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Distributed detection and mitigation of biasing attacks over multi-agent networks
, Article IEEE Transactions on Network Science and Engineering ; Volume 8, Issue 4 , 2021 , Pages 3465-3477 ; 23274697 (ISSN) ; Zarrabi, H ; Rabiee, H. R ; Khan, U. A ; Charalambous, T ; Sharif University of Technology
IEEE Computer Society
2021
Abstract
This paper proposes a distributed attack detection and mitigation technique based on distributed estimation over a multi-agent network, where the agents take partial system measurements susceptible to (possible) biasing attacks. In particular, we assume that the system is not locally observable via the measurements in the direct neighborhood of any agent. First, for performance analysis in the attack-free case, we show that the proposed distributed estimation is unbiased with bounded mean-square deviation in steady-state. Then, we propose a residual-based strategy to locally detect possible attacks at agents. In contrast to the deterministic thresholds in the literature assuming an upper...
Noise cancelation of epileptic interictal EEG data based on generalized eigenvalue decomposition
, Article 2012 35th International Conference on Telecommunications and Signal Processing, TSP 2012 - Proceedings ; 2012 , Pages 591-595 ; 9781467311182 (ISBN) ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology
2012
Abstract
Denoising is an important preprocessing stage in some Electroencephalography (EEG) applications such as epileptic source localization. In this paper, we propose a new algorithm for denoising the interictal EEG data. The proposed algorithm is based on Generalized Eigenvalue Decomposition of two covariance matrices of the observations. Since one of these matrices is related to the spike durations, we should estimate the occurrence time of the spike peaks and the exact spike durations. To this end, we propose a spike detection algorithm which is based on the available spike detection methods. The comparison of the results of the proposed algorithm with the results of two well-known ICA...
Energy loss estimation in distribution networks using stochastic simulation
, Article IEEE Power and Energy Society General Meeting, 26 July 2015 through 30 July 2015 ; Volume 2015-September , 2015 ; 19449925 (ISSN) ; 9781467380409 (ISBN) ; Ghanbari, N ; Mehrizi Sani, A ; Ehsan, M ; Sharif University of Technology
IEEE Computer Society
2015
Abstract
This paper presents an improved stochastic simulation method for calculating current dependent energy losses in distribution networks. The method is based on power load curves and integrates the stochastic nature of the load curves with power and voltage covariance matrices. The method reduces calculation effort using the factor analysis of covariance matrices and provides a few quantities needed to calculate energy losses. The method has no limitation for network configuration and gives accurate results several times faster than other existing methods. Therefore, it is appropriate for considering losses in optimization and decision making purposes in operating and planning of distribution...
Efficient 3-D positioning using time-delay and AOA measurements in MIMO radar systems
, Article IEEE Communications Letters ; 2017 ; 10897798 (ISSN) ; Behnia, F ; Zamani, H ; Sharif University of Technology
Abstract
This letter investigates the problem of threedimensional (3-D) target localization in multiple-input multipleoutput (MIMO) radars with distributed antennas, using hybrid timedelay (TD) and angle of arrival (AOA) measurements. We propose a closed-form positioning method based on weighted least squares (WLS) estimation. The proposed estimator is shown theoretically to achieve the Cramer-Rao lower bound (CRLB) under mild noise conditions. Numerical simulations also verify the theoretical developments. IEEE
Learning of tree-structured Gaussian graphical models on distributed data under communication constraints
, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) ; Motahari, S. A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our...
An efficient estimator for tdoa-based source localization with minimum number of sensors
, Article IEEE Communications Letters ; 2018 ; 10897798 (ISSN) ; Behnia, F ; Noroozi, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
In this letter, the problem of source localization using time difference of arrival (TDOA) is investigated. Then, a closedform two-stage solution is proposed based on estimation of the range nuisance parameter in the first stage and refinement of initial solution in the next stage. The proposed solution is shown analytically and verified by simulations to be an efficient estimate, which can attain the CRLB performance under mild Gaussian noise assumption. This method is able to locate the source with the minimum number of sensors required for N-dimensional localization. Numerical simulations demonstrate significant performance improvement of the proposed method compared with the...
Learning of tree-structured gaussian graphical models on distributed data under communication constraints
, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) ; Motahari, A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our...
Learning of tree-structured Gaussian graphical models on distributed data under communication constraints
, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) ; Motahari, S. A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our...