scholarly journals Real-time detection of the nanoparticle induced phytotoxicity in rice root tip through the visible red emissions of Eu3+ ions

2018 ◽  
Vol 17 (4) ◽  
pp. 499-504 ◽  
Author(s):  
Peng Du ◽  
Yunfei Wu ◽  
Jae Su Yu
Keyword(s):  
Real Time ◽  
Rice Root ◽  
Root Tip ◽  
Rice Seedlings ◽  
Prepared Nanoparticles ◽  
Real Time Detection

The rice seedlings are grown at different conditions. Luminescent image of the prepared nanoparticles excited by 396 nm light is shown. The rice root tip is treated with 100 μM NaBiF4:Eu3+ nanoparticles at 4 days after germination.

scholarly journals Other Real-Time Movement

2021 ◽  
pp. 207-211
Author(s):  
Tomoko M. Nakanishi
Keyword(s):  
Growth Inhibition ◽  
Real Time ◽  
Chemical Change ◽  
Harmful Effect ◽  
Rice Root ◽  
Ion Concentration ◽  
Root Tip ◽  
Interesting Phenomenon ◽  
Root Movement ◽  
Time Movement

AbstractIn the case of root movement, a very interesting phenomenon was found using the Super-HARP camera, which enabled the visualization of root movement in the dark.Although the first data on plants show that the harmful effect is growth inhibition, the first effect of the toxicity was to stop the rotation movement of the roots before growth inhibition occurred.When there was a chemical change in the environment, although the circumnutation of the root tip ceased, the root was able to elongate, and it was interesting that after a while the root movement resumed. In the case of a rice root, one round of movement of the rice root tip showed a constant time of approximately 50 minutes. However, this movement ceased when Al ion was supplied. The time needed for resuming the movement of the root tip was dependent on the Al ion concentration. It is not known what triggers the resumption of the movement of the root tip.

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

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
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