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.     

On the mechanical behavior of two directional symmetrical functionally graded beams under moving load

International Journal of Mechanics and Materials in Design - This article presents a different polynomial interpolation function based dynamic finite element model to study and analyze the...     

Vibration-assisted filling capability in thin wall investment casting

Understanding the mechanism of the vibration needed to fill thin section and clarifying the dominant control parameters of the vibration in thin wall investment casting are the keys to producing sound casting. The filling capability in thin wall investment casting method can be assessed by the metal head. It was found that the effect of the vibration on the metal head is markedly dependent on acceleration of applied vibration. Two potential mechanisms were observed from the experimental results during the filling process in thin wall casting: discontinuous propagation flow in vibration conditions and continuous propagation flow without vibration. These mechanisms can modify the contact angles between liquid metal and a wall of the mold. Experiments also showed that two features of the transition can be observed from the front of the morphology: coherent liquid metal front, this occurs in thin wall investment casting when the acceleration due to vibration is less than (1?g) and jetting at the free surface, this occurs in thin wall investment casting when the acceleration due to vibration exceeds 1?g.     

On the Performance of Downlink Multiuser Cognitive Radio Inspired Cooperative NOMA

This paper is considered as an application of a centralized control non orthogonal multiple access (NOMA) based cognitive radio network. Here, a base station (BS) sends simultaneously two information signals by employing the superposition coding scheme to two different types of users, i.e., group of near users and one far user. The near users, namely, the secondary users, exchange cooperatively their own received information among themselves ensuring the realization of maximal diversity gain. Besides, they are responsible for relaying information to the far user, namely, the primary user. One potential secondary user is selected to decode and forward the BS information signal to the primary user and the rest of the secondary users to reinforce the reliability, as well as, mitigate the non-decodable messages. Two equivalent cases of a relay (secondary user) selection scheme are proposed. In the first case, the selection aims at maximizing the minimum of the joint secondary to secondary (S to S) and secondary to primary (S to P) channels’ coefficients under a certain limit of interference condition. In the second case, the selection aims at maximizing the minimum of the BS to S and S to S paths while a certain quality of service of the primary user is strictly guaranteed. Assuming Rayleigh fading channels, new closed form expressions are derived for the achievable capacity associated with the two information signals. Simulation results reveal the advantage of our proposed schemes over the conventional orthogonal max–min approach and confirm the validity of our analysis.     

Sliding mode control for a fractional‐order non‐linear glucose‐insulin system

By providing the generalisation of integration and differentiation, and incorporating the memory and hereditary effects, fractional‐order modelling has gotten significant attention in the past few years. One of the extensively studied and utilised models to describe the glucose–insulin system of a human body is Bergman''s minimal model. This non‐linear model comprises of integer‐order differential equations. However, comparison with the experimental data shows that the fractional‐order version of Bergman''s minimal model is a better representative of the glucose–insulin system than its original integer‐order model. To design a control law for an artificial pancreas for a diabetic patient using a fractional‐order model, different techniques, including feedback linearisation, have been applied in the literature. The authors’ previous work shows that the fractional‐order version of Bergman''s model describes the glucose–insulin system in a better way than the integer‐order model. This study applies the sliding mode control technique and then compares the obtained simulation results with the ones obtained using feedback linearisation.Inspec keywords: nonlinear control systems, feedback, variable structure systems, differential equations, medical control systems, diseases, control system synthesis, sugar, nonlinear dynamical systemsOther keywords: fractional‐order nonlinear glucose‐insulin, hereditary effects, fractional‐order modelling, extensively, utilised models, glucose–insulin system, Bergman''s minimal model, nonlinear model, integer‐order differential equations, fractional‐order version, original integer‐order model, fractional‐order model, Bergman''s model, sliding mode control technique     

Study of the electrolyte-insulator-semiconductor field-effect transistor (EISFET) with applications in biosensor design

This paper presents a comprehensive review of the ion-sensitive field-effect transistor (ISFET) and its applications in biomolecular sensing and characterization of electrochemical interfaces. An introduction to the physics of field-effect transistors is presented, followed by a study of the properties of electrolytic solutions and electrolyte interface surface effects. Full modeling of the ion-sensitive transistor is given, followed by a survey of the different uses of the ISFET in biomedical and environmental applications. Particular attention is given to the use of the ion-sensitive transistors as replacements for microarrays in DNA gene expression analysis.