Pandemics often arise from the diffusion of infectious diseases over large regions, spanning multiple continents. Effective non-pharmaceutical interventions (NPIs) at the onset of an outbreak can significantly curb the spread. However, the complexity of human interactions can hinder the strict and timely imposition of such measures. Given the extensive damage caused by COVID-19, it is imperative to develop computational strategies that control and prevent the global spread of infectious diseases. This study proposes a model of COVID-19 spread networks, formulated based on daily confirmed cases per country, with mutual information as a measure of interdependence between countries. Utilizing graph theory, this approach identifies key nodes (countries) that play influential roles in the COVID-19 spread network. A control framework based on network theory is then introduced to mitigate and potentially eradicate further infection spread. Results demonstrate that the proposed framework holds substantial promise in preventing the transmission of COVID-19 and similar outbreaks when implemented promptly.

Enhancing lateral stability and driver comfort in the presence of driver behavior uncertainties is essential in the context of shared control for autonomous vehicles. In view of the absence of exact model based information in real time, this study harnesses the inverse reinforcement learning (IRL) procedure to establish the reward function for the automation model using expert data. In contrast to existing shared control studies that focus on automation counteracting driver behavior uncertainties, the novelty of the proposed study lies in developing human-like behavior within the shared control environment. Additionally, to achieve the overall objective of human like driving, RL based approach is employed to generate the automation road steer angle and driver automation (DA) relative weights, ensuring fulfillment of lane-keeping, vehicle lateral stability, and driver comfort objectives simultaneously. The reward function formulated for generating the DA relative weights and the automation model is integrated with the human arm muscular characteristics of the driver behavior model in the RL framework to develop the optimal shared steer angle. Comprehensive evaluations were performed to compare the driving performance of the suggested RL-based shared control system with existing adaptive shared control methods. Simulation outcomes indicate that the proposed control technique outperforms others by closely replicating human driving behavior. Additionally, a hardware-in-loop (HIL) setup was employed to validate the proposed shared control scheme under varying longitudinal speeds.

The landscape of cognitive states and their underlying neurobiological mechanisms has been significantly illuminated through advancements in neuroimaging and computational modeling. This study introduces an integrated approach that harnesses network analysis and machine learning techniques to characterize and differentiate cognitive groups—Normal Control (NC), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). Structural networks are formulated and analyzed based on diffusion tensor data through a fusion of graph theory and mass-spring model methodologies. Notably, features extracted from both graph theoretic and mass-spring model computations drive a two-step framework. This process commences with a random forest-based feature extraction, followed by a support vector-based classification approach, culminating in an impressive accuracy of 82.7% for classifying individuals across cognitive groups, with an AUC of 0.893. This study significance is underscored by the pressing need for enhanced cognitive impairment detection and differentiation strategies. The identified features offer nuanced insights into the intricate interplay among brain structure, dynamics, and cognitive function, thereby bridging gaps in our understanding of cognitive decline and neurodegeneration. By fortifying our diagnostic repertoire and facilitating personalized interventions, this research paves the way for refined clinical practices.

Dementia is one of the leading causes of mortality across the globe, yet, its treatment remains practically elusive. Preventing the later onsets of dementia by treating the early onset stands a better chance to forestall further surge in the cases of dementia around the world. Unfortunately, a lot of issues are associated with the detection of early onset as their clinical symptoms overlap with those of normal aging. Therefore, in this framework, the gray matter tissue probability map (TPM) is extracted from the magnetic resonance imaging (MRI) data of dementia related subjects. Generalized improved multiscale permutation entropy (GIMPE) based networks are formulated on the extracted gray matter TPM of normal control (NC), stable mild cognitive impairment (sMCI), progressive mild cognitive impairment (sMCI) and Alzheimer’s disease (AD) subjects. The network disruption of dementia onsets are assessed taking the networks of NC subjects as reference. A technique is developed and validated for the formulation of brain network from connectivity matrix and the formulated network topologies are quantified using graph theory metrics at nodal levels. The topological metrics at nodal levels are statistically analyzed using a non-parametric statistical (Kruskal-Wallis) test to extract the network signatures corresponding to NC, sMCI, pMCI and AD groups. Results show that the proposed framework is potentially viable for the detection and identification of the normal aging as well as the various stages of dementia onset at network level.

All existing physiological tremor filtering algorithms, developed for robotic microsurgery, use nonlinear phase prefilters to isolate the tremor signal. Such filters cause phase distortion to the filtered tremor signal and limit the filtering accuracy. We revisited this long-standing problem to enable filtering of the physiological tremor without any phase distortion. We developed a combined estimation–prediction paradigm that offers zero-phase type filtering. The estimation is achieved with the mathematically modified recursive singular spectrum analysis algorithm, and the prediction is delivered with the standard extreme learning machine. In addition, to limit the computational cost, we developed two moving window versions of this structure, which are appropriate for real-time implementation. The proposed paradigm preserved the natural phase of the filtered tremor. It achieved the key performance index of error limitation below$10\mu \text{m}$, yielding the estimation accuracy larger than 70%, at a time delay of 36 ms only. Both moving window versions of the proposed approach restricted the computational cost considerably while offering the same performance. It is the first time that the effective estimation of the physiological tremor is achieved, without any prefiltering and phase distortion. This proposed method is feasible for real-time implantation. Clinical translation of the proposed paradigm can significantly enhance the outcome in hand-held surgical robotics.Note to Practitioners—The imprecision caused by physiological hand tremor in microsurgeries has motivated researchers to innovate an efficient tremor compensating technique that can improve surgical performance. Yet, all the existing tremor filtering algorithms, implemented in hand-held surgical instruments, use nonlinear phase prefilters to separate the tremor signal. The inherent phase distortion caused by such prefilters restricts the filtering performance significantly and renders the existing methods inadequate for hand-held robotic surgery. Motivated by this, we proposed a novel estimator-predictor-based framework, by adopting the modified recursive singular spectrum analysis estimator and the extreme learning machine predictor. The proposed framework filters the tremor signal accurately, without distorting it, but at a small fixed lag. In a set of rigorous testing performed by emulating real-time processing, the proposed algorithm showed higher performance compared with the state-of-the-art algorithms. This validates not only its suitability for real-time implantation but also its potential to improve surgical performance, which has been limited by the distorted filtering. Nonetheless, we have presented a proof-of-principle framework for distortion-free filtering, but its full implementation in a real surgical instrument, such as Micron or ITrem, requires a substantial amount of experimental testing and verification. It can be also applicable in a wide range of areas, including health-care, digital manufacturing, smart automation and control, and various other robotic technologies where efficient filtering of advanced sensor data is highly desirable. In the future, we will develop the multidimensional model of the proposed framework to enable filtering of tremor in thexyz-axes simultaneously.

A lot of efforts have been made by researchers for easy detection of the prodromal phase of Alzheimer’s disease (AD) and other dementia to enable curative measures. Among the leading approaches that show promising results is the use of complex network theory on neuroimaging data such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), magnetoencephalogram (MEG), electroencephalogram (EEG) etc. However, exploring the network theory using the tissue probability Map (TPM) of magnetic resonance imaging (MRI) data has been quite unexplored. Therefore, in this paper, we developed the generalized improved multiscale permutation entropy (GIMPE) for the computation of complexity of grey matter (GM) TPM for all the considered region of interests (ROIs). In order to formulate a well defined network, the vectors, GIMPEs of all ROIs are taken as nodes and the edges between the nodes are defined by the Euclidean distance between the corresponding vectors (GIMPEs). The validation of our approach on MRI data accentuates the importance of the proposed approach as well as the significance of TPM based brain networks for the discrimination and differential diagnosis of normal aging, prodromal phase and the later phase of AD.

Neurocognitive impairments such as mild cognitive impairment (MCI), alzheimer's disease (AD) and vascular dementia (VD) effect the functional connectivity across the brain networks that aid proper neurocognitive functioning. Identification of dementia related disorders has remained a challenge due to their overlapping underlying complex structures. In this paper, we analyze the loss of functional connections of dementia networks in comparison with the network of average healthy control (HC) subjects. We then perform the topological quantification of the minimum spanning tree (MST) networks using graph theory metrics to identify the brain functional networks of MCI, AD and VD in comparison with healthy control (HC) subjects. A common reactive band is identified and MST is formed for all the subjects. The MST topological quantifications are used to identify the dementia related disorders based on the data recorded from 10 HC subjects and 30 dementia subjects. Our results show that the proposed approach has the potential to identify the dementia stages and can enhance the diagnosis of dementia related disorders.

In smart grid, energy resource management is highly complex large-scale optimization problem where the aim is to maximize the incomes while minimizing operational costs. Due to presence of mixed-integer variables and non-linear constraints, recently the use of evolutionary algorithms as a tool to find optimal and near-optimal solutions is becoming popular. The energy resource management problem further gets complicated if the uncertainty associated with the renewable generation, load forecast errors, electric vehicles scheduling and market prices are considered. Therefore, in a real-world scenario, it is essential to address the issues brought by the variability of demand, renewable energy, electric vehicles, and market price variations while maximizing the incomes and minimizing the total operation costs. In this paper, we analyze the performance of Differential Evolution with a stochastic selection on a large-scale energy resource management problem with uncertainty designed for competition at CEC 2018. The system comprises of a 25-bus microgrid representing a residential area with high penetration of Distributed Energy Resources (DER), Electric Vehicles (EVs), Demand Response (DR) programs etc.

To obtain a good dynamic control performance of permanent magnet synchronous motor (PMSM) drives, four sensors (one position, one DC-link voltage and at least two current sensors) are required for the control-loops. This paper deals with the unpredictable faults that occur in any one of the sensors using an advanced fault-tolerant control (FTC) scheme to provide continuous drive operation. The proposed FTC scheme comprises of two higher-order sliding mode (HOSM) observers and one Luenberger observer (LO) to generate the respective residuals, and provide the detection of all sensor faults. Moreover, HOSM controllers are designed to ensure finite-time convergence of the error trajectories. The designed scheme minimizes existing chattering phenomenon with good performance in terms of convergence speed and steady-state error. Evaluation results on a three-phase PMSM are presented to validate the effectiveness of the proposed FTC approach.

The tracking performance of the industrial servo systems are highly affected by the inherent unstructured uncertainties (external disturbances, and/or unmodeled dynamics), which degrades the reliability of the drive. This paper presents a higher-order terminal sliding mode controller to achieve high-accuracy motion-tracking control of DC motor drives. Fractional integral terminal sliding mode (ITSM) manifold is selected to eliminate the reaching time to the sliding hyperplane, which provides fast tracking error convergence in finite-time. Further, super-twisting control is employed to reduce the chattering while compensating the unwanted unstructured uncertainties compared with the traditional sliding mode control. Experimental results on a DC motor-based industrial mechatronic drives unit (IMDU) with belt-drive load are presented to show the effectiveness of the proposed controller.

The rollover status of a vehicle indicated by the lateral load transfer (LTR) as the vehicle traverses over various driving scenarios is critical in the implementation of antirollover control procedures. The determination of LTR is often carried out by the measurements of the roll angle, the lateral acceleration, vertically acting suspension forces, etc. In all these measurements, sensor faults may occur, which lead to a faulty computation of the rollover status, raising a false alarm. In this work, a scheme based on a robust higher order sliding-mode observer is proposed to estimate the states of a nonlinear two-wheel vehicular system affected by road disturbances, uncertainties in the height of the vehicle's center of gravity, and possible multiple sensor faults. Applying the proposed approach, the unknown inputs and sensor faults are reconstructed. To perform the estimations, adaptive sliding-mode-based observers that do not require the knowledge of the bounds of the uncertainties and unknown inputs are designed. Consequently, the true rollover status of the vehicle is determined, in spite of the presence of sensor faults. The validity of the proposed scheme has been assessed on the vehicle simulation software CarSim as the vehicle undergoes a double lane change maneuver.

For effective tremor compensation in robotics assisted hand-held device, accurate filtering of tremulous motion is necessary. The time-varying unknown phase delay that arises due to both software (filtering) and hardware (sensors) in these robotics instruments adversely affects the device performance. In this paper, moving window-based least squares support vector machines approach is formulated for multistep prediction of tremor to overcome the time-varying delay. This approach relies on the kernel-learning technique and does not require the knowledge of prediction horizon compared to the existing methods that require the delay to be known as a priori. The proposed method is evaluated through simulations and experiments with the tremor data recorded from surgeons and novice subjects. Comparison with the state-of-the-art techniques highlights the suitability and better performance of the proposed method.

In this paper, we shall examine the design of sliding mode observers for Lipschitz nonlinear systems for state and faults/unknown input estimations. The robust terms or the switching terms are designed such that the faults are tracked by their robust terms and so can be reconstructed from the sliding mode. The stability condition for the reduced order system is analyzed and the feedback gain is designed such that the reduced order system is stable. An application example to robotic manipulator is examined to demonstrate the effectiveness of the proposed method in reconstruction of unknown inputs/faults.

With the increasing popularity of actuators involving smart materials like piezoelectric, control of such materials becomes important. The existence of the inherent hysteretic behavior hinders the tracking accuracy of the actuators. To make matters worse, the hysteretic behavior changes with rate. One of the suggested ways is to have a feedforward controller to linearize the relationship between the input and output. Thus, the hysteretic behavior of the actuator must be first modeled by sensing the relationship between the input voltage and output displacement. Unfortunately, the hysteretic behavior is dependent on individual actuator and also environmental conditions like temperature. In this fast moving world, time is money and it is very costly to model the hysteresis regularly. In addition, the hysteretic behavior of the actuators also changes with age. Base on the studies done on the phenomena hysteretic behavior with rate, this paper proposes an adaptive rate-dependent feedforward controller with Prandtl-Ishlinskii (PI) hysteresis operators for piezoelectric actuators. This adaptive controller is achieved by adapting the coefficients to manipulate the weights of the play operators. Actual experiments are conducted to demonstrate the effectiveness of the adaptive controller.

In this paper, we design a discrete-time sliding mode (DSM) nonlinear observer for a class of nonlinear uncertain systems. Taylor series expansion together with a nonlinear state transformation is used to discretize the system. A strategy to avoid switching across the sliding manifold is employed, and the sliding trajectory is confined to a boundary layer once it converges to the sliding manifold. We call this phenomenon DSM. The conditions for existence of DSM are derived. The condition for the asymptotical stability of the estimation error is analyzed.