With the rapid development of rail transit, both the diversity and volume of data transmitted in Train Communication Network (TCN) have significantly increased. Conventional TCN technologies struggle to accommodate this expansion. TimeSensitive Networking (TSN), an enhanced technology built upon traditional Ethernet, not only supports periodic traffic with consistent latency but also maintains compatibility with standard Ethernet services. Its capability to handle high bandwidth, low latency and ensure reliability makes TSN a promising solution for train communications. This paper establishes a network topology and data flow model based on TSN. Building on Time-Aware Shaping (TAS), we propose the Priority-Sorted Multi-Routing No-Wait Scheduling Algorithm (PSMRS). PSMRS distinguishes between priorities and provides better service for high-priority traffic, while also enhancing bandwidth utilization, scheduling success rates, and real-time performance. Furthermore, experimental simulations are conducted using Python and OMNeT++. The results of these experiments demonstrate the feasibility of the proposed algorithm, specifically the findings verify that PSMRS ensures faster response time for high-priority traffic, boosts overall link utilization, increases data flow scheduling success rate, and delivers superior real-time performance in large-scale flow scheduling.

Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the diagnosis in clinical practices. We deploy a 3D Convolutional Neural Network (CNN) as the baseline approach for the PD severity classification and show the effectiveness. Due to the lack of data in clinical field, we explore the possibility of transfer learning from non-medical dataset and show that PD severity classification can benefit from it. To bridge the domain discrepancy between medical and non-medical datasets, we let the network focus more on the subtle temporal visual cues, i.e., the frequency of tremors, by designing a Temporal Self-Attention (TSA) mechanism. Seven tasks from the Movement Disorders Society - Unified PD rating scale (MDS-UPDRS) part III are investigated, which reveal the symptoms of bradykinesia and postural tremors. Furthermore, we propose a multi-domain learning method to predict the patient-level PD severity through task-assembling. We show the effectiveness of TSA and task-assembling method on our PD video dataset empirically. We achieve the best MCC of 0.55 on binary task-level and 0.39 on three-class patient-level classification.

This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control.

In this paper, we consider the trajectory tracking of a marine surface vessel in the presence of output constraints and uncertainties. An asymmetric barrier Lyapunov function is employed to cope with the output constraints. To handle the system uncertainties, we apply adaptive neural networks to approximate the unknown model parameters of a vessel. Both full state feedback control and output feedback control are proposed in this paper. The state feedback control law is designed by using the Moore-Penrose pseudoinverse in case that all states are known, and the output feedback control is designed using a high-gain observer. Under the proposed method the controller is able to achieve the constrained output. Meanwhile, the signals of the closed loop system are semiglobally uniformly bounded. Finally, numerical simulations are carried out to verify the feasibility of the proposed controller.

This paper studies the tracking control problem for an uncertain n -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The Moore-Penrose inverse term is employed in order to prevent the violation of the full-state constraints. A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. Simulation studies are performed to illustrate the effectiveness of the proposed control.

In this paper, we present adaptive neural network tracking control of a robotic manipulator with input deadzone and output constraint. A barrier Lyapunov function is employed to deal with the output constraints. Adaptive neural networks are used to approximate the deadzone function and the unknown model of the robotic manipulator. Both full state feedback control and output feedback control are considered in this paper. For the output feedback control, the high gain observer is used to estimate unmeasurable states. With the proposed control, the output constraints are not violated, and all the signals of the closed loop system are semi-globally uniformly bounded. The performance of the proposed control is illustrated through simulations.

We develop a completion pipeline for fragmented and damaged skulls. The goal of this work is to convert scanned incomplete skull fragments to a complete skull model for subsequent forensic or archeological tasks such as facial reconstruction. The proposed assembly and completion algorithms can also be used to repair other fragmented objects with inherent symmetry. A two-step assembly framework is proposed: (1) rough assembly by an ICP-like template matching algorithm integrated with the slippage features and spin-image descriptors; (2) assembly refinement by a global optimization on least square transformation error (LSTE) of break-curves. The assembled skull is finally repaired by a symmetry-based completion algorithm. Experiments on repairing scanned skull fragments demonstrate the efficacy and robustness of this framework.