Numerical solution of fractional delay differential equation by shifted Jacobi polynomials

In this paper, the fractional delay differential equation (FDDE) is considered for the purpose to develop an approximate scheme for its numerical solutions. The shifted Jacobi polynomial scheme is used to solve the results by deriving operational matrix for the fractional differentiation and integration in the Caputo and Riemann–Liouville sense, respectively. In addition to it, the Jacobi delay coefficient matrix is developed to solve the linear and nonlinear FDDE numerically. The error of the approximate solution of proposed method is discussed by applying the piecewise orthogonal technique. The applicability of this technique is shown by several examples like a mathematical model of houseflies and a model based on the effect of noise on light that reflected from laser to mirror. The obtained numerical results are tabulated and displayed graphically.     

Robust reliable H∞ control design for networked control systems with nonlinear actuator faults and randomly missing measurements

In this paper, the robust reliable H_∞ controller is designed for the problem of nonlinear actuator fault case in the uncertain networked control systems with randomly occurring missing data. More precisely, the occurrence of missing measurements is modeled by a stochastic variable in terms of Bernoulli random distribution. Also, the consideration of a nonlinear term in the input control scheme is a novel work for the proposed model. Suitable robust reliable design of control for a practical actuator fault model is constructed to guarantee the asymptotic stability of the system with H_∞ performance level. A new form of Lyapunov‐Krasovskii functional with triple integral terms are formulated, and the reciprocally convex technique is utilized to establish the sufficient stability criterion in the form of linear matrix inequalities. Finally, the effectiveness of the proposed control method is shown through numerical examples, and we can confirm that the derived condition attained less conservatism than the existing results.     

Fabrication of Arothron stellatus skin collagen film incorporated with Coccinia grandis as a durable wound construct

The wound dressing material has been fabricated with the novel Arothron stellatus fish skin collagen scaffold loaded with a bioactive extract obtained from Coccinia grandis (CPE) and drug Ciprofloxacin (D). The collagen scaffold was fabricated from the nonedible solid waste of marine origin (a cheaper source of raw material). The in vitro fluorescence staining of cells and in vivo evaluation of the fabricated scaffold exhibited enhanced cell adhesion and proliferation and indicated a more efficient wound healing paradigm than that of the control and other treated groups, respectively. The increased collagen synthesis and re-epithelialization of wound are proved to be potential wound constructs.     

Approximate controllability of stochastic nonlinear third‐order dispersion equation

In this work, we consider a class of control systems governed by the stochastic nonlinear third‐order dispersion equation in Hilbert spaces. We first prove the existence of mild solutions of stochastic nonlinear third‐order dispersion equation by using fixed point theory, infinite dimensional semigroup properties, stochastic analysis techniques, and then a new set of sufficient conditions are formulated which guarantees the approximate controllability of the main problem. Finally, an example is provided to illustrate the application of the obtained results.Copyright © 2012 John Wiley & Sons, Ltd.     

Pattern Analysis and Regressive Linear Measure for Botnet Detection

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers. However, certain limitations need to be addressed efficiently. The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints. The bots’ patterns or features over the network have to be analyzed in both linear and non-linear manner. The linear and non-linear features are composed of high-level and low-level features. The collected features are maintained over the Bag of Features (BoF) where the most influencing features are collected and provided into the classifier model. Here, the linearity and non-linearity of the threat are evaluated with Support Vector Machine (SVM). Next, with the collected BoF, the redundant features are eliminated as it triggers overhead towards the predictor model. Finally, a novel Incoming data Redundancy Elimination-based learning model (RedE-L) is built to classify the network features to provide robustness towards BotNets detection. The simulation is carried out in MATLAB environment, and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset (benchmark dataset). The proposed model intends to show better trade-off compared to the existing approaches like conventional SVM, C4.5, RepTree and so on. Here, various metrics like Accuracy, detection rate, Mathews Correlation Coefficient (MCC), and some other statistical analysis are performed to show the proposed RedE-L model's reliability. The F1-measure is 99.98%, precision is 99.93%, Accuracy is 99.84%, TPR is 99.92%, TNR is 99.94%, FNR is 0.06 and FPR is 0.06 respectively.     

A Model for Low Power,High Speed and Energy Efficient Early Landslide Detection System Using IoT

A landslide, also called as landslip appear to be the most threatening disaster of all time especially in the hillside regions. It involves enormous surface movements including debris flows, slopes failure, rock fall etc. It mainly occurs when the land slopes become unstable. Other measures that also lead to landslides are Ground water changes, Earthquakes, Floods, Volcano eruptions and Heavy rainfalls. People among these hill areas doesn’t know about the disaster that’s about to happen massively. The perfect way to avoid such hazards is by predicting it at initial phase with maximum accuracy. There are many wired and wireless supervising systems available to detect landslides which require higher cost and maintenance. But we have a suitable solution for this with Internet of Things (IoT) based approach. It improves objects control and detection remotely between various networks thereby creating possibilities for direct communication between physical and computer-based world. By this approach Landslides can be predicted at the initial phase. If there is a higher chance of Landslide then an alert will be sent to disaster management sector immediately to take necessary precautions so that enormous precious lives can be protected. This paper proposes a model for IoT based Landslide detection mechanisms in detail.

     

A novel cascade encryption algorithm for digital images based on anti-synchronized fractional order dynamical systems

In this paper, an active control technique is employed for anti-synchronization between two identical fractional order reverse butterfly-shaped hyperchaotic systems. We have shown that the convergence rate of anti-synchronization error is very faster by increasing the value of an active controller gain. A new algorithm for image encryption and decryption is introduced and established by anti-synchronized fractional order dynamical systems. Experimental results show that the proposed encryption algorithm has high level security against various attacks. Further, it confirms that the new algorithm is more efficient compared to other existing algorithms.