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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Pongiannan, R.K. Franklin, J. Richard Pravin, A. Maria, Marissa Mishra, Sarvottam Kumar Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India Computer Science Engineering Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331510022

BlindSpace is an innovative application aimed at improving the lives of visually challenged individuals. Leveraging cutting-edge image captioning and object detection technologies, the app allows users to capture images using their smartphone's camera, providing detailed scene descriptions and audibly identifying objects within those images. This not only enhances users' understanding of their environment but also fosters independence and confidence. With voice commands and text-to-speech technology, the application offers a user-friendly experience and can be customized to individual preferences. Operating efficiently offline, BlindSpace stands as a significant leap in accessibility technology, bridging the gap between the visual and non-visual worlds for visually challenged individuals and promoting inclusivity and autonomy. © 2024 IEEE.

关键词： Smartphones

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Distributed Hybrid Power System Integrated with Smart Microgrid

Distributed Hybrid Power System Integrated with Smart Microg...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Haider, Iftekhar Islam Siam, Md Sayful Nuhel, Ahsan Kabir Hriday, Taslima Akter Shaha, Debashish Electrical and Electronics Engineering Bangladesh Chandigarh University Computer Science and Engineering India

ISBN: (纸本)9781665489621

Renewable energy sources are the power of the present. Carbon dioxide (CO2) emissions from fossil fuel burning considerably contribute to global warming. The plan is to generate power with vertical axis wind turbines that will be coupled to a smart microgrid system. This system will transfer solar and wind energy into electrical energy utilizing solar panels and wind turbines. An Arduino UNO will supply all data from the solar panel and wind turbine. The system will be linked to an intelligent microgrid, which will allow for continuous power delivery. If the fault detector detects a problem with the system, SMS messages will be delivered via a GSM modem. This technology will make power generation and delivery easier. MATLAB/SIMULINK will be used to construct and test this system. In addition, a power monitoring system based on the Internet of Things (IoT) is implemented and utilized to collect and analyze data on electrical characteristics such as voltage, current, active power, and load energy usage. The Internet of Things (IoT) application 'Thing Speak' is used to collect real-time electrical data from users. Under the SG paradigm, consumers and utilities may utilize this information to better regulate use and minimize bills. © 2022 IEEE.

关键词： Simulink

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An Opto-Electronic HfOx-Based Transparent Memristive Synapse for Neuromorphic Computing System

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IEEE Transactions on Electron Devices 2023年第3期70卷 1351-1358页

作者： Saleem, Aftab Kumar, Dayanand Wu, Facai Keong, Lai Boon Tseng, Tseung-Yuen National Yang Ming Chiao Tung University Department of Electrical Engineering and Computer Science Hsinchu30010 Taiwan Laboratory Electrical and Computer Engineering Thuwal23955 Saudi Arabia Institute of Electronics National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

In this study, a transparent bilayer memristor showing both electrical and optical synapses along with good electrical properties after annealing is presented. In addition to 85% transparency, the device shows excellent electrical characteristics for 1000 cycles of stable LRS/HRS and more than 104 s retention at high temperatures. The annealed device also exhibits stable potentiation and depression cycles for more than 10 000 ac pulses with a low coefficient of nonlinearity. By applying consecutive ac pulses, synaptic properties of paired-pulse facilitation (PPF) and spike time-dependent plasticity (STDP) are calculated. The memristor is illuminated by a 405 nm light source in which different light intensities ranging from 20 to 40 mW/cm2 are used for achieving multilevel cell (MLC) characteristics. Learning/Forgetting curve (PSC) and optical PPF are measured to mimic optical synaptic function. An image recognition comparison of optical and electrical synaptic properties with a normalized loss rate of © 2023 IEEE.

关键词： Tin oxides

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Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images

Few-Shot Learning Approach on Tuberculosis Classification Ba...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Maulana, Muhammad Fazil Pramana, A. A. G. Yogi Permana, Faiz Ihza Fudholi, Dzikri Rahadian Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350391213

Tuberculosis (TB) is caused by the bacterium Mycobacteriumtuberculosis, primarily affecting the lungs. Early detection is crucial for improving treatment effectiveness and reducing transmission risk. Artificial intelligence (AI), particularly through image classification of chest X-rays, can assist in TB detection. However, class imbalance in TB chest X-ray datasets presents a challenge for accurate classification. In this paper, we propose a few-shot learning (FSL) approach using the Prototypical Network algorithm to address this issue. We compare the performance of ResNet-18, ResNet-50, and VGG16 in feature extraction from the TBX11K Chest X-ray dataset. Experiments with the model demonstrate classification accuracy results of 98.93% for ResNet-18, 98.60% for ResNet-50, and 33.33 % for VGG16. These findings indicate that the proposed method performs well on certain feature extraction algorithms in mitigating data imbalance. Ultimately the implementation of few-shot learning in handling data imbalance proves to be a promising solution for enhancing TB detection accuracy. © 2024 IEEE.

关键词： Zero-shot learning

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Revolutionizing Diabetic Retinopathy Recognition with Advanced Deep Learning Techniques 1

Revolutionizing Diabetic Retinopathy Recognition with Advanc...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Rajalingam, S. Kanagamalliga, S. Bhavani, U. Chouksey, Mausam Saveetha Engineering College Dept. of Electrical and Electronics Engineering Chennai India Saveetha Engineering College Dept. of Electronics and Communication Engineering Chennai India School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9798350353648

Diabetic retinopathy (DR) is a severe complications arising from diabetes and the primary cause of vision impairment worldwide. Early recognition is essential for efficient intervention and prevention of permanent vision loss. This research focuses on using Deep Learning Techniques (DLT) for accurate and efficient recognition of DR. It utilizes a dataset of retinal images from diabetes, covering several stages of DR progression. Convolutional Neural Networks (CNNs), is DLT subset, are employed for extracting features and performing classification. The proposed model effectively detects subtle retinal abnormalities indicative of DR. This research contributes to existing methods by introducing a novel deep learning approach that shows high specificity and sensitivity in DR detection. The DR-Color-Fundus-Images recognition using DLT were analyzed using available Color-Fundus-Retina datasets. Performance metrics, comprising the area beneath the curve receiver operating characteristic, specificity, and sensitivity, validate the model's accuracy. The outcomes have implications for developing automated screening tools, enabling timely intervention and improving healthcare outcomes for individuals with diabetic retinopathy. This research represents a significant advancement in harnessing deep learning for enhancing DR detection and highlights the importance of technological progress in ophthalmic healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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Multimodal Wearable System With Dual-Frequency Enhancement Network for Risk Recognition

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IEEE Internet of Things Journal 2025年第11期12卷 17634-17648页

作者： Yu, Feng Liu, Jiajie Yu, Hanchen Cheng, Wentao Liu, Li Jiang, Minghua Wuhan Textile University School of Computer Science and Artificial Intelligence Hubei Wuhan430200 China Engineering Research Center of Hubei Province for Clothing Information Hubei Wuhan430200 China Wuhan Textile University School of Electronics and Electrical Engineering Hubei Wuhan430200 China

Smart wearable systems can monitor users’ physiological data in real time, detect anomalies promptly through risk recognition technologies, provide early warnings, and assist users in taking preventive measures. However, single modal information is difficult to accurately recognize the behavioral state, expression state, and environmental conditions. Furthermore, multimodal data are often affected by noise and interference, complicating the accurate identification of risky behaviors. To address these challenges, we propose a smart wearable system based on the dual-frequency enhancement network (DFENet): 1) the multimodal sensor system is designed to combine behavioral recognition, expression recognition, and environmental recognition for comprehensive monitoring and recognition of multidimensional risk factors in complex scenarios;2) the DFENet is proposed to overcome challenges in feature extraction and accurate classification in complex environments;and 3) the behavioral recognition dataset and the expression recognition dataset are built to verify the effectiveness of the designed smart wearable system. Experimental results indicate that the proposed system can real-time achieve risk recognition across physical activity, expression state, and environmental conditions, and the proposed DFENet achieves excellent performance in accuracy, parameters, and floating-point operations (FLOPs) metrics on the three datasets. © 2014 IEEE.

关键词： Risk assessment

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Advancements in Smart Grids: Harnessing AI, IoT, and 5G Integration for Sustainable Energy Management 1

Advancements in Smart Grids: Harnessing AI, IoT, and 5G Inte...

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

作者： Mishra, Debani Prasad Preyaadarshini, Barsa Mishra, Kriti Kumar, Ayush Lenka, Rakesh Kumar International Institute of Information Techonology Electrical and Electronics Engineering Bhubaneswar India Central University of Odisha Department of Computer Science Odisha Koraput India

ISBN: (纸本)9798350355093

The conventional smart grid, while enhancing electricity networks, lacks advanced features such as automatic decision-making, security, scalability, and real-time monitoring. The integration of Artificial Intelligence (AI) and Machine to Machine (M2M) communication, supported by the evolving communication infrastructure, promises a transformative next-generation smart grid. This paper provides a comprehensive review of emerging trends and technological foundations, envisioning a future smart grid propelled by AI and empowered by the Internet of Things (IoT) and 5G. Addressing integration challenges, it proposes solutions and discusses standards crucial for optimal smart grid architecture. Future work will focus on implementing this integration using tools like Matlab, NS2/NS3, Open Daylight, and Mininet, comparing outcomes with existing literature. © 2024 IEEE.

关键词： Smart power grids

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Skin Cancer Prediction by Incorporating Bio-inspired Optimization in Deep Neural Network

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SN computer science 2024年第8期5卷 1-20页

作者： Mundada, Monica R. Sowmya, B.J. Supreeth, S. Prabhu, Smitha G. Mahesh, K. Vishwanath, Y. Rohith, S. Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bengaluru 560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru 560054 India School of Computer Science and Engineering REVA University Bengaluru 560064 India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering Sir M Visvesvaraya Institute of Technology Bengaluru India School of Computer Science & Engineering Presidency University Bengaluru 560064 India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Bengaluru 562164 India

This study offers a unified surgical conception of skin neoplasms using deep learning and genetic optimization techniques to improve classification values and speed of diagnosis. In this case, the architecture of Convolutional Neural Networks such as MobileNet, Xception and Inception is enhanced on the basis of the creative algorithms of Particle Swarm Optimization and Bat Algorithm to adjust the hyperparameters. The Heroku hosted cloud solution permits registered users to upload pictures of skin lesions via the web interface, where the picture is then processed through a Deep Neural Network (DNN) model that classifies the lesion as either melanoma or non-melanoma. The Convolution Neural Network (CNN) models were trained and evaluated on large publicly available datasets of skin lesions like ISIC, HAM10000 and the model performance was evaluated based on the effect of varying optimizers. Among all the architectures, Xception with Bat Algorithm (BA) performed best because of the more complex and deeper network architecture and extreme inception modules, which made it possible to extract and classify more complicated features. As a cloud-based solution, this system provides dermatologists and health practitioners with a more practical and workable solution for the detection of skin cancers. Furthermore, it can be utilized in other fields, which implies that this approach could extend its applications beyond dermatological imaging or imaging of patients at risk for other diseases, thus creating an adaptable and effective tool for clinical diagnosis in many areas of medicine. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bio-inspired optimization Deep neural network (DNN) Skin cancer prediction

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Design and Characterization of a Ka-Band Parasitic Patch Antenna for 5G Applications 9

Design and Characterization of a Ka-Band Parasitic Patch Ant...

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9th International Conference on Communication and electronics Systems, ICCES 2024

作者： Kouser, Kahkashan Hannan, Mohammad Shafi, Faizan Aknan, Mohammad Dept. of Computer Science & Engineering Bihar Motihari India Rajiv Gandhi Institute of Petroleum Technology Dept. of Electrical and Electronics Engineering Uttar Pradesh Amethi India Indian Institute of Technology Dept. of Electrical Electronics and Communication Engineering Dharwad India

ISBN: (纸本)9798350377972

He design of parasitic patch antennas for high-frequency applications, such as 5G millimeter-wave (mm-wave) communication, presents several challenges, including achieving high gain, wide bandwidth, and efficient impedance matching within a compact structure. Recent techniques, such as the use of parasitic elements and stepped impedance feedlines, have shown promise in enhancing antenna performance. However, these methods still face limitations related to fabrication complexity, signal degradation, and reliability under varying conditions. This work introduces a circular patch antenna with a parasitic reflector element specifically designed for Ka-band applications at 28 GHz. The proposed design addresses these challenges by optimizing the placement of parasitic elements to achieve high gain, a wide bandwidth of 2 GHz, and stable VSWR performance across the operating range. The single-layer, single-side etching structure further simplifies fabrication, making it suitable for compact wireless *** design of parasitic patch antennas for high-frequency applications, such as 5G millimeter-wave (mm-wave) communication, presents several challenges, including achieving high gain, wide bandwidth, and efficient impedance matching within a compact structure. Recent techniques, such as the use of parasitic elements and stepped impedance feedlines, have shown promise in enhancing antenna performance. However, these methods still face limitations related to fabrication complexity, signal degradation, and reliability under varying conditions. This work introduces a circular patch antenna with a parasitic reflector element specifically designed for Ka-band applications at 28 GHz. The proposed design addresses these challenges by optimizing the placement of parasitic elements to achieve high gain, a wide bandwidth of 2 GHz, and stable VSWR performance across the operating range. The single-layer, single-side etching structure further simplifies fabrication, making it suitable

关键词： Slot antennas

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Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm

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International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 438-453页

作者： Deepak S.Uplaonkar Virupakshappa Nagabhushan Patil Research Scholar in Electrical and Electronics Engineering Department Poojya Doddappa Appa College of EngineeringKalaburagiIndia Computer Science and Engineering Department Sharnbasva UniversityKalaburagiIndia Electrical and Electronics Engineering Department Poojya Doddappa Appa College of EngineeringKalaburagiIndia

Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the ***/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive histogram equalization approach(CLAHE)is applied as preprocessing,in order to enhance the visual quality of the images that helps in better ***,adaptively regularized kernel-based fuzzy C means(ARKFCM)is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set ***-The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation *** proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient,dice coefficient,precision,Matthews correlation coefficient,f-score and *** experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value,which is better than the existing *** implications-From the experimental analysis,the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based ***,the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based ***/value-The image preprocessing is carried out using CLAHE *** preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM *** this research,the proposed algorithm has advantages such as independence of clustering parameters,robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.

关键词： Adaptively regularized kernel-based fuzzy C means Contrast-limited adaptive histogram equalization Level set algorithm Liver tumor segmentation Local ternary pattern

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