Real-time auto white balancing using DWT-based multi-scale clustering

2006 ◽  
Author(s):  
N. Kehtarnavaz ◽  
N. Kim ◽  
M. Gamadia
Keyword(s):  
Real Time ◽  
Multi Scale ◽  
White Balancing

Development and Real-Time Implementation of Auto White Balancing Scoring Algorithm

2002 ◽  
Vol 8 (5) ◽  
pp. 379-386 ◽  
Author(s):  
N. Kehtarnavaz ◽  
H.-J. Oh ◽  
Y. Yoo
Keyword(s):  
Real Time ◽  
Scoring Algorithm ◽  
White Balancing

Towards Personalized Real-time Cardiodynamic Status Monitoring: Multi-scale Modeling of the Heart Sound

2021 ◽  
Author(s):  
Tung Jing Fang ◽  
Chen Han ◽  
Chuan Chia Wang ◽  
Lai Chung Lee ◽  
Whei Jane Wei
Keyword(s):  
Real Time ◽  
Heart Sound ◽  
Multi Scale ◽  
Status Monitoring ◽  
Scale Modeling ◽  
Multi Scale Modeling

scholarly journals Real-time simulation of large-scale HTS systems: multi-scale and homogeneous models using the T–A formulation

2019 ◽  
Vol 32 (6) ◽  
pp. 065003 ◽  
Author(s):  
Edgar Berrospe-Juarez ◽  
Víctor M R Zermeño ◽  
Frederic Trillaud ◽  
Francesco Grilli
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Real Time Simulation ◽  
Multi Scale ◽  
Time Simulation ◽  
Homogeneous Models

Robust real-time visual object tracking via multi-scale fully convolutional Siamese networks

2018 ◽  
Vol 77 (17) ◽  
pp. 22131-22143 ◽  
Author(s):  
Longchao Yang ◽  
Peilin Jiang ◽  
Fei Wang ◽  
Xuan Wang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Multi Scale ◽  
Siamese Networks

In-Process Data Fusion for Process Monitoring and Control of Metal Additive Manufacturing

2021 ◽  
Author(s):  
Zhuo Yang ◽  
Yan Lu ◽  
Simin Li ◽  
Jennifer Li ◽  
Yande Ndiaye ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Control ◽  
Data Fusion ◽  
Real Time ◽  
Process Data ◽  
Multi Scale ◽  
And Control ◽  
Fusion Framework ◽  
Scale Data ◽  
Metal Additive

Abstract To accelerate the adoption of Metal Additive Manufacturing (MAM) for production, an understanding of MAM process-structure-property (PSP) relationships is indispensable for quality control. A multitude of physical phenomena involved in MAM necessitates the use of multi-modal and in-process sensing techniques to model, monitor and control the process. The data generated from these sensors and process actuators are fused in various ways to advance our understanding of the process and to estimate both process status and part-in-progress states. This paper presents a hierarchical in-process data fusion framework for MAM, consisting of pointwise, trackwise, layerwise and partwise data analytics. Data fusion can be performed at raw data, feature, decision or mixed levels. The multi-scale data fusion framework is illustrated in detail using a laser powder bed fusion process for anomaly detection, material defect isolation, and part quality prediction. The multi-scale data fusion can be generally applied and integrated with real-time MAM process control, near-real-time layerwise repairing and buildwise decision making. The framework can be utilized by the AM research and standards community to rapidly develop and deploy interoperable tools and standards to analyze, process and exploit two or more different types of AM data. Common engineering standards for AM data fusion systems will dramatically improve the ability to detect, identify and locate part flaws, and then derive optimal policies for process control.

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