Staggered tomography for spatially continuous objects in compressive holography

Author(s):  
Zhenpeng Luo ◽  
Da Sun ◽  
Ping Su ◽  
Jianshe Ma ◽  
Liangcai Cao
2019 ◽  
Vol 57 (6) ◽  
pp. 93-99 ◽  
Author(s):  
Zhangbing Zhou ◽  
Yaqiang Zhang ◽  
Xuefang Yi ◽  
Chao Chen ◽  
Haodi Ping

2012 ◽  
Vol 51 (7) ◽  
pp. 1000 ◽  
Author(s):  
Hong Di ◽  
Kangfeng Zheng ◽  
Xin Zhang ◽  
Edmund Y. Lam ◽  
Taegeun Kim ◽  
...  

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 51875-51885 ◽  
Author(s):  
Taj Rahman ◽  
Xuanxia Yao ◽  
Gang Tao

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Pavel Skums ◽  
Leonid Bunimovich

Abstract Fractals are geometric objects that are self-similar at different scales and whose geometric dimensions differ from so-called fractal dimensions. Fractals describe complex continuous structures in nature. Although indications of self-similarity and fractality of complex networks has been previously observed, it is challenging to adapt the machinery from the theory of fractality of continuous objects to discrete objects such as networks. In this article, we identify and study fractal networks using the innate methods of graph theory and combinatorics. We establish analogues of topological (Lebesgue) and fractal (Hausdorff) dimensions for graphs and demonstrate that they are naturally related to known graph-theoretical characteristics: rank dimension and product dimension. Our approach reveals how self-similarity and fractality of a network are defined by a pattern of overlaps between densely connected network communities. It allows us to identify fractal graphs, explore the relations between graph fractality, graph colourings and graph descriptive complexity, and analyse the fractality of several classes of graphs and network models, as well as of a number of real-life networks. We demonstrate the application of our framework in evolutionary biology and virology by analysing networks of viral strains sampled at different stages of evolution inside their hosts. Our methodology revealed gradual self-organization of intra-host viral populations over the course of infection and their adaptation to the host environment. The obtained results lay a foundation for studying fractal properties of complex networks using combinatorial methods and algorithms.


2017 ◽  
Vol 25 (21) ◽  
pp. 24991 ◽  
Author(s):  
Hua Zhang ◽  
Liangcai Cao ◽  
Hao Zhang ◽  
Wenhui Zhang ◽  
Guofan Jin ◽  
...  

Author(s):  
Liangcai Cao ◽  
Hua Zhang ◽  
Wenhui Zhang ◽  
Hao Zhang ◽  
Guofan Jin

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