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Single and Dual-Polarized 22 - 46 GHz Multi-Band Phased Arrays for 5G MIMO

IEEE Transactions on Antennas and Propagation 2025年

作者： Liang, Tian Wang, Shufan Alhamed, Abdulrahman Rebeiz, Gabriel M. University of California at San Diego Department of Electrical and Computer Engineering La Jolla CA92093 United States Extreme Waves Inc United States Computer Electrical and Mathematical Sciences and Engineering Division Saudi Arabia

This paper presents wideband single and dual-polarized phased-arrays for multiband 5G systems. The antenna element incorporates a dipole-fed split-ring multimode resonator (SRR), enabling continuous operation in the 22-46 GHz (N257-N261 bands). The antenna is fabricated using a low-cost printed-circuit board, which facilitates interconnection with the TX/RX beamformer. For demonstration, the single-polarized antenna is employed in an 8 × 8 phased array, while the dual-polarized antenna is implemented in a 4 × 2 phased array. Post-calibration measurements show that the single-polarized 8 × 8 phased array exhibits an EIRP of 42-48 dBm and a scanning capability exceeding 55° at 22-46 GHz, which is ideal for customer premises equipment (CPE) application. The dual-polarized phased array achieves a saturated EIRP of 23-32 dBm at 21-44 GHz for each polarization. The high polarization isolation supports 2 × 2 multiple-input multiple-output (MIMO) applications. System level measurements were conducted using 5G-NR and 64 QAM modulation at 400 Mbaud. The dipole-fed SRR antenna offers the broadest operational bandwidth among single and dual-polarized planar phased array designs, making it a strong candidate for multi-standard 5G applications. © 1963-2012 IEEE.

关键词： Antenna phased arrays

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Guest Editorial Frontiers in Computational Electromagnetics

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IEEE Transactions on Antennas and Propagation 2023年第12期71卷 9175-9177页

作者： Notaros, Branislav M. Andriulli, Francesco P. Bagci, Hakan Colorado State University Department of Electrical and Computer Engineering Fort CollinsCO80 523 United States Politecnico di Torino Department of Electronics and Telecommunications Turin10129 Italy Division Thuwal23955 Saudi Arabia

Computational electromagnetics (CEM) seeks numerical solutions to practical engineering problems involving electromagnetic fields and waves and their interactions with materials and designed structures and systems. While physical measurement of electromagnetic fields is often expensive and impractical, CEM develops cost-effective and efficient simulation tools to analyze, design, and optimize real-world devices, structures, and systems. Advancements in CEM rely on those in mathematical representation of physical problems, numerical foundations of methods and algorithms, and computing hardware and software infrastructure. Indeed, CEM research and practice demand the seamless but also the most advanced combination of advancements in engineering, physics, mathematics, and computer science, holding immense potential for transformative impact. CEM is one of the most challenging areas of computational science and engineering due to the inherent complexity of electromagnetic problems. Unlike problems in other disciplines, electromagnetic problems are truly 3-D, volumetric, and vector-based and involve radiation and interaction at a distance. © 1963-2012 IEEE.

关键词： Computational electromagnetics

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Effect of Signal Conditioning and Evoked-Potential Based Representation on Stability and Distinctiveness of EEG Brain Signatures

Effect of Signal Conditioning and Evoked-Potential Based Rep...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Muhammed Esad Oztemel Ömer Muhammet Soysal Division of Electrical and Computer Engineering Louisiana State University Southeastern Louisiana University Computer Science

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Biometric patterns have been used for authentication purposes for more than decades. Although traditional biometric measurements including fingerprints, facial recognition, and sound recognition are often used for authentication purposes, there is ongoing concern about the risks associated with their repeatability and susceptibility to theft. The utilization of electroencephalography (EEG) has promising potential thanks to the distinctive patterns extracted from brain signals. The main goal of this study is to explore the effectiveness of signal conditioning and evoked potentials on uniqueness and permanence of individuals' EEG signals for authentication purpose. We utilized the Laplacian of Gaussian as a signal conditioning operator. In addition, three signifying functions were explored in obtaining evoked potential time-domain representation from an individual EEG signal. Different stimuli protocols were employed for comparison. A time-series and a statistical technique were utilized for uniqueness and permanence analysis.

关键词： Electric potential Protocols Laplace equations Authentication Fingerprint recognition Electroencephalography Stability analysis

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Finite Sample Analysis for a Class of Subspace Identification Methods 63

Finite Sample Analysis for a Class of Subspace Identificatio...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： He, Jiabao Ziemann, Ingvar Rojas, Cristian R. Hjalmarsson, Hakan Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Pennsylvania PhiladelphiaPA19104 United States

ISBN: (纸本)9798350316339

While subspace identification methods (SIMs) are appealing due to their simple parameterization for MIMO systems and robust numerical realizations, a comprehensive statistical analysis of SIMs remains an open problem, especially in the non-asymptotic regime. In this work, we provide a finite sample analysis for a class of SIMs, which reveals that the convergence rates for estimating Markov parameters and system matrices are O(1 / SN), in line with classical asymptotic results. Based on the observation that the model format in classical SIMs is non-causal because of a projection step, we choose a parsimonious SIM that bypasses the projection step and strictly enforces a causal model to facilitate the analysis, where a bank of ARX models are estimated in parallel. Leveraging recent results from a finite sample analysis of an individual ARX model, we obtain a union error bound for an array of ARX models and proceed to derive error bounds for system matrices using robustness results for the singular value decomposition. © 2024 IEEE.

关键词： Singular value decomposition

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Proton‑Prompted Ligand Exchange to Achieve High‑Efficiency CsPbI_(3) Quantum Dot Light‑Emitting Diodes

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Nano-Micro Letters 2024年第6期16卷 53-62页

作者： Yanming Li Ming Deng Xuanyu Zhang Lei Qian Chaoyu Xiang Laboratory of Advanced Nano‑Optoelectronic Materials and Devices Qianwan Institute of CNITECHNingbo 315300People’s Republic of China Division of Functional Materials and Nanodevices Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201People’s Republic of China Zhejiang Provincial Engineering Research Center of Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology&EngineeringChinese Academy of SciencesNingbo 315201People’s Republic of China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211ZhejiangPeople’s Republic of China University of Nottingham Ningbo China Ningbo 315100People’s Republic of China

CsPbI_(3)perovskite quantum dots(QDs)are ideal materials for the next generation of red light-emitting ***,the low phase stability of CsPbI_(3)QDs and long-chain insulating capping ligands hinder the improvement of device *** in-situ ligand replacement and ligand exchange after synthesis were often difficult to ***,we proposed a new ligand exchange strategy using a proton-prompted insitu exchange of short 5-aminopentanoic acid ligands with long-chain oleic acid and oleylamine ligands to obtain stable small-size CsPbI_(3)*** exchange strategy maintained the size and morphology of CsPbI_(3)QDs and improved the optical properties and the conductivity of CsPbI_(3)QDs *** a result,high-efficiency red QD-based light-emitting diodes with an emission wavelength of 645 nm demonstrated a record maximum external quantum efficiency of 24.45%and an operational half-life of 10.79 h.

关键词： CsPbI_(3) perovskite quantum dots Light-emitting diodes Ligand exchange Proton-prompted in-situ exchange

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Explainable Multiple Receptive Attention Network for Expert Cardiologist Compatible Incomplete Kawasaki Disease Diagnosis on Echocardiography 1

Explainable Multiple Receptive Attention Network for Expert ...

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Lee, Kyungsu Lee, Haeyun Lee, Moon Hwan Yang, Jaeseung Kim, Sewoong Eun, Youngsoon Eun, Lucy Youngmin Hwang, Jae Youn Department of Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Republic of Production Engineering Research Team Samsung SDI Yongin Korea Republic of Division of Pediatric Cardiology Yonsei University College of Medicine Seoul Korea Republic of

ISBN: (纸本)9798350371987

Identifying incomplete Kawasaki disease (KD) has been challenging due to its atypical clinical manifestations. Differentiating it from other febrile illnesses, including COVID-19, is crucial for pediatric patients. Early detection of coronary artery abnormalities through echocardiographic examination is vital for accurate diagnosis and favorable outcomes. With the increased prevalence of KD among pediatric populations, there is a need for continued research and innovative diagnostic tools to improve early detection and management. To address this, we introduce a Multiple Receptive Attention Network (MRANet) incorporating a multi-receptive attention layer, designed to enhance the discrimination of incomplete KD from echocardiographic images, achieving better sensitivity and specificity. A total of 147 echocardiographic imaging datasets were utilized for training MRANet and other state-of-the-art deep learning models. The performance of MRANet was compared with compatible deep learning networks for evaluation. The results demonstrate that MRANet outperforms other advanced deep learning methodologies. MRANet’s performance is comparable to that of an experienced pediatric cardiologist in detecting coronary artery abnormalities for accurate KD diagnosis. This study highlights the potential of MRANet as a valuable tool for aiding early detection and management of complex conditions in medical imaging and computer vision. Further research and validation are warranted to establish MRANet as a reliable tool in pediatric cardiology practice. ©2024 IEEE.

关键词： Echocardiography

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A Quantum-Inspired Multi-objective Portfolio Strategy Based on Trend Ratio Model in Global Financial Network

A Quantum-Inspired Multi-objective Portfolio Strategy Based ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Chou, Yao-Hsin Lai, Yun-Ting Tong, Yong Feng Young, Alvin Chang, Ming-Ho Wu, Kun-Min Jiang, Yu-Chi Kuo, Shu-Yu National Chi Nan University Department of Computer Science and Information Engineering Puli Taiwan National Center for Theoretical Sciences Physics Division Taipei Taiwan Princeton University Department of Electrical and Computer Engineering NJ United States National Taiwan University Center for Quantum Science and Engineering Department of Physics Taipei Taiwan

ISBN: (纸本)9798350308365

Portfolio strategy search is an essential topic in the financial and economic networks. Various risk preferences of investors should be considered to accommodate their investment choices, thereby introducing multi-objective portfolio optimization. This study utilizes an innovative and accurate evaluation model, the trend ratio (TR), to assess return and risk and construct a multi-objective optimization (MOO) model. Within a huge search space, computational intelligence techniques achieve high-performance computing through effective nature-inspired computing. The proposed TR-based MOO quantum-inspired model with an entanglement-enhanced local search (EL) mech-anism can search for Pareto optimal solutions (POSs) in global investment markets. The experimental results demonstrate that the TR-based EL mechanism effectively achieves a larger number of POSs and improves efficient frontiers to meet the different risk profiles of investors across global markets. A visualized Pareto front (PF) of the TR model helps a user identify a more straightforward return and risk balance evaluation, helping investors make informed decisions. The entanglement-simulated local search mechanism has been proven to improve the search for a better PF significantly. The TR-based, quantum-inspired MOO portfolio strategy search has paved the way for flexible solutions in global financial networks. © 2024 IEEE.

关键词： Quantum entanglement

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Efficient data-driven occupancy detection in office environments and feature impact analysis

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International Journal of Information Technology (Singapore) 2024年第5期17卷 3013-3023页

作者： Fouzi, Harrou Ramakrishna, Kini K. Madakyaru, Muddu Ying, Sun Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Computer Thuwal 23955-6900 Saudi Arabia Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Chemical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India

Occupancy detection is crucial in optimizing building energy efficiency and enhancing occupant comfort. This study introduces an innovative data-driven approach for accurate occupancy detection in an office room environment. Specifically, the methodology combines the advantages of Independent Component Analysis (ICA) to extract essential features from multivariate data and Kantorovitch distance (KD)-based schemes for detection sensitivity. The KD scheme’s detection threshold is computed nonparametrically using kernel density estimation to enhance the sensitivity of occupancy detection. The efficacy of this strategy is evaluated utilizing publicly available data recorded during winter in Mons, Belgium, capturing vital environmental parameters such as temperature, humidity, light, and CO2 levels through specialized sensors. Results demonstrate that the ICA-KD approach achieves an averaged accuracy of 98.355%, surpassing conventional approaches like Principal Component Analysis (PCA)-based, ICA-based, and other state-of-the-art methods. Additionally, the study uses Shapley Additive exPlanations (SHAP) with XGBoost to explore the impact of input variables on occupancy detection, highlighting the influence of various factors under different testing conditions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Building occupancy Data-driven Environmental data Occupancy detection Semi-supervised detection

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Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems

arXiv

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arXiv 2024年

作者： Sabouni, Ehsan Sabbir Ahmad, H.M. Giammarino, Vittorio Cassandras, Christos G. Paschalidis, Ioannis Ch. Li, Wenchao Division of Systems Engineering Boston University BostonMA02215 United States The Department of Electrical and Computer Engineering The Division of Systems Engineering Boston University BostonMA02215 United States The Department of Electrical and Computer Engineering Division of Systems Engineering Department of Biomedical Engineering Boston University BostonMA02215 United States

Optimal control methods provide solutions to safety-critical problems but easily become intractable. Control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance *** approach involves defining a performance objective alongside CBF-based safety constraints that must always be enforced. Unfortunately, both performance and solution feasibility can be significantly impacted by two key factors such as the selection of the cost function and associated parameters, and the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness. To address these challenges, we propose a Reinforcement Learning (RL)-based Receding Horizon Control (RHC) approach leveraging Model Predictive Control (MPC) with CBFs (MPC-CBF). In particular, we parameterize our controller and use bilevel optimization, where RL is used to learn the optimal parameters while MPC computes the optimal control input. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles (CAVs) at conflicting roadways. Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches used for tuning CBF-based controllers, showcasing the effectiveness of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation

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IEEE Transactions on Communications 2025年

作者： Khan, Nasir Abdallah, Asmaa Celik, Abdulkadir Eltawil, Ahmed M. Coleri, Sinem Koc University department of Electrical and Electronics Engineering Istanbul Turkey King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Thuwal23955 Saudi Arabia

Artificial intelligence (AI) is expected to significantly enhance radio resource management (RRM) in sixth-generation (6G) networks. However, the lack of explainability in complex deep learning (DL) models poses a challenge for practical implementation. This paper proposes a novel explainable AI (XAI)-based framework for feature selection and model complexity reduction in a model-agnostic manner. Applied to a multi-agent deep reinforcement learning (MADRL) setting, our approach addresses the joint sub-band assignment and power allocation problem in cellular vehicle-to-everything (V2X) communications. We propose a novel two-stage systematic explainability framework leveraging feature relevance-oriented XAI to simplify the DRL agents. While the former stage generates a state feature importance ranking of the trained models using Shapley additive explanations (SHAP)-based importance scores, the latter stage exploits these importance-based rankings to simplify the state space of the agents by removing the least important features from the model’s input. Simulation results demonstrate that the XAI-assisted methodology achieves ~97% of the original MADRL sum-rate performance while reducing optimal state features by ~28%, average training time by ~11%, and trainable weight parameters by ~46% in a network with eight vehicular pairs. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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