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Target Application Based Design Approach for RF MEMS Switches using Artificial Neural Networks
Lakshmi Narayana Thalluri,Samuyelu Bommu,Sathuluri Mallikharjuna Rao,K. Srinivasa Rao,Koushik Guha,S. S. Kiran 한국전기전자재료학회 2022 Transactions on Electrical and Electronic Material Vol.23 No.5
In this paper, a target application-based design approach for RF MEMS switches using artifi cial neural networks (ANN) is presented. ANN approach is used to decide the dimensions of the RF MEMS switch for the targeted application. The ANN approach design and analysis is done using MATLAB. The results obtained by ANN method are validated using the FEM tool simulation, which show that the developed models have good accuracy over the range of switch dimension values for the intended application. The switch dimensions are extracted for L, S, C, X, Ku, K, and Ka-band applications using ANN. Eventually, a novel RF MEMS switch based on the ANN approach for C-band applications is proposed. The required pullin voltage will increase because of perforation, and this problem is handled by adding extra weight to the membrane with pillars and slabs. Analyzed load distribution in membrane with perforation. FEM design is implemented using solid mechanics and electrostatic-based Multi-physics in COMSOL environment. Diff erent studies, i.e., stationary, time-dependent, and frequency domain are performed to extract electrical, mechanical, and Radio Frequency parameters of the proposed design. The switch designed for C-band applications off ering an actuation voltage of 7.4 V, Insertion loss of − 0.2 dB, and Isolationloss of − 57 dB.
Jacobian Based Nonlinear Algorithms for Prediction of Optimized RF MEMS Switch Dimensions
Lakshmi Narayana Thalluri,M Aravind Kumar,Mohamed Sultan Mohamed Ali,N. Britto Martin Paul,K. Srinivasa Rao,Koushik Guha,S. S. Kiran 한국전기전자재료학회 2023 Transactions on Electrical and Electronic Material Vol.24 No.5
This communication discusses the role of nonlinear algorithms in training the neural network, which predicts the optimized RF MEMS switch dimensions. A dedicated dataset, i.e., DrTLN-RF-MEMS-SWITCH-DATASET-v1, was created by considering the most appropriate input and output variable suitable to predict the cantilever dimensions, crab leg and serpentine structure-based RF MEMS switches. The distinct artificial neural networks (ANN) performance is analysed using various training methods. The hardware implementation possible neural network algorithms, i.e., Fitting and Cascade Feed Forward Network, are considered for learning and prediction. The ANN algorithm's performance in predicting and optimizing RF MEMS switch is analysed using nonlinear training methods like Levenberg–Marquardt (LM) and Scaled Conjugate Gradient (SCG). The cascaded forward network with LM training combination offers the best performance compared with other varieties. A comprehensive study is performed using neural networks and finite element simulation results. The study revealed that the error percentage is below 15.08% for most of the parameters.
Smart City IoT System Network Level Routing Analysis and Blockchain Security Based Implementation
Bommu Samuyelu,M Aravind Kumar,Babburu Kiranmai,N Srikanth,Thalluri Lakshmi Narayana,G V. Ganesh,Gopalan Anitha,Mallapati Purna Kishore,Guha Koushik,Mohammad Hayath Rajvee,S S. Kiran 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.2
This paper demonstrates, network-level performance analysis and implementation of smart city Internet of Things (IoT) system with Infrastructure as a Service (IaaS) level cloud computing architecture. The smart city IoT network topology performance is analyzed at the simulation level using the NS3 simulator by extracting most of the performance-deciding parameters. The performance-enhanced smart city topology is practically implemented in IaaS level architecture. The intended smart city IoT system can monitor the principal parameters like video surveillance with a thermal camera (to identify the virus-like COVID-19 infected people), transport, water quality, solar radiation, sound pollution, air quality (O3, NO2, CO, Particles), parking zones, iconic places, E-suggestions, PRO information over low power wide area network in 61.88 km × 61.88 km range. Primarily we have addressed the IoT network-level routing and quality of service (QoS) challenges and implementation level security challenges. The simulation level network topology analysis is performed to improve the routing and QoS. Blockchain technology-based decentralization is adopted to enrich the IoT system performance in terms of security.