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Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

Computer Systems Science & engineering 2023年第5期45卷 1743-1758页

作者： G.Sharmila M.K.Kavitha Devi Department of Computer Science&engineering Syed Ammal Engineering CollegeRamanathapuram623502India Department of Computer Science&engineering Thiagarajar College of EngineeringMadurai625015India

Over the past era,subgraph mining from a large collection of graph database is a crucial *** addition,scalability is another big problem due to insufficient *** are several security challenges associated with subgraph mining in today’s on-demand *** address this downside,our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs(BCCA-LSDG).The two-fold process is handled in the proposed BCCA-LSDG:graph indexing and authenticated query search(query processing).A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed *** resolve the issues and provide safe big data transmission,the proposed technique also combines blockchain with a consensus algorithm *** of the big data is ensured by dividing the BC network into distinct networks,each with a restricted number of allowed entities,data kept in the cloud gate server,and data analysis in the *** consensus algorithm is crucial for maintaining the speed,performance and security of the *** Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature ***,the graph index refinement process is undertaken to improve the query *** query error,fuzzy logic is used to refine the index of the graph *** proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems,and the combination of blockchain and subgraph provides a secure communication platform,according to the findings.

关键词： Big data blockchain consensus algorithm trust management graph index

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OFDM-CFO and Resource Scheduling Algorithm Using Fuzzy Linear-CFO

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Intelligent Automation & Soft Computing 2023年第2期35卷 2513-2525页

作者： M.Prabhu B.Muthu Kumar Department of Electronics and Communication Anna UniversityChennai600025India Department of Computer Science and engineering Syed Ammal Engineering CollegeRamanathapuram623502India

Orthogonal Frequency-Division Multiplexing(OFDM)is the form of a digital system and a way of encoding digital data across multiple frequency com-ponents that are used in telecommunication *** Frequency Offset(CFO)inaccuracy is a major disadvantage of *** paper proposed a fea-sible and elegant fuzzy-based resource allocation technique,that overcomes the constraints of the *** suggested Fuzzy linear CFO estimation(FL-CFO)not only estimates the CFO with increased precision but also allocates resources effectively,and achieves maximum utilization of dynamic *** sug-gested FL-CFO error estimation algorithm in OFDM systems employing 1-bit Quadrate errors ADC(1-bit QE)is utilized to extract the precise ***-ally,the base station(BS)manages the Resource Units(RU),which could be used to distribute resources in such a manner that the user requests are *** assign resources to a certain job,fuzzy rules are *** the residence duration exceeds the resource requirement time then the particular resources are provided to the highest-priority *** a result,the job performance will not be halted due to a lack of allocated *** metrics such as utilization,rate of failure,and life span,as well as energy consumption,are used to assess the pro-posed FL-CFO.

关键词： Orthogonal frequency division multiplexing(OFDM) carrier

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Steel Surface Defect Detection Using Learnable Memory Vision Transformer

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Computers, Materials & Continua 2025年第1期82卷 499-520页

作者： syed Tasnimul Karim Ayon Farhan Md.Siraj Jia Uddin Department of Computer Science and engineering BRAC UniversityDhaka1212Bangladesh Department of AI and Big Data Endicott CollegeWoosong UniversityDaejeon34606Republic of Korea

This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel *** awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and *** techniques mitigated overfitting,stabilized training,and improved generalization *** LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,*** findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature *** additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial *** instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often *** study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are *** research m

关键词： Learnable Memory Vision Transformer(LMViT) Convolutional Neural Networks(CNN) metal surface defect detection deep learning,computer vision image classification learnable memory gradient clipping label smoothing t-SNE visualization

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Evaluation of the Capacitance and Charge Distribution for Conducting Bodies by Circuit Modelling

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Circuits and Systems 2016年第4期7卷 280-291页

作者： Dhamodaran Muneeswaran Dhanasekaran Raghavan Department of Electronics and Communication engineering Syed Ammal Engineering College Ramanathapuram India Department of Electrical and Electronics engineering Syed Ammal Engineering College Ramanathapuram India

This paper presents a numerical analysis for computation of free space capacitance of different arbitrarily shaped conducting bodies based on the finite element method with triangular subsection modeling. Evaluation of capacitance of different arbitrary shapes is important for the electrostatic analysis. Capacitance computation is an important step in the prediction of electrostatic discharge which causes electromagnetic interference. We specifically illustrated capacitance computation of electrostatic models like unit cube, rectangular plate, triangular plate, T-shaped plate, sphere and two touching spheres. Numerical data on the capacitance of conducting objects are presented. The results are compared with other available results in the literature. We used the COMSOL Multiphysics software for the simulation. The models are designed in three-dimensional form using electrostatic environment and can be applied to any spacecraft circuit modeling design. The findings of this study show that the finite element method is a more accurate method and can be applied to any circuit modeling design.

关键词： Capacitance Spacecraft Circuit Modeling Electrostatic Analysis Electrostatic Discharge Finite Element Method

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Appropriate Sub-band Selection in Wavelet Packet Decomposition for Automated Glaucoma Diagnoses

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International Journal of Automation and computing 2015年第4期12卷 393-401页

作者： Chandrasekaran Raja Narayanan Gangatharan Department of Electronics and Communication engineering Anjalai Ammal Mahalingam Engineering College Department of Electronics and Communication engineering R.M.K.College of Engineering and Technology

The most common reason for blindness among human beings is *** increase of fluid pressure damages the optic nerve which gradually leads to irreversible loss of vision.A technique for automated screening of Glaucoma from the fundal retinal images is presented in this *** paper intends to explore the significance of both the approximate and detail coefficients through wavelet packet decomposition(WPD).Decomposition is done with "db3" wavelet function and the images are decomposed up to level-3producing 84 *** features,the energy and the entropy are calculated for each sub-band producing two feature matrices(158 images × 84 features).The above step is purely a statistical measure based on *** enhance the diagnostic accuracy,the second phase considers the structural(biological) region of interest(ROI) in the image and then extracts the same *** is worthy to note that direct biological features are not extracted to eliminate the drawbacks of segmentation whereas the biologically significant region is taken as ***,the detailed coefficient sub-bands(prominent edges) show more significance in the biological-ROI *** from enhancing the diagnostic accuracy by feature reduction,the paper intends to mark the significance indices,uniqueness and discrimination capability of the significant features(sub-bands) in both the ***,the crisp inputs are fed to the classifier ***,from the significant features of the biological-ROI feature matrices,the accuracy is raised to 85%which is notable than the accuracy of 79%achieved without considering the ROI.

关键词： Glaucoma wavelet packet decomposition feature reduction feature significance artificial neural networks.

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Assessment of Severity Level for Diabetic Macular Oedema Using Machine Learning Algorithms

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Circuits and Systems 2016年第7期7卷 1098-1105页

作者： S. Murugeswari R. Sukanesh syed ammal engineering college Ramanathapuram India Thiagarajar college of engineering Madurai India

The macula is an imperative part present in our human visual system which is most responsible for clear and colour vision. For the people suffering from diabetes, the various parts of the body including the retina of the eye are affected. These retinal damages cause swelling and other abnormalities nearby macula. The pathologies in macula due to diabetes are called Diabetic Macular oEdema (DME). It affects patients’ vision that may lead to vision loss. It can be overcome by advance identification of causes for swelling. The major causes for the swelling are neovascularization and other abnormalities occurring in the blood vessels nearby the macula. The aim of this work is to avoid vision loss by detecting the presence of abnormalities in macula in advance. The pathologies present in the abnormal images are detected by image segmentation technique viz. Fuzzy K-means algorithm. The classification is done by two different classifiers namely Cascade Neural Network and Partial Least Square which are employed to identify whether the image is normal or abnormal. The results of both the classifiers are compared with respect to classifier accuracy, sensitivity and specificity. The classifier accuracies of Cascade Neural Network and Partial Least Square are 96.84% and 94.36%, respectively. The information about the severity of the disease and the localization of pathologies are very useful to the ophthalmologist for diagnosing the disease and apply proper treatments to the patients to avoid the formation of any lesion and prevent vision loss.

关键词： Cascade Neural Network Diabetic Macular Oedema Grey Level Co-Occurrence Matrix Neovascularization Partial Least Square Classifier

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A New Front End Capacitive Converter Fed Switched Reluctance Motor for Torque Ripple Minimization

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Circuits and Systems 2016年第5期7卷 585-595页

作者： S. Muthulakshmi R. Dhanasekaran Department of Electrical and Electronics Syed Ammal Engineering College Ramanathapuram India

This paper presents new converter for torque ripple minimization of three phases Switched Reluctance Motor (SRM). The proposed converter has basic passive circuit which includes two diodes and one capacitor to the front end of an asymmetric converter with a specific end goal to get a high magnetization and demagnetization voltage. In view of this boost capacitor, the charge and demagnetization voltage are higher. Accordingly, it can reduce the negative torque generation from the tail current and enhance the output power. The proposed converter circuit is equipped for minimizing the SRM torque ripple furthermore enhancing the average torque when contrasted with traditional converter circuit. A three-phase SRM is modeled and the simulation output for no load and stacked condition depicts that the proposed converter has better performance when contrasted with traditional converter. It is appropriate for electric vehicle applications. The proposed framework is simulated by utilizing MATLAB/Simulink environment and their outcomes are examined extravagantly.

关键词： SRM MATLAB Torque Ripple Asymmetric Converter Magnetization Demagnetization

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Effect of temperature and strain rate on compressive response of extruded magnesium nano-composite

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Journal of Magnesium and Alloys 2015年第3期3卷 224-230页

作者： B.Selvam P.Marimuthu R.Narayanasamy V.Senthilkumar K.S.Tun M.Gupta Department of Mechanical engineering Mookambigai College of EngineeringPudukkottaiTamilnadu 622502India syed ammal engineering college RamanathapuramTamilnadu 623502India Department of Production engineering National Institute of TechnologyTiruchirappalliTamilnadu 620015India Department of Mechanical engineering National University of Singapore9 Engineering Drive 1Singapore 117576Singapore

The hot deformation behaviour of extruded magnesium-zinc oxide nano composite has been studied using hot compression *** test was conducted in the temperature range of 250-400℃ and in the strain rate range of 0.01 to 1.5 s^(−1).The processing map was obtained using the power dissipation efficiency with the functions of temperature and strain *** workability and instability domains were observed in the processing map for a nano *** optical microscopy(OM),scanning electron microscopy(SEM)and transmission electron microscopy(TEM)images were used to confirm the formation of dynamic recrystallization(DRX),dynamic recovery(DRY)and instability *** workability region of the composite was identified at a working temperature of 400℃ and the strain rate of 0.01 s^(−1) from the processing *** instability regions were observed at higher strain rates(>0.1 s^(−1))and temperatures(250-400℃).

关键词： Metal matrix composites Powder metallurgy Processing map Strain rate Instability Nano-composite

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Reconfigurable Logic Design of CORDIC Based FFT Architecture for 5G Communications

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Intelligent Automation & Soft Computing 2023年第6期36卷 2803-2818页

作者： C.Thiruvengadam M.Palanivelan Department of Electronics and Communication engineering Anjalai Ammal Mahalingam Engineering CollegeThiruvarur614403TamilnaduIndia Department of Electronics and Communication engineering Rajalakshmi Engineering CollegeChennai602105TamilnaduIndia

There are numerous goals in next-generation cellular networks(5G),which is expected to be available *** want to increase data rates,reduce end-to-end latencies,and improve end-user service *** networks need to change because there has been a significant rise in the number of base stations required to meet these needs and put the operators’low-cost constraints to the *** it can withstand interference from other wireless networks,and Adaptive Complex Multicarrier Modulation(ACMM)system is being looked at as a possible choice for the 5th Generation(5G)of wireless *** arithmetic units need to be used on the hardware side of multicarrier systems to do the pulse-shaping filters and inverse *** main goal of this study is to adapt complex multicarrier modulation(ACMM)for baseband transmission with low complexity and the ability to change *** found that this is the first recon-figurable architecture that lets you choose how many subcarriers a subband has while still having the same amount of hardware resources as ***,under the new design with a single selection line,it selects from a set of *** baseband modulating signal is evaluated and tested using a Field-Programmable Gate Array(FPGA)*** device is available from a commercial *** technology outperforms current technology in terms of computational com-plexity,simple design,and ease of ***,it has a higher power spectrum density,spectral efficiency,a lower bit error rate,and a higher peak to average power ratio than existing technology.

关键词： Error analysis pulse-shaping filters reconfigurable architectures FBMC OFDM UFMC

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Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification

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Computer Systems Science & engineering 2023年第2期44卷 1081-1097页

作者： P.Immaculate Rexi Jenifer S.Kannan Department of Computer Science and engineering Anjalai Ammal Mahalingam Engineering CollegeKoilvenni614403Tamil NaduIndia Department of CSE E.G.S.Pillay Engineering CollegeNagapattinam611002Tamil NaduIndia

Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)*** conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical *** recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification *** the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation *** this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image *** proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical *** addition,region growing segmentation technique is involved to determine the infected regions in the medical ***,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature ***,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification *** order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 *** experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different *** type your abstract here.

关键词： Medical image classiﬁcation spiking neural networks computer aided diagnosis medical imaging parameter optimization deep learning

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