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arXiv 2023年

作者： Saad-Falcon, Alex Howard, Christopher Romberg, Justin Allen, Kenneth Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Georgia Tech Research Institute Advanced Concepts Laboratory AtlantaGA30318 United States

Global optimization techniques are increasingly preferred over human-driven methods in the design of electromagnetic structures such as metasurfaces, and careful construction and parameterization of the physical structure is critical in ensuring computational efficiency and convergence of the optimization algorithm to a globally optimal solution. While many design variables in physical systems take discrete values, optimization algorithms often benefit from a continuous design space. This work demonstrates the use of level set functions as a continuous basis for designing material distributions for metasurface arrays and introduces an improved parameterization which is termed the periodic level set function. We explore the use of alternate norms in the definition of the level set function and define a new pseudo-inverse technique for upsampling basis coefficients with these norms. The level set method is compared to the fragmented parameterization and shows improved electromagnetic responses for two dissimilar cost functions: a narrowband objective and a broadband objective. Finally, we manufacture an optimized level set metasurface and measure its scattering parameters to demonstrate real-world performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Parameterization

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Multi-Agent Reinforcement Learning for Covert Semantic Communications over Wireless Networks

Multi-Agent Reinforcement Learning for Covert Semantic Commu...

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

作者： Yining Wang Ye Hu Hongyang Du Tao Luo Dusit Niyato Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical Engineering Columbia University New York NY USA School of Computer Science and Engineering (SCSE) NTU Singapore

In this paper, a covert semantic communication framework is proposed for image transmission over wireless networks. In the proposed framework, devices extract and selectively transmit semantic information of image data to a base station (BS). The semantic information consists of the objects in the image and a set of attributes of each object. A warden selects a device to detect and eavesdrops the semantic information. To ensure the security of semantic communications, a jammer, acts as the defender, requires to find a vulnerable device and transmits jamming signals to the vulnerable device. The metric to measure the performance of the covert semantic communications is defined as the difference in the average accuracy of the BS and the warden answering a set of questions for each image. To maximize the performance of covert semantic communications, each device and the jammer must jointly optimize their transmit power, determine the vulnerable device to be protected, and determine the partial semantic information that each device needs to transmit. To solve this problem, we propose a multi-agent policy gradient (MAPG) algorithm. The proposed algorithm enables each device and the jammer to cooperatively discover the vulnerable devices as well as find the semantic information transmission and power control policies that maximize the performance of the covert semantic communication system. Simulation results show that the proposed algorithm can improve the communication performance by up to 14.5% compared to the independent reinforcement learning.

关键词： Performance evaluation Wireless networks Simulation Semantics Signal processing algorithms Reinforcement learning Signal processing

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Characterizations of Second-order Nonlinear Optical Susceptibility for ϵ-phase Gallium Oxide

Characterizations of Second-order Nonlinear Optical Suscepti...

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2022 Compound Semiconductor Week, CSW 2022

作者： Zhou, Jingan Zhao, Xuan Xu, Rui Fu, Kai Li, Tao Xu, Mingfei He, Ziyi Robinson, Jacob T. Zhu, Hanyu Zhang, Xiaodong Zhao, Yuji Rice University Department of Electrical and Computer Engineering HoustonTX77005 United States Rice University Smalley-Curl Institute Applied Physics Graduate Program HoustonTX77005 United States Rice University Department of Materials Science and NanoEngineering HoustonTX77005 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85287 United States Suzhou Institute of Nano-tech and Nano-bionics CAS Key Laboratory of Nanodevices and Applications Suzhou215123 China

ISBN: (数字)9781665453400

ISBN: (纸本)9781665453400

In this work, we report the second-order nonlinear optical susceptibility χ(2) for epsilon phase Gallium Oxide (ϵ-Ga2O3) thin film on sapphire. ϵ-Ga2O3 exhibits hexagonal P63mc space group symmetry, which is a non-centrosymmetric structure with non-zero second order susceptibility. By focusing a pulsed laser beam on to a polished ϵ-Ga2O3 thin film, we collected the generated second harmonic photons with an ultra-sensitive femtowatt photodetector, obtaining effective second-order nonlinear optical susceptibility of χ(2) = 4.89×10-3 pm/V at wavelength of 800 nm. Two different measurement systems collecting reflected and transmitted photons were applied separately to make the result more convincing. The wavelength dependence from 790 nm to 900 nm and polarization dependence from TM mode to TE mode were measured as well. These results will be helpful for the design and fabrication of Ga2O3 based integrated photonics platform in the infrared-visible spectral range. © 2022 IEEE.

关键词： Gallium compounds

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Multi-Task Multi-Scale Feature Selection for Point Cloud Registration

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wu, Yue Luo, Chuang Gong, Maoguo Ding, Hangqi Sheng, Jinlong Miao, Qiguang Li, Hao Ma, Wenping He, Hao Xidian University School of Computer Science and Technology Xi'an710071 China Xidian University School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xi'an710071 China Inner Mongolia Normal University College of Mathematics Science Hohhot010028 China Xidian University School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xi'an710071 China

3D point cloud registration is a process of solving the geometric transformation between two point clouds. This process is an important issue in computer vision and pattern recognition. The registration methods based on geometric features are highly sensitive to the scale of feature extraction. Changes in scale can introduce inaccuracies in feature descriptions, thereby compromising the reliability of the registration results. To mitigate the impact of feature scale on the outcomes and the high-dimensional issue arising from features of different scales, we propose a method for multi-scale point cloud feature selection. We solve the high-dimensional problem of feature selection by designing a multi-task framework. By designing a mutual information dimensionality reduction method, we decomposed the high-dimensional feature selection task of different descriptors with multi-scale features into multiple related low-dimensional feature selection tasks. Then, by means of the knowledge transfer among these low-dimensional feature selection tasks, we sought the best feature subset to obtain more robust feature information. We evaluate the effectiveness of our method by conducting extensive experiments on various datasets. The experimental results show that the method outperforms other feature descriptors in terms of descriptive power and robustness and improves the effectiveness of point cloud registration. © 1997-2012 IEEE.

关键词： Feature Selection

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NOMA Cooperative Spectrum Sharing Average Capacity Improvement in 5G Network

NOMA Cooperative Spectrum Sharing Average Capacity Improveme...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mohamed Hassan Rashid A. Saeed Rania A. Mokhtar Khalid Hamid Elmustafa Sayed Ali Nidhal Odeh Manwinder Singh Othman O. Khalifa Shayla Islam Department of Wireless Communication Lovely Professional University Phagwara Punjab India Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Communication Systems Engineering University of Science & Technology Omdurman Sudan Department of Electronics Engineering Sudan University of Science and Technologu (SUST) Khartoum Sudan School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia & Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Department of Computer Science Institute of Computer Science and Digital Innovation UCSI University Malaysia Kuala Lumpur Malaysia

One of the most effective approaches to satisfying the requirements of the next generation of wireless services is non-orthogonal multiple access (NOMA). Spectrum efficiency and average capacity are two aspects of wireless communication that have become increasingly important as the number of mobile services in use has increased. Using both traditional MIMO and Massive MIMO (M-MIMO), this study proposes two new methods for increasing the average capacity of a 5G network's downlink (DL) NOMA power domain (PD) in conjunction with a Cooperative Cognitive Radio Network (CCRN). The initial method involves NOMA users competing for available rivalry channels (R-CH) on the CCRN. The second technique creates a dedicated channel (D-CH) for NOMA users. The MATLAB program is utilized to evaluate the proposed methods in three scenarios with varying distances, power location coefficients, and transmission power. Successive interference cancellation (SIC) and unstable channel conditions are also considered while assessing the analysis of the proposed system under the assumption of Rayleigh fading. Using DL NOMA PD systems, The results reveal that the higher average capacity of best at transmit power DL NOMA with CCRN (dedicated channel) improved U4 average capacity performance by M-MIMO DL NOMA with CCRN (D-CH) improved by when compared with convention DL NOMA. Formulas in closed form were found for average capacity. The resulting formulas are verified using a Monte Carlo simulation.

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Fast convergent on-board Li-ion battery state of charge estimation via back-forth moving horizon estimation

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IEEE Transactions on Power Electronics 2025年

作者： Liu, Zhihao Xiao, Yu Yuan, Yuan Xu, Xiaodong Luo, Biao Huang, Tingwen Ministry of Education School of Automation and Key Laboratory of Industrial Intelligence and Systems Central South University Changsha410083 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen China

Accurate state of charge (SOC) estimation is crucial for the safe operation of lithium-ion batteries (LIBs), yet existing methods are limited by sensitivity to initial SOC guess or high computational complexity. To address these challenges, this study proposes an onboard back-forth moving horizon estimation (BFMHE) framework for SOC estimation, achieving high precision, robustness, and computational efficiency compatible with microcontroller implementation. An efficient linear equivalent circuit model (ECM) is established, where the open-circuit voltage-SOC relationship is approximated by piecewise linear functions. The proposed framework employs two MHE estimators that iteratively operate in opposite directions over a finite time horizon, which enables rapid convergence. Moreover, this study provides a rigorous proof of the stability of the BFMHE method for the battery system with current input. The proposed approach is validated experimentally under various operating conditions and with different initial SOC guesses. Experimental results show that the proposed onboard BFMHE method achieves a maximum estimation error of less than 1% and quickly converges to the true SOC value, demonstrating strong robustness to initial estimation errors. © 1986-2012 IEEE.

关键词： Lithium-ion batteries

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Deep Linearization Mappings for Canonical Correlation Clustering

Deep Linearization Mappings for Canonical Correlation Cluste...

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Asilomar Conference on Signals, systems & computers

作者： Akshay Malhotra Ioannis D. Schizas João Morais InterDigital Communications Los Altos CA USA US Army Combat Capabilities Development Command US Army Research Laboratory MD USA School of Electrical Computer and Energy Engineering at Arizona State University Tempe AZ USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Kernelized canonical correlations analysis has been utilized as a means to carry out unsupervised data clustering. This work puts forth a method to obtain a non-linear mapping that transforms data to a feature space where the data has the potential to be more ‘linearly’ correlated. An auto encoder network is employed to minimize a reconstruction error and learn a proper non-linear mapping to transform the input data. The non-linear mapping found via the novel scheme goes beyond kernel functions and results in better clustering performance than linear and kernel-based canonical correlations as demonstrated by numerical tests conducted across two different data sets.

关键词： computers Correlation Transforms Kernel Standards

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Sample Complexity of Decentralized Tabular Q-Learning for Stochastic Games

Sample Complexity of Decentralized Tabular Q-Learning for St...

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American Control Conference (ACC)

作者： Zuguang Gao Qianqian Ma Tamer Başar John R. Birge Booth School of Business The University of Chicago Chicago USA Department of Electrical and Computer Engineering Boston University Boston USA Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana USA

In this paper, we carry out finite-sample analysis of decentralized Q-learning algorithms in the tabular setting for a significant subclass of general-sum stochastic games (SGs) – weakly acyclic SGs, which includes potential games and Markov team problems as special cases. In the practical while challenging decentralized setting, neither the rewards nor the actions of other agents can be observed by each agent. In fact, each agent can be completely oblivious to the presence of other decision makers. In this work, the sample complexity of the decentralized tabular Q-learning algorithm in [1] to converge to a Markov perfect equilibrium is developed.

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Developing Optimization-Based Inverter Models for Short Circuit Studies

arXiv

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arXiv 2023年

作者： Mabote, Thabiso R. Tabarez, Jose E. Barnes, Arthur K. Mate, Adam Bent, Russell W. Cotilla-Sanchez, Eduardo School of Electrical Engineering & Computer Science Oregon State University CorvallisOR United States Advanced Network Science Initiative Los Alamos National Laboratory Los AlamosNM United States

As inverter-based generation becomes more common in distribution networks, it is important to create models for use in optimization-based problems that accurately represent their non-linear behavior when saturated. This work presents models for grid-following and grid-forming inverters, and demonstrates their use in optimization-based fault studies. The developed models are shown to provide results in line with experimental tests from literature;however, grid-forming inverter models fail to provide feasible solutions for certain fault types even when currents, voltages, and powers from the models seem reasonable. This work lays down the foundation for the development of relaxed models for both grid-following and grid-forming inverter models for use in optimization problems. © 2023, CC BY.

关键词： Electric power distribution

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Combining Implicit-Explicit View Correlation for Light Field Semantic Segmentation

Combining Implicit-Explicit View Correlation for Light Field...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Ruixuan Cong Da Yang Rongshan Chen Sizhe Wang Zhenglong Cui Hao Sheng State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beihang Hangzhou Innovation Institute Yuhang Faculty of Applied Sciences Macao Polytechnic University

Since light field simultaneously records spatial information and angular information of light rays, it is considered to be beneficial for many potential applications, and semantic segmentation is one of them. The regular variation of image information across views facilitates a comprehensive scene understanding. However, in the case of limited memory, the high-dimensional property of light field makes the problem more intractable than generic semantic segmentation, manifested in the difficulty of fully exploiting the relationships among views while maintaining contextual information in single view. In this paper, we propose a novel network called LF-IENet for light field semantic segmentation. It contains two different manners to mine complementary information from surrounding views to segment central view. One is implicit feature integration that leverages attention mechanism to compute inter-view and intra-view similarity to modulate features of central view. The other is explicit feature propagation that directly warps features of other views to central view under the guidance of disparity. They complement each other and jointly realize complementary information fusion across views in light field. The proposed method achieves outperforming performance on both real-world and synthetic light field datasets, demonstrating the effectiveness of this new architecture.

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