Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Materials science & Computer science. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

Efficient design of an authenticated key agreement protocol for dew-assisted IoT systems

Abstract: Real-time communication is a significant aspect of Internet of Things (IoT). IoT-enabled devices requires the immediate adoption of the highly distributed and heterogeneous framework of collateral merits. Moreover, cloud-based streaming services for IoT have disadvantages such as the inability to provide low latency, mobility support, location-awareness, and real-time data handling, which makes ubiquitous connectivity between the IoT device and server. At the same time, the concept of dew computing modifies the current mechanism of cloud-based services for IoT. It minimizes the response time of comprehensive data, which was collected by nearby resources. However, speedy advancement in IoT directs the evolving security aspects to address emerging challenges. To address the security issues, a mutual authentication architecture has been introduced for dew computing, which ensures secure and authorized session establishment without the requirement of a trusted server. The main objective of the proposed framework is to avoid bottleneck situations without compromising efficiency and security in real-time communication to IoT users through dew computing. To ensure the correctness of protocol, proof of security and simulation using AVISPA are presented. Analysis of performance and comparative study is also conducted to show the advantage in efficiency.

Multiferroic Properties of Zn 1−x Mg x O Nanoparticles

Abstract: In present research communication, the synthesis of Zn 1−x Mg x O (x = 0.00, 0.05, 0.10, 0.15, and 0.20) nanoparticles has been carried out by sol–gel route. X-ray diffraction (XRD) patterns reveal the hexagonal structure of ZnO without the formation of any extra phase. Transmission electron microscopy (TEM) study indicates the spherical shape of nanoparticles, having an average particle size of 17 nm for undoped nanoparticles. The particle size has been found to be decreasing with the increase in Mg-doping concentration. High-resolution transmission electron microscopy (HRTEM) predicts the well crystalline nature of nanoparticles without any segregated extra phase or impurity, in agreement with energy dispersive spectroscopy (EDS). Electron spin resonance (ESR) analysis indicates the defects mediated long-range ferromagnetic interaction. Magnetization versus applied magnetic field (M– H) curves registers a decrease in saturation magnetization value with increase of Mg-doping concentration which can be related to decrease of oxygen vacancies in host nanoparticles. The dielectric versus frequency response has been explained by Maxwell–Wagner interfacial model. Improved ferroelectric behavior has been observed with Mg-doping concentration.

A weighted least squares optimisation strategy for medical image super resolution via multiscale convolutional neural networks for healthcare applications

Abstract: Medical imaging is an essential medical diagnosis system subsequently integrated with artificial intelligence for assistance in clinical diagnosis. The actual medical images acquired during the image capturing procedures generate poor quality images as a result of numerous physical restrictions of the imaging equipment and time constraints. Recently, medical image super-resolution (SR) has emerged as an indispensable research subject in the community of image processing to address such limitations. SR is a classical computer vision operation that attempts to restore a visually sharp high-resolution images from the degraded low-resolution images. In this study, an effective medical super-resolution approach based on weighted least squares optimisation via multiscale convolutional neural networks (CNNs) has been proposed for lesion localisation. The weighted least squares optimisation strategy that particularly is well-suited for progressively coarsening the original images and simultaneously extract multiscale information has been executed. Subsequently, a SR model by training CNNs based on wavelet analysis has been designed by carrying out wavelet decomposition of optimized images for multiscale representations. Then multiple CNNs have been trained separately to approximate the wavelet multiscale representations. The trained multiple convolutional neural networks characterize medical images in many directions and multiscale frequency bands, and thus facilitate image restoration subject to increased number of variations depicted in different dimensions and orientations. Finally, the trained CNNs regress wavelet multiscale representations from a LR medical images, followed by wavelet synthesis that forms a reconstructed HR medical image. The experimental performance indicates that the proposed model SR restoration approach achieve superior SR efficiency over existing comparative methods