Experimental implementation of wavefront sensorless real-time adaptive optics aberration correction control loop with a neural network

2020 ◽  
Author(s):  
Minzhao Liu ◽  
David N. Lopez ◽  
Gabriel C. Spalding
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Adaptive Optics ◽  
Aberration Correction ◽  
Control Loop ◽  
Experimental Implementation

scholarly journals Study on Aberration Correction of Adaptive Optics Based on Convolutional Neural Network

Photonics ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 377
Author(s):  
Jin Li ◽  
Luwei Wang ◽  
Yong Guo ◽  
Yangrui Huang ◽  
Zhigang Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Adaptive Optics ◽  
Correction Method ◽  
Limiting Factor ◽  
Aberration Correction ◽  
Microscopy Imaging ◽  
Imaging Resolution ◽  
Short Time ◽  
The Relationship

The existence of aberrations has always been an important limiting factor in the imaging field. Especially in optical microscopy imaging, the accumulated aberration of the optical system and the biological samples distorts the wavefront on the focal plane, thereby reducing the imaging resolution. Here, we propose an adaptive optical aberration correction method based on convolutional neural network. By establishing the relationship between the Zernike polynomial and the distorted wavefront, with the help of the fast calculation advantage of an artificial intelligence neural network, the distorted wavefront information can be output in a short time for the reconstruction of the wavefront to achieve the purpose of improving imaging resolution. Experimental results show that this method can effectively compensate the aberrations introduced by the system, agarose and HeLa cells. After correcting, the point spread function restored the doughnut-shape, and the resolution of the HeLa cell image increased about 20%.

scholarly journals Cascade Control of Coffee Roasting Degree in a Spouted Bed Batch Process Based on a Real-Time Imaging Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Omar R. Gómez-Gómez ◽  
Guadalupe M. Guatemala-Morales ◽  
J. Paulo García-Sandoval ◽  
Enrique Arriola-Guevara
Keyword(s):  
Real Time ◽  
Control Algorithm ◽  
Batch Process ◽  
Cascade Control ◽  
Control Loop ◽  
Spouted Bed ◽  
Experimental Implementation ◽  
Reference Images ◽  
Processing Techniques ◽  
Coffee Roasting

On this work, the experimental implementation of a cascade control algorithm to regulate coffee roasting degree in a batch spouted bed process is presented. The control algorithm is composed of an inner control loop that regulates the temperature of the hot air inflow used to roast coffee grains inside a spouted bed, while an outer control loop, based on imaging processing techniques, tracks on real time the coffee roasting degree and decides if the inflow air temperature must be modified. To achieve this goal, a colour-matching algorithm is used to compare a colour spectrum obtained from images acquired on real time, from a peephole on the spouted bed, with the colour spectrums of several reference images with different degrees of roasting. Match scores are computed based on the similarity between the colour spectrums. With the match scores, a roasting index is finally calculated to assess the degree of roasting, allowing to automatically track the roasting progress to decide if the batch roasting process has achieved the desired roasting degree. The experimental results show that the control scheme is able to robustly achieve the desired roasting degree with excellent effectiveness.

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.

Neural network for real-time synthesis of FIR filters

1989 ◽  
Vol 25 (17) ◽  
pp. 1199 ◽  
Author(s):  
G. Martinelli ◽  
R. Perfetti
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Fir Filters
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