Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

Architectural space planning using evolutionary computing approaches: a review

This paper presents various applications of evolutionary computing approach for architectural space planning problem. As such the problem of architectural space planning is NP-complete. Finding an optimal solution within a reasonable amount of time for these problems is impossible. However for architectural space planning problem we may not be even looking for an optimal but some feasible solution based on varied parameters. Many different computing approaches for space planning like procedural algorithms, heuristic search based methods, genetic algorithms, fuzzy logic, and artificial neural networks etc. have been developed and are being employed. In recent years evolutionary computation approaches have been applied to a wide variety of applications as it has the advantage of giving reasonably acceptable solution in a reasonable amount of time. There are also hybrid systems such as neural network and fuzzy logic which incorporates the features of evolutionary computing paradigm. The present paper aims to compare the various aspects and merits/demerits of each of these methods developed so far. Sixteen papers have been reviewed and compared on various parameters such as input features, output produced, set of constraints, scope of space coverage-single floor, multi-floor and urban spaces. Recent publications emphasized on energy aspect as well. The paper will help the better understanding of the Evolutionary computing perspective of solving architectural space planning problem. The findings of this paper provide useful insight into current developments and are beneficial for those who look for automating architectural space planning task within given design constraints.     

Home Security System Using Wireless Sensors Network

Wireless Personal Communications - In this paper, we propose a smart and robust home security system. This is for intrusion detection along with a proprietary Android application. Intruder-Spi...     

Insights into structural difference between sodium polyacrylate PAA and sodium polymethacrylate PMA in salt solutions investigated by molecular simulations

Journal of Materials Science - The difference in the structural properties of highly charged polyacrylic acid (PAA) and polymethacrylic acid (PMA) is investigated by atomistic molecular dynamics...     

A Particle Grey Wolf Hybrid Algorithm for Workflow Scheduling in Cloud Computing

Wireless Personal Communications - A workflow consists of a set of tasks that are dependent on each other and scheduling these dependent tasks to the virtual machines is one of the complex problems...     

Opportunistic Mobile Data Offloading Using Machine Learning Approach

Wireless Personal Communications - Currently, cellular networks both 3G and 4G are heavily overloaded due to increasing usage of mobile applications. Offloading mobile data traffic through...     

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study.     

An assessment of numerical techniques to find energy-minimizing microstructures associated with nonconvex potentials

Microstructural patterns emerge ubiquitously during phase transformations, deformation twinning, or crystal plasticity. Challenges are the prediction of such microstructural patterns and the resulting effective material behavior. Mathematically, the experimentally observed patterns are energy-minimizing sequences produced by an underlying non-(quasi)convex strain energy. Therefore, identifying the microstructure and effective response is linked to finding the quasiconvex, relaxed energy. Due to its nonlocal nature, quasiconvexification has traditionally been limited to (semi-)analytical techniques or has been dealt with by numerical techniques such as the finite element method (FEM). Both have been restricted to primarily simple material models. We here contrast three numerical techniques—FEM, a Fourier-based spectral formulation, and a meshless maximum-entropy (max-ent) method. We demonstrate their performance by minimizing the energy of a representative volume element for both hyperelasticity and finite-strain phase transformations. Unlike FEM, which fails to converge in most scenarios, the Fourier-based spectral formulation (FFT) scheme captures microstructures of intriguingly high resolution, whereas max-ent is superior at approximating the relaxed energy. None of the methods are capable of accurately predicting both microstructures and relaxed energy; yet, both FFT and max-ent show significant advantages over FEM. Numerical errors are explained by the energy associated with microstructural interfaces in the numerical techniques compared here.     

High-contrast, all-optical switching in bacteriorhodopsin films

We report experiments with nonlinear-absorption-based, high-contrast, all-optical switching in photochromic bacteriorhodopsin (BR) films. The switching action is accomplished by control of the transmission of a weak probe beam through a BR sample with the help of strong pump beam illumination at 532 nm wavelength. We found that the switching properties of BR films depend on several experimentally controllable parameters such as probe wavelength, pump beam intensity, and excitation rate. A comparative study of the switching behavior and other parameters of practical use was carried out at three probe wavelengths (543, 594, and 633 nm) and various beam powers and pump excitation rates. The results are presented for commercially available wild-type and D96N variant BR films.