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4th international conference on Energy Systems, Drives and Automations, ESDA 2021

作者： Kaur, Harpreet Umary, Ahsanullah Department of Computer Science Engineering Chandigarh University Punjab India Department of Electronic and Communication Engineering Chandigarh University Punjab India

ISBN: (纸本)9789819936908

Developing requests of the net clients is one of the motives which superiority utilizing dense wavelength division multiplexing (DWDM) systems to broadcast optical information. This proposed method has the ability that can transmit numerous wavelengths thru a single optical fiber. In this paper, execution analysis of dense wavelength multiplexing network (DWDM) system has been suggested to evaluate the performance of the suggested network using BER analysis over long distance. DWDM is the advanced type optical network that provide high bandwidth compare to available optical networks that accomplish the need for greater bandwidth. In DWDM, single optical fiber is utilized for transmission of different wavelength. The suggested network model system is implemented by using OptiSystem software, and output of the simulation model is analyzed in term of Q-factor and minimum BER;in general, capacity of the system is improved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Dense wavelength division multiplexing

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Design of L-Shape Slot Loaded Rectangular Microstrip Patch Antenna for IoT/WLAN/WiMAX Applications 5th

Design of L-Shape Slot Loaded Rectangular Microstrip Patch A...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Verma, Ramesh Kumar Kumar, Akhilesh Pattanayak, Prabina Chauhan, Puspraj Singh Bali, Vikram Mathur, Sonali Department of Computer Science and Engineering IMS Engineering College Uttar Pradesh Ghaziabad India Department of Electronics and Communication Engineering National Institute of Technology Silchar Assam India Department of Electronics and Communication Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

ISBN: (纸本)9789819719228

In this work, a rectangular shape microstrip patch antenna of single band resonating at frequency 2.50 GHz has been designed by loading two L-shape slots. The proposed antenna of single band shows impedance bandwidth of 574 MHz between frequency range 2.249–2.823 GHz. The proposed antenna resonates at frequency 2.50 GHz with return loss of −17.51 dB. However, the bandwidth of convention antenna is only 337 MHz. Hence, the bandwidth of proposed antenna (574 MHz) is increases 237 MHz compared to bandwidth of conventional antenna (337 MHz). The proposed antenna has VSWR, peak gain and efficiency 1.307, 3.08 dB and 98.2%, respectively, at frequency 2.50 GHz. Simulation and design is performed by using IE3D software with microstrip line feed of 50 Ω. The proposed antenna can be applicable for IoT/WLAN/WiMAX. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bandwidth

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Diagnosis of Alzheimer Disease from Multi-Modal Data: Role of Artificial Intelligence

Diagnosis of Alzheimer Disease from Multi-Modal Data: Role o...

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2024 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2024

作者： Julaiha, A.G. Noorul Vasudevan, B. Kannan, S. Rajesh Rajalakshmi Institute of Technology Anna University Department of Computer Science & Engineering Chennai India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331543617

Alzheimer's disease (AD) is another area of medical research concern mainly due to the current prognosis of the disease that is further impacting the patient and health care organizations. This paper aims at investigating the performance of the predictive model that employs artificial intelligence (AI) to forecast Alzheimer's disease based on clinical histories, neuro-images, and genomics. The CNN integrated with LSTM can allow for the analysis of such multifaceted data sources since it has outstanding performance in processing and interpreting the data. To understand Alzheimer's disease, we used a combination of CNNs and LSTMs. CNNs helped us identify patterns in brain scans, while LSTMs analyzed how clinical and genetic factors changed over time. The following model defines an enhanced decision-making approach that could prove useful in early diagnosis and treatment regimen planning compared to standard approaches. Based on the findings, multi-modal data contributes significantly to the identification of Alzheimer's disease prediction with high accuracy and less variability so that fruitful intervention trials and research would be well pioneered strategies. © 2024 IEEE.

关键词： electronic health record

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Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

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Intelligent Automation & Soft Computing 2023年第1期35卷 839-851页

作者： B.Murugeshwari D.Selvaraj K.Sudharson S.Radhika Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Information Technology Velammal Insitutue of TechnologyChennai601204India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600124India

Protecting the privacy of data in the multi-cloud is a crucial *** mining is a technique that protects the privacy of individual data while mining those *** most significant task entails obtaining data from numerous remote *** algorithms can obtain sensitive information once the data is in the data *** traditional algorithms/techniques promise to provide safe data transfer,storing,and retrieving over the cloud *** strategies are primarily concerned with protecting the privacy of user *** study aims to present data mining with privacy protection(DMPP)using precise elliptic curve cryptography(PECC),which builds upon that algebraic elliptic curve *** approach enables safe data exchange by utilizing a reliable data consolidation approach entirely reliant on rewritable data concealing ***,it outperforms data mining in terms of solid privacy procedures while maintaining the quality of the *** approximation error,computational cost,anonymizing time,and data loss are considered performance *** suggested approach is practical and applicable in real-world situations according to the experimentalfindings.

关键词： Data mining cryptography privacy preserving elliptic curve information security

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Methods and Applications for Sentiment Analysis in Online Social Networks 1

Methods and Applications for Sentiment Analysis in Online So...

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1st International Conference on Computing, Sciences and communications, ICCSC 2024

作者： Srivastava, Atul Chandra, Kunal Sharma, Vijay Shankar Pillai, Anuradha Amity School of Engineering and Technology AUUP Lucknow India Communication Engineering Manipal University Department of Computer Jaipur India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350353648

With hyped popularity of social media, sentiment analysis, a branch of natural language processing (NLP), has gained significant attention. Social media is a treasure trove for gaining insights into the thoughts, feelings, and opinions of the general public due to the abundance of user-generated content. This work offers a thorough review of sentiment analysis applied in social media, covering its methods, applications, and challenges. It discusses various approaches to sentiment analysis, from deep learning and machine learning models to rule-based techniques. Additionally, it examines the challenges associated with sentiment analysis of social media content, such as context dependence, slang, and sarcasm. The study also explores the various applications of sentiment analysis in social media, including tracking public opinion, political sentiment analysis, customer feedback analysis, and brand monitoring. By exploring these facets, this work seeks to throw light on the current state of the art in social media sentiment analysis and its implications. © 2024 IEEE.

关键词： Adversarial machine learning

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems Science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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Optimized Power Allocation in Downlink NOMA with Imperfect Successive Interference Cancellation

Optimized Power Allocation in Downlink NOMA with Imperfect S...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Biswas, Soumik Bera, Debasish Indian Institute of Information Technology Department of Electronics and Communication Engineering Kalyani India Indian Institute of Information Technology Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350350821

It is expected that physical-layer security will finally thrive to provide an extra layer of defence for a variety of devices in next-generation 6G wireless communication. In this work, physical-layer security using non-orthogonal multiple access (NOMA) with single-input-single-output (SISO) assumptions are considered where the users and eavesdroppers are spatially uniformly deployed. The channels between the transmitter (base station (BS)) and the NOMA users and eavesdroppers are considered to be independent Nakagami-m. We analyse the secrecy sum rate (SSR) of the NOMA system in the case of imperfect successive interfere cancellation (SIC) and propose an optimised power allocation scheme with minimised transmit power requirement to satisfy the Quality of Service (QoS) for all legitimate users. Closed-form solutions for the SSR are also derived. The performance of the proposed schemes has been evaluated both by analysis and by simulations. Results are evaluated for a wide range of the signal-to-noise ratio (SNR). © 2024 IEEE.

关键词： Channel state information

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High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

High-Fidelity Document Stain Removal via A Large-Scale Real-...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Li, Mingxian Sun, Hao Lei, Yingtie Zhang, Xiaofeng Dong, Yihang Zhou, Yilin Li, Zimeng Chen, Xuhang School of Computer Science and Engineering Huizhou University China The Ohio State University Department of Computer Science and Engineering United States School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Electronic and Communication Engineering Shenzhen Polytechnic University China

ISBN: (纸本)9798331510831

Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. The absence of a comprehensive stained document dataset has limited the effectiveness of existing document enhancement methods in removing stains while preserving fine-grained details. To address this challenge, we construct StainDoc, the first large-scale, high-resolution (2145 x 2245) dataset specifically designed for document stain removal. StainDoc comprises over 5,000 pairs of stained and clean document images across multiple scenes. This dataset encompasses a diverse range of stain types, severities, and document backgrounds, facilitating robust training and evaluation of document stain removal algorithms. Furthermore, we propose StainRestorer, a Transformer-based document stain removal approach. StainRestorer employs a memory-augmented Transformer architecture that captures hierarchical stain representations at part, instance, and semantic levels via the DocMemory module. The Stain Removal Transformer (SRTransformer) leverages these feature representations through a dual attention mechanism: an enhanced spatial attention with an expanded receptive field, and a channel attention captures channel-wise feature importance. This combination enables precise stain removal while preserving document content integrity. Extensive experiments demonstrate StainRestorer's superior performance over state-of-the-art methods on the Stain-Doc dataset and its variants *** and ***, establishing a new benchmark for document stain removal. Our work highlights the potential of memory-augmented Transformers for this task and contributes a valuable dataset to advance future research. © 2025 IEEE.

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Advancing Parkinson's Detection Through Intelligent Algorithms 10

Advancing Parkinson's Detection Through Intelligent Algorith...

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10th International Conference on Advanced Computing and communication Systems, ICACCS 2024

作者： Rasheedha, A. Lynsha Helena Pratheeba, H.P. Vinnetia, S. Caroline Devi, S. Sharmila Reethika, A. Gunanandhini, S. Sri Ramakrishna Engineering College Department of Biomedical Engineering Tamil Nadu Coimbatore India Cmr Institute of Technology Department of Computer Science and Engineering Bangaluru India Bannari Amman Institute of Technology Department of Biomedical Engineering Sathyamangalam India Dr.N.G.P.Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350384369

Parkinson's disease (PD) is a slowly developing nervous disorder that presents with signs like those of other illnesses. Appropriate diagnosis and therapy depend on early detection. One common method for identifying and evaluating Parkinson's disease is handwritten notes. Early Parkinson's disease (PD) detection has been studied using a number of machine learning techniques, although the most have poor performance accuracy. An effective deep learning model is suggested to help diagnose Parkinson's disease early. The model uses evolutionary algorithms, such as the K-Nearest Neighbour approach, to pick the most optimal features for high-performance accuracies. The model obtains an zone under curve of 0.92, detection accuracy of over 96%, and precision of 99%. To illustrate the effectiveness of the model, its performance is contrasted with cutting-edge machine learning and deep learning-based PD detection methods. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep MARL-Based Resilient Motion Planning for Decentralized Space Manipulator

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Space(Science & Technology) 2024年第1期4卷 160-169页

作者： Jiawei Zhang Chengchao Bai C.Patrick Yue Jifeng Guo The School of Astronautics Harbin Institute of TechnologyHarbinChina Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologyHong KongChina

Space manipulators play an important role in the on-orbit services and planetary surface *** the extreme environment of space,space manipulators are susceptible to a variety of unknown *** to have a resilient guarantee in failure or disturbance is the core capability of its future *** with traditional motion planning,learning-based motion planning has gradually become a hot spot in current ***,no matter what kind of research ideas,the single robotic manipulator is studied as an independent agent,making it unable to provide sufficient flexibility under conditions such as external force disturbance,observation noise,and mechanical ***,this paper puts forward the idea of“discretization of the traditional single manipulator”.Different discretization forms are given through the analysis of the multi-degree-of-freedom single-manipulator joint relationship,and a single-manipulator representation composed of multiple new subagents is ***,to verify the ability of the new multiagent representation to deal with interference,we adopted a centralized multiagent reinforcement learning *** influence of the number of agents and communication distances on learning-based planning results is analyzed in *** addition,by imposing joint locking failures on the manipulator and introducing observation and action interference,it is verified that the“multiagent robotic manipulator”obtained after discretization has stronger antidisturbance resilient capability than the traditional single manipulator.

关键词： planetary surface motion planning multiagent reinforcement learning space manipulators resilient motion planning robotic manipula decentralized space manipulator deep marl

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