In-Situ interactive image-based model building for Augmented Reality from a handheld device

Three-dimensional models of objects and their creation process are central for a variety of applications in Augmented Reality. In this article, we present a system that is designed for in-situ modeling using interactive techniques for two generic versions of handheld devices equipped with cameras. The system allows online building of 3D wireframe models through a combination of user interaction and automated methods. In particular, we concentrate in rigorous evaluation of the two devices and interaction methods in the context of 3D feature selection. We present the key components of our system, discuss our findings and results and identify design recommendations.     

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.     

Machine Learning-Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high-quality datasets at appropriate volumes. This article extends the suitability of such ML-enabled approaches to a new technological domain of EHD spraying and drop-on-demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree-based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.     

An effective deep learning model for grading abnormalities in retinal fundus images using variational auto-encoders

Diabetic retinopathy (DR) and Diabetic Macular Edema (DME) are severe diseases that affect the eyes due to damage in blood vessels. Computer-aided automated grading will help clinicians conduct disease diagnoses at ease. Experiments of automated image processing with deep learning techniques using CNN produce promising results, especially in the medical imaging domain. However, the disease grading tasks in retinal images using CNN struggle to retain high-quality information at the output. A novel deep learning model based on variational auto-encoder to grade DR and DME abnormalities in retinal images is proposed. The objective of the proposed model is to extract the most relevant retinal image features efficiently. It focuses on addressing less relevant candidate region generation and translational invariance present in images. The experiments are conducted in IDRID dataset and evaluated using accuracy, U-kappa, sensitivity, specificity and precision metrics. The results outperform compared with other state-of-art techniques.     

Multi-objective parameter estimation of induction motor using particle swarm optimization

In order to simplify the offline parameter estimation of induction motor, a method based on optimization using a particle swarm optimization (PSO) technique is presented. Three different induction motor models such as approximate, exact and deep bar circuit models are considered. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the manufacturer data or from tests. The optimization problem is formulated as multi-objective function to minimize the error between the estimated and the manufacturer data. The sensitivity analysis is also performed to identify parameters, which have the most impact on motor performance. The feasibility of the proposed method is demonstrated for two different motors and it is compared with the genetic algorithm and the classical parameter estimation method. Simulation results show that the proposed PSO method was indeed capable of estimating the parameters over a wide operating range of the motor.     

Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms

This paper aims to solve the balanced multi-robot task allocation problem. Multi-robot systems are becoming more and more significant in industrial, commercial and scientific applications. Effectively allocating tasks to multi-robots i.e. utilizing all robots in a cost effective manner becomes a tedious process. The current attempts made by the researchers concentrate only on minimizing the distance between the robots and the tasks, and not much importance is given to the balancing of work loads among robots. It is also found from the literature that the multi-robot system is analogous to Multiple Travelling Salesman Problem (MTSP). This paper attempts to develop mechanism to address the above two issues with objective of minimizing the distance travelled by ‘m’ robots and balancing the work load between ‘m’ robots equally. The proposed approach has two fold, first develops a mathematical model for balanced multi-robot task allocation problem, and secondly proposes a methodology to solve the model in three stages. Stage I groups the ‘N’ tasks into ‘n’ clusters of tasks using K-means clustering technique with the objective of minimizing the distance between the tasks, stage II calculates the travel cost of robot and clusters combination, stage III allocates the robot to the clusters in order to utilise all robot in a cost effective manner.     

Scalable satisfiability checking and test data generation from modeling diagrams

We explore the automatic generation of test data that respect constraints expressed in the Object-Role Modeling (ORM) language. ORM is a popular conceptual modeling language, primarily targeting database applications, with significant uses in practice. The general problem of even checking whether an ORM diagram is satisfiable is quite hard: restricted forms are easily NP-hard and the problem is undecidable for some expressive formulations of ORM. Brute-force mapping to input for constraint and SAT solvers does not scale: state-of-the-art solvers fail to find data to satisfy uniqueness and mandatory constraints in realistic time even for small examples. We instead define a restricted subset of ORM that allows efficient reasoning yet contains most constraints overwhelmingly used in practice. We show that the problem of deciding whether these constraints are consistent (i.e., whether we can generate appropriate test data) is solvable in polynomial time, and we produce a highly efficient (interactive speed) checker. Additionally, we analyze over 160 ORM diagrams that capture data models from industrial practice and demonstrate that our subset of ORM is expressive enough to handle their vast majority.     

Influence ofβ-cyclodextrin as an encapsule and as an inclusion complex dopant on conducting polyaniline

An investigation on the effect ofβ-cyclodextrin (CD) in both free and inclusion-complexed forms with a guest anionic metal complex, dioxalatodiaquochromate(III) (DDC), on the characteristics of conducting polyaniline (PANI) is carried out. Four materials, PANI (i.e. PANI-SO ₄ ²⁻ ), PANI-DDC, PANI-CD and PANI-CD + DDC were prepared byin situ chemical oxidative polymerization in aqueous H ₂SO₄ at pH 1 and subjected to electrical conductivity and spectral (IR and UV-vis bd measurements. DDC and CD when separately incorporated, reduce the conductivity of PANI by about half whilst their inclusion complex CD + DDC enhances it. Spectral characterization reveals that DDC as a dopant and CD as an encapsule exhibit their effects through adverse interaction with imine-amine N centres and benzenoid moiety of PANI. The inclusion complex CD + DDC, on the contrary, functions as a dopant by lying in between the chains and seems to promote the extended conformation of PANI chain and hence theπ -electron delocalization. Exposure of the material to methanol vapour causes a decrease in conductivity in PANI and PANI-CD while an increase in PANI-CD + DDC. This study makes explicit the distinct role of CD as an encapsule and CD + DDC inclusion complex as a dopant in altering the electrical property of PANI.     

Query Optimization in Multidatabase Systems

Global query execution in a multidatabase system can be done parallelly, as all the local databases are independent. In this paper, a cost model that considers parallel execution of subqueries for a global query is developed. In order to obtain maximum parallelism in query execution, it is required to find a query execution plan that is represented in the form of a bushy tree and this query tree should be balanced to the maximal possible extent with respect to execution time. A new bottom up approach called Agglomerative Approach (AA) is proposed to construct balanced bushy trees with respect to execution time. By the deterministic nature of this approach, it generates local optimal solutions. This local minima problem will be severe in the case of graph queries, i.e., queries that are represented with a graph structure. A Simulated annealing Approach (SA) is employed to obtain a (near) optimal solution. These approaches (AA and SA) are suitable for handling on-line and off-line queries respectively. A Hybrid Approach (HA), that is an integration of AA and SA, is proposed to optimize queries for which the estimated time to be spent on optimization is known a priori. Results obtained with AA and SA on both tree and graph structured queries are presented.