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...     

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.     

A novel multimodal clustering framework for images with diverse associated text

Multimedia Tools and Applications - With the enormous growth in the number of images on the web, image clustering has become an essential part of any image retrieval system. Since web images are...     

Synthesis of novel counter electrode by combination of mesoporous–macroporous CZTS films for enhanced performance of quantum-dots sensitized solar cells

This present study aims to manifest the potential of CZTS films as a low-cost counter electrode (CE) in quantum-dots sensitized solar cells (QDSSCs). Hitherto, numerous researchers have reported the application of either CZTS nano particles or films as a counter electrode in dye sensitized solar cells. However, its use in QDSSCs is scarcely reported. Herein, CdS quantum-dots sensitized ZnO film is used as photoanode. In the beginning, as a counter electrode, two different CZTS films (mesoporous and macroporous) are prepared using two different deposition techniques (spray pyrolysis and spin coating, respectively). For the meso-CZTS film, high V_OC and FF are observed, whereas, for the macro-CZTS film, high J_SC is observed. Hence, to take the advantage of both, subsequently, a film (meso–macro-CZTS) comprising mesoporous film upon the macroporous film is prepared and applied as CE. For the meso–macro-CZTS, substantial enhancement in power conversion efficiency (PCE) is observed. Additionally, to compare the results with commonly reported CEs (SnS/FTO and Pt–FTO) are also applied in QDSSCs. Moreover, to improve the PCE combination of these CEs along with meso–macro-CZTS, for instance, CZTS/SnS/FTO and CZTS/Pt–FTO are also applied as CE. The highest efficiency of 4.34% is achieved with CZTS/Pt–FTO.     

An online‐offline prognosis model for fatigue life prediction under biaxial cyclic loading with overloads

This paper presents a robust online‐offline model for the prediction of crack propagation under complex in‐phase biaxial fatigue loading in the presence of overloads of different magnitudes. The online prognosis model comprises a combination of finite element analysis and data‐driven regression to predict the crack propagation under constant loading, while the offline model is trained using experimental data to inform the post‐overload crack growth retardation behavior to the online model. The developed methodology is validated by conducting biaxial fatigue experiments using aluminum AA7075‐T651 alloy cruciform specimens. A close correlation is observed between the experimental results and model predictions. The results show that the model successfully predicts the crack retardation behavior under the influence of overloads with different magnitudes occurring at different stages of fatigue crack growth. Error analysis is conducted to investigate the sensitivities of the number of training points and crack increments to the prediction accuracy. In addition, the error propagation with respect to the crack length is studied, which provides constructive suggestions for further model improvement.