Omni-OTPE: Omnidirectional Optimal Real-Time Ground Target Position Estimation System for Moving Lightweight Unmanned Aerial Vehicle.

Ground target detection and positioning systems based on lightweight unmanned aerial vehicles (UAVs) are increasing in value for aerial reconnaissance and surveillance. However, the current method for estimating the target's position is limited by the field of view angle, rendering it challenging to fulfill the demands of a real-time omnidirectional reconnaissance operation. To address this issue, we propose an Omnidirectional Optimal Real-Time Ground Target Position Estimation System (Omni-OTPE) that utilizes a fisheye camera and LiDAR sensors. The object of interest is first identified in the fisheye image, and then, the image-based target position is obtained by solving using the fisheye projection model and the target center extraction algorithm based on the detected edge information. Next, the LiDAR's real-time point cloud data are filtered based on position-direction constraints using the image-based target position information. This step allows for the determination of point cloud clusters that are relevant to the characterization of the target's position information. Finally, the target positions obtained from the two methods are fused using an optimal Kalman fuser to obtain the optimal target position information. In order to evaluate the positioning accuracy, we designed a hardware and software setup, mounted on a lightweight UAV, and tested it in a real scenario. The experimental results validate that our method exhibits significant advantages over traditional methods and achieves a real-time high-performance ground target position estimation function.

Real-Time Ocean Current Compensation for AUV Trajectory Tracking Control Using a Meta-Learning and Self-Adaptation Hybrid Approach.

Autonomous underwater vehicles (AUVs) may deviate from their predetermined trajectory in underwater currents due to the complex effects of hydrodynamics on their maneuverability. Model-based control methods are commonly employed to address this problem, but they suffer from issues related to the time-variability of parameters and the inaccuracy of mathematical models. To improve these, a meta-learning and self-adaptation hybrid approach is proposed in this paper to enable an underwater robot to adapt to ocean currents. Instead of using a traditional complex mathematical model, a deep neural network (DNN) serving as the basis function is trained to learn a high-order hydrodynamic model offline; then, a set of linear coefficients is adjusted dynamically by an adaptive law online. By conjoining these two strategies for real-time thrust compensation, the proposed method leverages the potent representational capacity of DNN along with the rapid response of adaptive control. This combination achieves a significant enhancement in tracking performance compared to alternative controllers, as observed in simulations. These findings substantiate that the AUV can adeptly adapt to new speeds of ocean currents.

Daphnetin ameliorates Aß pathogenesis via STAT3/GFAP signaling in an APP/PS1 double-transgenic mouse model of Alzheimer's disease.

Alzheimer's disease (AD) has become a major public health problem that affects the elderly population. Therapeutic compounds with curative effects are not available due to the complex pathogenesis of AD. Daphnetin, a natural coumarin derivative and inhibitor of various kinases, has anti-inflammatory and antioxidant activities. In this study, we found that daphnetin improved spatial learning and memory in an amyloid precursor protein (APP)/presenilin 1 (PS1) double-transgenic mouse model of AD. Daphnetin markedly decreased the levels of amyloid-ß peptide 1-40 (Aß40) and 1-42 (Aß42) in the cerebral cortex, downregulated the expressions of enzymes involved in APP processing, e.g., beta-site APP-cleaving enzyme (BACE), nicastrin and presenilin enhancer protein 2 (PEN2). We further found the reduced serum levels of inflammatory factors, including interleukin-1ß (IL-1ß), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and chemokine (C-C motif) ligand 3 (CCL3), while daphnetin increased total antioxidant capacity (T-AOC) and superoxide dismutase (SOD) levels in the serum. Interestingly, daphnetin markedly decreased the expression of glial fibrillary acidic protein (GFAP) and the upstream regulatory molecule- phosphorylated signal transducer and activator of transcription 3 (p-STAT3) in APP/PS1 mice, and mainly inhibited the phosphorylation of STAT3 at Ser727 to decrease GFAP expression evidenced in a LPS-activated glial cell model. These results suggest that daphnetin ameliorates cognitive deficits and that Aß deposition in APP/PS1 mice is mainly correlated with astrocyte activation and APP processing.

Advanced Autonomous Underwater Vehicles Attitude Control with L 1 Backstepping Adaptive Control Strategy.

This paper presents a novel attitude control design, which combines L 1 adaptive control and backstepping control together, for Autonomous Underwater Vehicles (AUVs) in a highly dynamic and uncertain environment. The Euler angle representation is adopted in this paper to represent the attitude propagation. Kinematics and dynamics of the attitude are in the strict feedback form, which leads the backstepping control strategy serving as the baseline controller. Moreover, by bringing fast and robust adaptation into the backstepping control architecture, our controller is capable of dealing with time-varying uncertainties from modeling and external disturbances in dynamics. This attitude controller is proposed for coupled pitch-yaw channels. For inevitable roll excursions, a Lyapunov function-based optimum linearization method is presented to analyze the stability of the roll angle in the operation region. Theoretical analysis and simulation results are given to demonstrate the feasibility of the developed control strategy.