scholarly journals Non invasive rail track detection system using microwave sensor

2009 ◽  
Vol 178 ◽  
pp. 012033 ◽  
Author(s):  
K Vijayakumar ◽  
S R Wylie ◽  
J D Cullen ◽  
C C Wright ◽  
A I Ai-Shamma'a
2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110169
Author(s):  
Ritu Gaur ◽  
Dipesh Kumar Verma ◽  
Ritin Mohindra ◽  
Kapil Goyal ◽  
Shipra Gupta ◽  
...  

Introduction The current gold standard for detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA involves subjecting nasopharyngeal or oropharyngeal swabs to reverse transcription quantitative PCR (RT-qPCR). However, both sample types need to be collected by trained professionals. Using self-collected buccal swabs as an alternative could simplify and accelerate diagnosis of coronavirus disease 2019 (COVID-19). Objective To assess self-collected buccal swab samples as an alternative method for SARS-CoV-2 detection in patients with COVID-19. Methods Buccal swab samples were self-collected by 73 patients with COVID-19. Total RNA was extracted using Qiagen kits. RNA encoding the SARS-CoV-2 Env protein and human RNase P as an internal control was amplified using the TRUPCR® SARS-CoV-2 RT-qPCR kit version 2.1 and a Bio-Rad CFX96 Real-Time Detection System. Result The sensitivity of RT-qPCR from buccal swabs was 58.9% (43/73; 95% confidence interval [CI] 46.77%–70.27%) and that of RT-qPCR from saliva was 62.90% (39/62; 95% CI 49.69%–74.84%) taking positive SARS-CoV-2 RT-qPCR from nasopharyngeal swabs as the gold standard. Conclusion Self-collected buccal swabs are promising alternatives to nasopharyngeal or oropharyngeal swabs for SARS CoV-2 detection.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter P. Ricci ◽  
Otto J. Gregory

AbstractThe presence of ammonia within the body has long been linked to complications stemming from the liver, kidneys, and stomach. These complications can be the result of serious conditions such as chronic kidney disease (CKD), peptic ulcers, and recently COVID-19. Limited liver and kidney function leads to increased blood urea nitrogen (BUN) within the body resulting in elevated levels of ammonia in the mouth, nose, and skin. Similarly, peptic ulcers, commonly from H. pylori, result in ammonia production from urea within the stomach. The presence of these biomarkers enables a potential screening protocol to be considered for frequent, non-invasive monitoring of these conditions. Unfortunately, detection of ammonia in these mediums is rather challenging due to relatively small concentrations and an abundance of interferents. Currently, there are no options available for non-invasive screening of these conditions continuously and in real-time. Here we demonstrate the selective detection of ammonia using a vapor phase thermodynamic sensing platform capable of being employed as part of a health screening protocol. The results show that our detection system has the remarkable ability to selectively detect trace levels of ammonia in the vapor phase using a single catalyst. Additionally, detection was demonstrated in the presence of interferents such as carbon dioxide (CO2) and acetone common in human breath. These results show that our thermodynamic sensors are well suited to selectively detect ammonia at levels that could potentially be useful for health screening applications.


2021 ◽  
pp. 1-1
Author(s):  
Chong Hyun Lee ◽  
Yoon-Sang Jeong ◽  
Hina Ashraf

2018 ◽  
Vol 10 (3) ◽  
pp. 10-29 ◽  
Author(s):  
George Shaker ◽  
Karly Smith ◽  
Ala Eldin Omer ◽  
Shuo Liu ◽  
Clement Csech ◽  
...  

This article discusses recent developments in the authors' experiments using Google's Soli alpha kit to develop a non-invasive blood glucose detection system. The Soli system (co-developed by Google and Infineon) is a 60 GHz mm-wave radar that promises a small, mobile, and wearable platform intended for gesture recognition. They have retrofitted the setup for the system and their experiments outline a proof-of-concept prototype to detect changes of the dielectric properties of solutions with different levels of glucose and distinguish between different concentrations. Preliminary results indicated that mm-waves are suitable for glucose detection among biological mediums at concentrations similar to blood glucose concentrations of diabetic patients. The authors discuss improving the repeatability and scalability of the system, other systems of glucose detection, and potential user constraints of implementation.


2019 ◽  
Vol 19 (10) ◽  
pp. 6187-6191 ◽  
Author(s):  
Seung Ho Lee ◽  
Min Seok Kim ◽  
Ok-Kyun Kim ◽  
Hyung-Hwan Baik ◽  
Ji-Hye Kim

2017 ◽  
Vol 50 (1) ◽  
pp. 438-445 ◽  
Author(s):  
Gergely Takács ◽  
Karol Ondrejkovič ◽  
Gabriel Hulkó

2013 ◽  
Vol 278-280 ◽  
pp. 1143-1147
Author(s):  
Liang Sun ◽  
Jun Zhang ◽  
Zhe Chen ◽  
Long Guo ◽  
Jia Ge ◽  
...  

Rail track surface detection system was constructed to detect the rail track surface. The mismatch between the train speed, rail track location and camera trigger speed was solved during the real-time acquisition process about the rail track using optical encoder and GPS module. In our system, optical encoder was used to reflect the train speed and output pulse to externally trigger the line scan high-speed camera exposure. At the same time, another pulse of optical encoder was transmitted to the computer to save the GPS location information. So the rail track images are saved with the track location information.


2013 ◽  
Vol 278-280 ◽  
pp. 856-860
Author(s):  
Long Guo ◽  
Jun Zhang ◽  
Zhe Chen ◽  
Liang Sun ◽  
Jia Ge ◽  
...  

Rail track surface defects detection is an important part of the monitoring of railroad safety. In this paper, rail track images obtained by detection system of rail track surface image are processed. Firstly, the Hough transform is applied to process the images of the track surface to locate and extract the image of the track surface, which overcomes the influence of incline and unfixed width of track surface images caused by vehicle vibration. Secondly, improved Sobel operator and area filter are used together to extract track surface defects from the original images. Finally, the defects images are classified based on circularity and length-width ratio of minimum enclosing rectangular of defects images.Results of experiments show that the algorithm can identify and classify the defects images of track surface. The minimum detection region in rail track surface is 0.0068 cm2.


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