scholarly journals Event Guided Depth Sensing

2021 ◽  
Author(s):  
Manasi Muglikar ◽  
Diederik Paul Moeys ◽  
Davide Scaramuzza
Keyword(s):  
Depth Sensing

scholarly journals Depth-Sensing Indentation as a Micro- and Nanomechanical Approach to Characterisation of Mechanical Properties of Soft, Biological, and Biomimetic Materials

Nanomaterials ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 15 ◽  
Author(s):  
Nikolay V. Perepelkin ◽  
Feodor M. Borodich ◽  
Alexander E. Kovalev ◽  
Stanislav N. Gorb
Keyword(s):  
Review Paper ◽  
Soft Materials ◽  
Data Fitting ◽  
Depth Sensing ◽  
Adhesive Properties ◽  
Distinctive Features ◽  
Experimental Part ◽  
Properties Of Materials ◽  
The Mathematical Model ◽  
Non Destructive

Classical methods of material testing become extremely complicated or impossible at micro-/nanoscale. At the same time, depth-sensing indentation (DSI) can be applied without much change at various length scales. However, interpretation of the DSI data needs to be done carefully, as length-scale dependent effects, such as adhesion, should be taken into account. This review paper is focused on different DSI approaches and factors that can lead to erroneous results, if conventional DSI methods are used for micro-/nanomechanical testing, or testing soft materials. We also review our recent advances in the development of a method that intrinsically takes adhesion effects in DSI into account: the Borodich–Galanov (BG) method, and its extended variant (eBG). The BG/eBG methods can be considered a framework made of the experimental part (DSI by means of spherical indenters), and the data processing part (data fitting based on the mathematical model of the experiment), with such distinctive features as intrinsic model-based account of adhesion, the ability to simultaneously estimate elastic and adhesive properties of materials, and non-destructive nature.

Micro-Hardness and Morphology of LDPE Influenced by Beta Radiation

2014 ◽  
Vol 606 ◽  
pp. 253-256 ◽  
Author(s):  
Martin Ovsik ◽  
Petr Kratky ◽  
David Manas ◽  
Miroslav Manas ◽  
Michal Stanek ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Hardness Test ◽  
Polymer Materials ◽  
Beta Radiation ◽  
Micro Hardness ◽  
X Ray Diffraction ◽  
Depth Sensing ◽  
Surface Layers ◽  
Hardness Values ◽  
Different Doses

This article deals with the influence of different doses of Beta radiation to the structure and mico-mechanical properties of Low-density polyethylene (LDPE). Hard surface layers of polymer materials, especially LDPE, can be formed by radiation cross-linking by β radiation with doses of 33, 66 and 99 kGy. Material properties created by β radiation are measured by micro-hardness test using the DSI method (Depth Sensing Indentation). Individual radiation doses caused structural and micro-mechanical changes which have a significant effect on the final properties of the LDPE tested. The highest values of micro-mechanical properties were reached at radiation dose of 66 and 99 kGy, when the micro-hardness values increased by about 21%. The changes were examined and confirmed by X-ray diffraction.

scholarly journals A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 546
Author(s):  
Zhenni Li ◽  
Haoyi Sun ◽  
Yuliang Gao ◽  
Jiao Wang
Keyword(s):  
Real Time ◽  
Super Resolution ◽  
Depth Map ◽  
Acquisition System ◽  
Depth Image ◽  
Fpga Design ◽  
Depth Sensing ◽  
Residual Network ◽  
Real Time Processing ◽  
Depth Maps

Depth maps obtained through sensors are often unsatisfactory because of their low-resolution and noise interference. In this paper, we propose a real-time depth map enhancement system based on a residual network which uses dual channels to process depth maps and intensity maps respectively and cancels the preprocessing process, and the algorithm proposed can achieve real-time processing speed at more than 30 fps. Furthermore, the FPGA design and implementation for depth sensing is also introduced. In this FPGA design, intensity image and depth image are captured by the dual-camera synchronous acquisition system as the input of neural network. Experiments on various depth map restoration shows our algorithms has better performance than existing LRMC, DE-CNN and DDTF algorithms on standard datasets and has a better depth map super-resolution, and our FPGA completed the test of the system to ensure that the data throughput of the USB 3.0 interface of the acquisition system is stable at 226 Mbps, and support dual-camera to work at full speed, that is, 54 fps@ (1280 × 960 + 328 × 248 × 3).

scholarly journals Temperature-Dependent Creep Behavior and Quasi-Static Mechanical Properties of Heat-Treated Wood

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 968
Author(s):  
Dong Xing ◽  
Xinzhou Wang ◽  
Siqun Wang
Keyword(s):  
Mechanical Properties ◽  
Creep Behavior ◽  
Cell Walls ◽  
Treated Wood ◽  
Crosslinking Reaction ◽  
Depth Sensing ◽  
Temperature Dependent ◽  
Heat Treated ◽  
Static Mechanical ◽  
Static Mechanical Properties

In this paper, Berkovich depth-sensing indentation has been used to study the effects of the temperature-dependent quasi-static mechanical properties and creep deformation of heat-treated wood at temperatures from 20 °C to 180 °C. The characteristics of the load–depth curve, creep strain rate, creep compliance, and creep stress exponent of heat-treated wood are evaluated. The results showed that high temperature heat treatment improved the hardness of wood cell walls and reduced the creep rate of wood cell walls. This is mainly due to the improvement of the crystallinity of the cellulose, and the recondensation and crosslinking reaction of the lignocellulose structure. The Burgers model is well fitted to study the creep behavior of heat-treated wood cell walls under different temperatures.

scholarly journals BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2144
Author(s):  
Stefan Reitmann ◽  
Lorenzo Neumann ◽  
Bernhard Jung
Keyword(s):  
Point Clouds ◽  
Training Data ◽  
Sensor Data ◽  
Generation Process ◽  
Data Generation ◽  
Depth Sensor ◽  
Depth Sensing ◽  
Depth Sensors ◽  
3D Point Clouds ◽  
Wide Range

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

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