scholarly journals Selective Change Driven Imaging: A Biomimetic Visual Sensing Strategy

Sensors ◽  
2011 ◽  
Vol 11 (11) ◽  
pp. 11000-11020 ◽  
Author(s):  
Jose Boluda ◽  
Pedro Zuccarello ◽  
Fernando Pardo ◽  
Francisco Vegara
Keyword(s):  
Visual Sensing ◽  
Selective Change

Visual Sensing Is Seeing: Why Mindsight, in Hindsight, Is Blind

2004 ◽  
Author(s):  
Daniel J. Simons ◽  
Gabriel Nevarez ◽  
Walter R. Boot
Keyword(s):  
Visual Sensing

Real-time and visual sensing devices based on pH-control assembled lanthanide-barium nano-cluster

2021 ◽  
Vol 413 ◽  
pp. 125291
Author(s):  
Min Liu ◽  
Haoran Li ◽  
Lan Bai ◽  
Kai Zheng ◽  
Zhipeng Zhao ◽  
...  
Keyword(s):  
Real Time ◽  
Ph Control ◽  
Visual Sensing ◽  
Nano Cluster

scholarly journals Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 639
Author(s):  
Chen Ma ◽  
Haifei Dang ◽  
Jun Du ◽  
Pengfei He ◽  
Minbo Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Manufacturing Process ◽  
Support Vector ◽  
Defect Classification ◽  
Droplet Deposition ◽  
Network Support ◽  
Visual Sensing ◽  
Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

A D-π-A-π-D organic conjugated molecule with multiple chelating points: Spectral property and its reversible visual sensing Cu2+

2019 ◽  
Vol 165 ◽  
pp. 217-222 ◽  
Author(s):  
Qi Zhao ◽  
Hua Yuan ◽  
Xinming Xu ◽  
Lei Hu ◽  
Peiwei Gong ◽  
...  
Keyword(s):  
Spectral Property ◽  
Visual Sensing ◽  
Conjugated Molecule

A Ratiometric Fluorescence Probe for Selective Visual Sensing of Zn2+

2005 ◽  
Vol 127 (43) ◽  
pp. 14962-14963 ◽  
Author(s):  
Ayyappanpillai Ajayaghosh ◽  
Priya Carol ◽  
Sivaramapanicker Sreejith
Keyword(s):  
Fluorescence Probe ◽  
Ratiometric Fluorescence ◽  
Visual Sensing

Cephalopod-Inspired Chromotropic Ionic Skin with Rapid Visual Sensing Capabilities to Multiple Stimuli

ACS Nano ◽  
2021 ◽  
Vol 15 (2) ◽  
pp. 3509-3521
Author(s):  
Yunpeng Wang ◽  
Xianfei Cao ◽  
Jie Cheng ◽  
Bowen Yao ◽  
Yusen Zhao ◽  
...  
Keyword(s):  
Visual Sensing

Pyridinium-based tripodal chemosensor in visual sensing of AMP in water by indicator displacement assay (IDA)

2013 ◽  
Vol 11 (34) ◽  
pp. 5666 ◽  
Author(s):  
Kumaresh Ghosh ◽  
Sk Sarfaraj Ali ◽  
Avik Ranjan Sarkar ◽  
Asmita Samadder ◽  
Anisur Rahman Khuda-Bukhsh ◽  
...  
Keyword(s):  
Visual Sensing ◽  
Displacement Assay

Effect of Interaction Modeling in AFM-Based Nanomanipulation

2008 ◽  
Author(s):  
Fakhreddine Landolsi ◽  
Fathi H. Ghorbel ◽  
James B. Dabney
Keyword(s):  
Experimental Data ◽  
Numerical Simulations ◽  
Interaction Model ◽  
Collision Process ◽  
Visual Sensing ◽  
Proposed Model ◽  
Interaction Modeling

AFM-based nanomanipulation is very challenging because of the complex mechanics in tip-sample interactions and the limitations in AFM visual sensing capabilities. In the present paper, we investigate the modeling of AFM-based nanomanipulation emphasizing the effects of the relevant interactions at the nanoscale. The major contribution of the present work is the use of a combined DMT-JKR interaction model in order to describe the complete collision process between the AFM tip and the sample. The coupling between the interactions and the friction at the nanoscale is emphasized. The efficacy of the proposed model to reproduce experimental data is demonstrated via numerical simulations.

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