scholarly journals Real-time dynamic range and signal to noise enhancement in beam-scanning microscopy by integration of sensor characteristics, data acquisition hardware, and statistical methods

2013 ◽  
Author(s):  
David J. Kissick ◽  
Ryan D. Muir ◽  
Shane Z. Sullivan ◽  
Robert A. Oglesbee ◽  
Garth J. Simpson
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Statistical Methods ◽  
Dynamic Range ◽  
Scanning Microscopy ◽  
Signal To Noise ◽  
Beam Scanning ◽  
Time Dynamic

scholarly journals Synchronous digitization for high dynamic range lock-in amplification in beam-scanning microscopy

2014 ◽  
Vol 85 (3) ◽  
pp. 033703 ◽  
Author(s):  
Ryan D. Muir ◽  
Shane Z. Sullivan ◽  
Robert A. Oglesbee ◽  
Garth J. Simpson
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Scanning Microscopy ◽  
Beam Scanning ◽  
High Dynamic ◽  
Lock In

scholarly journals Real-time high dynamic range laser scanning microscopy

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
C. Vinegoni ◽  
C. Leon Swisher ◽  
P. Fumene Feruglio ◽  
R. J. Giedt ◽  
D. L. Rousso ◽  
...  
Keyword(s):  
Real Time ◽  
Laser Scanning ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Laser Scanning Microscopy ◽  
Scanning Microscopy ◽  
High Dynamic

Data acquisition and processing circuit for high-temperature logging up to 200°C

2020 ◽  
Vol 37 (3) ◽  
pp. 131-138
Author(s):  
Jingxuan Peng ◽  
Jingjing Cheng ◽  
Lei Wu ◽  
Qiong Li
Keyword(s):  
High Temperature ◽  
Power Consumption ◽  
Data Acquisition ◽  
Dynamic Range ◽  
Dynamic Performance ◽  
Harmonic Distortion ◽  
Signal To Noise ◽  
Content Type ◽  
Data Acquisition And Processing ◽  
Processing Circuit

Purpose This paper aims to study a high-temperature (up to 200 °C) data acquisition and processing circuit for logging. Design/methodology/approach With the decrease in thermal resistance by system-in package technology and exquisite power consumption distribution design, the circuit worked well at high temperatures environment from both theoretical analysis and real experiments evaluation. Findings In thermal simulation, considering on board chips’ power consumption as additional heat source, the highest temperature point reached by all the chips in the circuit is only 211 °C at work temperature of 200 °C. In addition, the proposed circuit was validated by long time high-temperature experiments. The circuit showed good dynamic performance during a 4-h test in a 200-°C oven, and maintained a signal-to-noise ratio of 92.54 dB, a signal-to-noise and distortion ratio of 91.81 dB, a total harmonic distortion of −99.89 dB and a spurious free dynamic range of 100.28 dB. Originality/value The proposed circuit and methodology showed great potential for application in deep-well logging systems and other high-temperature situations.

Real-time dynamic PC image generation techniques for high performance and high dynamic range fidelity

2008 ◽  
Author(s):  
Dennis H. Bunfield ◽  
Darian E. Trimble ◽  
Thomas Fronckowiak, Jr. ◽  
Gary Ballard ◽  
Joesph Morris
Keyword(s):  
Real Time ◽  
High Performance ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Image Generation ◽  
Time Dynamic ◽  
High Dynamic

Control Model Constructing on Real-Time Dynamic Reversible Lane in Condition of Connected Vehicle

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Lina Mao ◽  
Wenquan Li ◽  
Pengsen Hu ◽  
Guiliang Zhou ◽  
Huiting Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Control Model ◽  
Connected Vehicle ◽  
Time Dynamic

An Over 120dB Dynamic Range Linear Response Single Exposure CMOS Image Sensor with Two-stage Lateral Overflow Integration Trench Capacitors

2020 ◽  
Vol 2020 (7) ◽  
pp. 143-1-143-6 ◽  
Author(s):  
Yasuyuki Fujihara ◽  
Maasa Murata ◽  
Shota Nakayama ◽  
Rihito Kuroda ◽  
Shigetoshi Sugawa
Keyword(s):  
Linear Response ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Signal To Noise ◽  
Single Exposure ◽  
Two Stage ◽  
Noise Ratio ◽  
Maximum Signal

This paper presents a prototype linear response single exposure CMOS image sensor with two-stage lateral overflow integration trench capacitors (LOFITreCs) exhibiting over 120dB dynamic range with 11.4Me- full well capacity (FWC) and maximum signal-to-noise ratio (SNR) of 70dB. The measured SNR at all switching points were over 35dB thanks to the proposed two-stage LOFITreCs.

scholarly journals High frame-rate multichannel beam-scanning microscopy based on Lissajous trajectories

2014 ◽  
Vol 22 (20) ◽  
pp. 24224 ◽  
Author(s):  
Shane Z. Sullivan ◽  
Ryan D. Muir ◽  
Justin A. Newman ◽  
Mark S. Carlsen ◽  
Suhas Sreehari ◽  
...  
Keyword(s):  
Frame Rate ◽  
Scanning Microscopy ◽  
Beam Scanning ◽  
High Frame Rate ◽  
Lissajous Trajectories

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

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