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检索条件"机构=Departments of Computer Science and Engineering and Electronics and Communication Engineering"

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Analysis of Uniform Amplitude Butler Beam Forming Network for X/Ku-Band for Air-Borne Synthetic Aperture Radar Applications

Analysis of Uniform Amplitude Butler Beam Forming Network fo...

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Wireless Antenna and Microwave Symposium (WAMS), IEEE

作者： Irshad Ahammad Shaik Venkatesh Nallagorla Kula Deepak Gaddam Sai Dheeraj Reddy Bobba Praveena Kati Venkata Kishore Kothapudi Srinivas Kiran Gottapu Department of Electronics and Communication Engineering Centre of Excellence Advanced RF Microwave & Wireless Communications School of Electrical Electronics and Communication Engineering Vignan's Foundation for Science Technology and Research (VFSTR) Andhra Pradesh India Department of Engineering and computer Sciences Benedict College Columbia SC USA

ISBN: (数字)9798350358926

ISBN: (纸本)9798350358933

In this article, the signal processing technique known as beamforming is discussed in relation to a number of industries, such as telecommunications, wireless communication systems, radar, sonar, and acoustics. The recommended frequencies for the Ku band at 13.265 GHz and the X band at 9. ***, crossovers, and phase shifters are produced as a result of the components, which are utilised to realise the design of beamforming networks. Insertion loss for the proposed crossover is less than 2dB. Butler matrix-based beamforming network with substrate with $\epsilon_{\mathrm{r}}{=2.2}$ , height $=0.8$ mm with 8 different beamforms at- 20°,-15°,-10°,-5°,5°,10°, 15°,200. the theoretically predicted total power of 9.030dB, By merging various signals or waveforms to create a concentrated, directional beam, beamforming networks (also known as Butler matrices) can theoretically increase power by amplifying the targeted signal and eliminating interference and noise from other directions. The significance of beamforming is found in its capacity to enhance signal quality. By reducing route loss and attenuations, beamforming increases communication range and quality. Target localisation depends heavily on beamforming.

关键词： Couplings Butler matrices Wireless communication Microwave antennas Array signal processing Phase shifters Antenna feeds

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Personal Protective Equipment On Hand Gloves -A Review Study 8

Personal Protective Equipment On Hand Gloves -A Review Study

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8th International Conference on Advanced Computing and communication Systems, ICACCS 2022

作者： Saradhamani, P. Anitha, S. Keerthana, K.P. Karthikeyan, K. Dr. N.G.P Institute of Technology Department of Management Studies Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Sns College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665408165

To continue to aid others, health professionals around the world prioritize self-defense. This includes learning how to prevent and control illnesses, as well as how to use personal protective equipment (PPE), properly wash hands, and dispose of potentially dangerous substances. The World Health Organization (WHO) defines protective gear (PPE) as garments are worn to safeguard health professionals or other people from contamination. Face protection, goggles and masks or face protection, gloves, gloves or caps, headgear, and rubber boots are all examples of medical PPE. This study, which took a long time and effort to achieve, is about medical PPE to protect against viruses or potentially infectious chemicals. This study, which took a lot of time and effort, focused on medical PPE to prevent potential foreign infectious pathogens or substances. © 2022 IEEE.

关键词： Health

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Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus

Prediction model using SMOTE, genetic algorithm and decision...

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作者： Azad, Chandrashekhar Bhushan, Bharat Sharma, Rohit Shankar, Achyut Singh, Krishna Kant Khamparia, Aditya Department of Computer Applications National Institute of Technology Jamshedpur India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Electronics & Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology NCR Campus Delhi- NCR Campus Ghaziabad India Department of CSE ASET Amity University Noida201301 India Bengaluru India School of Computer Science and Engineering Lovely Professional University Punjab India

Diabetes mellitus is a well-known chronic disease that diminishes the insulin producing capability of the human body. This results in high blood sugar level which might lead to various complications such as eye damage, nerve damage, cardiovascular damage, kidney damage and stroke. Although diabetes has attracted huge research attention, the overall performance of such medical disease classification using machine learning techniques is relatively low, majorly due to existence of class imbalance and missing values in the data. In this paper, we propose a novel Prediction Model using Synthetic Minority Oversampling Technique, Genetic Algorithm and Decision Tree (PMSGD) for Classification of Diabetes Mellitus on Pima Indians Diabetes Database (PIDD) dataset. The framework of the proposed PMSGD prediction model is composed of four different layers. The first layer is the pre-processing layer which is responsible for handling missing values, detection of outlier and oversampling the minority class. In the second layer, the most significant features are selected using correlation and genetic algorithm. In the third layer, the proposed model is trained, and its effectiveness is evaluated in the fourth layer in terms of classification accuracy (CA), classification error (CE), precision, recall (sensitivity), measure (FM), and Area_Under_ROC (AUROC). The proposed PMSGD algorithm clearly outperforms its counterparts and achieves a remarkable accuracy of 82.1256%. The best outcome achieved by the proposed system in terms of CA, CE, precision, sensitivity, FM and AUROC is 82.1256%, 17.8744%, 0.8070%, 0.8598, 0.8326 and 0.8511, respectively. The obtained simulation results show the effectiveness and superiority of our proposed PMSGD model and their by reduced error rate to help in decision-making process. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision trees

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Deep Neural Networks in Neuroimaging: A U-Net Model for Precise Brain Tumor Detection

Deep Neural Networks in Neuroimaging: A U-Net Model for Prec...

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communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This work holds the promise of being a considerable breakthrough in medical imaging diagnosis by studying advanced U-Net topologies for accurate brain tumour segmentation from magnetic resonance imaging (MRI) data. In this research, we present a more sophisticated pipeline that covers data preprocessing, robust model validation, and architecture tuning of the deep learning techniques. Performing accurately at 94.7% accuracy, the model showed a decent precision and recall rates with extremely promising results against traditional segmentation approaches as well as current deep learning frameworks. These results show how U-Net models could improve the diagnoses accuracy and guide personalized treatment tactics. Our research demonstrates the transformative ability of deep learning in medical diagnostics and opens a pathway for additional strides in individualized therapy, albeit diffuse tumour border segmentation barrier and MRI image heterogeneity limitations.

关键词： Deep learning Image segmentation Accuracy Magnetic resonance imaging Brain modeling Robustness Topology Telecommunication computing Planning Tuning

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Intelligent and Smart Health Care Monitoring System based on Deep Convolutional Neural Network Model

Intelligent and Smart Health Care Monitoring System based on...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Anitha, G. Sankar, R. Vimala Juliet, A. Dhivya, V. Thiyaneswaran, B. Kishore Kumar, A.T.A. Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of ECE Vel Tech MultiTech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of ECE Sona college of Technology Salem India Department of ECE School of Engineering Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India

ISBN: (纸本)9798350305708

Remote health monitoring apps have revolutionized the healthcare industry as a whole with the advent of Internet of Things (IoT) technology. Strategies used to get insights through the analysis of lifestyle patterns as well as behaviors are also crucial to tailored healthcare and preventative services. Opportunities for both the investigation of illnesses and the forecasting of abnormal health situations are made possible by the use of intelligent data retrieval as well as categorization methods. The broad use of IoT has improved telemedicine capabilities and introduced more precise remote patient monitoring, both of which have had a favorable effect on the world at large. The impact of the Internet of Things (IoT) becomes deeper as it enhances healthcare infrastructure for patients, doctors, and hospitals. Through the usage of secure patient portals, healthcare providers have easier access to up-to-date patient information, allowing for more precise diagnosis and treatment. The prototype's potential for efficient healthcare monitoring is reinforced by the success of similar systems. In this research, we automated the AlexNet Model building process by experimenting with several approaches. In addition, a whole new method of diagnosis has been created. The experimental data shows that the suggested algorithm is superior to the state-of-the-art methods. The total accuracy of the suggested model is 97.13%, which is significantly higher than the accuracy of any of the other models compared. This demonstrates the model's efficiency and places it as a potentially game-changing breakthrough in healthcare. © 2023 IEEE.

关键词： AlexNet Model Deep Convolutional Neural Network Smart Health Care Monitoring System

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Futuristic Banking: Streamlining ATM Transactions with Fingerprint and Contactless Authentication

Futuristic Banking: Streamlining ATM Transactions with Finge...

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Smart electronics and communication (ICOSEC), International Conference on

作者： A. Essaki Muthu A. Justin Diraviam R. Niranjana K. Saravanan S. Sundararajan R. Santhana Krishnan Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering College of Engineering Guindy Anna University Chennai India Department of Mechanical Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India

Modern banking relies heavily on Automated Teller Machines (ATMs), although card-based transactions have historically faced security issues. This study proposes Near-Field communication (NFC) Card Emulation and fingerprint technology as secure alternatives for ATM cash transactions. NFC Card Emulation enables secure data exchanges within a short distance, ideal for handling sensitive information. To augment security during authentication, fingerprint technology is proposed as an additional layer of protection. By employing fingerprint sensors, user identification can be established without reliance on physical cards or NFC tags. This integration ensures a heightened level of security during the authentication process, surpassing the current card-based approach employed in ATMs. The system eliminates the need for physical cards, enhancing security and mitigating risks such as ATM tampering and magnetic strip damage. A high level of security is guaranteed during ATM transactions as authentication becomes more effective and secure. This novel strategy strengthens ATM security and provides users with a seamless and safe banking experience by incorporating NFC Card Emulation and fingerprint technologies.

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Development of Kalman Filter-Based Tracker and Visualizer for Millimeter-Wave Radars in Python

Development of Kalman Filter-Based Tracker and Visualizer fo...

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IEEE International Conference on electronics, Computing and communication Technologies (CONECCT)

作者： Aditya Aravind Hari Ganesan Sanjeev Gurugopinath Ganesan Thiagarajan Department of Electronics and Communication Engineering PES University Bengaluru India Department of Computer Science and Engineering PES University Bengaluru India MMRFIC Technology Private Limited Bengaluru India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

In this paper, we present a Kalman filter (KF)based tracker and visualizer in a tightly-coupled architecture for millimeter-wave (mmWave) radar applications. For the tracker segment, we consider an interactive multiple model (IMM)based KF framework, which combines constant acceleration and velocity models. The visualizer unit uses a multi-thread approach ensuring thread safety, and can be implemented on software with limited memory and computational power. Moreover, the tracker and visualizer unit can be run on different computers due to the TCP/IP socket connection. The tracker-visualizer duo is developed in Python, as a scalable solution, which makes it platform-agnostic. Through real-world experimental data, we demonstrate the efficacy of the proposed tracker and visualizer.

关键词： Computational modeling Sockets Data visualization TCPIP Millimeter wave radar Radar tracking Software

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Implementing Front Axle Reference Points for Enhanced Autonomous Vehicle Navigation

Implementing Front Axle Reference Points for Enhanced Autono...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Premkumar Duraisamy Sibishree M Nandhini S Parvathy K Anukarthika S Devatharani G S Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Dr NGP Institute of Technology Coimbatore India Department of Artificial Intelligence and Data Science Sri Eswar College of Engineering Coimbatore India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

In response to the rapid progress of autonomous vehicle technology, the imperative for sophisticated navigation systems has emerged. This paper extensively investigates the fundamental mechanisms employed for reference and positioning in autonomous vehicles, with a specific focus on the Global Positioning System (GPS), Lidar (Light Detection and Ranging), Radar (Radio Detection and Ranging), and Camera-Based Vision Systems. Amidst the evolving landscape of autonomous navigation, numerous challenges persist, ranging from environmental variability to the need for real-time accuracy. A comprehensive comparative analysis is conducted for each technology, elucidating their fundamental operations, advantages, and limitations. This study highlights the performance of the front axle reference point mechanism and compared it the rear and center axle reference points. This study analyzes the distinct roles and applications of these mechanisms, underscoring the significance of the front axle reference point in achieving precision and reliability in navigation, especially in intricate and dynamic environments. The integration of these diverse technologies, with a specific emphasis on the front axle reference point, is pivotal for enabling autonomous vehicles to navigate with enhanced accuracy and safety. Additionally, the paper explores future trends, potential challenges, and emerging technologies, providing valuable insights into the evolving landscape of autonomous vehicle navigation. In essence, this paper endeavors to furnish a detailed overview of current technologies while offering a new perspective on how these systems, particularly those focused on the front axle reference point, will continue to mold the trajectory of autonomous transportation in the future.

关键词： Accuracy Laser radar Navigation Machine vision Wheels Distance measurement Vehicle dynamics

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Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

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Electrical electronics and Computing Technologies (ICEECT), International Conference on

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology.

关键词： Phased arrays Beam steering Adaptive systems Array signal processing 5G mobile communication Precoding Wireless networks Bit error rate Interference Throughput

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An Efficient Analysis of Various Deep learning Model in Brain tumor Detection with MRI

An Efficient Analysis of Various Deep learning Model in Brai...

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communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This paper compares three of the popular deep learning models being VGG-16, VGG-19 and ResNet50 with respect to how they perform in the classification task. We investigate how much each model learns, the reduction of errors and inferences from data over epochs, through an in-depth analysis of training and testing phases. The authors concluded that all the models perform efficiently; however, from the study results of performance and accuracy, VGG-19 and ResNet50 have a high-performance scale as well as super accuracy in their deep learning model which encourages business for application towards complex pattern recognition or predictive modelling. These results provide important intuitions on what deep learning architectures can do, and pave the path for further works in this area, b

关键词： Deep learning Training Analytical models Accuracy Computational modeling Predictive models Brain modeling Data models Residual neural networks Testing

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