Morphotypic analysis and classification of bacteria and bacterial colonies using laser light-scattering, pattern recognition, and machine-learning system

2009 ◽  
Author(s):  
Bartek Rajwa ◽  
Murat Dundar ◽  
Valeri Patsekin ◽  
Karleigh Huff ◽  
Arun Bhunia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Light Scattering ◽  
Laser Light ◽  
Laser Light Scattering ◽  
Learning System ◽  
Scattering Pattern

On-chip classification of micro-particles using laser light scattering and machine learning

2021 ◽  
Author(s):  
Mubashir Hussain ◽  
Xiaolong Liu ◽  
Jun Zou ◽  
Jian Yang ◽  
Zeeshan Ali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Light Scattering ◽  
Laser Light ◽  
Laser Light Scattering ◽  
Micro Particles ◽  
On Chip

Determination of wood grain direction from laser light scattering pattern

2004 ◽  
Vol 41 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Simo-Pekka Simonaho ◽  
Jari Palviainen ◽  
Yrjö Tolonen ◽  
Raimo Silvennoinen
Keyword(s):  
Light Scattering ◽  
Laser Light ◽  
Laser Light Scattering ◽  
Scattering Pattern ◽  
Wood Grain

In-Situ Dual-Wavelength Ellipsometry and Light Scattering Monitoring of Si/Si1−xGex Heterostructures and Multiuantum Wells

1993 ◽  
Vol 324 ◽  
Author(s):  
C. Pickering ◽  
D.A.O. Hope ◽  
W.Y. Leong ◽  
D.J. Robbins ◽  
R. Greef
Keyword(s):  
Growth Rate ◽  
Light Scattering ◽  
Laser Light ◽  
Growth Period ◽  
Laser Light Scattering ◽  
Heterojunction Bipolar Transistor ◽  
Dual Wavelength ◽  
Multi Quantum Well ◽  
Refractive Index Data

AbstractIn-situ dual-wavelength ellipsometry and laser light scattering have been used to monitor growth of Si/Si1−x,Gex heterojunction bipolar transistor and multi-quantum well (MQW) structures. The growth rate of B-doped Si0 8Ge0.2 has been shown to be linear, but that of As-doped Si is non-linear, decreasing with time. Refractive index data have been obtained at the growth temperature for x = 0.15, 0.20, 0.25. Interface regions ∼ 6-20Å thickness have been detected at hetero-interfaces and during interrupted alloy growth. Period-to-period repeatability of MQW structures has been shown to be ±lML.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

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