Photon-limited depth and reflectivity imaging with sparsity regularization

2017 ◽  
Vol 392 ◽  
pp. 25-30 ◽  
Author(s):  
Kang Yan ◽  
Li Lifei ◽  
Duan Xuejie ◽  
Zhang Tongyi ◽  
Li Dongjian ◽  
...  
Keyword(s):  
Sparsity Regularization ◽  
Limited Depth ◽  
Reflectivity Imaging

Design options for self-cleansing storm sewers

1996 ◽  
Vol 33 (9) ◽  
pp. 215-220 ◽  
Author(s):  
Chandramouli Nalluri ◽  
Aminuddin Ab. Ghani
Keyword(s):  
Cohesive Sediments ◽  
Stress Criterion ◽  
Sediment Bed ◽  
Sediment Size ◽  
Codes Of Practice ◽  
Design Charts ◽  
Design Options ◽  
Minimum Velocity ◽  
Limited Depth ◽  
Storm Sewers

A list of available codes of practice for self-cleansing sewers is presented and a review of appraisals of minimum velocity criterion is summarised. Comparisons of newly developed “minimum velocity” criteria and “minimum shear stress” criterion are presented. Some design charts are also given. These charts are applicable to non-cohesive sediments (typically storm sewers). It appears that sediment size and concentration need to be taken into account, and that a limited depth of sediment bed is recommended for large pipes (diameters > 1000 mm) to maximise their transport capacity.

scholarly journals Can Liquid Lenses Increase Depth of Field in Head Mounted Video See-Through Devices?

2021 ◽  
Vol 7 (8) ◽  
pp. 138
Author(s):  
Marina Carbone ◽  
Davide Domeneghetti ◽  
Fabrizio Cutolo ◽  
Renzo D’Amato ◽  
Emanuele Cigna ◽  
...  
Keyword(s):  
Control System ◽  
Clinical Practice ◽  
Augmented Reality ◽  
Time Of Flight ◽  
Depth Of Field ◽  
Liquid Lenses ◽  
Good Potential ◽  
Distance Sensor ◽  
Limited Depth ◽  
Lens Optics

Wearable Video See-Through (VST) devices for Augmented Reality (AR) and for obtaining a Magnified View are taking hold in the medical and surgical fields. However, these devices are not yet usable in daily clinical practice, due to focusing problems and a limited depth of field. This study investigates the use of liquid-lens optics to create an autofocus system for wearable VST visors. The autofocus system is based on a Time of Flight (TOF) distance sensor and an active autofocus control system. The integrated autofocus system in the wearable VST viewers showed good potential in terms of providing rapid focus at various distances and a magnified view.

Norm sensitivity of sparsity regularization with respect to p

2012 ◽  
Vol 28 (10) ◽  
pp. 104009 ◽  
Author(s):  
K S Kazimierski ◽  
P Maass ◽  
R Strehlow
Keyword(s):  
Sparsity Regularization

Block-matching sparsity regularization-based image reconstruction for low-dose computed tomography

2018 ◽  
Vol 45 (6) ◽  
pp. 2439-2452 ◽  
Author(s):  
Ailong Cai ◽  
Lei Li ◽  
Zhizhong Zheng ◽  
Linyuan Wang ◽  
Bin Yan
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Block Matching ◽  
Sparsity Regularization ◽  
Low Dose Computed Tomography

scholarly journals PyDL8.5: a Library for Learning Optimal Decision Trees

2020 ◽  
Author(s):  
Gaël Aglin ◽  
Siegfried Nijssen ◽  
Pierre Schaus
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Efficient Algorithm ◽  
Optimal Decision ◽  
Learning Tasks ◽  
Explainable Ai ◽  
Classification Tasks ◽  
Interpretable Models ◽  
Limited Depth

Decision Trees (DTs) are widely used Machine Learning (ML) models with a broad range of applications. The interest in these models has increased even further in the context of Explainable AI (XAI), as decision trees of limited depth are very interpretable models. However, traditional algorithms for learning DTs are heuristic in nature; they may produce trees that are of suboptimal quality under depth constraints. We introduce PyDL8.5, a Python library to infer depth-constrained Optimal Decision Trees (ODTs). PyDL8.5 provides an interface for DL8.5, an efficient algorithm for inferring depth-constrained ODTs. The library provides an easy-to-use scikit-learn compatible interface. It cannot only be used for classification tasks, but also for regression, clustering, and other tasks. We introduce an interface that allows users to easily implement these other learning tasks. We provide a number of examples of how to use this library.

Non-convex sparsity regularization for wave back restoration of space object images

2016 ◽  
Vol 24 (4) ◽  
pp. 902-912
Author(s):  
郭从洲 GUO Cong-zhou ◽  
时文俊 SHI Wen-jun ◽  
秦志远 QIN ZHi-yuan ◽  
耿则勋 GENG Ze-xun
Keyword(s):  
Space Object ◽  
Sparsity Regularization
Sign in / Sign up

Export Citation Format

Share Document