2019 53rd Asilomar Conference on Signals, Systems, and Computers最新文献_第3页

Hardware-Efficient Seizure Detection 硬件高效的癫痫检测

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9049047

Bingzhao Zhu, Mahsa Shoaran

引用次数: 3

Optimal deception attack on networked vehicular cyber physical systems 网络化车辆网络物理系统的最优欺骗攻击

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048730

Moulik Choraria, Arpan Chattopadhyay, U. Mitra, E. Ström

{"title":"Optimal deception attack on networked vehicular cyber physical systems","authors":"Moulik Choraria, Arpan Chattopadhyay, U. Mitra, E. Ström","doi":"10.1109/IEEECONF44664.2019.9048730","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048730","url":null,"abstract":"Herein, design of false data injection attack on a distributed cyber-physical system is considered. A stochastic process with linear dynamics and Gaussian noise is measured by multiple agent nodes, each equipped with multiple sensors. The agent nodes form a multi-hop network among themselves. Each agent node computes an estimate of the process by using its sensor observations and messages obtained from neighbouring nodes, via Kalman-consensus filtering. An external attacker, capable of arbitrarily manipulating the sensor observations of some or all agent nodes, injects errors into those sensor observations. The goal of the attacker is to steer the estimates at the agent nodes as close as possible to a pre-specified value, while respecting a constraint on the attack detection probability. To this end, a constrained optimization problem is formulated to find the optimal parameter values of a certain class of linear attacks. The parameters of linear attack are learnt on-line via a combination of stochastic approximation and online stochastic gradient descent. Numerical results demonstrate the efficacy of the attack.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"1 1","pages":"1131-1135"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90155503","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

Secure Regularized Zero Forcing for Multiuser MIMOME Channels 多用户MIMOME信道的安全正则化零强制

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048726

S. Asaad, Ali Bereyhi, R. Müller, R. Schaefer

引用次数: 6

Smooth Fictitious Play in N × 2 Potential Games N × 2潜在游戏中的平滑虚拟玩法

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048995

Brian Swenson, H. Poor

引用次数: 3

Convergence of Iterative Hard Thresholding Variants with Application to Asynchronous Parallel Methods for Sparse Recovery 迭代硬阈值变量的收敛性及其在异步并行稀疏恢复方法中的应用

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048787

Jamie Haddock, D. Needell, Alireza Zaeemzadeh, N. Rahnavard

引用次数: 0

IEEECONF 2019 Author Index ieee econf 2019作者索引

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/ieeeconf44664.2019.9048725

引用次数: 0

Unsupervised Learning of Nonlinear Mixtures: Identifiability and Algorithm 非线性混合的无监督学习:可辨识性与算法

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048661

Bo Yang, Xiao Fu, N. Sidiropoulos, Kejun Huang

{"title":"Unsupervised Learning of Nonlinear Mixtures: Identifiability and Algorithm","authors":"Bo Yang, Xiao Fu, N. Sidiropoulos, Kejun Huang","doi":"10.1109/IEEECONF44664.2019.9048661","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048661","url":null,"abstract":"Linear mixture models (LMMs) have proven very useful in a plethora of applications, e.g., topic modeling, clustering, and speech / audio separation. As a critical aspect of the LMM, identifiability of the model parameters is well-studied, under frameworks such as independent component analysis and constrained matrix factorization. Nevertheless, when the linear mixtures are distorted by unknown nonlinear functions – which is well-motivated and more realistic in many cases – the associated identifiability issues are far less studied. This work focuses on parameter identification of a nonlinear mixture model that is motivated by a number of real-world applications, e.g., hyperspectral imaging and magnetic resonance imaging. A novel identification criterion is proposed and the associated identifiability issues are studied. A practical implementation based on a judiciously designed neural network is proposed to realize the criterion, and an effective learning algorithm is proposed. Numerical results on synthetic and real application data corroborate the effectiveness of the proposed method.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"23 1","pages":"1040-1044"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79650393","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: 1

Reinforcement Learning for Cognitive Radar Task Scheduling 认知雷达任务调度的强化学习

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048892

M. Gaafar, M. Shaghaghi, R. Adve, Z. Ding

引用次数: 6

Fast Color-guided Depth Denoising for RGB-D Images by Graph Filtering 基于图形滤波的RGB-D图像快速颜色引导深度去噪

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048703

Qiwei Huang, Ruikang Li, Zidong Jiang, Wei Feng, Sijie Lin, Hui Feng, Bo Hu

引用次数: 3

Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging 综合功率分析:经验评价及在认知神经影像学中的应用

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048971

Peiye Zhuang, Bliss Chapman, Ran Li, Oluwasanmi Koyejo

引用次数: 1