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Simulation and implementation of a reconfigurable dual-function pixel

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Optoelectronics Letters 2024年

作者： Shaher dwik Gurusamy Sasikala department of Electronics & Communication Engineering School of Electrical & Communication Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology

This paper presents the simulation and implementation of a reconfigurable pixel that serves both data acquisition and energy harvesting purposes. The main topic focuses on switching between the two operating modes of the photodiode: photoconductive and photovoltaic. This proposed model can be used to design novel optical sensors with energy harvesting capability， such as Position Sensitive device (PSd) and CMOS image sensors， which can extend the battery lifetime of the whole optical system. Thus， we can overcome power supply problems like wiring and changing batteries frequently， especially in hard-to-reach places like space (cube satellites) or even Underwater Wireless Optical Communication (UWOC). The proposed pixel architecture offers the advantage of a minimalistic design with only four transistors. Nevertheless， it does come with a drawback in the form of higher noise levels. The simulation was achieved using MATLAB， and the implementation was performed using the Programmable System-on-Chip (PSoC) microcontroller. The results showed that the functionality of the dual-function pixel is correct， and the scheduling of both energy harvesting and signal sensing functions was successfully achieved.

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Exploring deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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Computer Modeling in Engineering & sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer science and Engineering Vellore Institute of TechnologyChennai600127India department of Computer science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic department of Computer science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India department of Mechanical Engineering and University Centre for research&development Chandigarh UniversityMohali140413India department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other media.deep learning(dL)methods are more successful than other traditional machine learning(ML)methods inCV.dL techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face recognition.In this review,a structured discussion on the history,methods,and applications of dL methods to CV problems is presented.The sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of dL methods and CV.This review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

Adaptive Marine Predator Optimization Algorithm(AOMA)-deep Supervised Learning Classification(dSLC)based IdS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India department of Computer science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India department of Computer science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India department of Computer science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India department of Computer science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India department of Computer science Moulay Ismail UniversityMeknes 5003Morocco

due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks today.In MANET,the Intrusion detection System(IdS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular operation.different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET.However,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of misclassification.Therefore,the goal of this paper is to create an intelligent IdS framework for significantly enhancing MANET security through the use of deep learning models.Here,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of classifier.Then,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier.Moreover,the deep Supervise Learning Classification(dSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations.during evaluation,the performance and results of the proposed AOMA-dSLC based IdS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion detection System(IdS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) deep Supervise Learning Classification(dSLC)

MOEAGAC:an energy aware model with genetic algorithm for efficient scheduling in cloud computing

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International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 318-329页

作者： Nageswara Prasadhu Marri N.r.rajalakshmi CSE Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennaiIndia

Purpose-Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism.This research aims to propose the optimization of makespan,energy consumption and data transfer time(dTT)by considering the priority tasks.The research work is concentrated on the multi-objective approach based on the genetic algorithm(GA)and energy aware model to increase the efficiency of the task scheduling.design/methodology/approach-Cloud computing is the recent advancement of the distributed and cluster computing.Cloud computing offers different services to the clients based on their requirements,and it works on the environment of virtualization.Cloud environment contains the number of data centers which are distributed geographically.Major challenges faced by the cloud environment are energy consumption of the data centers.Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan.This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm.This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines.The energy model is developed for picking the task based on the fitness function.The simulation results show the performance of the multi-objective model with respect to makespan,dTT and energy consumption.Findings-The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine.The directed acyclic graph is used to represent the task dependencies.The proposed model recorded 5% less makespan compared against the MOdPSO and 0.7% less compared against the HEFT algorithms.The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/value-This paper proposed the multi-objective model based on bio-inspired approach c

关键词： Cloud computing Priority roulette wheel selection Genetic algorithm Population

Modelling of electric vehicle charging station and controlled by fuzzy logic controller with different modes of operation

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Journal of Control and decision 2023年第1期10卷 26-39页

作者： S.C.Vijay Kumar K.Karunanithi S.P.raja S.ramesh P.Chandrasekar r.Obulupathy Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology chennaitamil naduindia school of computer science and engineering vellore institute of technologyvelloretamil naduindia

The main objective of implementing charging stations is to ensure the good charging to theElectric Vehicles by using a solar PV array which is interconnected to the battery energy storagesystems. The charging station regulates the supply voltage and frequency without the use of amechanical speed governor. It also assures that energy gained from grid or by the dG set willhave the unity power factor (UPF) when the load is nonlinear. Besides this, the Point of CommonCoupling (PCC) voltage is synchronised with the grid/generator voltage in order to providecontinuous charging. In order to increase the optimal efficiency of the charging stations, thecharging stations will perform the active/reactive power transfer from the vehicle to grid, vehicleto house and vehicle to vehicle (V2V) power transfer. The operational experiment of the chargingstation is simulated and verified by using MATLAB/SIMULINK.

关键词： Electric vehicle charging station solar photo-voltaic generation energy transfer diesel-generator set fuzzy logic controller total harmonic distortion

High accuracy offering attention mechanisms based deep learning approach using CNN/bi-LSTM for sentiment analysis

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International Journal of Intelligent Computing and Cybernetics 2022年第1期15卷 61-74页

作者： Venkateswara rao Kota Shyamala devi Munisamy CSE Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennaiIndia CSE Andhra Loyola Institute of Engineering and TechnologyVijayawadaIndia

Purpose-Neural network(NN)-based deep learning(dL)approach is considered for sentiment analysis(SA)by incorporating convolutional neural network(CNN),bi-directional long short-term memory(Bi-LSTM)and attention methods.Unlike the conventional supervised machine learning natural language processing algorithms,the authors have used unsupervised deep learning algorithms.design/methodology/approach-The method presented for sentiment analysis is designed using CNN,Bi-LSTM and the attention mechanism.Word2vec word embedding is used for natural language processing(NLP).The discussed approach is designed for sentence-level SA which consists of one embedding layer,two convolutional layers with max-pooling,oneLSTMlayer and two fully connected(FC)layers.Overall the system training time is 30 min.Findings-The method performance is analyzed using metrics like precision,recall,F1 score,and accuracy.CNN is helped to reduce the complexity and Bi-LSTM is helped to process the long sequence input text.Originality/value-The attention mechanism is adopted to decide the significance of every hidden state and give a weighted sum of all the features fed as input.

关键词： Sentiment analysis NLP Neural networks Bi-LSTM Attention mechanism Word embedding dropout Fully connected(FC)layer Performance metrics

deep Learning-Based FOPId Controller for Cascaded dC-dC Converters

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Computer Systems science & Engineering 2023年第8期46卷 1503-1519页

作者： S.Hema Y.Sukhi department of EEE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India department of EEE R.M.K.Engineering CollegeKavaraipettaiChennai601206India

Smart grids and their technologies transform the traditional electric grids to assure safe,secure,cost-effective,and reliable power transmission.Non-linear phenomena in power systems,such as voltage collapse and oscillatory phenomena,can be investigated by chaos theory.recently,renewable energy resources,such as wind turbines,and solar photovoltaic(PV)arrays,have been widely used for electric power generation.The design of the controller for the direct Current(dC)converter in a PV system is performed based on the linearized model at an appropriate operating point.However,these operating points are everchanging in a PV system,and the design of the controller is usually accomplished based on a low irradiance level.This study designs a fractional-order proportional-integrated-derivative(FOPId)controller using deep learning(dL)with quasi-oppositional Archimedes Optimization algorithm(FOPId-QOAOA)for cascaded dC-dC converters in micro-grid applications.The presented FOPIdQOAOA model is designed to enhance the overall efficiency of the cascaded dC-dC boost converter.In addition,the proposed model develops a FOPId controller using a stacked sparse autoencoder(SSAE)model to regulate the converter output voltage.To tune the hyper-parameters related to the SSAE model,the QOAOA is derived by the including of the quasi-oppositional based learning(QOBL)with traditional AOA.Moreover,an objective function with the including of the integral of time multiplied by squared error(ITSE)is considered in this study.For validating the efficiency of the FOPId-QOAOA method,a sequence of simulations was performed under distinct aspects.A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.

关键词： Micro-grid deep learning archimedes optimization algorithm FOPId controller objective function

A Parallel Approach for Sentiment Analysis on Social Networks Using Spark

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Intelligent Automation & Soft Computing 2023年第2期35卷 1831-1842页

作者： M.Mohamed Iqbal K.Latha Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai600062India University College of Engineering Anna University(B.I.T Campus)Tiruchirappalli620024India

The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics.As a result,social media has emerged as the most effective and largest open source for obtaining public opinion.Single node computational methods are inefﬁcient for sentiment analysis on such large datasets.Supercomputers or parallel or distributed proces-sing are two options for dealing with such large amounts of data.Most parallel programming frameworks,such as MPI(Message Processing Interface),are dif-ﬁcult to use and scale in environments where supercomputers are expensive.Using the Apache Spark Parallel Model,this proposed work presents a scalable system for sentiment analysis on Twitter.A Spark-based Naive Bayes training technique is suggested for this purpose;unlike prior research,this algorithm does not need any disk access.Millions of tweets have been classiﬁed using the trained model.Experiments with various-sized clusters reveal that the suggested strategy is extremely scalable and cost-effective for larger data sets.It is nearly 12 times quicker than the Map reduce-based model and nearly 21 times faster than the Naive Bayes Classiﬁer in Apache Mahout.To evaluate the framework’s scalabil-ity,we gathered a large training corpus from Twitter.The accuracy of the classi-ﬁer trained with this new dataset was more than 80%.

关键词： Social networks sentiment analysis big data spark tweets classiﬁcation

Process Modelling and Experimental Analysis of Optimal Specimen Selection in Organic CMCs

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Computers, Materials & Continua 2022年第2期70卷 2415-2433页

作者： P.V.rajesh Kanak Kalita Xiao-Zhi Gao Faculty of Mechanical Engineering Saranathan College of EngineeringTrichy620012India department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India School of Computing University of Eastern FinlandKuopioFI-70211Finland

Bone grafting is a surgical restructuring procedure of replacing broken bones and reconstructing missing bone pieces so that complex bone fractures can be repaired to avoid any serious health risk as well as permanent bone disfiguration.Normally,human bones tend to regenerate and heal completely from fracture.But it needs a small scaffold to provide the necessary space to grow.Bone implants allow a broken bone to grow seamlessly.Traditionally,non-corrosive metal alloys are used for fixing broken bones.A metal plate is fastened between two ends of broken bones to join them.However,issues like high weight,high cost,low wear resistance,etc.led to the emergence of ceramics and ceramic-based composites in surgical engineering.recent trends indicate the usage of organic ceramics and their associated composites as biomimeticmaterials for prostheses and other biomedical applications.This research paper deals with the fabrication of one such type of ceramic matrix composite(CMC)specimen with sea sponge and cuttlebone using powder metallurgy process by varying composition of cuttlebone,the particle size of the ceramics and sintering temperature of green compacts.Evaluation of thermo-mechanical properties and optimization of process parameters is carried out using the preferential selection index(PSI)method.The results obtained from this technique are further validated usingMulti-Level General Factorial design(MLGFd).

关键词： Grafting implants ceramics composites prostheses powder metallurgy optimization