The fracture toughness of Ni/Al2O3 laminates by digital image correlation II: Bridging-stresses and R-curve models

A composite of metal and brittle ceramic layers have increased fracture toughness as compared to ceramic monoliths. The property controlling the toughness enhancement is the, ‘bridging-stress’, exerted by the ductile phase astride the crack in the ceramic. This bridging-stress is a function of the crack-opening displacement (COD) which is a function of the size of the crack and the position along its profile. Depending on the accuracy of estimation of the bridging-stress, the modeled R-curve and experimental one match. In this study, a weight function based approach to generate the R-curve is reported and compared with the experimental results for Al₂O₃/Ni multilayer laminates.     

A unified analytical framework for distributed variable step size LMS algorithms in sensor networks

Internet of Things (IoT) is helping to create a smart world by connecting sensors in a seamless fashion. With the forthcoming fifth generation (5G) wireless communication systems, IoT is becoming increasingly important since 5G will be an important enabler for the IoT. Sensor networks for IoT are increasingly used in diverse areas, e.g., in situational and location awareness, leading to proliferation of sensors at the edge of physical world. There exist several variable step-size strategies in literature to improve the performance of diffusion-based Least Mean Square (LMS) algorithm for estimation in wireless sensor networks. However, a major drawback is the complexity in the theoretical analysis of the resultant algorithms. Researchers use several assumptions to find closed-form analytical solutions. This work presents a unified analytical framework for distributed variable step-size LMS algorithms. This analysis is then extended to the case of diffusion based wireless sensor networks for estimating a compressible system and steady state analysis is carried out. The approach is applied to several variable step-size strategies for compressible systems. Theoretical and simulation results are presented and compared with the existing algorithms to show the superiority of proposed work.     

Full-Duplex Relaying Schemes for Cooperative NOMA Networks with SWIPT

Wireless Personal Communications - Existing security approaches for safeguarding data exchange among the sensor nodes are investigated in presence of apriori information of an adversary in...     

AdDroid: Rule-Based Machine Learning Framework for Android Malware Analysis

Mobile Networks and Applications - Recent years have witnessed huge growth in Android malware development. Colossal reliance on Android applications for day to day working and their massive...     

Energy Cooperation with Sleep Mechanism in Renewable Energy Assisted Cellular HetNets

The emerging fifth generation (5G) and beyond radio access networks are expected to be extremely dense and heterogeneous as compared to the current networks, involving a large number of different classes of base stations (BSs), namely macro, micro, femto and pico BSs. Among several performance requirements 5G and beyond systems aim to achieve, energy efficiency is one of the crucial requirements. In order to achieve energy-efficient design in dense heterogeneous 5G networks, various approaches in terms of resource allocation, off-loading techniques, hardware solutions and energy harvesting are being considered. In this regard, this paper develops an energy usage optimization framework in a cellular heterogeneous network (HetNet) consisting of a central macro-BS and a number of micro-BSs, equipped with renewable energy sources (RESs) such as solar panels and wind turbines. The proposed framework incorporates an energy cooperation mechanism along with a sleep mechanism (BS ON/OFF switching), in which the BSs having lean traffic are put into a sleep mode and their traffic load gets shared by the central BS. The surplus harvested energy from RESs of the sleeping BSs can then be sold back to the grid. An optimization problem for maximizing the utilization of RES and minimizing the usage of the traditional sources, such as utility and generator, is formulated and this mixed integer non-linear programming problem is solved through an interior point method. The presented results for various HetNet sizes demonstrate the significant savings in the energy cost with the proposed RES-enabled HetNet sleep mechanism model over the conventional approaches.

     

Learning Patterns from COVID-19 Instances

Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization (WHO). Coronavirus disease is also termed COVID-19. It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images. Therefore, Chest X-Ray alone may play a vital role in identifying COVID-19 cases. In this paper, we propose a Machine Learning (ML) approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images. The article also explores traditional, and Deep Learning (DL) approaches for COVID-19 patterns from Chest X-Ray images to predict, analyze, and further understand this virus. The experimental evaluation of the proposed approach achieves 97.5% detection performance using the DL model for COVID-19 versus normal cases. In contrast, for COVID-19 versus Pneumonia Virus scenario, we achieve 94.5% accurate detections. Our extensive evaluation in the experimental section guides and helps in the selection of an appropriate model for similar tasks. Thus, the approach can be used for medical usages and is particularly pertinent in detecting COVID-19 positive patients using X-Ray images alone.     

A power control technique for anti-collision schemes in RFID systems

The emerging technology of Radio Frequency IDentification (RFID) has enabled a wide range of automated tracking and monitoring applications. However, the process of interrogating a set of RFID tags usually involves sharing a wireless communication medium by an RFID reader and many tags. Tag collisions result in a significant delay to the interrogation process, and such collisions are hard to overcome because of the limited capabilities of passive RFID tags and their inability to sense the communication medium. While existing anti-collision schemes assume reading all tags at once which results in many collisions, we propose a novel approach in which the interrogation zone of an RFID reader is divided into a number of clusters (annuli), and tags of different clusters are read separately. Therefore, the likelihood of collisions is reduced as a result of reducing the number of tags that share the same channel at the same time.In this paper, we consider two optimization problems whose objective is minimizing the interrogation delay. The first one aims at finding the optimal clustering scheme assuming an ideal setting in which the transmission range of the RFID reader can be tuned with high precision. In the second one, we consider another scenario in which the RFID reader has a finite set of discrete transmission ranges. For each problem, we present a delay mathematical analysis and devise an algorithm to efficiently find the optimal number of clusters. The proposed approach can be integrated with any existing anti-collision scheme to improve its performance and, hence, meet the demand of large scale RFID applications. Simulation results show that our approach makes significant improvements in reducing collisions and delay.     

Verification of surfactant CHAPS effect using AFM for making biomemory device consisting of recombinant azurin monolayer

In this study, a protein-based biomemory device was developed using a surface modified recombinant azurin layer and its surface characteristics were analyzed by atomic force microscopy. The cysteine-modified azurin used for this purpose was a metalloprotein that had redox properties. To immobilize the metalloprotein on the Au substrates, the cysteine-modified azurin layer was self-assembled on the Au surface through a covalent bond between the thiol group on the cysteine and the Au surface. In our previous work, we showed that this protein layer was formed as cohesive clusters on Au surface through physical adsorption. To reduce the formation of these cohesion clusters, a zwitterionic surfactant, (3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate) (CHAPS) was introduced to modify the surface properties. Using this approach, we found that CHAPS significantly reduced the amount of cysteine-modified azurin aggregates that nonspecifically adsorbed to the Au substrate. Atomic force microscopy was used to analyze the modified-surface. Based on this analysis, the size of the recombinant azurin clusters when CHAPS was used were about 15–25 nm whereas aggregates of 150–200 nm were observed in the absence of CHAPS. In addition, Raman spectroscopy was performed to confirm the retention of azurin molecules self-assembled on the Au surface. Electrochemical results using cyclic voltammetry indicated that recombinant azurin was successfully immobilized onto the Au surface with CHAPS and its redox property remained intact. Chronoamperometry was then used to demonstrate the memory characteristics of this azurin-based fabricated memory device. The combined results of this study show that CHAPS can significantly reduce the size of protein aggregates that become immobilized on the surface without a loss of the electrochemical properties of the protein.