetic behaviours. This article summarises these processes, discusses their measurement, and highlights some salient examples.

An analytical formulation of ZOA-based machining stability for complex tool geometries: application to 5-axis ball-end milling

The paper describes a computationally convenient analytical formulation of the stability of the cutting process with respect to self-excited vibrations in the case of five-axis milling based on the commonly used zero-order approximation. In the case of five-axis milling with general milling cutters, it is difficult to calculate stable machining process conditions for two main reasons. The first reason is the difficulty of calculating the mean value of the cutting force Jacobian with respect to the regenerative displacement (closely related to a milling directional matrix) for a generally inclined tool, and the second reason is the nonlinearity of this Jacobian with respect to the process parameters, which means that the problem cannot be reduced to a linear eigenvalue problem as is usual for linear cases (e.g. cylindrical milling with respect to the axial depth of cut). In the first part, this paper presents a modification of the calculation of the machining stability limits for a nonlinearly dependent cutting force Jacobian. A new formulation of this Jacobian for a general tool based on the surface integral over the tool and workpiece engagement region is presented which leads directly to the mean value of the Jacobian of the cutting force (direction matrix) without the need to calculate it as a function of time and then calculate the mean value over one revolution. The advantage is that if we can analytically describe the engagement area, we also obtain an analytical relation for the cutting force Jacobian. This is presented with the practical example of a generally inclined ball-end mill. This analytical formulation of the force Jacobian allows the calculation of its derivatives with respect to the technological parameters (depth of cut, step over, tilt and lead angles), which is useful both for the calculation of stability diagrams and for solving optimization problems related to machining stability.

Feature-Based Compliance Control for Precise Peg-in-Hole Assembly

This article focuses on solving the problem of precise peg-in-hole assembly. The main contribution of this article is to propose a control method to improve the assembly precision. The method is capable to realize the precise insertion assembly tasks of cylindrical or prismatic objects with clearance or interference fit. In this article, a feature space and a response space are first constructed according to the relative pose and equivalent forces and moments. Then, the contact states are segmented in the feature space and the segmentation boundaries are mapped into the response space. Furthermore, a feature-based compliance control (FBCC) method is proposed based on boundary mapping. In FBCC method, a direction matrix is designed to execute accurate adjustment and an integral element is applied to eliminate residual responses. Finally, the simulations and experiments demonstrate the superiority and robustness of FBCC method.