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.
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.
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 <i>B</i>-spline finite element method (TBFEM). In contrast to tensor-product <i>B</i>-splines, which is widely used in medical imaging, triangular <i>B</i>-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 <i>B</i>-splines, our method can explicitly model the transformation between temporal mammogram pairs over irregular region of interest(ROI), using a collection of triangular <i>B</i>-splines. In addition, it is also capable of describing C<sup>0</sup> 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 C<sup>0</sup> continuities of the displacement field helps to further increase the registration quality considerably.