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A high aspect ratio surface micromachined accelerometer based on a siC-CNT composite material

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Microsystems & Nanoengineering 2024年第2期10卷 219-230页

作者： Jiarui Mo shreyas shankar Roberto Pezone Guoqi Zhang sten Vollebregt Laboratory of Electronic Components Technology and Materials(ECTM)Department of MicroelectronicsDelft University of TechnologyDelftThe Netherlands

silicon carbide(siC)is recognized as an excellent material for microelectromechanical systems(MEMs),especially those operating in challenging environments,such as high temperature,high radiation,and corrosive environments.However,siC bulk micromachining is still a challenge,which hinders the development of complex siC MEMs.To address this problem,we present the use of a carbon nanotube(CNT)array coated with amorphous siC(a-siC)as an alternative composite material to enable high aspect ratio(HAR)surface micromachining.By using a prepatterned catalyst layer,a HAR CNT array can be grown as a structural template and then densified by uniformly filling the CNT bundle with LPCVD a-siC.The electrical properties of the resulting siC-CNT composite were characterized,and the results indicated that the electrical resistivity was dominated by the CNTs.To demonstrate the use of this composite in MEMs applications,a capacitive accelerometer was designed,fabricated,and measured.The fabrication results showed that the composite is fully compatible with the manufacturing of surface micromachining devices.The Young’s modulus of the composite was extracted from the measured spring constant,and the results show a great improvement in the mechanical properties of the CNTs after coating with a-siC.The accelerometer was electrically characterized,and its functionality was confirmed using a mechanical shaker.

关键词： composite surface electrical

Comparative measurement of CO_(2),CH_(4)and CO at two traffic interjunctions having inflated vehicular flow in Delhi

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Journal of Environmental sciences 2024年第7期141卷 314-329页

作者： Komal Daya soni Khem singh shankar G.Aggarwal CsIR-National Physical Laboratory Dr.K.S.Krishnan MargNew Delhi 110012India Academy of scientific&Innovative Research(AcsIR) Ghaziabad 201002India

Vehicular emissions are considered one of the major anthropogenic sources of greenhouse gases and poor air quality in metropolitan cities.This study aims to see the correlation of CO_(2),CH_(4),and CO through monitoring over a period from December 2020 to October 2021 covering three seasons’winter,summer,and monsoon at two different traffic locations of Delhi having different traffic volumes,road patterns,and traffic management.The annual average morning concentration of CO_(2),CH_(4)and CO was found(533±105),(7.3±3.1),(10.7±3.0)ppm at Najafgarh and(480±70),(5.2±1.8),(7.8±2.8)ppm at Rajendra Place,respectively.A relationship between concentration of all three gases and meteorological parameters such as temperature,humidity,wind speed and wind direction has also been investigated using Pearson correlation coefficient and pollution rose diagram.A comparable pattern in concentration was observed for all three gases in spatial(location)and temporal(diurnal)distribution.The concentration trend of CO_(2)in different seasons is winter>summer>monsoon,while in the case of CH_(4)winter=summer>monsoon but not any seasonal trend was noted in CO case.It is observed that CO_(2)has a good relation with CO(a tracer for vehicular emission)in terms of diurnal variation,whereas,CH_(4)does not represent a relation with CO and CO_(2)diurnally,suggesting that vehicles are the source of CO_(2)but not much contributing to other greenhouse gases like CH_(4).

关键词： Vehicular emission Greenhouse gases Harmful pollutants Meteorological parameters

An Interactive Operating Demand Response Approach for Hybrid Power systems Integrating Renewable Energy sources

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Protection and Control of Modern Power systems 2024年第3期9卷 174-194页

作者： Abhishek saxena Ravi shankar IEEE the Department of Electrical Engineering National Institute of Technology PatnaIndia

The increased deployment of renewable energy in existing power networks has jeopardized rotational inertia,resulting in system degradation and insta-bility.To address the issue,this paper proposes a demand response strategy for ensuring the future reliability of the electrical power system.In addition,a modified fuzzy logic control topology-based two-degree-of-freedom(fractional order proportional integral)-tilt derivative controller is designed to regulate the frequency within a demand response framework of a hybrid two-area deregulated power system.The test system includes thermal power plants,renewable energy sources(such as wind,parabolic trough solar thermal plant,biogas),and electric vehicle assets.To adaptively tune the controller’s coefficients,a quasi-opposition-based harris hawks optimization(QOHHO)algorithm is developed.The effectiveness of this algorithm is compared to other optimization algorithms,and the stability of the system is evaluated.The results demonstrate that the designed control algorithm significantly enhances system frequency stability in various scenarios,including uncertainties,physical constraints,and high penetration of renewables,compared to existing work.Additionally,an experimental assessment through OPAL-RT is conducted to verify the practicality of the proposed strategy,considering source and load intermittencies.

关键词： Area control error demand response energy storage vertically integrated utility

A Framework for Cybersecurity Alert Distribution and Response Network (ADRIAN)

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Journal of software Engineering and Applications 2024年第5期17卷 396-420页

作者： Akarshita shankar Vijay Madisetti school of Cybersecurity and Privacy Georgia Institute of Technology Atlanta USA

security Information and Event Management (sIEM) platforms are critical for organizations to monitor and manage their security operations centers. However, organizations using sIEM platforms have several challenges such as inefficiency of alert management and integration with real-time communication tools. These challenges cause delays and cost penalties for organizations in their efforts to resolve the alerts and potential security breaches. This paper introduces a cybersecurity Alert Distribution and Response Network (Adrian) system. Adrian introduces a novel enhancement to sIEM platforms by integrating sIEM functionalities with real-time collaboration platforms. Adrian leverages the uniquity of mobile applications of collaboration platforms to provide real-time alerts, enabling a two-way communication channel that facilitates immediate response to security incidents and efficient sIEM platform management. To demonstrate Adrian’s capabilities, we have introduced a case-study that integrates Wazuh, a sIEM platform, to slack, a collaboration platform. The case study demonstrates all the functionalities of Adrian including the real-time alert distribution, alert customization, alert categorization, and enablement of management activities, thereby increasing the responsiveness and efficiency of Adrian’s capabilities. The study concludes with a discussion on the potential expansion of Adrian’s capabilities including the incorporation of artificial intelligence (AI) for enhanced alert prioritization and response automation.

关键词： sIEM Platforms Alert Distribution Incident Response Automation sIEM Management Collaboration Platform

Enhancing security in QR Code Technology Using AI: Exploration and Mitigation strategies

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International Journal of Intelligence science 2024年第2期14卷 49-57页

作者： saranya Vaithilingam santhosh Aradhya Mohan shankar Department of Information Technology University of the Cumberlands Williamsburg KY USA

The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potential security risks that malicious actors can exploit. QR code Phishing, or “Quishing”, is a type of phishing attack that leverages QR codes to deceive individuals into visiting malicious websites or downloading harmful software. These attacks can be particularly effective due to the growing popularity and trust in QR codes. This paper examines the importance of enhancing the security of QR codes through the utilization of artificial intelligence (AI). The abstract investigates the integration of AI methods for identifying and mitigating security threats associated with QR code usage. By assessing the current state of QR code security and evaluating the effectiveness of AI-driven solutions, this research aims to propose comprehensive strategies for strengthening QR code technology’s resilience. The study contributes to discussions on secure data encoding and retrieval, providing valuable insights into the evolving synergy between QR codes and AI for the advancement of secure digital communication.

关键词： Artificial Intelligence Cyber security QR Codes Quishing AI Framework Machine Learning AI-Enhanced security

Frequency domain analysis of pre-stressed elastomeric vibration isolators

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Defence Technology（防务技术） 2023年第7期25卷 33-47页

作者： s.somanath R.Marimuthu shankar Krishnapillai Indian space Research Organisation Anthariksh BhavanNew BEL RoadBengaluru560231India sTR/VssC Indian Space Research OrganisationThiruvananthapuram695022India Department of Mechanical Engineering IIT MadrasChennai600036India

Two types of elastomeric vibration isolators used for equipment vibration isolation in aerospace vehicles are considered for the present study. These isolators are constructed using elastomers mounted in steel encasings. These isolators are initially deformed statically and dynamic loads are applied on the deformed configuration. To capture the static deformation, equivalent static load corresponding to its load rating and specified displacements are created. static deformation is computed using Finite Element methods with four node axi-symmetric element which include the geometric non-linear effect for steel and with standard Yeoh hyper-elastic material model for elastomers(Muhammed and Zu, 2012) [1]. Yeoh material constants are derived from uni-axial tension test data of the elastomer specimen. These isolators are subjected to harmonic and random excitations in the pre-deformed state. For numerical analysis, elastomeric constants at dynamic conditions are obtained as complex function of frequency using Dynamic Mechanical Analyzer(DMA) for a range of frequencies. The standard material model of Yeoh is modified incorporating frequency dependant material characteristics and damping in the range of frequencies of interest. A multiplicative non-separable variables law is derived for Yeoh material model to include the effect of static pre-stress, based on the methodology given in literature(Nashif et al.,1985;Beda et al., 2014) [2,3]. The modifications of Yeoh model suitable for frequency domain analysis is the novelty in the present study. In the analysis, while dynamic loads are applied, the configuration is updated considering initial static loading. The frequency response of the isolators is computed using material properties evaluated at progressive dynamic strains until a match in natural frequency is observed. Appropriate damping corrections are then incorporated to match the test observed transmissibility. Then updated material properties are used to compute the random response which showed good agreement wit

关键词： Elastomer Isolator Axi-symmetric Frequency Random Response strain amplitude Complex modulus Transmissibility

Decision Tree Based Key Management for secure Group Communication

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Computer systems science & Engineering 2022年第8期42卷 561-575页

作者： P.Parthasarathi s.shankar Department of Computer science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India

Group communication is widely used by most of the emerging network applications like telecommunication,video conferencing,simulation applications,distributed and other interactive systems.secured group communication plays a vital role in case of providing the integrity,authenticity,confidentiality,and availability of the message delivered among the group members with respect to communicate securely between the inter group or else within the group.In secure group communications,the time cost associated with the key updating in the proceedings of the member join and departure is an important aspect of the quality of service,particularly in the large groups with highly active membership.Hence,the paper is aimed to achieve better cost and time efficiency through an improved DC multicast routing protocol which is used to expose the path between the nodes participating in the group communication.During this process,each node constructs an adaptive Ptolemy decision tree for the purpose of generating the contributory key.Each of the node is comprised of three keys which will be exchanged between the nodes for considering the group key for the purpose of secure and cost-efficient group communication.The rekeying process is performed when a member leaves or adds into the group.The performance metrics of novel approach is measured depending on the important factors such as computational and communicational cost,rekeying process and formation of the group.It is concluded from the study that the technique has reduced the computational and communicational cost of the secure group communication when compared to the other existing methods.

关键词： Key generation adaptive Ptolemy decision tree cost reduction secure group communication

semantic Based Greedy Levy Gradient Boosting Algorithm for Phishing Detection

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Computer systems science & Engineering 2022年第5期41卷 525-538页

作者： R.sakunthala Jenni s.shankar Department of Computer science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India

The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable.Phishing websites also create traffic in the entire network.Another phishing issue is the broadening malware of the entire network,thus highlighting the demand for their detection while massive datasets(i.e.,big data)are processed.Despite the application of boosting mechanisms in phishing detection,these methods are prone to significant errors in their output,specifically due to the combination of all website features in the training state.The upcoming big data system requires MapReduce,a popular parallel programming,to process massive datasets.To address these issues,a probabilistic latent semantic and greedy levy gradient boosting(PLs-GLGB)algorithm for website phishing detection using MapReduce is proposed.A feature selection-based model is provided using a probabilistic intersective latent semantic preprocessing model to minimize errors in website phishing detection.Here,the missing data in each URL are identified and discarded for further processing to ensure data quality.subsequently,with the preprocessed features(URLs),feature vectors are updated by the greedy levy divergence gradient(model)that selects the optimal features in the URL and accurately detects the websites.Thus,greedy levy efficiently differentiates between phishing websites and legitimate websites.Experiments are conducted using one of the largest public corpora of a website phish tank dataset.Results show that the PLs-GLGB algorithm for website phishing detection outperforms stateof-the-art phishing detection methods.significant amounts of phishing detection time and errors are also saved during the detection of website phishing.

关键词： Web service providers probabilistic intersective latent semantic greedy levy divergence gradient phishing detection big data

Optimal Data Placement and Replication Approach for sIoT with Edge

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Computer systems science & Engineering 2022年第5期41卷 661-676页

作者： B.Prabhu shankar s.Chitra Department of Information Technology Er Perumal Manimekalai College of EngineeringHosur635117TamilnaduIndia Department of Computer science and Engineering Er Perumal Manimekalai College of EngineeringHosur63117TamilnaduIndia

social networks(sNs)are sources with extreme number of users around the world who are all sharing data like images,audio,and video to their friends using IoT devices.This concept is the so-called social Internet of Things(sIot).The evolving nature of edge-cloud computing has enabled storage of a large volume of data from various sources,and this task demands an efficient storage procedure.For this kind of large volume of data storage,the usage of data replication using edge with geo-distributed cloud service area is suited to fulfill the user’s expectations with low latency.The major issue is the way to store the data and replicate these large data items optimally and allocate the request from the data center efficiently.For efficient storage of these data,we use edge server,which is part of the cloud server,in this study.Thus,the data are distributed and stored with quick access,which will reduce the latency with response.The proposed data placement approach learns with machine learning(ML)algorithm called radial basis kernel function assisted with support vector machine(RBF-sVM)to classify the data center for storing the user and friend’s data from the sIoT devices.These learning algorithms will be used to predict the workload of the data stored in the data center as either edge or cloud depending on the existing time slots.The data placement with dynamic nature is also optimized using the proposed dynamic graph partitioning(GP)method to meet the individual user’s demand of low latency with minimum costs.This way will keep the sIoT data placement efficient and effective over time.Accordingly,this proposed data placement and replication approach introduces three kinds of innovations compared with the existing data placement approach.(i)Rather than storing the user data in a single cloud,this study uses the edge server closest to the sIoT devices for faster access with reduced response time.(ii)The classification algorithm called RBF-sVM is used to find storage for user for reducing data replication.(iii)Dynamic

关键词： Data placement data replication social network social internet of things edge computing cloud computing graph partitioning support vector machine machine learning radial basis function latency storage cost