2018 IEEE Statistical Signal Processing Workshop (SSP)最新文献_第5页

Network Intrusion Detection Using Flow Statistics 基于流量统计的网络入侵检测

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450709

B. Atli, Y. Miché, Alexander Jung

引用次数: 6

Robust PCA and Robust Subspace Tracking: A Comparative Evaluation 鲁棒主成分分析与鲁棒子空间跟踪:比较评价

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450718

S. Javed, Praneeth Narayanamurthy, T. Bouwmans, Namrata Vaswani

引用次数: 31

Sliding Window Based Linear Signal Detection Using 1-Bit Quantization and Oversampling for Large-Scale Multiple-Antenna Systems 基于滑动窗口的大规模多天线系统1位量化和过采样线性信号检测

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450809

Z. Shao, L. Landau, R. D. Lamare

引用次数: 12

Subspace Averaging for Source Enumeration in Large Arrays 大型数组中源枚举的子空间平均

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450837

I. Santamaría, D. Ramírez, L. Scharf

引用次数: 4

All-Digital Massive Mimo With a Fronthaul Constraint 具有前传约束的全数字大规模Mimo

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450779

Sven Jacobsson, Yasaman Ettefagh, G. Durisi, Christoph Studer

引用次数: 7

The Potential of Smartlnb Networks for Rainfall Estimation Smartlnb网络在降雨估计方面的潜力

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450692

F. Giannetti, M. Moretti, R. Reggiannini, A. Petrolino, G. Bacci, E. Adirosi, L. Baldini, L. Facheris, S. Melani, A. Ortolani

引用次数: 5

Distributed Particle Metropolis-Hastings Schemes 分布粒子大都会-黑斯廷斯方案

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450763

Luca Martino, V. Elvira, Gustau Camps-Valls

引用次数: 10

Shape-Constrained and Unconstrained Density Estimation Using Geometric Exploration 基于几何探测的形状约束和无约束密度估计

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450768

Sutanoy Dasgupta, D. Pati, Ian H. Jermyn, Anuj Srivastava

{"title":"Shape-Constrained and Unconstrained Density Estimation Using Geometric Exploration","authors":"Sutanoy Dasgupta, D. Pati, Ian H. Jermyn, Anuj Srivastava","doi":"10.1109/SSP.2018.8450768","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450768","url":null,"abstract":"The problem of nonparametrically estimating probability density functions (pdfs) from observed data requires posing and solving optimization problems on the space of pdfs. We take a geometric approach and explore this space for optimization using actions of a time-warping group. One action, termed area preserving, is transitive and is applicable to the case of unconstrained density estimation. In this case, we take a two-step approach that involves obtaining any initial estimate of the pdf and then transforming it via this warping action to reach the final estimate by maximizing the log-likelihood function. Another action, termed mode-preserving, is useful in situations where the pdf is constrained in shape, i.e. the number of its modes is known. As earlier, we initialize the estimation with an arbitrary element of the correct shape class, and then search over all time warpings to reach the optimal pdf within that shape class. Optimization over warping functions is performed numerically using the geometry of the group of warping functions. These methods are illustrated using a number of simulated examples.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121184177","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

Network Topology Inference From Input-Output Diffusion Pairs 基于输入-输出扩散对的网络拓扑推断

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450838

Santiago Segarra, A. Marques, Mohak Goyal, Samuel Rey-Escudero

{"title":"Network Topology Inference From Input-Output Diffusion Pairs","authors":"Santiago Segarra, A. Marques, Mohak Goyal, Samuel Rey-Escudero","doi":"10.1109/SSP.2018.8450838","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450838","url":null,"abstract":"We consider a scenario where a steady network state (output graph signal) has been generated by an initial network state (input graph signal) locally diffused through the edges of the network according to an auto-regressive and/or moving average dynamics of order one. The input graph signal can represent, for example, an initial opinion profile and the output the consensus opinion formed after individuals exchange their views with their friends. For that scenario, we analyze how a set of input-output pairs (each corresponding to a different opinion cascade) can be used to infer the topology of the underlying graph. The problem is formulated as a least squares minimization augmented with topological constraints, which include sparsity on the graph edges. While the original network recovery problem is non-convex, suitable convex relaxations along with theoretical recovery guarantees are presented. We first look at the case where all input-output pairs have been generated with the same graph but the diffusion coefficients for each observation are different. We then discuss the case where the graphs are related but not exactly the same. Numerical tests showcase the effectiveness of the proposed algorithms in recovering different types of networks with synthetic signals.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128715597","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: 7

Adaptive Period Estimation For Sparse Point Processes 稀疏点过程的自适应周期估计

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450856

Hans-Peter Bernhard, A. Springer

引用次数: 1