Wind Farm Layout Design Using Cuckoo Search Algorithms

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for a better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization (PSO) algorithms, which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and PSO algorithms for the given test scenarios in terms of yearly power output and efficiency.     

Formation Control and Tracking for Co-operative Robots with Non-holonomic Constraints

This paper mainly addresses formation control problem of non-holonomic systems in an optimized manner. Instead of using linearization to solve this problem approximately, we designed control laws with guaranteed global convergence by leveraging nonlinear transformations. Under this nonlinear transformation, consensus of non-holonomic robots can be converted into a stabilization problem, to which optimal treatment applies. This concept is then extended to the formation control and tracking problem for a team of robots following leader-follower strategy. A trajectory generator prescribes the feasible motion of virtual reference robot, a decentralized control law is used for each robot to track the reference while maintaining the formation. The asymptotic convergence of follower robots to the position and orientation of the reference robot is ensured using the Lyapunov function which is also generated using chained form differential equations. In order to witness the efficacy of the scheme, simulations results are presented for Unicycle and Car-like robots.     

Computer Vision Techniques Applied for Diagnostic Analysis of Retinal OCT Images: A Review

The retina is a tiny layer at the posterior pole of an eye and is made up of tissues sensitive to light, these tissues generate nerve signals that pass through the optic nerve to the brain. A retinal disorder occurs when the retina malfunctions; glaucoma, diabetic retinopathy and pathologic myopia are retinal disorders and principal causes of blindness worldwide. These retinal disorders are often diagnosed and treated by an ophthalmologist. However, to accurately assess a retinal disease, ophthalmologist would need qualitative and quantitative analysis of the disease, it’s early and current statistics, but acquisition of these measurements are not possible through manual techniques, there should be automated computer aided diagnosis (CAD) systems to assist ophthalmologists. In this comprehensive review, an analysis and evaluation has been performed of different computer vision and image processing approaches applied to OCT images for automatic diagnosis of retinal disorders. We also reported disease causes, symptoms and pathologies manifestations within OCT images, which can serve as baseline knowledge for development of an automated CAD system. Hence, this disease specific review offers a good understanding to analyze visual impairments from retinal OCT images which will help researcher to design enhanced therapeutic systems for retinal disorders.     

A Novel Framework for Human Activity Recognition with Time Labelled Real Time Sensor Data

Human activity recognition is an effective approach for identifying the characteristics of historical data. In the past decades, different shallow classifiers and handcrafted features were used to identify the activities from the sensor data. These approaches are configured for offline processing and are not suitable for sequential data. This article proposes an adaptive framework for human activity recognition using a deep learning mechanism. This deep learning approach forms the deep belief network (DBN), which contains a visible layer and hidden layers. The processing of raw sensor data is performed by these layers and the activity is identified at the top most layers. The DBN is tested using the real time environment with the help of mobile devices that contain an accelerometer, a magnetometer, and a gyroscope. The results are analyzed with the metrics of precision, recall, and the F1-score. The results proved that the proposed method has a higher F1_score when compared to the existing approach.     

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

In this paper, the problem of outsourcing the selective encryption of a medical image to cloud by resource-constrained devices such as smart phone is addressed, without revealing the cover image to cloud using steganography. In the proposed framework, the region of interest of the medical image is first detected using a visual saliency model. The detected important data is then embedded in a host image, producing a stego image which is outsourced to cloud for encryption. The cloud which has powerful resources, encrypts the image and sent back the encrypted marked image to the client. The client can then extract the selectively encrypted region of interest and can combine it with the region of non-interest to form a selectively encrypted image, which can be sent to medical specialists and healthcare centers. Experimental results and analysis validate the effectiveness of the proposed framework in terms of security, image quality, and computational complexity and verify its applicability in remote patient monitoring centers.     

Nonlinear robust state feedback control system design for nonlinear uncertain systems

This paper presents a systematic approach to the design of a nonlinear robust dynamic state feedback controller for nonlinear uncertain systems using copies of the plant nonlinearities. The technique is based on the use of integral quadratic constraints and minimax linear quadratic regulator control, and uses a structured uncertainty representation. The approach combines a linear state feedback guaranteed cost controller and copies of the plant nonlinearities to form a robust nonlinear controller with a novel control architecture. A nonlinear state feedback controller is designed for a synchronous machine using the proposed method. The design provides improved stability and transient response in the presence of uncertainty and nonlinearity in the system and also provides a guaranteed bound on the cost function. An automatic voltage regulator to track reference terminal voltage is also provided by a state feedback equivalent robust nonlinear proportional integral controller. Copyright © 2016 John Wiley & Sons, Ltd.     

Laminar convection in the annulus of a double-pipe with triangular fins

A steady and laminar convective flow has been numerically simulated in the fully-developed annular region of a finned double-pipe subjected to the constant heat flux boundary condition imposed at the inner-pipe wall. Finite element method has been employed in this study. Friction factor and Nusselt number have been studied as flow characteristics against variations in the ratio of radii of the inner and the outer pipes, the fin height, the fin half angle and the number of fins. The results show significant enhancement in the heat transfer rate in both the cases when sufficient pumping power is available and when it is not. The minimum and maximum increase in the product of friction factor and Reynolds number relative to the finless geometry is more than one time and more than 40 times respectively while gain in the relative value of Nusselt number lies in the range 1–177. This provides an evidence of more than four times enhancement in the heat transfer coefficient relative to that in the pressure loss as a result of extended fin surfaces.     

Diffusion dynamics of open source software: An agent-based computational economics (ACE) approach

The rising popularity of open source software (OSS) calls for a better understanding of the drivers of its adoption and diffusion. In this research, we propose an integrated framework that simultaneously investigates a broad range of social and economic factors on the diffusion dynamics of OSS using an Agent Based Computational Economics (ACE) approach. We find that interoperability costs, variability of OSS support costs, and duration of PS upgrade cycle are major determinants of OSS diffusion. Furthermore, there are interaction effects between network topology, network density and interoperability costs, which strongly influence the diffusion dynamics of OSS. The proposed model can be used as a building block to further investigate complex competitive dynamics in software markets.