In this paper, design of composite state convergence controller for teleoperating a multi-degrees-of-freedom manipulator is presented. The composite variables corresponding to the joints of the master and slave manipulators as well as operator’s force are transmitted across the communication channel. The control laws for the master and slave systems are defined according to the composite state convergence scheme with the addition of cancellation terms containing the nonlinear dynamics of master and slave manipulators. It is shown that convergence of composite variables of respective master and slave joints guarantee the convergence of respective joint positions of the master and slave systems under the composite state convergence control laws. MATLAB simulations on time-delayed teleoperation model are performed to validate the proposal.

We established a mathematical model based on the sense of biological survey in the field of agriculture and introduced various control methods on how to prevent the crops from destructive pests. Basically, there are two main stages in the life cycle of natural enemies like insects: mature and immature. Here, we construct a food chain model of plant pest natural enemy. In natural enemies, there are two stages of construction. Also, we consider three classes of diseases in the pest population, namely, susceptible, exposed, and infectious in this proposed work. In order to categorize the considered models into the class of Impulsive Differential Equations (IDEs), in our study, we specifically consider two ecosystems, which define the impact of control mechanisms on the impulsive releasing of virus particle natural enemies and infectious pests at particular time. Additionally, the importance of spraying virus particles in pest control is discussed; then, we obtain two types of periodic solutions for the system, namely, plant pest extinction and pest extinction. By utilizing the small amplitude perturbation techniques and Floquet theory of the impulsive equation, we obtain the local stability of both periodic solutions. Moreover, the comparison technique of IDE shows the sufficient conditions for the global attractivity of a pest extinction periodic solution. With the assistance of the comparison results, we draw a numerical calculation for the addressed models. Finally, we extend the study of the two models for pest management models: with and without the existence of virus particle.

Soft Computing (SC) techniques are oriented towards the analysis and design of intelligent systems covering an interdisciplinary methodological framework. Such techniques include fuzzy logic, genetic algorithms, artificial neural networks, machine learning, deep learning, expert systems , and hybrids of these techniques. All these components are complementary rather than competitive. Soft computing as it was defined by Professor Lotfi Zadeh is a collection of methodologies that cope with the main disadvantage of the conventional (hard) computing the poor performances when working in uncertain conditions. There are many applications in control, decision making, robotics, pattern recognition and many others. In this case the systems are tolerant to uncertainty, imprecision, and even partial truth.

Theory of Graphs could offer a plenty to enrich the analysis and modeling to generate datasets out of the systems and processes regarding the spread of a disease that affects humans, animals, plants, crops etc., In this paper first we show graphs can serve as a model for cattle movements from one farm to another. Second, we give a crisp explanation regarding disease transmission models on contact graphs/networks. It is possible to indicate how a regular tree exhibits relations among graph structure and the infectious disease spread and how certain properties of it akin to diameter and density of graph, affect the duration of an outbreak. Third, we elaborate on the presence of a suitable environment for exploiting several streams of data such as genetic temporal and spatial to locate case clusters one dependent on the other of a disease that is infectious. Here a graph for each stream of data joining all cases that are created with pairwise distance among them as edge weights and altered by omitting exceeding distances of a cutoff assigned that relies on already existing assumptions and rate of spread of a disease information. Fourth we provide an overview of epidemiology, disease transmission, fatality rate and clinical features of zoonotic viral infections of epidemic and pandemic magnitude since 2000. Fifth we indicate how the clinical data and virus spread data can be exploited for the creation of health knowledge graph. Graph Theory is an ideal tool to model, predict, form an opinion to devise strategies to quickly arrest the outbreak and minimize the devastating effect of zoonotic viral infections.

Notice that the synapsis of brain is a form of communication. As communication demands connectivity, it is not a surprise that "graph theory" is a fastest growing area of research in the life sciences. It attempts to explain the connections and communication between networks of neurons. Alzheimer’s disease (AD) progression in brain is due to a deposition and development of amyloid plaque and the loss of communication between nerve cells. Graph/network theory can provide incredible insights into the incorrect wiring leading to memory loss in a progressive manner. Network in AD is slanted towards investigating the intricate patterns of interconnections found in the pathogenesis of brain. Here, we see how the notions of graph/network theory can be prudently exploited to comprehend the Alzheimer’s disease. We begin with introducing concepts of graph/network theory as a model for specific genetic hubs of the brain regions and cellular signalling. We begin with a brief introduction of prevalence and causes of AD followed by outlining its genetic and signalling pathogenesis. We then present some of the network-applied outcome in assessing the disease-signalling interactions, signal transduction of protein-protein interaction, disturbed genetics and signalling pathways as compelling targets of pathogenesis of the disease.

Composite state convergence scheme is a reduced-complexity version of the state convergence controller for teleoperation system. It employs a smaller number of control gains and communication channels used to synchronize the motion of a single master-slave system in a desired dynamic way. The present study aims at generalizing the composite state convergence scheme so that l-slave systems can follow the weighted motion of k-master systems. To achieve this, at first, composite variables of all master and slave systems are transmitted across the communication channel along with operators' forces and a set of k+l+2kl control gains is defined. In the second stage, the design procedure of the existing composite state convergence scheme is extended for multiple systems and the control gains are determined through the solution of coupled equations. Finally, to validate the findings, simulations and semi-real time experiments are performed in MAFLAB/Sirntilink/QUARC environment by considering different configurations of teleoperation systems.

Based on composite variables, three-channel state convergence is a novel architecture for the bilateral control of teleoperation systems modelled on state space. Although simple to design and easy to implement, this bilateral control algorithm relies on model parameters. To lower this dependence, this article proposes a disturbance-observer-supported three-channel state convergence architecture. At first, extended state observers are used for estimating position and velocity states of master and slave systems along with their lumped uncertainties. These position and velocity estimates are then fused to form composite variables which are transmitted along with the operator's force. With the knowledge of composite variables and the estimates of uncertainties, bilateral control laws are developed for the master and slave systems by following the method of state convergence. To validate the proposal, simulations as well as semi-realtime experiments are performed in MATLAB/Simulink environment by considering a single degree-of-freedom time-delayed teleoperation system.

Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

The need for a behavioural interview scoring strategy is a critical element in order to ensure an optimal organizational human capital. Behavioural interview based on storytelling approach is a technique through which career seekers are required to provide clear details of how they have handled such workloads in the past. The whole literature assumes the existence of strong correlations between the score received on the selection interview and subsequent job performance, so in this paper we intend to highlight the relationship between these two assessments as well as the modelling using fuzzy logic of a CAR alternative system for scoring the selection interview. The results demonstrated that there is a very significant association between the classic interview score and work performance (r=0.894 to p<0.01). Furthermore, there is also a significant correlation coefficient of r=0.925 at a p<0.01, between the fuzzy CAR score and job performance, thus the validity and the optimization of the procedure are fully proven.

Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links load and reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an important issue. Recently, the tremendous growth of Mobile Edge Computing (MEC) empowers the edge network nodes with more computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within the mobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the caching performance, the suitable methodology is to consider both context awareness and intelligence so that the caching strategy is aware of the environment while caching the appropriate content by making the right decision. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we present a modified reinforcement learning (mRL) to cache contents in the network edges. Our proposed solution aims to maximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent (SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.

Wireless Sensor Networks (WSNs) are consists of many tiny sensing devices called nodes to sense, compute and transmit the data. These nodes are made of transducer, memory, battery, micro-controller and an antenna for the communication of data from node to sinks. Nodes used in the networks are having energy constraints as they are usually battery powered and hence it is not always feasible to change the battery due to odd terrain of deployment. For the managements of these limited energy resources of WSNs various algorithms have been proposed and implemented. In this paper we have implemented the Sine Cosine Optimization algorithm in routing and clustering of WSNs for the optimizing lifetime of the Wireless Sensor Networks. Higher energy nodes are deployed to work as the cluster head to enhance the lifetime of WSNs and effect of using higher energy nodes are also studied for two different position of base station (BS). Results of this algorithm are compared with the Genetic Algorithm and Particle Swarm Optimization Algorithm.

In this proposed work, a substantial comparative performance optimisation has been established between the PI, lead, lead-lag and fuzzy logic controllers towards the closed loop control strategies of a simplified permanent magnet synchronous motor (PMSM) drive. By the introduction of sinusoidal pulse width modulation (PWM) control strategy, it is expected that the nature of armature current would be nearly sinusoidal and generated torque ripples will be lesser. In this proposed structure of a PMSM drive, the speed reference has been incorporated with a speed controller to fortify that the exact speed of the proposed motor match with the base speed with null speed error. The overall structure of the PMSM drive is separated into two loop control structure, inner current loop and outer speed loop. All the necessary performance indices of the proposed PMSM drive system are tested in a MATLAB/Simulink environment. Moreover, the performance of a fuzzy logic speed controlled PMSM drive as compared to all classical controllers provides better dynamic as well as steady state performance with reduced torque ripples. Therefore, the entire performance of the proposed simplified PMSM drive in closed loop control strategy is executed and efficacy of controllers is resolved under various operating conditions. Hence, the superiority of intelligent speed controller (fuzzy logic controller) for this proposed PMSM drive model over all classical controllers is validated and optimised for high performance applications. Finally, an auto-tuning control strategy for the fuzzy intelligent speed controller is also proposed for optimal operation of the drive system.

Performing an activity satisfactorily requires a certain amount of time, and in general many activities are needed to be performed. Management of the available time of a researcher is important for effective and efficient execution of one or more tasks, and inadvertently affects the time of collaborators, managers or supervisors as well. Managing time well ensures that one can get the most of what is set out for in any given day.

Effectiveness in research output is based on several aspects like developing skills to be able to convey good reasons to believe and accept the conclusions made, deal with the criticisms that may come one’s way from the supervisor, the reviewers or the examiners, and the ability to use the peer review process to one’s advantage to do research that has an impact and contributes to one’s field of study.

This paper proposes a new and original fuzzy approach to circular iris segmentation based on isolines, sine law and lookup tables. All isolines found in the eye image together form the space in which the inner and outer boundaries of the iris are searched for, while the sine law is used for identifying clusters of concyclic points within any given and possibly noisy isoline. The new segmentation procedure proved a failure rate of 5.83% when tested on 116,564 eye images (LG2200 subset of ND-CrossSenssor-Iris-2013 database) at an average speed of four images per second on a single Xeon L5420 core.

The time-delay in a system model is inherent to any physical system and it induces infinite roots in its characteristic equation forcing the exact analysis to be computationally difficult tasks.

Foot plantar pressure characteristics can be used to investigate and characterize diabetic patients. The current work proposed an effective method for analyzing plantar pressure images in order to obtain the key areas of foot plantar pressure characteristics. A collected data of plantar pressure of diabetic patients is involved to evaluate the proposed method based on image analysis. Initially, the plantar pressure imaging dataset was preprocessed by using watershed transformation to determine the region of interest (ROI) as well as to decrease the computation complexity. Afterward, the convolutional neural network (CNN) based K-mean clustering and parameterized manifold learning using an improved isometric mapping algorithm (ISOMAP) were applied to attain segments of the imaging dataset. The proposed method was discussed and was compared on ten areas of plantar including toes, mid-foots and heels. For the clustering result, the experiments established superior performance with root mean square error (RMSE) of 70%, average accuracy of 80% and 80% time consuming. Furthermore, the proposed manifold learning method achieved an average accuracy of 87.2%, which was superior to other seven algorithms including multi-dimensional scaling (MDS), principal components analysis (PCA), locally linear embedding (LLE), Hessian LLE, Laplacian eigenmap method (LE), diffusion map, and local tangent space alignment (LTSA). The proposed approach established potential application on shoe-last customization of diabetic foot.

This paper reports a modified and computationally efficient energy detection method for spectrum sensing in cognitive radio (CR) system utilising hybrid soft computing paradigms. The proposed soft computing method uses artificial neural network and fuzzy logic for learning and decision making as a solution to the problems when CR system is subjected to threshold uncertainty and uncertainties in noise variance. The proposed soft computing method should be referred thereafter as Neuro-Fuzzy double threshold (NFDT) technique. The developed technique utilizes the concept of double sensing credibility while formulating the fuzzy decision rule and artificial neural network to learn the threshold function. Through simulation results it is validated that proposed method leads to overall improved probability of detection compared to conventional and existing methods.

This chapter deals with the stability analysis of linear time-delay systems without and with parametric uncertainties. The stability analysis for both constant and time-varying delay in the states is considered. The focus of this chapter is to review the existing methods on delay-dependent stability analysis in an LMI framework based on Lyapunov-Krasovskii approach and consequently the improved results on delay-dependent stability analysis are presented. The results of the proposed techniques are validated by considering numerical examples and compared with existing results.