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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Kim, Jungyeon Choi, Jinseok Park, Jeonghun Alkhateeb, Ahmed Lee, Namyoon Pohang University of Science and Technology Department of Electrical Engineering Pohang Korea Republic of Korea Advanced Institute of Science and Technology School of Electrical Engineering Daejeon Korea Republic of Yonsei University School of Electrical and Electronic Engineering Seoul Korea Republic of Arizona State University School of Electrical Computer and Energy Engineering AZ United States Korea University School of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350351255

In frequency-division duplexing (FDD) multipleinput multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) becomes challenging due to the tremendous feedback overhead that increases with the number of antennas. Using uplink pilots to estimate downlink CSI is a promising approach that can eliminate the need for CSI feedback, but its accuracy decreases significantly as the number of channel paths increases. In this paper, we propose a mean square error (MSE)-optimal downlink channel reconstruction method that jointly utilizes uplink CSI and limited downlink CSI. With the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for channel phase quantization and show the optimal feedback bit allocation method to minimize the MSE. Harnessing robust downlink precoding, we demonstrate that the proposed downlink channel reconstruction is sufficient to achieve a sum-spectral efficiency comparable to that with perfect downlink CSI. © 2024 IEEE.

关键词： Mean square error

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Microring Resonator based Photonic Circuits as Limited Precision Differential Equation Solvers

Microring Resonator based Photonic Circuits as Limited Preci...

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2024 IEEE Photonics Conference, IPC 2024

作者： Abdelghany, Mahmoud On, Mehmet Berkay Charalampous, Georgios Lee, Yun-Jhu Wang, Ji Nasirov, Aslan Bai, Zhaojun Oskay, Caglar Ben Yoo, S.J. University of California Davis Department of Electrical and Computer Engineering DavisCA95616 United States University of California Davis Department of Computer Science DavisCA95616 United States Vanderbilt University Department of Civil and Environmental Engineering NashvilleTN37235-1831 United States

ISBN: (纸本)9798350361957

We propose programmable photonic integrated circuits based on microring resonator crossbar arrays, which implement the Jacobi method as an iterative differential equation solver. We investigate the precision of the scalar multiplication unit experimentally, simulate the matrix multiplication numerically, and discuss methods to improve the precision. © 2024 IEEE.

关键词： Photonic integrated circuits

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Energy Efficient Probabilistic Semantic Communication over SAGIN

Energy Efficient Probabilistic Semantic Communication over S...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe Gan, Xu Huang, Chongwen Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Hangzhou China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Southeast University National Mobile Communications Research Laboratory Nanjing China

ISBN: (纸本)9798350351255

In this paper, the energy efficiency maximization problem in space-air-ground integrated network (SAGIN)-enabled probabilistic semantic communication (PSC) is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitted data, while the GTs can automatically recover the original data. In the considered PSC system, shared probability graphs serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Therefore, it is important to study the trade-off between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, semantic compression ratio, and UAV location constraints. To solve this non-convex problem, we propose an alternating algorithm. Numerical results show the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Emergence of composite many-body exciton states in WS2 and MoSe2 monolayers

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Physical Review B 2024年第4期109卷 L041304-L041304页

作者： J. Choi J. Li D. Van Tuan H. Dery S. A. Crooker National High Magnetic Field Laboratory Los Alamos New Mexico 87545 USA Advanced Instrumentation Institute Korea Research Institute of Standards and Science Daejeon 34113 Korea Wuhan National High Magnetic Field Center and School of Physics Huazhong University of Science and Technology Hubei 430074 China Department of Electrical and Computer Engineering University of Rochester Rochester New York 14627 USA Department of Physics and Astronomy University of Rochester Rochester New York 14627 USA

When doped with a high density of mobile charge carriers, monolayer transition-metal dichalcogenide (TMD) semiconductors can host new types of composite many-particle exciton states that do not exist in conventional semiconductors. Such multiparticle bound states arise when a photoexcited electron-hole pair couples not to just a single Fermi sea that is quantum-mechanically distinguishable (as in the case of conventional charged excitons or trions), but rather couples simultaneously to multiple Fermi seas, each having distinct spin and valley quantum numbers. Composite six-particle “hexciton” states were recently identified in electron-doped WSe2 monolayers, but under suitable conditions they should also form in all other members of the monolayer TMD family. Here we present spectroscopic evidence demonstrating the emergence of many-body hexcitons in charge-tunable WS2 monolayers (at the A-exciton) and MoSe2 monolayers (at the B-exciton). The roles of distinguishability and carrier screening on the stability of hexcitons are discussed.

关键词： Excitons Trions Transition metal dichalcogenides Two-dimensional electron system

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SF_(6)decomposition and insulation condition monitoring of GIE:A review

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High Voltage 2021年第6期6卷 955-966页

作者： Fuping Zeng Haotian Li Hongtu Cheng Ju Tang Yilu Liu School of Electrical Engineering and Automation Wuhan UniversityWuhanChina Department of Electrical Engineering and Computer Science USA Oak Ridge National Laboratory KnoxvilleTennesseeUSA

The statistics of the Conference International des Grands Reseaux Electriques(CIGRE)indicate that the operational reliability of SF6 gas‐insulated equipment(GIE)is very high;however,the failure rate of the GIE in operation is much higher than that of the IEC standard,and the fault occurs frequently in the GIE at a high voltage *** reason is due to the complex and strong on‐site electromagnetic interference environment and fully enclosed structure of *** key method and technology for effective on‐line monitoring and fault diagnosis of GIE are still *** the partial strong electromagnetic energy and high temperature induced by early latent insulation faults in the equipment,SF6 gas insulation presents different degrees of *** decomposition products mainly include SO_(2)F_(2),SOF_(2),SO_(2),HF,and *** decomposition characteristics of SF6 are closely related to the property of insulation *** present,this area is attracting attention from the power industry and research *** study summarises the current research on SF_(6)decomposition component analysis(DCA).The content mainly includes the latest progress of SF_(6)decomposition characteristics and mechanism under fault conditions,and fault diagnosis methods based on decomposition components.

关键词： insulation fault diagnosis

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Online Container Scheduling for Data-intensive Applications in Serverless Edge Computing 42

Online Container Scheduling for Data-intensive Applications ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shang, Xiaojun Mao, Yingling Liu, Yu Huang, Yaodong Liu, Zhenhua Yang, Yuanyuan Stony Brook University Department of Electrical and Computer Engineering United States Shenzhen University College of Computer Science and Software Engineering Shenzhen China Stony Brook University Department of Applied Mathematics and Statistics United States

ISBN: (纸本)9798350334142

Introducing the emerging serverless paradigm into edge computing could avoid over- and under-provisioning of limited edge resources and make complex edge resource management transparent to application developers, which largely facilitates the cost-effectiveness, portability, and short time-to-market of edge applications. However, the computation/data dispersion and device/network heterogeneity of edge environments prevent current serverless computing platforms from acclimating to the network edge. In this paper, we address such challenges by formulating a container placement and data flow routing problem, which fully considers the heterogeneity of edge networks and the overhead of operating serverless platforms on resource-limited edge servers. We design an online algorithm to solve the problem. We further show its local optimum for each arriving container and prove its theoretical guarantee to the optimal offline solution. We also conduct extensive simulations based on practical experiment results to show the advantages of the proposed algorithm over existing baselines. © 2023 IEEE.

关键词： Edge computing

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A Transfer Learning Approach for Neurodegenerative Disease Classification from Brain MRI Images: Distinguishing Alzheimer's, Parkinson's, and Control Cases 6

A Transfer Learning Approach for Neurodegenerative Disease C...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Siddiqua, Ayesha Oni, Atib Mohammad Miah, Md. Jarez Northern University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Electrical & Electronic Engineering Dhaka Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798350385779

Like many other critical medical conditions, different neurogenerative diseases, including Alzheimer's and Parkinson's diseases, need to get diagnosed in the primary stage. Deep learning algorithms show excellent performance in detecting neurodegenerative diseases from images obtained by magnetic resonance imaging (MRI) of the human brain. Recently, transfer learning has also shown promising outcomes in the classification of neurological conditions using brain MRI data. Here, we examine the efficacy of the transfer learning strategy utilizing four distinct CNN architectures, namely EfficientNetB0, ResNet50, InceptionV3,and Xception. Used dataset for the study has three classes;Parkinson's disease (PD), Alzheimer's disease (AD), and control (healthy). The study compares the accuracy, precision, recall, and F1- score metrics for the investigated CNN models. The result demonstrates that the EfficientNetB0 model shows the best training and testing accuracy, reaching an accuracy as high as 99.4%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Learning Skill-based Industrial Robot Tasks with User Priors 18

Learning Skill-based Industrial Robot Tasks with User Priors

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18th IEEE International Conference on Automation science and engineering, CASE 2022

作者： Mayr, Matthias Hvarfner, Carl Chatzilygeroudis, Konstantinos Nardi, Luigi Krueger, Volker Department of Computer Science LundSE 221 00 Sweden Greece Stanford University Department of Computer Science and Electrical Engineering CA94305 United States

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Nevertheless, given their prior experience, robot operators should be able to help the learning process further by providing educated guesses about where in the parameter space potential optimal solutions could be found. Interestingly, such prior knowledge is not exploited in current robot learning frameworks. We introduce an approach that combines user priors and Bayesian optimization to allow fast optimization of robot industrial tasks at robot deployment time. We evaluate our method on three tasks that are learned in simulation as well as on two tasks that are learned directly on a real robot system. Additionally, we transfer knowledge from the corresponding simulation tasks by automatically constructing priors from well-performing configurations for learning on the real system. To handle potentially contradicting task objectives, the tasks are modeled as multi-objective problems. Our results show that operator priors, both user-specified and transferred, vastly accelerate the discovery of rich Pareto fronts, and typically produce final performance far superior to proposed baselines. © 2022 IEEE.

关键词： Learning systems

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm With Particle Swarm Optimization K-Means for Banking Data 6

Performance Comparison of the Invasive Weed Optimization K-M...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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Deep Learning Techniques for Enhancing Near-Field Spatially Varying Channels In Wireless Communications

Deep Learning Techniques for Enhancing Near-Field Spatially ...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Srividhya, Ch. Sapthami, I. Nagpal, Amandeep Pachauri, Praveen Chandrashekar, Rakesh Jabbar, Ahmed Sabah Abed AL-Zahra Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Government Polytechnic Siwan India New Horizon College Of Engineering Bangalore India College Of Medical Technology The Islamic University Medical Laboratory Technology Department Najaf Iraq

ISBN: (纸本)9798350366846

In the ever-growing field of radio communications, better ways of modeling and boosting channels are needed, especially for channels that change location in the near field. Here, we present a new way to use deep learning to improve the performance of radio communication channels. Our plan, called the 'Proposed Method,' uses complex neural network structures to model and improve channel features. We tested the proposed method against six well-known ones, including Orthogonal Matching Pursuit, Maximum Likelihood Estimation, Least Squares, Compressive Sensing, Singular Value Decomposition, and Beamforming, to see how well it works. To rate how well something works, we use things like Mean Absolute Error (MAE), Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Computational Complexity, and Root Mean Squared Error (RMSE). The results show that the Proposed Method is better because it models and estimates channels more accurately and has lower RMSE, MAE, BER, and MSE. The Proposed Method also has a higher signal-to-noise ratio (SNR), which means the quality of the data is better. In addition, because it is simple to compute, it is a good answer for real-world problems. © 2024 IEEE.

关键词： Beamforming

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