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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sirigineedi, Manikanta Sowmiya, M. Hemavath, I.U. Bharathi, V.C. Karmode, Sayali S. Sathya, R. Vishnu Institute of Technology Department of Information Technology Bhimavaram Andhra Pradesh Vishnupur India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science and Engineering Avadi600062 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Mahatma Gandhi Mission's College of Engineering & Technology Kamothe Maharashtra Navi Mumbai India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798331540364

This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon classification and the YOLO algorithm for real-time detection in live camera feeds. The proposed system generates email alerts to promptly inform users of detected weapons, enabling timely action to mitigate risks. By combining these advanced modules, the proposed system offers an effective solution for addressing weapon-related violence and improving security measures in various environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Radio Map Estimation via Latent-Domain Plug-and-Play Denoisers

Radio Map Estimation via Latent-Domain Plug-and-Play Denoise...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Le Xu Lei Cheng Junting Chen Wenqiang Pu Xiao Fu School of Electrical Engineering and Computer Science Oregon State University Corvallis USA College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Radio map estimation (RME) aims to construct a map of radio strength across multiple domains (e.g., space and frequency) from limited measurements. data-driven deep neural model-based RME showed promising performance, yet requiring excessive training resources. This work puts forth an RME approach that can effectively incorporate learned information without training over radio map data. Our idea is to employ the plug-and-play (PnP) denoising scheme from computational imaging. The PnP framework allows incorporating denoisers trained over natural images to handle other types of data, e.g., ocean sound fields and medical images, due to the similarity of their denoising processes. Conventional PnP methods mostly use the learned denoisers in the data domain. Instead, the proposed approach applies PnP in the latent domain through a tailored algorithm design and spatial-spectral factorization of radio maps. This way, the proposed RME method exhibits enhanced scalability and noise robustness. Simulations are used to illustrate the effectiveness of the proposed approach.

关键词： Training Computational modeling Scalability Oceans Signal processing algorithms Estimation Sea measurements Numerical simulation Robustness Speech processing

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LSTM-DFE equalizer for 25 Gbit/s/λ IM/DD Super-PON 20

LSTM-DFE equalizer for 25 Gbit/s/λ IM/DD Super-PON

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20th International Conference on Optical Communications and Networks, ICOCN 2022

作者： Zhang, Qianwu Zhi, Xuzhuang Zhan, Shucheng Zhang, Jing Wu, Yating Li, Zhengxuan Cao, Bingyao Shanghai Institute for Advanced Communication and Data Science Shanghai University Key Laboratory of Specialty Optics and Optical Access Networks Shanghai200444 China University of Electronic Science and Technology of China School of Communication and Information Engineering Sichuan Chengdu611731 China

A long short-Term memory-DFE equalizer for 25 \mathbf{Gbit}/\mathbf{s}/\lambda IM/DD Super-PON is proposed. Simulation results show that a 7 dB sensitivity improvement is obtained, compared with long short-Term memory... 详细信息

ISBN: (数字)9781665458986

ISBN: (纸本)9781665458986

关键词： Long short-term memory

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$ \tt {zkFL}$zkFL: Zero-Knowledge Proof-Based Gradient Aggregation for Federated Learning

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IEEE Transactions on Big data 2024年第2期11卷 447-460页

作者： Zhipeng Wang Nanqing Dong Jiahao Sun William Knottenbelt Yike Guo Department of Computing Imperial College London London U.K. Shanghai Artificial Intelligence Laboratory Shanghai China FLock.io London U.K. Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong SAR China Data Science Institute Imperial College London London U.K.

Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator. FL can be a scalable machine learning solution in Big data scenarios. Traditional FL relies on the trust assumption of the central aggregator, which forms cohorts of clients honestly. However, a malicious aggregator, in reality, could abandon and replace the client's training models, or insert fake clients, to manipulate the final training results. In this work, we introduce $ \tt {zkFL}$ , which leverages zero-knowledge proofs to tackle the issue of a malicious aggregator during the training model aggregation process. To guarantee the correct aggregation results, the aggregator provides a proof per round, demonstrating to the clients that the aggregator executes the intended behavior faithfully. To further reduce the verification cost of clients, we use blockchain to handle the proof in a zero-knowledge way, where miners (i.e., the participants validating and maintaining the blockchain data) can verify the proof without knowing the clients’ local and aggregated models. The theoretical analysis and empirical results show that $ \tt {zkFL}$ achieves better security and privacy than traditional FL, without modifying the underlying FL network structure or heavily compromising the training speed.

关键词： Blockchains Big data Training Computational modeling data models Privacy Arithmetic

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Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks

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IEEE Internet of Things Journal 2025年

作者： Yu, Hanzhi Liu, Yuchen Yang, Zhaohui Sun, Haijian Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT), which serves as a virtual representation of the physical network. The considered network includes a set of base stations (BSs) that must allocate its limited spectrum resources to serve a set of users while also transmitting its partially observed physical network information to a cloud server to generate the DNT. Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users. However, if the DNT does not receive the physical network information of the BSs over a large time period, the DNT's accuracy in representing the physical network may degrade. To this end, each BS must decide when to send the physical network information to the cloud server to update the DNT, while also determining the spectrum resource allocation policy for both DNT synchronization and serving the users. We formulate this resource allocation task as an optimization problem, aiming to maximize the total data rate of all users while minimizing the asynchronization between the physical network and the DNT. The formulated problem is challenging to solve by traditional optimization methods, as each BS can only observe a partial physical network, making it difficult to find an optimal spectrum allocation strategy for the entire network. To address this problem, we propose a method based on the gated recurrent units (GRUs) and the value decomposition network (VDN). The GRU component allows the DNT to predict future status using the historical data, effectively updating itself when the BSs do not transmit the physical network information. The VDN algorithm enables each BS to learn the relationship between its local observation and the team reward of all BSs, allowing it to collaborate with others in determining whe

关键词： Resource allocation

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Movable Superdirective Pairs: A Phase Shifter-Free Approach to mmWave Communications

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IEEE Wireless Communications Letters 2025年第6期14卷 1806-1810页

作者： Han, Liangcheng Yin, Haifan Gao, Mengying Zhang, Rui Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

In this letter, we propose a novel Movable Superdirective Pairs (MSP) approach that combines movable antennas with superdirective pair arrays to enhance millimeter-wave (mmWave) communications. By controlling the rotation angles and positions of superdirective antenna pairs via low-power actuators on the user side, the proposed MSP approach maximizes the received signal-to-noise ratio (SNR) of multipath signals without the need for phase shifters, thereby reducing hardware costs and insertion losses. An efficient algorithm based on alternating optimization and gradient projection is developed to solve the non-convex rotation–position design problem. Moreover, MSP is shown to be robust in both line-of-sight (LOS) and non-line-ofsight (NLOS) channels. Simulation results demonstrate that MSP achieves significant performance gains over fixed-position arrays, while maintaining low system complexity. © 2012 IEEE.

关键词： Microwave antennas

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Multiscale study of the dynamic friction coefficient due to asperity plowing

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Friction 2021年第4期9卷 822-839页

作者： Jianqiao HU Hengxu SONG Stefan SANDFELD Xiaoming LIU Yueguang WEI State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing 100190China School of Engineering Science University of Chinese Academy of SciencesBeijing 100049China Institute for Advanced Simulation IAS-9:Materials Data Science and Informatics Forschungszentrum Juelich GmbHJuelich 52425Germany Department of Mechanics and Engineering Science College of EngineeringPeking UniversityBeijing 100871China

A macroscopically nominal flat surface is rough at the nanoscale level and consists of ***,the frictional properties of the macroscale-level rough surface are determined by the mechanical behaviors of nanoasperity contact pairs under *** this work,we first used molecular dynamics simulations to study the non-adhesive shear between single contact ***,to estimate the friction coefficient of rough surfaces,we implemented the frictional behavior of a single contact pair into a Greenwood-Williamson-type statistical *** employing the present multiscale approach,we used the size,rate,and orientation effects,which originated from nanoscale dislocation plasticity,to determine the dependence of the macroscale friction coefficient on system parameters,such as the surface roughness,separation,loading velocity,and *** model predicts an unconventional dependence of the friction coefficient on the normal contact load,which has been observed in nanoscale frictional ***,this model represents one step toward understanding some of the relevant macroscopic phenomena of surface friction at the nanoscale level.

关键词： multiscale friction asperity plowing dislocation plasticity size/velocity effect crystal orientation statistical model

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Decoupled Sequential Power Flow Study in MT-MVDC Distribution Systems based on Novel NR/Estimation-Correction Algorithm 11

Decoupled Sequential Power Flow Study in MT-MVDC Distributio...

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Sun, Pingyang Wu, Rongcheng Li, Gen Khalid, Muhammad Town, Graham Konstantinou, Georgios Unsw School of Electrical Engineering and Telecommunications Sydney Australia University of Technology Data Science Institute Sydney Australia Dtu Department of Engineering Technology and Didactics Electric Energy Denmark King Fahd University of Petroleum & Minerals Electrical Engineering Department Saudi Arabia Macquarie University School of Engineering Australia

ISBN: (纸本)9788957083505

In order to address the gap in comprehensive power flow (PF) analysis of multi-terminal medium-voltage direct current (MT-MVDC) distribution systems, this paper proposes a novel decoupled sequential PF algorithm based on Newton-Raphson (NR)/estimation-correction method. The presented algorithm is designed for an MT-MVDC distribution network and takes into account the PF equations of both generic ac/dc and dc/dc converters. The MVDC PF study is the main focus and the power losses of voltage source converters (VSCs) & dc/dc converters are considered for accurately deriving the dc powers at MVDC links. The proposed algorithm only requires to define a single dc power or current bus type, hence the bus type definition is simplified and the solution of multiple sub-Jacobian matrices can be avoided. A binary search method is employed to correct the estimated initial dc power/current values, which contributes to the searching time reduction. Although an external correction iteration is required in the dc PF derivation, the proposed algorithm still improves on the computation efficiency in comparison with coupled sequential ac/dc PF algorithms. An MT-MVDC distribution network incorporated into IEEE 14 bus system verifies the validity and efficiency of the proposed algorithm. © 2023 The Korean institute of Power Electronics.

关键词： Electric power distribution

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A Fuzzy based CNN Method for Covid 19 Classification in Noisy Chest X-ray Images

A Fuzzy based CNN Method for Covid 19 Classification in Nois...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Mitra, Ankita Esakki Madura, E. Kiruba, B. Basker, N. Grace, N.Vini Antony Glory, E. Dr. B. C. Roy Engineering College Department of Ece Durgapur India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sona College of Technology Department of Cse Salem India R.M.D. Engineering College Department of Ece Kavaraipettai India Saveetha School of Engineering Department of Biosciences Chennai India

ISBN: (纸本)9798350315455

The COVID-19 pandemic has caused a global health crisis. Chest X-ray (CXR) imaging is most commonly utilized techniques for diagnosing COVID-19. However, accuracy of CXR-based diagnosis is limited by the presence of noisy labels. Noisy labels are labels that are incorrect or inaccurate. They can be caused by a variety of factors, such as human error, image artifacts, and variations in imaging protocols. In this paper, a novel method is introduced for COVID-19 CXR image classification in the presence of noisy labels. The primary objective of this research is to improve the accuracy of COVID-19 detection in X-ray images that are characterized by the presence of noise. To achieve this, proposed method incorporates direction-based fuzzy rules into our methodology, which allows to effectively identify and analyse edges and textures within the X-ray images. The labels are then subjected to undergone manifold learning and multidimensional scaling to eliminate noise. High confidence samples are further chosen as training set for label-recovered dataset in order to increase constancy and precision of a classification framework without compromising its intrinsic performance. The proposed method is evaluated on three publicly available COVID-19 CXR datasets. The results showed that our method can achieve an accuracy of 96.59% on the large dataset, 97.12% on the medium dataset, 98.79% on the small dataset. These results are comparable to the state-of-the-art methods for COVID-19 CXR image classification. This can help to improve the early diagnosis and treatment of COVID-19 patients. © 2023 IEEE.

关键词： chest X-ray COVID-19 Fuzzy membership noisy labels semisupervised learning

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Design and Implementation of Robust Multicast Trees for Lossy Wireless Ad-Hoc Networks 15

Design and Implementation of Robust Multicast Trees for Loss...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Preetha, M. Sohal, Jagmeet Sampangi Rama Reddy, B.R. Sharma, Nakul S. Hemelatha, S. Singh, Manpreet Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Department of Physics & Electronics Karnataka Bangalore India Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350370249

This paper affords a layout and implementation of a sturdy multicast tree technique for loss wireless advert-hoc networks. The proposed technique seeks to provide dependable delivery of statistics in dynamic environments where hyperlinks revel in excessive degrees of packet loss. This method is based on a mixture of spanning tree clustering, hyperlink-country routing, and acknowledgements-primarily based recuperation mechanisms, using re-transmissions and adaptive retransmission periods to catch up on packet loss. The usage of these combined techniques permits for minimal deterioration of verbal exchange performance because of packet losses, whilst retaining a green network layout. Simulations had been conducted to evaluate the overall performance of the proposed method in assessment to previous works. The effects showed that the proposed method outperformed the other techniques in terms of multicast message transport fee. The proposed approach is also proven to reduce overhead site visitors because of retransmissions while as compared to previous works. The proposed method affords a suitable and efficient solution for reliable multicasting in loss wireless ad-hoc networks. © 2024 IEEE.

关键词： Adaptive Affords Compared Conducted Periods Previous Retransmission Transport

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