Wiring Effects Mitigation for Through-Wall Human Motion Micro-Doppler Signatures Using a Generative Adversarial Network

2021 ◽  
pp. 1-1
Author(s):  
Shuoguang Wang ◽  
Qiang An ◽  
Shiyong Li ◽  
Guoqiang Zhao ◽  
Houjun Sun
Keyword(s):  
Human Motion ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Through-the-Wall Micro-Doppler De-Wiring Technique via Cycle-Consistent Adversarial Network

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 124
Author(s):  
Shuoguang Wang ◽  
Ke Miao ◽  
Shiyong Li ◽  
Qiang An
Keyword(s):  
Human Motion ◽  
Ultra Wideband ◽  
Activity Detection ◽  
Generative Adversarial Network ◽  
Time Frequency ◽  
Adversarial Network ◽  
Uwb Radar ◽  
Frequency Representation ◽  
Human Motion Monitoring ◽  
Human Activity Detection

The radar penetrating technique has aroused a keen interest in the research community, due to its superior abilities for through-the-wall indoor human motion monitoring. Micro-Doppler signatures in this situation play a significant role in recognition and classification for human activities. However, the live wire buried in the wall introduces additive clutters to the spectrograms. Such degraded spectrograms drastically affect the performance of behind-the-wall human activity detection. In this paper, an ultra-wideband (UWB) radar system is utilized in the through-the-wall scenario to get the feature enhanced micro-Doppler signature called range-max time-frequency representation (R-max TFR). Then, a recently introduced Cycle-Consistent Generative Adversarial Network (Cycle GAN) is employed to realize the end-to-end de-wiring task. Cycle GAN can learn the mapping between spectrograms with and without the live wire effect. To minimize the wiring clutters, a loss function called identity loss is introduced in this work. Finally, the proposed de-wiring approach is evaluated through classification. The results show that the proposed Cycle GAN architecture outperforms other state-of-art de-wiring methods.

Efficient human motion prediction using temporal convolutional generative adversarial network

2021 ◽  
Vol 545 ◽  
pp. 427-447
Author(s):  
Qiongjie Cui ◽  
Huaijiang Sun ◽  
Yue Kong ◽  
Xiaoqian Zhang ◽  
Yanmeng Li
Keyword(s):  
Human Motion ◽  
Motion Prediction ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Human Motion Prediction

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.

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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