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A novel map matching algorithm for real-time location using low frequency floating trajectory data
The continuous enhancement of technologies and modern well-equipped infrastructures are necessary for easy life. Road accident and missing vehicle ratio are very challenging in preventing misshapenness because these are continually increasing due to traffic hazards. The single way to protect human life from such type of conditions that is more reliable navigation services such as correct location tracking of vehicles on the road network. The real-time location tracking methods fully depends on the map matching algorithms, which also compute a reliable path on the road network. A smart vehicle can provide more reliable tracking services during or before any misshaping using proposed map matching algorithm. This work contributes to ensure correct location for necessary action during misshaping, alert accident zone and communicate messages without wasting valuable time. The proposed approach is validated on the real tracking data and is compared against poor GPS service. Copyright 2023 Inderscience Enterprises Ltd. -
A novel model for speech to text conversion /
International Refered Journal of Engineering And Science, Vol-3 (1), pp. 39-41,ISSN-2319-183X. -
A novel moems sensor design simulation and analysis with MEEP /
International Journal Of Engineering Technology Science And Research, Vol.2, Issue 8, pp.319-325, ISSN No: 2394-3386. -
A novel multi functional multi parameter concealed cluster based data aggregation scheme for wireless sensor networks (NMFMP-CDA)
Data aggregation is a promising solution for minimizing the communication overhead by merging redundant data thereby prolonging the lifetime of energy starving Wireless Sensor Network (WSN). Deployment of heterogeneous sensors for measuring different kinds of physical parameter requires the aggregator to combine diverse data in a smooth and secure manner. Supporting multi functional data aggregation can reduce the transmission cost wherein the base station can compute multiple statistical operations in one query. In this paper, we propose a novel secure energy efficient scheme for aggregating data of diverse parameters by representing sensed data as number of occurrences of different range value using binary encoded form thereby enabling the base station to compute multiple statistical functions over the obtained aggregate of each single parameter in one query. This also facilitates aggregation at every hop with less communication overhead and allows the network size to grow dynamically which in turn meets the need of large scale WSN. To support the recovery of parameter wise elaborated view from the multi parameter aggregate a novelty is employed in additive aggregation. End to end confidentiality of the data is secured by adopting elliptic curve based homomorphic encryption scheme. In addition, signature is attached with the cipher text to preserve the data integrity and authenticity of the node both at the base station and the aggregator which filters out false data at the earliest there by saving bandwidth. The efficiency of the proposed scheme is analyzed in terms of computation and communication overhead with respect to various schemes for various network sizes. This scheme is also validated against various attacks and proved to be efficient for aggregating more number of parameters. To the best of our understanding, our proposed scheme is the first to meet all of the above stated quality measures with a good performance. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
A novel optimised method for speckle reduction in medical ultrasound images
The advancement of medical imaging techniques evolving from X-ray to PET images and the medical image analysis helped medical experts to detect, diagnose and offer treatments for complex disorders and deadly diseases in the human body. Among the various modalities used, Ultrasound imaging is the most widely accepted modality because of its affordability, non-invasive nature and various other features. But the presence of speckle noise in ultrasound image lowers the image quality and reduces diagnostic value. This article states an improved hybrid speckle noise reduction method, a combined application of Kuan and non-local means filters. In this method, Kuan filter is used to sharpen the edges and thereafter the speckle noise elimination is done by using the non-local means. In addition, the performance of the proposed hybrid filter and its design parameters are optimised by using a meta-heuristic called grey wolf optimiser. The performance of hybrid method is evaluated by analysing a chosen set of well-known post filtering methods used for speckle reduction with given ultrasound B-mode images. The comparison of test results using remarkable performance metrics and computation time demonstrate that the hybrid method can be used as the efficient speckle reduction method for image analysis. Copyright 2022 Inderscience Enterprises Ltd. -
A Novel Paradigm for IoT Security: ResNet-GRU Model Revolutionizes Botnet Attack Detection
The rapid proliferation of the Internet of Things (IoT) has engendered substantial security apprehensions, chiefly due to the emergence of botnet attacks. This research study delves into the realm of Intrusion Detection Systems (IDS) by leveraging the IoT23 dataset, with a specific emphasis on the intricate domain of IoT at the network's edge. The evolution of edge computing underscores the exigency for tailored security solutions. An array of statistical methodologies, encompassing ANOVA, Kruskal-Wallis, and Friedman tests, is systematically employed to illuminate the evolving trends across multiple facets of the study. Given the intricacies entailed in feature selection within edge environments, Chi-square analyses, Recursive Feature Elimination (RFE), and Lasso-based techniques are strategically harnessed to unearth meaningful feature subsets. A meticulous evaluation encompassing 19 classifiers, meticulously selected from both machine learning (ML) and deep learning (DL) paradigms, is rigorously conducted. Initial findings underscore the potential of the Gated Recurrent Unit (GRU) model, especially when coupled with intrinsic lasso-based feature selection. This promising outcome catalyzes the formulation of an ensemble approach that harnesses multiple LassoCV models, aimed at amplifying feature selection proficiency. Furthermore, an optimized ResNet-GRU model emerges from the fusion of the GRU and ResNet architectures, with the objective of augmenting classification performance. In response to mounting concerns regarding data privacy at the edge, a resilient federated learning ecosystem is meticulously crafted. The seamless integration of the optimized ResNet-GRU model into this framework facilitates the employment of FedAvg, a widely acclaimed federated learning methodology, to adeptly navigate the intricacies associated with data sharing challenges. A comprehensive performance evaluation is undertaken, wherein the ResNet-GRU model is benchmarked against FedAvg and a diverse array of other federated learning algorithms, including FedProx and Fed+. This extensive comparative analysis encompasses a spectrum of performance metrics and processing time benchmarks, shedding comprehensive light on the capabilities of the model. (2023), (Science and Information Organization). All Rights Reserved. -
A novel route for isomerization of ?-pinene oxide at room temperature under irradiation of light-emitting diodes
Present investigation demonstrates the potential use of HY-zeolite for photochemical applications in the selective isomerization of ?-pinene oxide to carveol. In this study, ultraviolet lamp and LED (390 nm) light sources were employed under atmospheric conditions. The results revealed that light penetration through protonated zeolite cavity promotes the hydrogen radical formation, facilitating the isomerization reaction in the presence of dimethylacetamide solvent to achieve up to 60% and 40% conversion of ?-pinene oxide to selective carveol (71%) under light irradiation. Here, using in situ spectroscopic studies (EPR and fluorescence), to confirm the hydrogen radical generation after light irradiation on the reaction mixture. Besides, the mechanistic pathway is proposed based on the experimental evidence of the formation of radicals, which is validated by the Density Functional Theory (DFT). By comparing electrical energy consumption for the same reaction using different reaction setups, it is understood that the energy requirement is nearly the same in the case of a reaction performed using a thermal reactor. The power consumption in reactions conducted using thermal, UV lamp and LED-based reactors was 1.6 kW/h, 1.5 kW/h, and 0.00144 kW/h, respectively. It is clear that the energy consumption in thermal and UV lamp-based reactors is higher than that of LED-based reactors, which was 1111 and 1041 times more than LED reactors respectively. Notably, the catalyst was found to be recyclable at least five consecutive runs, and the successful protocol was demonstrated up to 50 g scale. 2023 Elsevier Ltd -
A novel security framework for healthcare data through IOT sensors
The Internet of Things (IoT) has played a crucial role in the distribution of health records and poses security issues to the patient-specific health information needed for remote hospital attention. The majority of publicly accessible security mechanisms for health information do not concentrate on the flow of information from IoT different sensors installed upon the person's blood through networking devices to primary health care centers. In this paper, we investigated the potential risks of unprotected transmission data, particularly among IoT sensor systems and network gateways. The study encourages the transmission of health insurance data to hospitals remotely. The proposed health care information model would encode immediately so that the sensing element before even being transferred to cryptographic techniques. To use a laboratory configuration with two-stage cryptography at the IoT sensor and two-stage decoding at the physician's surgery receptor, the prototype system was validated. The test results for a complete safety system for IoT - based on the transmission of healthcare data seem good. The study opens up new avenues for information security on IoT devices. 2022 The Authors -
A novel SIW based dual-band power divider using double-circular complementary split ring resonators
This article presents a novel design of substrate integrated waveguide (SIW) dual-band power divider loaded with double-circular complementary split-ring resonators (CSRRs). The double-circular CSRRs are etched on the top layer of the proposed structure to obtain the dual-band characteristic. The proposed geometry provides a passband frequency below the cut-off frequency of the SIW due to the electric dipole nature of the CSRRs. By changing the dimensions of the CSRRs, various passband characteristics are studied. To validate the design idea, a compact dual band power divider with equal power division operating at 8.4 and 11.7 GHz is designed, fabricated, and tested. A good steadiness is found between simulated and tested results. The proposed idea provides features of compact size, dual-band operation, and good isolation. The size of the fabricated prototype excluding microstrip transition is 0.473?g 0.284?g, where ?g is the guided wave length at the center frequency of first band. 2019 Wiley Periodicals, Inc. -
A novel survey for young substellar objects with the W-band filter IV: detection and characterization of low-mass brown dwarfs in Serpens Core
We present spectroscopic confirmation of nine M5 or later Serpens Core candidate members, identified using a combination of CFHT WIRCam photometry and IRTF SpeX spectroscopy. Through spectral fitting, we find that the latest of these nine candidate members is best fit by an L0 spectral standard (in the range of M8L2), implying a mass of ?0.010.035M?. If confirmed as a cluster member, this would be one of the lowest mass Serpens Core objects ever discovered. We present analysis of the physical properties of the sample, as well as the likely membership of the candidate Serpens Core members. 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
A Novel Survey for Young Substellar Objects with the W-band Filter. V. IC 348 and Barnard 5 in the Perseus Cloud
We report the discovery of substellar objects in the young star cluster IC 348 and the neighboring Barnard 5 dark cloud, both at the eastern end of the Perseus star-forming complex. The substellar candidates are selected using narrowband imaging, i.e., on and off photometric technique with a filter centered around the water absorption feature at 1.45 ?m, a technique proven to be efficient in detecting water-bearing substellar objects. Our spectroscopic observations confirm three brown dwarfs in IC 348. In addition, the source WBIS 03492858+3258064, reported in this work, is the first confirmed brown dwarf discovered toward Barnard 5. Together with the young stellar population selected via near- and mid-infrared colors using the Two Micron All Sky Survey and the Wide-field Infrared Survey Explorer, we diagnose the relation between stellar versus substellar objects with the associated molecular clouds. Analyzed by Gaia EDR3 parallaxes and kinematics of the cloud members across the Perseus region, we propose the star formation scenario of the complex under influence of the nearby OB association. 2022. The Author(s). Published by the American Astronomical Society. -
A Novel Survey for Young Substellar Objects with the W-band Filter. VI. Spectroscopic Census of Substellar Members and the IMF of the ? Orionis Cluster
Low-mass stars and substellar objects are essential in tracing the initial mass function (IMF). We study the nearby young ? Orionis cluster (d ? 408 pc, age ? 1.8 Myr) using deep near-infrared (NIR) photometric data in the J, W, and H bands from WIRCam on the Canada-France-Hawaii Telescope. We use the water absorption feature to select brown dwarfs photometrically and confirm their nature spectroscopically with IRTF-SpeX. Additionally we select candidate low-mass stars for spectroscopy and analyze their membership and those of literature sources using astrometry from Gaia DR3. We obtain NIR spectra for 28 very-low-mass stars and brown dwarfs and estimate their spectral type between M3 and M8.5 (masses ranging between 0.3 and 0.01 M ?). Apart from these, we also identify five new planetary-mass candidates which require further spectroscopic confirmation of youth. We compile a comprehensive catalog of 170 spectroscopically confirmed members in the central region of the cluster, for a wide mass range of ?19-0.004 M ?. We estimate the star-to-brown-dwarf ratio to be ?4, within the range reported for other nearby star-forming regions. With the updated catalog of members we trace the IMF down to 4 M Jup and we find that a two-segment power law fits the substellar IMF better than a log-normal distribution. 2023. The Author(s). Published by the American Astronomical Society. -
A novel technique for leaf disease classification using Legion Kernels with parallel support vector machine (LK-PSVM) and fuzzy C means image segmentation
Detection of plant disease and classificationare being investigated in many parts of the worldto save precious medical plants from becoming extinct.Major problem in this task, include the lack of advanced and technology driven solution. Manual identification is often time-consuming and prone to inaccuracies. Therefore, there is an urgent need for an automated and efficient method that can accurately identify and classify plant diseases. This article focuses on detecting the disease through classificationthrough a new technique using leaf images for automatic classification. This paper proposes a novel segmentation technique using Fuzzy C means and Particle Swarm Optimization for effective segmentation of leaf images and feature extraction that can help in classification of disease.The approach emphasizes on the integration of techniques such as image processing, segmentation and feature extraction and finally the classification, which offers a comprehensive solution for the disease detection. The work leverages on the advantages of Legion Kernels and Parallal support vector Machine (LK-PSVM) clubbed with fuzzy C means Image segmentation to offer a framework that can handle diverse leaf images and which can effectively differentiate the type of the disease.The proposed method LK-PSVM combined with Fuzzy C means presents a novel approach that is significantly deviated from the conventional methods of leaf disease classification.The proposed wok brings an integrated framework which can synergistically combine the Legion Kernels with the PSVM technique coupled with Fuzzy C Means Image segmentation which can handle the issue of overlapped data sets and support vector machines are used to handle the situation where the number of dimensions are more than the number of samples, which is more probable in the classification problem under consideration.By integrating these components, the proposed method achieves more accuracy and robustness when compared to the existing methods in the literature. The segmentation is carried out using PSO after pre-processing of images. The Gaussian functions are used to eliminate the background subtraction. Different features of the images are then computed. A total of 55,400 images were used for the experiment consisting of various plants leaves spreading across 38 labels. A classifier is then proposed using Machine learning methods for the detection of disease in apple fruit leaves. The experiments prove that the proposed method have high degree of classification accuracy when compared to existing methods. The proposed method not only cater to the need in terms of accuracy but also making it scalable for different types of leaves. 2024 The Authors -
A Novel Threshold based Method for Vessel Intensity Detection and Extraction from Retinal Images
Retinal vessel segmentation is an active research area in medical image processing. Several research outcomes on retinal vessel segmentation have emerged in recent years. Each method has its own pros and cons, either in the vessel detection stage or in its extraction. Based on a detailed empirical investigation, a novel retinal vessel extraction architecture is proposed, which makes use of a couple of existing algorithms. In the proposed algorithm, vessel detection is carried out using a cumulative distribution function-based thresholding scheme. The resultant vessel intensities are extracted based on the hysteresis thresholding scheme. Experiments are carried out with retinal images from DRIVE and STARE databases. The results in terms of Sensitivity, Specificity, and Accuracy are compared with five standard methods. The proposed method outperforms all methods in terms of Sensitivity and Accuracy for the DRIVE data set, whereas for STARE, the performance is comparable with the best method. 2021. All Rights Reserved. -
A novel two-tier feature selection model for Alzheimers disease prediction
The interdisciplinary research studies of artificial intelligence in health sector is bringing drastic life saving changes in the healthcare domain. One such aspect is the early disease prediction using machine learning and regression algorithms. The purpose of this research is to improve the prediction accuracy of Alzheimers disease by analysing the correlation of unexplored Alzheimer causing diseases. The work proposes Chi square-lasso ridge linear (Chi-LRL) model, a new two-tier feature ranking model which recognizes the significance of including diabetes, blood pressure and body mass index as potential Alzhiemer predictive parameters. The newly added predictive parameters of Alzheimers disease were statistically verified along with the conventional prediction parameters using chi-square method (Chi) as Tier 1 and an embedded model of lasso, ridge and linear (LRL) Regression for feature ranking as Tier 2. The performance of the proposed Chi-LRL model with selected features were then analysed using machine learning algorithms for performance analysis. The result shows a noticeable performance by selecting eleven significant features and a 4.5% increase in the prediction accuracy of Alzheirmer disease. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
A numerical approach to the coupled atmospheric ocean model using a fractional operator
In the present framework, the coupled mathematical model of the atmosphere-ocean system called El Nino-Southern Oscillation (ENSO) is analyzed with the aid Adams-Bashforth numerical scheme. The fundamental aim of the present work is to demonstrate the chaotic behaviour of the coupled fractional-order system. The existence and uniqueness are demonstrated within the frame of the fixed-point hypothesis with the CaputoFabrizio fractional operator. Moreover, we captured the chaotic behaviour for the attained results with diverse order. The effect of the perturbation parameter and others associated with the model is captured. The obtained results elucidate that, the present study helps to understand the importance of fractional order and also initial conditions for the nonlinear models to analyze and capture the corresponding consequence of the fractional-order dynamical systems. 2021 by the authors. -
A Numerical Investigation on Thermal Gradients and Stresses in High Temperature PEM Fuel Cell During Start-up
The High Temperature Polymer Electrolyte Fuel Cell (HT-PEMFC) stacks using polybenzimidazole (PBI) based membranes have an inability to internally heat up at low temperatures to their nominal operating temperature (160C180C) during the start-up process. Several strategies, such as direct electrical heating, coolant/gas channel heating, catalytic hydrogen-oxygen combustion, etc., are proposed in the literature to assist the heating for quick start-up situations. However, little knowledge exists on the transient thermomechanical stresses induced during the start-up heating process due to non-uniformity in heat supply and disparity in thermal properties of the cell components. The objective of the present research is to analyze the thermal gradients and thermal stresses developed in the HT-PEMFC structure during the start-up with various heating methods discussed in the literature, as well as during the cell operation by exploiting the Fluid-Structure Interaction (FSI) approach. The use of polyalkylene glycol (Fragoltherm S-15-A) based Heat Transfer Fluid (HTF) in the coolant channel has substantially improved the start-up time due to the high Nusselt number. However, a significant gradient in temperature distribution is observed during the preheating process, which resulted in great inhomogeneous stresses in the membrane, particularly in the in-plane direction. Interestingly, the degree of uniformity in membrane current density distribution during cell operation is increased. A detailed heat analysis in the cell showed that the heat generated in the cell due to electrochemical reactions is sufficient to raise the cell temperature from 120C to operating temperature in a short time. Being subjected to a compressive stress of above 40 MPa, which is higher than the ultimate strength of a typical acid doped PBI membrane, the electrolyte is the most vulnerable component during the start-up. Hence, to inhibit the concomitant effect on cell performance and degradation, a novel start-up strategy should be implemented. 2021 Elsevier Ltd -
A pair of kinematically related space curves
We investigate the relation between two types of space curves, the Mannheim curves and constant-pitch curves and primarily explicate a method of deriving Mannheim curves and constant-pitch curves from each other by means of a suitable deformation of a space curve. We define a "radius" function and a "pitch" function for any arbitrary regular space curve and use these to characterize the two classes of curves. A few non-trivial examples of both Mannheim and constant pitch curves are discussed. The geometric nature of Mannheim curves is established by using the notion of osculating helices. The Frenet-Serret motion of a rigid body in theoretical kinematics is studied for the special case of a Mannheim curve and the axodes in this case are deduced. In particular, we show that the fixed axode is developable if and only if the motion trajectory is a Mannheim curve. 2018 World Scientific Publishing Company. -
A Paradigm shift in Family therapy in India : Exploration from Socioeconomic, Cultural and Spiritual Perspectives
International Journal of Physical and Social Sciences Vol. 3, Issue 3,pp. 153-166 , ISSN No. 2249-5894 -
A Perspective on Challenges and Opportunities of Supply Chain Management
Global Journal of Arts and Management Vol. 2, No. 3, pp. 227 - 231, ISSN No. 2249-2658