Academic Positions

  • Present 2015

    Assistant Professor

    Concordia University, Concordia Institute of Information Systems Engineering (CIISE)

  • 2015 2013

    Postdoctoral Fellow

    University of Toronto, Department of Electrical and Computer Engineering

Education & Training

  • Ph.D. 2013

    Ph.D. in Electrical Engineering

    York University Department of Electrical Engineering and Computer Science

  • M.Sc.2007

    Master of Science in Biomedical Engineering

    Amirkabir University of Technology (Tehran Polytechnic)

  • B.A.2005

    Bachelor of Electrical Engineering

    University of Tehran

Bibliometrics

  •                          

Professional Service

They did not know it was impossible so they did it. Mark Twain.

Honors, Awards and Grants

  • Feb. 2014
    Eshrat Arjomandi Award for Outstanding Ph.D. Dissertation
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    Best Ph.D. Thesis Award from Department of Electrical Engineering and Computer Science (EECS), York University.
  • July 2012
    IEEE Information Fusion Student Paper Award
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    One of 10 recipients of Student Paper Awards at 15th IEEE International Conference on Information Fusion, 2012, for my paper entitled: "Decentralized Sensor Selection based on the Distributed Posterior Cramer-Rao Lower Bound"
  • Dec. 2011
    IEEE CAMSAP Student Researcher Travel Grant
    IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2011). The Grant was supported by the U.S. Office of Naval Research (7 recipients).

Research Summary

We live in an era of data deluge. The volume, variety, and velocity of data is exploding and the ability to process such large amounts of information promises to limit the spread of epidemics, learn the dynamics of emergent social-computational systems, and protect critical infrastructures. Of particular interest to this talk is the big data collected from Cyber-Physical Systems (CPS), which exhibit a wide range of diversities. The CPSs are engineering systems with embedded control, communication and sensing capabilities that can interact with humans through cyber space. Recently there has been a surge of interest in practical and opportunistic applications of CPSs including: (i) State prediction for analyzing contingencies and taking preventive actions against possible failures in smart power grids; (ii) Optimizing the reliability of CPSs using decentralized sensor resource management techniques, and; (iii) Surveillance applications for following a reference target in decentralized camera networks. State forecasting is the core part of all these problems, which is the focus of this talk.

Interests

  • Statistical Signal Processing
  • Systems with Non-Linear Dynamics
  • Cyber-Physical Systems
  • Distributed Agent/ Sensor Networks
  • Information Fusion
  • Large-Scale Dynamical Systems
  • Complex-Valued Systems
  • Predictive Analytics
  • Smart Grids

Prospective Students

Research Assistant Positions

  • I am seeking energetic, innovative, and hard-working graduate students interested in working with me on statistical signal/image processing, secure/event-based processing in Cyber-Physical Systems (CPS), information fusion, and communications.

  • If you are interested in joining my research group, please read my research publications and send me an email including your CV and a list of your research publications (if any). Your email should also indicate: (i) The research area of your interest and why you are interested; (ii) Your research plans (if any), e.g., prepare a research proposal, and; (iii) Your past research experiences.

  • Please note that I will respond to an email only if I am interested in your background. Otherwise, please assume that I am not interested.

  • A Successful candidate should have strong background in systems and mathematics, strong analytical skills, and familiarity with Matlab.

  • Advice for Prospective Research Students

Current Teaching

  • Winter 2019

    COEN-231: Introduction to Discrete Mathematics

    This course contains some mathematical background required in many other engineering advanced courses and many real world engineering applications. By the end of this course, students should learn a particular set of mathematical facts and how to apply them and more importantly should be able to think logically and mathematically. Five important themes are interwoven in the textbook and lectures: mathematical reasoning, combinatorial analysis, discrete structures, algorithmic thinking, and application and modeling.

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    INSE-6320: Risk Analysis for Information and Systems Engineering

    This course examines the fundamental concepts, techniques and tools for risk analysis and decision making strategies in the context of information and systems engineering. This includes qualitative and quantitative risk assessment, risks in systems engineering, environmental risks, security risks; methods of risk analysis, fault trees and event trees; quantification of probabilities, use of data, models, and expert judgements; risks and decisions, interlinking risk analysis with risk management; decision analysis; system analysis and quantification; uncertainty modeling and risk measurement; and project risk management.

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Previous Teaching

  • Winter 2018

    COEN-231: Introduction to Discrete Mathematics

    This course contains some mathematical background required in many other engineering advanced courses and many real world engineering applications. By the end of this course, students should learn a particular set of mathematical facts and how to apply them and more importantly should be able to think logically and mathematically. Five important themes are interwoven in the textbook and lectures: mathematical reasoning, combinatorial analysis, discrete structures, algorithmic thinking, and application and modeling.

    image image

    INSE-6310: Systems Engineering Maintenance Management

    This course teaches basic concepts, models, methods and tools in maintenance management. The related reliability concepts, deterministic replacement, preventive maintenance and condition based maintenance will be discussed. Case studies will be performed.

    image image
  • Winter 2017

    INSE-6310: Introduction to Discrete Mathematics

    This course teaches basic concepts, models, methods and tools in maintenance management. The related reliability concepts, deterministic replacement, preventive maintenance and condition based maintenance will be discussed. Case studies will be performed.

    image image
  • Fall 2016

    COEN-231: Systems Engineering Maintenance Management

    This course contains some mathematical background required in many other engineering advanced courses and many real world engineering applications. By the end of this course, students should learn a particular set of mathematical facts and how to apply them and more importantly should be able to think logically and mathematically. Five important themes are interwoven in the textbook and lectures: mathematical reasoning, combinatorial analysis, discrete structures, algorithmic thinking, and application and modeling.

    image image
  • Winter 2016

    INSE-6310: Introduction to Discrete Mathematics

    This course teaches basic concepts, models, methods and tools in maintenance management. The related reliability concepts, deterministic replacement, preventive maintenance and condition based maintenance will be discussed. Case studies will be performed.

    image
  • Fall 2012

    CSE3451: Signals and Systems

    York University, Toronto, Canada, course offered by the Departments of Computer Sciece and Engineering.

Teaching Experience as Tutorial Teaching Assistant

  • 2008 2013

    CSE4215: Mobile Communication

    York University, Toronto, Canada, course offered by the Departments of Computer Sciece and Engineering.

    CSE3451: Signals and Systems

    York University, Toronto, Canada, course offered by the Departments of Computer Sciece and Engineering.

Teaching Experience as Teaching Assistant

  • 2008 2013

    CSE1020: Introduciton to Computer Science I

    York University, Toronto, Canada, course offered by the Departments of Computer Sciece and Engineering.

    CSE1030: Introduciton to Computer Science II

    York University, Toronto, Canada, course offered by the Departments of Computer Sciece and Engineering.

    CSE1560: Introduction to Computing for Mathematics and Statistics

    York University, Toronto, Canada, course offered by the Departments of Computer Sciece and Engineering.

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Event-Based Estimation With Information-Based Triggering and Adaptive Update

Arash Mohammadi and Konstantinos N. Plataniotis
Journal Paper 23 IEEE Transactions on Signal Processing, vol. 65, no. 18, pp. 4924-4939, Sept. 15, 2017

Abstract

This paper is motivated by recent advancements of cyber-physical systems and significance of managing limited communication resources in their applications. We propose an open-loop estimation strategy with an information-based triggering mechanism coupled with an adaptive event-based fusion framework. In the open-loop topology considered in this paper, a sensor transfers its measurements to a remote estimator only in occurrence of specific events (asynchronously). Each event is identified using a local stochastic triggering mechanism without incorporation of a feedback from the remote estimator and/or implementation of a local filter at the sensor level. We propose a particular stochastic triggering criterion based on the projection of local observation into the state-space, which in turn is a measure of the achievable gain in the local information state vector. Then, we investigate an unsupervised fusion model at the estimation side where the estimator blindly listens to its communication channel without having a priori information of the triggering mechanism of the sensor. An update mechanism with a Bayesian collapsing strategy is proposed to adaptively form state estimates at the estimator side in an unsupervised fashion. The estimator is adaptive in the sense that it is able to distinguish between having received an actual measurement or noise. The simulation results show that the proposed information-based triggering mechanism significantly outperforms its counterparts specifically in low communication rates, and confirms the effectiveness of the proposed unsupervised fusion methodology.

Ternary Event-based State Estimation with Joint Point, Quantized, and Set-valued Measurements

Arash Mohammadi, Somayeh Davar, and Konstantinos N. Plataniotis
Journal Paper 22 IEEE Signal Processing Letter, vol. 25, no. 5, pp. 665-669, 2018.

Abstract

This letter proposes a novel ternary-event-based particle filtering (TEB-PF) framework by introducing the ternary-event-triggering mechanism coupled with a non-Gaussian fusion strategy that jointly incorporates point-valued, quantized, and set-valued measurements. In contrast to the existing binary-event-triggering solutions, the TEB-PF is a distributed state estimation architecture where the remote sensor communicates its measurements to the estimator, residing at the fusion centre, in a ternary-event-based fashion, i.e., holds on to its observation during idle epochs, transfers quantized ones during the transitional epochs, and only communicates raw observations during event epochs. Due to joint utilization of quantized and set-valued measurements in addition to the point-valued ones, the proposed TEB-PF simultaneously reduces the communication overhead, in comparison to its binary triggering counterparts, while also improving the estimation accuracy specially in low communication rates.

Non-Circular Attacks on Phasor Measurement Units for State Estimation in Smart Grid

Arash Mohammadi and Konstantinos N. Plataniotis
Journal Paper 21 IEEE Journal of Selected Topics in Signal Processing, Revision Submitted, 2018.

Abstract

With the evolution of phasor measurement units (PMUs) and the proposition to incorporate a large number of PMUs in future smart grids, it is critical to identify and prevent potential (zero-day) cyber attacks on phasor signals. The PMUs are the forefront of sensor technologies used in the smart grid and produce phasor voltage and current readings, which are complex-valued in nature. In this regard, the paper investigates potential attacks on complex-valued PMU signals and proposes the new paradigm of data-injection attacks, referred to as non-circular attacks. Existing state estimation algorithms and attack monitoring solutions assume that the PMU observations have statistical characteristics similar to that of real-valued signals. This assumption makes PMUs extremely defenseless against the proposed non-circular attacks. In this paper, we: (i) Introduce the non-circular attack model; (ii) Evaluate (both analytically and via experiments) the potential destructive nature of such attacks; (iii) Propose a Bhattacharyya distance (BD)- detector for monitoring the system against cyber attacks by transforming the detection problem to an equivalent problem of comparing innovation sequences in distribution via statistical distance measures, and; (iv) Propose a circularization approach, which enables the conventional detection algorithms to identify non-circular attacks.

CEASE: A Collaborative Event-triggered Average-consensus Sampled-data framework with Performance Guarantees for Multi-agent Systems

Amir Amini, Arash Mohammadi, and Amir Asif
Journal Paper 20 IEEE Transactions on Signal Processing, Submitted, 2018.

Abstract

The paper proposes a distributed framework for Collaborative, Event-triggered, Average consensus, Sampled data (CEASE) algorithms for multiagent systems with two classes of performance guarantees. Referred to as the E-CEASE algorithm, the first approach ensures an exponential rate of convergence and derives associated conditions and optimal design parameters using the Lyapunov-Krasovskii stability theorem. The second approach provides a structured trade-off between the number of transmissions and rate of consensus convergence based on a guaranteed cost and is referred to as G-CEASE. The two implementations of CEASE are event-driven in the sense that agents transmit within their respective neighbourhoods only on the triggering of an event. To reduce communication and processing, the triggering condition in CEASE is monitored at discrete-time steps. Both E-CEASE and G-CEASE support switching topologies in multi-agent systems. Monte-Carlo simulations on randomized networks quantify the effectiveness of the proposed approaches.

Optimized Double Band Spectra-Spatial Filters Coupled with Ternary ECOC for BCIs

Soroosh Shahtalebi, and Arash Mohammadi
Journal Paper 19 IEEE Transactions on Neural Systems & Rehabilitation Engineering, Submitted, 2018.

Abstract

Motivated by the promising emergence of Brain Computer Interfaces (BCIs) within assistive/rehabilitative systems for therapeutic applications, the paper proposes a novel Bayesian framework that simultaneously optimizes a number subject-specific filter banks and spatial filters. Referred to as the ECCSP framework, optimized double band spectra-spatial filters are derived based on Common Spatial Patterns (CSP) coupled with the error correcting output coding (ECOC) classifiers. The proposed ECCSP framework constructs optimized subject-specific spectral filters in an intuitive fashion resulting in creation of significantly discriminant features, which is a crucial requirement for any EEG-based BCI system. Through incorporation of the ECOC approach, the classification problem is then modeled as communication over a noisy channel where the misclassification error is corrected by error correction techniques borrowed from information theory. The paper also proposes a modified version of the ECOC adopted to EEG classification problems by deploying ternary class codewords to account for ambiguous EEG epochs (which is a common phenomenon due to the cocktail-party nature of brain signals). The proposed ECCSP framework and its variants are evaluated over two different datasets from the BCI Competition (i.e., BCIC-IV2a and BCIC-IV2b). The results indicate that the proposed approach significantly outperforms its state-of-the-art counterparts and introduces a robust framework for motor imagery studies.

WAKE: Wavelet Decomposition Coupled with Adaptive Kalman Filtering for Pathological Tremor Extraction

Soroosh Shahtalebi, Arash Mohammadi, Farokh Atashzar, Rajni Patel
Journal Paper 18 Biomedical Signal Processing and Control, Submitted, 2017.

Abstract

Pathological Hand Tremor (PHT) is among common symptoms of several neurological movement disorders, which can significantly degrade quality of life of affected individuals. Beside pharmaceutical and surgical therapies, mechatronic technologies have been utilized to control PHTs. Most of these technologies function based on estimation, extraction, and characterization of tremor movement signals. Real-time extraction of tremor signal is of paramount importance because of its application in assistive and rehabilitative devices. In this paper, we propose a novel on-line adaptive method which can adjust the hyper-parameters of the filter to the variable characteristics of the tremor. The proposed technique (i.e., WAKE) is composed of a new adaptive Kalman filter and a wavelet transform core to provide indirect prediction of the tremor, one sample ahead of time, to be used for its suppression. In this paper, the design, implementation and evaluation of WAKE are given. The performance is evaluated on two different datasets. One dataset is recorded from patients with PHTs and the other one is a synthetic dataset, developed in this work, that simulates hand tremor under ten different conditions. The results demonstrate a significant improvement in the estimation accuracy in comparison with two well regarded techniques in literature.

Guest Editorial Distributed Signal Processing for Security and Privacy in Networked Cyber-Physical Systems.

Arash Mohammadi, P. Cheng, Vincenzo Piuri, Konstantinos N. Plataniotis, P. Campisi:
Journal Paper 17 IEEE Transactions on Signal and Information Processing over Networks, vol. 4, no. 1, pp. 1-3, 2018.

Abstract

The papers in this special section address distributed signal processing applications that support security and privacy in networked cyber-physical systems. Networked cyber-physical systems (CPSs) are engineering systems with integrated computational and communication capabilities that interact with humans through cyber space. The CPSs have recently emerged in several practical applications of significant engineering importance including aerospace, industrial/manufacturing process control, multimedia networks, transportation systems, power grids, and medical systems. The CPSs typically consist of both wireless and wired sensor/agent networks with different capacity/reliability levels where the emphasis is on real-time operations, and performing distributed, secure, and optimal sensing/processing is the key concern. To satisfy these requirements of the CPSs, it is of paramount importance to design innovative “Signal Processing” tools to provide unprecedented performance and resource utilization efficiency.

Distributed-Graph-Based Statistical Approach for Intrusion Detection in Cyber-Physical Systems

Hamidreza Sadreazami, Arash Mohammadi, Amir Asif, and Konstantinos N. Plataniotis
Journal Paper 16 IEEE Transactions on Signal and Information Processing over Networks, vol. 4, no. 1, pp. 137-147, 2018.

Abstract

Cyber-physical systems have recently emerged in several practical engineering applications where security and privacy are of paramount importance. This motivated the paper and a recent surge of interest in development of innovative and novel anomaly and intrusion detection technologies. This paper proposes a novel distributed blind intrusion detection framework by modeling sensor measurements as the target graph-signal and utilizing the statistical properties of the graph-signal for intrusion detection. To fully take into account the underlying network structure, the graph similarity matrix is constructed using both the data measured by the sensors and sensors' proximity resulting in a data-adaptive and structure-aware monitoring solution. In the proposed supervised detection framework, the magnitude of the captured data is modeled by Gaussian Markov random field and the corresponding precision matrix is estimated by learning a graph Laplacian matrix from sensor measurements adaptively. The proposed intrusion detection methodology is designed based on a modified Bayesian likelihood ratio test and the closed-form expressions are derived for the test statistic. Finally, temporal analysis of the network behavior is established by computing the Bhattacharyya distance between the measurement distributions at the consecutive time instants. Experiments are conducted to evaluate the performance of the proposed method and to compare it with that of the state-of-the-art methods. The results show that the proposed intrusion detection framework provides a detection performance superior to those provided by the other existing schemes.

Iterative Graph-Based Filtering for Image Abstraction and Stylization

Hamidreza Sadreazami, Amir Asif, and Arash Mohammadi
Journal Paper 15 IEEE Transactions on Circuits and Systems, vol. 65-II, no. 2, pp. 251-255, 2018.

Abstract

In this brief, motivated by the recent advances in graph signal processing, we address the problem of image abstraction and stylization. A novel unified graph-based multi-layer framework is proposed to perform iterative filtering without requiring any weight updates. The proposed graph-based filtering approach is shown to be superior to other existing methods due to iteratively using the filtered Laplacian in order to enhance the smoothened image signal at each layer. In order to render real images into painterly style ones and create a simple stylized format from color images, the low-contrast regions of an image are first smoothened using the proposed iterative graph filters in either vertex or spectral domains. The abstracted image is then quantized and sharpened using the proposed iterative highpass graph filter. The effectiveness of the graph-based image stylization method is verified through several experiments. It is shown that the proposed method can yield significantly improved visual quality for stylized images as compared to other existing methods.

Attack Detection/Isolation via a Secure Multi-Sensor Fusion Framework for Cyber-Physical Systems

Arash Mohammadi Chun Yang, and Qing-wei Chen
Journal Paper 14 Complexity, 2018.

Abstract

Motivated by rapid growth of cyberphysical systems (CPSs) and the necessity to provide secure state estimates against potential data injection attacks in their application domains, the paper proposes a secure and innovative attack detection and isolation fusion framework. The proposed multisensor fusion framework provides secure state estimates by using ideas from interactive multiple models (IMM) combined with a novel fuzzy-based attack detection/isolation mechanism. The IMM filter is used to adjust the system’s uncertainty adaptively via model probabilities by using a hybrid state model consisting of two behaviour modes, one corresponding to the ideal scenario and one associated with the attack behaviour mode. The state chi-square test is then incorporated through the proposed fuzzy-based fusion framework to detect and isolate potential data injection attacks. In other words, the validation probability of each sensor is calculated based on the value of the chi-square test. Finally, by incorporation of the validation probability of each sensor, the weights of its associated subsystem are computed. To be concrete, an integrated navigation system is simulated with three types of attacks ranging from a constant bias attack to a non-Gaussian stochastic attack to evaluate the proposed attack detection and isolation fusion framework.

Improper Complex-Valued Bhattacharyya Distance

Arash Mohammadi and Konstantinos N. Plataniotis
Journal Paper 13 IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 5, pp. 1049-1064, 2016.

Abstract

Motivated by application of complex-valued signal processing techniques in statistical pattern recognition, classification, and Gaussian mixture (GM) modeling, this paper derives analytical expressions for computing the Bhattacharyya coefficient/distance (BC/BD) between two improper complex-valued Gaussian distributions. The BC/BD is one of the most widely used statistical measures for evaluating class separability in classification problems, feature extraction in pattern recognition, and for GM reduction (GMR) purposes. The BC provides an upper bound on the Bayes error, which is commonly known as the best criterion to evaluate feature sets. Although the computation of the BC/BD between real-valued signals is a well-known result, it has not yet been extended to the case of improper complex-valued Gaussian densities. This paper addresses this gap. We analyze the role of the pseudocovariance matrix, which characterizes the noncircularity of the signal, and show that it carries critical second-order statistical information for computing the BC/BD. We derive upper and lower bounds on the BD in terms of the eigenvalues of the covariance and pseudocovariance matrices of the underlying densities. The theoretical bounds are then used to introduce the concept of β -dominance in the context of statistical distance measures. The BC is pseudometric, since it fails to satisfy the triangle inequality. Using the Matusita distance (a full-metric variant of the BC), we propose an intuitively pleasing indirect distance measure for comparing two general GMs. Finally, we investigate the application of the proposed BC/BD measures for GMR purposes and develop two BC-based GMR algorithms.

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter

Chun Yang, Arash Mohammadi, Qingwei Chen,
Journal Paper 12 Sensors, vol. 16, no. 11, 2016.

Abstract

Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system’s error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the proposed fault tolerant fusion framework provides superior performance over its traditional counterparts.

Distributed Widely Linear Multiple-Model Adaptive Estimation

Arash Mohammadi and Konstantinos N. Plataniotis
Journal Paper 11 IEEE Transactions on Signal and Information Processing over Networks, vol. 1, no. 3, pp. 164-179, 2015.

Abstract

The paper considers the problem of estimating the state of a complex-valued stochastic hybrid system observed distributively using an agent/sensor network (AN/SN) with complex-valued (possibly noncircular) observations. In several distributed estimation problems, a suitable model to describe the underlying system is unknown a priori, i.e., distributed state estimation with structural uncertainty. Motivated by application of widely linear processing techniques in such problems, the paper proposes a class of distributed multiple-model adaptive estimation algorithms, referred to as the CD/MMAE. By incorporating the particular structure of the complex-valued observations on the second moment, first we develop two hierarchical CD/MMAE implementations and then use them as the building blocks and develop a diffusion-based hybrid estimator for decentralized estimation without incorporation of a fusion centre. The paper derives a new form of the adaptation law and a new form of information fusion, which takes advantage of the full second-order statistical properties of the underlying observations. Convergence properties of the proposed diffusion-based CD/MMAE are then investigated. We show that the adaptive weight of all local nodes converges to the true mode with probability one. Simulation results indicate that the proposed hybrid estimators provide improved performance and convergence properties over their traditional counterparts.

Structure-Induced Complex Kalman Filter for Decentralized Sequential Bayesian Estimation

Arash Mohammadi and Konstantinos N. Plataniotis
Journal Paper 10 IEEE Signal Processing Letters, vol. 22, no. 9, pp. 1419-1423, 2015.

Abstract

The letter considers a multi-sensor state estimation problem configured in a decentralized architecture where local complex statistics are communicated to the central processing unit for fusion instead of the raw observations. Naive adaptation of the augmented complex statistics to develop a decentralized state estimation algorithm results in increased local computations, and introduces extensive communication overhead, making it practically unattractive. The letter proposes a structure-induced complex Kalman filter framework with reduced communication overhead. In order to further reduce the local computations, the letter proposes a non-circularity criterion which allows each node to examine the non-circularity of its local observations. A local sensor node disregards its extra second-order statistical information when the non-circularity coefficient is small. In cases where the local observations are highly non-circular, an intuitively pleasing circularization approach is proposed to avoid computation and communication of the pseudo-covariance matrices. Simulation results indicate that the proposed structured-induced complex Kalman filter (SCKF) provides significant performance improvements over its traditional counterparts.

Improper Complex-Valued Multiple-Model Adaptive Estimation

Arash Mohammadi and Konstantinos N. Plataniotis
Journal Paper 9 IEEE Transactions on Signal Processing, vol. 63, no. 6, pp. 1528-1542, 2015.

Abstract

Motivated by the problem of estimating the discrete and continuous states of an improper complex-valued stochastic hybrid system, the paper proposes a class of widely linear (augmented) multiple model adaptive estimation algorithms, referred to as the C/MMAE. We show that for an improper complex-valued signal, pseudo-covariance of the innovation sequence is not zero and, therefore, carries useful statistical information regarding the unknown behaviour mode of the hybrid system. A new Bayesian law is, therefore, derived as a function of the pseudo-covariance of the innovation sequence and used to compute the probability that a hypothesized model is in effect at a certain time. We show that the C/MMAE, which uses the new Bayesian law and utilizes the complete second-order statistical characterization of the complex-valued innovation sequence, convergencies faster than its counterpart, which only uses the conventional covariance of the innovation sequence. In order to simplify the computational complexity, we develop two circularized versions of the C/MMAE using a preprocessing step, referred to as the circularizing filter (CF). The CF is incorporated to convert the improper observations/innovations into the proper ones in order to reduce the computational complexity of the hypothesis testing step. Finally, an interacting version of the C/MMAE, referred to as C/IMM, is developed for improper complex-valued systems with Markovian switching coefficients. Simulation results indicate that the proposed hybrid estimators provide improved performance and convergence properties over their traditional counterparts.

Complex-Valued Gaussian Sum Filter for Nonlinear Filtering of Non-Gaussian/Non-Circular Noise

Arash Mohammadi and Konstantinos N. Plataniotis
Journal Paper 8 IEEE Signal Processing Letters, vol. 22, no. 4, pp. 440-444, 2015.

Abstract

Distributed Consensus + Innovation Particle Filtering for Bearing/Range Tracking with Communication Constraints

Arash Mohammadi and Amir Asif
Journal Paper 7 IEEE Transactions on Signal Processing, vol. 63, no. 3, pp. 620-635, 2015.

Abstract

Consensus-based Distributed Dynamic Sensor Selection in Decentralised Sensor Networks Using the Posterior Cramer-Rao Lower Bound

Arash Mohammadi and Amir Asif
Journal Paper 6 Signal Processing, vol. 108, pp. 558-575, 2015.

Abstract

Full and Reduced-order Distributed Bayesian Estimation: Analytical Performance Bounds

Arash Mohammadi and Amir Asif
Journal Paper 5 IEEE Transaction on Aerospace and Electronic Systems, vol. 50, issue 4, pp. 2468-2488, 2014.

Abstract

A Distributed Particle Filtering Approach for Multiple Acoustic Source Tracking Using an Acoustic Vector Sensor Network

Xionghu Zhong, Arash Mohammadi, A. B. Premkumar, and Amir Asif
Journal Paper 4 Signal Processing, 108, pp. 589-603, 2015.

Abstract

Distributed Particle Filter Implementation with Intermittent/Irregular Consensus Convergence

Arash Mohammadi, and Amir Asif
Journal Paper 3 IEEE Transactions on Signal Processing, vol. 61, no. 10, pp. 2572-2587, May 15, 2013.

Abstract

Decentralized Conditional Posterior Cramer-Rao Lower Bound for Nonlinear Distributed Estimation

Arash Mohammadi, and Amir Asif
Journal Paper 2 IEEE Signal Processing Letters, vol. 20, no. 2, pp. 165-168, Feb. 2013.

Abstract

Reconstruction of Missing Features by Means of Multivariate Laplace Distribution (MLD) for Noise Robust Speech Recognition

Arash Mohammadi, and Farshad Almasganj
Journal Paper 1 Expert Systems with Applications, vol. 38, no. 4, pp. 3918-3930, 2011.

Abstract

Secure State Estimation in Industrial Control Systems

Arash Mohammadi and Konstantinos N. Plataniotis
Book Chapter 2 CRC Press | March 18, 2016 | ISBN: 9781498734738
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Cyber Security for Industrial Control Systems: from the Viewpoint of Close-Loop

Motivated by recent evolution of cutting-edge sensor technologies with complex-valued measurements, this chapter analyzes attack models and diagnostic solutions for monitoring industrial control systems against complex-valued cyber attacks. By capitalizing on the knowledge that the existing detection and closed loop estimation algorithms ignore the full second-order statistical properties of the received measurements, we show that an adversary can attack the system by maximizing the correlations between the real and imaginary parts of the reported measurements. Consequently, the adversary can pass the conventional attack detection methodologies and change the underlying system beyond repair.

In ths chapter, the first section surveys recent developments in secure closed-loop state estimation methodologies, and then reviews the fundamentals of complex-valued signals and their applications. Second section highlights the drawbacks of~the state-of-the-art estimation methodologies and illustrates their vulnerability to cyber attacks. In the third section, first we review the existing attack models and then introduce the non-circular attack model. The forth section first surveys the state-of-the-art attack detection diagnostics and shows how to transform cyber-attack detection problem into the problem of comparing statistical distance measures between probability distributions. Fifth section provides illustrative examples followed by future research directions and conclusions.

Consensus-based Particle Filter Implementations for Distributed Non-linear Systems

Arash Mohammadi, Amir Asif
Book Chapter 1 Nova Science Publishers | November, 2012 | ISBN: 978-1-61942-898-0
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Nonlinear Estimation and Applications to Industrial Systems Control

The chapter proposes three consensus-based, distributed implementations of the particle filter for non-linear state estimation problems with non-Gaussian excitation. Our approaches range from a simple but still intuitive approach, referred to as the global likelihood constrained implementation of the particle filter (GLC/DPF), included to illustrate the underlying concepts to near-optimal distributed approaches. The unscented, consensusbased, distributed implementation of the particle filter (UCD/DPF) is the second approach that couples the unscented Kalman filter (UKF) with the particle filter such that the UKF estimates the Gaussian approximation of the proposal distribution, which is then used as the proposal distribution in the particle filter. The UCD/DPF requires each node to wait until consensus is reached before running the next iteration of the particle filter and is suitable for networks where communication is relatively inexpensive as compared to sensing. The third approach is the global channel filter based distributed particle filter (GCF/DPF), which does not require the consensus algorithms to converge between two consecutive observations. These approaches are successfully tested by running simulations of bearings-only tracking (BOT) applications of moving targets arising in radar surveillance, underwater submarine tracking, and robotics.

Brain Tumor Classification via Capsule Nets

Parnian Afshar, Arash Mohammadi, and K.N. Plataniotis
Conference Papers 56 Submitted to IEEE International Conference on Image Processing (ICIP), 2018.

Abstract

Explainability Properties of Capusle Nets

Atefeh Shahroudnejad, Arash Mohammadi, and K.N. Plataniotis
Conference Papers 55 Submitted to IEEE International Conference on Image Processing (ICIP), 2018.

Abstract

CARISI: Convolutional Autoencoder-based Inter-Slice Interpolation of Brain Tumor Images

Parnian Afshar, Atefeh Shahroudnejad, Arash Mohammadi, and K.N. Plataniotis
Conference Papers 54 Submitted to IEEE International Conference on Image Processing (ICIP), 2018.

Abstract

Analyzing the Effect of Bluetooth Low Energy (BLE) with Randomized MAC Addresses in IoT Applications

Golnar Kalantar, Arash Mohammadi, and Nima Sadrieh
Conference Papers 53 Submitted to IEEE International Conference on Internet of Things (iThings), 2018.

Abstract

Graph-based Model of EEG Signals via Functional Clustering and Total Variation Measure for Brain Computer Interfacing

Golnar Kalantar, and Arash Mohammadi
Conference Papers 52 ACCEPTED in IEEE International Engineering in Medicine and Biology Conference (EMBC), 2018.

Abstract

Interactive Multiple Model Particle Filters for Generalized Degradation Path Modeling

Ali Al-Dulaimi, Arash Mohammadi, and Amir Asif
Conference Papers 51 ACCEPTED in Institute of Industrial & Systems Engineers (IISE) Annual Conference \& Expo, 2018.

Abstract

Autonomous and self-aware systems, Autonomous Vehicles, Event-triggered estimation, Particle filtering

Somayeh Davar, Arash Mohammadi, and K.N. Plataniotis
Conference Papers 50 ACCEPTED in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

Abstract

Resilient Event-Triggered Consensus with Exponential Convergence in Multi-agent Systems

Amir Amini, Amir Asif, and Arash Mohammadi
Conference Papers 49 ACCEPTED in IEEE American Control Conference (ACC), 2018.

Abstract

A Bayesian Framework to Optimize Double Band Spectra Spatial Filters for Motor Imagery Classification

Soroosh Shahtalebi and Arash Mohammadi
Conference Papers 48 ACCEPTED in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

Abstract

Multiple-Model and Reduced-Order Kalman Filtering for Pathological Hand Tremor Extraction

Vahid Khorasani, Arash Mohammadi, S.F. Atashzar, R. Patel
Conference Papers 47 ACCEPTED in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

Abstract

WAKE-BPAT: Wavelet-based Adaptive Kalman Filtering for Blood Pressure Estimation via Fusion of Pulse Arrival Times

Golnar Kalantar, S.K. Mukhopadhyay, F. Marefat, P. Mohseni, and Arash Mohammadi
Conference Papers 46 ACCEPTED in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

Abstract

An Event-triggered Average Consensus Algorithm with Performance Guarantees for Distributed Sensor Networks

Amir Amini, Amir Asif, and Arash Mohammadi
Conference Papers 45 ACCEPTED in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

Abstract

A Robust Event-Triggered Consensus Strategy for Linear Multi-agent Systems with Uncertain Network Topology

Amir Amini, Amir Asif, and Arash Mohammadi
Conference Papers 44 ACCEPTED in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

Abstract

Real-Time and Event-Triggered Object Detection, Recognition, and Tracking

D. Blizzard, Somayeh Davar, and Arash Mohammadi
Conference Papers 43 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 2017.

Abstract

Progressive Fusion of Multi-rate Motor Imagery Classification for Brain Computer Interfaces

T. Maloney, Golnar Kalantar, and Arash Mohammadi
Conference Papers 42 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 2017.

Abstract

A Late Adaptive Graph-Based Edge-Aware Filtering with Iterative Weight Updating Process

H. Sadreazami, Amir Asif, and Arash Mohammadi
Conference Papers 41 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 2017.

Abstract

Interactive Gaussian-Sum Filtering for Estimating Systematic Risk in Financial Econometrics

Arash Mohammadi, X.P. Zhang, and K.N. Plataniotis
Conference Papers 40 IEEE Global Signal Processing Conference (GlobalSip), 2017.

Abstract

Adaptive Dimentionality Reduction Method using Graph-based Spectral Decomposition for Motor Imagery-based Brain-Computer Interfaces

Golnar Kalantar, H. Sadreazami, Arash Mohammadi, and Amir. Asif
Conference Papers 39 IEEE Global Signal Processing Conference (GlobalSip), 2017.

Abstract

A Guaranteed Cons LMI-based Approach for Event-Triggered Average Consensus in Multi-Agent Networks

Amir Amini, Amir Asif, and Arash Mohammadi
Conference Papers 38 IEEE Global Signal Processing Conference (GlobalSip), 2017.

Abstract

Dynamic Estimation Strategy for E-BMFLC Filters in Analyzing Pathological Hand Tremors

Vahid Khorasani, Arash Mohammadi, S.F. Atashzar, R. Patel
Conference Papers 37 IEEE Global Signal Processing Conference (GlobalSip), 2017.

Abstract

A Multi-rate and Auto-Adjustable Wavelet Decomposition Framework for Pathological Hand Tremor Extraction

Soroosh Shahtalebi, Arash Mohammadi, S.F. Atashzar, and R. Patel
Conference Papers 36 IEEE Global Signal Processing Conference (GlobalSip), 2017.

Abstract

Ternary ECOC Classifiers Coupled with Optimized Saptio-Spectral Patterns for Multiclass Motor Imagery Classification

Soroosh Shahtalebi and Arash Mohammadi
Conference Papers 35 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017.

Abstract

Error Correction Output Codding Coupled with the CSP for Motor Imagery BCI

Soroosh Shahtalebi and Arash Mohammadi
Conference Papers 34 European Signal Processing Conference (EUSIPCO), 2017.

Abstract

Event-based Particle Filtering with Point and Set-valued Measurements

Somayeh Davar and Arash Mohammadi
Conference Papers 33 European Signal Processing Conference (EUSIPCO), 2017.

Abstract

Multi-Sensor and Information-Based Event Triggered Distributed Estimation

Somayeh Davar and Arash Mohammadi
Conference Papers 32 International Conference on Distributed Computing in Sensor Systems (DCOSS), 2017.

Abstract

Event-based Consensus for a class of heterogeneous multi-agent systems: An LMI approach

Amir Amini, Arash Mohammadi, and A. Asif
Conference Papers 31 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017.

Abstract

Generalized Degradation Model for Health Management of Mission Critical Systems

Ali Al-Dulaimi, Arash Mohammadi, and A. Asif
Conference Papers 30 Institute of Industrial & Systems Engineers (IISE) Annual Conference & Expo, 2017.

Abstract

Data-Adaptive Color Image Denoising and Enhancement Using Graph-Based Filtering

H. Sadreazami, A. Asif, and Arash Mohammadi
Conference Papers 29 IEEE International Symposium on Circuits & Systems (ISCAS), 2017.

Abstract

Data-driven Image Stylization Using Graph-based Filtering

H. Sadreazami, A. Asif, and Arash Mohammadi
Conference Papers 28 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2017.

Abstract

Diffusive Particle Filtering for Distributed Multisensor Estimation

Arash Mohammadi, and Amir Asif
Conference Papers 27 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016.

Abstract

Widely-Linear Gaussian Sum Filter

Arash Mohammadi, and K.N. Plataniotis
Conference Papers 26 IEEE Sensor Array & Multichannel Signal Processing Workshop (SAM), Special Session on Non-Circular Signals & Widely Linear Processing, 2016.

Abstract

Secure Estimation Against Complex-valued Attacks

Arash Mohammadi, A. Margoosian, and K.N. Plataniotis
Conference Papers 25 IEEE Statistical Signal Processing (SSP), 2016.

Abstract

Distributed Particle Filter Implementations with Minimal Consensus Runs

Arash Mohammadi, and Amir Asif
Conference Papers 24 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 4-9, Florence, Italy, 2014.

Abstract

Reduced Order Distributed Particle Filter for Electric Power Grids

Amir Asif, Arash Mohammadi and S. Saxena
Conference Papers 23 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 4-9, Florence, Italy, 2014.

Abstract

A Distributed Particle Filter for Acoustic Source Tracking Using an Acoustic Vector Sensor Network

Xionghu Zhong, Arash Mohammadi, A. B. Premkumar, and Amir Asif
Conference Papers 22 IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014.

Abstract

Full Order Nonlinear Distributed Estimation in Intermittently Connected Networks

Arash Mohammadi and Amir Asif
Conference Papers 21 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 6327-6331, May 26-31, Vancouver, Canada, 2013.

Abstract

Decentralized Computation of the Conditional Posterior Cramer-Rao Lower Bound: Application to Adaptive Sensor Selection

Arash Mohammadi, and Amir Asif
Conference Papers 20 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 5278-5282, May 26-31, Vancouver, Canada, 2013.

Abstract

Acoustic Source Tracking in a Reverberant Environment Using a Pairwise Synchronous Microphone Network

Xionghu Zhong, Arash Mohammadi, W. Wan, A. B. Premkumar, and Amir Asif
Conference Papers 19IEEE International Conference on Information Fusion (FUSION), Istanbul, Turkey, 2013.

Abstract

Decentralized Bayesian Estimation with Quantized Observations: Theoretical Performance Bounds

Arash Mohammadi, Amir Asif, Xionghu Zhong, and A. B. Premkumar
Conference Papers 18 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Cambridge, MA, pp. 149-156, 2013.

Abstract

Decentralized Sensor Selection based on the Distributed Posterior Cramer-Rao Lower Bound

Arash Mohammadi and Amir Asif
Conference Papers 17 IEEE International Conference on Information Fusion (FUSION), Singapore, pp. 1668-1675, 2012.

Abstract

Theoretical Performance Bounds for Reduced-order Linear and Nonlinear Distributed Estimation

Arash Mohammadi and Amir Asif
Conference Papers 16 IEEE Global Telecommunications Conference (GLOBECOM), Anaheim, CA, pp. 3929–3935, 2012.

Abstract

A Constraint Sufficient Statistics based Distributed Particle Filter for Bearing Only Tracking

Arash Mohammadi and Amir Asif
Conference Papers 15 IEEE International Communications Conference (ICC), Ottawa, Canada, pp. 3670-3675, June 12-15, 2012.

Abstract

Distributed State Estimation for Large-scale Nonlinear Systems: A Reduced Order Particle Filter Implementation

Arash Mohammadi and Amir Asif
Conference Papers 14 IEEE Statistical Signal Processing (SSP), Arbor, MI, pp. 249-252, 2012.

Abstract

Full Order Distributed Particle Filters for Intermittent Connections: Feedback From Fusion Filters to Local Filters Improves Performanc

Arash Mohammadi and Amir Asif
Conference Papers 13 IEEE Statistical Signal Processing (SSP), Arbor, MI, pp. 524-527, 2012.

Abstract

Distributed Posterior Cramer-Rao Lower Bound for Nonlinear Sequential Bayesian Estimation

Arash Mohammadi and Amir Asif
Conference Papers 12IEEE Sensor Array and Multichannel Signal Processing (SAM), pp. 509-512, 2012.

Abstract

A Consensus/Fusion based Distributed Implementation of the Particle Filter

Arash Mohammadi and Amir Asif
Conference Papers 11 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, San Juan, Puerto Rico, pp. 285-288, Dec. 2011.

Abstract

Consensus-Based Distributed Unscented Particle Filter

Arash Mohammadi and Amir Asif
Conference Papers 10 IEEE Statistical Signal Processing (SSP), Nice, France, pp. 237-240, 2011.

Abstract

Distributed Particle Filtering for Large Scale Dynamical Systems

Arash Mohammadi and Amir Asif
Conference Papers 9 IEEE International Multi-topic Conference (INMIC), vol. 1, pp. 1-5, Dec. 2009.

Abstract

Missing Feature Reconstruction with Multivariate Laplace Distribution (MLD) for Noise Robust Phoneme Recognition

Arash Mohammadi, Farshad Almasganj, A. Taherkhani, and F. Naderkhani
Conference Papers 8 IEEE International Symposium on Communications, Control, and Signal Processing (ISCCSP), pp pp 836-840, March 12-14, 2008.

Abstract

Incomplete Spectrogram Reconstruction with Kalman Filter for Noise Robust Speech Recognition

Arash Mohammadi Farshad Almasganj, S.N. Sadrieh, and A. Zandi
Conference Papers 7 IEEE International Symposium on Communications, Control, and Signal Processing (ISCCSP), pp 814-818, March 12-14, 2008.

Abstract

Using Phoneme Segmentation in Conjunction with Missing Feature Approaches for Noise Robust Speech Recognition

Arash Mohammadi, Farshad Almasganj, A. Taherkhani, and F. Naderkhani
Conference Papers 6 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp 297-301, December 15-18, 2007.

Abstract

Investigation of Probability Distribution of Speech Signals in Mel Filter Bank Domain

Arash Mohammadi and Farshad Almasganj
Conference Papers 5 Proceeding of (IKT), 2007.

Abstract

Design of Chaotic Neural Network for Robust Phoneme Recognition

A. Taherkhani, S. A. Seyyedsalehi, Arash Mohammadi, and H. Davande
Conference Papers IEEE World Congress on Computational Intelligence (IWCCI), pp 3500-3504, June 1-6, 2008.

Abstract

Design of a Chaotic Neural Network by Using Chaotic Nodes and NDRAM Network

A. Taherkhani, S. A. Seyyedsalehi, Arash Mohammadi and M.H. Moradi
Conference Papers 3 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp 1171-1175, December 15-18, 2007.

Abstract

A New Method for Summation of short time CAF's Based on RLCS Motion

S. N. Sadrieh1, R. Saadat and Arash Mohammadi
Conference Papers 2 IEEE ICEE, 2008.

Abstract

Design of Chaotic Neural Network for Robust Phoneme Recognition

A. Taherkhani, S. A. Seyyedsalehi and Arash Mohammadi
Conference Papers 1 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2008.

Abstract

Contact & Meet Me

I would be happy to talk to you if you need my assistance in your research or would like to collaborate on potential research projects. Please feel free to contact me using the contact information on the right. I would also be happy to meet you in person at my office, please drop me an e-mail to arrange a meeting time. My office information is as follows:

1515 Rue Ste-Catherine, Montreal, QC, Canada, H3G-2W1.

My office is located in EV009.187

  •    office: (514) 848-2424 ext. 2712
  •    arashmoh "at" encs.concordia.ca
  •    arash.mohammadi "at" concordia.ca
  •    ca.linkedin.com/in/arash