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2nd International Conference on Power electronics and IoT Applications in Renewable Energy and its Control, PARC 2022

作者： Kaliappan, Balamurugan Srirevathi, B. Balaganesan, S.M. Rajesh, A. Sri Ramakrishna Engineering College Department of Electrical and Electronics Engineering Coimbatore India Vellore Institute of Technology Department of Electrical and Electronics Engineering Chennai India Erode Sungunthar Engineering College Department of Computer Applications Erode India

ISBN: (纸本)9781665432153

Brushless Direct Current (BLDC) motors have many rewards like high reliability, high effectiveness, low maintenance and so forth. Because of its benefits, BLDC motor is broadly used in servo frameworks and in low power drive frameworks. In the recent decade, a number of controller techniques are utilized for the speed control and torque ripple of brushless DC motor. Here, PIC microcontroller with MOSFET switching is used, further, the parameter is optimized with the use of Bat Algorithm (BA) and the performance is upgraded with the assistance of Internet of Things (IoT). © 2022 IEEE.

关键词： Controllers

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Towards Seamless Connectivity: Implementing 6G Communication Technologies in Next-Generation Networks 3

Towards Seamless Connectivity: Implementing 6G Communication...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Kalaiselvi, K. Sankar, R. Supriya, S. Kaushik, G. Swamy, Hemanth Devika, M. Saveetha College of Liberal Arts and Sciences SIMATS Department of Data Analytics Tamilnadu Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of AI& DS Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). Despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can't meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, AI-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues. © 2024 IEEE.

关键词： Internet of Everything

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On Neural-Network-Based Detection for Hypertensive Subjects Using Classification of Retinal Fundus Photographs 53

On Neural-Network-Based Detection for Hypertensive Subjects ...

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53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Sonetsuji, Yuki Isokawa, Teijiro Kamiura, Naotake Tabuchi, Hitoshi University of Hyogo Graduate School of Engineering Dept. of Electronics and Computer Science Himeji Japan Hiroshima University Dept. of of Technology and Design Thinking for Medicine Hiroshima Japan

ISBN: (纸本)9781665464161

In this paper, a method of detecting hypertensive subjects is proposed, using retinal fundus photographs classified by convolutional neural networks (CNNs for short). The proposed method employs Inception-v3 model as a CNN. The data to be presented to the proposed model are prepared from retinal fundus photographs. The scheme of fine tuning is conducted to construct a discrimination model. The model specifies the photographs corresponding to the subjects being in hypertensive states. Experimental results establish that the proposed method can achieves favorable metric values. Especially, Accuracy value achieved by the proposed method is comparatively high for the small scale of dataset, compared with the previously proposed method. © 2023 IEEE.

关键词： Ophthalmology

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Classification of Cotton Fabric, Pineapple Fabric and Cotton Pineapple Blend Fabric with VGG16 using Keras 15

Classification of Cotton Fabric, Pineapple Fabric and Cotton...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Villaverde, Jocelyn F. Ferrer, Marlou D. MacAbeo, Julia Alexandra T. Masilungan-Manuel, Joanna Tess Mapua University Department of Electrical Electronics and Computer Engineering Manila Philippines Philippine Textile Research Institute Research and Development Division Department of Science and Technology Taguig City Philippines

ISBN: (纸本)9798350381177

Plant leaves represent valuable raw materials for fabric production, offering an eco-friendly alternative to traditional textiles. Converting these leaves into fibers opens opportunities for versatile textile applications. Traditionally, fabric classification relied heavily on manual, time-consuming microscopic analysis of fiber composition and texture, imposing limitations on efficiency and scalability. To address this challenge, the researchers developed a robust computer Vision System on Raspberry Pi, capable of accurately classifying cotton, pineapple, and pineapple-cotton fabrics. Leveraging the powerful machine learning libraries, Tensorflow and Keras, the researchers utilized the VGG16 model, training and fine-tuning it for specialized fabric classification tasks. Notably, the VGG16 model came pretrained with weights from the ImageNet dataset, further enhancing its performance. The dataset comprised 300 images, with equal representation from cotton, pineapple, and cotton-pineapple blend fabrics. These images underwent comprehensive image pre-preprocessing, utilizing techniques such as flipping, adjusting brightness and contrast, and color augmentation. The results of this study are highly promising, with the system achieving an impressive overall accuracy rate of 91.11%. This research not only showcases the system's proficiency in fabric classification but also highlights its potential for practical applications in real-world fabric classification scenarios. © 2023 IEEE.

关键词： Cotton

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An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

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computers, Materials & Continua 2022年第1期70卷 1315-1334页

作者： Naglaa F.Soliman Naglaa S.Ali Mahmoud I.Aly Abeer D.Algarni Walid El-Shafai Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt International Academy for Engineering and Media Science October CityEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent *** diagnosis is essential to handle breast cancer patients for treatment at the right *** with mammography is the preferred examination for breast cancer,as it is available worldwide and ***-Aided Detection(CAD)systems are used to analyze medical images to detect breast cancer,*** death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after *** of mammogram images has four main steps:pre-processing,segmentation of the region of interest,feature extraction and classification of the images into normal or abnormal *** paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect *** pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter,removal of artifacts using morphological operations,and contrast enhancement using a fuzzy enhancement *** proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure(EME)and local contrast *** comparison shows an outstanding performance of the proposed technique from the visual and numerical *** segmentation process is performed using Otsu’smultiple thresholding *** method segments the image regions into five classes with variable intensities using four *** effectiveness is measured based on visual quality of the segmentation output,as it gives details about the image and positions of *** performance of the proposed framework is measured using Dice coefficient,Hausdorff,and Peak Signal-to-Noise Ratio(PSNR)*** segmented tumor region with the proposed segmentation method is 81%of the ground truth region provided by an ***,the proposed framew

关键词： Breast cancer mammogram images CAD contrast enhancement fuzzy logic segmentation

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Analysis of Sensors Used in Medical Body Area Networks for Alzheimer’s Patients 7th

Analysis of Sensors Used in Medical Body Area Networks for A...

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7th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2022

作者： Daddi, Abhiraj Bose, Aryan Chaudhary, Sakhi Jadhav, Renu Raj, Aman Jadhav, Parul School of Electronics and Communication Dr. Vishwanath Karad MIT World Peace University Pune India Computer Science Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9789811993039

In this paper, a survey about the current types of sensors used in a medical body area network (MBAN) related to Alzheimer’s patients has been set forth. Alzheimer’s disease (AD) affects the cognitive function of a patient due to which the patient is unable to perform basic and straightforward everyday tasks. The patient needs to be looked after with intensive care by their family members. This survey about the various sensors can be studied for the development of a healthcare system that aims toward providing support to the family members to take care of the patient and provide mental relief to the patient to some extent. The detailed architecture and working of various sensors have been explained in this survey paper. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health care

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RIS-Assisted Flexible Receive Spatial Modulation for Wireless Communication

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IEEE Wireless Communications Letters 2025年

作者： Yue, Ming Peng, Yuyang Jin, Qi Lee, Juho Macau University of Science and Technology School of Computer Science and Engineering China Samsung Electronics Samsung Research Seoul Korea Republic of

In this letter, we propose a novel transmission scheme called Reconfigurable Intelligent Surface (RIS)-assisted Flexible Receive Spatial Modulation (RIS-FRSM). In this scheme, the number of active Receive Antennas (RAs) is dynamically determined by additional information bits, and the RIS is divided into corresponding parts to facilitate the flexible configuration. It not only achieves high Spectral Efficiency (SE) transmission but also allows for the adjustment of system settings according to diverse communication requirements. Furthermore, we derive an upper bound for the average Bit Error Rate (BER). The results of computer simulations demonstrate that the proposed scheme outperforms the benchmark schemes in terms of error performance. © 2012 IEEE.

关键词： Signal modulation

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IoT botnet detection from software defined network using American zebra optimization algorithm with SSRNN-ELM

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International Journal of Information technology (Singapore) 2025年第2期17卷 959-967页

作者： Bindu, Nalluri Venkata Madhu Nassa, Vinay Kumar Vasuki, P. Manikandan, G. Jeena, R. Mahaveerakannan, R. Department of CSE-AIML Malla Reddy Engineering College (Autonomous) Medchal Secunderabad Malkajgiri (Dist) India Ellenki College of Engineering and Technology (ECET) Patelguda (v) Ameenpur (m) Sangareddy (D) Hyderabad Telangana India Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

Software-defined networking (SDN) offers centralized control over large-scale networks, enhancing flexibility and enabling tailored network applications. However, SDN introduces new security vulnerabilities, including the risk of botnet attacks, which can compromise systems and steal data. This paper proposes a novel approach for detecting botnet attacks in SDN environments by leveraging a hybrid model that integrates recurrent neural networks (RNNs) and extreme learning machines (ELMs). The proposed method utilizes RNNs for feature learning and ELMs for classification, with the American zebra optimization algorithm (AZOA) optimizing ELM weights. This hybrid SSRNN-ELM (supervised subset recurrent neural network—extreme learning machine) approach is evaluated using N-BaIoT dataset and performance metrics, demonstrating effective detection of complex botnet attacks with high accuracy. The results demonstrate that the hybrid model effectively identifies complex botnet attacks, achieving a detection accuracy of 96.60%. The integration of recurrent neural networks (RNNs) for feature learning and extreme learning machines (ELMs) for classification, along with optimization by the American zebra optimization algorithm (AZOA), significantly improves detection performance as compared to the recent existing studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： American zebra optimization algorithm (AZOA) BotNet attack detection Extreme learning machine (ELM) Recurrent neural network (RNN) Software-defined network (SDN)

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Edge detection using adjusted Chebyshev polynomials on contrast-enhanced images by modified histogram equalization

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International Journal of Information technology (Singapore) 2022年第6期14卷 3031-3038页

作者： Acharya, Kuldip Ghoshal, Dibyendu Department of Computer Science and Engineering National Institute of Technology Agartala Barjala Tripura Jirania 799046 India Department of Electronics and Communication Engineering National Institute of Technology Agartala Barjala Tripura Jirania 799046 India

Edge detection is a technique for determining the object lines in a digital image. Edge detection processes are considerably improved by noise reduction and contrast enhancement. This paper represents a new edge detection algorithm for images using the adjusted Chebyshev polynomial curve fitting method on contrast-enhanced images. In the first step, a new image enhancement method is proposed to enhance the contrast of an image, using the weighted standard deviation to compute a clipping threshold value to restrict the rate of over-enhancement, and a geometric mean is used to partition the histogram into equal parts. In the second step, the histogram of an enhanced image obtained from step one has been processed by a novel approach using a modified Chebyshev polynomial to compute a threshold value which is applied to the gradient image to detect the edges. Image quality metrics are calculated on the proposed method on both the original image and on the proposed enhanced image. The quantitative evaluation shows that EBCM is increased by 8.00%, PR is increased by 1.00%, and ESSIM is increased by 3% by the proposed edge detection method applied to the proposed enhanced image than on an original image. The proposed method is compared with the state-of-art and latest edge detection algorithms, and experimental results show that the proposed edge detection method’s overall performance is superior to that of state-of-the-art and recent edge detection methods. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Chebyshev polynomial Contrast enhancement Edge detection Geometric mean Histogram equalization Thresholding Weighted standard deviation

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Performance Evaluation of Synthetic Aperture Radar Images 8

Performance Evaluation of Synthetic Aperture Radar Images

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8th International Conference on Signal Processing and Communication, ICSC 2022

作者： Upadhyay, Kritika Kumar, Sandeep Bharti, Manisha Agarwal, Prabhakar National Institute of Technology Delhi Dept. of Electronics and Communication Engineering Delhi India National Institute of Technology Delhi Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665454308

Synthetic Aperture Radar (SAR) imaginary is used extensively for Military applications in the modern era of technology. In this work, we have evaluated the performance of SAR images for Radar Systems. The SAR length, Resolution, and Footprint of SAR networks have been compared by varying the parameters of Radar signals like Bandwidth, Operating Frequency, and Satellite Altitude. From the results, it was observed that Antenna Footprints vary inversely with the Operating Frequency and Grazing Angle of Radar © 2022 IEEE.

关键词： Synthetic aperture radar

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