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pSO-DBNet for peak-to-Average power Ratio Reduction Using Deep Belief Network

Computer Systems Science & Engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh p.sivaranjani D.Mansoor Hussain Venkat padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(pApR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of pApR in 5G wireless *** transmit sequence(pTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,pApR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed pApR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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AI Method for Improving Crop Yield prediction Accuracy Using ANN

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Computer Systems Science & Engineering 2023年第10期47卷 153-170页

作者： T.sivaranjani S.p.Vimal Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical CampusCoimbatore642109India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering CollegeCoimbatore641022India

Crop Yield prediction(CYp)is critical to world food *** safety is a top priority for *** rely on reliable CYp to make import and export decisions that must be fulfilled before launching an agricultural *** Yield(CY)is a complex variable influenced by multiple factors,including genotype,environment,and their *** is a significant agrarian ***,CYp is the main task due to many composite factors,such as climatic conditions and soil *** Learning(ML)is a powerful tool for supporting CYp decisions,including decision support on which crops to grow in a specific ***,Artificial Neural Networks(ANN)are usually used to predict the behaviour of complex non-linear *** a result,this research paper attempts to determine the correlations between climatic variables,soil nutrients,and CYwith the available ***,threemethods,Levenberg-Marquardt(LM),Bayesian regularisation(BR),and scaled conjugate gradient(SCG),are used to train the neural network(NN)model and then compared to determine prediction *** performance measures of the training,as declared above,such as Mean Squared Error(MSE)and correlation coefficient(R),were determined to assess the ANN models that had been *** experimental study proves that LM training algorithms are better,while BR and SCG have minimal performance.

关键词： Crop prediction accuracy ANN precision agriculture crop yield

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Artificial potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot path prediction

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Intelligent Automation & Soft Computing 2023年第1期35卷 1135-1150页

作者： A.sivaranjani B.Vinod Department of Robotics and Automation Engineering PSG College of TechnologyCoimbatore641004India Department of Electrical and Electronics Engineering PSG College of TechnologyCoimbatore641004India

Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an *** Learning methods enable a state action function in mobile robots suited to their *** trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its *** Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the *** it requires a large number of training *** research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial potential Field(ApF)***,a TB3 Waffle pi DQN is built and trained to reach the *** the ApF shortest path algorithm is incorporated into the DQN *** proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and ApF gives a better optimal path and takes less time when compared to the conventional DQN *** performance improvement rate of the proposed DQN+ApF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+ApF in comparison with DQN in terms of average time is achieved by 0.331 *** performance of the proposed DQN+ApF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.

关键词： Artificial potentialfield deep reinforcement learning mobile robot turtle bot deep Q network path prediction

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Monitoring the Turmeric Finger Disease and Growth Characteristics Using Sensor Based Embedded System —A Novel Method

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Circuits and Systems 2016年第8期7卷 1280-1296页

作者： Maheswaran Shanmugam Asokan Ramasamy sivaranjani paramasivam priyadharshini prabhakaran Department of ECE Kongu Engineering College Erode India Kongu Nadu College of Engineering and Technology Trichy India

In this cyber era, novelty plays a prime role in the field of agriculture that majorly depends on computer-based measurements and control. Herein before, it was totally controlled and performed by the agriculturists. One of the technological innovative methods to measure and monitor the turmeric finger growth characteristics is the embedded system that is based on sensor array module such as flex sensor, temperature sensor and pH sensor. The experimental work has been designed and tested with five set of nodes and the growth of turmeric finger is tenuously monitored by measuring the change in flex resistance. Out of five nodes, two nodes were diseased. Deliberately, one node was left as such and the other node was treated with natural pesticides (pseudomonas and viride) to restrict the rhizome rot disease attack. After cultivation, it was found that the rhizome rot disease attack on the node which was treated with pesticides was comparatively lesser than the other node. The five different nodes have been used in the experimental work with an average flex sensor resistance of 3.962 cm/kΩ. In a nutshell, this proposed method manifests the farmers to detect the rhizome rot disease at its earlier stage and to prevent it as well by screening the growth of the turmeric fingers when it is under the soil.

关键词： Embedded System Turmeric Finger Growth Rhizome Rot Disease Natural pesticides Flex Sensor

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Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid particle Swarm Optimization Algorithm-Harmony Search Algorithm

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Circuits and Systems 2016年第5期7卷 562-573页

作者： sivaranjani paramasivam Senthilkumar Athappan Eswari Devi Natrajan Maheswaran Shanmugam Department of Electronics and Communication Engineering Kongu Engineering College Erode India Department of Electrical and Electronics Engineering Dr. Mahalingam College of Engineering and Technology Pollachi India

Floorplanning is a prominent area in the Very Large-Scale Integrated (VLSI) circuit design automation, because it influences the performance, size, yield and reliability of the VLSI chips. It is the process of estimating the positions and shapes of the modules. A high packing density, small feature size and high clock frequency make the Integrated Circuit (IC) to dissipate large amount of heat. So, in this paper, a methodology is presented to distribute the temperature of the module on the layout while simultaneously optimizing the total area and wirelength by using a hybrid particle Swarm Optimization-Harmony Search (HpSOHS) algorithm. This hybrid algorithm employs diversification technique (pSO) to obtain global optima and intensification strategy (HS) to achieve the best solution at the local level and Modified Corner List algorithm (MCL) for floorplan representation. A thermal modelling tool called hotspot tool is integrated with the proposed algorithm to obtain the temperature at the block level. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solution.

关键词： VLSI Non-Slicing Floorplan Modified Corner List (MCL) Algorithm Hybrid particle Swarm Optimization-Harmony Search Algorithm (HpSOHS)

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Synthesis and characterization of ZnO–CuO nanocomposites powder by modified perfume spray pyrolysis method and its antimicrobial investigation

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Journal of Semiconductors 2018年第3期39卷 1-7页

作者： D.Saravanakkumar S.sivaranjani K.Kaviyarasu A.Ayeshamariam B.Ravikumar S.pandiarajan C.Veeralakshmi M.Jayachandran M.Maaza Research and Development Center Bharathiyar UniversityCoimbatore641046India Department of physics SBM College of Engineering&TechnologyDindigul624001India UNESCO-UNISA Africa Chair in Nanoscience's/Nanotechnology Laboratories College of Graduate StudiesUniversity of South Africa(UNISA)Muckleneuk RidgePretoriaSouth Africa Nanosciences African network(NANOAFNET) Materials Research Group(MRG)iThemba LABS-National Research Foundation(NRF)Somerset WestWestern Cape ProvinceSouth Africa Department of physics Khadir Mohideen CollegeAdirampattinam614701India Department of physics Devanga Arts CollegeAruppukkottai626101India Department of physics Sethu Institute of TechnologyPullorKariapatti626115India

pure ZnO, ZnO–CuO nanocomposites can be synthesized by using a modified perfume spray pyrolysis method（MSp）. The crystallite size of the nanoparticles（Nps） has been observed by X-ray diffraction pattern and is nearly 36 nm. Morphological studies have been analyzed by using Field Emission Scanning Electron Microscopy（FESEM） and its elemental analysis was reported by Elemental X-ray Analysis（EDX）; these studies confirmed that ZnO and CuO have hexagonal structure and monoclinic structure respectively. Fourier Transform Infrared（FTIR） spectra revealed that the presence of functional frequencies of ZnO and CuO were observed at 443 and 616 *** average bandgap value at 3.25 e V using UV–vis spectra for the entitled composite has described a blue shift that has been observed here. The antibacterial study against both gram positive and negative bacteria has been studied by the disc diffusion method. To the best of our knowledge, it is the first report on ZnO–CuO nanocomposite synthesized by a modified perfume spray pyrolysis method.

关键词： MSp method XRD FTIR FESEM CuO nanoparticles

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