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In this letter, we explore a general-purpose microstrip coupling model for computer aided design of new bandpass filters. The J-inverter topology of the model facilitates the study of coupling behaviours of different microstrip structures leading to a quick comparison of their coupling strengths. Based on such comparison, microstrip filters with interdigital capacitors and etched slots yielding relatively higher coupling coefficients are designed, fabricated and tested. The filters exhibit relatively wider bandwidths which are easily adjustable by way of changing the geometrical parameters of interdigital capacitors and slots. [ABSTRACT FROM PUBLISHER]

This work proposes an active collision avoidance between autonomous driving vehicle and pedestrian with motion uncertainty under urban road. A candidate trajectory planning method considering spatial and time sequences is proposed, which combines the polynomial path planning and the velocity planning with variable safety velocity. Then, a pedestrian-vehicle interaction model is constructed, which takes the pedestrian’s uncertain motion as a superposition of the Markov process without interference and the motion caused by the vehicle, and predicts the pedestrian’s motion probabilistically. On these bases, the optimal trajectory is evaluated from the candidate trajectories by safety, stability, and efficiency, as well as different driving styles. The proposed collision avoidance strategy is verified in conventional and emergency simulation scenarios. Simulation results show that it can effectively plan a safe, stable and efficient trajectory under normal and emergency conditions. [ABSTRACT FROM AUTHOR]

A correction to the article "A Novel Current Memory Circuit for Active-Matrix Organic Light-Emitting Diode (AMOLED)" that was published in the previous issue is presented.

The hybrid brain-computer interface (hBCI) combining motor imagery (MI) and steady-state visual evoked potential (SSVEP) has been proven to have better performance than a pure MI- or SSVEP-based brain-computer interface (BCI). In most studies on hBCIs, subjects have been required to focus their attention on flickering light-emitting diodes (LEDs) or blocks while imagining body movements. However, these two classical tasks performed concurrently have a poor correlation. Therefore, it is necessary to reduce the task complexity of such a system and improve its user-friendliness. Aiming to achieve this goal, this study proposes a novel hybrid BCI that combines MI and intermodulation SSVEPs. In the proposed system, images of both hands flicker at the same frequency (i.e., 30 Hz) but at different grasp frequencies (i.e., 1 Hz for the left hand, and 1.5 Hz for the right hand), resulting in different intermodulation frequencies for encoding targets. Additionally, movement observation for subjects can help to perform the MI task better. In this study, two types of brain signals are classified independently and then fused by a scoring mechanism based on the probability distribution of relevant parameters. The online verification results showed that the average accuracies of 12 healthy subjects and 11 stroke patients were 92.40 ± 7.45% and 73.07 ± 9.07%, respectively. The average accuracies of 10 healthy subjects in the MI, SSVEP, and hybrid tasks were 84.00 ± 12.81%, 80.75 ± 8.08%, and 89.00 ± 9.94%, respectively. The high recognition accuracy verifies the feasibility and robustness of the proposed system. This study provides a novel and natural paradigm for a hybrid BCI based on MI and SSVEP. [ABSTRACT FROM AUTHOR]

In the switching process of driving authority for man-machine cooperative driving, it should be ensured that the driver can take over the vehicle safely, stably and efficiently. This paper proposes a driving authority allocation strategy based on real-time allocation domain (RAD), which mainly includes the establishment of RAD and the dynamic optimization. The RAD is a comprehensive dynamic allocation internal, whose range is determined by the corresponding allocation values of driver’s cognitive state, driver’s muscle state and environment state. The dynamic optimization is to search for the optimal driving authority in RAD at any time, and its optimization objectives and constraints all contain time-varying parameters. This work proposes an improved simulated annealing algorithm to solve this problem. Simulation results show that the proposed driving authority allocation strategy can effectively allocate the driving authority according to the current condition and driver’s state, and ensure the safety, stability and efficiency of the take-over process. [ABSTRACT FROM AUTHOR]

In this article, fixed-time cooperative guidance laws are designed for multiple missiles to simultaneously attack a maneuvering target at desired terminal angles. The 3-D guidance commands consist of tangential acceleration along line-of-sight (LOS) direction to achieve a salvo attack and normal acceleration perpendicular to the LOS direction to realize a terminal angle-constrained interception. First, the tangential acceleration is derived from a distributed cooperative protocol to guarantee the fixed-time consensus of impact times for multiple missiles. Second, based on the integral sliding manifold and fixed-time reaching law, the normal acceleration is developed for each missile to converge the LOS angle to the desired value within a fixed time. In particular, the unknown target acceleration components are estimated by fixed-time observers and compensated in the guidance commands. The fixed-time stability of the proposed cooperative guidance law is rigorously proved and the explicit estimate for the convergence time is theoretically provided. Then, the proposed methods are utilized to realize the cooperative mission in the planar guidance scenario. Finally, the effectiveness and advantages of the proposed cooperative guidance laws are demonstrated through numerical simulations with comparative studies. [ABSTRACT FROM AUTHOR]

In order to solve the manual labelling, long-tail effect and driving conservatism of the existing decision-making algorithm. This paper proposed an integrated decision-making framework (IDF) for highway autonomous vehicles. Firstly, states of the highway traffic are extracted by the velocity, time headway (TH) and the probabilistic lane distribution of the surrounding vehicles. With the extracted traffic state, the reinforcement learning (RL) is adopted to learn the optimal state-action pair for specific scenario. Analogously, by mapping millions of traffic scenarios, huge amounts of state-action pairs can be stored in the experience pool. Then the imitation learning (IL) is further employed to memorize the experience pool by deep neural networks. The learning result shows that the accuracy of the decision network can reach 94.17%. Besides, for some imperfect decisions of the network, the rule-based method is taken to rectify by judging the long-term reward. Finally, the IDF is simulated in G25 highway and has promising results, which can always drive the vehicle to the state with high efficiency while ensuring safety. [ABSTRACT FROM AUTHOR]

Uncertain cut-in maneuver of vehicles from adjacent lanes makes it difficult for vehicle's automatic speed control strategy to make judgments and effective control decisions. In this paper, an intelligent speed control strategy for uncertain cut-in scenarios is established based on a basic autonomous driving system. This strategy judges cut-in maneuver from surrounding vehicles and outputs adaptive control action under current environment according to Q value of state-action pair based on a Q network. In addition, according to the analysis of cut-in scenarios, the Q network is trained based on a novel reinforcement learning method named as experience screening deep Q-learning network (ES-DQN). The proposed ES-DQN is an extension of double deep Q-learning network (DDQN) algorithm, and includes two parts: experience screening and policy learning. Based on the experience screened from the experience screening part, the proposed learning method can train an intelligent speed control strategy which has stronger adaptability and control effect in uncertain cut-in scenarios. According to simulation results, the proposed intelligent speed control strategy trained by ES-DQN has better performance under uncertain cut-in scenarios than DDQN method and traditional ACC strategy. Meanwhile, by adjusting weight value in reward function, the system can realize different control target. [ABSTRACT FROM AUTHOR]

In order to improve the fault-tolerant performance of wheel angle sensor of steer-by-wire (SBW) vehicle, a fault diagnosis and fault-tolerant compensation (FDFTC) strategy for wheel angle sensor of SBW vehicle via extended Kalman filter (EKF) is proposed in this paper. Firstly, the steering motor model, rack and pinion model, vehicle dynamics model and variable transmission ratio model are established. Then, based on the analytical redundancy method, the EKF algorithm is adopted to obtain the wheel angle estimation signal. On this basis, a sensor FDFTC strategy with improved majority voting scheme is proposed, which is composed of fault diagnosis module and fault isolation compensation module. Finally, combined with Matlab/Simulink and Carsim, the estimation results are analyzed under sinusoidal condition and double lane change condition, and the performance of the proposed FDFTC strategy is further verified under the sensor stuck fault condition, deviation fault condition and noise interference condition. The results indicate that the proposed FDFTC strategy can not only accurately diagnose the fault signal, but also reconstruct the correct wheel angle signal, which is of great significance to improve the safety and reliability of SBW vehicle. [ABSTRACT FROM AUTHOR]

This article deals with sensor fault tolerant control design and large delay compensation for a class of linear time-invariant systems. Based on predictor-based state transformation and predictive descriptor observer design, both the state and output feedback control laws are constructed. Within the framework of Lyapunov–Krasovskii functionals, a set of stability conditions are identified. The closed-loop systems is globally asymptotically stable at the origin under the state feedback and is ultimately bounded under the output feedback. Finally, the proposed design is validated through numerical simulations and then implemented on a quadrotor trajectory tracking test. Both simulation and experimental results demonstrate the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]