Mr. Kexiang Wang Profile

Kexiang Wang

at Stony Brook Univ

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Publications (3)

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Proceedings Article | 31 August 2018 Paper

Mission oriented self-organizing network methodology of UAV

Xueli Zheng, Tao Gu, Kexiang Wang

Proceedings Volume 10835, 1083517 (2018) https://doi.org/10.1117/12.2504684

KEYWORDS: Network architectures, Unmanned aerial vehicles, Avionic systems, Mobile communications, Motion models

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Mission resources scheduling and network topology of UAV multi-platform avionics system represents a feature of time and space isolation, and traditional aeronautical Ad Hoc network research and mission resources scheduling research is relatively independent. Aeronautical Ad Hoc network research lack of correlation of missions and resources, which makes the networks of UAV multi-platform avionics system cannot satisfy the dynamic mission demand to resources. In order to solve this problem, we propose a mission driven network and resource mapping architecture of UAV multi-platform avionics system based on a network structure from the perspective of network topology. It studies the quantitative mission, mission scheduling, node mobile model and network dynamic clustering method, through these studies the mission requirements and platform resources of UAV multi-platform avionics system are correlated. And the mission oriented adaptive networks architecture of UAV multi-platform avionics system is proposed, it provide a reference for the mission resource management and the aeronautical Ad Hoc network study of UAV multi-platform avionics system. The proposed network architecture could adaptive adjust the network clustering structure according the mission requirements of UAV multi-platform avionics system, and support the resource connecting and sharing of UAV multi-platform avionics system.

Proceedings Article | 6 March 2013 Paper

TV-Stokes strategy for sparse-view CT image reconstruction

Yan Liu, Lin Chen, Hao Zhang, Ke Wang, Jianhua Ma, Zhengrong Liang

Proceedings Volume 8668, 86683D (2013) https://doi.org/10.1117/12.2006957

KEYWORDS: Televisions, CT reconstruction, Computed tomography, Neuroimaging, Brain, Image restoration, Image resolution, Data acquisition, Scanners, Image analysis

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This paper introduces a new strategy to reconstruct computed tomography (CT) images from sparse-view projection data based on total variation stokes (TVS) strategy. Previous works have shown that CT images can be reconstructed from sparse-view data by solving a constrained TV problem. Considering the incompressible property of the voxels along the tangent direction of isophote lines, a tangent vector is consolidated in this newly-proposed algorithm for normal vector estimation. Then, a minimization problem based on this estimated normal vector is addressed and resolved in computation. The to-be-estimated image is obtained by executing this two-step framework iteratively with projection data fidelity constraints. By introducing this normal vector estimation, the edge information of the image is well preserved and the artifacts are efficiently inhibited. In addition, the new proposed algorithm can mitigate the staircase effects which are usually observed from the results of the conventional constrained TV method. In this study, the TVS method was evaluated by patients’ brain raw data which was acquired from Siemens SOMATOM Sensation 16-slice CT scanner. The results suggest that the proposed TVS strategy can accurately reconstruct the brain images and produce comparable results relative to the TV-projection onto convex sets (TV-POCS) method and its general case: adaptiveweighted TV-POCS (AwTV-POCS) method from 232,116 projection views. In addition, an improvement was observed when using only 77 views for TVS method compared to the AwTV/TV-POCS methods. In the quantitative evaluation, the TVS method showed adequate noise-resolution property and highest universal quality index value.

Proceedings Article | 10 March 2006 Paper

Temporal registration of 2D x-ray mammogram using triangular B-splines finite element method (TBFEM)

Kexiang Wang, Ying He, Hong Qin, Paul Fisher, Wei Zhao

Proceedings Volume 6144, 614436 (2006) https://doi.org/10.1117/12.654259

KEYWORDS: Image registration, Mammography, Breast, Finite element methods, X-rays, Image processing, Tissues, 3D modeling, Image segmentation, Medical imaging

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In this paper we develop a novel image processing technique to register two dimensional temporal mammograms for effective diagnosis and therapy. Our registration framework is founded upon triangular B-spline finite element method (TBFEM). In contrast to tensor-product B-splines, which is widely used in medical imaging, triangular B-splines are much more powerful, associated with many desirable advantages for image registration, such as flexible triangular domain, local control, space-varying smoothness, and sharp feature modeling. Empowered by the rigorous theory of triangular B-splines, our method can explicitly model the transformation between temporal mammogram pairs over irregular region of interest(ROI), using a collection of triangular B-splines. In addition, it is also capable of describing C⁰ continuous deformation at the interfaces between different elastic tissues, while the overall displacement field is smooth. Our registration process consists of two steps: 1) The template image is first nonlinearly deformed using TBFEM model, subject to pre-segmented feature constraints; 2) The deformed template image is further perturbed by applying pseudo image forces, aiming to reducing intensity-based discrepancies. The proposed registration framework has been tested extensively on practical clinical data, and the experimental results demonstrates that the registration accuracy is improved comparing to using conventional FEMs. Besides, the modeling of local C⁰ continuities of the displacement field helps to further increase the registration quality considerably.

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