An EIT reconstruction algorithm: comparison of one-step and iterative versions

2004 ◽  
Author(s):  
Agnieszka Janczulewicz ◽  
Jerzy Wtorek
Keyword(s):  
Reconstruction Algorithm ◽  
Algorithm Comparison ◽  
One Step

scholarly journals Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 197
Author(s):  
Emil Dumic ◽  
Anamaria Bjelopera ◽  
Andreas Nüchter
Keyword(s):  
Surface Reconstruction ◽  
Video Compression ◽  
Point Cloud ◽  
Voronoi Diagrams ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
Algorithm Comparison ◽  
Point To Point ◽  
Original Point

In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.

scholarly journals Higher-Ranked Annotation Polymorphic Dependency Analysis

2020 ◽  
pp. 656-683 ◽  
Author(s):  
Fabian Thorand ◽  
Jurriaan Hage
Keyword(s):  
Static Analysis ◽  
Type System ◽  
Reconstruction Algorithm ◽  
Context Sensitivity ◽  
Functional Languages ◽  
Dependency Analysis ◽  
One Step ◽  
Type Reconstruction ◽  
Generic Analysis

AbstractThe precision of a static analysis can be improved by increasing the context-sensitivity of the analysis. In a type-based formulation of static analysis for functional languages this can be achieved by, e.g., introducing let-polyvariance or subtyping. In this paper we go one step further by defining a higher-ranked polyvariant type system so that even properties of lambda-bound identifiers can be generalized over. We do this for dependency analysis, a generic analysis that can be instantiated to a range of different analyses that in this way all can profit.We prove that our analysis is sound with respect to a call-by-name semantics and that it satisfies a so-called noninterference property. We provide a type reconstruction algorithm that we have proven to be terminating, and sound and complete with respect to its declarative specification. Our principled description can serve as a blueprint for making other analyses higher-ranked.

scholarly journals Cardiac Functional Analysis with Multi–Detector Row CT and Segmental Reconstruction Algorithm: Comparison with Echocardiography, SPECT, and MR Imaging

Radiology ◽  
2005 ◽  
Vol 234 (2) ◽  
pp. 381-390 ◽  
Author(s):  
Masaki Yamamuro ◽  
Eiji Tadamura ◽  
Shigeto Kubo ◽  
Hiroshi Toyoda ◽  
Takeshi Nishina ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Mr Imaging ◽  
Reconstruction Algorithm ◽  
Algorithm Comparison

scholarly journals Optimization Algorithm’s Problems: Comparison Study

2017 ◽  
Vol 2 (3) ◽  
pp. 25-31
Author(s):  
Rebaz M. Nabi ◽  
Rania Azad ◽  
Soran Saeed ◽  
Rebwar M. Nabi
Keyword(s):  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Network Flows ◽  
Optimization Problems ◽  
Solution Process ◽  
Maximum Matching ◽  
Future Research ◽  
Comparison Study ◽  
Algorithm Comparison ◽  
One Step

Currently, in various fields and disciplines problem optimization are used commonly. In this concern, we have to define solutions which are two known concepts optimal or near optimal optimization problems in regards to some objects. Usually, it is surely difficult to sort problems out in only one step, but some processes can be followed by us which people usually call it problem solving. Frequently, the solution process is split into various steps which are accomplishing one after the other. Therefore, in this paper we consider some algorithms that help us to sort out problems, for exemplify, finding the shortest path, minimum spanning tree, maximum network flows and maximum matching. More importantly, the algorithm comparison will be presented. Additionally, the limitation of each algorithm. The last but not the least, the future research in this area will be approached.

scholarly journals A Fast Reconstruction Algorithm for Fluorescence Optical Diffusion Tomography Based on Preiteration

2007 ◽  
Vol 2007 ◽  
pp. 1-6 ◽  
Author(s):  
Xiaolei Song ◽  
Xiaoyun Xiong ◽  
Jing Bai
Keyword(s):  
Near Infrared ◽  
Iteration Process ◽  
Reconstruction Algorithm ◽  
Data Inversion ◽  
Matrix Vector Multiplication ◽  
Generalized Inverse Matrix ◽  
One Step ◽  
Fast Reconstruction ◽  
Optical Diffusion Tomography ◽  
Matrix Vector

Fluorescence optical diffusion tomography in the near-infrared (NIR) bandwidth is considered to be one of the most promising ways for noninvasive molecular-based imaging. Many reconstructive approaches to it utilize iterative methods for data inversion. However, they are time-consuming and they are far from meeting the real-time imaging demands. In this work, a fast preiteration algorithm based on the generalized inverse matrix is proposed. This method needs only one step of matrix-vector multiplication online, by pushing the iteration process to be executed offline. In the preiteration process, the second-order iterative format is employed to exponentially accelerate the convergence. Simulations based on an analytical diffusion model show that the distribution of fluorescent yield can be well estimated by this algorithm and the reconstructed speed is remarkably increased.

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