2020 54th Asilomar Conference on Signals, Systems, and Computers最新文献_第7页

Semi-supervised Convolutional Triplet Neural Networks for Assessing Paper Texture Similarity 纸张纹理相似度评估的半监督卷积三重神经网络

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443454

Leah Lackey, Arick Grootveld, A.G. Klein

引用次数: 0

Detecting novel genomic structural variants through negative binomial optimization 通过负二项优化检测新的基因组结构变异

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443308

Andrew Lazar, Mario Banuelos, Suzanne S. Sindi, Roummel F. Marcia

{"title":"Detecting novel genomic structural variants through negative binomial optimization","authors":"Andrew Lazar, Mario Banuelos, Suzanne S. Sindi, Roummel F. Marcia","doi":"10.1109/IEEECONF51394.2020.9443308","DOIUrl":"https://doi.org/10.1109/IEEECONF51394.2020.9443308","url":null,"abstract":"Structural variants (SVs) are short sequences of DNA, larger than one nucleotide, that can vary between members of the same species. Although SVs are relatively rare, compared to single nucleotide variants (SNVs) they are an important source of genetic variation and some SVs have been associated with diseases and susceptibility to certain types of cancer. SV detection is commonly performed by aligning sequenced fragments of an individual’s genome to a high-quality reference genome. Candidate SVs correspond to discordant mapped configurations of fragments; however, errors in the sequencing also lead to potential discordant mappings. Because of this error, many candidate SVs are in fact false positives. When sequencing coverage is high, SV detection is more accurate, but this comes at higher sequencing cost. Sequencing at low coverage does reduce cost, but increases error and complexity of SV detection. The goal of our work is to use mathematical optimization to improve SV detection in low-coverage DNA sequencing data. Previous studies of SV detection have modeled coverage with a Poisson distribution, but this assumes the mean and variance are the same. In an effort more closely model the experimental data we use the negative binomial distribution, which allows for the mean and variance to differ, and contains the Poisson distribution as a special case. Our approach also control false positive predictions by simultaneously considering simultaneous SV prediction in a parent and child. We assume that most SVs carried by a child are inherited from a parent but a small fraction may be novel to the child. We balance the rarity of novel versus inherited SVs by enforcing sparsity through an l1-penalty and compare this negative binomial reconstruction algorithm to the Poisson reconstruction algorithm by testing both on the same simulated data sets.","PeriodicalId":248190,"journal":{"name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115374555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Study on Conditional Features for a Flow-based Neural Vocoder 基于流的神经声码器条件特征研究

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443273

Hyungseob Lim, Suhyeon Oh, Kyungguen Byun, Hong-Goo Kang

引用次数: 0

Time-varying graph analysis comparing speech perception in healthy and aphasic brains 健康与失语大脑语言感知的时变图分析

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443478

Sudha Yellapantula, Colin Noe, S. Fischer-Baum, B. Aazhang

{"title":"Time-varying graph analysis comparing speech perception in healthy and aphasic brains","authors":"Sudha Yellapantula, Colin Noe, S. Fischer-Baum, B. Aazhang","doi":"10.1109/IEEECONF51394.2020.9443478","DOIUrl":"https://doi.org/10.1109/IEEECONF51394.2020.9443478","url":null,"abstract":"Aphasia affects millions of patients each year, causing communication difficulties and impeding their quality of life. While the nature of the language impairment after stroke is very patient specific, characterizing each person’s impairment is useful for treatment and research. Recently, language processing frameworks as a whole are moving towards whole-brain network based analyses. Reflecting this, network based approaches to normal and impaired EEG data are also needed. We measured some aspects of language processing impairments with electroencephalography (EEG). Using EEG recordings from subjects engaged in a speech perception task, we devised a framework based on graph signal processing and information theory, to reveal features of the brain dynamics that separate the 21 healthy subjects from an aphasic patient. This patient has focal difficulty in speech perception. We performed a robust principal component analysis to jointly infer a time-invariant low rank structure and time-varying sparse matrices to characterize brain dynamics in the healthy and the aphasic subjects. With this graph theoretic approach, we demonstrate the possibility to characterize impairment at a patient specific level, that sheds insight into cognitive processes that were disrupted at the network level. This allows for a complementary measure of patient impairment to traditional ERP metrics.","PeriodicalId":248190,"journal":{"name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125428145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autotuning Plug-and-Play Algorithms for MRI MRI的自动调谐即插即用算法

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443493

S. Shastri, R. Ahmad, P. Schniter

引用次数: 1

Performance Comparison of Time-Domain and Frequency-Domain RF Self-Interference Cancellation in Full-Duplex Wireless Systems 全双工无线系统中时域和频域射频自干扰消除性能比较

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9457545

Sasank Garikapati, Aditya Gaonkar, A. Nagulu, Tingjun Chen, G. Zussman, H. Krishnaswamy

{"title":"Performance Comparison of Time-Domain and Frequency-Domain RF Self-Interference Cancellation in Full-Duplex Wireless Systems","authors":"Sasank Garikapati, Aditya Gaonkar, A. Nagulu, Tingjun Chen, G. Zussman, H. Krishnaswamy","doi":"10.1109/IEEECONF51394.2020.9457545","DOIUrl":"https://doi.org/10.1109/IEEECONF51394.2020.9457545","url":null,"abstract":"Full-Duplex is a wireless communication paradigm which potentially provides a 2x improvement in the data-rate by a more efficient usage of available spectrum. A critical bottle neck in FD operation is the powerful Self-Interference (SI) on top of the desired received signal which lies in the same frequency range, potentially desensitizing the receive chain and thereby corrupting reception. To achieve FD operation, the SI has to be suppressed below the RX noise floor through different stages of cancellation as frequency selective filtering is not possible. Wide-band SI cancellation (SIC) requires a cancellation network which accurately emulates the frequency response of the SI channel over the desired bandwidth. This work examines two ways of achieving wide-band SIC, one from a frequency domain perspective by using a bank of RF filters and another from a time domain perspective by using finite impulse response (FIR) filters. Using data from measurements, we compare the two aforementioned approaches and discuss challenges involved in implementing these networks.","PeriodicalId":248190,"journal":{"name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128237732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Probabilistic Optimization for Source Separation 源分离的概率优化

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443564

G. Schuller, Oleg Golokolenko

引用次数: 2

On-Chip Optical and Analog Processing in 180nm CMOS for Holography 用于全息摄影的180nm CMOS片上光学和模拟处理

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443533

Mel J White, V. Boominathan, A. Veeraraghavan, A. Molnar

引用次数: 0

A Lightweight Model for Deep Frame Prediction in Video Coding 视频编码中深度帧预测的轻量级模型

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443427

Hyomin Choi, I. Bajić

引用次数: 0

Graph Laplacian-based Tumor Segmentation and Denoising in Brain Magnetic Resonance Imaging 脑磁共振成像中基于图拉普拉斯的肿瘤分割与去噪

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443557

A. Hanif, M. Doroslovački

引用次数: 1