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This paper presents a broadband frequency tunable and polarization reconfigurable circularly polarized (CP) antenna, using a novel active electromagnetic band gap (EBG) structure. The EBG surface employs identical metallic rectangular patch arrays on both sides of a thin substrate, but rotated by 90 from each other. The active bias circuits are also orthogonal for each surface, enabling the reflection phases for orthogonal incident waves to be tuned independently in a wide frequency range. By placing a wideband coplanar waveguide (CPW) fed monopole antenna above the EBG surface, and properly tuning the bias voltages across the varactors in each direction, CP waves can be generated at any desired frequency over a broad band. In accordance with simulations, the measured 3 dB axial ratio (AR) bandwidth reaches 40% (1.03-1.54 GHz), with good input matching and radiation patterns at six presented sampling frequencies. The polarization reconfigurability is verified by simulations and measurements, and shown to be capable of switching between left hand circular polarization (LHCP) and right hand circular polarization (RHCP).

Current analyses on insect dynamic flight stability are based on linear theory and limited to small disturbance motions. However, insects' aerial environment is filled with swirling eddies and wind gusts, and large disturbances are common. Here, we numerically solve the equations of motion coupled with the Navier-Stokes equations to simulate the large disturbance motions and analyse the nonlinear flight dynamics of hovering model insects. We consider two representative model insects, a model hawkmoth (large size, low wingbeat frequency) and a model dronefly (small size, high wingbeat frequency). For small and large initial disturbances, the disturbance motion grows with time, and the insects tumble and never return to the equilibrium state; the hovering flight is inherently ( passively) unstable. The instability is caused by a pitch moment produced by forward/backward motion and/or a roll moment produced by side motion of the insect.

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Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspectbased sentiment analysis (TABSA). However, existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. This may result in targets or aspects having the same vector representations in different contexts and losing the contextdependent information. To address this problem, we propose a novel method to refine the embeddings of targets and aspects. Such pivotal embedding refinement utilizes a sparse coefficient vector to adjust the embeddings of target and aspect from the context. Hence the embeddings of targets and aspects can be refined from the highly correlative words instead of using context-independent or randomly initialized vectors. Experiment results on two benchmark datasets show that our approach yields the state-of-the-art performance in TABSA task.

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