scholarly journals Detection for Multisatellite Downlink Signal Based on Generative Adversarial Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qing-yang Guan ◽  
Wu Shuang
Keyword(s):  
Signal Detection ◽  
High Precision ◽  
Error Signal ◽  
Generative Adversarial Network ◽  
Error Matrix ◽  
Satellite Link ◽  
Optimal Weights ◽  
Adversarial Network ◽  
Downlink Signal ◽  
Local Generator

A method for satellite downlink signal detection based on a generative adversarial network is proposed. The generator adversarial network and adversarial network are established, respectively. The generator network realizes the local generator of satellite signals, and the adversarial network is used for high-precision signal detection. The error network is generated by the error signal to form the satellite link downlink. The network reconstructs the optimal weights by generating errors, forms an error matrix for different satellite downlink, and then forms an adaptive matrix weight adjustment. Through the reconstruction of the optimal detection matrix, detection for the downlink signals of multiple satellites is completed. The proposed generative adversarial network can realize the high-precision detection for the downlink signal.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Analysis and Simulation of Altimeter Performance for the Production of Ice Sheet Topographic Maps

1986 ◽  
Vol 8 ◽  
pp. 141-145 ◽  
Author(s):  
K.C. Partington ◽  
C.G. Rapley
Keyword(s):  
High Precision ◽  
Systems Analysis ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Data Interpretation ◽  
Topographic Maps ◽  
Error Signal ◽  
Tracking Systems ◽  
Tracking Mode ◽  
Non Linear

Satellite-borne, radar altimeters have already demonstrated an ability to produce high-precision, topographic maps of the ice sheets. Seasat operated in a tracking mode, designed for use over oceans, but successfully tracked much of the flatter regions of the ice sheet to ± 72° latitude. ERS-1 will extend coverage to ± 82° latitude and will be equipped with an ocean mode similar to that of Seasat and an ice mode designed to permit tracking of the steeper, peripheral regions. The ocean mode will be used over the flatter regions, because of its greater precision.Altimeter performance over the ice sheets has been investigated through a study of Seasat tracking behaviour and the use of an altimeter performance simulator, with a view to assessing the likely performance of ERS-1 and the design of improved tracking systems. Analysis of Seasat data shows that lock was frequently lost, as a result of possessing a non-linear height error signal over the width of the range window. Having lost lock, the tracker frequently failed to transfer rapidly and effectively to track mode. Use of the altimeter performance simulator confirms many of the findings from Seasat data and it is being used to facilitate data interpretation and mapping, through the modelling of waveform sequence.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

scholarly journals Realistic Sonar Image Simulation Using Generative Adversarial Network

2019 ◽  
Vol 52 (21) ◽  
pp. 291-296 ◽  
Author(s):  
Minsung Sung ◽  
Jason Kim ◽  
Juhwan Kim ◽  
Son-Cheol Yu
Keyword(s):  
Image Simulation ◽  
Generative Adversarial Network ◽  
Sonar Image ◽  
Adversarial Network
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