Real-Time Vision Using a Smart Sensor System

Author(s):  
Ahmed Nabil Belbachir ◽  
Martin Litzenberger ◽  
Christoph Posch ◽  
Peter Schon
Keyword(s):  
2021 ◽  
pp. 155335062110314
Author(s):  
Mario V. Roser ◽  
Alexander H. R. Frank ◽  
Lea Henrichs ◽  
Christian Heiliger ◽  
Dorian Andrade ◽  
...  

Background: For centuries, surgeons have relied on surgical drains during postoperative care. Despite all advances in modern medicine and the area of digitalization, as of today, most if not all assessment of abdominal secretions excreted via surgical drains are carried out manually. We here introduce a novel integrated Smart Sensor System ( Smart Drain) that allows for real-time characterization and digitalization of postoperative abdominal drain output at the patient’s bedside. Methods: A prototype of the Smart Drain was developed using a sophisticated spectrometer for assessment of drain output. The prototype measures 10 × 6 × 6 cm and therefore easily fits at the bedside. At the time of measurement with our Smart Drain, the drain output was additionally sent off to be analyzed in our routine laboratory for typical markers of interest in abdominal surgery such as bilirubin, lipase, amylase, triglycerides, urea, protein, and red blood cells. A total of 45 samples from 19 patients were included. Results: The measurements generated were found to correlate with conventional laboratory measurements for bilirubin (r = .658, P = .000), lipase (r = .490, P = .002), amylase (r = .571, P = .000), triglycerides (r = .803, P = .000), urea (r = .326, P = .033), protein (r = .387, P = .012), and red blood cells (r = .904, P = .000). Conclusions: To our best knowledge, for the first time we describe a device using a sophisticated spectrometer that allows for real-time characterization and digitalization of postoperative abdominal drain output at the patient’s bedside.


2021 ◽  
Vol 714 (4) ◽  
pp. 042046
Author(s):  
Jiangping Nan ◽  
Yajuan Jia ◽  
Xuezhen Dai ◽  
Yinglu Liu ◽  
Xiaowen Ren ◽  
...  

2013 ◽  
Vol 718-720 ◽  
pp. 1740-1745
Author(s):  
Tulu Muluneh Mekonnen ◽  
De Ning Jiang ◽  
Yong Xin Feng

Vehicle collision sensor system and reporting accident to police is an electronic device installed in a vehicle to inform police man in case of accident to track the vehicles location. This system works using pressure sensor, GPS and GSM technology. These technology embedded together to sense the vehicle collision and indicate the position of the vehicle or locate the place of accident in order to solve the problem immediately (as soon as possible).For doing so AT89S52 microcontroller is interfaced serially to a GSM modem, GPS receiver, and pressure sensor. A GSM modem is used to send the position (Latitude and Longitude) of the vehicle, the plate of the vehicle and the SMS text from the accident place. The GPS modem will continuously give the data (longitude and latitude) and Load sensor senses the collision of the vehicle against obstacles and input to microcontroller. As load sensor senses the collision, the GSM start to send the plate of the vehicle, text message and the position of the vehicle in terms of latitude and longitude in real time.


Inventions ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 42
Author(s):  
Worasit Sangjan ◽  
Arron H. Carter ◽  
Michael O. Pumphrey ◽  
Vadim Jitkov ◽  
Sindhuja Sankaran

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.


2011 ◽  
Vol 50 (35) ◽  
pp. 6430 ◽  
Author(s):  
James Parkhurst ◽  
Gareth Price ◽  
Phil Sharrock ◽  
Christopher Moore

2002 ◽  
Vol 02 (03) ◽  
pp. 481-499
Author(s):  
JANE YOU ◽  
DAVID ZHANG

This paper presents a new approach to smart sensor system design for real-time remote sensing. A combination of techniques for image analysis and image compression is investigated. The proposed algorithms include: (1) a fractional discrimination function for image analysis, (2) a comparison of effective algorithms for image compression, (3) a pipeline architecture for parallel image classification and compression on-board satellites, and (4) a task control strategy for mapping image computing models to hardware processing elements. The efficiency and accuracy of the proposed techniques are demonstrated throughout system simulation.


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