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Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

Journal of Systems engineering and Electronics 2024年第2期35卷 316-329页

作者： WEI Shaopeng ZHANG Lei LU Jingyue LIU Hongwei College of Oceanography and Space Informatics China University of Petroleum(East China)Qingdao 266580China School of Electronics Engineering Xidian UniversityXi’an 710071China School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen UniversityGuangzhou 518107China School of Computer Science and Technology Xidian UniversityXi’an 710071China

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation *** paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary *** the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time *** dictionary learning is followed to separate real signals from jamming *** the learned representation,time-varying MISRJs are suppressed *** simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

关键词： synthetic aperture radar(SAR) modulated interrupt sampling jamming(MISRJ) online dictionary learning

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AN 8-WAY RF FEED NETWORK FOR 5.3 GHZ C-BAND SAR: DESIGN, SIMULATION, AND ANALYSIS USING STANDARD METHODS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 21-43页

作者： Kothapudi, Venkata Kishore Thokala, Niharika Vallabhaneni, Ramyasri Sanaka, Poojitha Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Bhattacharyya, Arka Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St ColumbiaSC29204 United States

This study proposes the design and analysis of an eight-way power divider for unequal division at 5.3 GHz for C-band frequency. Many transmission line pieces make up the current power divider. These transmission lines were printed on a substrate material (RT/duroid 5880, 1.6 mm thick, permittivity 2.2, and loss tangent 0.0002). The eight-way power divider’s unequal amplitudes are constructed using Taylor’s 1-parameter method to achieve the side projection levels –17 dB down as from the main beam. The proposed power divider consists of using two transmission lines with a 3/4 frequency connected to branched microstriplines, providing excellent isolation and matching between the power divider’s two output ports. This power divider is composed of three stages. A 1:2 power divider is used in the first stage, a 1:4 power divider is used in the second stage, and a 1:8 power divider is used in the third stage. According to the simulation results, 1:2 delivers half of the input power, 1:4 produces one-fourth of the power, and 1:8 delivers one-eighth of the input power. All of these assessments were done with the advanced design system (ADS) simulation program. According to both simulated and measured data, the return loss exceeds –35 dB, and the isolation across the output terminals exceeds –40 dB. The proposed circuit operates effectively at C-band frequencies in terms of return loss and isolation (4 GHz and 8 GHz). © 2025 by Begell House, Inc.

关键词： Integrated circuit design

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Benchmarking Semantic communications for Image Transmission Over MIMO Interference Channels 16

Benchmarking Semantic Communications for Image Transmission ...

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16th International Conference on Wireless communications and Signal Processing, WCSP 2024

作者： Wang, Yanhu Guo, Shuaishuai Dong, Anming Zhao, Hui School of Control Science and Engineering Shandong University China School of Computer Science and Technology Qilu University of Technology China Eurecom Communication Systems Department France

ISBN: (纸本)9798350390643

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-tonoise (SNR) regimes. © 2024 IEEE.

关键词： Radio transceivers

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Biosignal Sequence Real-time Prediction for Game Users Based on Features Fusion of Local-Global and Time-Frequency Domain

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IEEE Transactions on Games 2025年

作者： Li, Rongyang Ding, Jianguo Ning, Huansheng Mao, Lingfeng Hebei Normal University of Science and Technology China Department of Computer Science Blekinge Institute of Technology Sweden School of Computer and Communication Engineering University of Science and Technology Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing China

Biosignal sequence real-time prediction (BSRP) is essential for predicting the future emotional experience of game users. However, BSRP for game users faces challenges, including poor real-time performance and limited feature fusion dimensions. To address these issues, we proposed a method for BSRP based on the features fusion of Local-Global and Time-Frequency domain (LGTF) for game users, which integrates real-time capabilities with multi-dimensional features fusion. Specifically, LGTF meets real-time requirements and achieves the features fusion of Local-Global through multi-channel synchronized adaptive convolution. In addition, LGTF implements the features fusion of inter- and intra-band in the frequency domain and the features fusion of time-frequency domain by incorporating the Self-Attention mechanism and Fourier Transform. Furthermore, we conducted comprehensive validation experiments on LGTF using the public dataset. The results indicate that: 1) In the comparison study, LGTF outperformed other methods, achieving the lowest average MSE and MAE values across different prediction lengths of 0.61 and 0.47, respectively. 2) Ablation studies revealed that the addition of time-frequency domain feature fusion (TF) and local-global feature fusion (LG) both have the positive effect on the prediction performance, reducing the average MSE by 0.11 and 0.09, respectively. 3) Generalization study shows that LGTF exhibits stable performance and generalization across different subjects and shows performance advantages in specific game scenarios. 4) Time performance analysis suggests LGTF has the real-time performance.5) Case study demonstrates that LGTF is practical for predicting game users' future emotions and enhancing their emotional experiences. © 2018 IEEE.

关键词： Frequency domain analysis

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Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking

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computers, Materials & Continua 2023年第9期76卷 3131-3150页

作者： Arif Hussain Magsi Ghulam Muhammad Sajida Karim Saifullah Memon Zulfiqar Ali State Key Laboratory ofNetworking and Switching Technology Beijing University of Post and TelecommunicationBeijing100876China Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK

Recent advancements in the Vehicular Ad-hoc Network(VANET)have tremendously addressed road-related ***,Named Data Networking(NDN)in VANET has emerged as a vital technology due to its outstanding ***,the NDN communication framework fails to address two important *** current NDN employs a pull-based content retrieval network,which is inefficient in disseminating crucial content in Vehicular Named Data Networking(VNDN).Additionally,VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous *** various solutions have been proposed for detecting vehicles’behavior,they inadequately addressed the challenges specific to *** deal with these two issues,we propose a novel push-based crucial content dissemination scheme that extends the scope of VNDN from pullbased content retrieval to a push-based content forwarding *** addition,we exploitMachine Learning(ML)techniques within VNDN to detect the behavior of vehicles and classify them as attackers or *** trained and tested our system on the publicly accessible dataset Vehicular Reference Misbehavior(VeReMi).We employed fiveML classification algorithms and constructed the bestmodel for illusion attack *** results indicate that RandomForest(RF)achieved excellent accuracy in detecting all illusion attack types in VeReMi,with an accuracy rate of 100%for type 1 and type 2,96%for type 4 and type 16,and 95%for type ***,RF can effectively evaluate the behavior of vehicles and identify attacker vehicles with high *** ultimate goal of our research is to improve content exchange and ***,ourML-based attack detection and preventionmechanismensures trustworthy content dissemination and prevents attacker vehicles from sharing misleading information in VNDN.

关键词： Named data networking vehicular networks pull-push illusion attack machine learning

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Data Security Storage Mechanism Based on Blockchain Network

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computers, Materials & Continua 2023年第3期74卷 4933-4950页

作者： Jin Wang WeiOu Wenhai Wang RSimon Sherratt Yongjun Ren Xiaofeng Yu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Control Science and Engineering Zhejiang UniversityHangzhou310058China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China School of Business Nanjing UniversityNanjing210093China

With the rapid development of information technology,the development of blockchain technology has also been deeply *** performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage *** the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the *** traditional blockchain system uses theMerkle Tree method to store *** verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and *** order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication *** realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.

关键词： Blockchain cryptographic commitment smart contract data storage

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Lung sound disease detection using attention over pre-trained efficientnet architecture

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Multimedia Tools and Applications 2024年 1-35页

作者： Nair, Anuja Vadher, Himanshu Patel, Pal Vyas, Tarjni Bhatt, Chintan Bruno, Alessandro Department of Computer Science & amp Engineering School of Technology Nirma University Gujarat Ahmedabad382481 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382007 India Department of Business Law Economics Consumer Behaviour ’Carlo A. Ricciardi’ Faculty of Communication IULM University Milan20143 Italy

Air pollution has been on the rise for quite a while now and with it is the increasing number of cases involving respiratory diseases. These respiratory diseases range from the mild ones to the most severe ones. Therefore, we need to detect these diseases. The most efficient way of checking the health of the lung is through the use of lung sounds. But the lung sounds carry with them the minute variations that a doctor or any human sometimes misses. To tackle the same, this paper proposes a deep learning model that detects a person’s lung disease by processing lung sounds. The ICBHI 2017 dataset comprising the lung sound audios and their corresponding labels is used to train the proposed model. The dataset consists of lung sounds and some additional background noise has been removed using the bandpass Butterworth filter, whose frequency range has been set to 250Hz - 2000Hz. Later, the amount of data is multiplied through data augmentation for audio signals. The augmented data is later converted to mel spectrograms, which are, in turn, fed into the deep learning model. The model that gives the best accuracy is EfficientNet-B0 + attention with a training accuracy of 99.72%, validation accuracy of 99.82%, precision, recall and f1-scores of 99.82% as well. Furthermore, a comparison of the pre-trained model’s training and inference times has been performed. Although the proposed model takes a significant amount of training time, it takes the least amount of inference time compared to the other pre-trained models that have been tested. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Butterworth filters

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A pilot allocation method for multi-cell multi-user massive MIMO system

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Journal of Systems engineering and Electronics 2021年第2期32卷 399-407页

作者： LI Yiming DU Liping CHEN Yueyun School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Wireless and Space Communication Laboratory Shunde Graduate School of University of Science and Technology BeijingFoshan 528000China

Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output(MIMO)*** paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral *** pilots of the terminals are allocated onebit orthogonal identifier to diminish the cell categories by the operation of exclusive OR(XOR).At the same time,the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm,and different pilot sequences are assigned to different *** simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.

关键词： massive multiple input multiple output(MIMO) pilot allocation cell classification users grouping(UG) spectral efficiency

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From Social Media to Ballot Box:Leveraging Location-Aware Sentiment Analysis for Election Predictions

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computers, Materials & Continua 2023年第12期77卷 3037-3055页

作者： Asif Khan Nada Boudjellal Huaping Zhang Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media ***,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and *** light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral *** achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country *** predicts the support and opposing strength of each political party/*** determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/*** sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case *** applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state ***,at the country level,the LEPM model outperforms traditional polling *** a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other *** the extensive social media data,the LEPM model provides nuanc

关键词： Sentiment analysis big data machine learning election predictions social media analysis

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Leveraging IR based sequence and graph features for source-binary code alignment 4

Leveraging IR based sequence and graph features for source-b...

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4th International Conference on Neural Networks, Information and communication engineering, NNICE 2024

作者： Yu, Zhouqian Zhang, Wei Xu, Tao Beijing Information Science and Technology University Computer School Beijing China Beijing Information Science and Technology University Beijing Laboratory of National Economic Security Early-Warning Engineering Beijing China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350394375

Code similarity analysis is a versatile technique that can be applied across various domains, including code clone detection, code search, malware detection, patch analysis, and vulnerability search. The core of source-binary code similarity analysis lies in effective modeling of the both of source code and binary code. Existing researches have overlooked the fact that code has both text and graph structure features, so they usually only use one of these features for modeling. In this paper, we propose a method that combines sequence features based on intermediate representation and program graph features derived from intermediate representation to enhance the capture of program semantics. Our approach involves the parallel embedding and learning of LLVM IR and the derived program graphs, leading to the extraction of the final code feature representation. Furthermore, we employ a triplet-loss network to identify code characteristic disparities and produce similarity rankings. The experimental results have demonstrated that our approach outperforms other models on the baseline, achieving a 6% improvement in accuracy for source-binary code similarity tasks. © 2024 IEEE.

关键词： Graph neural networks

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