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The traditional Simultaneous Localization and Mapping (SLAM) systems are based on the strong static assumption, and their performance will degrade significantly due to the presence of dynamic objects located in dynamic environments. To decrease the effects of the dynamic objects, based on the ORB-SLAM2 system, a novel dynamic semantic SLAM system called MOR-SLAM is presented using a mask repair method, which can accurately detect dynamic objects and realize high-precision positioning and tracking of the system in dynamic indoor environments. First, an instance segmentation module is added to the front end of ORB-SLAM2 to distinguish dynamic and static objects in the environment and obtain a preliminary mask. Next, to overcome the under-segmentation problem in instance segmentation, a new mask inpainting model is proposed to ensure that the integrity of object masks, which repairs large objects and small objects in the image with the depth value fusion method and morphological method, respectively. Then, a reliable basic matrix can be obtained based on the above-repaired mask. Finally, the potential dynamic feature points in the environment are detected and removed through the reliable basic matrix, and the remaining static feature points are input into the tracking module of the system to realize the high-precision positioning and tracking in dynamic environments. The experiments on the public TUM dataset show that, compared with ORB-SLAM2, the MOR-SLAM improves the absolute trajectory accuracy by 95.55%. In addition, compared with DynaSLAM and DS-SLAM on the high-dynamic sequences (fr3/w/rpy and fr3/w/static), the MOR-SLAM improves the absolute trajectory accuracy by 15.20% and 59.71%, respectively.

Complex-valued time-varying Sylvester equation (CVTVSE) has been successfully applied into mathematics and control domain. However, the computation load of solving CVTVSE will rise significantly with the increase of sampling rate, and it is a challenging job to tackle the CVTVSE online. In this paper, a new sign-multi-power activation function is designed. Based on this new activation function, an improved complex-valued Zhang neural network (ICZNN) model for tackling the CVTVSE is established. Furthermore, the strict proof for the maximum time of global convergence of the ICZNN is given in detail. A total of two numerical experiments are employed to verify the performance of the proposed ICZNN model, and the results show that, as compared with the previous Zhang neural network (ZNN) models with different nonlinear activation functions, this ICZNN model with the sign-multi-power activation function has a faster convergence speed to tackle the CVTVSE.

Objective: Preeclampsia is a common complication of pregnancy that is characterized by high blood pressure. Preeclampsia can have both negative maternal and fetal effects, including impaired fetal development from reduced placental perfusion. Because umbilical vessels provide fetal blood supply, morphological changes in these vessels can critically affect fetal function. This study investigated morphological and structural changes in umbilical veins from 17 controls and 19 preeclampsia cases. Methods: 36 umbilical cords were collected, and take umbilical cord of placental, middle, and fetal segment to H&E and orange G staining. Microscope images were obtained by digital microscope, and wall thickness, lumen diameter, wall-luminal ratio and tunica media thickness, Area of smooth muscle, area of nuclei, nuclei density, and nuclear area density of smooth muscle in tunica media were tested by Image-pro Plus. Results: Lumen diameter of umbilical veins gradually decreased, while wall thickness, tunica media thickness, and wall-luminal ratio gradually increased, from placental to fetal segments of umbilical veins. Area of smooth muscle, area of nuclei, nuclei density, and nuclear area density did not significantly differ from placental to fetal segments of umbilical veins. Compared to controls, (1) Preeclampsia cases had a smaller lumen diameter and greater wall thickness, tunica media thickness, wall-luminal ratio; (2) Preeclampsia cases had higher nuclei density and nuclear area density, and had no difference in area of smooth muscle and area of nuclei. In fetal segments of umbilical veins from preeclampsia cases, correlation analysis showed that wall-luminal ratio negatively correlated with gestation age, birth weight, and Apgar score. Conclusions: These results indicate that umbilical vein of preeclampsia cases has a thickened tunica media from smooth muscle proliferation, which may be an adapted reaction to hypertension. Altogether, this study demonstrates that morphological changes in umbilical veins adversely affect fetal development in preeclampsia cases.

In this paper, an improved Zhang neural network (IZNN) is proposed by using a kind of novel nonlinear activation function to solve the complex-valued systems of linear equation. Compared with the previous ZNN models, the convergence rate of the IZNN model has been accelerated. To do so, a kind of novel nonlinear activation function is first proposed to establish the novel recurrent neural network. Then, the corresponding maximum convergent time is given according to the randomly generated initial error vector, and the theoretical proof is described in detail in this paper. Finally, the experiment results illustrate that the new recurrent neural network using the proposed activation function has higher convergence rate than the previous neural networks using the linear activation function or the tunable activation function.

The problem of consensus-based distributed tracking in wireless sensor networks (WSNs) with switching network topologies and outlier-corrupted sensor observations is considered. First, to attack the outlier-corrupted measurements, a robust Kalman filtering (RKF) scheme with weighted matrices on innovation sequences is introduced. The proposed RKF possesses high robustness against outliers while having similar computational burden as traditional Kalman filter. Then, each node estimates the network-wide agreement on target state using only communications between one-hop neighbors. In order to improve the convergent speed of the consensus filter in case of switching topologies, an adaptive weight update strategy is proposed. Note that the proposed algorithm relaxes the requirement of Gaussian noise statistics in contrast to the decentralized/distributed Kalman filters. Besides, unlike the existing consensus-based filters, we do not need to perform consensus filtering on the covariance matrices, which will reduce the computational and communicational burden abundantly. Finally, simulation examples are included to demonstrate the robustness of the proposed RKF and effectiveness of adaptive consensus approach.

Optimizing the design of tracking system under energy and bandwidth constraints in wireless sensor networks (WSN) is of paramount importance. In this paper, the problem of collaborative target tracking in WSNs is considered in a framework of quantized measurement fusion. First, the measurement in each local sensor is quantized by probabilistic quantization scheme and transmitted to a fusion center (FC). Then, the quantized messages are fused and sequential importance resampling (SIR) particle filtering is employed to estimate the target state. In the FC, quantized measurement fusion via both augmented approach and weighted approach is investigated. For both approaches, the closed-form solution to the optimization problem of bandwidth scheduling is given, where the total energy consumption is minimized subject to a constraint on the fusion performance. Finally, posterior Cramer-Rao lower bounds (CRLBs) on the tracking accuracy using quantized measurement fusion are derived. Simulation results reveal that both approaches perform very closely to the posterior CRLB while obtaining average communication energy saving up to 72.8% and 45.1%, respectively.

This paper deals with the problem of integral sliding mode disturbance observer (ISDOB)-based preview repetitive control (PRC) for a class of nonlinear systems subject to external disturbances and nonlinearity. First, an ISDOB-based PRC law is presented to estimate and compensate the exogenous disturbances and nonlinearity. Next, by using a new equality constraint, a continuous-discrete two-dimensional (2D) system is established. Then, the stability of the combined controller-observer system is derived on the basis of a Lyapunov analysis, 2D system theory, and the singular-value-decomposition technique. By solving an linear matrix inequality (LMI), the gains of the controller and state observer can be obtained. Finally, two numerical examples are provided to illustrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]