A quantum mechanical study on the application of inorganic BC2N nanotubes in the Na-ion batteries

Ai, Ling-Yan; Chang, Chunrui; Liu, Ying; Ebrahimiasl, Saeideh

doi:10.1016/j.inoche.2020.107886

Cited by 4 publications

(1 citation statement)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the imaging process of super-resolution images, degradation processes such as optical and motion blur, down sampling and noise are often generated due to the physical conditions of the imaging system or many external factors, which make the resolution of the image not reach the people's requirements [12][13][14][15][16]. Therefore, the effective enhancement of small features of super-resolution images has become a problem to be solved in this field [17][18]. And the small feature enhancement method for super-resolution images can improve the sharpness and resolution of the image by enhancing the edge information of the image target, while also retaining the feature information of the original image is an effective way to solve the above problems, which has attracted the attention of many experts and scholars [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Study on the application of multi-angle imaging related technology in the construction process

Gao

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

The mufti-angle imaging technique is proposed to address the wall position offset and the side flaps and gate flaps caused by the sparse array in the ultra-wide band through-wall radar building layout imaging, using the characteristic that the azimuth upward distance profile is similar around the main flap but different around the side flap/gate flap. Firstly, after the backward projection algorithm obtains the wall image of each viewpoint, the mufti-directional two-dimensional matching filter is used to enhance the image of each viewpoint and eliminate the effect of side flaps and raster flaps. Then the images of each viewpoint are decomposed by combining the multistage transform, i.e., the non-down sampling contour let transform, and the obtained low-frequency components and high-frequency components are fused using the corresponding fusion rules, respectively. Finally, the final fused building layout image is obtained by non-down sampling contour let inverse transform. The simulation experimental data show that the running time of the method in this paper is 10.99s. It shows that the proposed method enhances the outline and detail information of the building wall, and the final fused image obtained has prominent wall image, clearer edge contours, and good retention of overall detail information. The quality of image fusion is greatly improved than other methods, which paves the way for subsequent fusion methods including chatterers such as construction building corners and windows and doors.

show abstract