Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective...Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective implementation of massive MIMO challenging,due to the size and weight limits of the masssive MIMO that are located on each BS.Therefore,in order to miniaturize the massive MIMO,it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis.In this paper,a multiple-pattern synthesis is considered towards convex optimization(CO).The joint convex optimization(JCO)based synthesis is proposed to construct a codebook for beamforming.Then,a criterion containing multiple constraints is developed,in which the sparse array is required to fullfill all constraints.Finally,extensive evaluations are performed under realistic simulation settings.The results show that with the same number of antenna elements,sparse array using the proposed JCO-based synthesis outperforms not only the uniform array,but also the sparse array with the existing CO-based synthesis method.Furthermore,with a half of the number of antenna elements that on the uniform array,the performance of the JCO-based sparse array approaches to that of the uniform array.展开更多
A novel Direction-Of-Arrival (DOA) estimation method is proposed in the presence of mutual coupling using the joint sparse recovery. In the proposed method, the eigenvector corresponding to the maximum eigenvalue of c...A novel Direction-Of-Arrival (DOA) estimation method is proposed in the presence of mutual coupling using the joint sparse recovery. In the proposed method, the eigenvector corresponding to the maximum eigenvalue of covariance matrix of array measurement is viewed as the signal to be represented. By exploiting the geometrical property in steering vectors and the symmetric Toeplitz structure of Mutual Coupling Matrix (MCM), the redundant dictionaries containing the DOA information are constructed. Consequently, the optimization model based on joint sparse recovery is built and then is solved through Second Order Cone Program (SOCP) and Interior Point Method (IPM). The DOA estimates are gotten according to the positions of nonzeros elements. At last, computer simulations demonstrate the excellent performance of the proposed method.展开更多
The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representat...The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representation-based median filter as the in-loop filter in depth video codec. It constructs depth candidate set which contains relevant neighboring depth pixel based on depth and intensity similarity weighted sparse coding, then the median operation is performed on this set to select a neighboring depth pixel as the result of the filtering. The experimental results indicate that the depth bitrate is reduced by about 9% compared with anchor method. It is confirmed that the proposed method is more effective in reducing the required depth bitrates for a given synthesis quality level.展开更多
Medical Image Fusion is the synthesizing technology for fusing multi-modal medical information using mathematical procedures to generate better visual on the image content and high-quality image output.Medical image f...Medical Image Fusion is the synthesizing technology for fusing multi-modal medical information using mathematical procedures to generate better visual on the image content and high-quality image output.Medical image fusion represents an indispensible role infixing major solutions for the complicated medical predicaments,while the recent research results have an enhanced affinity towards the preservation of medical image details,leaving color distortion and halo artifacts to remain unaddressed.This paper proposes a novel method of fusing Computer Tomography(CT)and Magnetic Resonance Imaging(MRI)using a hybrid model of Non Sub-sampled Contourlet Transform(NSCT)and Joint Sparse Representation(JSR).This model gratifies the need for precise integration of medical images of different modalities,which is an essential requirement in the diagnosing process towards clinical activities and treating the patients accordingly.In the proposed model,the medical image is decomposed using NSCT which is an efficient shift variant decomposition transformation method.JSR is exercised to extricate the common features of the medical image for the fusion process.The performance analysis of the proposed system proves that the proposed image fusion technique for medical image fusion is more efficient,provides better results,and a high level of distinctness by integrating the advantages of complementary images.The comparative analysis proves that the proposed technique exhibits better-quality than the existing medical image fusion practices.展开更多
In many practical applications,we need to recover block sparse signals.In this paper,we encounter the system model where joint sparse signals exhibit block structure.To reconstruct this category of signals,we propose ...In many practical applications,we need to recover block sparse signals.In this paper,we encounter the system model where joint sparse signals exhibit block structure.To reconstruct this category of signals,we propose a new algorithm called block signal subspace matching pursuit(BSSMP)for the block joint sparse recovery problem in compressed sensing,which simultaneously reconstructs the support of block jointly sparse signals from a common sensing matrix.To begin with,we consider the case where block joint sparse matrix X has full column rank and any r nonzero rowblocks are linearly independent.Based on these assumptions,our theoretical analysis indicates that the BSSMP algorithm could reconstruct the support of X through at most K-r+[r/L]iterations if sensing matrix A satisfies the block restricted isometry property of order L(K-r)+r+1 with δB_(L(K-r)+r+1)<max{√r/√K+r/4+√r/4,√L/√Kd+√L}.This condition improves the existing result.展开更多
New methods of synthetizing nonequidistant sparse antenna arrays based on the properties of magic squares are studied.The methods of construction and algorithms of synthesis of two-dimensional antennas based on them p...New methods of synthetizing nonequidistant sparse antenna arrays based on the properties of magic squares are studied.The methods of construction and algorithms of synthesis of two-dimensional antennas based on them providing a high degreeof dilution and sufficiently small side radiation are proposed.The methods for construction of such antennas and their maincharacteristics are considered.展开更多
针对过程复杂且结构未知的对象,在保证模型有效性的前提下,根据数据信息构建简单模型来简化控制器的求解是亟待解决的问题。以受控自回归模型为例,提出一种基于修正最小角回归算法的稀疏辨识方法。首先将系统模型转化为过参数化的高维...针对过程复杂且结构未知的对象,在保证模型有效性的前提下,根据数据信息构建简单模型来简化控制器的求解是亟待解决的问题。以受控自回归模型为例,提出一种基于修正最小角回归算法的稀疏辨识方法。首先将系统模型转化为过参数化的高维稀疏模型,然后将最小角回归算法用于稀疏系统辨识,并提出绝对角度停止准则,使算法经过少量的迭代即可获得模型的稀疏参数估计,并同时获得有效的时滞和阶次估计。结合辨识得到的受控自回归模型,引入一种基于指定相位点频率和增益的比例-积分-微分(proportional integral derivative,PID)控制器。数值仿真和平衡机器人的姿态控制仿真表明,该稀疏辨识算法在低数据量下具有较高的辨识精度,建立的模型具有较好的泛化性能,控制器具有良好的控制效果。展开更多
文摘Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective implementation of massive MIMO challenging,due to the size and weight limits of the masssive MIMO that are located on each BS.Therefore,in order to miniaturize the massive MIMO,it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis.In this paper,a multiple-pattern synthesis is considered towards convex optimization(CO).The joint convex optimization(JCO)based synthesis is proposed to construct a codebook for beamforming.Then,a criterion containing multiple constraints is developed,in which the sparse array is required to fullfill all constraints.Finally,extensive evaluations are performed under realistic simulation settings.The results show that with the same number of antenna elements,sparse array using the proposed JCO-based synthesis outperforms not only the uniform array,but also the sparse array with the existing CO-based synthesis method.Furthermore,with a half of the number of antenna elements that on the uniform array,the performance of the JCO-based sparse array approaches to that of the uniform array.
基金Supported by the Innovation Foundation for Outstanding Postgraduates in the Electronic Engineering Institute of PLA (No. 2009YB005)
文摘A novel Direction-Of-Arrival (DOA) estimation method is proposed in the presence of mutual coupling using the joint sparse recovery. In the proposed method, the eigenvector corresponding to the maximum eigenvalue of covariance matrix of array measurement is viewed as the signal to be represented. By exploiting the geometrical property in steering vectors and the symmetric Toeplitz structure of Mutual Coupling Matrix (MCM), the redundant dictionaries containing the DOA information are constructed. Consequently, the optimization model based on joint sparse recovery is built and then is solved through Second Order Cone Program (SOCP) and Interior Point Method (IPM). The DOA estimates are gotten according to the positions of nonzeros elements. At last, computer simulations demonstrate the excellent performance of the proposed method.
基金Supported by the National Natural Science Foundation of China(61462048)
文摘The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representation-based median filter as the in-loop filter in depth video codec. It constructs depth candidate set which contains relevant neighboring depth pixel based on depth and intensity similarity weighted sparse coding, then the median operation is performed on this set to select a neighboring depth pixel as the result of the filtering. The experimental results indicate that the depth bitrate is reduced by about 9% compared with anchor method. It is confirmed that the proposed method is more effective in reducing the required depth bitrates for a given synthesis quality level.
文摘Medical Image Fusion is the synthesizing technology for fusing multi-modal medical information using mathematical procedures to generate better visual on the image content and high-quality image output.Medical image fusion represents an indispensible role infixing major solutions for the complicated medical predicaments,while the recent research results have an enhanced affinity towards the preservation of medical image details,leaving color distortion and halo artifacts to remain unaddressed.This paper proposes a novel method of fusing Computer Tomography(CT)and Magnetic Resonance Imaging(MRI)using a hybrid model of Non Sub-sampled Contourlet Transform(NSCT)and Joint Sparse Representation(JSR).This model gratifies the need for precise integration of medical images of different modalities,which is an essential requirement in the diagnosing process towards clinical activities and treating the patients accordingly.In the proposed model,the medical image is decomposed using NSCT which is an efficient shift variant decomposition transformation method.JSR is exercised to extricate the common features of the medical image for the fusion process.The performance analysis of the proposed system proves that the proposed image fusion technique for medical image fusion is more efficient,provides better results,and a high level of distinctness by integrating the advantages of complementary images.The comparative analysis proves that the proposed technique exhibits better-quality than the existing medical image fusion practices.
基金partially supported by the Natural Science Foundation of Henan Province(Grant Nos.252300420326,242300420252)in part by the Key Scientifc Research Project of Colleges and Universities in Henan Province(Grant No.24A120007)+1 种基金in part by Training Program for Young Backbone Teachers in Higher Education Institutions of Henan Province(Grant No.2023GGJS037)in part by the National Natural Science Foundation of China(Grant Nos.12271215,12326378 and 11871248)。
文摘In many practical applications,we need to recover block sparse signals.In this paper,we encounter the system model where joint sparse signals exhibit block structure.To reconstruct this category of signals,we propose a new algorithm called block signal subspace matching pursuit(BSSMP)for the block joint sparse recovery problem in compressed sensing,which simultaneously reconstructs the support of block jointly sparse signals from a common sensing matrix.To begin with,we consider the case where block joint sparse matrix X has full column rank and any r nonzero rowblocks are linearly independent.Based on these assumptions,our theoretical analysis indicates that the BSSMP algorithm could reconstruct the support of X through at most K-r+[r/L]iterations if sensing matrix A satisfies the block restricted isometry property of order L(K-r)+r+1 with δB_(L(K-r)+r+1)<max{√r/√K+r/4+√r/4,√L/√Kd+√L}.This condition improves the existing result.
文摘New methods of synthetizing nonequidistant sparse antenna arrays based on the properties of magic squares are studied.The methods of construction and algorithms of synthesis of two-dimensional antennas based on them providing a high degreeof dilution and sufficiently small side radiation are proposed.The methods for construction of such antennas and their maincharacteristics are considered.
文摘针对过程复杂且结构未知的对象,在保证模型有效性的前提下,根据数据信息构建简单模型来简化控制器的求解是亟待解决的问题。以受控自回归模型为例,提出一种基于修正最小角回归算法的稀疏辨识方法。首先将系统模型转化为过参数化的高维稀疏模型,然后将最小角回归算法用于稀疏系统辨识,并提出绝对角度停止准则,使算法经过少量的迭代即可获得模型的稀疏参数估计,并同时获得有效的时滞和阶次估计。结合辨识得到的受控自回归模型,引入一种基于指定相位点频率和增益的比例-积分-微分(proportional integral derivative,PID)控制器。数值仿真和平衡机器人的姿态控制仿真表明,该稀疏辨识算法在低数据量下具有较高的辨识精度,建立的模型具有较好的泛化性能,控制器具有良好的控制效果。
