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2024 IEEE International Conference on recent Advances in science and Engineering technology, ICrASET 2024

作者： Bhuvaneswari, Govindappagari Gomathi, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Computer Science and Engineering Avadi Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

recent developments in Wireless Body Area Network (WBAN) technology have created a wide range of sensors capable of monitoring various physiological parameters in patients. However, the limited battery life of these sensors has constrained the potential of WBAN, highlighting the need for energy-efficient algorithms to address their substantial energy demands. To address this challenge, this paper introduces a novel Energy Efficient Hybrid Meta-Heuristic Approach (NEEMA) for WBANs. NEEMA combines the Tunicate Swarm Algorithm (TSA) and Genetic Algorithm (GA), collectively known as T-GA, to achieve high convergence and broad exploitation and exploration capabilities. The approach incorporates a clustering methodology to determine the Cluster Head (CH) using a fitness function derived from T-GA, which considers innovative parameters for conserving energy across the network. The study underscores the importance of multi-hop communication between patients equipped with sensor nodes, enabling data transmission to healthcare providers through multiple relay Head (rH) nodes, transmitting data to subsequent rHs and ultimately to the central data sink. The process for selecting rHs mirrors that of CHs. Experimental results demonstrate that NEEMA outperforms existing algorithms and offers advantages for various WBAN applications. © 2024 IEEE.

关键词： Sensor nodes

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IoT-rPC Based remote Controlling System for Energy-Efficient Air Conditioning and Ventilation in Shopping Malls

IoT-RPC Based Remote Controlling System for Energy-Efficient...

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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Arun, C.A. Gadde, Mukesh Narayana Sharma, Aditya raj, Tanya Dept of Ece Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (纸本)9798350394580

The rapid increase in public places, such as shopping malls, theatres, and supermarkets, has made it imperative to maintain a comfortable indoor environment for visitors. This work proposes a remote-controlling system to optimize the operation of air conditioners, ventilators, lights, and fans in shopping malls, ensuring both visitor comfort and energy efficiency. The system integrates the ESP32 microcontroller and the ThingsBoard IoT platform for seamless communication between devices and by utilizing the JSON-rPC protocol over MQTT communication. The proposed hardware setup involves deploying ESP32 microcontrollers on each shopping mall floor. MQTT communication is established between the ESP32 microcontroller and the ThingsBoard platform, enabling remote control of devices through an intuitive dashboard. The results demonstrate effective control over air conditioners and ventilators, leading to significant energy savings and enhanced visitor comfort. © 2023 IEEE.

关键词： dHT22 ESP32 MQTT PZEM004T rPC ThingsBoard

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IoT based detection and Monitoring for Coronary Artery disease 9

IoT based Detection and Monitoring for Coronary Artery Disea...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Aanandha Saravanan, K. Vignesh Prasanna, N. Ezilarasan, M.r. Aloy Anuja Mary, G. Sathyasri, B. MuthuKumaran, d. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350384208

Cardio vascular disease is the dangerous coronary artery disease (CAd) today. According to world health organization (WHO) the death cases are gradually increasing. The invasive diagnosis like Angiography causes pain and cost-effective treatment. Whereas non-invasive treatment like Electrocardiography (ECG), Heart Flow Analysis (HFA), X-rays are minimal expenses and painless treatment compare to invasive treatment. To predict and monitor this coronary artery disease we have designed to prevent the serious damages of patients. recently the researchers work to develop software to help the doctors to resolve both forecast and Verdict of heart syndrome. deep learning techniques and algorithms have been pragmatic in numerous medicinal datasets to systematize analyze of huge and multifaceted statistics. The accuracy level of Binary model and Categorical model is compared. In which Binary model consisted as more accuracy than Categorical model. Prediction of occurrence of heart disease in medical field gathering more information using data analytics. By using Fire database, the data is stored and monitored in GUI Application of User devices. © 2023 IEEE.

关键词： Heart

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design of CPW Fed Quad Port MIMO Antenna for Sub-6 GHz 5G Application 3

Design of CPW Fed Quad Port MIMO Antenna for Sub-6 GHz 5G Ap...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： dileepan, d. Ippili, Sai Vivek Balineni, ravichandu Yarramalli, Gayathri Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Dept of ECE Chennai India

ISBN: (纸本)9798350317022

Compatibility and efficiency are the two important factors that need to be considered in current wireless technology. Maintaining good MIMO parameters, such as ECC and dG, can help achieve these goals. A compact dimension of 45 x 45 mm2 is utilized to build the quad-port MIMO structure. A compact CPW-fed single element of dimension 20 mm x 20 mm with a -10 dB bandwidth between 5.1 GHz and 5.85 GHz is used to design a compact MIMO structure. The distance between each single antenna element is 5 mm, and the elements are placed in an orthogonal orientation, which enables good isolation. The MIMO design has good isolation over the entire operational band, with a value of less than -30 dB. The proposed antenna design has an ECC value of 0.0003 and a dG value of around 10 dB. Also, the design shows good radiation characteristics and positive gain. Ansys HFSS software is used to carry out all necessary simulations. © 2023 IEEE.

关键词： 5G dG ECC MIMO Quad port

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Speed directive for Connected Autonomous Vehicles(CAV) on freeways 7

Speed directive for Connected Autonomous Vehicles(CAV) on fr...

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7th International Conference on Circuit Power and Computing technologies, ICCPCT 2024

作者： Vaishnavi, T. deva Shahila, d Ferlin Nalini, N. rosi, A. Ashwini, A. Priya, M. Sangeetha Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Chennai India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372816

Connected Autonomous Vehicles (CAV) is happening due to sensors incorporation in all road conditions and make driving judgements, and these sensors must be reliable for the vehicles to be safe. Accelerated devices hold the main segment among entire hindrance recognition devices. Generally, estimated to be built on vehicles more often. To identify obstacles, these sensors analyze ultrasonic emission and reflection data. Using the intrinsic weaknesses in sensors, we developed adaptive attacks using jamming, random spoofing, and spoofing on ultrasonic sensors. By doing this, we were able to a car into stopping when it shouldn't have and let it keep going. It substantiates our assaults using independent devices and mobile automobiles, like a Tesla Model that has Autopilot. Here, results show that occurrences induce blindness and malfunctions in both autonomous vehicles and sensors, which could lead towards crashes. The proposed deuce resistance strategies nearly improve the safety of accelerated devices and CAV with two methods of verifying signals. The suggested verified signals are sensor leveling physical shift authentication and multiple sensor consistency check, which uses multiple sensors to verify signals at the system level. © 2024 IEEE.

关键词： Ultrasonic sensors

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deep Learning Based drowsiness detection With Alert System Using raspberry Pi Pico

Deep Learning Based Drowsiness Detection With Alert System U...

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2023 International Conference on data science, Agents and Artificial Intelligence, ICdSAAI 2023

作者： Ashwini, A. Vaishnavi, T. rosi, A. Shahila, d. Ferlin deva Nalini, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350348910

Most people are losing their lives each year as a result of vehicle accidents caused by sleepy drivers. A device that can identify tiredness and notify the driver to it is necessary to prevent car accidents and save lives. Finding drowsy drivers is perhaps the most important step in stopping any traffic accident, everywhere in the globe. The goal of this work is to create alert strategy for smart vehicles which immediately halt drowsy driving problems. However, feeling tired is a normal physical phenomenon that can happen for a number of different causes. In order to stop the accident's cause, an accurate alarm system must be developed. The system for alerting sleepy drivers that has been developed using this technique is discussed in the proposed study where Video Stream Processing (VSP) is processed using the blinking criteria based on the eyes Euclidean distance. Additionally, the face marking method is created. The results of proposed work uses dlib, an OpenCV deep learning technique using raspberry Pi with a mounted camera for accurate detection, showing good results for detecting drowsiness, reducing the accident occurrence on the roads. The facial recognition algorithm developed by Haar starts with collected photographs as its input and outputs faces that are identified. © 2023 IEEE.

关键词： Aspect ratio

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Maize Seed Quality detection using resNet V2 deep Learning technique

Maize Seed Quality detection using ResNet V2 Deep Learning T...

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2024 Asian Conference on Intelligent technologies, ACOIT 2024

作者： Kavitha, r. Murugananthan, V. Kavitha, M. Srinivasan, r. rajathi, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Technology Park Malaysia Kuala Lumpur57000 Malaysia

ISBN: (纸本)9798350374933

recent advancements in agricultural technology have seen the application of computational methods for seed quality detection. A range of studies have explored the use of deep learning in seed quality detection in agriculture. For the agricultural sector to achieve maximum crop yields and food security, the quality of the maize seeds must be guaranteed. The conventional techniques for evaluating the quality of seeds, which depend on visual inspection and biochemical analyses, are labor-intensive, time-consuming, and frequently prone to human error. We suggest using the resNet v2 deep learning architecture in an automated maize seed quality detection system to overcome these issues. In the proposed work, we assembled an extensive dataset of photos of maize seeds. To assess the performance of the model, the dataset was divided into subsets for training, validation, and testing. To improve the model's capacity for generalization, data augmentation methods including rotation, zooming, and flipping were used during training. Starting with pre-trained weights on ImageNet, the resNet v2 model was refined via transfer learning. Stochastic gradient descent with a learning rate scheduler was then used to further optimize the model. This research employs images of maize seeds for the purpose of classification. The process of classification is facilitated through the utilization of a Convolutional Neural Network (CNN) that operates on the resNet152 v2 architecture. The chosen hyperparameters include a learning rate set at 0.001, a batch size of 512, and an epoch of 150. The optimization technique employed is Adaptive Moment Estimation (Adam). The distribution between training and validation data is maintained at a ratio of 80:20. Analysis of the validation outcomes reveals an accuracy rate of 96% compared to other traditional models. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced thermal stability and performance of kenaf fibre-reinforced hybrid composites with silicon carbide fillers

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Interactions 2024年第1期245卷 1-13页

作者： Karthick, M. Meikandan, M. Paul, d.L. Belgin Karthik, P. Karthik, K. rajesh, d. Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Chennai India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India

Natural fibre indused hybrid polymer has emerged over the past decade along various sectors including automobile, structural and much more. The biodegradability of the hybrid composite makes it more versatile in the current global industry. The present work focuses on fabrication such hybrid composite from kenaf fibre, silicon carbide, and epoxy resin polymer. Adding the silicon carbide filler material enhances the electrical and thermal characteristic where as the kenaf fibre act as reinforcement for structural integrity. A set of six composite specimens were produced, with variations in the weight% of silicon carbide. After that, these samples were put through several thermal tests, such as "differential scanning calorimetric (dSC), thermo gravimetric analysis (TGA), coefficient of thermal expansion (CTE), thermal conductivity (TC), and heat deflection temperature (HdT)". This work has been intended to expand the range of possible applications for composites reinforced with silicon carbide or kenaf fibres by emphasizing their potential for enhanced thermal stability, improved performance, and reduced thermal expansion in comparison to traditionally used other filler materials with composite structures. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Silicon carbide

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Locating Patient Health data Theft Using Gradient Boosting with Hybrid Microwave Transmission-Based Wearable device

Locating Patient Health Data Theft Using Gradient Boosting w...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Allimuthu, Udayakumar Singh, Edwin C. Sivaraman, V. ratnavathi, Konagalla Kumar reddy, Y. Pavan Krishna, O. Vamsi Surekha, Addanki Venkata Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Deemed to be University Tamil Nadu Chennai600062 India Madanapalle institute of Technology & Science Andhra Pradesh India Veltech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331540685

The research article addresses the intersection of healthcare monitoring and crime prevention via wearable devices in hospital settings. It proposes a system leveraging blockchain, decision-making algorithms, and wireless communication, particularly IoT and gradient-boosting approaches. Existing health care monitoring lacks real-time intervention capabilities, and conventional crime prevention methods are often reactive rather than proactive. The revolutionary combination of gradient boosting and hybrid microwave transmission-based wearable devices is the ultimate solution to combat patient health data theft. With this cutting-edge technology, this research can confidently detect and prevent unauthorised access to sensitive healthcare information, ensuring the safety and privacy of patients. The proposed system ensures continuous health monitoring and prompt interventions while enhancing crime prevention through mobile data analysis. Blockchain and decision-making algorithms are integrated to securely store patient data and enable authorised personnel access for review. IoT and gradient-boosting algorithms facilitate real-time health monitoring and accurate prediction of crime-related patterns through mobile data analysis. The proposed gradient boosting algorithm has an rMSE of 0.105 and an MAE of 0.010, suggesting great prediction accuracy. With a V AF score of 96% and an r-squared score of 0.94, it captures data variability while demonstrating predictive solid power. This comprehensive strategy utilises cutting-edge technology to enhance healthcare monitoring via wearable devices, contribute to crime prevention, and address crucial concerns in healthcare and public safety. © 2024 IEEE.

关键词： data privacy

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Compressive Strength Estimation of Concrete Blocks using Fully Connected deep Neural Network 14

Compressive Strength Estimation of Concrete Blocks using Ful...

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14th International Conference on Advances in Computing, Control, and Telecommunication technologies, ACT 2023

作者： Malarvizhi, N. Shyamala devi, M. Pandian, J. Arun dinesh, C. Naga Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India

Compression strength refers to a structure ability to endure loads that cause it to shrink in size. The ultimate load in Newtones scaled by the gross cross section area of the block in square millimeters yields the bearing capacity of a concrete masonry unit. The machine learning technology could extend the interdisciplinary field for the estimation of concrete block compressive strength. With this review, this project aims to estimate the concrete block compressive strength retrieved from the KAGGLE machine-learning repository. The compressive concrete blocks dataset contains 9 features and 1050 concrete block and it is processed with encoding and missing values. The exploratory data analysis is done to analyze the correlation of all the features. The raw dataset is applied to linear regression, Linear Kernel SVr, Polynomial Kernel SVr, radial Basis Function SVr, Sigmoid Kernel SVr, decision Tree to analyze the performance of compressive strength estimation of concrete blocks. The proposed model uses Fully Connected deep Neural Network (FCdNN) where the compressive concrete blocks dataset is applied to estimate the compressive strength estimation of concrete blocks. The performance metrics utilized for analysis are Mean Squared Error, r-Squared error. Experimental results shows that the Fully Connected deep Neural Network shows Mean Squared Error of 29.5196 and r-Squared error of 0.885 when analyzed with the all regression models. © Grenze Scientific Society, 2023.

关键词： Compressive strength

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