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

作者： Suresh, N. ramesh, M. rosi, A. Periyar Selvam, K. Vaishnavi, T. Nalini, N. AVN Institute of Engineering & Technology Electronics & Communication Engineering Hyderabad501510 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Electronics & Communication Engineering India GRT Institute of Engineering and Technology Electronics & Communication Engineering Tiruttani India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Electronics & Communication Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Electronics & Communication Engineering Chennai India

ISBN: (纸本)9798350372816

This paper Proposed to design a CMOS Based LC-Voltage Control Oscillator Using Substrate Bias Effect and Low-Voltage Folded-Cascade Current Mirror Circuit using an LC tank circuit at 2.5GHz oscillation Frequency. The paper delves into the substrate-biasing technique, which adjusts the threshold voltage of a CMOS transistor, and the low-voltage folded-cascade current mirror circuit that utilizes CMOS technology to improve the performance of the current mirror circuit. The folded-cascade current mirror circuit comprises of a cascade structure in the input stage and a folded structure in the output stage, which is used to increase the output impedance and provide a more stable output current. The paper concludes that the folded-cascade current mirror circuit is a powerful tool in CMOS circuit design, offering improved performance and accuracy over the traditional current mirror circuit. In this simulation is carried out through cadence virtuoso generic process design kit (GPdK) with 90nm technology. The simulation results exhibits phase noise of -154.92 dBc/Hz @ 1MHz, low power consumption of 4.8mW and the Figure of Merit -216.09 dBc/Hz at 2.5GHz oscillation frequency © 2024 IEEE.

关键词： CMOS integrated circuits

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Artificial Intelligence-Aided Trajectory Planning for UAV 9th

Artificial Intelligence-Aided Trajectory Planning for UAV

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9th International Conference on Signal Processing and Integrated Networks, SPIN 2022

作者： debnath, Sanjoy Teja, Achanta Sai Mani Naveen, Miriyala Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819913114

In this work, we investigate the multi-UAV trajectory planning for optimal resource allocation and energy-efficiency maximization. Considering different terrain models, evaluate effective path for UAV to avoid the obstacle and maximize the energy efficiency of the UAV. With the motivation of reducing the total length of UAV trajectory path while maintaining high QoS and energy efficiency, a stochastic obstacle avoidance game based on q-learning is modeled. Here we re-frame the reward function of q-learning algorithm for optimal trajectory path evaluation. Based on the designed reward function, the optimal trajectory path for the UAV deployment is evaluated from the stochastic analysis. It is observed from the analysis that UAV trajectory is improved by zero collision with the obstacle. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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Proactive Preterm Labor Prediction Using Ptl-Simple rNN deep Learning 2

Proactive Preterm Labor Prediction Using Ptl-Simple RNN Deep...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Sivaranjani, S. Aruna, r. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

Preterm labour presents a substantial obstacle in nature of obstetrics, requiring precise prediction models for prompt intervention. Utilizing deep learning, particularly simple rNN, presents encouraging opportunities for making such predictions. Simple rNN models are highly effective in capturing sequential dependencies, which is essential for studying temporal patterns in risk variables associated with premature labour. Simple rNN models may detect minor connections that indicate the start of premature labour by integrating several information sources, such as maternal health records, genetic predispositions, and environmental variables. Their ability to analyze time-series data allows for dynamic risk evaluation, resulting in improved early warning systems. In addition, Simple-rNN models can adjust to changing patient profiles, ensuring that customized risk assessment also provides higher accuracy. Simple rNN-based prediction models have great promise to reduce the effect of preterm labour by allowing preventative treatments and enhancing perinatal outcomes. This research realize this potential through ongoing improvement and integration with clinical practice. © 2024 IEEE.

关键词： Prediction models

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Virtual reality for real Estate: revolutionizing Property Visualization and Enhancing Client Engagement 4

Virtual Reality for Real Estate: Revolutionizing Property Vi...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Prema, K. Chhetri, Abish Singh, Sowmya Pratap rao, d. Yaswanth Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (纸本)9798331518981

Modern Virtual reality with a huge impact on the ever-diversifying branches of this new digital economy, Virtual reality is now such an integral part of our contemporary reality. Several factors - from decreasing component prices, fast technological market maturity to the easy availability of wide array of high-tech products that can leverage this new technology has made it a practical idea. In this article, we would like to address an investigation in the sense of what it is about establishing anything resembling a real estate management system using artificial intelligence and virtual reality technologies. At its core, this research centers around augmented reality and virtual reality applications and use cases for real estate. The panorama camera feature allows you to photograph an entire space in a 3d plane, which can then be stitched together resulting your viewer having the ability to look around within this room and view other events as they happen from his point of view. This allows for the option of presenting content in an entirely new light. Although 360-degree camera can shoot in better quality, not all of the people have it so scanning with smartphone is enough. This application indicates that virtual reality enriches the understanding and increases clients' intention to purchase, while the manipulation of virtual reality content remains as an important factor affecting both clients' satisfaction towards future housing sales, as well as developer's acceptance. © 2024 IEEE.

关键词： Augmented reality

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Emotion recognition in Speech: A Natural Language Processing Perspective

Emotion Recognition in Speech: A Natural Language Processing...

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2024 International Conference on Advances in Computing research on science Engineering and technology, ACrOSET 2024

作者： Suganya, V. Prema, K. Hari Krishna, Cherukuri Siva reddy, Ch. Jaswanth, E. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (纸本)9798350388800

Speech emotion recognition is the process of accurately interpreting an individual's emotion from their speech. This paper introduces a python-based approach using natural language processing techniques for real-time emotion recognition in speech. The system utilizes a combination of feature extraction, acoustic modeling, and sentiment analysis to accurately identify and categorize different emotions expressed in spoken language. The feature extraction process involves extracting relevant acoustic features such as from the speech signal. These features are then used to train machine learning algorithms to classify emotions. sentiment analysis techniques are incorporated to capture additional emotional cues present in the text of the speech. The system demonstrates promising results in real-time emotion recognition, achieving high classification accuracies for a range of emotions. The system's efficacy and productivity are further enhanced by leveraging popular Python libraries such as NLTK, Scikit-learn, and Keras for natural language processing and machine learning tasks. The ultimate goal is to enhance emotion recognition accuracy significantly, with observed improvements ranging from 95% to 98%. Through this work experimental evaluation of the system on various emotion datasets shows its capability to recognize emotions in real-time speech with real- world applications © 2024 IEEE.

关键词： Modeling languages

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Transforming diabetes Care with Predictive Machine Learning techniques 4

Transforming Diabetes Care with Predictive Machine Learning ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Mariammal, G. Jasmine, r. Megiba Siva rama Lingham, N. Poongothai, E. Prema, S. Sai Charan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Ponjesly College of Engineering Nagercoil India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331508845

diabetes mellitus is a chronic metabolic disorder characterized by elevated blood sugar levels, affecting millions of individuals worldwide. Early detection and management of diabetes are crucial in preventing complications and improving patient outcomes. In recent years, machine learning techniques have shown promise in predicting diabetes risk based on various factors such as demographic information, medical history, and lifestyle habits. Verticaling through a not-too-well designed case study - This paper is an extensive examination and comparison of different ML techniques for diabetes prediction. It goes through the methodologies, datasets, features, performance metrics and challenges of current models that are based on machine learning. In addition, understand strengths and weaknesses of all the algorithms- SVM, decision Trees, random Forests, Logistic regression etc. Here the logistic regression algorithm is used to develop a predictive model for diabetes based on demographic and clinical features. After preprocessing the data, including handling missing values and scaling numerical features, trained a logistic regression model using a portion of the dataset. The model achieved a high accuracy of 90% on the testing set, indicating its effectiveness in distinguishing be- tween diabetic and non-diabetic individuals. © 2024 IEEE.

关键词： Logistic regression

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Net defender: Enhancing SdN Security with deep Learning Against BOTNET Threats 3

Net Defender: Enhancing SDN Security with Deep Learning Agai...

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3rd International Conference on Electrical, Electronics, Information and Communication technologies, ICEEICT 2024

作者： Anandaraj, A. Peter Soosai Ananthi, A. Swetha, Moldireddy Gari Gandla, Vishnu Vardhan Abhiram, d.V.S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350369083

Computer network defenses are particularly vulnerable to botnet attacks when it comes to Software-defined Networks (SdNs). The purpose of this research is to examine the use of deep Learning techniques inside software-defined networks (SdNs) to detect and block assaults of this kind. Predetermined signatures or heuristic criteria are commonly used by existing botnet detection systems, which limits their ability to adapt to constantly evolving attack strategies. Furthermore, situations involving changing network circumstances may not be amenable to the scalability and efficiency of conventional mitigation solutions. SdN-dL combines the programmability and centralized control of software-defined networks (SdNs) with deep learning's powerful pattern recognition capabilities. Software-defined networking (SdN) controllers that use deep Learning models enable real-time monitoring of network traffic patterns. This makes it possible to proactively find botnet activities and stop them. The use of SdN-dL enables the support of dynamic network adaptation and response, which lessens the window of vulnerability to botnet attacks and limits the amount of false positives. Software-defined Network security has been enhanced due to the method's success in accurately detecting and countering botnet attacks. Extensive testing and modeling have shown this. © 2024 IEEE.

关键词： Bot (Internet)

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development and Analysis of Malaria Vector by Mathematical Modeling 3rd

Development and Analysis of Malaria Vector by Mathematical M...

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3rd International Conference on Emergent Converging technologies and Biomedical Systems, ETBS 2023

作者： Jothi, Naresh Kumar Lakshmi, A. Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Avadi Chennai India

ISBN: (纸本)9789819986453

In this paper we apply new mathematical model concept is developed for dynamic between malaria disease. We found out the disease-free equilibrium points and write the parameter values, using the mathematical equation. Applied local stability concept to control infection rate and prevent the spread of malaria between people. Using MATLAB to generate numerical solution of the model and stability of human and malaria vector population are free from the disease and the infection rate is stable. The government of India should take steps to control the disease and spread the awareness among the peoples and the society. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： diseases

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An intelligent speech enhancement model using enhanced heuristic-based residual convolutional neural network with encoder-decoder architecture

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International Journal of Speech technology 2024年第3期27卷 637-656页

作者： Balasubrahmanyam, M. Valarmathi, r.S. Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

As the listening capacity exists in humans, they are facing the critical issues of understanding the speeches even in the presence of some background or other noises in the world. To diminish the noises, Speech Enhancement (SE) is the process of improving the quality of the speech signal by applying some techniques without degrading any such information. Hence, it is used for hearing-aid people, speech recognition, etc. recent researchers have developed some works to increase speech intelligibility. Substantial success has been achieved by executing the supervised learning methods. Nevertheless, the existing process incurs such shortcomings as attaining maximum error, computation burden, and so on. To overcome that, certain deep learning methodologies are immensely involved in SE for determining the spectrogram magnitude when reconstructing the signal source by removing the noise. Thus, it also becomes a more challenging task to acquire a clean speech signal. Though these methods aim to present the speech as more clear and intelligible, it may arise such intricacies that degrade the quality and efficiency. As it contains beneficial structure and resources, still it is in the scope of developing the novel SE model. To conquer these complexities, a successful SE task is offered utilizing deep learning in this paper. This recommended work performs the SE which employs deep learning to denoise a noisy speech to generate a quality speech. At first, the speech signal that contains noises such as cooler noise or fan noise, restaurant noise, railway station noise, factory noise, traffic in the journey, bus-stand noise, cinema theater noise, and clouding areas noise are gathered from the standard online sources. After that, this noisy speech signal is forwarded to Adaptive residual Convolutional Neural Networks with Encoder-decoder (ArCNNetEd) architecture, where the parameters involved in this framework are optimized with the support of random revised drawer Algorithm (rrdA

关键词： Speech enhancement

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IoT-Enhanced Automatic Child detection System for Cars 5

IoT-Enhanced Automatic Child Detection System for Cars

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kavitha, P. Teja, K. Pavan rahul, S.G. Chikkam, Naga ramakrishna Andra, deepika Tanakala, Venkannababu Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350379945

The increasing number of heatstroke-related fatalities among children left in vehicles highlights the urgent need for an effective preventive solution. This paper presents an innovative control system that detects the presence of children in the rear seats of vehicles and promptly alerts their parents. Unlike traditional systems that either invade privacy through camera use or generate frequent false alerts due to pressure sensors, our approach leverages IoT technology combined with advanced sensors and Arduino components to accurately identify children and differentiate them from inanimate objects. This system aims to reduce false alerts, protect privacy, and provide a reliable means of preventing heatstroke deaths by ensuring timely notifications to parents. By addressing the critical issue of child safety in vehicles, this solution offers a significant improvement over existing methods. © 2024 IEEE.

关键词： Pressure sensors

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