Oligomer based real-time detection of microorganisms producing nuclease enzymes

The Analyst ◽  
2019 ◽  
Vol 144 (4) ◽  
pp. 1379-1385 ◽  
Author(s):  
Erkan Mozioğlu ◽  
Sema Akyürek ◽  
Simay Gündüz ◽  
Muslum Akgoz ◽  
Ahmet C. Gören ◽  
...  
Keyword(s):  
Real Time ◽  
Culture Media ◽  
Real Time Detection ◽  
Fluorescently Labeled Oligonucleotides

In this study, we provide a method using fluorescently labeled oligonucleotides for the diagnosis of microorganisms producing nucleases in real time, while growing them in culture media.

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown
Keyword(s):  
Real Time ◽  
Steering Wheel ◽  
Real Time Detection

Walking Motion Real-time Detection Based on Walking Stick Equipped with MPU and Raspberry Pi

2019 ◽  
Author(s):  
Junyi Wang ◽  
Mohamad Saada ◽  
Haibin Cai ◽  
Qinggang Meng
Keyword(s):  
Real Time ◽  
Raspberry Pi ◽  
Walking Motion ◽  
Real Time Detection ◽  
Walking Stick

Summer 1999 Mount Weather/Aberdeen Proving Grounds Field Test Report: Real-Time Detection of Military Ground Targets Using a VIS/NIR Hyperspectral Sensor

2000 ◽  
Author(s):  
Frederick M. Olchowski ◽  
Christopher M. Stellman ◽  
Joseph V. Michalowicz
Keyword(s):  
Real Time ◽  
Field Test ◽  
Test Report ◽  
Real Time Detection ◽  
Hyperspectral Sensor

Real Time Detection of Sodium in Size-segregated Marine Aerosols

1997 ◽  
Author(s):  
Eric S. Saltzman ◽  
Anthony J. Hynes
Keyword(s):  
Real Time ◽  
Marine Aerosols ◽  
Real Time Detection

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

All‐in‐One DNA Extraction Tube for Facilitated Real‐Time Detection of Infectious Pathogens

2021 ◽  
pp. 2100430
Author(s):  
Younseong Song ◽  
Yong Tae Kim ◽  
Yunho Choi ◽  
Hogi Kim ◽  
Min Hee Yeom ◽  
...  
Keyword(s):  
Real Time ◽  
Dna Extraction ◽  
Extraction Tube ◽  
Real Time Detection

scholarly journals Framework for Real-Time Detection and Identification of possible patients of COVID-19 at public places

2021 ◽  
pp. 102605
Author(s):  
Bharati Peddinti ◽  
Amir Shaikh ◽  
Bhavya K.R. ◽  
Nithin Kumar K.C.
Keyword(s):  
Real Time ◽  
Public Places ◽  
Detection And Identification ◽  
Real Time Detection
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