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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Singh, Sheetal Wairya, Subodh Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Lucknow226021 India

ISBN: (纸本)9798350385199

Tunnel field effect transistors (TFET) have become a viable alternative to classic CMOS due to their advantages, such as substantially decreased leakage current and steeper sub-threshold slope. The paper overviews several electrical noises that might arise in a fundamental TFET device. The presence of diverse noise sources in TFET, like random telegraph noise (RTN), flicker noise, shot noise, etc., has low/high-frequency noise that affects performance across different frequency bands. A different prototype with analytical capability has been created to investigate the alteration in electrostatic potential caused by a charged trap in the oxide of the gate of TFET by changing the materials. III-V materials like Ge/Si, GaAsSb/InGaAs, etc., hetero-junction TFET have been analyzed to compare the Gaussian trap;a uniform trap yields a higher Ion to Ioff ratio and lower subthreshold swing. The present research also investigates the influence of electrical noise on the efficiency of different TFET configurations. This study also compares five TFET structures: Ge-based Optimized work, SiGe-S-NW-TFET, Si based nanotube TFET, Si/SiGe HTG-TFET, and Ge-Source vTFET. The Ge-based TFET achieves the highest Ion of 80.9 μ A/ μm at VGS is 0.55 V, while the Ge-Source vTFET exhibits the lowest SS of 21.2 mV/dec, highlighting their potential for low-power applications. © 2024 IEEE.

关键词： III-V semiconductors

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Modeling and Simulation of Virtual Reality Assistive Tool for Children with Autism Spectral Disorder 2nd

Modeling and Simulation of Virtual Reality Assistive Tool fo...

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2nd International Conference on Deep Sciences for Computing and communications, IconDeepCom 2023

作者： NirmalaPriya, G. Shree Aravindh, S.S. Tharun, D. Deepak, M. Hermalin, Cynthiya S. Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9783031689079

Autism spectrum disorder (ASD) is a prevalent issue contributing to deprivation among people in terms of their capability to maintain social relationships and interact normally with fellow members of society. Teens with ASD battle to exercise proper sociable behavior as well as mingle with others sharing the same age range. It affects all sorts of races, ethnicities and budgets alike. Despite its long- term nature, ASD has therapies and treatments that can help address issues associated with it such as restlessness, aggression, repetitive behavior, focus difficulties, worry and depression through the use of medicine. The use of virtual reality-based training, in addition to medical intervention, may be used to address various behavioral tradeoffs through repeated conditioning and learning. A variety of learning scenarios that would be unsafe, challenging, or time-consuming to set up or frequently recreate in the real world can now be simulated using virtual reality (VR) technology. In this paper, a virtual reality simulator for with the aid of social educators, achieve this goal. This study used the gaming program Social Activity Trainer to simulate and construct a virtual reality (VR)-based assistive aid (SAT). The assistive tool was created to help youngsters with ASD interact and use social skills. Based on three key skills—facial expression recognition, reading comprehension, and task delivery—seven tasks were created for the VR assistive tool. There are seven modules in a virtual reality scenario, including one for music therapy. The EEG signals are looked at to assess the degree of autism in 10 ASD children who participated in the test. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diseases

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CNOL: A Convoluted Neural Optimization Logic to Predict Leptospirosis Disease Detection based on Medical Data Evaluation Scheme 3

CNOL: A Convoluted Neural Optimization Logic to Predict Lept...

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

作者： Sajiv, G. Meenakshisundaram, N. Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Leptospirosis is a widespread zoonotic disease that can cause severe health issues if not detected and treated early. This study presents a Convoluted Neural Optimization Logic (CNOL) framework designed to predict Leptospirosis Disease using both structured and unstructured medical data. The proposed model integrates convolutional layers for feature extraction from complex datasets, including clinical information and medical images, and utilizes advanced optimization techniques like Adam to enhance learning. The CNOL framework tested on a Leptospirosis Disease Dataset, and results show a significant improvement in predictive accuracy. The model achieved an accuracy of 95.67%, outperforming traditional machine learning models such as Logistic Regression (85.45%) and Random Forest (89.56%), and deep learning models like Deep Neural Networks (93.10%). Additionally, the CNOL framework exhibited superior precision (94.85%) and recall (96.42%), highlighting its effectiveness in both identifying true positive cases and minimizing false negatives. This study demonstrates the potential of combining convolutional layers and advanced optimization techniques for enhancing disease prediction, providing a robust tool for early diagnosis and intervention. © 2025 IEEE.

关键词： Convolutional neural networks

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Industrial wireless sensor networks with real time data access

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Materials Today: Proceedings 2023年 80卷 2760-2764页

作者： Shanthi, K.G. Sesha Vidhya, S. Vishali, G. Uma, R.V. Thanuja, M. Srinitha, S. Department of Electronics and Communication Engineering R.M.K College of Engineering and Technology Tamil Nadu Chennai India

This paper targets to transmit industrial data from one sensor node to another sensor node, wirelessly and securely. However as the nodes have Internet access and are typically positioned in an unsupervised context with minimal individual intervention;security and seclusion are the prime challenges in Industrial Wireless Sensor Networks (IWSN). To use IWSN, a user must be able to get real time information straight from the source nodes and this is accomplished by Radio Frequency Identification (RFID). It yields a cost-effective solution of identification problems, but it frequently suffers from diverse safety and privacy difficulties. This paper resolves the issues of RFID systems using Global System for Mobile communications (GSM) module that generates a One-Time Password (OTP) for accessing the data, and thereby ensures user's privacy and security in Industrial Wireless Sensor Network. Furthermore, eight evaluation criteria have been met by the proposed system surpassing the existing systems. © 2021

关键词： Global system for mobile communications

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Image Classification using Deep Learning Models in Computer Vision 3

Image Classification using Deep Learning Models in Computer ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Jampa, Pavithra Kolluri, Uday Kiran Venkat, Peruri Sumalata, G.L. Gokaraju Rangaraju Institute of Engineering and Technology Dept.Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350375190

This research focuses on comparison of deep learning architectures employed in computer vision specifically image classification, for classification we employ three cutting edge deep learning architectures: Xception, DenseNet, and the Vision Transformer (ViT).We evaluate and compare the performance of these models in accurately classifying images of Birds. This comprehensive study aims to provide insights into the effectiveness of different deep learning models, shedding light on their strengths and weaknesses in image classification. This research delves into the exploration of ImageNet21k, a subset featuring 10 carefully choosen classes of Birds, ImageNet stands out as a cornerstone, serving as a benchmark for evaluating the capabilities of deep neural networks and a huge dataset with millions of labelled images spread over thousands of categories, has become the touchstone for testing the efficacy of deep learning models. The task involves not only accurately classifying objects within images but also detecting and localizing multiple objects, pushing the boundaries of artificial intelligence in visual comprehension. © 2024 IEEE.

关键词： Image classification

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A Multi-decision Fusion Strategy for PolSAR Image Classification 24

A Multi-decision Fusion Strategy for PolSAR Image Classifica...

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7th International Conference on Data Storage and Data engineering, DSDE 2024

作者： Song, Wanying Chen, Yuang Wang, Chi Wu, Yan Xi'an Key Laboratory Of Network Convergence Communication School Of Communication And Information Technology Xi'an University of Science and Technology China School Of Electronics Engineering Xidian University China

ISBN: (纸本)9798400716935

To effectively fuse the high-dimensional features and make full use of the contribution of different classifiers to image classification, we propose a polarimetric synthetic aperture radar (PolSAR) image classification method fusing SVM iterative classification and JSRC decisions, SVM-JSRC for short. This method combines the idea of semi-supervised classification and uses SVM classifiers for multiple iterative classification, which is used to expand the training sample set and reduce the influence of insufficient PolSAR image label samples. Since various characteristics would contribute differently to image classification, the extended training sample set was used to extract the high-dimensional polarization and texture features. And then constructing the feature dictionaries, respectively, which are used to make joint sparse representation classification (JSRC) decisions. To get classification results, SVM classification and JSRC decisions are combined by using Dempster-Shafer (D-S) evidentiary theory. The efficacy of the suggested strategy is demonstrated by experiments using actual PolSAR images. © 2024 ACM.

关键词： Image classification

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Detection of Diabetic and Hypertensive Retinopathy Using Deep Learning Models 3

Detection of Diabetic and Hypertensive Retinopathy Using Dee...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Gurthula, Rasagna Vanukuru, Chandana Chiluka, Vighnesh Sumalata, M.S.G.L. Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350375190

The diagnosis of diabetic and hypertensive retinopathy, a major cause of blindness in long-term diabetes patients, is a critical topic that we address in this work. Traditional diagnostic techniques like Fluorescein angiography, an imaging test which is used to view the condition of blood vessels present in the retina of eye, are not so reliable and they are not widely available. So, we suggest a sophisticated method that makes use of two different datasets, ODIR-5K and Messidor-2 to improve diagnosis accuracy by utilizing deep learning techniques. The popular deep learning architectures used in this approach are ResNet101, ResNet152, InceptionV3, Inception ResNetv2, DenseNet201, and Xception. Adding to the foundational work, we present the Xception model, obtaining impressive 98% and 89% accuracy for Messidor-2 and ODIR-5K, respectively. This innovative method provides a more feasible and efficient alternative to existing diagnostic techniques for the timely diagnosis of hypertensive retinopathy (HR) and diabetic retinopathy (DR). The combination of sophisticated models with a variety of datasets makes easy to identify severity of disease of the patients. © 2024 IEEE.

关键词： Blood vessels

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Eye Disease Classification using CNN and Vision Transformers 1

Eye Disease Classification using CNN and Vision Transformers

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1st IEEE International Conference on Advances in Signal Processing, Power, communication, and Computing, ASPCC 2024

作者： Prathibha, G. Acharya Nagarjuna University Anu College of Engineering and Technology Electronics and Communication Engineering Guntur India

ISBN: (纸本)9798350355093

Many people in the world are suffering with different eye Diseases. Early diagnosis of Eye diseases can save the patient from risk of losing Vision. Enormous work is done on classification of diseased eyes using Machine Learning Techniques. In this work, the classification of Eye diseases are detected using features from two different architectures of CNN which are fused and classified. A total of 9825 images consisting of ACRIMA, ODIR-5K, Glaucoma, ORIGA6, Cataract and Retina diseased images are considered from which features are extracted using the different pre-trained networks. The extracted features from two different pre-trained network are fused which are further classified using the Convolution Neural Network resulting in an accuracy of 97.32% using EfficientNetB0 and VGG16. Further, the dataset is also classified using Vision transformers resulting in an accuracy of 98.02% which is superior compared to the result obtained from fused features of CNN. © 2024 IEEE.

关键词： Accuracy CNN Concatenation Pre-Trained Networks Vison Transformer

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MangoLeafNet: Transforming Mango Leaf Disease Management with Deep Learning 4

MangoLeafNet: Transforming Mango Leaf Disease Management wit...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Raj, J. Relin Francis Raja, S.R. Nithila, Ezhil E. Haribabu, K. Krishnan, R. Santhana Muthu, A. Essaki Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Hindustan Institute of Technology and Science Chennai India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331540364

Effective mango leaf disease detection is critical for optimizing crop health and yield, utilizing advanced models for accurate diagnosis. To discover important mango leaf diseases, this study uses two different datasets to investigate MangoLeafNet, an advanced deep-learning model based on EfficientN etB0. Four images are included in each dataset: healthy leaves, powdery mildew, Anthracnose, and sooty mold (also known as sooty mold). MangoLeafNet was chosen for its effective compound scaling methodology and evaluated for its classification performance. Metrics like accuracy, precision, recall, and Fl score were used to assess the Model's performance. In order to evaluate MangoLeafNet's comparative efficacy, it was compared with VGG16, MobileNetV1, and AlexNet, among other models. Because of its sophisticated feature extraction capabilities, the Model can classify data precisely, which makes it an invaluable tool for controlling diseases in the mango agricultural sector. © 2024 IEEE.

关键词： Fruits

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Design of a Compact Dual Band Antenna for 5G and Wimax Applications 3

Design of a Compact Dual Band Antenna for 5G and Wimax Appli...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and electronics, ICDCECE 2024

作者： Suganyadevi, S. Priyadharsini, S. Raaghul Ganesh, S. Ramakrishnan, R. Sowmiya, R. Srinithi, P. Kpr Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

For 5th Generation (5G) communications, a dual-band microstrip patch antenna system design is presented in this paper. It operates at two different frequencies: S-band (5.2 GHz) for Worldwide Inter-operability for Microwave Access (WiMAX) and sub-6 (3.5 GHz) for 5G communication. The antenna is meticulously designed to meet the exacting requirements of modern wireless communication systems, providing enhanced performance and versatility. Using a FR-4 substrate with a dielectric constant of 4.3 and a thickness of 2.4 mm, the antenna was developed. The VSWR value obtained is 1.38 dB and 1.75 dB which is acceptable. This study enhances connections for satellite navigation systems and 5G networks by combining sub-6 and S-band frequencies into a single antenna design, providing a small and affordable solution for a variety of communication needs. The suggested antenna design has excellent performance characteristics, which makes it a useful component for next-generation wireless communication technologies. © 2024 IEEE.

关键词： Wimax

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