The relay node with linear relaying transmits the linear combination of its past received signals.The optimization of two-hop relay channel with linear relaying is discussed in this paper.The capacity for the two-hop ...The relay node with linear relaying transmits the linear combination of its past received signals.The optimization of two-hop relay channel with linear relaying is discussed in this paper.The capacity for the two-hop Gaussian relay channel with linear relaying is derived,which can be formulated as an optimization problem over the relaying matrix and the covariance matrix of the signals transmitted at the source.It is proved that the solution to this optimization problem is equivalent to a "single-letter" optimization problem.We also show that the solution to this "single-letter" optimization problem has the same form as the expression of the rate achieved by Time-Sharing Amplify and Forward(TSAF).In order to solve this equivalent problem,we proposed an iterative algorithm.Simulation results show that if channel gain of one hop is relatively smaller,the achievable rate with TSAF is closer to the max-flow min-cut capacity bound,but at a lower complexity.展开更多
In this paper, a new 7×7 matrix spectral problem, which is associated with the super AKNS equation isconstructed.With the use of the binary nonlinearization method, a new integrable decomposition of the super AKN...In this paper, a new 7×7 matrix spectral problem, which is associated with the super AKNS equation isconstructed.With the use of the binary nonlinearization method, a new integrable decomposition of the super AKNSequation is presented.展开更多
This paper has been done on study kinematic problem of Persian joint in a general way. In this study, instead of using simulation analysis method as in the previous researches, the 3D rotation matrix method is applied...This paper has been done on study kinematic problem of Persian joint in a general way. In this study, instead of using simulation analysis method as in the previous researches, the 3D rotation matrix method is applied to present the relationship of angular velocities of input shaft and output shaft. The result shows that when the angle between intersecting shafts changes from 0 to 135°, the angular velocity is maintained constant. This new result completely matches with analysis from kinematic simulation of this mechanism. The obtained result is an important base to solve dynamic problem in order to develop the applicability of this joint in reality.展开更多
A new approach to construct a new 4×4 matrix spectral problem from a normal 2×2 matrix spectral problem is presented.AKNS spectral problem is discussed as an example.The isospectral evolution equation of the...A new approach to construct a new 4×4 matrix spectral problem from a normal 2×2 matrix spectral problem is presented.AKNS spectral problem is discussed as an example.The isospectral evolution equation of the new 4×4 matrix spectral problem is nothing but the famous AKNS equation hierarchy.With the aid of the binary nonlino earization method,the authors get new integrable decompositions of the AKNS equation. In this process,the r-matrix is used to get the result.展开更多
This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal...This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal criterion is NP-complete. The authors propose an iterative algorithm to pursue a suboptimal solution. Furthermore, in order to improve the bandwidth and energy efficiency of the wireless sensor networks, the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem. In the case of unknown covariance matrix, the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator. Simulation results show the good performance of the proposed algorithms.展开更多
Recently, the 1-bit compressive sensing （1-bit CS） has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or ...Recently, the 1-bit compressive sensing （1-bit CS） has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or the sign of a signal that can be exactly recovered with a decoding method. We first show that a necessary assumption （that has been overlooked in the literature） should be made for some existing theories and discussions for 1-bit CS. Without such an assumption, the found solution by some existing decoding algorithms might be inconsistent with 1-bit measurements. This motivates us to pursue a new direction to develop uniform and nonuniform recovery theories for 1-bit CS with a new decoding method which always generates a solution consistent with 1-bit measurements. We focus on an extreme case of 1-bit CS, in which the measurements capture only the sign of the product of a sensing matrix and a signal. We show that the 1-bit CS model can be reformulated equivalently as an t0-minimization problem with linear constraints. This reformulation naturally leads to a new linear-program-based decoding method, referred to as the 1-bit basis pursuit, which is remarkably different from existing formulations. It turns out that the uniqueness condition for the solution of the 1-bit basis pursuit yields the so-called restricted range space property （RRSP） of the transposed sensing matrix. This concept provides a basis to develop sign recovery conditions for sparse signals through 1-bit measurements. We prove that if the sign of a sparse signal can be exactly recovered from 1-bit measurements with 1-bit basis pursuit, then the sensing matrix must admit a certain RRSP, and that if the sensing matrix admits a slightly enhanced RRSP, then the sign of a k-sparse signal can be exactly recovered with 1-bit basis pursuit.展开更多
In this paper,a stepwise coupled-mode model with the use of the direct global matrix approach is proposed.This method is capable of handling two-dimensional problems with either a point source in cylindrical geometry ...In this paper,a stepwise coupled-mode model with the use of the direct global matrix approach is proposed.This method is capable of handling two-dimensional problems with either a point source in cylindrical geometry or a line source in plane geometry.With the use of the direct global matrix approach,this method is numerically stable.In addition,by introducing appropriately normalized range solutions,this model is free from the numerical overflow problem.Furthermore,we put forward source conditions appropriate for the line-source problem in plane geometry.As a result,this method is capable of addressing the scenario with a line source on top of a sloping bottom.Closed-form expressions for coupling matrices are derived and applied in this paper for handling problems with pressure-release boundaries and a homogeneous water column.The numerical simulations indicate that the proposed model is accurate,efficient,and numerically stable.Consequently,this model can serve as a benchmark model in range-dependent propagation modeling.Although this method is verified by an ideal wedge problem in this paper,the formulation applies to realistic problems as well.展开更多
A soliton hierarchy of multicomponent AKNS equations is generated from an arbitraryorder matrix spectral problem, along with its bi-Hamiltonian formulation. Adjoint symmetry constraints are presented to manipulate bi...A soliton hierarchy of multicomponent AKNS equations is generated from an arbitraryorder matrix spectral problem, along with its bi-Hamiltonian formulation. Adjoint symmetry constraints are presented to manipulate binary nonlinearization for the associated arbitrary order matrix spectral problem. The resulting spatial and temporal constrained flows are shown to provide integrable decompositions of the multicomponent AKNS equations.展开更多
基金supported by the National Natural Science Foundation of China under Grants No.60972045,No.61071089the Natural Science Foundation of Jiangsu Province under Grant No. BK2010077+4 种基金the Open Project of State Key Laboratory of Networking and Switching under Grant No.SKLNST-2009-1-12the Priority Academic Program Development of Jiangsu Provincethe University Postgraduate Research and Innovation Project in Jiangsu Province under Grant No.CXZZ11_0395the Fundamental Research Funds for the Central Universities under Grant No.2009B32114the Excellent Innovative Research Team of High Schools in Jiangsu Province under Grant No.TJ208029
文摘The relay node with linear relaying transmits the linear combination of its past received signals.The optimization of two-hop relay channel with linear relaying is discussed in this paper.The capacity for the two-hop Gaussian relay channel with linear relaying is derived,which can be formulated as an optimization problem over the relaying matrix and the covariance matrix of the signals transmitted at the source.It is proved that the solution to this optimization problem is equivalent to a "single-letter" optimization problem.We also show that the solution to this "single-letter" optimization problem has the same form as the expression of the rate achieved by Time-Sharing Amplify and Forward(TSAF).In order to solve this equivalent problem,we proposed an iterative algorithm.Simulation results show that if channel gain of one hop is relatively smaller,the achievable rate with TSAF is closer to the max-flow min-cut capacity bound,but at a lower complexity.
基金Supported by the National Natural Science Foundation of China under Grant No.10926036the Education Department of Zhejiang Province under Grant No.Y200906909the Zhejiang Provincial Natural Science Foundation of China under Grant No.Y6090172
文摘In this paper, a new 7×7 matrix spectral problem, which is associated with the super AKNS equation isconstructed.With the use of the binary nonlinearization method, a new integrable decomposition of the super AKNSequation is presented.
文摘This paper has been done on study kinematic problem of Persian joint in a general way. In this study, instead of using simulation analysis method as in the previous researches, the 3D rotation matrix method is applied to present the relationship of angular velocities of input shaft and output shaft. The result shows that when the angle between intersecting shafts changes from 0 to 135°, the angular velocity is maintained constant. This new result completely matches with analysis from kinematic simulation of this mechanism. The obtained result is an important base to solve dynamic problem in order to develop the applicability of this joint in reality.
基金the National Natural Science Foundation of China(No.10671121).
文摘A new approach to construct a new 4×4 matrix spectral problem from a normal 2×2 matrix spectral problem is presented.AKNS spectral problem is discussed as an example.The isospectral evolution equation of the new 4×4 matrix spectral problem is nothing but the famous AKNS equation hierarchy.With the aid of the binary nonlino earization method,the authors get new integrable decompositions of the AKNS equation. In this process,the r-matrix is used to get the result.
基金supported by the National Natural Science Foundation of China-Key Program under Grant No. 61032001the National Natural Science Foundation of China under Grant No.60828006
文摘This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal criterion is NP-complete. The authors propose an iterative algorithm to pursue a suboptimal solution. Furthermore, in order to improve the bandwidth and energy efficiency of the wireless sensor networks, the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem. In the case of unknown covariance matrix, the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator. Simulation results show the good performance of the proposed algorithms.
基金supported by the Engineering and Physical Sciences Research Council of UK (Grant No. #EP/K00946X/1)
文摘Recently, the 1-bit compressive sensing （1-bit CS） has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or the sign of a signal that can be exactly recovered with a decoding method. We first show that a necessary assumption （that has been overlooked in the literature） should be made for some existing theories and discussions for 1-bit CS. Without such an assumption, the found solution by some existing decoding algorithms might be inconsistent with 1-bit measurements. This motivates us to pursue a new direction to develop uniform and nonuniform recovery theories for 1-bit CS with a new decoding method which always generates a solution consistent with 1-bit measurements. We focus on an extreme case of 1-bit CS, in which the measurements capture only the sign of the product of a sensing matrix and a signal. We show that the 1-bit CS model can be reformulated equivalently as an t0-minimization problem with linear constraints. This reformulation naturally leads to a new linear-program-based decoding method, referred to as the 1-bit basis pursuit, which is remarkably different from existing formulations. It turns out that the uniqueness condition for the solution of the 1-bit basis pursuit yields the so-called restricted range space property （RRSP） of the transposed sensing matrix. This concept provides a basis to develop sign recovery conditions for sparse signals through 1-bit measurements. We prove that if the sign of a sparse signal can be exactly recovered from 1-bit measurements with 1-bit basis pursuit, then the sensing matrix must admit a certain RRSP, and that if the sensing matrix admits a slightly enhanced RRSP, then the sign of a k-sparse signal can be exactly recovered with 1-bit basis pursuit.
基金supported by the National Natural Science Foundation of China (Grant Nos. 10734100 and 11125420)the Knowledge Innovation Program of Chinese Academy of Sciences
文摘In this paper,a stepwise coupled-mode model with the use of the direct global matrix approach is proposed.This method is capable of handling two-dimensional problems with either a point source in cylindrical geometry or a line source in plane geometry.With the use of the direct global matrix approach,this method is numerically stable.In addition,by introducing appropriately normalized range solutions,this model is free from the numerical overflow problem.Furthermore,we put forward source conditions appropriate for the line-source problem in plane geometry.As a result,this method is capable of addressing the scenario with a line source on top of a sloping bottom.Closed-form expressions for coupling matrices are derived and applied in this paper for handling problems with pressure-release boundaries and a homogeneous water column.The numerical simulations indicate that the proposed model is accurate,efficient,and numerically stable.Consequently,this model can serve as a benchmark model in range-dependent propagation modeling.Although this method is verified by an ideal wedge problem in this paper,the formulation applies to realistic problems as well.
基金Research Grants Council of Hong Kong(CERG 9040466)City University of Hong Kong(SRGs 7001041,7001178)+2 种基金National Science Foundation of China(No.19801031)Special Grant of Excellent PhD Thesis(No.200013)Special Funds for Major State Basjc Reaca
文摘A soliton hierarchy of multicomponent AKNS equations is generated from an arbitraryorder matrix spectral problem, along with its bi-Hamiltonian formulation. Adjoint symmetry constraints are presented to manipulate binary nonlinearization for the associated arbitrary order matrix spectral problem. The resulting spatial and temporal constrained flows are shown to provide integrable decompositions of the multicomponent AKNS equations.
