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Mixed RF/FSO Communication systems With Rate Splitting

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1621-1634页

作者： Zhang, Yiran Xiao, Zhichen Yang, Liang Bithas, Petros S. Hasna, Mazen O. Karagiannidis, George K. Hunan University College of Computer Science and Electronic Engineering Changsha410082 China National and Kapodistrian University of Athens Department of Digital Industry Technologies Athens157 72 Greece Qatar University Department of Electrical Engineering Doha2713 Qatar Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki541 24 Greece Artificial Intelligence and Cyber Systems Research Center Beirut0379-7751 Lebanon

In this paper, rate splitting (RS) has been applied in a mixed dual-hop radio frequency/free space optical (RF/FSO) relay system. In particular, assuming fixed-gain amplify-and-forward (AF) and decode-and-forward (DF) relay protocols, the performance of both heterodyne detection and intensity-modulated direct detection (IM-DD) has been analytically evaluated. In this framework, it is assumed that the RF channel suffers from Rayleigh fading, while the FSO channel is affected by the atmospheric turbulence-induced fading with Málaga (M) distribution and pointing errors. For the considered system, we derive expressions for the outage probability, the average bit error rate (BER), and the average channel capacity. In addition, the asymptotic performance of the system at high signal-to-noise ratios regime is analyzed, special cases are discussed, and useful insights have been extracted. Finally, numerical results are presented to verify the accuracy of the analytical and asymptotic expressions. © 1983-2012 IEEE.

关键词： Heterodyne detection

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Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks 41

Provable Multi-Task Representation Learning by Two-Layer ReL...

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41st International Conference on Machine Learning, ICML 2024

作者： Collins, Liam Hassani, Hamed Soltanolkotabi, Mahdi Mokhtari, Aryan Shakkottai, Sanjay Department of Electrical and Computer Engineering University of Texas at Austin AustinTX United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States

An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear layer of the network. This approach yields strong downstream performance in a variety of contexts, demonstrating that multitask pretraining leads to effective feature learning. Although several recent theoretical studies have shown that shallow NNs learn meaningful features when either (i) they are trained on a single task or (ii) they are linear, very little is known about the closer-to-practice case of nonlinear NNs trained on multiple tasks. In this work, we present the first results proving that feature learning occurs during training with a nonlinear model on multiple tasks. Our key insight is that multi-task pretraining induces a pseudo-contrastive loss that favors representations that align points that typically have the same label across tasks. Using this observation, we show that when the tasks are binary classification tasks with labels depending on the projection of the data onto an r-dimensional subspace within the d rdimensional input space, a simple gradient-based multitask learning algorithm on a two-layer ReLU NN recovers this projection, allowing for generalization to downstream tasks with sample and neuron complexity independent of d. In contrast, we show that with high probability over the draw of a single task, training on this single task cannot guarantee to learn all r ground-truth features. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

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computers, Materials & Continua 2023年第1期74卷 1819-1835页

作者： Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil Radzi Ambar Ahmed Abdu Alattab Anwar Ali Yahya Yousef Asiri Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu Pahat86400JohorMalaysia Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran61441Saudi Arabia Department of Computer Science Faculty of Computer Science and Info.SystemsThamar UniversityDhamar87246Yemen Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

关键词： Cardiac short-axis MRI images automatic left ventricle localization deep learning models faster R-CNN

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Airstacknet: A Stacking Ensemble-Based Approach for Air Quality Prediction

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computers, Materials & Continua 2023年第1期74卷 2073-2096页

作者： Amel Ksibi Amina Salhi Ala Saleh Alluhaidan Sahar A.El-Rahman Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairoEgypt

The quality of the airwe breathe during the courses of our daily lives has a significant impact on our health and well-being as ***,personal air quality measurement remains *** this study,we investigate the use of first-person photos for the prediction of air *** main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution *** consists of two layers and four regression models,where the first layer generates meta-data fromLight Gradient Boosting Machine(Light-GBM),Extreme Gradient Boosting Regression(XGBoost)and CatBoost Regression(CatBoost),whereas the second layer computes the final prediction from the meta-data of the first layer using Extra Tree Regression(ET).The performance of the proposed AirStackNet model is validated using public Personal Air Quality Dataset(PAQD).Our experiments are evaluated using Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Coefficient of Determination(R2),Mean Squared Error(MSE),Root Mean Squared Logarithmic Error(RMSLE),and Mean Absolute Percentage Error(MAPE).Experimental Results indicate that the proposed AirStackNet model not only can effectively improve air pollution prediction performance by overcoming the Bias-Variance tradeoff,but also outperforms baseline and state of the art models.

关键词： Personal air quality prediction airstacknet ensemble learning feature extraction stacking

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ATOM: An Automatic Topology Synthesis Framework for Operational Amplifiers

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 1193-1198页

作者： Shen, Jinyi Yang, Fan Shang, Li Yan, Changhao Bi, Zhaori Zhou, Dian Zeng, Xuan Fudan University State Key Laboratory of Integrated Chips and Systems School of Microelectronics Shanghai200433 China Fudan University State Key Laboratory of Integrated Chips and Systems The School of Computer Science Shanghai200433 China Fudan University State Key Laboratory of Integrated Chips and Systems Microelectronics Department Shanghai200433 China University of Texas at Dallas Department of Electrical Engineering DallasTX75080 United States

Bayesian optimization (BO) is more efficient in automatically synthesizing operational amplifier (opamp) topologies compared to conventional methods. However, the design space for behavior-level opamp topologies involves numerous connections that are difficult to comprehend, and evaluating each topology incurs substantial computational costs. To tackle these challenges, this brief introduces ATOM, an automatic opamp topology synthesis framework. We construct a concise design space for behavior-level opamp topologies, consisting of topologies that designers can easily understand. We propose an opamp topology optimization method that incorporates freeze-thaw BO. This method efficiently explores the design space and expedites the evaluation process. Experimental studies demonstrate that ATOM outperforms state-of-the-art topology synthesis methods in terms of success rate and optimization results while reducing the number of required simulations by up to 8.15 times. The source code for ATOM is available at https://***/Jinyi-Shen/ATOM. © 2024 IEEE.

关键词： Thawing

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Techno-economic analysis of green hydrogen as an energy-storage medium for commercial buildings

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Clean Energy 2023年第1期7卷 84-98页

作者： Rahul Rajeevkumar Urs Assia Chadly Ameena Al Sumaiti Ahmad Mayyas Department of Industrial and Systems Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Electrical Engineering and Computer Science Advanced Power and Energy CenterKhalifa UniversityAbu Dhabi 127788UAE

Green-hydrogen production is vital in mitigating carbon emissions and is being adopted *** its transition to a more diverse energy mix with a bigger share for renewable energy,United Arab Emirates(UAE)has committed to investing billions of dollars in the production of green *** study presents the results of the techno-economic assessment of a green-hydrogen-based commercial-building microgrid design in the *** microgrid has been designed based on the building load demand,green-hydrogen production potential utilizing solar photovoltaic(PV)energy and discrete stack reversible fuel cell electricity generation during non-PV *** the current market conditions and the hot humid climate of the UAE,a performance analysis is derived to evaluate the technical and economic feasibility of this *** study aims at maximizing both the building microgrid’s independence from the main grid and its renewable *** results indicate that the designed system is capable of meeting three-quarters of its load demand independently from the main grid and is supported by a 78%renewable-energy *** economic analysis demonstrates a 3.117-$/kg levelized cost of hydrogen production and a 0.248-$/kWh levelized cost for storing hydrogen as ***,the levelized cost of system energy was found to be less than the current utility costs in the *** analysis shows the significant impact of the capital cost and discount rate on the levelized cost of hydrogen generation and storage.

关键词： fuel cell electrolyser green hydrogen microgrid photovoltaic

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A deep decentralized privacy-preservation framework for online social networks

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Blockchain(Research and Applications) 2024年第4期5卷 163-174页

作者： Samuel Akwasi Frimpong Mu Han Emmanuel Kwame Effah Joseph Kwame Adjei Isaac Hanson Percy Brown School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212013China Department of Computer Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Electrical&Electronic Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Computer Science and Information Systems Ashesi UniversityAccra3042Ghana Department of Telecommunication Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana

This paper addresses the critical challenge of privacy in Online Social Networks(OSNs),where centralized designs compromise user *** propose a novel privacy-preservation framework that integrates blockchain technology with deep learning to overcome these *** methodology employs a two-tier architecture:the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm(ePSOGSA)for optimizing feature selection,while the second tier employs an enhanced Non-symmetric Deep Autoencoder(e-NDAE)for anomaly ***,a blockchain network secures users’data via smart contracts,ensuring robust data *** tested on the NSL-KDD dataset,our framework achieves 98.79%accuracy,a 10%false alarm rate,and a 98.99%detection rate,surpassing existing *** integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.

关键词： Preprocessing Privacy-preservation Blockchain Deep learning Online social network

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A review on simulation models of cascading failures in power systems

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iEnergy 2023年第4期2卷 284-296页

作者： Zhenping Guo Kai Sun Xiaowen Su Srdjan Simunovic Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTNUSA Department of Electrical and Computer Engineering University of Texas at San AntonioSan AntonioTXUSA Computational Sciences&Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

Among various power system disturbances,cascading failures are considered the most serious and extreme threats to grid operations,potentially leading to significant stability issues or even widespread power *** power systems’behaviors during cascading failures is of great importance to comprehend how failures originate and propagate,as well as to develop effective preventive and mitigative control *** intricate mechanism of cascading failures,characterized by multi-timescale dynamics,presents exceptional challenges for their *** paper provides a comprehensive review of simulation models for cascading failures,providing a systematic categorization and a comparison of these *** challenges and potential research directions for the future are also discussed.

关键词： Cascading failures power systems security and reliability simulation models

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Federated Multi-armed Bandits Under Byzantine Attacks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1488-1501页

作者： Saday, Artun Demirel, Ilker Yildirim, Yigit Tekin, Cem Bilkent University Department of Electrical and Electronics Engineering Ankara06800 Turkey Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory CambridgeMA02139 United States

Multi-armed bandits (MAB) is a sequential decision-making model in which the learner controls the trade-off between exploration and exploitation to maximize its cumulative reward. Federated multi-armed bandits (FMAB) is an emerging framework where a cohort of learners with heterogeneous local models play a MAB game and communicate their aggregated feedback to a server to learn a globally optimal arm. Two key hurdles in FMAB are communication-efficient learning and resilience to adversarial attacks. To address these issues, we study the FMAB problem in the presence of Byzantine clients who can send false model updates threatening the learning process. We analyze the sample complexity and the regret of β-optimal arm identification. We borrow tools from robust statistics and propose a median-of-means (MoM)-based online algorithm, Fed-MoM-UCB, to cope with Byzantine clients. In particular, we show that if the Byzantine clients constitute less than half of the cohort, the cumulative regret with respect to β-optimal arms is bounded over time with high probability, showcasing both communication efficiency and Byzantine resilience. We analyze the interplay between the algorithm parameters, a discernibility margin, regret, communication cost, and the arms' suboptimality gaps. We demonstrate Fed-MoM-UCB's effectiveness against the baselines in the presence of Byzantine attacks via experiments. © 2020 IEEE.

关键词： Federated learning

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Emergent classical spin liquid phases in an Ising lattice via size effects

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Physical Review B 2024年第2期109卷 L020303-L020303页

作者： Pratik Brahma Sayeef Salahuddin Department of Electrical Engineering and Computer Sciences University of California Berkeley California 94720 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA

We show that a classical spin liquid phase can emerge from an ordered magnetic state in the two-dimensional frustrated Shastry-Sutherland Ising lattice due to lateral confinement. Two distinct classical spin liquid states are stabilized: (i) long-range spin-correlated dimers, and (ii) exponentially decaying spin-correlated disordered states, depending on widths of W=3n, 3n+1 or W=3n+2,n being a positive integer. Stabilization of spin liquids in a square-triangular lattice moves beyond the conventional geometric paradigm of kagome, triangular, or tetrahedral arrangements of antiferromagnetic ions, where spin liquids have been discussed conventionally.

关键词： Phase diagrams Spin liquid Classical spin models Lattice types Ising model Transfer matrix calculations

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