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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Venkatachalapathy, M.V. Venkateshappa Cyril Prasanna Raj, P. Reva University School of Electronics and Communication Engineering Bengaluru India Cambridge Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375442

Convolution Neural Networks (CNNs) are pivotal in image processing. Prominent CNN models include SqueezeNet, MobileNet, Xception, VGG-16, and AlexNet. These models transform 2D data to 1D, emphasizing maximum intensity pixels for data flattening. Traditional systolic arrays are primarily linear and 2D;however, advancements in VLSI and packing technology have increased interest in 3D systolic architectures. This study proposes a 3D systolic architecture where input data is arranged on the X-Y plane, and filter coefficients are fed through the Z-X plane. The architecture utilizes multiplexers, multipliers, adders, 2:1 muxes, and delay registers. The model was developed using MATLAB and verified through HDL simulation on a Field-Programmable Gate-Array (FPGA). The 3D systolic architecture demonstrated an increase in area and operating frequency compared to conventional architectures, achieving a maximum frequency of 315 MHz and power dissipation below 5 W. The proposed 3D systolic architecture enhances the efficiency of CNN operations, offering a reusable subsystem for complex computations. © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Low light image enhancement using reflection model and wavelet fusion

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Multimedia Tools and Applications 2024年 1-29页

作者： Singh, Pallavi Bhandari, Ashish Kumar Kumar, Reman Department of Electronics and Communication Engineering National Institute of Technology Patna Patna800005 India

In this article, a fresh method for enhancing low-light images (LIME) is introduced, utilizing reflection models and wavelet fusion. The algorithm proposed adapts dynamically to dark images by utilizing reflection models and the multiscale techniques. Initially, an input RGB color image undergoes stretching to rectify any form of color distortion before being transform to the HSV color space. Using the multiscale theory concept, the illumination coefficient of the V component is computed. Subsequently, an image brightness enhancement scheme is applied, leveraging the Fechner principle, which dynamically adjusts the parameters of the enhancement function. Furthermore, a method for image fusion based on wavelets is developed to extract important features from the two images. At last, after applying wavelet fusion the improved image is obtained. When compared to alternative methods, the proposed approach demonstrates superior results in subjective and objective evaluations. The improved images exhibit clarity, brightness, and a natural appearance. The suggested method is assessed with images from the Ex-Dark as well as LOL datasets, and its performance is compared with many other commonly used techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image fusion

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Demand-based dynamic bandwidth allocation in multi-beam satellites using machine learning concepts

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Intelligent and Converged Networks 2024年第2期5卷 147-166页

作者： Shwet Kashyap Nisha Gupta Department of Electronics and Communication Engineering Birla Institute of Technology MesraRanchi 835215India

In the realm of satellite communication,where the importance of efficient spectrum utilization is growing day by day due to the increasing significance of this technology,dynamic resource management has emerged as a pivotal consideration in the design of contemporary multi-beam satellites,facilitating the flexible allocation of resources based on user *** research paper delves into the pivotal role played by machine learning and artificial intelligence within the domain of satellite communication,particularly focusing on spot beam *** study encompasses an evaluation of machine learning’s application,whereby an extensive dataset capturing user demand across a specific geographical area is subjected to *** analysis involves determining the optimal number of beams/clusters,achieved through the utilization of the knee-elbow method predicated on within-cluster sum of ***,the demand data are equitably segmented employing the weighted k-means clustering *** proposed solution introduces a straightforward yet efficient model for bandwidth allocation,contrasting with conventional fixed beam illumination *** approach not only enhances spectrum utilization but also leads to noteworthy power savings,thereby addressing the growing importance of efficient resource management in satellite communication.

关键词： satellite communications multi-beam satellites machine learning weighted k-means clustering Voronoi tessellation knee-elbow method

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Breast Mass Detection and Classification Using Machine Learning Approaches on Two-Dimensional Mammogram: A Review

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Critical Reviews in Biomedical engineering 2024年第4期52卷 41-60页

作者： Shankari, N. Kudva, Vidya Hegde, Roopa B. Department of Electronics and Communication Engineering NMAM Institute of Technology Karnataka Nitte574110 India

Breast cancer is a leading cause of mortality among women, both in India and globally. The prevalence of breast masses is notably common in women aged 20 to 60. These breast masses are classified, according to the breast imaging-reporting and data systems (BI-RADS) standard, into categories such as fibroadenoma, breast cysts, benign, and malignant masses. To aid in the diagnosis of breast disorders, imaging plays a vital role, with mammography being the most widely used modality for detecting breast abnormalities over the years. However, the process of identifying breast diseases through mammograms can be time-consuming, requiring experienced radiologists to review a significant volume of images. Early detection of breast masses is crucial for effective disease management, ultimately reducing mortality rates. To address this challenge, advancements in image processing techniques, specifically utilizing artificial intelligence (AI) and machine learning (ML), have tiled the way for the development of decision support systems. These systems assist radiologists in the accurate identification and classification of breast disorders. This paper presents a review of various studies where diverse machine learning approaches have been applied to digital mammograms. These approaches aim to identify breast masses and classify them into distinct subclasses such as normal, benign and malignant. Additionally, the paper highlights both the advantages and limitations of existing techniques, offering valuable insights for the benefit of future research endeavors in this critical area of medical imaging and breast health. © 2024 by Begell House, Inc.

关键词： Feature extraction

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Design and Development of Readout electronics for Thermopile Array Detector Using FPGA 5

Design and Development of Readout Electronics for Thermopile...

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5th International Conference on Circuits, Control, communication and Computing, I4C 2024

作者： Rashmi Priyadarshini, B.K. Rajendra Prasad, P. Sarvagya, Mrinal Naresh, Banothu Ravi Kumar, M.G. School of Electronics and Communication Engineering Reva University Bangalore India Ramaiah Institute of Technology Department of Electronics and Communication Engineering Bangalore560054 India Bangalore India Nagarjuna College of Engineering Department of Electronics and Communication Engineering & Technology Bangalore India

ISBN: (纸本)9798331528539

Thermopile detector arrays, integral to infrared sensors, find widespread application in temperature measurement and thermal imaging. This paper introduces a novel approach by designing dedicated readout electronics for thermopile detectors, aiming at their characterization for space applications, specifically in a safe mode earth sensor. The proposed system employs a reprogrammable Field Programmable Gate Array (FPGA) for implementing the readout electronics, encompassing tasks such as extracting raw pixel data from the detector and configuring the sensor for different parameter settings. The safe mode earth sensor, crucial for spacecraft attitude determination, provides 2-axes attitude referencing Earth in the pitch and roll axes. Leveraging thermopile-based earth sensors, the system detects Earth edges in the IR band, facilitating the calculation of roll and pitch angles. The compact design makes it particularly suitable for deployment in Micro-Satellites and other satellites as a safe mode sensor. The readout electronics and data acquisition system play a pivotal role in evaluating, characterizing, and screening thermopile detectors, ensuring their suitability for deployment in safe mode earth sensor applications. This integrated approach not only enhances the efficiency of thermopile detectors but also opens avenues for their broader application in space-based sensing systems. © 2024 IEEE.

关键词： Micro satellites

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Wavelet Transform based Enhanced Statistical Voice Conversion of Non-Audible Murmur (NAM) for Improved Speech Intelligibility 2

Wavelet Transform based Enhanced Statistical Voice Conversio...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Veera Boopathy, E. Ramjan Begam, A. Anandhakumar, P. Vignesh, M. Mydhili, K.S. Rajeshwaran, K. Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Akshaya College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India VSB Engineering College Department of Electronics and Communication Engineering India Pollachi Institute of Engineering and Technology Department of Electronics and Communication Engineering Pollachi India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331534950

This article offers a novel wavelet transformation based statistical approach for transforming Non-Audible Murmur (NAM) signals into speech sounds in order to overcome the problems arising from the low energy and low-frequency content of NAMs. The wavelet transform of the NAM signals safely partitions the signals into detailed time-frequency features, enabling both noise reduction and feature enhancement. Some of the proposed system design steps involve pre-processing, framing, wavelet analysis of the underlying signal, and reconstruction of the enhanced signal. The simulation results show that this method can effectively reduce the standard deviation of the converted speech to 98% of the word accuracy rate. This shows that the wavelet transform is beneficial in enhancing the quality of the speech by filtering out noise and making it easier to implement in any application where clear speech reproduction is crucial. © 2024 IEEE.

关键词： Noise abatement

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Verification of news and certificate, and plagiarism detector

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Multimedia Tools and Applications 2025年 1-28页

作者： Jain, Pushpak Anand, Dewansh Rajni Behal, Vansh Kapoor, Vipul Sharma, Neha Jindal, Neeru Department of Electronics and Communication Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Patiala India

Fake news, Fake certification, and Plagiarism are the most common issues arising these days. During this COVID-19 situation, there are a lot of rumors and fake news spreading and some of us are using fake certification to avoid vaccination and plagiarism became the most common topic during online studies. It is found that several apps and websites are present for the detection of false news and plagiarism. However, there is no single website for the detection of fake certificates. Moreover, nothing is made that consists of all these features in one platform. So, this proposed work develops a website for detecting fake news, Fake certificate, and plagiarism detector. The proposed method uses Natural Language Processing techniques for detecting NPC (news, plagiarism, and certificate). The proposed work uses image processing with some Python libraries like OpenCV in all the detections and that makes it quite interesting. For detecting NPC (news, plagiarism, and certificate) the algorithm is built using a k-mean model, logistic regression, and neural network. The proposed work uses the datasets that are trained using the count vectorizer method for the detection and to get its accuracy which will be tested using machine learning algorithms. The proposed method has shown an accuracy rate of 94.28% on Logistics Regression in comparison to existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fake news Machine learning Natural language processing Plagiarism Social media platform

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YOLOv7 for brain tumour detection using morphological transfer learning model

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Neural Computing and Applications 2024年第32期36卷 20321-20340页

作者： Pandey, Sanat Kumar Bhandari, Ashish Kumar Department of Electronics and Communication Engineering National Institute of Technology Patna Patna800005 India

An accurate diagnosis of a brain tumour in its early stages is required to improve the possibility of survival for cancer patients. Due to the structural complexity of the brain, it has become very difficult and tedious for neurologists and radiologists to diagnose brain tumours in the initial stages with the help of various common manual approaches to tumour diagnosis. To improve the performance of the diagnosis, some computer-aided diagnosis-based systems are developed with the concepts of artificial intelligence. In this proposed manuscript, we analyse various computer-aided design (CAD)-based approaches and design a modern approach with ideas of transfer learning over deep learning on magnetic resonance imaging (MRI). In this study, we apply a transfer learning approach with the object detection model YOLO (You Only Look Once) and analyse the MRI dataset with the various modified versions of YOLO. After the analysis, we propose an object detection model based on the modified YOLOv7 with a morphological filtering approach to reach an efficient and accurate diagnosis. To enhance the performance accuracy of this suggested model, we also analyse the various versions of YOLOv7 models and find that the proposed model having the YOLOv7-E6E object detection technique gives the optimum value of performance indicators as precision, recall, F1, and mAP@50 as 1, 0.92, 0.958333, and 0.974, respectively. The value of mAP@50 improves to 0.992 by introducing a morphological filtering approach before the object detection technique. During the complete analysis of the suggested model, we use the BraTS 2021 dataset. The BraTS 2021 dataset has brain MR images from the RSNA-MICCAI brain tumour radiogenetic competition, and the complete dataset is labelled using the online tool MakeSense AI. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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Soft Attention Mechanism Based Network to Extract Blood Vessels from Retinal Image Modality

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3408-3418页

作者： Preity Bhandari, Ashish Kumar Shahnawazuddin, Syed National Institute of Technology Patna Department of Electronics and Communication Engineering Patna800005 India

In the field of ophthalmology, most of the ocular diseases are associated with vessel abnormalities. Hence, vessel segmentation plays an important role to diagnose ocular disease accurately. Deep learning (DL) techniques are grabbing attention these days due to their accurate analysis and extraction of retinal vessels. However, the vessels with low contrast, thin vessels, and vessels with improper boundaries are still overlooked. To cover this gap, we have introduced an attention mechanism based DL network that includes encoder and decoder blocks. Basically, the architecture is an extension of U-net model in which soft attention blocks are introduced in between the down-sampling and up-sampling phases of the network. The attention blocks learn to focus on the target structures and, therefore, the relevant information gets effectively extracted. In our proposed approach, for the preprocessing of the image, we first extracted the green channel (GE) and then applied contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement. The preprocessed image was then fed as the input to the model for vessel segmentation. For the quantitative analysis of our model, we have used four benchmark datasets [digital retinal image for vessel extraction (DRIVE), structural analysis of retina (STARE), child heart and health study in England (CHASE), and high resolution fundus (HRF)]. A comparative analysis is also done with the state-of-the-art techniques using several performance parameters. The method proposed in this study is found to be more efficient in extracting retinal vessels. The average accuracy achieved by our method is 98%. © 2020 IEEE.

关键词： Image segmentation

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Artificial intelligence based succored power prediction in stand alone PV system

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Australian Journal of Electrical and electronics engineering 2025年第1期22卷 66-79页

作者： Sayeed, Farrukh Ahmed, K. Rafeeq Swamy, S.M. Department of Electrical and Electronics Engineering ACE College of Engineering Thiruvananthapuram India Department of Electronics and Communication Engineering School of Engineering Presidency University Bangalore India Department of AI&ML RT Chennai India

Accurate forecasting of solar power generation is crucial for effective management of renewable energy resources. This literature review emphasises the effectiveness of Artificial Neural Networks (ANNs) in predicting solar energy potential. While traditional ANNs with single hidden layers and predefined neuron configurations have shown success in various applications, they are not suitable for predicting nonlinear time-series databases in the context of this research. The limitations of traditional ANNs in predicting solar power potential necessitate the exploration of alternative approaches. Configuring the structure of traditional ANNs for solar power prediction is a time-consuming process that often requires manual or trial-and-error methods. To address this issue, optimisation techniques are incorporated into the ANN model. Various traditional methods have been explored in this research, but none has achieved satisfactory performance. Drawing from experiences with traditional techniques, a novel Evolution of Cub to Predator (ECP) technique is proposed. The investigation results demonstrate that the incorporation of optimisation techniques yields superior performance compared to traditional ANNs. The proposed ECP technique achieves an impressive prediction accuracy of 97.2%, surpassing existing optimisation techniques. These findings highlight the potential of the ECP technique for accurate solar power prediction and its contribution to optimising renewable energy resource management. ©, Engineers Australia.

关键词： Solar power generation

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