2017 51st Asilomar Conference on Signals, Systems, and Computers最新文献_第4页

Through-the-wall radar imaging using a distributed Quasi-Newton method 采用分布式准牛顿方法的穿壁雷达成像

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335142

Haroon Raja, W. Bajwa, F. Ahmad

引用次数: 2

Graph slepians to probe into large-scale network organization of resting-state functional connectivity 绘制睡眠图，探讨静息状态功能连通性的大规模网络组织

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335615

M. Preti, D. Ville

{"title":"Graph slepians to probe into large-scale network organization of resting-state functional connectivity","authors":"M. Preti, D. Ville","doi":"10.1109/ACSSC.2017.8335615","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335615","url":null,"abstract":"Functional magnetic resonance imaging (fMRI) is providing large amounts of data about brain function. Measuring correlations between spontaneous activity time courses from resting-state fMRI has revealed large-scale network organization. In the graph-based approach for functional connectivity analysis, a graph is built where nodes are brain regions and edge weights are pairwise correlations between the associated time courses. Here, we propose to apply recent approaches from graph signal processing to analyze fMRI data. First, the graph is constructed from structural connectivity, then, the corresponding graph spectrum is obtained such that the graph Slepian design can be deployed. In particular, graph Slepians are band-limited (i.e., using only graph Laplacian eigenvectors with lowest eigenvalues) with optimal energy concentration in predefined subgraphs. The subgraphs selected here are default-mode network (DMN) and fronto-parietal network (FPN), known as task-negative and — positive networks, respectively. While their activity appears anti-correlated during resting-state, a much more complicated interplay has been suggested recently using dynamic and time-resolved approaches. Preliminary results using data from the Human Connectome Project show that the proposed framework can direct the analysis to specific parts of the network and bring to light interactions between local and global aspects of network organization that were hidden before.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121500777","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: 3

Radar detection in K-distributed clutter plus noise using L-statistics 基于l统计量的k分布杂波加噪声雷达检测

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335532

J. Ritcey

引用次数: 1

Directional timing synchronization in wideband millimeter wave cellular systems with low-resolution ADCs 带低分辨率adc的宽带毫米波蜂窝系统的定向定时同步

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335131

Dalin Zhu, R. Heath

引用次数: 6

Pilot decontamination under imperfect power control 飞行员在不完善的动力控制下进行净化

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335513

Jitendra Tugnait

引用次数: 1

Parallel GF(2n) multipliers 并行GF(2n)乘法器

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335505

Trenton J. Grale, E. Swartzlander

引用次数: 2

Adaptive sequential refinement: A tractable approach for ambiguity function shaping in cognitive radar 自适应序列细化:认知雷达中模糊函数形成的一种易于处理的方法

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335406

Omar Aldayel, Tiantong Guo, V. Monga, M. Rangaswamy

{"title":"Adaptive sequential refinement: A tractable approach for ambiguity function shaping in cognitive radar","authors":"Omar Aldayel, Tiantong Guo, V. Monga, M. Rangaswamy","doi":"10.1109/ACSSC.2017.8335406","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335406","url":null,"abstract":"Ambiguity function shaping continues to be one of the most challenging open problems in cognitive radar. Analytically, a complex quartic function should be optimized as a function of the radar waveform code. Practical considerations further require that the waveform be constant modulus, which exacerbates the issue and leads to a hard non-convex problem. We develop a new approach called Adaptive Sequential Refinement (ASR) to suppress the clutter returns for a desired range-Doppler, i.e. ambiguity function response. ASR solves the aforementioned optimization problem in a unique iterative manner such that the formulation is updated depending on the iteration index. We establish formally that: 1.) the problem in each step of the iteration has a closed form solution, and 2.) monotonic decrease of the cost function until convergence is guaranteed. Experimental validation shows that ASR produces a radar waveform with higher Signal to Interference Ratio (SIR) and superior ambiguity function shaping than state of the art alternatives even as its computational burden is orders of magnitude lower.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134115772","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: 10

Whole brain reconstruction from multilayered sections of a mouse model of status epilepticus 癫痫持续状态小鼠模型多层切片全脑重建

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335554

Haoyi Liang, N. Dabrowska, J. Kapur, D. Weller

{"title":"Whole brain reconstruction from multilayered sections of a mouse model of status epilepticus","authors":"Haoyi Liang, N. Dabrowska, J. Kapur, D. Weller","doi":"10.1109/ACSSC.2017.8335554","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335554","url":null,"abstract":"This research concerns confocal fluorescence microscopy imaging of the whole brain of C57BL/6 mice with single-cell resolution. These brains are too large for specimen holders in available 3D microscopes, so this research develops a set of volume reconstruction methods to reproduce a whole brain from multilayered, thin sections of the brain imaged using a confocal microscope. As the sections are in solution during imaging, their shapes warp differently, and their structures no longer align. The proposed two-stage reconstruction procedure consists of single-section correction and section-to-section alignment, towards producing a whole brain volume. In the first stage, the proposed method carefully unwarps the distorted shapes of each section. The second stage aligns prominent features between the layers of neighboring sections. This paper also newly considers how these stages influence each other in the broader context of whole brain volume reconstruction. Experimental results portraying each stage with real image data suggest that the proposed approach can produce consistent 3D volumes and largely correct the observed distortions.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134137828","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

Latent variable models for hippocampal sequence analysis 海马序列分析的潜在变量模型

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335439

E. Ackermann, C. Kemere, Kourosh Maboudi, K. Diba

引用次数: 1

Multiple interface brain and head models for EEG: A surface charge approach 脑电图的多界面脑和头部模型:表面电荷方法

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335568

F. J. Solisi, A. Papandreou-Suppappola

引用次数: 3