101 results about "Matrix optimization" patented technology

The invention discloses a precoding and drive current joint optimization method for a four-color visible light communication system, which aims at minimizing sum mean square error (MSE) of signal estimation in a corresponding light channel while lighting quality requirements are met. Compared with the existing three-color visible light communication precoding matrix optimization problem, the four-color visible light communication system adds a light communication wavelength division multiplexing channel, and an ability of adjusting indoor lighting quality is also provided. In view of the optimization problem, nonlinear color rendering index constraints are firstly converted into linear light mixing ratio constraints via linear light mixing equations, and optimization solution is carried out on the MSE minimal value through introducing semidefinite relaxation algorithm.

The invention discloses a test optimization selecting method considering key failures. The method includes the steps of obtaining a failure-test relevance matrix, establishing an optimization model, solving the optimization model and the like. According to the method, from the angle of system security, the importance of the key failures is analyzed; then based on the relevance matrix, with the test price minimization as the optimization target, with the failure detection rate and isolation rate and the key failure detection rate and isolation rate as the constraints, the test optimization selecting model considering the key failures is established; finally, solution is conducted by means of a binary particle swarm algorithm based on mass center improvement and inertia weight self-adaptiveadjustment. The severe threats caused by key failure detection omission to device security can be effectively eliminated.

The invention discloses a bidimensional compressed sensing image acquisition and reconstruction method based on discrete cosine transformation (DCT) and discrete Fourier transformation (DFT), belongs to the technical field of designs of measurement matrixes and optimization of reconstruction matrixes in the compressed sensing process and provides a method for firstly determining the measurement matrix and a sparse matrix and then optimizing the reconstruction matrix. In a measurement stage, the 0-1 sparse matrix is adopted; in a reconstruction stage, a Gaussian matrix is adopted; and therefore, an after-optimization method capable of easily implementing hardware and guaranteeing a signal reconstruction effect can be realized. The method comprises the following steps of: performing row vector orthogonal normalization and column vector unitization on the reconstruction matrix obtained by the (i-1)th iteration calculation through ith iteration, optimizing the reconstruction matrix on the basis of maximum values of absolute values of relevant coefficients among row and column vectors, the convergence stability of row vector modules and the number of rows and the number of columns which obey the Gaussian distribution, and finishing the after-optimization on measurement data subjected to one-dimensional and two-dimensional sparse transformation and the measurement matrixes by calculating a transitional matrix and a proximity matrix. The method lays a foundation for the compressed sensing from theoretical research to industrialization.

The invention discloses an unmanned aerial vehicle (UAV)-carried RIS auxiliary communication system capacity optimization method and device, and the method comprises the steps: taking a UAV-carried RIS as a relay, and constructing a UAV-carried RIS auxiliary communication system; converting a system capacity optimization problem of the UAV carrying the RIS auxiliary communication system into a UAV trajectory and RIS beam forming matrix optimization problem with user rate guarantee and UAV energy consumption constraint; based on a deep reinforcement learning algorithm, an optimal UAV trajectory and an optimal RIS beamforming matrix are obtained, and system capacity maximization is achieved under the condition that the minimum target data rate of a user and UAV energy consumption constraints are met. According to the method, the track and phase shift problems are solved, and meanwhile, the system capacity can be maximized under the energy consumption constraint.

The present invention discloses a digital halftoning method. The method comprises steps of: (a1) dividing an original image into non-overlapping blocks; (a2) obtaining a Least-Mean-Square trained (LMS-trained) filter by comparing at least a training image and a halftone result corresponding to the training image (a3) optimizing a class matrix with the LMS-trained filter, which involves the diffused area and the diffused weightings; and (a4) processing the non-overlapping blocks by performing a dot diffusion procedure with the optimized class matrix and the diffused weightings to generate a halftone image corresponding to the original image. A detailed class matrix optimizing method as in the above-mentioned step (a3) is also disclosed.