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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Taiba Ali Monika Singh Raveesh Hegde Amar Choudhary Anindrita Sen Electronics and Communication Engg. CMR Institute of Technology Bengaluru India Electronics and Communication Engg. Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

During climax of COVID-19, research revealed that the virus primarily spreads through close contact with respiratory droplets from infected people and by touching surfaces that have been contaminated. To curb the transmission of this virus and similar viral infections, healthcare experts advised frequent cleaning and disinfecting practices. This paper proposes a contactless sanitization system designed to enhance human safety. The system incorporates various features, including height detection, body temperature checks, and sanitization capabilities, to minimize the risk of contamination. Additionally, it is designed to be accessible to individuals with disabilities, such as those with hearing or visual impairments. By addressing these needs, the system aims to provide a comprehensive solution for reducing the spread of infections while ensuring inclusivity for all users. This approach not only focuses on preventing disease transmission but also emphasizes the importance of accessibility and user-friendliness in public health measures.

关键词： Computer viruses Surface contamination Visual impairment Signal processing Feature extraction Cleaning Pollution measurement Safety Public healthcare Diseases

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Creation of a Vertical Gallium Arsenide (GaAs) Channel High Electron Mobility Transistor (HEMT) for Power Applications in Integrated Circuit (IC) Technology 15

Creation of a Vertical Gallium Arsenide (GaAs) Channel High ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Geethanjali, G.C. Pavithra, G. Manjunath, T.C. VLSI Design & Embedded Systems Specialization Dept. of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore India Dept. of Electronics & Communication Engg. Dayananda Sagar College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331300579

The research article investigates the development of Vertical Gallium Arsenide (GaAs) Channel High Electron Mobility Transistor (HEMT) for Power Applications in IC Technology. This specialized transistor, the Vertical GaAs Channel HEMT, is designed with unique properties enabling its application in high-frequency and high-power operations, particularly in power-related contexts. The abstract succinctly encapsulates the ongoing progress in this development, emphasizing its primary focus on enhancing power efficiency and performance within integrated circuits (ICs). The innovation seeks to facilitate the creation of more compact and efficient electronic devices by integrating power applications seamlessly into ICs. Notably, the technology's ability to operate at higher frequencies renders it pertinent for applications in communication and radar systems. The study also explores the potential for reducing power consumption, aligning with the industry's overarching commitment to energy-efficient electronics. The broader applications of Vertical GaAs Channel HEMT extend into emerging fields such as 5G communication and satellite technology, signifying substantial advancements in the realm of advanced and efficient electronic systems. The abstract concludes by recommending researchers refer to recent academic journals and industry publications for the latest updates on semiconductor advancements. © Grenze Scientific Society, 2024.

关键词： Gallium arsenide

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AI/ML Applications in Spoofing Detection in 5G Networks: Challenges and Solutions

AI/ML Applications in Spoofing Detection in 5G Networks: Cha...

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Intelligent and Innovative Technologies in Computing, Electrical and electronics (IITCEE), International Conference

作者： Monika Singh Raveesh Hegde Mahesh Kumar Jha Amar Choudhary Rubini P Electronics and Communication Engg. CMR Institute of Technology Bengaluru India Electronics and Communication Engg. Alliance College of Engineering and Design Alliance University Bengaluru India Computer Science and Engg. School of Engineering and Technology CMR University Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

5G networks has advanced connectivity, high data, low latency and reliability. Along with these features new challenges come with respect to security, particularly spoofing attacks. One of the necessity in 5G and future wireless communication is to prevent the spoofing attacks depending on the applications. Artificial Intelligence (AI) and Machine Learning (ML) present promising solutions for detecting and mitigating spoofing threats in 5G networks. This paper explains the different types of spoofing and applications, challenges, and future prospects of AI/ML in addressing spoofing in 5G networks. AI/ML algorithms can be used in detection of the location of attackers. This paper also analyses current methodologies, highlight key challenges, and discuss potential advancements that could enhance the security of 5G networks.

关键词： Wireless communication Industries Quantum computing Machine learning algorithms 5G mobile communication Prevention and mitigation Real-time systems Security Reliability Low latency communication

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Revolutionizing and Enhancing Medical Diagnostics with Conditional GANs for Cross-Modality Image Translation 18

Revolutionizing and Enhancing Medical Diagnostics with Condi...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Maji, Prasenjit Dhibar, Kunal Mondal, Hemanta Kumar National Institute of Technology Bcrec Ece Computer Sci. and Design Durgapur India Computer Science and Engg. Bengal College of Engg. & Tech Durgapur India National Institute of Technology Electronics and Communication Engineering Durgapur India

ISBN: (纸本)9789380544519

This pioneering study introduces Conditional Generative Adversarial Networks (cGANs) in medical diagnostics, explicitly focusing on translating images between disparate modalities, exemplified by the conversion from Photoplethysmography (PPG) to Electrocardiography (ECG). The method harnesses inherent data relationships to generate high-fidelity cross-modality images, offering valuable anatomical insights, aiding disease identification, and facilitating effective treatment planning. The study highlights cGANs' transformative role in overcoming modality challenges and emphasizes their potential to revolutionize medical diagnostics. The proposed methodology contributes to dataset expansion through data synthesis, enhancing patient monitoring efficacy. Ethical considerations address synthetic data usage in medical settings, emphasizing responsible technology application. Rigorous evaluation validates the approach, affirming its effectiveness in preserving diagnostic features and establishing potential widespread adoption in medical imaging. This research signifies a new era in patient care, highlighting cGANs' pivotal role in advancing diagnostic innovation and pushing healthcare boundaries responsibly. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cConditional Generative Adversarial Networks (cGANs) Computed Tomography (CT) cross-modality translation data synthesis Electro-cardio-gram (ECG) ethical implications Photo-plethysmo-gram (PPG)

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Deep Learning for Brain Tumor Detection with FPGA Pathway 10

Deep Learning for Brain Tumor Detection with FPGA Pathway

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Mallick, Aniruddha Prasad, Hriya Maji, Prasenjit Banerjee, Subhabrata Mondal, Hemanta Kumar Institute of Engineering & Management Electronics and Commu. Engg. Kolkata India National Institute of Technology Electronics and Commu. Engg. Durgapur India Dr. B. C. Roy Engineering College NIT Durgapur Computer Science and Design ECE Durgapur India Institute of Engineering & Management University of Engineering and Management Electronics and Communication Engineering Kolkata India National Institute of Technology Electronics and Communication Engineering Durgapur India

ISBN: (纸本)9798331533229

Brain tumors, particularly gliomas, present significant challenges in medical science because of their high incidence, recurrence, and mortality rates. Perfect diagnosis using Magnetic Resonance Imaging (MRI) is crucial. Recent developement in deep learning, particularly in Convolutional Neural Networks (CNNs) and transfer learning models, have significantly enhanced the automation and accuracy of medical image analysis. This work proposes a comprehensive framework for classification of brain tumor using MRI data, integrating CNN, VGG16, and EfficientNetB0 models with a Field Programmable Gate Array (FPGA) implementation pathway. Our approach enhances the models' resilience and applicability using robust data augmentation methods, including geometrical transformation. Experimental results demonstrate that both CNN and EfficientNetB0 achieve classification accuracies exceeding 90%, showcasing their effectiveness in distinguishing between gliomas, meningiomas, and pituitary tumors. The FPGA-based accelerator significantly improves processing efficiency. This framework advances tumor classification and addresses computational challenges, paving the way for practical applications in medical diagnostics. © 2024 IEEE.

关键词： Convolutional neural networks

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Revolutionizing and Enhancing Medical Diagnostics with Conditional GANs for Cross-Modality Image Translation

Revolutionizing and Enhancing Medical Diagnostics with Condi...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Prasenjit Maji Kunal Dhibar Hemanta Kumar Mondal ECE Computer Sci. and Design National Institute of Technology & BCREC Durgapur India Computer Science and Engg. Bengal College of Engg. & Tech Durgapur India Electronics and Communication Engineering National Institute of Technology Durgapur India

This pioneering study introduces Conditional Generative Adversarial Networks (cGANs) in medical diagnostics, explicitly focusing on translating images between disparate modalities, exemplified by the conversion from Photoplethysmography (PPG) to Electrocardiography (ECG). The method harnesses inherent data relationships to generate high-fidelity cross-modality images, offering valuable anatomical insights, aiding disease identification, and facilitating effective treatment planning. The study highlights cGANs’ transformative role in overcoming modality challenges and emphasizes their potential to revolutionize medical diagnostics. The proposed methodology contributes to dataset expansion through data synthesis, enhancing patient monitoring efficacy. Ethical considerations address synthetic data usage in medical settings, emphasizing responsible technology application. Rigorous evaluation validates the approach, affirming its effectiveness in preserving diagnostic features and establishing potential widespread adoption in medical imaging. This research signifies a new era in patient care, highlighting cGANs’ pivotal role in advancing diagnostic innovation and pushing healthcare boundaries responsibly.

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Deep Learning for Brain Tumor Detection with FPGA Pathway

Deep Learning for Brain Tumor Detection with FPGA Pathway

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IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Aniruddha Mallick Hriya Prasad Prasenjit Maji Subhabrata Banerjee Hemanta Kumar Mondal Electronics and Commu. Engg. Institute of Engineering & Management Kolkata India Electronics and Commu. Engg. National Institute of Technology Durgapur India Computer Science and Design ECE Dr. B. C. Roy Engineering College NIT Durgapur Durgapur India Electronics and Communication Engineering Institute of Engineering & Management University of Engineering and Management Kolkata India Electronics and Communication Engineering National Institute of Technology Durgapur India

ISBN: (数字)9798331533229

ISBN: (纸本)9798331533236

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Brain modeling Data models Convolutional neural networks Medical diagnosis Field programmable gate arrays Medical diagnostic imaging

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EASYDIAGNOS: A FRAMEWORK FOR ACCURATE FEATURE SELECTION FOR AUTOMATIC DIAGNOSIS IN SMART HEALTHCARE

arXiv

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arXiv 2024年

作者： Maji, Prasenjit Mondal, Amit Kumar Mondal, Hemanta Kumar Mahanty, Saraju P. Computer Science and Design Dr. B. C. Roy Engineering College WB 713206 India Computer Science and Engineering Bengal College of Engg. and Tech. WB 713212 India Electronics and Communication Engg. National Institute of Technology WB Durgapur713209 India Dept. of Computer Science and Engineering University of North Texas United States

The rapid advancements in artificial intelligence (AI) have revolutionized smart healthcare, driving innovations in wearable technologies, continuous monitoring devices, and intelligent diagnostic systems. However, security, explainability, robustness, and performance optimization challenges remain critical barriers to widespread adoption in clinical environments. This research presents an innovative algorithmic method using the Adaptive Feature Evaluator (AFE) algorithm to improve feature selection in healthcare datasets and overcome problems. AFE integrating Genetic Algorithms (GA), Explainable Artificial Intelligence (XAI), and Permutation and Combination Techniques (PCT), the algorithm optimizes Clinical Decision Support Systems (CDSS), thereby enhancing predictive accuracy and interpretability. The proposed method is validated across three diverse healthcare datasets using six distinct machine learning algorithms, demonstrating its robustness and superiority over conventional feature selection techniques. The results underscore the transformative potential of AFE in smart healthcare, enabling personalized and transparent patient care. Notably, the AFE algorithm, when combined with a Multi-layer Perceptron (MLP), achieved an accuracy of up to 98.5%, highlighting its capability to improve clinical decision-making processes in real-world healthcare applications. © 2024, CC BY.

关键词： Electronic health record

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A Novel PR Controller with Improved Performance for Single-Phase UPS Inverter 4

A Novel PR Controller with Improved Performance for Single-P...

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4th Biennial International Conference on Nascent Technologies in engineering, ICNET 2021

作者： Prabhakaran, Prajof Krishna, S Mohan Febin, Daya J. L Perumal, Thinagaran National Institute of Technology Karnataka Dept. of Electrical and Electronics Engg. Surathkal India Alliance College of Engineering and Design Alliance University Dept. of Electrical and Electronics Engg. Bangalore India Electric Vehicles Incubation Testing and Research Centre Vellore Institute of Technology Chennai India University Putra Malaysia Faculty of Computer Science and Information Technology Department of Computer Science Serdang Selangor43400 Malaysia

ISBN: (纸本)9781728190617

Modern uninterrupted power supplies (UPS) are gaining popularity as they can deliver clean and high-quality power under extreme load condition for sensitive devices (like medical, military and communication equipment). Traditional proportional-integral (PI) based control for UPS system provides high-quality output but suffers from the poor dynamic response. In this paper, a novel proportional resonant (PR) controller is proposed for controlling the output voltage of a single-phase inverter. The proposed controller provides a fast-dynamic response, low or zero steady-state error, and reduced total harmonic distortion (THD). A detailed step by step procedure to design the novel controller is presented based on the specific criteria (like steady-state error, overshoot, etc.). Due to the fast response, the proposed PR controller is also suitable for EV charging applications (specifically to the vehicle to load charging). Simulation and experimental results are presented to validate the feasibility and efficacy of the proposed controller. © 2021 IEEE.

关键词： Controllers

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Modelling, Simulation and Analysis of NPDA for Sub-retinal implant using Nano Materials

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IOP Conference Series: Materials Science and engineering 2022年第1期1225卷

作者： M. VijaiMeyyappan R. Joseph Daniel Assistant Professor M.I.E.T. Engineering College Dept. of Electronics and Communication Engg.Gundur Trichy 620 007 Professor NPMaSS MEMS Design Centre Dept. of Electronics and Instrumentation Engg. FEAT Annamalai University 608 002

There are two primary illnesses that afflict the elderly worldwide those are Retinitis Pigmentosa (RP) and Age-related Macula Degeneration (AMD). Presently there has been a large research focus to develop treatment methods that would cure AMD induced vision disability and RP diseases. Silicon Micro Photo Diode Array, ARGUS II, and ALPHA IMS are a few of the latest therapeutic approaches suggested for curing these diseases (MPDA). These systems have led to many undesirable side effects in patients. The objective of this research work is to create an affordable device that improves the vision recovery. Photovoltaic cells composed of nanomaterials such as carbon nanotubes and graphene are known as Nano Photo Diode Arrays (NPDA). Under incident light, they may generate stimulating electrical pulses to restore vision, photoreceptor cells are stimulated in the retina. The authors of this study sought to optimise the several geometries of Graphene/SWCNT-C60/Al bilayer hetero junction photodiodes that would constitute the NPDA for subretinal implantation.

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