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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Aslam, S. Shamkuwar, Madhavi Babu, S. B. G. Tilak Suresh, J. Pavithra, G. Natrayan, L. Chaitanya Bharathi Institute of Technology Electrical And Electronics Engineering Andhra Pradesh Proddatur516360 India Zeal Institute of Business Administration Computer Application And Research Maharashtra Pune India Aditya Engineering College Department of Ece Surampalem India Ramachandra College of Engineering Electrical & Electronics Engineering A.P Eluru534007 India Dayananda Sagar College of Engineering Electronics & Communication Engineering Karnataka Bangalore560111 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350365092

The aircraft industry has long used cutting-edge technologies to ensure equipment security, dependability, and performance. In aerospace applications, fault tolerance-the ability of a system to perform reliably despite errors or failures-is essential. Developing devices that work well even when things go wrong is vital. Using a Smart Multiphase Power Converter (SMPC) and a Model Reference Adaptive Control (MRAC) algorithm, this abstract improves aircraft fault tolerance. Multiphase power converters are increasingly used in airplanes due to their high power density, fault tolerance, and efficiency. The MRAC algorithm controls this operation. MRAC continuously optimizes system settings to match the reference model. MRAC algorithms adapt aeronautical control systems to maintain peak performance despite external interference, changing operational parameters, and broken parts. Combining the SMPC and MRAC algorithms in aerospace fault-tolerant machine design has many benefits. First, it checks the system's state and adjusts the controller parameters to account for any deviations from the ideal, ensuring great reliability. In conclusion, smart multiphase power converters and model reference adaptive control can improve aeronautical fault-tolerant machine architecture. This technology benefits the aircraft sector with its fault tolerance, efficiency, and adaptability. As aerospace technology advances, imaginative solutions are essential for safe and reliable future missions in Earth's atmosphere and beyond. Space operations can be safer and more efficient because to this study's implications for machinery design that can withstand extreme circumstances. © 2024 IEEE.

关键词： Fault tolerance

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Industrial and Home Automation using Facebook Messenger Chatbot and PaaS 3

Industrial and Home Automation using Facebook Messenger Chat...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Selvalakshmi, S. Ravi, S. Kumar, A.T.A. Kishore Devi, S. Durga Sunheriya, Neeraj Attar, Hani Rmk Engineering College Department of Electronics and Instrumentation Engineering Chennai India Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India School of Engineering Department of Ece Andhra Pradesh Tirupati India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Department of Energy Engineer Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509675

A set of communication protocols that allows interconnected devices on the internet to communicate with each other. A proposed IHM-FMP model integrates Messenger Chat bot functionality and Platform as a Service (PaaS) architecture for efficient home automation and security. Utilizing hardware components like Raspberry Pi, relay modules, and sensors, along with TCP/IP protocol for internet connectivity, the system enables remote control of home appliances and security features. Through Messenger Chat bot integration, users can easily command devices via Facebook Messenger, enhancing accessibility. The PaaS method ensures control with layers for remote access and management. Experimental results demonstrate the model's superiority in network throughput, delay, routing overhead, transmission accuracy, and energy efficiency compared to existing techniques. This holistic approach to home automation and security offers convenience, accessibility, and robust performance, making it an effective solution for modern households. © 2025 IEEE.

关键词： Chatbots

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MPC Controller Design For Multiple Input And Multiple Output System

MPC Controller Design For Multiple Input And Multiple Output...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Lavanya, M. Jayamanmadharao, M. Sonia, Bankuru Praveen, R.V.S. Sarkar, Rashel Chitra, D. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electrical & Electronics Engineering Thandalam India Aditya Institute of Technology and Management Department of Ece Tekkali India Department of Eee Visakhapatnam India Organization - LTIMindtree India The Assam Royal Global University Department of Computer Science and Engineering Assam India Panimalar Engineering College Mba Department Varadharajapuram India

ISBN: (纸本)9798350375442

This paper explores the application of Model Predictive Control (MPC) in multiple input and multiple output (MIMO) processes, focusing on redesigning the MPC system for a four-tank system known as Quadruple. MPC is utilized in process industries to forecast future controller outputs and minimize system costs by determining optimal future control actions. Control action rates and plant outputs are adjusted by modifying weight matrices. The specified prediction and control horizons serve as key control parameters. The redesign of the MPC system for the Quadruple system involves designing and implementing it using LABVIEW software. LABVIEW simplifies the complexity of MPC controller design and implementation. The primary objective of four-tank system is to maintain consistent fluid levels in each tank, with LABVIEW facilitating the implementation of automatic controllers for effective control in process plants. © 2024 IEEE.

关键词： Model predictive control

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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Comparative Analyses of Multi-Wavelength Converters Based on Electro Absorption Modulators at Various Data Rates

Comparative Analyses of Multi-Wavelength Converters Based on...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Kaur, Taranjeet Kaur, Jatinder Jambu, Mohit Saxena, Aditi Shah, Sanjeev Kumar Saxena, Antima Chandigarh Engineering College Department of Ece Jhanjeri Mohali140307 India Gulzar Group of Institutes Eee Department Punjab Khanna141401 India Gla University Department of Ece Mathura281406 India Uttaranchal Institute of Technology Uttaranchal University Electronics and Communication Engineering Dehradun248007 India Ies College of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350310061

The performance analysis of an electro absorption modulator based multi-wavelength converter that uses a radio frequency generator has been investigated at various input powers (ranging from -3 dBm to 10 dBm) and at various data rates (5 Gbps, 10 Gbps, and 20 Gbps). This was done in order to eliminate the need for an additional pump signal by utilising the RZ (Return to Zero) modulation format. This was done in order to make the solution a more cost-effective one. In this article, a comprehensive investigation of the trade-off between diverse power and varied data rate has been carried out. The input power and data rate are the two most essential operational parameters for wavelength conversion. Because of this, the effect of input power and data rate on the performance of wavelength conversion is investigated in great depth by monitoring the timing diagrams of the signal that has been wavelength converted. © 2023 IEEE.

关键词： Cost effectiveness

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The Future of EV: Real-Time Development of an Intelligent Wireless Charging System for Electric Vehicles 1

The Future of EV: Real-Time Development of an Intelligent Wi...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Rajalingam, A. Darwin Nesakumar, A. Sathya, S. Krishnankutty, Jishor Erat Abid, Y. Pagunuran, Jubert R. College of Engineering and Technology University of Technology and Applied Sciences Engineering Department Shinas Oman R. M. K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of VLSI Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350361780

The world is always on the lookout for new ways to put technology to use, and it is changing at a rate no one has ever seen before. For decades, the car industry has done nothing to alter. The advent of electric vehicles, which run on electricity rather than fossil fuels, was the big breakthrough. There have been many investigations and appreciated structures as well as outcomes for wirelessly powering electric automobiles, both passively and rapidly. This might result in a more rapid acceptance of electric vehicles globally, as it is an emerging technology in automotive manufacturing that has not yet been widely used. We cross-validated our Intelligent Wireless Charging System (IWCS), a wireless power transmission charging circuit for electric vehicles, with the Classical Charging System (CCS) to assess the efficacy of the proposed model. To solve the problem of battery overheating during charging while plugged in, as well as to extend the vehicle's battery life, the suggested model is employed. Batteries temperature handling is the primary concern for EVs due to the increased heat losses while plugged-in charging, which in turn reduces the battery's lifespan. Currently, electric cars mostly employ water-cooled systems to cool the battery, which isn't ideal for EVs due to the added size and weight. Air cooling is another option for cooling systems;it's already used in internal combustion engines but isn't very effective in electric vehicles since charging the battery produces too much heat. There is a considerable decrease in thermal output when we employ wireless charging technology. The proposed system's primary objective is to provide wireless power transfer using inductive coupling as its basis. © 2024 IEEE.

关键词： Energy transfer

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Innovative Techniques in the Construction of Very Large Scale Integration Circuits for Low-Power and High-Performance Circuits 2

Innovative Techniques in the Construction of Very Large Scal...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Saravanan, V. Sasikala, G. Gomathi, P. Kirubakaran, D. Rajalingam, A. Pagunuran, Jubert R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India V.S.B. Engineering College Department of Ece India R.M.K Engineering College Department of Computer Science and Engineering India University of Technology and Applied Sciences College of Engineering and Technology Engineering Department Shinas Oman

ISBN: (纸本)9798350388916

Portable electronic gadgets and high-performance computing systems have increased demand for low-power, high-performance integrated circuits (ICs). VLSI technology, which creates integrated circuits with dozens to millions of transistors on a chip, is leading these efforts. This research investigates low-power, high-performance VLSI circuit construction methods. We start with VLSI design fundamentals and power management and performance optimization concerns. The constraints of traditional VLSI design methods in satisfying current electronics' strict requirements are examined. VLSI design methodology development and evaluation are the focus of this research. Advanced power optimization methods include MTCMOS, adaptive body biasing, and power gating. We also study clock tree improvement, signal integrity enhancement, and new materials and device topologies for high performance. In addition, we study how machine learning techniques automate and optimize circuit placement, routing, and timing in VLSI design. AI handles the complexity of current VLSI designs and finds optimal design parameters for minimal power and high performance. Scaling VLSI circuits to newer technology nodes raises process variance, leakage current, and thermal management difficulties, which the research tackles. These issues can be overcome with new ways to keep VLSI circuits scaling while preserving performance and power efficiency. Finally, case studies and experimental findings show that the offered methods work in real-world applications. These findings demonstrate significant power usage and performance benefits over typical VLSI design. In conclusion, our research advances VLSI technology by developing low-power, high-performance integrated circuits. The proposed VLSI design methods and solutions could lead to more efficient and powerful electronic systems. © 2024 IEEE.

关键词： VLSI circuits

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A Novel Hybridclustering Model Forwireless Sensor Networks

A Novel Hybridclustering Model Forwireless Sensor Networks

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Vijayalakshmi, P. Saravanan, M. Rani, M. Thillai Ashok, M. Palaniappan, Ramya Nagaraj, V. Department of Computer Science and Engineering Knowledge Institute of Technology Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Ece Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Cse Madanapalle Institute of Technology and Science Andhrapradesh Chitoor India Department of Ece Annapoorna Engineering College Tamil Nadu Salem India

ISBN: (纸本)9798350334357

Energy efficiency is the largest critical provocation in the wireless sensor network. Communicationenergy is the ultimate consuming work in sensor junctions, especially in the great distance. Thus, clustered routing modelsaresystematicapproachesutilizedfor lowering the communication energy and expanding the lifespan of the networkwhichis the objective of this paper. This work proposes a hybrid clustered routingmodel for energyexpansion in WSN. Several clustering designs examine the residual power and distance in the middle of the junctions in the cluster heads selection and further rotate the cluster heads selection periodically. The proposed clustering design, K-Means, and LEACH protocol techniques are applied. The clustering planpermitsforreducing intra-cluster transmissions and to gain powerability for sensor junctions. The hybrid integration of K-means and Leach protocol is used toimprove the nodes' power and the network lifespan. © 2023 IEEE.

关键词： Wireless sensor networks

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Deep Learning Applications in Microscopy and Holography for near-field Signal Processing 13

Deep Learning Applications in Microscopy and Holography for ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Mohseni, Milad Thilagham, K.T. Aravinda, K.A. Kumar, B Santhosh Nagpal, Amandeep Geetha, B.T. Islamic Azad University Department of Computer and Engineering Tabriz Branch Tabriz Iran Government College of Engineering. Salem Metallurgical Engineering Tamilnadu India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Saveetha School of Engineering Simats Department of Ece Tamilnadu India

ISBN: (纸本)9798350305463

Focusing on near-field signal processing, we provide a novel and comprehensive strategy for improving picture quality, obtaining super-resolution, and performing quantitative analysis in the disciplines of microscopy and holography. Three deep learning algorithms - Enhanced ImageNet (EINet), HoloReconGAN, and QuantSegNet - are combined in this method to maximize their potential. In order to improve images, super-resolve them, and analyze them quantitatively, many algorithms have been developed. In order to better comprehend each method, mathematical equations are provided to describe the main steps involved. With the use of convolutional neural networks, noise is reduced and finer features are brought into focus with the help of the Enhanced ImageNet (EINet) method. To accomplish super-resolution and 3D reconstruction from holographic data, HoloReconGAN combines generative adversarial networks (GANs) with variational autoencoders (VAEs). Label-free segmentation and quantitative analysis of structures in microscopy and holography pictures are the focus of QuantSegNet, a semantic segmentation network. Our suggested technique has been shown to outperform six established methods over a broad variety of assessment criteria. Image quality, noise suppression, feature recognition, computational efficiency, and resilience are just few of the areas where it shines. It's also faster, uses less memory, is more accurate, easier to use, cheaper, and faster. Applications in several scientific fields might be facilitated, and the area of microscopy and holography as a whole could see significant advancements as a result. © 2024 IEEE.

关键词： Image enhancement

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Resource Allocation in Multi-Access Edge Computing using Teaching-Learning Based Optimization: A Multi-Objective Approach 2

Resource Allocation in Multi-Access Edge Computing using Tea...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Al-Attabi, Kassem Kumar, Sachin Naik, Ashitha V. Bhagavanthu, M. Reddy, R. Archana The Islamic University Najaf Iraq Nit Department of Electronics and Communication Engineering Karnataka Raichur India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India Cvr College of Engineering Department of Information Technology Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India

ISBN: (纸本)9798350382952

Efficient resource allocation in Multi-access Edge Computing (MEC) plays a pivotal role in achieving high throughput, low latency, energy efficiency, and user fairness. Traditional optimization approaches often address these goals separately, leading to suboptimal solutions in dynamic environments with multifaceted user demands. This research proposes a multi-objective framework for resource allocation in MEC by leveraging the Teaching-Learning Based Optimization (TLBO) algorithm. The TLBO algorithm, inspired by the classroom learning process, iteratively improves a population of candidate solutions by sharing knowledge among learners and guidance from a 'teacher.' The research formulate the resource allocation problem as a multi-objective optimization problem and demonstrate how TLBO can effectively discover Pareto-optimal solutions that represent trade-offs between conflicting objectives. Experimental results on simulated MEC scenarios demonstrate the superiority of with throughput of 150 mbps the proposed approach compared to baseline strategies such as greedy of 135 mbps and weighted round robin of 142 mbps. © 2024 IEEE.

关键词： Energy efficiency

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