A Real-Time Transport System for High-Resolution Measure Image

2011 ◽  
Vol 467-469 ◽  
pp. 104-107 ◽  
Author(s):  
Feng Zhang ◽  
Guo Qiang Ren ◽  
Qin Zhang Wu
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Transport System ◽  
Image Data ◽  
The Real ◽  
Resolution Measure

This document explains and demonstrates how to use SRIO interface in the real-time transport platform for high-resolution measure image. The raw CCD data is first captured in a FIFO implemented in FPGA, and then transferred from FPGA to DSP through the SRIO interface. When one frame image data is captured, it is displayed on the screen of PC computer.

The Research of the Real-Time High-Resolution Image Acquisition Based on Embedded System

2014 ◽  
Vol 631-632 ◽  
pp. 508-511
Author(s):  
Xi Ye Feng ◽  
Xiu Qing Huang
Keyword(s):  
High Resolution ◽  
Embedded System ◽  
Real Time ◽  
Low Cost ◽  
Image Acquisition ◽  
Image Data ◽  
Image Sensor ◽  
High Definition ◽  
The Real ◽  
Embedded Linux System

This paper presents the design of a real-time high-definition image acquisition. The hardware platform combines Intel Xscale PXA270 processor, high-resolution camera and SAA7114H. The system is based on the embedded Linux system. Beetween the image sensor and the system memory,there is a quick capture interface.The interface receives the data from the image sensor,and converts the raw image data to a suitable format, and sends H.264 stream to the memory via the DMA channel. The result shows that the design can realize the real-time and high-definition image acquisition in a complicated environment. The advantage of this system is small volume, low power consumption and low cost. It can be widely used in agricultural and hydrological monitoring, intelligent transportation, security monitoring and intelligent home.

Real-Time Water Injection Monitoring with Distributed Fiber Optics Using Physics-Informed Machine Learning

2021 ◽  
Author(s):  
Teymur Sadigov ◽  
Cagri Cerrahoglu ◽  
James Ramsay ◽  
Laurence Burchell ◽  
Sean Cavalero ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Real Time ◽  
Fiber Optics ◽  
Water Injection ◽  
The Real ◽  
Start Up ◽  
Naturally Fractured ◽  
Monitoring Application ◽  
Time Injection

Abstract This paper introduces a novel technique that allows real-time injection monitoring with distributed fiber optics using physics-informed machine learning methods and presents results from Clair Ridge asset where a cloud-based, real-time application is deployed. Clair Ridge is a structural high comprising of naturally fractured Devonian to Carboniferous continental sandstones, with a significantly naturally fractured ridge area. The fractured nature of the reservoir lends itself to permanent deployment of Distributed Fiber Optic Sensing (DFOS) to enable real-time injection monitoring to maximise recovery from the field. In addition to their default limitations, such as providing a snapshot measurement and disturbing the natural well flow with up and down flowing passes, wireline-conveyed production logs (PL) are also unable to provide a high-resolution profile of the water injection along the reservoir due to the completion type. DFOS offers unique surveillance capability when permanently installed along the reservoir interface and continuously providing injection profiles with full visibility along the reservoir section without the need for an intervention. The real-time injection monitoring application uses both distributed acoustic and temperature sensing (DAS & DTS) and is based on physics-informed machine learning models. It is now running and available to all asset users on the cloud. So far, the application has generated high-resolution injection profiles over a dozen multi-rate injection periods automatically and the results are cross-checked against the profiles from the warmback analyses that were also generated automatically as part of the same application. The real-time monitoring insights have been effectively applied to provide significant business value using the capability for start-up optimization to manage and improve injection conformance, monitor fractured formations and caprock monitoring.

scholarly journals Rapid Diagnosis of Tuberculosis by Real-Time High-Resolution Imaging of Mycobacterium tuberculosis Colonies

2015 ◽  
Vol 53 (8) ◽  
pp. 2693-2696 ◽  
Author(s):  
Ramzi Ghodbane ◽  
Shady Asmar ◽  
Marlena Betzner ◽  
Marie Linet ◽  
Joseph Pierquin ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
High Resolution ◽  
Real Time ◽  
Solid Medium ◽  
Contamination Rate ◽  
High Resolution Imaging ◽  
Standard Protocol ◽  
Content Type ◽  
The Real ◽  
Resolution Imaging

Culture remains the cornerstone of diagnosis for pulmonary tuberculosis, but the fastidiousness ofMycobacterium tuberculosismay delay culture-based diagnosis for weeks. We evaluated the performance of real-time high-resolution imaging for the rapid detection ofM. tuberculosiscolonies growing on a solid medium. A total of 50 clinical specimens, including 42 sputum specimens, 4 stool specimens, 2 bronchoalveolar lavage fluid specimens, and 2 bronchial aspirate fluid specimens were prospectively inoculated into (i) a commercially available Middlebrook broth and evaluated for mycobacterial growth indirectly detected by measuring oxygen consumption (standard protocol) and (ii) a home-made solid medium incubated in an incubator featuring real-time high-resolution imaging of colonies (real-time protocol). Isolates were identified by Ziehl-Neelsen staining and matrix-assisted laser desorption ionization–time of flight mass spectrometry. Use of the standard protocol yielded 14/50 (28%)M. tuberculosisisolates, which is not significantly different from the 13/50 (26%)M. tuberculosisisolates found using the real-time protocol (P= 1.00 by Fisher's exact test), and the contamination rate of 1/50 (2%) was not significantly different from the contamination rate of 2/50 (4%) using the real-time protocol (P= 1.00). The real-time imaging protocol showed a 4.4-fold reduction in time to detection, 82 ± 54 h versus 360 ± 142 h (P< 0.05). These preliminary data give the proof of concept that real-time high-resolution imaging ofM. tuberculosiscolonies is a new technology that shortens the time to growth detection and the laboratory diagnosis of pulmonary tuberculosis.

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