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In this work, we propose a novel strategy to ensure infants, who inadvertently displace their quilts during sleep, are promptly and accurately re-covered. Our approach is formulated into two subsequent steps: interference resolution and quilt spreading. By leveraging the DWPose human skeletal detection and the Segment Anything instance segmentation models, the proposed method can accurately recognize the states of the infant and the quilt over her, which involves addressing the interferences resulted from an infant's limbs laid on part of the quilt. Building upon prior research, the EM*D deep learning model is employed to forecast quilt state transitions before and after quilt spreading actions. To improve the sensitivity of the network in distinguishing state variation of the handled quilt, we introduce an enhanced loss function that translates the voxelized quilt state into a more representative one. Both simulation and real-world experiments validate the efficacy of our method, in spreading and recover a quilt over an infant.

A passive underwater acoustic marker is presented and its feasibility for underwater recognition, positioning, and navigation is proved by a numerical method and experimental results. These markers are composed of acrylic elastic objects designed by backscattering strong peaks associated with the subsonic Rayleigh wave resonance of a polymer target excited by a broadband pulse, and having a unique acoustic signature for a selected frequency band, akin to acoustic barcodes. Therefore, the backscattering response of markers can be regulated by changing the geometry of elastic objects. These acoustic barcodes naturally operate in a wider frequency band, and have a longer lifetime and lower cost, compared with active acoustic markers.