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Full-Text Articles in Physical Sciences and Mathematics
An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell Iv, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan-Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong-Wen Deng, Chaoyang Zhang
An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell Iv, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan-Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong-Wen Deng, Chaoyang Zhang
Faculty Publications
Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a …
Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple
Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple
Faculty Publications
Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from …