Application of 3D documentation and geometric reconstruction methods in traffic accident analysis: With high resolution surface scanning, radiological MSCT/MRI scanning and real data based animation

Our Palaeolithic ancestors did not make good representations of themselves on the rocky surfaces of caves and barring certain exceptions – such as the case of La Marche (found on small slabs of stone or plaquettes) or the Cueva de Ambrosio – the few known examples can only be referred to as anthropomorphs. As such, only hand stencils give us a real picture of the people who came before us. Hand stencils and imprints provide us with a large amount of information that allows us to approach not only their physical appearance but also to infer less tangible details, such as the preferential use of one hand over the other (i.e., handedness). Both new and/or mature technologies as well as digital processing of images, computers with the ability to process very high resolution images, and a more extensive knowledge of the Palaeolithic figures all help us to analyse thoroughly the hands in El Castillo cave. The interdisciplinary study presented here contributes many novel developments based on real data, representing a major step forward in knowledge about our predecessors.

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Development of AEBS Simulation Model for Traffic Accident Analysis of Vehicle with ADAS

Transactions of Korean Society of Automotive Engineers ◽

10.7467/ksae.2021.29.11.995 ◽

2021 ◽

Vol 29 (11) ◽

pp. 995-1001

Author(s):

Seryong Baek ◽

Cheonho Kim ◽

Jonghyuk Kim ◽

Hasun Park ◽

Jonghan Lim

Keyword(s):

Simulation Model ◽

Traffic Accident ◽

Accident Analysis

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Road Traffic Accident Analysis and Visualization of Accident Prone Areas

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.33280 ◽

2021 ◽

Vol 9 (3) ◽

pp. 552-561

Author(s):

Megna T Sreedhar

Keyword(s):

Road Traffic Accident ◽

Traffic Accident ◽

Road Traffic ◽

Accident Analysis

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Sparse Signal Recovery from Modulo Observations

10.21203/rs.3.rs-42731/v1 ◽

2020 ◽

Author(s):

Viraj Shah ◽

Chinmay Hegde

Keyword(s):

Phase Retrieval ◽

Dynamic Range ◽

Signal Reconstruction ◽

Real Data ◽

Superior Performance ◽

Signal Recovery ◽

Reconstruction Methods ◽

Novel Approach ◽

Sparsity Constraints ◽

Improved Performance

Abstract We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.

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A practical guideline for T1 reconstruction from various flip angles in MRI

Journal of Algorithms & Computational Technology ◽

10.1177/1748301816656288 ◽

2016 ◽

Vol 10 (4) ◽

pp. 213-223 ◽

Cited By ~ 1

Author(s):

Constantin Sandmann ◽

Erlend Hodneland ◽

Jan Modersitzki

Keyword(s):

Sensitivity Analysis ◽

Linear Method ◽

Real Data ◽

Flip Angle ◽

Robust Method ◽

Variable Flip Angle ◽

Nonlinear Approach ◽

Reconstruction Methods ◽

The Family ◽

Linear Reconstruction

A fast and robust method for T1 estimation in MRI is the so-called variable flip angle technique. We introduce a novel family of T1 reconstruction methods from data acquired with various flip angles and propose a family member which combines the robustness of a nonlinear- with the computational advantages of a linear reconstruction. The constructed family contains the most common approaches for T1 estimation, namely a linear and a nonlinear approach. A general sensitivity analysis for arbitrary members of the family is established. Advantages of the optimized reconstruction are demonstrated on phantom- as well as real data, showing improvements of up to 24% as compared with the linear method. As a further means to stabilize T1 estimation, spatial stabilization methods are compared. We demonstrate on phantom and on real data that improved results can be obtained if not only T1 but also a second unknown M0 in the reconstruction is stabilized.

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