Surface functionalization allowing repetitive use of optical sensors for real-time detection of antibody-bacteria interaction

2015 ◽  
Vol 9 (7) ◽  
pp. 730-737 ◽  
Author(s):  
Marika Kutscher ◽  
Manuel Rosenberger ◽  
Bernhard Schmauss ◽  
Lorenz Meinel ◽  
Udo Lorenz ◽  
...  
Keyword(s):  
Real Time ◽  
Surface Functionalization ◽  
Optical Sensors ◽  
Real Time Detection

scholarly journals Biosensors for the Analysis of Food- and Waterborne Pathogens and Their Toxins

2006 ◽  
Vol 89 (3) ◽  
pp. 873-883 ◽  
Author(s):  
Avraham Rasooly ◽  
Keith E Herold
Keyword(s):  
Real Time ◽  
Optical Sensors ◽  
Food Production ◽  
Control Point ◽  
Food Microbiology ◽  
Microbial Pathogens ◽  
Critical Control Point ◽  
Microbial Analysis ◽  
Real Time Detection ◽  
Microbial Toxins

Abstract Biosensors are devices which combine a biochemical recognition element with a physical transducer. There are various types of biosensors, including electrochemical, acoustical, and optical sensors. Biosensors are used for medical applications and for environmental testing. Although biosensors are not commonly used for food microbial analysis, they have great potential for the detection of microbial pathogens and their toxins in food. They enable fast or real-time detection, portability, and multipathogen detection for both field and laboratory analysis. Several applications have been developed for microbial analysis of food pathogens, including E. coli O157:H7, Staphylococcus aureus, Salmonella, and Listeria monocytogenes, as well as various microbial toxins such as staphylococcal enterotoxins and mycotoxins. Biosensors have several potential advantages over other methods of analysis, including sensitivity in the range of ng/mL for microbial toxins and <100 colony-forming units/mL for bacteria. Fast or real-time detection can provide almost immediate interactive information about the sample tested, enabling users to take corrective measures before consumption or further contamination can occur. Miniaturization of biosensors enables biosensor integration into various food production equipment and machinery. Potential uses of biosensors for food microbiology include online process microbial monitoring to provide real-time information in food production and analysis ofmicrobial pathogens and their toxins in finished food. Biosensors can also be integrated into Hazard Analysis and Critical Control Point programs, enabling critical microbial analysis of the entire food manufacturing process. In this review, the main biosensor approaches, technologies, instrumentation, and applications for food microbial analysis are described.

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

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.

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