Convenient room-temperature synthesis of sulfur and nitrogen co-doped NiCo-LDH coupled with CNTs on NiCo foam as battery-type electrode for high performance hybrid supercapacitor

The convenient synthesis of the composite electrode with high supercapacitance performance plays an important role in practical application but is challenging.Herein,the carbon nanotubes(CNTs)coupled with lowcrystalline sulfur and nitrogen co-doped Ni Co-LDH(denoted as SN-Ni Co-LDH)nanosheets array are grown on Ni Co foam(NCF)substrate by two convenient steps of metal induced self-assembly and corrosion engineering,which present the advantages of operating at roomtemperature and low preparation costs.Benefiting from the S–N co-doping and low-crystallinity of Ni Co-LDH,the prepared SN-Ni Co-LDH@CNTs@NCF electrode presents a topping charge capacity of 2470 C·g^(-1)(4.94 C·cm^(-2))at 5 m A·cm^(-2).Furthermore,the fabricated asymmetry supercapacitor(ASC)achieves an extraordinary energy density of 77 Wh·kg^(-1)(0.617 m Wh·cm^(-2))at a power density of 438 W·kg^(-1)(3.5 m W·cm^(-2))and outstanding stability(91%capacity retention after 5000 cycles at20 m A·cm^(-2)).Impressively,the structure evolution of Ni Co-LDH during the charge/discharge processes has been thoroughly elucidated by in-situ Raman spectra.Therefore,this work verifies a powerful strategy and practical value for preparing composite electrodes with high supercapacitance performance,and also provides guidance for the rational design of the smart electrodes.

Bayesian moving object detection in dynamic scenes using an adaptive foreground model

Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation between neighboring pixels can be used to achieve high levels of detection accuracy in the presence of dynamic background. However, color similarity between foreground and background will cause many foreground pixels to be misclassified. In this paper, an adaptive foreground model is exploited to detect moving objects in dynamic scenes. The foreground model provides an effective description of foreground by adaptively combining the temporal persistence and spatial coherence of moving objects. Building on the advantages of MAP-MRF (the maximum a posteriori in the Markov random field) decision framework, the proposed method performs well in addressing the challenging problem of missed detection caused by similarity in color between foreground and background pixels. Experimental results on real dynamic scenes show that the proposed method is robust and efficient.