Xiongxiong He

Gradient-based algorithm for designing sensing matrix considering real mutual coherence for compressed sensing systems

This study deals with the issue of designing the sensing matrix for a compressed sensing (CS) system assuming that the dictionary is given. Traditionally, the measurement of small mutual coherence is considered to design the optimal sensing matrix so that the Gram of the equivalent dictionary is as close to the target Gram as possible, where the equivalent dictionary is not normalised. In other words, these algorithms are designed to solve the CS problem using an optimisation stage followed by normalisation. To achieve a global solution, a novel strategy of the sensing matrix design is proposed by using a gradient-based method, in which the measure of real mutual coherence for the equivalent dictionary is considered. According to this approach, a minimised objective function based on alternating minimisation is also developed through searching the target Gram within a set of relaxed equiangular tight frames. Some experiments are done to compare the performance of the newly designed sensing matrix with the existing ones under the condition that the dictionary is fixed. For the simulations of synthetic data and real image, the proposed approach provides better signal reconstruction accuracy.

A new anti-swing control law for overhead crane systems

In this paper, a novel anti-swing control method is proposed for underactuated overhead crane systems, which is based on an improved damping signal constructed to suppress the payload swing. Specifically, to increase the anti-swing performance of overhead crane systems, an improved damping anti-swing signal introduced into the trolley motion is investigated based on a swing-related storage function. Consequently, a novel anti-swing control method is designed straightforwardly, and the equilibrium point of the overall closed-loop system is proven to be asymptotically stable by Lyapunov techniques and LaSalles invariance theorem. Numerical simulation results are provided to demonstrate the feasibility and flexibility of the proposed control method as well as its robustness against parameter variations and uncertain disturbances.

Pulse neural network–based adaptive iterative learning control for uncertain robots

An adaptive iterative learning control algorithm based on pulse neural network (PNN) is proposed for trajectory tracking of uncertain robot system. Sliding mode variable structure control is used to improve the robustness to disturbance and perturbation, and boundary layer is used to eliminate the chattering of sliding mode control. In the iterative domain, the unknown parameters are tuned and used for part of the controller. Running in parallel, the PNN can perform real-time state estimation for improving the system convergence. We analyze the stability and convergence of this algorithm by using the Lyapunove-like methodology. The simulation results show that the expected control purpose can be achieved using the proposed algorithm.

An improved node localization based on adaptive iterated unscented Kalman filter for WSN

In this paper, by combining iterative strategy and adaptive factor, an adaptive iterated unscented Kalman filter (AIUKF) is presented for the node positioning system in Wireless Sensor Network (WSN), which is based on unscented Kalman filter(UKF). According to the range-based localization algorithm model, RSSI is used to measure distance; an improved method which consisted by maximum likelihood estimation and regional constraint condition is utilized to realize the node initial positioning, and AIUKF is applied to achieve precise positioning finally, meanwhile using RSSI as the measurement values of observation equation directly. The simulation results show that the performance of the proposed positioning algorithm is improved obviously compared with EKF algorithm and UKF algorithm.

Exponential synchronization of complex networks with nonidentical time varying coupling delays and switching topology

In this paper, the exponential synchronization is discussed for the complex dynamical networks with nonidentical time varying coupling delays and switching topology. By adopting the average dwell time and free weight matrix approach of the switched systems, the delay-dependent sufficient conditions are derived to ensure the exponential synchronization of the complex networks where all subnetworks are self-synchronizing. The inferior bound of the ratio between total active time of self-synchronizing and nonsynchronizing is given to guarantee the exponential synchronization of the entire networks where some subnetworks are nonsynchronizing as well as the average dwell time.

On Collaborative Compressive Sensing Systems: The Framework, Design, and Algorithm

We propose a collaborative compressive sensing (CCS) framework consisting of a bank of

A novel camera calibration method combined with calibration toolbox and genetic algorithm

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Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization for Block-sparse Compressive Sensing

compressive sensing (CS) systems that share the same sensing matrix but have different sparsifying dictionaries. This CCS system is guaranteed to yield better performance than each individual CS system in a statistical sense, while with the parallel computing strategy, it requires the same time as that needed for each individual CS system to conduct compression and signal recovery. We then provide an approach to designing optimal CCS systems by utilizing a measure that involves both the sensing matrix and dictionaries and hence allows us to simultaneously optimize the sensing matrix and all the

A novel multi-dictionary framework with global sensing matrix design for compressed sensing

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Finite-Time Adaptive Attitude Stabilization for Spacecraft Based on Modified Power Reaching Law

dictionaries under the same scheme. An alternating minimization-based algorithm is derived for solving the corresponding optimal design problem. We provide a rigorous convergence analysis to show that the proposed algorithm is convergent. Experiments with real images are carried out and show that the proposed CCS system significantly improves on existing CS systems in terms of the signal recovery accuracy.