Earlier Research Work Research Articles

A review of “performance analysis and optimization of car air spoilers”

Before last decade, due to surplus amount of fuel, cars are designed for high speed operation, comfort and safety. As scarcity of fuel increases due to high consumption of fuel in automobiles, many researchers started working on the idea of alternative fuels, redesign of car body and reducing aerodynamic losses. To overcome these losses spoilers are used and hence it is needed to optimize its shape. This study is primarily focused to find out optimized shape of car spoiler so that mass can be minimized which help in reducing fuel consumption without affecting its aerodynamic properties and strength. Computational Fluid Dynamics (CFD) analysis of two dimensional model of spoiler is done whose results are validated by earlier research work in this field for understanding variation of aerodynamic property of cross-section. Three dimensional CFD analysis of spoiler gives aerodynamic property and pressure data which is needed to compare result of optimized model formed by optimized cross-sectional shape. Shape optimization is done by Shape Optimization tool of ANSYS 14.0 which is further tested for design failure in ABAQUS 6.11. The result of shape optimization has saved 18.74% of material keeping all its strength and aerodynamic property intact. This study open provides a numerical tool for improvement of future model of spoiler in terms of reduction of mass.

A BrainNet Classification Technique Based on Deep Convolutional Neural Network for Detection of Brain Tumor in FLAIR MRI Images

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. The logical gap between the visual representation of data captured by MRI device and the information apparent to the person evaluating poses a key challenge in the medical field. Research in computerized segmentation of tumor is widely gaining popularity nowadays, which may lead to an accurate analysis of MRI images and planned treatment of patients. The recent field of deep learning and neural networks promises to classify images with higher accuracy. This work proposes a new BrainNet classification technique that combines fuzzy c means, morphological operators and CNN to identify image regions that are suspicious. The proposed method is assessed with the help of imaging data obtained from Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2015 and IXI dataset. The effectiveness of the proposed method is computed with traditional machine learning and Convolutional Neural Networks. Experimental results show that our proposed method outperforms state-of-the-art classification on the BRATS 2015 dataset.

Performance Analysis of Data Encryption Algorithm

In this paper, author have performed experimental analysis of Jumbling-Salting (JS) algorithm for larger text size. In the earlier research work, JS Algorithm was symmetric-password encryption algorithm. JS algorithm consists of two prominent cryptographic processes namely Jumbling and Salting. Jumbling consists of three major randomized processes viz. Addition, Selection and Reverse. Jumbling process jumbles the random characters into password string. Salting process adds a random string based on some timestamp value. The output of Jumbling and Salting process is given to predefined AES block to perform 128-bit key encryption to maintain the cipher text size uniform. In this research, the capability of JS algorithm is enhanced. The paper therefore shows the performance of JS algorithm regarding length of cipher text size with respect to AES and DES algorithms. This extended research ensures that JS algorithm is not only suitable for smaller text like password, pin, passcode etc. but it is also favorable for larger texts.

Perspectives in Water Quality Assessment

One can assess the quality of water by water quality index method and it is a mathematical method to evaluate the water quality based on physical or chemical parameters, using WQI one can evaluate the water quality of both ground water and surface water, while determining the WQI one can use the standards or guidelines provided by some standard organizations like WHO (world health organization), National Standard Body of India (BIS), ICMR (Indian Council for Medical Research) etc. To assign grade to the water samples under study, apart from the WQI there are some other popular techniques used to evaluate water quality they are statistical methods, multivariate statistics, neural networks, fuzzy logic and machine learning algorithms. The aim of this study is to give insight into various methods used or developed to evaluate water quality of both ground and surface water by earlier research works.

Heat Transfer in Hydromagnetic Flow over an Unsteady Stretching Permeable Sheet

Unsteady laminar boundary layer flow of viscous incompressible electrically conducting fluid along a continuous stretched permeable surface with the magnetic field effect is investigated. The defining characteristics of unsteady laminar boundary layer flow are governed a more than one independent variables, stretching velocity and surface temperature of the field. Governing equations are obtained for influencing parameters and transformed into ordinary differential equations by taking convenient similarity variables. Runge-Kutta fourth order method in corporation by the shooting technique is introduced to carry out numerical computations of the investigation. Velocity and temperature profiles are computed and represented graphically for the influences of suction/injection parameter, unsteadiness parameter, magnetic parameter and Prandtl number, while numerical solutions of local skin friction coefficient and local Nusselt number are discussed through tables. For non-magnetic condition, results are found in concordance with earlier research work.

A Game Theoretic Interference Management Scheme in Full Duplex Cellular Systems under Infeasible QoS Requirements

Several emerging mobile applications and services (e.g., autonomous cars) require higher wireless throughput than ever before. This demand stresses the need for investigating novel methods that have the potential to dramatically increase the spectral efficiency (SE) of wireless systems. An evolving approach is the Single-channel full duplex (SCFD) communication where each node may simultaneously receive and transmit over the same frequency channel, and, hence, this could potentially double the current SE figures. In an earlier research work, we derived a model of the signal to interference plus noise ratio (SINR) in an SCFD-based cellular system with imperfect self interference cancellation, and investigated interference management under feasible QoS requirements. In this paper, game theoretic results are exploited to investigate the intercell interference management in SCFD-based cellular networks under infeasible QoS requirements. The investigation starts with a game formulation that captures two different cases. Then, the existence and uniqueness of the Nash equilibrium point are established. After that, a computationally efficient distributed algorithm, which realizes best effort and fair wireless services, is designed. The merit of this scheme is that, when the QoS requirements are feasible, they will be achieved with minimum energy consumption. Results of extensive simulation experiments are presented to show the effectiveness of the proposed schemes.

A deep neural network based classifier for brain tumor diagnosis

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.

A new two-level implicit scheme based on cubic spline approximations for the 1D time-dependent quasilinear biharmonic problems

In this article, we present a new two-level implicit cubic spline numerical method of accuracy 2 in time and 4 in spatial direction for the numerical solution of 1D time-dependent quasilinear biharmonic equation subject to appropriate initial and natural boundary conditions prescribed. The easiness of the proposed numerical method lies in their 3-point discretization in which we use two points $$ x \pm (h/2) $$ and a central point ‘x’ in spatial direction. Using the continuity of the first-order derivative of cubic spline function, we derive the fourth-order accurate numerical method for the time-dependent biharmonic equation on a uniform mesh. The stability consideration of the proposed method is discussed using a model linear problem. The proposed cubic spline method successfully implements on generalized Kuramoto–Sivashinsky and extended Fisher–Kolmogorov equations. From the numerical experiments, we obtain better computational results compared to the results discussed in earlier research work.

Finite-Element Approach to Camera Modelling and Calibration

This paper is focused on the finite-element (FE) method of camera calibration. The FE method enables the modelling of systematic error effects, including those which cannot be recovered by standard modelling procedures, e.g., those based on Brown’s distortion model. The FE approach to camera modelling has been previously published a number of times; however, some important aspects were not sufficiently addressed in this earlier research work. In addition, the computing power was too low to test the finite-element method with high-resolution FE grid. The proposed FE implementation is fully independent of any polynomial model and includes correction of the distance-dependent distortion effect. Besides modelling the effects such as lens distortion and sensor unflatness, the approach also accommodates the calibration of non-perspective lenses such as fisheye lenses. In addition to introducing the proposed FE calibration method, this paper addresses the related issues of sufficient target density, correction pattern smoothness and FE grid size. It also reports on experimental testing of the new FE implementation using the acceptance test procedure of the German VDI guideline 2634. Two different cameras were calibrated within the acceptance tests to analyse the impacts of the sensor size and field of view of the lens. For comparison with the FE method, both data sets were also processed using standard photogrammetric software (AICON 3D Studio). The results have proven the ability of the proposed FE modification to recover any systematic effects and to model ultra-wide field-of-view lenses, while achieving highly accurate measurements. The method is able to model the distance-dependent distortion effect, but requires a very large number of observations, which may be expensive and difficult to establish in practise. The proposed method, which can be characterised by utilising a high-resolution grid, is mostly intended for laboratory calibration of highly stable camera systems and not for on-the-job type calibration, where the target density would likely not be sufficiently large.

Roll forward validation based decision tree classification for detecting data integrity attacks in industrial internet of things

Data Integrity attack is a major hindrance to the evolution of Industrial Internet of Things (IIoT) as it leads to immense financial loss or even human fatality. The existing security features in Software Defined Networking (SDN), which is emphatically superior to the traditional networks mitigate the integrity attacks to some extent. However, a generic, robust, secure and resilient Intrusion Detection System (IDS) for IIoT is still lacking in the literature. Towards this goal, a generic IDS is already proposed in our earlier research work which combines both anomaly as well as rule-based intrusion detection techniques and successfully tested against the real-time dataset obtained from the water purification process in a test bed at the Singapore University of Technology and Design (SUTD). This research work proposes a supervised learning approach that utilizes Roll-forward technique for validation and Classification and Regression Trees (CART) with invariants for categorization to find anomalousness in the water treatment process. The proposed work incorporates the capability to substantiate time-series data through Roll-forward validation which is then succeeded by utilization of the CART with invariants for classification. The proposed work is simulated using Mininet tool and the train and test accuracies are 99.9% and 98.1% respectively.

SR-MLC: Machine Learning Classifiers in Cyber Security-An Optimal Approach

The Digital flexibility is a quickly developing perspective that is accomplishing acknowledgment. Negative Cyber-assaults are those that oppositely impact the accessibility, uprightness or secrecy of IT arrange frameworks and related administrations and data. Earlier research works have carried on information control by an adversary as a worry, yet their works neglected to sum up the experiments. Many focused on formulating assault vectors inverse to explicit AI calculations and applications, for example, the Support Vector Machine (SVM) classifier. In our proposed work, an autonomous methodology on flexibility assessment and the development of enemy versatile classifiers utilizing Cluster Tree Map (CTM) Algorithm is finished. All information types in the area of Cyber Network information investigation are focused. The goal is to make a familiarity with any such strategy able to do effectively demonstrating the innovativeness and expertise of digital assailants and in this manner creating solo learning model. Better expected precision is achieved by utilizing Scalable Resilience Machine Learning Classifiers (SR-MLC).
 Keywords: Resilience, Cluster, Classifiers, Cyber attackers Resilience, Cluster, Cloud, Cyber Security, Artificial Intelligence, Machine Learning, Network, Network Security, Analytics.

SR-MLC: Machine Learning Classifiers in Cyber Security-An Optimal Approach

The Digital flexibility is a quickly developing perspective that is accomplishing acknowledgment. Negative Cyber-assaults are those that oppositely impact the accessibility, uprightness or secrecy of IT arrange frameworks and related administrations and data. Earlier research works have carried on information control by an adversary as a worry, yet their works neglected to sum up the experiments. Many focused on formulating assault vectors inverse to explicit AI calculations and applications, for example, the Support Vector Machine (SVM) classifier. In our proposed work, an autonomous methodology on flexibility assessment and the development of enemy versatile classifiers utilizing Cluster Tree Map (CTM) Algorithm is finished. All information types in the area of Cyber Network information investigation are focused. The goal is to make a familiarity with any such strategy able to do effectively demonstrating the innovativeness and expertise of digital assailants and in this manner creating solo learning model. Better expected precision is achieved by utilizing Scalable Resilience Machine Learning Classifiers (SR-MLC).

Dynamic scheduling of tasks for multi‐core real‐time systems based on optimum energy and throughput

One of the critical design issues in real-time systems is energy consumption, especially in battery-operated systems. Generally higher processor voltage generates higher throughput of the system while decreasing voltage can perform energy minimisation. Instead of lowering processor voltage, this paper presents an optimum energy efficient real-time scheduling to adjust voltage dynamically to achieve optimum throughput. Earlier research works have considered random new tasks, which have been divided into jobs using pfair scheduling to fit into idle times of different cores of the system. In this paper we consider each job has different power levels and execution time at each power level can be found using normalised execution time. Based on the power levels and their corresponding execution time, we find different combinations of energy signature of the system and derive the optimum state of the system using a weighted average of the energy of the system and corresponding throughput. We verify the proposed model using generated task sets and the results show that the model performs excellently in all the cases and significantly reduced the total energy consumption of the system with respect to some popular and relatively new scheduling schemes.

Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living

Abstract Activity recognition(AR) is a popular subject of research in the recent past. Recognition of activities performed by human beings, enables the addressing of challenges posed by many real-world applications such as health monitoring, providing security etc. Segmentation plays a vital role in AR. This paper evaluates the efficiency of Area-Based Segmentation using different performance measures. Area-Based segmentation was proposed in our earlier research work. The evaluation of the Area-Based segmentation technique is conducted on four real world datasets viz. Aruba17, Shib010, HH102, and HH113 comprising of data pertaining to an individual, living in the test bed home. Machine learning classifiers, SVM-R, SVM-P, NB and KNN are adopted to validate the performance of Area-Based segmentation. Amongst the four chosen classification algorithms SVM-R exhibits better in all the four datasets. Area-Based segmentation recognise the four test bed activities with accuracies of 0.74, 0.98, 0.66, and 0.99 respectively. The results reveal that Area based segmentation can efficiently segment sensor data stream which aids in accurate recognition of smart home activities.

Measuring the Impact of Motivations on Travelers’ Strategic Decisions in Different Traffic Conditions: Data Collection, Analysis, and Modeling

Continued growth in travel demand and the corresponding congestion occurrence accentuates the need for active transportation and demand management (ATDM) for predictive rather than reactive congestion mitigation strategies. These strategies (applications) reduce demand and thus improve the performance of different surface transportation facilities. Earlier research work mainly utilized simulations and field experiments to suggest improvements to ATDM applications associated with pricing and/or provision of information to travelers; however, few such studies considered the dynamics of travelers’ motivations as an essential component in the performance of a given ATDM application. In other words, how travelers’ motivations change as a function of the ATDM applications’ characteristics remains largely unexplored. This study thus examines the theory of planned behavior (TPB) to analyze travelers’ motivations associated with different mode choices and captures and measures the corresponding dynamics of motivations when facing different surrounding circumstances and ATDM regimes. Utilizing a survey and an online data collection platform to estimate motivational patterns behind travelers’ mode choices, following and extending the TPB paradigm, the study concludes that: (a) travelers’ mode choices are primarily determined by intentions (motivations); (b) income and age are two additional characteristics that influence mode choices; (c) travelers’ reasoned choices are mainly attitude-oriented; and (d) different attitudinal aspects are accentuated or compromised along with the changes of travel conditions and ATDM applications.

Bridging Waves and Crucial Events in the Dynamics of the Brain.

Earlier research work on the dynamics of the brain, disclosing the existence of crucial events, is revisited for the purpose of making the action of crucial events, responsible for the 1/f −noise in the brain, compatible with the wave-like nature of the brain processes. We review the relevant neurophysiological literature to make clear that crucial events are generated by criticality. We also show that although criticality generates a strong deviation from the regular wave-like behavior, under the form of Rapid Transition Processes, the brain dynamics also host crucial events in regions of nearly coherent oscillations, thereby making many crucial events virtually invisible. Furthermore, the anomalous scaling generated by the crucial events can be established with high accuracy by means of direct analysis of raw data, suggested by a theoretical perspective not requiring the crucial events to yield a visible physical effect. The latter follows from the fact that periodicity, waves and crucial events are the consequences of a spontaneous process of self-organization. We obtain three main results: (a) the important role of crucial events is confirmed and established with greater accuracy than previously; (b) we demonstrate the theoretical tools necessary to understand the joint action of crucial events and periodicity; (c) we argue that the results of this paper can be used to shed light on the nature of this important process of self-organization, thereby contributing to the understanding of cognition.

Non-Linear Radiation and Chemical Reaction Impacts on Hydromagnetic Particle Suspension Flow in Non-Newtonian Fluids

Abstract A two dimensional (2D) boundary layer two-phase MHD (magneto hydrodynamics) flow of Maxwell and Oldroyd-B fluid over a stretching sheet has been explored. Heat and mass transfer phenomena is inspected through Non-linear radiation, viscous dissipation, joule heating, Soret (thermo-diffusion) and Dufour (diffusion-thermo) Impact. The boundary layer governing differential equations are modelled and transformed to a system of ODE’S with the aid of similarity transformations. The final controlled equations along boundary restrictions are resolved numerically by Runge-Kutta Felhberg method. The graphical analysis has been emphasized for the fluid and dust phase velocity, temperature and concentration fields to the influence of sundry dynamical flow quantities. Furthermore, for authentication of the present computation, the achieved results are distinguished with earlier research works in specific cases and marvellous agreement has been noted. The outcomes conveyed here manifest that velocity and boundary layer thickness escalate with boost up the values of ${K_1}$ . Velocity and boundary layer thickness declines with boost up the values of $M$ . Opposite trend is seen in temperature and concentration profiles. The specific heat ratio parameter $\gamma$ enhances the temperature profile declines. Boost up the values of Soret number $Sr$ temperature profile declines and concentration profile enhances. Skin friction factor declines with increasing values $\beta _v$ verses $M$ .

Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications

Particle Swarm Optimization (PSO) algorithms often face premature convergence problem, specially in multimodal problems as it may get stuck in specific point. In this paper, we have enhanced Dynamic-PSO i.e. and an extention of our earlier research work. This newly proposed algorithm Guided Dynamic-PSO (GDPSO) also targets the particles whose personal best get stuck i.e. their personal best does not improve for fixed number of iterations similar to DPSO, however a new approach is proposed for replacing personal bests of such particles. The replacement of this new personal best is done on the basis of sharing fitness so that better diversity can be provided to avoid the problem. The performance of GDPSO has been compared with PSO and its variants including DPSO over 24 benchmark functions provided by Black-Box Optimization Benchmarking (BBOB 2015). Results show that the performance of GDPSO is better in comparison with other peer algorithms. Further effectiveness of GDPSO is demonstrated in digital image watermarking. Digital image watermarking schemes primarily focus on providing good tradeoff between imperceptibility and robustness along with reliability in watermarked images produced for wide variety of applications. To support watermarking scheme in achieving this tradeoff, suitable watermark strength is identified in the form of scaling factor using GDPSO for colored images. Results achieved through GDPSO are compared with PSO and other widely accepted variants of PSO over different combination of host and watermark images. Experiment results demonstrate that performance of underline watermarking scheme when used with GDPSO, in terms of imperceptibility and robustness, is better than other variants of PSO.

The Implementation of Spatial Planning Objects in a 3D Cadastral Model

The paper concerns spatial planning in Poland and its connection with the cadastre. The Polish spatial planning system defines the set of colours, lines, hatches, etc. destined for the preparations of spatial plans, though this has so far not been followed by a spatial planning model or application schema. The aim of this paper is to create a preliminary concept of the unified modelling language (UML) schema of database integrating 3D cadastre and 3D spatial planning. The authors initially define five unified modelling language classes representing spatial planning objects (four representing spatial objects and one a dictionary list). As spatial planning and cadastres are very strongly connected, these classes are implemented into a cadastral model that had been earlier enriched with 3D classes. The final results of this research are UML diagrams based on the Polish cadastral model as defined earlier in legal regulations. They comprise original cadastral model classes, 3D cadastral objects added in earlier research work, classes representing spatial planning objects and the relationships among them. Such a solution better connects cadastre and spatial planning on a structural level and introduces 3D elements into spatial planning which has basically been done in two dimensions.

Performance Improvement of Multi-DER Microgrid for Small- and Large-Signal Disturbances and Nonlinear Loads: Novel Complementary Control Loop and Fuzzy Controller in a Hierarchical Droop-Based Control Scheme

This paper presents new control scheme for a microgrid including several distributed energy resources that is completely analyzed so that the three hierarchical control levels are modeled for islanded and grid-connected modes. However, decentralized control methods of voltage-source converter (VSC)-based microgrids and microgrids’ hierarchical control scheme in the earlier research works have been limited to primary and secondary control levels. The presented hierarchical control scheme exploits new control loop to control the reactive power reference by a nonlinear fuzzy logic controller to improve performance of microgrid, not only for small signal events, but also respect to large signal disturbances such as short circuits, line outage, heavy motor starting, etc. and moreover, nonlinear loads and consequently, have the benefit of increasing the system stability margins and fault ride through capability. Besides, this control scheme can sufficiently provide power sharing to loads and network. Simulation studies are performed on a microgrid consisting of four distributed energy resources with local loads in MATLAB/Simulink environment to show the effectiveness of the proposed hierarchical control strategy.