A. Asif

A. Asif
York University

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150
Publications
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Introduction
Skills and Expertise

Publications

Publications (150)
Chapter
The presented research focuses on Hand Gesture Recognition (HGR) utilizing Surface-Electromyogram (sEMG) signals. This is due to its unique potential for decoding wearable data to interpret human intent for immersion in Mixed Reality (MR) environments. The existing solutions so far rely on complicated and heavy-weighted Deep Neural Networks (DNNs),...
Article
Full-text available
Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals have recently shown considerable potential for development of advanced myoelectric-controlled prosthesis. Although deep learning techniques can improve HGR accuracy compared to their classical counterparts, classifying hand movements based on sparse multich...
Article
This article proposes a resilient strategy for leaderless and leader–following consensus in general linear multiagent systems under simultaneous presence of false data injection and denial-of-service (DoS) attacks. To save energy, local control updates and communication between the neighboring agents are based on a distributed periodic event-trigge...
Article
Objective The paper focuses on development of robust and accurate processing solutions for continuous and cuff-less blood pressure (BP) monitoring. In this regard, a robust deep learning-based framework is proposed for computation of low latency and continuous upper and lower bounds on the systolic and diastolic BP. Methods Referred to as the BP-N...
Preprint
Full-text available
Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals has recently shown significant potential for development of advanced myoelectric-controlled prosthesis. Existing deep learning approaches, typically, include only one model as such can hardly maintain acceptable generalization performance in changing scenar...
Article
Full-text available
Recent advancements in Electroencephalographic (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI) in several practical applications, ranging from rehabilitation systems to smart consumer technologies. When it comes to Signal Processing (SP) for BCI, there has been a...
Preprint
Full-text available
Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability, and is also time-consuming, considering the fact that only experts are capable of providin...
Preprint
Full-text available
Recent advancements in Electroencephalography (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI). When it comes to Signal Processing (SP) for BCI, there has been a surge of interest on Steady-State motion-Visual Evoked Potentials (SSmVEP), where motion stimulation i...
Preprint
Full-text available
Objective: The paper focuses on development of robust and accurate processing solutions for continuous and cuff-less blood pressure (BP) monitoring. In this regard, a robust deep learning-based framework is proposed for computation of low latency, continuous, and calibration-free upper and lower bounds on the systolic and diastolic BP. Method: Refe...
Preprint
Full-text available
Advances in biosignal signal processing and machine learning, in particular Deep Neural Networks (DNNs), have paved the way for the development of innovative Human-Machine Interfaces for decoding the human intent and controlling artificial limbs. DNN models have shown promising results with respect to other algorithms for decoding muscle electrical...
Preprint
Full-text available
Brain iron deposition, in particular deep gray matter nuclei, increases with advancing age. Hereditary Hemochromatosis (HH) is the most common inherited disorder of systemic iron excess in Europeans and recent studies claimed high brain iron accumulation in patient with Hemochromatosis. In this study, we focus on Artificial Intelligence (AI)-based...
Preprint
Full-text available
There has been a surge of recent interest in Machine Learning (ML), particularly Deep Neural Network (DNN)-based models, to decode muscle activities from surface Electromyography (sEMG) signals for myoelectric control of neurorobotic systems. DNN-based models, however, require large training sets and, typically, have high structural complexity, i.e...
Poster
Full-text available
Autonomous Systems (AS) are advanced intelligent systems for implementing brain-inspired and cognitive behaviors by computational intelligence beyond traditional reflexive, imperative, and adaptive systems. The IEEE International Conference on Autonomous Systems (ICAS’21, https://2021.ieee-icas.org/ ) will be a premier international forum for prese...
Conference Paper
Full-text available
Autonomous systems are advanced intelligent systems and general AI technologies triggered by the transdisciplinary development in intelligence science, system science, brain science, cognitive science, robotics, computational intelligence, and intelligent mathematics. AS are driven by the increasing demands in the modern industries of cognitive com...
Article
Full-text available
This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces. DNNs have been recently used for detecting the intended hand gesture through the processing of surface electromyogram (sEMG) signals. Objective: Although DNNs have shown superior accuracy compared to convention...
Article
Full-text available
The paper proposes a co-design framework for event-triggered stabilization control of a class of networked control systems (NCS) under unknown DoS attacks. To reduce the number of control inputs, a sampled-data dynamic event-triggering (S-DET) scheme is developed. Both the state measurements and monitoring of the S-DET are conducted periodically. T...
Preprint
Full-text available
This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces. DNNs have been recently used for detecting the intended hand gesture through processing of surface electromyogram (sEMG) signals. The ultimate goal of these approaches is to realize high-performance controllers...
Conference Paper
In the recent years, multiple pedestrian tracking (MPT) has been one of the most important components in a wide range of applications in computer vision, such as video surveillance, traffic monitoring, and sports analysis, to name a few. In these applications, the scene is in continuous motion hence typical tracking systems that are using backgroun...
Conference Paper
Full-text available
The parallel hybrid models of different deep neural networks architectures are the most promising approaches for remaining useful life (RUL) estimation. In light of that, this paper introduces for the first time in the literature a new parallel hybrid deep neural network (DNN) solution for RUL estimation, named the Noisy Multipath Parallel Hybrid M...
Conference Paper
The paper proposes a multi-path parallel noisy hybrid framework for RUL estimation, named as the Noisy Parallel Hybrid Model for Remaining Useful Life Estimation (NPHM). The NPHM combines the most successful deep neural networks architectures for RUL estimation, based on three noisy parallel paths to utilize the different extracted features from ea...
Article
Full-text available
The paper proposes a novel approach for formation-containment control based on a dynamic event-triggering mechanism for multi-agent systems. The leader-leader and follower-follower communications are reduced by utilizing the distributed dynamic event-triggered framework. We consider two separate sets of design parameters: one set comprising control...
Article
Accurate and robust estimation of Remaining Useful life (RUL) is of paramount importance for development of advanced smart and predictive maintenance strategies. To this aim, the paper proposes a new hybrid framework, referred to as the NPBGRU, developed by integration of three fully noisy deep learning architectures. Noisy CNN (NCNN) and Noisy Bi-...
Conference Paper
Full-text available
Motivated by the inconceivable capability of human brain in simultaneously processing multi-modal signals and its real-time feedback to the outer world events, there has been a surge of interest in establishing a communication bridge between the human brain and a computer, which are referred to as Brain-computer Interfaces (BCI). To this aim, monit...
Conference Paper
Full-text available
This paper studies formation control in general linear multi-agent systems where communication between the neighbouring agents is based on the fulfillment of dynamic event-triggering (DET) conditions. A novel co-design optimization is proposed to simultaneously design all required control and DET parameters. We use the flexibility of the proposed c...
Preprint
Full-text available
The paper proposes a novel approach for formation-containment control based on a dynamic event-triggering mechanism for multi-agent systems. The leader-leader and follower-follower communications are reduced by utilizing the distributed dynamic event-triggered framework. We consider two separate sets of design parameters: one set comprising control...
Preprint
Full-text available
Motivated by the inconceivable capability of the human brain in simultaneously processing multi-modal signals and its real-time feedback to the outer world events, there has been a surge of interest in establishing a communication bridge between the human brain and a computer, which are referred to as Brain-computer Interfaces (BCI). To this aim, m...
Article
Motivated by the potentials of deep learning models in significantly improving myoelectric control of neuroprosthetic robotic limbs, this paper proposes two novel deep learning architectures, namely the [Formula: see text] ([Formula: see text]) and the [Formula: see text] ([Formula: see text]), for performing Hand Gesture Recognition (HGR) via mult...
Article
Referred to as the RQ-CEASE, this article proposes a resilient framework for quantized, event-triggered (ET), sampled-data, average consensus in multiagent systems subject to denial of service (DoS) attacks. The DoS attacks typically attempt to block the measurement and communication channels in the network. Two different ET approaches are consider...
Poster
Full-text available
Autonomous Systems (AS) are advanced intelligent systems for implementing brain-inspired and cognitive behaviors by computational intelligence beyond traditional reflexive, imperative, and adaptive systems. The IEEE International Conference on Autonomous Systems (ICAS’20) is a new initiative on theoretical, experimental, and applied AS'. ICAS’20 co...
Article
Smart manufacturing and industrial Internet of Things (IoT) have transformed the maintenance management concept from the conventional perspective of being reactive to being predictive. Recent advancements in this regard has resulted in development of effective Prognostic Health Management (PHM) frameworks, which coupled with deep learning architect...
Preprint
Capitalizing on the need for addressing the existing challenges associated with gesture recognition via sparse multichannel surface Electromyography (sEMG) signals, the paper proposes a novel deep learning model, referred to as the XceptionTime architecture. The proposed innovative XceptionTime is designed by integration of depthwise separable conv...
Article
Aging critical infrastructures and valuable machineries together with recent catastrophic incidents such as the collapse of Morandi bridge calls for an urgent quest to design advanced and innovative data-driven solutions and efficiently incorporate multi-sensor streaming data sources for condition-based maintenance. Remaining Useful Life (RUL) is a...
Article
The volume, variety, and velocity of medical imaging data are exploding, making it impractical for clinicians to properly utilize such available information resources in an efficient fashion. At the same time, the interpretation of such a large amount of medical imaging data by humans is significantly error prone, reducing the possibility of extrac...
Article
Full-text available
The paper proposes a distributed framework for Collaborative, Event-triggered, Average consensus, Sampled data (CEASE) algorithms for undirected networked multi-agent 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 conditio...
Conference Paper
Recent advancements in cyber-physical systems (CPS) necessitate the development of degradation models representing the ever-changing dynamics of the degradation process with accuracy. Structural uncertainties of the degradation path (DP) and it's often non-linear/non-Gaussian random nature make the development of an accurate model that adaptively f...
Article
In the single-input multiple-output radar, the system transmits scaled (coherent) versions of a single waveform. The multiple-input multiple-output (MIMO) radar uses multiple antennas to simultaneously transmit several non-coherent waveforms and exploits multiple antennas to receive the reflected signals (echoes). This diversity in term of waveform...
Conference Paper
In modern cyber-physical energy systems, the time of the failure is typically determined by utilizing a degradation model derived from measurements of critical parameters of relevance to the system. Developing an accurate model for the degradation process is a key challenge in the prognostic and health management of such systems. A unified degradat...
Article
This paper proposes a distributed consensus algorithm for linear event-based heterogeneous multi-agent systems (MAS). The proposed scheme is event-triggered in the sense that an agent selectively transmits its information within its local neighbourhood based on a directed network topology under the fulfillment of certain conditions. Using the Lyapu...
Article
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 multilayer 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 existi...
Article
This paper proposes a nonlinear particle filter (PF) method for dynamic state estimation in droop-controlled islanded microgrids (IMGs). The PFs are normally applied to systems that (1) have highly nonlinear system dynamics, and (2) do not require the additive process or observation noise to be Gaussian. This flexibility allows the PFs to handle no...
Article
The paper derives closed-form expressions of the deterministic Cramer-Rao bound (CRB) bounds for the direction of arrival (DOA) and Doppler frequency of a moving target in a multipath environment for both conventional multiple-input multiple-output (MIMO) and time reversal (TR) MIMO frameworks. Incorporating the adaptive TR waveform processing feat...
Conference Paper
Recent developments in microgrids place strict constraints on the underlying state estimation technology, including the need for a dynamic and distributed approach. Since the problem is reminiscent of classical information fusion [2], the paper explores the application of a fusion-based reduced order, distributed unscented particle filter (FR/DUPF)...
Article
The paper considers the problem of dynamic sensor scheduling for non-linear tracking problems in distributed sensor/agent networks (AN/SN), where channel limitations restrict how many sensors can simultaneously participate in the estimation mechanism. Commonly referred to as sensor selection, the basic objective is to select a subset of sensors fro...
Article
Different centralized approaches such as least-squares (LS) and particle filtering (PF) algorithms have been developed to localize an acoustic source by using a distributed acoustic vector sensor (AVS) array. However, such algorithms are either not applicable for multiple sources or rely heavily on sensor-processor communication. In this paper, a d...
Article
A constrained sufficient statistic (CSS) based distributed particle filter (CSS/DPF) implementation is proposed for nonlinear bearing-only and joint bearing/range tracking applications in sensor networks. The CSS/DPF runs localized particle filters at nodes constituting the sensor network and uses the resulting local sufficient statistics (LSS) to...
Article
Motivated by the resource management problem in nonlinear multi-sensor tracking networks, the paper derives online, distributed estimation algorithms for computing the posterior Cramer-Rao lower bound (PCRLB) for full-order and ´ reduced-order distributed Bayesian estimators without requiring a fusion centre and with nodal communications limited to...
Article
Full-text available
The paper develops a fusion-based, reduced order, distributed implementation of the unscented particle filter (FR/DUPF) for state estimation in complex nonlinear electric power grids (EPG). Based on partitioning the overall EPG system into n localized but dynamically coupled subsystems, the near-optimal FR/DUPF provides a computational saving of up...
Article
Motivated by the problem of distributed signal processing in sensor networks, the paper considers the general problem of state estimation in geographically dispersed systems with nonlinear dynamics operating in an uncertain environment with communication constraints. Distributed particle filter implementations used as nonlinear state estimators int...
Conference Paper
This paper presents a distributed particle filtering (PF) approach for wideband acoustic source tracking using an acoustic vector sensor (AVS) network. At each distributed AVS node of the AVS network, the unscented information PF (UIPF) provides local estimates of the source location. A distributed consensus algorithm, based on the first and second...
Article
Full-text available
The conditional posterior Cramer-Rao lower bound (PCRLB) is an effective sensor resource management criteria for large, geographically distributed sensor networks. Existing algorithms for distributed computation of the PCRLB (dPCRLB) are based on raw observations leading to significant communication overhead to the estimation mechanism. This letter...
Conference Paper
This paper considers acoustic source tracking in a room environment using a distributed microphone pair network. Existing time-delay of arrival (TDOA) based approaches usually require all received signals to be transmitted to central processor and synchronized to extract the TDOA measurements. The source positions are then obtained by using a subse...
Conference Paper
The paper derives closed form (nonmatrix) expression for the deterministic Cramér-Rao bound (CRB) for the direction-of-arrival associated with a target embedded in a noisy, multipath channel using the time reversal (TR) MIMO system. By incorporating the TR built-in adaptive waveform processing feature to reshape the MIMO probing signals, we prove t...
Conference Paper
The paper considers the problem of performing distributed particle filtering in intermittently connected networks with nonlinear state dynamics. In the context of large, geographically-distributed sensor networks, communication delays affect the convergence of the consensus algorithms used to derive the global state estimate from local estimates. W...
Conference Paper
Motivated by the problem of adaptive resource management in decentralized sensor networks, the paper derives an algorithm for the distributed computation of the conditional posterior Cramér-Rao lower bound (PCRLB) for nonlinear tracking applications as an alternative to the non-conditional (conventional) PCRLB. Using the proposed conditional bound,...
Conference Paper
The posterior Cramέr Rao lower bound (PCRLB) has recently been proposed as an effective selection criteria for sensor resource management in large, geographically distributed sensor networks. Existing algorithms (in particular the decentralized approaches with no central fusion centre) designed for computing the PCRLB are based on raw observations...
Article
Full-text available
Motivated by the decentralized adaptive resource management problems, the letter derives recursive expressions for online computation of the conditional decentralized posterior Cramér-Rao lower bound (PCRLB). Compared to the non-conditional PCRLB, the conditional PCRLB is a function of the past history of observations made and, therefore, a more ac...
Conference Paper
Full-text available
In sensor networks deployed over large-scale, multidimensional physical systems with limited spatial observability, reduced-order, distributed estimation is a practical alternative to centralized estimation. For such reduced-order systems, centralized computation of the posterior Cramér Rao lower bound (CRLB) is not possible as the global estimate...
Conference Paper
The paper derives analytical Cramér-Rao bound expressions for the Doppler-Angle estimators used in multiple-input multiple-output (MIMO) communication systems. Our motivation is two folds. First, these analytical expressions are used as a measure of the optimal performance that an Angle-Doppler MIMO estimator can potentially achieve. Second, we ill...
Chapter
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 illustrat...
Conference Paper
Motivated by state estimation problems in power distribution networks (PDN), the paper proposes a fusion based, reduced order, distributed implementation of the particle filter (FR/DPF) for large scale, nonlinear dynamical systems with localized sensor observations. Direct application of the centralized particle filter is computationally challengin...
Conference Paper
In [1, 2], we proposed a consensus/fusion based distributed implementation of the particle filter (CF/DPF) for non-linear systems with non-Gaussian excitation and intermittent communication connectivity. To recap, the CF/DPF implemented two filters at each node: (i) A localized particle filter based only on the host node's observations, and; (ii) A...
Conference Paper
This paper designs time reversal (TR) based angle-Doppler estimators for multiple-input-multiple-output (TRAD/MIMO) radars with the goal of using the spatial diversity of multipath propagation in urban scenarios to their advantage. Simulation results show that the angle-Doppler diversity is successfully utilized by the TRAD/MIMO radars to achieve s...
Conference Paper
The paper proposes a time reversal based uniform rectangular array (TR/URA) system for joint estimation of azimuth and elevation of a stationary target. For target localization in a rich scattering environment, conventional radars fail due to interference from multipath signal reflections. The TR/URA system uses multipath to its advantage by utiliz...