Linearity Distortion Analysis of Junctionless Quadruple Gate MOSFETs for Analog Applications

Silicon - This paper examines a Junctionless quadruple gate (JLQG) MOSFET for analog and linearity distortion performance by numerically calculating transconductance and its higher order...     

Heterogeneous Catalytic Polymerization of Ethylene in Microtubular Reactor Systems

The feasibility of using a microtubular reactor for heterogeneous polymerization of ethylene was investigated experimentally. Chemically inert polymer tubing of 800–2300 μm in inner diameter was fabricated and used as a polymerization reactor. Nonporous silica nanoparticles with a diameter of 400 nm were synthesized and used as support for the high‐activity rac‐ethylene(indenyl)₂ZrCl₂ catalyst with methylaluminoxane as cocatalyst and toluene as diluent. Large‐diameter microtubular reactors were also successfully used to conduct heterogeneous polymerization of ethylene in continuous reaction operations. High initial catalyst activity was obtained and the overall polymerization activity per volume or reactor length was quite high. No particle fragmentation occurred and the polymer particles were covered with small subgrains or nanofibrils with a diameter of 30 nm.     

Dietary oxidised lipids,health consequences and novel food technologies that thwart food lipid oxidation: an update

Processed foods are popular and their consumption is expected to grow globally. Food processing and manufacturing promote lipid oxidation in foods rich in polyunsaturated fatty acids and cholesterol. This review focuses on how various food manufacturing/processing techniques promote lipid oxidation in grains, meats and meat products, dairy and fats/oils. This review also considers emerging evidence from animal and human studies that suggest a link between dietary oxidised lipid consumption and chronic disease risk. An update on novel food technologies that limit food lipid oxidation is discussed so as to inform both food scientists and dietitians/nutritionists to direct future efforts in not only continuing to bring these novel technologies to the market place but also conduct clinical trials to establish their role in human health.     

Evolutionary gaming approach for decision making of Tier‐3 Internet service provider networks migration to SoDIP6 networks

With the increasing number of Internet of Things (IoT) devices, current networking world is suffering in terms of management and operations with lack of IPv4 addresses leading to issues like network address translation (NAT) proliferation, security and quality of services. Software‐defined networking (SDN) and Internet Protocol version 6 (IPv6) are the new networking paradigms evolved to address related issues of legacy IPv4 networking. To adapt with global competitive environment and avoid all existing issues in legacy networking system, network service providers have to migrate their networks into IPv6 and SDN‐enabled networks. But immediate transformations of existing network are not viable due to several factors like higher cost of migration, lack of technical human resources, lack of standards and protocols during transitions, and many more. In this paper, we present the migration analysis for proper decision making of network transition in terms of customer demand, traffic engineering, and organizational strength with operation expenditure for network migration using evolutionary gaming approach. Joint migration to SDN‐enabled IPv6 network from game theoretic perspective is modeled and is validated using numerical results obtained from simulations. Our empirical analysis shows the evolutionary process of network migration while different internal and external factors in the organization affect the overall migration. Evolutionary game in migration planning is supportive in decision making for service providers to develop suitable strategy for their network migration. The proposed approach for migration decision making is mostly applicable to fairly sustained service providers who lack economics, regulation/policy, and resources strengths.     

Development of optoelectronic-based pulsed current sensor to detect the poor laser discharge condition

A current transformer (CT)-based sensor has been developed to detect poor discharge conditions in copper vapour laser. The optoelectronic-based pulsed current sensor architecture involves the optical transmitter–receiver HFBR, high-frequency current transformer, and fiber optic link. The CT has been designed and calibrated to ensure generation of an optical signal at the current threshold crossover. Bandwidth analysis of the CT is carried out using the bode plot. The optoelectronic inter-conversion of the pulsed voltage of the CT and transmission via fiber optic link provides the non-contact current sensing and remote signal processing of the signal. This study discusses the details of the sensor.     

Deep Learning Approach for Analysis and Characterization of COVID-19

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images of patients, developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases, while 397 belong to negative cases of COVID-19. Our experiment resulted in an accuracy of 98.4%, sensitivity of 98.5%, specificity of 98.3%, precision of 97.1%, and F1-score of 97.8%. The additional parameters of classification error, mean absolute error (MAE), root-mean-square error (RMSE), and Matthew’s correlation coefficient (MCC) are used to evaluate our proposed work. The obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases.     

RP-LPP : a random permutation based locality preserving projection for cancelable biometric recognition

Biometrics are being increasingly used across the world, but it also raises privacy and security concerns of the enrolled identities. The main reason is due to the fact that biometrics are not cancelable and if compromised may give access to the intruder. Cancelable biometric template is a solution to this problem which can be reissued if compromised. In this paper, we suggest a simple and powerful method called Random Permutation Locality Preserving Projection (RP-LPP) for Cancelable Biometric Recognition. Here, we exploit the mathematical relationship between the eigenvalues and eigenvectors of the original biometric image and its randomly permuted version is exploited for carrying out cancelable biometric recognition. The proposed technique work in a cryptic manner by accepting the cancelable biometric template and a key (called PIN) issued to a user. The effectiveness of the proposed techniques is demonstrated on three freely available face (ORL), iris (UBIRIS) and ear (IITD) datasets against state-of-the-art methods. The advantages of proposed technique are (i) the classification accuracy remains unaffected due to cancelable biometric templates generated using random permutation, (ii) security and quality of generated templates and (iii) robustness across different biometrics. In addition, no image registration is required for performing recognition.