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A simple and environmentally-friendly bio-oxidation approach to produce graphite oxide nanosheets is described.

This paper presents a novel robust fixed‐time fault‐tolerant control for global fixed‐time tracking of uncertain robot manipulators with actuator effectiveness faults. With the sufficient consideration of the effects on uncertain dynamics, external disturbances and actuator effectiveness faults to the trajectory tracking performance, a singularity‐free robust fault‐tolerant control with an auxiliary vector is constructed for the fixed‐time tracking control of uncertain robot manipulators. Lyapunov stability theory is employed to prove the global fixed‐time stability ensuring that both the position and velocity tracking errors converge globally to the origin within a fixed time. The appealing advantages of the proposed control are as follows: (i) it is easy to implement with the global robust fixed‐time fault‐tolerant tracking control for uncertain robot manipulators featuring with faster transient convergence rate and higher steady‐state tracking precision; (ii) the settling time is independent of the initial states of closed‐loop system and can be calculated in advance for robot manipulators with uncertain dynamics, external disturbances and actuator faults. Extensive simulations on a two‐DOFs robot are presented to demonstrate the effectiveness and improved performances of the proposed approach.

This paper addresses an adaptive fault-tolerant tracking control for robot manipulators. By fully considering the effects of uncertainties and actuator effectiveness faults (UAEFs), a robust fault-tolerant tracking control combining with an auxiliary function and an integral sliding manifold is first developed for uncertain robot manipulators. Then, an adaptive law for unknown parameters of the upper bounded uncertainties is constructed to obtain a robust fault-tolerant approach with the elimination of the reaching phase of sliding mode control (SMC). The stability of the proposed approaches is accomplished by Lyapunov stable theory. The key contributions of the proposed approach are as follows: i) the reaching phase of SMC is removed in the control design and then the sliding mode starts at very beginning; (ii) the nominal control term is eliminated in the design of integral sliding surface and then the algebraic loop problem is also avoided in the proposed approach for robot manipulators; (iii) the simple control structure with an adaptive law is obtained for improving chattering-restraining ability of the proposed approach and then the effects of time delay and computational burden are also restrained from the proposed approach. Simulation and experimental comparisons have been accomplished for verifying the effectiveness of the proposed approach.

Abstract-This paper presents an electrical tunable bandpass filter based on tunable LC resonators loaded with semiconductor varactors. Magnetic dominated mixed coupling between the tunable resonators is utilized to compensate the bandwidth of the tunable filter. Cross coupling is created by using magnetic dominated mixed coupling between the resonators and source to load electrical coupling, and two transmission zeros are generated beside the passband. The tunable mechanism of the proposed filter is studied. The tunable filter is analyzed, designed, fabricated and measured. The measurement shows that the filter can be tuned from 70 MHz to 270 MHz with a fractional bandwidth from 27% to 21%.

A novel C-L-L π-type feeding network is presented to tune the working frequency and impedance matching of antenna. Two varactors are used in the tunable feeding network as tunable elements for antenna resonating frequency and impedance matching tuning. The tunable capability of the network is studied, and a patch antenna is used to verify the tunable feeding network. The tunable feeding network is designed, fabricated and measured. The measurement results show that the patch antenna can be tuned from 630 MHz to 1.04 GHz with a maximum impedance bandwidth of 24 MHz of S 11 less than −10 dB.

Image classification is a basic and important task in computer vision. Recently, various neural networks have been designed and proved to be very powerful models for image classification. It is natural for thinking of how to gather their strengths together, which refers to feature combination tasks. Traditional combination methods mainly focus on designing specific combination algorithms to achieve higher performance. However, few works consider how to utilize their agreement on a given target (for example, a specific class) to achieve better combinations. This paper presents a novel dynamic feature combination method (DFCA) for image classification problems based on the agreement of the individual features. DFCA promisingly takes the agreement of not only the commonalities, but also the individualities of different features by dynamically updating the weighting coefficients of given features using a routing module. Experiment and extensive analysis on CIFAR-10 prove the effectiveness and promising characteristics of the proposed method.

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