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Institution

Northeastern University (China)

EducationShenyang, China
About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).


Papers
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Journal ArticleDOI
TL;DR: A novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views and it is indicated that the reconstruction can be efficiently modeled as angular restoration on an epipolar plane image (EPI).
Abstract: In this paper, a novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views. We indicate that the reconstruction can be efficiently modeled as angular restoration on an epipolar plane image (EPI). The main problem in direct reconstruction on the EPI involves an information asymmetry between the spatial and angular dimensions, where the detailed portion in the angular dimensions is damaged by undersampling. Directly upsampling or super-resolving the light field in the angular dimensions causes ghosting effects. To suppress these ghosting effects, we contribute a novel “blur-restoration-deblur” framework. First, the “blur” step is applied to extract the low-frequency components of the light field in the spatial dimensions by convolving each EPI slice with a selected blur kernel. Then, the “restoration” step is implemented by a CNN, which is trained to restore the angular details of the EPI. Finally, we use a non-blind “deblur” operation to recover the spatial high frequencies suppressed by the EPI blur. We evaluate our approach on several datasets, including synthetic scenes, real-world scenes and challenging microscope light field data. We demonstrate the high performance and robustness of the proposed framework compared with state-of-the-art algorithms. We further show extended applications, including depth enhancement and interpolation for unstructured input. More importantly, a novel rendering approach is presented by combining the proposed framework and depth information to handle large disparities.

104 citations

Journal ArticleDOI
TL;DR: The single-atom dielectric resonance represents the physical limit of the electromagnetic response of condensed matter, and thus might open up a new avenue for designing electromagnetic wave absorption materials.
Abstract: We synthesize two Nb/C composites with an order of magnitude difference in the density of single niobium atoms substituted into defective graphitic layers. The concentration and sites of single Nb atoms are identified using aberration-corrected scanning transmission electron microscopy and density functional theory. Comparing the experimental complex permittivity spectra reveals that a representative dielectric resonance at ∼16 GHz originates from the intrinsic polarization of single Nb atom sites, which is confirmed by theoretical simulations. The single-atom dielectric resonance represents the physical limit of the electromagnetic response of condensed matter, and thus might open up a new avenue for designing electromagnetic wave absorption materials. Single-atom resonance also has important implications in understanding the correlation between the macroscopic dielectric behaviors and the atomic-scale structural origin.

104 citations

Journal ArticleDOI
TL;DR: This paper deals with reducing the conservatism of stability conditions for continuous-time T-S fuzzy systems by relying on analysis of the positive and negative characteristics of the time derivatives of normalized fuzzy weighting functions.

104 citations

Journal ArticleDOI
TL;DR: In this article, a self-powered wearable sweat-lactate analyzer for building sports big data has been realized based on sweat-evaporation-biosensing coupling effect.

104 citations

Journal ArticleDOI
TL;DR: In this article, a 2D mesoscopic damage model was used to numerically quantify the thermal stresses and crack development of a cement-based composite subjected to elevated temperatures, and the results showed that the mechanisms of thermal damage and fracture of the composite depend on the difference between the coefficients of thermal expansion (CTE) of the inclusion and the cementbased matrix, the strength of materials, and heterogeneity of materials at meso-scale.
Abstract: This paper presents the development and verification of 2-D mesoscopic thermoelastic damage model used to numerically quantify the thermal stresses and crack development of a cement-based composite subjected to elevated temperatures. The program is then used to study the thermal fracture behavior of a cement-based matrix with a single inclusion. The results show that the mechanisms of thermal damage and fracture of the composite depend on (i) the difference between the coefficients of thermal expansion (CTE) of the inclusion and the cement-based matrix, (ii) the strengths of materials, and (iii) the heterogeneity of materials at meso-scale. The thermal cracking is an evolution process from diffused damage, nucleation, and finally linkage of cracks. If the CTE of the inclusion is greater than that of the matrix, radial cracks will form in the matrix. On the other hand, inclusion cracks and tangential cracks at the interface between inclusion and matrix will form if the CTE of the inclusion is smaller than that of the matrix.

104 citations


Authors

Showing all 36436 results

NameH-indexPapersCitations
Rui Zhang1512625107917
Hui-Ming Cheng147880111921
Yonggang Huang13679769290
Yang Liu1292506122380
Tao Zhang123277283866
J. R. Dahn12083266025
Terence G. Langdon117115861603
Frank L. Lewis114104560497
Xin Li114277871389
Peng Wang108167254529
David J. Hill107136457746
Jian Zhang107306469715
Xuemin Shen106122144959
Yi Zhang102181753417
Tao Li102248360947
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023166
2022906
20214,691
20204,118
20193,653
20182,878