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The frequency estimation problem is studied in this work in the presence of missing measurements. The approach developed in this work is mainly inspired by sparse signal theory. To find a sparse representation of frequency estimation problem, a DFT-like matrix is created in which the frequency sparsity is discovered. The missing measurements are modeled by a sparse representation as well where missing...
In this paper, we present an efficient algorithm for sparse signal recovery with high exact recovery rate. The main idea of the algorithm is to combine two existing methods: linearized Bregman algorithm and reweighting technique. Compared with other available methods, such as reweighted Basis Pursuit (BP) and linearized Bregman, the proposed algorithm has a much lower computational complexity with...
In this work, we develop a minimum mean square error (MMSE) estimator for the underdetermined systems when the signal of interest is sparse. To address the uncertainty issue introduced in the measurement system, robust approaches are developed based on stochastic and worst case optimization techniques under the minimax framework. To solve the optimization problem, different constraints on the unknown...
The research with respect to Particle Swarm Optimization is concentrated in improving their performance on avoiding local maxima. Since standard Particle Swarm Optimization does not perform well in many cases, we propose double chaotic particle swarm optimization algorithm based on logistic map. This chaotic movement has good randomness and ergodic statistics property of chaos sequence. We propose...
In order to meet the need of source authentication in a wireless sensor network composed of IRIS nodes, this paper presents the implementation and performance analysis of the elliptic curve digital signature algorithm (ECDSA) on IRIS nodes. We choose 7 kinds of optimization algorithms for ECDSA to test and compare their performance further. By turning these optimizations on or off, we compare their...
For the problem of the high power LED heat radiator cooling slowly and having a higher temperature, it will affect the service life of the LED. Studying thermal characteristics of the different power of fin-tubular radiator temperature field distribution and heat flux vector distribution of lamps and lanterns. According to heat transfer knowledge, we make a qualitative analysis of the heat dissipation...
Robust pole assignment problem for linear systems with uncertainty is studied in this paper. The proposed gradient flow optimization algorithm is used to solve the Sylvester equations, in order that the close-loop control systems have the desired robust poles, namely, the uniformly asymptotically stable performance. The feedback gain matrix of the synthesized system can be derived from the gradient...
The parameters plays an important role to the performance of support vector regression(SVR). In order to solve the problem of the Parameter optimization for SVR, first, we transform the problem of Parameter optimization into a problem of nonlinear system state estimation, then, we propose a novel algorithm based on Dual Recursive Variational Bayesian Adaptive Square-Cubature Kalman Filter (DRVB-ASCKF),...
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