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Influence analysis of calculated horizontally layered soil parameters on grounding parameters

High Voltage 2023年第2期8卷 421-430页

作者： Yihua Dan Junjun Yin Jian Yang Huizhang Yang Department of Electronic Engineering Tsinghua UniversityBeijingChina School of Computer&Communication Engineering University of Science and Technology BeijingBeijingChina

Calculated parameters(soil layer resistivity,soil layer thickness,and the number of soil layers)of horizontally layered soil are usually obtained based on the measured apparent resistivity under different measurement distances,which are significant for the design,operation,and maintenance of grounding *** existing calculation methods of soil parameters are just trying to make the calculation results approach the measurement data,ignoring the relationship among measurement data,the calculated soil parameters,and grounding parameters,which would increase the workload of the *** better balance the distance range of the measurement data and the influence of the calculated horizontally layered soil on grounding parameters,this paper systematically studies the relationship among measured apparent soil resistivity,calculated horizontally layered soil parameters,and grounding *** basic theories of apparent resistivity measurement,soil parameter calculation,and grounding parameter calculation are given,the influence of soil layer thickness on the measured apparent resistivity is studied,and the influence of the calculated soil parameters on the grounding resistance of different grounding models is *** on different scales of grounding grids,the results give a corresponding reference distance range of measured apparent soil ***,this paper can help decrease the workload of soil resistivity measurement during grounding parameters analysis,which has far-reaching engineering significance.

关键词： soil parameters apparent

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Joint Rain Streaks & Haze Removal Network for Object Detection

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computers, Materials & Continua 2024年第6期79卷 4683-4702页

作者： Ragini Thatikonda Prakash Kodali Ramalingaswamy Cheruku Eswaramoorthy K.V Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Department of Computer Science and Engineering National Institute of TechnologyWarangal506004India Department of Electronics and Communication Engineering Indian Institute of Information TechnologyDesign and ManufacturingKurnool518008India

In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant *** emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior ***,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational *** response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature *** methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map *** the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.

关键词： Image deraining Selective Dense Residual Module(SDRM) Selective Kernel Fusion Module(SKFM) Selective KernelDense Residual M-Shaped Network(SKDRMNet)

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A review on Gujarati language based automatic speech recognition (ASR) systems

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International Journal of Speech Technology 2024年第1期27卷 133-156页

作者： Dua, Mohit Bhagat, Bhavesh Dua, Shelza Chakravarty, Nidhi Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Electronics Communication and Engineering Punjab Engineering College Chandigarh Chandigarh India

Automatic speech recognition (ASR) plays a crucial role in facilitating natural and efficient human–computer interaction. This paper offers a comprehensive review of ASR systems tailored specifically for the Gujarati language. Existing literature on Gujarati ASR indicates that most survey papers have focused solely on back-end classification methods. This paper aims to fill this gap by presenting a comprehensive survey that encompasses feature extraction techniques, backend models, speech datasets, and evaluation metrics. This review provides an in-depth analysis of the cutting-edge methodologies employed in the development of Gujarati ASR systems. Firstly, the paper discusses various feature extraction techniques. Secondly, it covers classification models and their impact on ASR performance. Thirdly, the study delves into available speech datasets relevant to the Gujarati language, offering valuable insights for researchers and practitioners. Finally, the paper reviews the evaluation parameters used in ASR systems. Additionally, it gives an overview of online toolkits, resources, and language models pertinent to Gujarati ASR. This review serves as a comprehensive reference for academics and professionals involved in the research and development of ASR systems for the Gujarati language. The parameters selected for comparison include front-end feature extraction methods, back-end classification techniques, speech dataset, and evaluation metrics. Furthermore, the paper discusses both the contributions and limitations encountered by current ASR systems during the review. Finally, it addresses various challenges that still persist and provides directions for future research in this critical field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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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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Two-Dimensional Golay Complementary Array Sets with Arbitrary Lengths for Omnidirectional MIMO Transmission

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IEEE Transactions on communications 2024年第2期72卷 1135-1145页

作者： Zhao, You-Qi Pai, Cheng-Yu Huang, Zhen-Ming Liu, Zilong Chen, Chao-Yu National Cheng Kung University Department of Engineering Science Tainan701 Taiwan National Cheng Kung University Department of Engineering Science and the Institute of Computer and Communication Engineering Tainan701 Taiwan University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom National Cheng Kung University Department of Electrical Engineering and the Institute of Computer and Communication Engineering Tainan701 Taiwan

This paper presents a coding approach for achieving omnidirectional transmission of certain common signals in massive multi-input multi-output (MIMO) networks such that the received power at any direction in a cell remains constant for any given distance. Specifically, two-dimensional (2D) Golay complementary array set (GCAS) can be used to design the massive MIMO precoding matrix so as to achieve omnidirectional transmission due to its complementary autocorrelation property. In this paper, novel constructions of new 2D GCASs with arbitrary array lengths are proposed. Our key idea is to carefully truncate the columns of certain larger arrays generated by 2D generalized Boolean functions. Finally, the power radiation patterns and numerical results are provided to verify the omnidirectional property of the GCAS-based precoding. The error performances of the proposed precoding scheme are presented to validate its superiority over the existing alternatives. © 1972-2012 IEEE.

关键词： Peak to average power ratio

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Underwater video transmission with video enhancement using reduce hazing algorithm

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Journal of Optical communications 2024年第2期45卷 379-388页

作者： Mathiazhagan, Janarthanan Gauni, Sabitha Mohan, Rajesvari Department of Electronic and Communication Engineering SRM Institute of Science and Technology Tamil Nadu Chennai India

Underwater video regulation is an insightful research field that can help engineers with bettering investigation on the lowered condition. Submerged video preparing has been utilized in a many fields, such as submerged infinitesimal location, landscape examining, mine identification, media transmission connections, and self-proficient lowered vehicles. Be that as it may, submerged video experiences solid assimilation, dissipating, shading contortion, and clamor from the manufactured light sources, causing video obscure, cloudiness, and a somewhat blue or greenish tone. In this way, the improvement can be separated into two techniques, submerged video de-preliminaries and underexposed video concealing remaking. Relentless in remote correspondence structures, for instance 3G, 4G, and so on, a coming crisis is endless deftly of the nonattendance of consistently Radio Frequency (RF) resources;this deterrent in moving speed cannot strengthen the improvement notable for high information speed. So the new innovation of Light-Fidelity (Li-Fi) came into picture. This innovation can be contrasted to that of Wi-Fi and offers points of interest like expanded available spectrum efficiency, effectiveness, security, low idleness and a lot higher speed. communication is accomplished by exchanging light-emitting diode (LED) lights on and off at a speed higher than what is detectable to the human eye. This paper presents the explanation behind underexposed picture corruption and surveys the cutting-edge knowledge calculations like video reduce hazing algorithm. In this calculation, it uses two different de-hazing methods, simple Dark Channel Prior (DCP) and Approximate Dark Channel Prior (ADCP), to reduce haze in a video. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Light

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Security to text (S2T): multi-layered based security approaches for secret text content

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Multimedia Tools and Applications 2025年第15期84卷 15151-15171页

作者： Kashid, Shamal Awasthi, Lalit K. Berwal, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand IN Srinagar India Faculty of Communication Engineering Military College of Telecommunication Engineering IN Mhow India

In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of techniques in text mining, information retrieval, and natural language processing (NLP). To solve this issue, we introduce two novel approaches: keyword extraction (KE) and a multi-layered secret sharing scheme (MLSS) to provide security to extracted keywords rather than overall text documents. The KE approach encompasses a sequence of text pre-processing procedures, including tokenization, stopword removal, stemming, and bag of words representation, followed by indexing. This methodology aims to efficiently extract keywords from text datasets. For this research work, we have proposed three datasets, including text messages, whatsapp messages, and electronic mail. MLSS enhances the security of extracted textual data by leveraging text pre-processing steps. This scheme ensures better confidentiality and the non-revealment of sensitive information. Additionally, we evaluate our KE model on our dataset as well as on standard datasets. Experimental results demonstrate the effectiveness of our proposed security to text (S2T) model, which outperform existing state-of-the-art approaches. The model obtains a 100% correlation between the reconstructed text and the original text. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

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Digital communications and Networks 2024年第1期10卷 53-62页

作者： Zhipeng Cheng Minghui Liwang Ning Chen Lianfen Huang Nadra Guizani Xiaojiang Du Department of Information and Communication Engineering Xiamen UniversityXiamen361005China School of Electrical and Computer Engineering University of Texas at ArlingtonArlingtonTX76019USA Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ07030USA

Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in ***,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective *** this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground *** goal was to mitigate co-channel interference while maximizing long-term system *** problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this *** simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

关键词： UAV-user association Multi-connectivity Resource allocation Power control Multi-agent deep reinforcement learning

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A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images

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International Journal of Intelligent Networks 2024年第1期5卷 10-18页

作者： George, Neetha Shine, Linu N, Ambily Abraham, Bejoy Ramachandran, Sivakumar Department of Electronics and Communication Engineering Rajiv Gandhi Institute of Technology Kottayam Kerala India Department of Electronics and Communication Engineering College of Engineering Trivandrum Kerala India Department of Electronics and Communication Engineering Government Engineering College Idukki Kerala India Department of Computer Science and Engineering College of Engineering Muttathara Kerala Thiruvananthapuram India Department of Electronics and Communication Engineering Government Engineering College Wayanad Kerala India

The advancements in medical imaging techniques have brought exponential increase in the quantity and complexity of data which often require human expertise for interpretation and decision making. However, in real-world clinical settings, there is often a shortage of experts available for timely diagnosis and triage. An automated diagnostic technique aids clinicians in the precise diagnosis and effective management of diseases. In this article, a two-stage classification model using convolutional neural network (CNN) is proposed for the classification and severity analysis of retinal and choroidal diseases in optical coherence tomography (OCT) images. The proposed model can identify abnormal conditions such as Pachychoroid disorders, macular edema, and Drusen. The images are initially classified into four categories-healthy, Drusen, Pachychoroid and macular edema classes. The severity of Pachychoroid conditions, including central serous chorioretinopathy, polypoid choroidal vasculopathy, and choroidal neovascularization, is subsequently determined through a second-level classification. A modified version of the VGG16 architecture is used for the initial classification. A fine-tuned CNN with the same architecture is then employed to determine the severity of Pachychoroid diseases. Further, we make use of the UNet architecture for assessing the severity of macular edema. The proposed hybrid approach for classification and analysis achieved promising results, demonstrating consistency on par with human experts in diagnosing and grading ocular diseases. © 2024

关键词： Optical tomography

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Preamble detection for broadband power line communication systems

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Telecommunication Systems 2024年第1期87卷 47-60页

作者： Su, Szu-Lin Huang, Nan-Hsiung Chen, Yu-Hsuan Lai, Kuei-Chiang Institute of Computer and Communication Engineering Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

Power line communication (PLC) systems enable information exchange over existing electric power grids at a low deployment cost. Such systems constitute a crucial component of smart grids. However, PLC is negatively affected by multipath channel, random impulsive noise (IN), and background noise. Numerous studies have evaluated the data detection performance of PLC systems;however, few studies have examined the preamble detection performance of these systems under IN. Accordingly, the present study evaluated preamble detection schemes for PLC systems. Specifically, the study compared two schemes, namely, an energy detection scheme and an autocorrelation detection scheme. The study results indicated that the energy detection scheme exhibited lower complexity and had a shorter mean time to correct detection than did the autocorrelation detection scheme. Therefore, the energy detection scheme was determined to be more suitable than the autocorrelation detection scheme for preamble detection in power line channels, a finding that is in contrast to expectations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Impulse noise

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