Enhanced Real-Time Wellbore Stability Monitoring Using High Definition Imaging With Wired-Pipe Telemetry

2009 ◽  
Author(s):  
Christopher Allen Wolfe ◽  
Stephen A. Morris ◽  
Ansgar Baule
Keyword(s):  
Real Time ◽  
Wellbore Stability ◽  
High Definition ◽  
Stability Monitoring

Well of the Future: A New Approach to Real-Time Wellbore Stability Monitoring in the Colombian Foothills

2015 ◽  
Author(s):  
J. C. Almeida ◽  
M. Rangel ◽  
G. Rey ◽  
M. Pacione ◽  
C. López ◽  
...  
Keyword(s):  
Real Time ◽  
Wellbore Stability ◽  
New Approach ◽  
Stability Monitoring ◽  
The Future

Application of Real-Time Wellbore Stability Monitoring on a Deepwater ERD Well

2005 ◽  
Author(s):  
J.A. Greenwood ◽  
A. Brehm ◽  
E. van Oort ◽  
D.R. Algu ◽  
Y.E. Volokitin
Keyword(s):  
Real Time ◽  
Wellbore Stability ◽  
Stability Monitoring

Evaluation and Application of Real-Time Image and Caliper Data as Part of a Wellbore Stability Monitoring Provision

2006 ◽  
Author(s):  
Jeremy Alexander Greenwood ◽  
Peter Michael Bowler ◽  
Juan Francisco Sarmiento ◽  
Stephen M. Willson ◽  
Stephen T. Edwards
Keyword(s):  
Real Time ◽  
Wellbore Stability ◽  
Stability Monitoring ◽  
Real Time Image ◽  
Time Image

Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey
Keyword(s):  
Graduate Students ◽  
Data Collection ◽  
Real Time ◽  
Clinical Supervision ◽  
Video Recording ◽  
Advanced Technology ◽  
High Definition ◽  
Recording Technology ◽  
Self Confidence ◽  
Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

MobileNet-Yolo Based wildlife detection model: A case study in yunnan tongbiguan nature reserve, China

2021 ◽  
pp. 1-11
Author(s):  
Tingting Zhao ◽  
Xiaoli Yi ◽  
Zhiyong Zeng ◽  
Tao Feng
Keyword(s):  
Real Time ◽  
Nature Reserve ◽  
Complex Model ◽  
Training Data ◽  
Mean Average Precision ◽  
Detection Accuracy ◽  
High Definition ◽  
Average Precision ◽  
Data Set ◽  
Real Time Detection

YTNR (Yunnan Tongbiguan Nature Reserve) is located in the westernmost part of China’s tropical regions and is the only area in China with the tropical biota of the Irrawaddy River system. The reserve has abundant tropical flora and fauna resources. In order to realize the real-time detection of wild animals in this area, this paper proposes an improved YOLO (You only look once) network. The original YOLO model can achieve higher detection accuracy, but due to the complex model structure, it cannot achieve a faster detection speed on the CPU detection platform. Therefore, the lightweight network MobileNet is introduced to replace the backbone feature extraction network in YOLO, which realizes real-time detection on the CPU platform. In response to the difficulty in collecting wild animal image data, the research team deployed 50 high-definition cameras in the study area and conducted continuous observations for more than 1,000 hours. In the end, this research uses 1410 images of wildlife collected in the field and 1577 wildlife images from the internet to construct a research data set combined with the manual annotation of domain experts. At the same time, transfer learning is introduced to solve the problem of insufficient training data and the network is difficult to fit. The experimental results show that our model trained on a training set containing 2419 animal images has a mean average precision of 93.6% and an FPS (Frame Per Second) of 3.8 under the CPU. Compared with YOLO, the mean average precision is increased by 7.7%, and the FPS value is increased by 3.

Blue-light imaging compared with high-definition white light for real-time histology prediction of colorectal polyps less than 1 centimeter: a prospective randomized study

2019 ◽  
Vol 89 (3) ◽  
pp. 554-564.e1 ◽  
Author(s):  
Emanuele Rondonotti ◽  
Silvia Paggi ◽  
Arnaldo Amato ◽  
Giuseppe Mogavero ◽  
Alida Andrealli ◽  
...  
Keyword(s):  
Real Time ◽  
Blue Light ◽  
White Light ◽  
Randomized Study ◽  
Colorectal Polyps ◽  
Prospective Randomized Study ◽  
High Definition

Real Time Stability Monitoring

2014 ◽  
pp. 59-98
Author(s):  
Mladen Kezunovic ◽  
Sakis Meliopoulos ◽  
Vaithianathan Venkatasubramanian ◽  
Vijay Vittal
Keyword(s):  
Real Time ◽  
Time Stability ◽  
Stability Monitoring ◽  
Real Time Stability

scholarly journals Motion Estimation Architecture Using Efficient Adder-Compressors for HDTV Video Coding

2010 ◽  
Vol 5 (1) ◽  
pp. 78-88 ◽  
Author(s):  
Marcelo Porto ◽  
André Silva ◽  
Sergo Almeida ◽  
Eduardo Da Costa ◽  
Sergio Bampi
Keyword(s):  
Motion Estimation ◽  
Real Time ◽  
Search Algorithm ◽  
Absolute Difference ◽  
High Definition ◽  
Case Processing ◽  
Worst Case ◽  
Average Case ◽  
High Definition Television ◽  
Internal Structures

This paper presents real time HDTV (High Definition Television) architecture for Motion Estimation (ME) using efficient adder compressors. The architecture is based on the Quarter Sub-sampled Diamond Search algorithm (QSDS) with Dynamic Iteration Control (DIC) algorithm. The main characteristic of the proposed architecture is the large amount of Processing Units (PUs) that are used to calculate the SAD (Sum of Absolute Difference) metric. The internal structures of the PUs are composed by a large number of addition operations to calculate the SADs. In this paper, efficient 4-2 and 8-2 adder compressors are used in the PUs architecture to achieve the performance to work with HDTV (High Definition Television) videos in real time at 30 frames per second. These adder compressors enable the simultaneous addition of 4 and 8 operands respectively. The PUs, using adder compressors, were applied to the ME architecture. The implemented architecture was described in VHDL and synthesized to FPGA and, with Leonardo Spectrum tool, to the TSMC 0.18μm CMOS standard cell technology. Synthesis results indicate that the new QSDS-DIC architecture reach the best performance result and enable gains of 12% in terms of processing rate. The architecture can reach real time for full HDTV (1920x1080 pixels) in the worst case processing 65 frames per second, and it can process 269 HDTV frames per second in the average case.

scholarly journals High-Quality Transmission of Cardiotocogram and Fetal Information Using a 5G System: Pilot Experiment (Preprint)

2020 ◽  
Author(s):  
Katsuhiko Naruse ◽  
Tomoya Yamashita ◽  
Yukari Onishi ◽  
Yuhi Niitaka ◽  
Fumikage Uchida ◽  
...  
Keyword(s):  
Real Time ◽  
Video Camera ◽  
Ultrasound Images ◽  
Radio Waves ◽  
High Definition ◽  
Waveform Data ◽  
High Definition Video ◽  
Other Information ◽  
Emergency Transport ◽  
The Status

BACKGROUND A cardiotocogram (CTG) is a device used to perceive the status of a fetus in utero in real time. There are a few reports of its use at home or during emergency transport. OBJECTIVE The aim of this study was to test whether CTG and other perinatal information can be transmitted accurately using an experimental station with a 5G transmission system. METHODS In the research institute, real-time fetal heart rate waveform data from the CTG device, high-definition video ultrasound images of the fetus, and high-definition video taken with a video camera on a single line were transmitted by 5G radio waves from the transmitting station to the receiving station. RESULTS All data were proven to be transmitted with a minimum delay of less than 1 second. The CTG waveform image quality was not inferior, and there was no interruption in transmission. Images of the transmitted ultrasound examination and video movie were fine and smooth. CONCLUSIONS CTG and other information about the fetuses and pregnant women were successfully transmitted by a 5G system. This finding will lead to prompt and accurate medical treatment and improve the prognosis of newborns.

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