An Assessment of State-of-the-Art Offgas Sensor Technology for Process Control in Steel Combustion Furnaces

2021 ◽  
Author(s):  
K. Grieshaber ◽  
E. Huelson
Keyword(s):  
Process Control ◽  
State Of The Art ◽  
Sensor Technology

scholarly journals Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 864 ◽  
Author(s):  
Ju Wang ◽  
Nicolai Spicher ◽  
Joana M. Warnecke ◽  
Mostafa Haghi ◽  
Jonas Schwartze ◽  
...  
Keyword(s):  
Literature Review ◽  
Health Monitoring ◽  
Smart Home ◽  
State Of The Art ◽  
Home Health ◽  
Environmental Data ◽  
Behavioral Data ◽  
Sensor Technology ◽  
Current Sensor ◽  
Home Health Monitoring

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking.

Thermal Laser Separation – A Novel Dicing Technology Fulfilling the Demands of Volume Manufacturing of 4H-SiC Devices

2015 ◽  
Vol 821-823 ◽  
pp. 528-532 ◽  
Author(s):  
Dirk Lewke ◽  
Karl Otto Dohnke ◽  
Hans Ulrich Zühlke ◽  
Mercedes Cerezuela Barret ◽  
Martin Schellenberger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Process Control ◽  
Tool Wear ◽  
Large Scale ◽  
State Of The Art ◽  
Scale Production ◽  
High Quality ◽  
Large Scale Production ◽  
Laser Separation

One challenge for volume manufacturing of 4H-SiC devices is the state-of-the-art wafer dicing technology – the mechanical blade dicing which suffers from high tool wear and low feed rates. In this paper we discuss Thermal Laser Separation (TLS) as a novel dicing technology for large scale production of SiC devices. We compare the latest TLS experimental data resulting from fully processed 4H-SiC wafers with results obtained by mechanical dicing technology. Especially typical product relevant features like process control monitoring (PCM) structures and backside metallization, quality of diced SiC-devices as well as productivity are considered. It could be shown that with feed rates up to two orders of magnitude higher than state-of-the-art, no tool wear and high quality of diced chips, TLS has a very promising potential to fulfill the demands of volume manufacturing of 4H-SiC devices.

Enabling Requirements for High Volume Thermo compression Bonding

2015 ◽  
Vol 2015 (DPC) ◽  
pp. 000995-001015
Author(s):  
Tom Strothmann
Keyword(s):  
Process Control ◽  
Cost Reduction ◽  
State Of The Art ◽  
High Volume ◽  
Manufacturing Technology ◽  
Data Logging ◽  
Key Factors ◽  
Review State ◽  
Memory Applications

The potential of Thermo compression Bonding (TCB) has been widely discussed for several years, but it has not previously achieved widespread production use. TCB has now begun the transition to an accepted high volume manufacturing technology driven primarily by the memory market, but with wider adoption close for non-memory applications. Several key factors have enabled this transition, including advanced TCB equipment with higher UPH for cost reduction and advanced methods of inline process control. The unique requirements of TCB demand absolute process control, simultaneous data logging capability for multiple key factors in the process and portability of the process between tools. This introduces a level of sophistication that has not previously been required for BE assembly processes. This presentation will review state of the art TCB technology and the fundamental equipment requirements to support the transition to HVM.

Deep Image Demosaicing for Submicron Image Sensors

2019 ◽  
Vol 63 (6) ◽  
pp. 60410-1-60410-12
Author(s):  
Irina Kim ◽  
Seongwook Song ◽  
Soonkeun Chang ◽  
Sukhwan Lim ◽  
Kai Guo
Keyword(s):  
Image Quality ◽  
Mobile Devices ◽  
State Of The Art ◽  
Image Sensor ◽  
Sensor Technology ◽  
Color Filter ◽  
Computationally Efficient ◽  
Linear Feature ◽  
Visual Artifacts ◽  
Very High

Abstract Latest trend in image sensor technology allowing submicron pixel size for high-end mobile devices comes at very high image resolutions and with irregularly sampled Quad Bayer color filter array (CFA). Sustaining image quality becomes a challenge for the image signal processor (ISP), namely for demosaicing. Inspired by the success of deep learning approach to standard Bayer demosaicing, we aim to investigate how artifacts-prone Quad Bayer array can benefit from it. We found that deeper networks are capable to improve image quality and reduce artifacts; however, deeper networks can be hardly deployed on mobile devices given very high image resolutions: 24MP, 36MP, 48MP. In this article, we propose an efficient end-to-end solution to bridge this gap—a duplex pyramid network (DPN). Deep hierarchical structure, residual learning, and linear feature map depth growth allow very large receptive field, yielding better details restoration and artifacts reduction, while staying computationally efficient. Experiments show that the proposed network outperforms state of the art for standard and Quad Bayer demosaicing. For the challenging Quad Bayer CFA, the proposed method reduces visual artifacts better than state-of-the-art deep networks including artifacts existing in conventional commercial solutions. While superior in image quality, it is 2‐25 times faster than state-of-the-art deep neural networks and therefore feasible for deployment on mobile devices, paving the way for a new era of on-device deep ISPs.

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