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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhatt, Upendra Mohan Sharma, Somya Francis, G. Arun Kaushik, Neeraj Hemelatha, S. Sharma, Y.K. Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India School of Commerce and Management Iimt University Meerut India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Tmu Department of Ece India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350357769

This study examines the typical performance of a wirelessly fueled transceiver in which an electricity source interacts with a target while being supplied by a specialized power transmitter (PB). As a transmission device, it is believed that the PB is competent of frequency prediction, cloud based spatial multiplexing, and spectral detection. When using a night before going to bed technique, the provider first collects information from either a PB with numerous receivers before sending data to the target. Theoretical formulas for the annualized pace for two distinct transmit antennas, particularly regarding postpone accepting and postponement incompatible, are obtained under the assumption of Nakagami-m path loss. Additionally, in some specific situations, such as the high send wattage domain and a huge assortment of antennas, closed-form methods for the ideal time spread that optimize the typical transmission are found. The effect of co-channel crosstalk is then researched. The total output can be increased at the PB by adding more broadcast transmitters, and since interruption can be used as an additional energy source, it may even be possible to use interruption to your advantage. This has been demonstrated by analytical and modeling findings. Additionally, the cruising pace is not significantly impacted by the energy conversion link's waning intensity level, particularly if there are a lot of PB receivers. Lastly, it is noted that the average flow is significantly influenced by the source. © 2023 IEEE.

关键词： Cellular data transmission Co-channel crosstalk Energy conversion link Frequency prediction Multiple access systems Optimization Performance evaluation Power beacons Power transmitter System model System performance Telecommunication networks Transmit antennas Wireless power transfer

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Design and Development of a Touch Free Smart Home Controlling System Based on Virtual Reality (VR) Technology

Design and Development of a Touch Free Smart Home Controllin...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Devi, S. Rukmani Raghi, K.R. Priya, S. Geetha Sathi, G. Kumar, Sarva Naveen Dinesh, M. Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

The Touch-Free Smart Home Controlling System based on Virtual Reality (VR) Technology represents an innovative approach to home automation, leveraging the immersive capabilities of VR to provide users with seamless and intuitive control over their smart home environment without the need for physical touch. This system integrates VR headsets with motion sensors, depth cameras, and gesture recognition technology to enable users to navigate and interact with a virtual representation of their home environment. Smart home devices such as lights, thermostats, door locks, and entertainment systems are interconnected and controlled through the VR interface, offering users a compelling and immersive way to manage their living space. The central hub acts as the backbone of the system, facilitating communication between the VR interface, sensors, and smart home devices. Performance metrics such as response time, latency, throughput, and power consumption are monitored to ensure optimal system operation. Security measures including user authentication and data encryption are implemented to protect user privacy and prevent unauthorized access. The Touch-Free Smart Home Controlling System based on VR Technology not only enhances convenience and accessibility but also represents a significant step forward in the evolution of smart home automation towards more intuitive and immersive user experiences.. © 2024 IEEE.

关键词： Gesture recognition

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A Rail-To-Rail Transconductance Amplifier Based on Current Generator Circuits

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2023年第10期31卷 1624-1628页

作者： Akbari, Meysam Hussein, Safwan Mawlood Hashim, Yasir Khateb, Fabian Tang, Kea-Tiong University of Kurdistan Department of Electronics and Communication Engineering Sanandaj66177-15175 Iran Tishk International University Faculty of Engineering Department of Computer Engineering Erbil44001 Iraq Department of Microelectronics Brno University of Technology Brno601 90 Czech Republic Brno University of Defense Department of Electrical Engineering Brno66210 Czech Republic Czech Technical University in Prague Faculty of Biomedical Engineering Kladno3105 Czech Republic National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

In this brief, two current generator circuits are used to design a self-biasing transconductance amplifier. The current generators are configured using two n-channel and p-channel cascode current mirrors by which a high input dynamic range is achieved. Since such a topology creates positive feedback, the transconductance of the circuit is also increased causing higher performance. To ensure the stability of the circuit, constant current sources can be paralleled with the current mirror topologies, which of course are implemented using input drivers. Therefore, two n-channel and p-channel input differential pairs are added to the current generator circuits by which not only a rail-To-rail operation is achieved but also the amplifier is stabilized. The proposed circuit was fabricated in the TSMC 0.18-μm CMOS process with a silicon area of μm54.1× 71 μm. Under a 1.8-V supply voltage, the experimental results showed a high input common-mode range (ICMR), while a gain bandwidth (GBW) of 83.9 MHz was measured for a capacitive load of μm2×6μm pF. In addition, a dc gain and a slew rate (SR) of 68.4 dB and 71.7 V/ μm , respectively, were achieved. © 1993-2012 IEEE.

关键词： Operational amplifiers

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Decoding Financial Markets: Unleashing the Power of Bi-LSTM in Sentiment Analysis for Cutting-Edge Stock Price Prediction

Decoding Financial Markets: Unleashing the Power of Bi-LSTM ...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Aluvala, Srinivas Balamurugan, V. Jeyasudha, J. Kumar, R. Dinesh Pradeep Kumar, S. Sr University School of Computer Science & Artificial Intelligence Warangal India Sathyabama Institute of Science and Technology Department of Ece Chennai India Srm Insitute of Science and Technology Department of Computational Intelligence Chennai India Saveetha School of Engineering Department of Ece Thandalam India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Predicting stock prices accurately is a challenging task due to various influencing factors. This paper addresses the need for precise stock price prediction by presenting an enhanced sentiment analysis methodology using deep learning, particularly focusing on the Bi-LSTM model. The challenge of accurate sentiment analysis in stock prediction is overcome through advanced data preprocessing techniques tailored to maintain sentiment context effectively. The proposed Bi-LSTM (Bidirectional long Short-Term Memory) and CNN (Convolutional Neural Network) model demonstrates superior performance in classifying sentiments with high precision, recall, F1-score, and accuracy. The numerical validation demonstrates the effectiveness of the Bi-LSTM model in achieving a balance across various metrics, highlighting its potential for practical implementation in stock market prediction. The Bi-LSTM and CNN model achieves remarkable precision 90.00%, recall 90.00%, F1-score 90.00%, and accuracy 91.00%. In comparison to established methods, such as Multivariate Sequential Long Short-Term Memory Autoencoder (MSLSTMA), long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), the Bidirectional long Short-Term Memory (Bi-LSTM) model exhibits its competitive edge in stock price prediction as supported by evaluation metrics. The results emphasize the importance of tailored data preprocessing and deep learning techniques, showcasing the efficiency of the proposed methodology in enhancing stock price prediction accuracy while maintaining high percentages of precision, recall, and F1-score, along with impressive accuracy. © 2023 IEEE.

关键词： Bidirectional long Short-Term Memory Convolutional Neural Network Deep Learning Sentiment Analysis Stock Price Prediction

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Traffic Sign Detection Problems using Convolutional Neural Techniques in Image Processing

Traffic Sign Detection Problems using Convolutional Neural T...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Subhamathi, A.S.F. Sathiyapriyan, C. Anuradha, T. Jose Anand, A. Rheem, Abdul Arun, M.R. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Cyber Security Chennai603104 India Kcg College of Technolog Department of Electrical and Electronics Engineering Tamil Nadu Chennai600097 India Kcg College of Technology Department of Ece Karapakkam Chennai600 097 India Mullana Department of Mathematics and Humanities Haryana Ambala India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (纸本)9798350375237

Traffic sign detection is a critical component in advancing intelligent transportation systems and ensuring road safety. Convolutional Neural Networks (CNNs) have emerged as powerful tools in image processing and computer vision, demonstrating remarkable success in various domains, including traffic sign recognition. This paper presents a comprehensive exploration of the challenges associated with traffic sign detection and the application of CNN techniques to address these issues. The paper reviews the traditional methods used for traffic sign detection and emphasizes the limitations that motivate the adoption of CNNs. The core of the paper delves into the application of convolutional neural techniques for traffic sign detection. Various CNN architectures are surveyed and highlight their strengths and weaknesses in handling specific challenges inherent to traffic sign recognition. Attention mechanisms, transfer learning, and other advancements in CNNs are explored for their potential to enhance detection accuracy and robustness. In conclusion, this paper contributes to the understanding of how convolutional neural techniques can be leveraged to tackle traffic sign detection challenges. © 2024 IEEE.

关键词： Object detection

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FMICS: A Robust Learning Methodology to Predict Skin Cancer Disease Using Fused Melanoma Image Classification Scheme

FMICS: A Robust Learning Methodology to Predict Skin Cancer ...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bhavani, G. Rama, M. Padma, S. Kumar, P. Praveen Kumar, K. Suresh Rajeshwari, R. R.M.K. Engineering College Department of Electronics and Communication Engineering Chennai India Qis College of Engineering and Technology Department of It AndhraPradesh Ongole India Sona College of Technology Department of Eee Salem636005 India Qis College of Engineering and Technology Department of Ece AndhraPradesh Ongole India Panimalar Engineering College Department of Mba Chennai India S.A Engineering College Department of Master of Computer Application Chennai India

ISBN: (纸本)9798331543617

Skin cancer (SC), particularly melanoma, is a significant health issue due to its high mortality rate when not detected early. This study introduces a novel approach, FMICS: A Robust Learning Methodology (Hybrid ANN + XGBoost) for predicting SC through fused melanoma image classification. The methodology integrates Artificial Neural Networks (ANN) for feature extraction and XGBoost for classification, leveraging the strengths of both models. Preprocessing techniques such as image filtering, contrast enhancement, and hair removal were applied to improve image quality. The study utilized dermoscopic images from the PH2 and HAM10000 datasets, containing both benign and malignant lesions. The hybrid model achieved superior performance compared to individual models, with an accuracy of 94.80%, sensitivity of 94.90%, and specificity of 94.70%. Data augmentation techniques, including rotation, scaling, and flipping, were applied to enhance the model's robustness. Evaluation metrics such as the F1-score and ROC-AUC were used to assess the performance. The hybrid approach significantly reduced false negatives, making it highly reliable for melanoma detection. These results suggest that the FMICS methodology offers a promising solution for accurate and efficient SC diagnosis, with potential applications in clinical and mobile environments. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Music Recommendations with Emotional Insight: A Facial Expression Approach in AI 7

Enhancing Music Recommendations with Emotional Insight: A Fa...

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7th International Conference on electronics, Communication and Aerospace Technology, ICECA 2023

作者： Gupta, Manish Venkatesh, S.N. Suraskar, Rupali Praveena, K. Jegadesan, S. Suneetha, Sampathirao GLA University Department of Electronics and Communication Engineering Uttar Pradesh Mathura India Brindavan College of Engineering Department of ECE Karnataka Bangalore India Larsen and Toubro Edutech Department of Electronic Maharashtra Nagpur India Department of ECE Tirupati India Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India Andhra University College of Engineering Andhra University Department of Computer Science & Systems Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350340600

Music recommendation systems are only one of the many fields that artificial intelligence (AI) has transformed. A developing field called 'emotion-aware music recommendation' aims to improve user experience by including emotional context into the suggestion procedure. This study investigates how AI -powered music recommendation systems can incorporate facial expression analysis. The proposed system suggests a unique method that makes use of deep learning methods to instantly recognize and assess user facial emotions. The proposed system creates music suggestions that fit the user's emotional state by identifying emotions like joy, melancholy, and enthusiasm. Experimental findings that show how our strategy might increase user engagement and pleasure. The results show how emotion-aware AI systems have the ability to produce individualized and emotionally impactful musical experiences. © 2023 IEEE.

关键词： Music

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Innovative Communication Protocols and Network Architectures in 5G Technologies 3

Innovative Communication Protocols and Network Architectures...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Senthilkumar, C. Vijay, J. Sankar, R. Akula, Akhila Supriya, S. Purushothaman, V. Saveetha School of Engineering Department of Electronics and Communiation Engineering Tamil Nadu Chennai India Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Department of Electronics and Communication Engineering Salem India Chennai Institute of Technology Kundrathur Department of EEE Chennai India MLR Institute of Technology Department of ECE Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

A new era of connectedness marked by unheard-of speed, minimal latency, and increased capacity to serve an abundance of devices and apps is brought about by the introduction of 5G technology. This paper delves into the innovative communication protocols and network architectures that underpin 5G technology, which collectively aim to meet the diverse and demanding requirements of modern communication networks. We investigate the architecture and application of cutting-edge protocols, including improved Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and New Radio (NR). Furthermore, this research delves into innovative network designs such as Massive MIMO, Edge Computing, and Network Slicing, emphasizing their functions in streamlining network operations, allocating resources, and augmenting user satisfaction. The integration of these cutting-edge protocols and architectures within the 5G framework promises to achieve higher efficiency, flexibility, and scalability. However, the deployment of 5 G networks is not without challenges. This paper addresses key issues such as security vulnerabilities, interoperability among heterogeneous devices and networks, and the substantial investments required for infrastructure development. Additionally, we discuss the regulatory and standardization hurdles that must be overcome to realize the full potential of 5G technology. Using a thorough study, we highlight how 5G technology is revolutionizing a number of industries, including telemedicine, the Internet of Things (IoT), driverless cars, and smart cities. The last section of the paper paints a picture of how global connectivity will develop in the future, with 5G acting as a key technology that will open the door to a more intelligent, connected, and productive society. © 2024 IEEE.

关键词： Internet of things

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Machine Learning Model used Efficiently to Manage the Smart Grid and Renewable Energy

Machine Learning Model used Efficiently to Manage the Smart ...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Narayan, Yogendra Selvaraj, Sivakumar Suryawanshi, Mahesh A. Ramya, S. Chandigarh University Department of Ece Mohali India VelTech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India Bharati Vidyapeeth College of Engineering Department of Chemical Engineering Navi Mumbai India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350317060

A new technology, a Smart Grid Management System (SGMS), explains how it uses machine learning algorithms to distribute power more effectively. This article overviews intelligent grid management systems, including their design, advantages, and problems. Machine learning methods, including decision trees, neural networks, and Support Vector Machines (SVM), are also discussed in this study as they are employed in intelligent grid management systems. Increased efficiency, less waste, enhanced dependability, and decreased expenses are a few benefits of using deep learning techniques in intelligent energy systems for management. Protection of data, Security, and capacity are only some issues that arise when applying machine learning techniques to innovativegrid management systems. The report finishes with a discussion of potentialnext steps for machine research learning-based intelligent control systems for grids. © 2023 IEEE.

关键词： Support vector machines

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Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction 3

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, R. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods. © 2025 IEEE.

关键词： Smart contract

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