Analysis of a Disc-Loaded Circular Waveguide for Interaction Impedance of a Gyrotron Amplifier

A rigorous electromagnetic analysis of a circular waveguide loaded with axially periodic annular discs was developed in the fast-wave regime, considering finite axial disc thickness and taking into account the effect of higher order space harmonics in the disc-free region and higher order modal harmonics in the disc-occupied region of the structure. The quality of the disc-loaded circular waveguide was evaluated with respect to its azimuthal interaction impedance that has relevance to the gain of a gyrotron millimeter-wave amplifier (gyro-traveling-wave tube) in which such a loaded waveguide finds application as a wideband interaction structure. The results of electromagnetic analysis of the structure with respect to both the dispersion and azimuthal interaction impedance characteristics were validated against the commercially available code: high frequency structure simulator (HFSS). The analysis predicts that the value of the interaction impedance at a given frequency decreases with the increase of the disc hole radius and disc periodicity. The change of the axial disc thickness does not significantly change the value of the interaction impedance though it shifts the frequency range over which appreciable interaction impedance is obtained. Out of the three disc parameters, namely the disc hole radius, thickness and periodicity, the lattermost is most effective in controlling the value of the azimuthal interaction impedance. However, the passband of frequencies and the center frequency of the passband both decrease with the increase of the disc periodicity. Moreover, the disc periodicity that provides large azimuthal interaction impedance would in general be different from that giving the desired dispersion shape for wideband interaction in a gyro-TWT, suggesting a trade-off in the value of the disc periodicity to be chosen.     

Retina disease prediction using modified convolutional neural network based on Inception-ResNet model with support vector machine classifier

Artificial intelligence and deep learning have aided ocular disease through experiments including automatic illness recognition from images of the iris, fundus, or retina. Automated diagnosis systems (ADSs) provide services for the benefit of humanity and are essential in the early detection of harmful diseases. In fact, early detection is essential to avoid total blindness. In real life, several diagnostic tests such as visual ocular tonometry, retinal exam, and acuity test are performed, but they are conclusively time demanding and stressful for the patient. To consume time and detect the retinal disease earlier, an efficient prediction method is designed. In this proposed model, the first process is data collection that consists of a retinal disease dataset for testing and training. The second process is pre-processing, which executes image resizing and noise filter for feature extraction. The third step is feature extraction, which extracts the image's form, size, color, and texture for classification with CNN based on Inception-ResNet V2. The classification process is done by using the SVM with the extracted features. The prediction of diseases is classified such as normal, cataract, glaucoma, and retinal disease. The suggested model's performance is assessed using performance indicators such as accuracy, error, sensitivity, precision, and so forth. The suggested model's accuracy, error, sensitivity, and precision are 0.96, 0.962, 0.964, and 0.04, respectively, higher than existing techniques such as VGG16, Mobilenet V1, ResNet, and AlexNet. Thus, the proposed model instantly predicts retinal disease.     

Comprehensive studies on polyethylenimine filled polypropylene and its potential application in carbon dioxide sequestration

Polypropylene (PP) was blended with branched polyethylenimine (PEI) with the aim to prepare blends having CO₂ adsorption property. The CO₂ adsorption properties will be conferred due to the presence of variety of amine functionality in PEI. PEI contains primary, secondary as well as tertiary amine groups. Before testing CO₂ adsorption, PP–PEI blends were characterized using variety of techniques, for example, differential scanning calorimetry, thermogravimetric analysis, dynamic mechanical analysis, scanning electron microscopy, and polarized light optical microscopy. In this work, we have studied in detail both compatibilized as well as noncompatibilized blends of PP and PEI. The compatibilization was achieved via addition of maleic anhydride grafted PP. Finally, all the compatibilized as well as noncompatibilized blends were studied for CO₂ adsorption. The compatibilized blends showed better thermal, mechanical as well as CO₂ adsorption properties as compared to the noncompatibilized blends. POLYM. ENG. SCI., 59:2092–2102, 2019. © 2019 Society of Plastics Engineers     

Micro-mechanical Behaviour of AA2014 Alloy During Hot Tensile Testing

Nowadays, critical aircraft application demands high end performance of respective aerospace materials. Al–4.5 %Cu alloy known as duralumin have extensive application on spacecraft, as ring rolled and heat treated condition. However, behaviour of the same alloy at high temperature is always a thirst area to improve the understanding. This paper mainly focussed on the behaviour of duralumin alloy during hot tensile testing. Hot tensile testings were done at six different temperatures ranging from room temperature to 300 °C. Mechanical properties at various temperatures were measured and studied along with the microstructural changes at all experimental conditions. Thermodynamic software package FactSage simulation was employed to identify the precipitates expected to precipitate at high temperature. Important conclusions were drawn from both theoretical and experimental observations.     

Deep learning-based extractive text summarization with word-level attention mechanism

Multimedia Tools and Applications - With the rise in the amount of textual data over the internet, the demand for summarizing it in a short, readable, easy-to-understand form has increased. Much of...     

Fusion of multi-exposure images using recursive and Gaussian filter

Multidimensional Systems and Signal Processing - This paper proposes a novel technique to create a high-resolution image by combining the bracketed exposure sequence without a priori...     

Secure data processing for IoT middleware systems

The Journal of Supercomputing - Increasingly, more manufacturing companies are equipping their products with smart capabilities which allow them to provide more informed services to customers....     

Efficient data management and control over WSNs using SDN‐enabled aerial networks

The recent developments in collaborative search, acquisition, and tracking have hoisted the geographical barrier. The network between unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) is one such collaboration, which comprises battery‐powered static sensor nodes that act as sources and sinks and UAVs that act as relays. This collaborative network presents with opportunities and advantages, but at the same time, configuration of such networks is an arduous task. The WSN nodes are characterized by constant depleting power. Their network itself requires constant management and reconfiguration. These requisites can be slaked through the formation of an efficient data dissemination algorithm, which acclimates according to the network state. Considering this, a data dissemination approach is presented in this paper, which constructs a virtual topology predicated on the charge of WSN nodes utilizing software‐defined networks (SDNs) through UAVs. The topology is constantly monitored and reconfigured when required. The aerial nodes are equipped with multiple‐input multiple‐output (MIMO) antennas in order to facilitate simultaneous communication with the ground nodes, the base station, and the SDN controller. An efficient sleep timer and backoff counter strategies are also utilized by the proposed approach. The SDN controller facilitates the topology formation and maintenance of a sleep timer and a backoff counter. The proposed model is compared with clustered hierarchical layouts and hexagonal cell layouts through the network simulations. The results suggest significant improvements in the proposed model for various metrics, such as lifetime, delay, latency, delivery ratio, and throughput in comparison with the existing solutions.     

Energy harvesting from far field RF signals

In this article, an alternative source of energy harvesting is proposed. It is based on the concept of charge pump electronics circuit and radio frequency (RF) signal amplifier. The RF signals are acquired by the Dickson charge pump circuit, amplified, and converted into a desired DC signal. To ensure the maximum power extraction, the proposed energy harvester circuit includes multiple circuit level approach. The diode‐capacitor charge pump generated the step‐up stage in the system. The proposed idea is designed and implemented using a suitable hardware successfully. Initially, the designed circuit is simulated and tested using the MultiSim software and then hardware implemented to obtained the desired 1‐5 V DC signal. The presented circuit can be used in various applications such as electronic devices charging, power supply, energy harvesting, etc.     

Non-unified Compact Residual-Distribution Methods for Scalar Advection–Diffusion Problems

This paper solves the advection–diffusion equation by treating both advection and diffusion residuals in a separate (non-unified) manner. An alternative residual distribution (RD) method combined with the Galerkin method is proposed to solve the advection–diffusion problem. This Flux-Difference RD method maintains a compact-stencil and the whole process of solving advection–diffusion does not require additional equations to be solved. A general mathematical analysis reveals that the new RD method is linearity preserving on arbitrary grids for the steady-state advection–diffusion equation. The numerical results show that the flux difference RD method preserves second-order accuracy on various unstructured grids including highly randomized anisotropic grids on both the linear and nonlinear scalar advection–diffusion cases.