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Cell adhesion was the first step of bone reconstruction. While hydroxyapatite (HA)/graphene composites had been utilized for improving the cell adhesion and bone osteogenesis, the impact of cell adhesion and HA/graphene composites, especially HA/hydrophilic graphene (HG) composites, on internal interaction force and external surface properties remained poorly understood. Here, higher stability HA/HG composites were synthesized without extra ion introduction with in situ self-assembling method. And with XRD, FT-IR, XPS and Raman analyses, the evidences of the formation of HA and the introduction of HG was clear. TEM and SEM images showed the net-like spatial structure due to the internal interaction force between HA and HG, which provided the strain stimulation for cell adhesion. Subsequently, the external surface properties of HA/HG composites demonstrated that the roughness and hydrophilic ability of HA/HG composites could be artificially regulated by increasing the content of HG. Besides, the cell proliferation rate of HA/HG composites had been investigated. Compared to the intrinsic HA, HA/5%HG possessed the higher cell proliferation rate (264.81%) and promoted the spreading and growth of MC3T3-E1 cells. Finally, the regulation mechanism between HA/HG and cell adhesion were illuminated in detail. The excellent regular behavior of HA/HG composites for cell adhesion made them promising candidates for bone reconstruction and repairing. The present work provided the reference for the design of modifiable biomaterials and offered much inspiration for the future research of bone reconstruction engineering.

This paper studies distributed secure fusion estimation problem in presence of eavesdroppers, where an eavesdropper can collect the messages from sensors to the fusion center through communication networks. To prevent eavesdropping, an insertion method of artificial noises is developed based on the fusion estimation error covariance matrix such that: i) The local/fusion estimation error covariance of the eavesdropper is unbounded; ii) The fusion estimation error covariance of the defender is bounded. Moreover, a sufficient condition is derived to guarantee the effective noise insertion strategy. Simulations are given to show the effectiveness of the proposed methods.

This paper considers optimal control data scheduling for finite-horizon linear quadratic regulation (LQR) control of scalar systems with limited controller-plant communication. Both the single-system and multiple-system scenarios are studied. For the first scenario, we derive the necessary and sufficient condition for a comparison function to be positive. Using this condition, the optimality of an explicit schedule is extended from unstable systems in the existing work to general systems. For the second scenario, we are able to construct explicit optimal scheduling policies for three particular classes of problems. Numerical examples are provided to illustrate the proposed results.

Data fusion for nonlinear systems is one of the challenging topics in state estimation and target tracking recently. We study decentralized cubature Kalman fusion in this paper. Cubature Kalman filter (CKF) is a more effective method than the conventional nonlinear filters, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF). For most of the practical cases, there are correlative between process and measurement noises (Correlation I) and among measurement noises (Correlation II). So, it is more attractive to design fusion algorithms based on the CKF for the systems with complex correlated noises. Firstly, a cubature Kalman filter with correlation I (CKF-CN) is derived. Secondly, by introducing the EKF with correlated noises (EKF-CN) and its information filter EIF-CN, the CKF-CN is embedded in the EIF-CN framework to get a cubature information filter with correlated noises (CIF-CN). Consequently, a square-root cubature Kalman filter with noise correlation I (SCKF-CN) and the associated information filter SCIF-CN are presented to improve computational performance. Finally, based on the proposed SCIF-CN and matrix diagonalization, a decentralized nonlinear fusion algorithm is proposed for the multisensor system with Correlation I and Correlation II. Simulation examples are demonstrated to validate the proposed filters and fusion algorithms.

Wireless sensor networks (WSNs) fusion systems are usually faced with the communication bandwidth constraints, so it is necessary to adopt quantization. The existing quantization is usually under the assumption of known distribution function of data. However, obtaining the accurate distribution function of data is impossible. Therefore, in this paper, based on the obtained measurements, a kind of non-uniform quantization strategy under the principle of least square sum of quantization error is proposed firstly. Then, the solving method of non-uniform quantizing points based on adaptive quantization interval is presented. Next, we further provide a recursive updating method of quantizing points which include semi real-time update and real-time update. Finally, on the basis of the new method, we establish the Kalman Filter based on the new idea of Non-uniform Quantization and give some analysis of performance, which includes the theoretical part and experiment simulation.

Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

A novel filter design is presented for a class of multivariate nonlinear stochastic systems with uncertain dynamics. The system is subject to non-Gaussian process noises. The entire filter system is proved to be bounded in terms of a specified performance index, provided that the weighting function satisfies a certain condition. An analytical solution of optimal filter gain matrix is also given. Further, the estimation error system is exponentially and ultimately bounded in the mean square sense, even with the presence of system uncertainties. Finally, a simulation example is demonstrated to validate the proposed scheme.