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Performance Improvement in E-Gun Deposited SiOx- Based RRAM Device by Switching Material Thickness Reduction
A performance improvement by reduction in switching material thickness in a e-gun deposited SiOx based resistive switching memory device was investigated. Reduction in thickness cause thinner filamentary path formation during ON-state by controlling the vacancydefects. Thinner filament cause lowering of operation current from 500 ?A to 100 ?A and also improves the reset current (from >400 ?A to <100 ?A). Switching material thickness reductionalso cause the forming free ability in the device. All these electrical parametric improvements enhance the device reliability performances. The device show >200 dc endurance, >3-hour dataretention and >1000 P/E endurance with 100 ns pulses. 2022 Institute of Physics Publishing. All rights reserved. -
Performance improvement of triple band truncated spiked triangular patch antenna
In this paper, the design of a novel triple band triangular microstrip patch antenna with inset feed is proposed. The triangular patch is designed for a resonant frequency of 2 GHz. The inset feed is placed at a depth of 1/3rd of height from the bottom of the patch for improved return loss. The insertion of two slots and two tabs causes the antenna to resonate at multiple frequencies. The proposed antenna resonates at three frequencies: 1.939 GHz, 2.515 GHz and 3.212 GHz. The truncation of the edges of the patch and the tabs improves the gain and directivity of the antenna. 2016 IEEE. -
Performance inquisition of web services using soap UI and JMeter
Web Service is a managed code through which the user can expose the existing functionality over the network. Web Service allows multiple applications to communicate with each other. The communication involves passing the data or interaction of two services for a specified action. There are commercial and open source tools available for testing web services. This paper describes about two popular open source tools to test the performance of the web services in terms of response time. The performance is tested based on the time acquired by each service. The comparison study will help in understanding the usage of web service testing tools and adoption of these tools for testing purpose. 2017 IEEE. -
Performance optimization for extraction, transformation, loading and reporting of data
Enterprise Resource Planning has become the cornerstone for making data acquisition and related operations more efficient. Recent advances in hardware and software technologies have enabled us to think about performance optimization. Ninety percent of ERP projects spend more than their allocated budgets and have exceeded the time schedule for implementation. There are many factors that can be attributed to the low success rate of implementation but one main factor is the performance of the ERP package itself. In this paper, we have described the Business Intelligence tool and database which is related to Systems, Applications and Products. It is popularly known as SAP. Based on this, a new, mulch-dimensional performance metric is proposed for extracting, transforming, loading and reporting the data. 2015 IEEE. -
Person re-identification using part based hybrid descriptor
Real time person re-identification systems require robust descriptors for useful feature extraction. This paper focuses on a novel descriptor which can efficiently re-identify persons in varied views and change in illumination. The descriptors detect the features by dividing the person image into multiple parts. We use a combination of local and global feature descriptors to form a reliable descriptor. Performance evaluation is done on a benchmarking dataset. 2016 IEEE. -
Pertaining analysis of fracture risk in Osteoporotic patients using Machine Learning Techniques
Bone fractures in the spine or hip are the most severe complications of Osteoporosis. Older subjects with Osteoporosis are vulnerable to falls. This paper aims to review the breakthrough in machine learning methods over the past four years in assessing fracture risk in osteoporotic patients. Machine learning is applied in the healthcare and medical field. Machine learning professionals can accurately predict disease onset by analyzing a large amount of data. Osteoporosis is one of the healthcare domains in which new Machine learning and Artificial Intelligence techniques can be implemented. The objective of this research is to give an overview of the recent advancements in machine learning methods in finding out the risk factors for fractures or predicting the onset of disease. A systematic search was conducted in PubMed to get research papers published on Machine learning methods to detect, classify, or predict osteoporosis-related fracture risk. The articles belonging to Fracture prediction and risks (n=14), Osteoporosis classification(n=3), Diagnosis of fracture(n=3), and Predicting length of stay (n=1) were identified. The quality of the articles is assessed. Most articles described the efforts to create the model and showcased excellent results in predicting the risks. Significant limitations were in the form of inadequate data splitting and data validations. More validation studies are needed in various large groups to improve the model. Most of the participants in significant studies were in their initial stage of the disease, and the reproducibility analysis was done with major disease issues. 2023 IEEE. -
Phonon limited diffusion thermopower in phosphorene
A theoretical investigation of diffusion thermopower, Sd, of phosphorene employing Boltzmann transport formalism is presented. We assume carriers in phosphorene to be scattered by in-plane single and flexural two-phonon processes via deformation potential coupling. Our calculations of Sd in phosphorene show that, at low temperatures (T?< 20 K) Sd increases linearly with temperature and for the range of temperatures considered single phonon contribution to Sd dominates. As function of carrier concentration, ns, considered (1016?1018 m-2), at T = 300K, Sd decreases from 189?V/K to 9.9 ?V/K. 2017 Author(s). -
Photometric and spectroscopic study of candidate be stars in the magellanic clouds
[No abstract available] -
Photon, Electron, Proton and Alpha Particle Interaction Parameters of Different Clays
Modern life has made human beings and nature vulnerable to harmful radiations at different levels. This can be a great health hazard of our times. Since there is no probability of dodging the harmful influence, the practical way out is having protective shielding. Lead, the most efficient attenuator in current use has the drawbacks of being heavy, toxic and capable of producing secondary radiations. Other attenuators concrete, glass etc. have similar deficits in use. This is the context of the scientific world's quest for a perfect shielding material which can provide protection from harmful radiations effectively, economically and environment friendly. This work attempts a computational study on the radiation shielding efficiency of different types of clays, understanding of which would enable its applications for radiation shielding. The presence of high Z elements and the layered structure of clay along with its good thermal stability make it ideal filler for an effective radiation shield. In this work, we have performed a systematic study of the mass attenuation coefficients, effective atomic number and electron density of various clay samples. 2022 American Institute of Physics Inc.. All rights reserved. -
Physical layer impairment-aware routing and wavelength assignment (PLI-RWA) strategy for mixed line rate (MLR) wavelength division multiplexed (WDM) optical networks
The ever increasing global Internet traffic is resulting in a serious upgrade of the current optical networks' capacity. The legacy infrastructure can be enhanced not only by increasing the capacity, but also by adopting advance modulation formats, having increased spectral efficiency at higher data rate. In a mixed-line-rate (MLR) optical network, different line rates, on different wavelengths, can coexist on the same fiber. Further, studies have shown that migration to data rates higher than 10Gbps requires implementation of phase modulation schemes. However, the co-existing On-Off Keying (OOK) channels cause critical physical layer impairments (PLIs) to the phase modulated channels, mainly due to cross-phase modulation (XPM), which in turn limits the network's performance. In order to mitigate this effect, a more sophisticated PLI-Routing and Wavelength Assignment (PLI-RWA) scheme needs to be adopted. In this work, we investigate the critical impairment for each data rate and the way it affects the quality of transmission (QoT). We propose a novel PLI-RWA algorithm for MLR optical networks. The proposed algorithm is compared through simulations with the existing shortest path and minimum hop routing schemes. 2015 IEEE -
Physical Unclonable Function and OAuth 2.0 Based Secure Authentication Scheme for Internet of Medical Things
With ubiquitous computing and penetration of high-speed data networks, the Internet of Medical Things (IoMT) has found widespread application. Digital healthcare helps medical professionals monitor patients and provide services remotely. With the increased adoption of IoMT comes an increased risk profile. Private and confidential medical data is gathered across various IoMT devices and transmitted to medical servers. Privacy breach or unauthorized access to personal medical data has far-reaching consequences. However, heterogeneity, limited computational resources, and lack of standardization in authentication schemes prevent a robust IoMT security framework. This paper introduces a secure lightweight authentication and authorization scheme. The use of the Physical Unclonable Function (PUF) reduces pressure on computational resources and establishes the authenticity of the IoMT. The use of OAuth 2.0 open standard for authorization allows interoperability between different vendors. The resilience of the model to impersonation and replay attacks is analyzed. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Pixels to Pathogens: A Deep Learning Approach to Plant Pathology Detection
It is known that accurately identifying, early and timely treatment and elimination of the plant diseases is essential for crop protection and healthy crop growth. In traditional or conventional methods, identification and classification were done by testing in laboratories or through visual inspection by farmers. Now going through the testing in labs is very time consuming, while the visual inspection requires enough experience and knowledge. To solve this problem, our study proposes a robust plant pathogen detection method based on a Deep Learning approach on a large dataset containing about 38 categories of different species like Maize, Potatoes, Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases like rust, molds, blight (late and early). This crop disease detection model leverages the power of the EfficientNetB3 architecture, a state-of-art convolutional neural network(CNN). The main backbone is served by EfficientNetB3and then it is fine-tuned using different hyperparameters and other regularization techniques like weight decay, dropout method and optimizers like RAdam,to enhance the overall accuracy coupled with dynamic learning rate adjustment. In the testing set of the dataset, the proposed model shows encouraging accuracy of about 99.25%, high precision of about 97.35%. A thorough evaluation of the model's functionality is given by the help of training and validation line chart and loss chart that gives the in-depth information on the prediction. And then we implemented the detection model in our mobile application whose interface screen shots are given below. In the application the image can be taken by camera or fed from folders and it will detect the type of disease. 2024 IEEE. -
Plant Disease Detection and Classification using Emperor Penguin Optimizer (EPO) based Region Convolutional Neural Network (RCNN)
Agriculture stands as India's most crucial industry, despite grappling with a 35% annual loss in crop yield attributed to plant diseases. Traditionally, the detection of plant diseases has been a laborious process, hampered by insufficient laboratory infrastructure and expert knowledge. Plant disease detection methods that are automated provide a useful way to expedite the labor-intensive process of keeping an eye on large-scale agricultural fields and recognizing disease symptoms as soon as they appear on plant leaves. Current developments in deep learning (DL) and computer vision have highlighted the benefits of creating autonomous models for plant disease identification based on visible symptoms on leaves. In this study, we propose a novel method for detecting and classifying plant diseases by combining the Emperor Penguin Optimizer (EPO) with a Region Convolutional Neural Network (RCNN). The suggested methodology uses EPO to improve the discriminative power of features extracted from plant pictures, allowing for a more robust and accurate classification procedure. The Classification Region Convolutional Neural Network (RCNN) is used to leverage spatial correlations within the image, allowing for exact disease region localization. The goal of this integration is to increase the overall efficiency and dependability of plant disease detection systems. The investigations made use of the well-known PlantVillage dataset, which comprises 54,305 data of different plant disease types in 38 categories. Furthermore, an analysis was carried out in comparison with similar advanced investigations. According to the experiment results, RCNN-EPO outperformed in terms of classification accuracy, achieving 94.552%. 2024 IEEE. -
Plasma sprayed magnesium aluminate and alumina composite coatings from waste aluminum dross
The absence of structured waste management practices for tons of black aluminum dross (Al-dross) when land-filled affects the ecosystem we live in. Researchers and technologists are now working towards three goals (a) minimization of Al-dross production (b) reducing its toxic effects on the environment and (c) treating the Al-dross to beneficiate useful materials from it in an environmentally friendly manner and to generate useful industrial products. The third aspect has been addressed in this study. Al-dross is an aluminum industry generated waste that mainly contains Al metal (oxidized during processing), Aluminum Nitride (AlN), ?-aluminum oxide (?-Al2O3) and magnesium aluminate (MgAl2O4). The oxides are highly suitable for refractory and thermally insulating material applications, but AlN is detrimental for two reasons - (a) thermal conductivity higher than the oxides and (b) carcinogenic gas evolution during processing. Hence AlN must be removed from Al-dross for further processing into refractories. In this work, AlN with minor quantities of halides were removed from Al-dross to extract the major useful refractory oxide constituents in an environmentally friendly manner. The process methodology involved sieving Al-dross to < 600 m particles, aqueous media treatment to remove the nitrides in the form of NH3 gas, oven drying and calcination at 10001150 C for 2 h (in an electrical muffle furnace in ambient air atmosphere) to obtain a mixture of the composite oxide powder of ? 99.0% purity. The calcined compound was mixed with suitable organic binders and sieved to obtain plasma sprayable powder and plasma spray-coated onto bond coated (commercial NiCrAlY) steel substrates. XRD and SEM with EDS facility were used to characterize the powders and coatings. A polished metallographic cross-section was prepared to study the microstructure and interface characteristics. The findings are presented. 2022 -
Plasma sprayed nano refractory coatings
Nano powders may be reconstituted into micron sized plasma sprayable powders either by using a spray drier or a manual process by employing organic binders to agglomerate them. This paper deals with the synthesis of nano sized alumino-silicate plasma sprayable powders and plasma sprayed coatings prepared from them. Nano sized raw materials involving kyanite and andalusite refractory powders were converted into plasma sprayable powders by using polyvinyl alcohol (PVA) binders. The preparation methodology involved obtaining free flowing, micron sized agglomerated nano-alumino-silicates particles which could be plasma spray coated by using an Atmospheric Spray Coating Facility. About 220 microns thick nano-alumino silicate coatings were deposited on 75 microns thick commercial NiCrAlY bond coat on stainless steel substrates. The challenges involved in plasma spray coating the nano material with low density was in obtaining good deposition efficiency, retaining the nano micro structures and the structural phase composition of the coating. The coatings were evaluated for materials characteristics such as crystal structural phase via XRD, microstructure via SEM and chemical composition via EDS. The microstructure depicted fine grained nano-sized surface morphologies, kyanite and andalusite phase structure, with high potential for application as refractory coatings. Published under licence by IOP Publishing Ltd. -
Plasma Sprayed Refractory Coatings from Aluminium Dross
Refractory coatings on metals offer a unique blend of chemical inertness, stability and mechanical properties at temperatures higher than what the metal can normally withstand. However, a balance must be struck with many factors: Thickness, adhesion, performance, durability, economy and suitability for specific end use requirements. The present-day technology requires the coating to give effective service under extreme temperatures while being environmentally friendly and be easily available. One application of refractory coating is in steel industry-pipe linings. This research works highlights the potential to use aluminum dross, an industrial waste material to generate refractory coatings, comprised of Al2O3 and MgAl2O4 after suitable processing. Al dross is a byproduct of the Aluminium smelting process which can be recycled mechanically to separate the residual Aluminium metal from the Aluminium oxide. These are usually produced in tones every year and are found to be dumped in landfills and other empty spaces which generate toxic fumes like methane and other gases when reacted with moisture. The Aluminium dross used in this work was analyzed and found to comprise of its usual constituents such as metallic Al, MgAl2O4, Al2O3, AlN and other oxides and nitrides in minute quantities. Manual procedures were conducted to synthesize plasma spray-able dross which was further introduced to standard laboratory tests for the removal of undesirable constituents like AlN and other nitrides which led to the optimization of quality of powders. Atmospheric plasma spray (APS) coating methodology was used to deposit 250?m thick coatings of re-processed Al dross, involving the spraying of the processed powder onto a bond coated (NiCrAlY) steel substrate. The raw, reprocessed and the plasma sprayed coated Al dross were evaluated for their material characteristics by employing X-ray Diffractometry (XRD) for crystal structural phases, microstructure and chemical composition by employing sophisticated microscopy (SEM) technique and EDS associated with the SEM. The paper is presented keeping in in view the aptness of reprocessed Al dross, an industrial waste material to be utilized as refractories for use in engineering industries. 2019 Elsevier Ltd. -
PMFRO: Personalized Mens Fashion Recommendation Using Dynamic Ontological Models
There is a thriving need for an expert intelligent system for recommending fashion especially focusing on mens fashion. As it is an area which is neglected both in terms of fashion and modelling intelligent systems. So, in this paper the PMFRO framework for mens recommendation has been put forth which indicates the semantic similarity schemes with auxiliary knowledge and machine intelligence in a very systematic manner. The framework intelligently creates mapping of the preprocessed preferences and the user records and clicks with that of the items in the profile. So, this model aggregates community user profiles and also maps the mens fashion ontology using strategic semantic similarity schemes. Semantic similarity is evaluated using Lesk similarity and NPMI measures at several stages and instances with differential set thresholds and the dataset is classified using the feature control, machine learning bagging classifier which is an ensemble model in order to recommend the mens fashion. The PMFRO framework is an intelligent amalgamation and integration of auxiliary knowledge, strategic knowledge, user profile preferences as well as machine learning paradigms and semantic similarity models for recommending mens fashion and overall precision of 94.68% and FDR of 0.06 was achieved using the PMFRO model. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Pneumonia Detection using Ensemble Transfer Learning
Pneumonia is among the most common illnesses and causes to death among the young children worldwide. It is more serious in under-developed countries as it is hard to diagnose due to the absence of specialists. Chest X-ray images have essentially been utilized in the diagnosis of this disease. Examining chest X-rays is a difficult task, even for an experienced radiologist. Information Technology, especially Artificial Intelligence, have started contributing to accurate diagnosis of pneumonia from chest X-ray images. In this work, we used deep learning, transfer learning, and ensemble voting to increase the accuracy of pneumonia detection. The models utilized are VGG16, MobileNetV2, and InceptionV3, all pre-trained on ImageNet, and used the Kaggle RSNA CXR image dataset. The results from these models are ensembled using the weighted average ensemble approach to achieve better accuracy and obtained 98.63% test accuracy. The results are promising, and the proposed model can assist doctors in detecting pneumonia quickly and accurately from Chest X-Ray. 2022 IEEE. -
Political Optimizer Algorithm for Optimal Location and Sizing of Photovoltaic Distribution Generation in Electrical Distribution Network
In this paper, the political optimizer (PO), a new and efficient socio-inspired meta-heuristic search algorithm, is proposed for the first time in this research for determining the ideal locations and capacities of photovoltaic (PV) distribution generation (DG) in electrical distribution networks (EDN). A multi-objective function is designed to lower distribution losses and voltage deviation indexes and maximize voltage stability, among other objectives. The computational efficiency of PO when solving the optimal allocation of PV systems in EDN is investigated on an IEEE 33-bus EDN. The results indicate that integrating small DGs at multiple locations has a better EDN performance than integrating a single significant DG in the network. The results also suggest that, as demonstrated by a comparative analysis of PO results and those of other related literature works, PO can deal with complex multi-variable optimization problems. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Polynomial time algorithm for inferring subclasses of parallel internal column contextual array languages
In [2,16] a new method of description of pictures of digitized rectangular arrays is introduced based on contextual grammars, called parallel internal contextual array grammars. In this paper, we pay our attention on parallel internal column contextual array grammars and observe that the languages generated by these grammars are not inferable from positive data only. We define two subclasses of parallel internal column contextual array languages, namely, k-uniform and strictly parallel internal column contextual languages which are incomparable and not disjoint classes and provide identification algorithms to learn these classes. Springer International Publishing AG 2017.