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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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Study the Improved Efficiency of the Lead-Free CsSnI3 Perovskite Solar Cells with Variation of Absorber Layer in SCAPS-1D

Study the Improved Efficiency of the Lead-Free CsSnI3 Perovs...

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Arijit Kumar Barik Himadri Sekhar Das Sudipta Banerjee Heranmoy Maity Santanu Mishra Mousam Chatterjee Bidrohi Bhattachrya Asim Kumar Jana Gourisankar Roymahpatra Electronics and communication Engineering Ramakrishna Mission Vidyamandira Howrah India Electronics and communication Engineering Haldia Institute of Technology Haldia India Computer Science and Enginerring Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India Department of Applied Science Haldia Institute of Technology Haldia India Electronics and Communication Engineering B. P. Poddar Institute of Management and Technology Kolkata India Electrical and Electronics Engineering Pailan college of Management and Technology Kolkata India

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

In this research work, a novel orderly study was conducted to explore the different parameters effect of thickness of the protection tier on adeptness of the FTO/TiO 2 /CsSnI 3 /Cu 2 O/Au Perovskite solar cell by SCAPS-1D. To realize the optimized power conversion efficiency (PCE), acceptor density of the Perovskite absorption layer is (N A =2×10 19 cm -3 ) with a 40nm TiO 2 layer, 300 nm for the Perovskite absorption layer (CsSnI 3 ) and 10 nm Cu2o as HTL, with Au as electrode. After optimization process, efficiency reached from 13.60% to 17.17%. A result indicates the enhancement performance of the Perovskite solar cells (PSC).

关键词： Electrodes Performance evaluation Absorption Photovoltaic cells Instruments Lead Perovskites Power conversion Protection Optimization

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Adaptive Error Correction and Machine Learning for Robust Optical Fibre communication

Adaptive Error Correction and Machine Learning for Robust Op...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： K. Vembarasi S. Praveen Kumar T. Senthil Kumar S. Senthilkumar M. Rajakumaran R. Ganeshamoorthy Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Electronics and Communication Engineering M Kumarasamy college of engineering Thalavapalayam Tamil Nadu School of Computing SASTRA Deemed University Thanjavur Tamil Nadu India Department of Electronics and Communication Engineering Rajalakshmi Engineering college Chennai Tamil Nadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Optical fibre communication systems are key for high-speed data transmission but they face challenges such as transmission errors and signal degradation. Hence this study proposes an innovative method that combines adaptive error correction technique with machine learning algorithm to enhance the robustness and reliability of optic fibre communication. Adaptive error correction methods are utilized, specifically low-density parity check codes and Turbo codes which are known for their efficiency in handling high error rates in data transmission. These techniques dynamically adjust their error correction parameters based on the different conditions of optical fibre communication channel. Then, the integration of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are utilized to detect and correct errors in real time. For training and evaluation, the International Telecommunication Union (ITU-T) are employed. This dataset includes different signal parameters and error scenarios. Experimental evaluation demonstrate that this method eliminates the bit error rate and enhance the signal to noise ratio compared to traditional error correction methods. It also ensures minimal latency, making the method suitable for high-speed optical fibre communication system. This study contributes to the field of optical fibre communication by providing a robust solution that utilizes the strengths of both adaptive error correction and machine learning.

关键词： Recurrent neural networks Bit error rate Machine learning Turbo codes Optical fiber communication Error correction Data communication Convolutional neural networks Telecommunication network reliability Signal to noise ratio

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Medical Internet of Things using Deep Learning Techniques for Skin Cancer Detection 5

Medical Internet of Things using Deep Learning Techniques fo...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Veeraiah, Vivek Ravikaumar, G.K. Kalpana, R. Sreenivasulu, K. Singh, Yashpal Shukla, Surendra Kumar Adichunchanagiri University Department of Computer Science Karnataka Mandya India Adichunchanagiri University Department of Computer Science and Engineering Karnataka Mandya India Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Department of Electronics and Communication Engineering Tamil Nadu Chennai India KLEF Deemed University Department of Biotechnology Andhra Pradesh Guntur India Jain Deemed to Be University Department of Computer Science & Engineering Karnataka Benguluru India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

It has always been difficult to diagnose skin cancer using eye inspection and manual study of photographs of skin lesions. It might take a lot of time and effort to manually examine skin lesions to look for melanoma. Skin cancer, especially melanoma, is one of the deadliest conditions. It is more challenging to identify and diagnose different skin lesions due to the similarities between them in colour skin imaging, such as carcinoma and nevi. Many deep learning models and machine learning methods have evolved for the interpretation of medical pictures, particularly the images of skin lesions, as a result of technological innovation and the rapid rise in processing resources. Early skin cancer identification and classification enable patients to receive appropriate diagnosis and care. This research presents a novel 'deep learning framework' for skin lesion identification in skin images that is supported by the 'internet of health and things (IoHT)' and is based on transfer learning. In order to obtain pertinent data and logical arguments from many sources, this research work has concentrated on taking the secondary qualitative technique into consideration. © 2022 IEEE.

关键词： Internet of things

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Location based Context Awareness with Multipoint Transmission by ELAML Algorithm

Location based Context Awareness with Multipoint Transmissio...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： S. Ram Prasath K. Gokulakrishnan M. Subramanian N. Mohan Department of Computer Science and Engineering SCAD College of Engineering and Technology Tirunelveli India Department of Electronics and Communication Engineering Anna University Regional Campus Tirunelveli India

Wireless network and communication engineers have a query that when, where and how mmWave will be used in volte+ network? The future generation network depends on multiple speed Radio Access Technology (RAT), but it makes use of different access in downlink with multipoint transmission in the presence of multilink distributed wireless channel information. Context awareness means that systems can sense and react their environment based on location which is capable of identifying nearest network locator. The location of particular system in a distributed network can be determined by Navigational Network Locator (NNL). Next, Vehicular Systems demand high data transfer links of mmWave communication. By the Convolution Neural Networks, it passes the result to the upcoming vehicular signal and multiple links shares the data to all mobile networks. The Enhanced Location Awareness Machine Learning (ELAML) combined with Mobile Edge Computing (MEC) technologies to enable multiple links ultra-high speed data downloads. The real-world implementations of Location based machine learning network architecture using traffic patterns are identified. The main aim of this research paper is about implementing multiple ultra-high data rate with volte+ mmWave vehicular network.

关键词： Location awareness Base stations Wireless sensor networks Machine learning algorithms Wireless networks Machine learning Context awareness

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Performance analysis of IEEE 802.11 WLANs with frame aggregation and unsaturated traffic load

Performance analysis of IEEE 802.11 WLANs with frame aggrega...

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International Black Sea Conference on communications and Networking (BlackSeaCom)

作者： Shinnazar Seytnazarov Dong Geun Jeong Wha Sook Jeon Faculty of Computer Science and Engineering Innopolis University Innopolis Russia Department of Electronics Engineering and Applied Communication Research Center Hankuk University of Foreign Studies Yongin-si South Korea Department of Computer Science and Engineering Seoul National University Seoul South Korea

ISBN: (数字)9798350351859

ISBN: (纸本)9798350351866

Existing performance models of modern IEEE 802.11 networks with frame aggregation do not correctly reflect the operations for usual/unsaturated traffic conditions. Some of them assume a predefined service-time distribution. Others model very specific cases of unsaturated traffic load, such as stations defering transmission until $K$ packets are gathered or stations always having at least one packet in their buffers. We propose a new performance model for 802.11 networks with frame aggregation and unsaturated traffic load, where the aggregation size, and the service times are dynamically determined by a number of factors such as the offered traffic load, the random backoff process, the number of stations, etc. Performance evaluations show that the proposed model produces a more accurate throughput for different numbers of nodes and various traffic loads.

关键词： Performance evaluation Solid modeling Analytical models Three-dimensional displays Simulation Telecommunication traffic Throughput

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Blockchain Based Future Banking by Decentralized Exchanges 3

Blockchain Based Future Banking by Decentralized Exchanges

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Raman, Ramakrishnan Mary Rexy V, Arul Viswanathan, C. Shrirvastava, Ankit Ganesh, E.N. Symbiosis Institute of Business Management Pune & Symbiosis International Deemed University Maharashtra Pune India Tamil Nadu Chennai India Alpha College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Symbiosis Law School Uttar Pradesh Noida India Maharashtra Pune India Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306231

Decentralized Finance (DeFi) is a blockchain-based financial system that offers open and transparent financial services. This paradigm will enhance traditional banking security, transparency, inclusivity, and efficiency. Decentralized financial systems employ blockchain decentralized exchanges (DEX)'s without intermediaries and their transactions are protected by cryptography. Public ledger users may verify blockchain transactions. Without ID or address, traditional banks decline customers. Anyone with internet may trade finance on DEXs'. This inclusion may aid unbanked and underbanked individuals, enhancing global prosperity. Future banks will employ DEX for efficiency and intermediaries and complex processes of conventional banking are removed. DEX speeds up transactions by eliminating intermediaries. Scalability issues must be addressed as platforms grow. Future banking on DEX-based blockchain requires study into the ecosystem's multiple applications and advancements. © 2023 IEEE.

关键词： Smart contract

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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electronics and Renewable Systems (ICEARS), International Conference on

作者： Velmurugan M Shinzeer C K G. Maya R. Ramya R Kanimozhi Syed Ismail Abdul Lathif Department Of Computer Science & Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science And Technology Chennai Department of School of Computer Applications Lovely Professional University Phagwara Punjab Faculty of Management SRM Institute of Science and Technology Kattankulathur Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Samayapuram Computer Science and Engineering Muthayammal Engineering College Rasipuram Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions.

关键词： Resistance Privacy Fault tolerance Smart contracts Fault tolerant systems Throughput Energy efficiency Blockchains Identity management systems Interoperability

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IoT Controlled Device to Manage the EMG Signals of the Patient and Alert in Real Time 2

IoT Controlled Device to Manage the EMG Signals of the Patie...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Arunsankar, G. Sampathrajan, Rajeshkumar Vanathi, A. Gopi, B. Sasikala, S. Srinivasan, C. Department of Electrical and Electronics Engineering Gojan School of Business and Technology Redhills Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Tamil Nadu Rasipuram India Kakkaveri Rasipuram Tamil Nadu Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

IoT technology has created tremendous healthcare potential. This research study introduces an IoT-controlled gadget that manages patient electromyography (EMG) readings and provides real-time notifications. EMG signals help diagnose and monitor neuromuscular problems, making proper measurement and analysis essential for optimal healthcare. Wearable EMG sensors, a microprocessor, and cloud infrastructure comprise the IoT-controlled gadget. The microcontroller receives EMG signals from the patient's wearable sensors. The microcontroller uses IoT to securely communicate data to a cloud-based infrastructure for analysis. EMG signals are analyzed using modern algorithms and machine learning on the cloud. These algorithms examine signal patterns, identify abnormalities, and categorize data based on predetermined criteria. The technology also alerts healthcare providers for important deviations or aberrant EMG patterns. IoT-controlled EMG monitoring devices have various benefits. Wireless connection allows patients to roam freely for continuous monitoring. Real-time notifications improve patient safety and reaction time in emergency scenarios. The cloud-based architecture also allows remote consultations and patient monitoring using EMG data. The suggested gadget was extensively tested with a varied population of patients. The findings show the device's accurate EMG signal capture and real-time alarms. This study introduces an IoT-controlled gadget for patient EMG signal management and real-time notifications. IoT technologies, wearable sensors, and cloud-based analytics streamline EMG data monitoring and analysis, improving patient care and healthcare outcomes. This work contributes to IoT-enabled healthcare solution research and advances neuromuscular condition management. © 2023 IEEE.

关键词： Electromyography Healthcare Internet of Things Medical Information Processing Remote Patient Monitoring

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Optimizing Cybersecurity: Leveraging Support Vector Machines for Real-Time Threat Detection

Optimizing Cybersecurity: Leveraging Support Vector Machines...

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Innovations in communications, Electrical and computer engineering (ICICEC), International Conference on

作者： Ankit Kumar Dubey Rohit Kumar Dubey Anil Shukla Poornanand Dubey Department of Computer Science and Engineering Baderia Global Institute of Engineering and Management Jabalpur India Department of Engineering Mathematics Baderia Global Institute of Engineering and Management Jabalpur India Department of Electronics and Communication Engineering Baderia Global Institute of Engineering and Management Jabalpur India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

In the current digital era, that is marked by the enhanced use of information technology, there are also enhanced threats which are in the form of cybercrimes. This research deals with enhancing the cyber security through Support Vector Machines (SVMs) in threat detection in real time. SVMs are a class of machine learning algorithms favored for the accuracy and efficiency they provide when it comes to the classification processes; they would be very useful in fast and accurate finding of potential threats. This forms the basis of the study under which the authors propose a new approach using SVMs in a real time data mining system that can quickly detect anomalous activities across networks. Using the detection rates for different further commonly used machine learning models, we show the advantages of our concept to the development of SVMs in view of detection accuracy, velocity and resource demand. The findings demonstrate that the use of SVM based system can alleviate the false positive incident and improve the security situation of organizations. This research extends the field of cybersecurity by developing a customizable and transferable model that can be deployed in different contexts to strengthen existing defenses against the emerging and evolving cyber threats.

关键词： Support vector machines Training Technological innovation Accuracy System performance Scalability Real-time systems Threat assessment Resource management computer crime

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Extraction of Water Bodies from Visible Color Satellite Images Using PCA Feature Map

Extraction of Water Bodies from Visible Color Satellite Imag...

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2021 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2021

作者： Sharma, Deepa Sharma, Trapti Singhai, Jyoti Indian Institute of Information Technology Vit Bhopal University -Maulana Azad National Institute of Technology Department of Electronics & Communication Engineering Department of Computer Science & Engineering Department of Electronics & Communication Engineering Bhopal India

ISBN: (纸本)9781665442497

Water body extraction from remote sensing imagery is important in planning and monitoring of water resources. It is a complex task due to the diversity of water quality, and similarity of spectral characteristics with the background information. In this paper, an algorithm is proposed to extract the water bodies from the RGB visible color satellite images using principal component analysis (PCA) feature. First, the PCA features are extracted from the input image and select PCA component 2 feature and segmented it into binary classes by using threshold method. Further, this binary outcome is enhanced by morphological erode operation to obtain the final water regions. The qualitative analysis of experimental outcomes illustrate that the proposed algorithm extract most of the water regions with least false prediction rate. © 2021 IEEE.

关键词： Extraction

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