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Fundamental Limits of Two-Hop MIMO Channels: An Asymptotic Approach

IEEE Transactions on Information Theory 2025年第6期71卷 4069-4111页

作者： Zhuang, Zeyan Zhang, Xin Xu, Dongfang Song, Shenghui The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong The Hong Kong University of Science and Technology Division of Integrative Systems and Design Hong Kong

Multi-antenna relays and intelligent reflecting surfaces (IRSs) have been utilized to construct favorable channels to improve the performance of wireless systems. A common feature between relay systems and IRS-aided systems is the two-hop multiple-input multiple-output (MIMO) channel. As a result, the mutual information (MI) of two-hop MIMO channels has been widely investigated with very engaging results. However, a rigorous investigation on the fundamental limits of two-hop MIMO channels, i.e., the first and second-order analysis, is not yet available in the literature, due to the difficulties caused by the two-hop (product) channel and the noise introduced by the relay (active IRS). In this paper, we employ large random matrix theory, specifically Gaussian tools, to derive the closed-form deterministic approximation for the mean and variance of the MI. Additionally, we determine the convergence rate for the mean, variance and the characteristic function of the MI, and prove the asymptotic Gaussianity. Furthermore, we also investigate the analytical properties of the fundamental equations that describe the closed-form approximation and prove the existence and uniqueness of the solution. An iterative algorithm is then proposed to obtain the solutions for the fundamental equations. Numerical results validate the accuracy of the theoretical analysis. © 1963-2012 IEEE.

关键词： Iterative methods

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Decentralized MIMO Systems With Imperfect CSI Using LMMSE Receivers

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IEEE Journal on Selected Topics in Signal Processing 2025年第2期19卷 331-348页

作者： Zhuang, Zeyan Zhang, Xin Xu, Dongfang Song, Shenghui Eldar, Yonina C. The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Division of Integrative Systems and Design Hong Kong Hong Kong The Weizmann Institute of Science Department of Mathematics and Computer Science Rehovot7610001 Israel

Centralized baseband processing (CBP) is required to achieve the full potential of massive multiple-input multiple-output (MIMO) systems. However, due to the large number of antennas, CBP suffers from two major issues: 1) Extensive data interconnection between radio frequency (RF) circuitry and the central processing unit;and 2) high-dimensional computation. To this end, decentralized baseband processing (DBP) has been proposed, where the antennas at the base station are partitioned into clusters connected to separate RF circuits and equipped with separate computing units. However, the optimal fusion scheme that maximizes signal-to-interference-and-noise ratio (SINR) and the related performance analysis for DBP with general spatial correlation and imperfect channel state information (CSI) have not been studied. In this paper, we consider a decentralized MIMO system where all clusters adopt linear minimum mean-square error (LMMSE) receivers. We first establish an optimal linear fusion scheme that has high computational and data input/output costs. To reduce the cost, we then propose two suboptimal fusion schemes with reduced complexity. For all three schemes, we study the SINR performance by leveraging random matrix theory and demonstrate conditions under which the suboptimal schemes are optimal. Furthermore, we determine the optimal regularization parameter for the LMMSE receiver, identify the best antenna partitioning strategy, and prove that the SINR will decrease as the number of clusters increases. Numerical simulations validate the accuracy of the theoretical results. © 2025 IEEE.

关键词： Mean square error

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Understanding, Implementing, and Supporting Security Assurance Cases in Safety-Critical Domains

arXiv

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arXiv 2025年

作者： Mohamad, Mazen Department of Computer Science & Engineering Division of Interaction Design and Software Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden

The increasing demand for connectivity in safety-critical domains has made security assurance a crucial consideration. In safety-critical industry, software, and connectivity have become integral to meeting market expectations. Regulatory bodies now require security assurance cases (SAC) to verify compliance, as demonstrated in ISO/SAE-21434 for automotive. However, existing approaches for creating SACs do not adequately address industry-specific constraints and requirements. In this thesis, we present CASCADE, an approach for creating SACs that aligns with ISO/SAE-21434 and integrates quality assurance measures. CASCADE is developed based on insights from industry needs and a systematic literature review. We explore various factors driving SAC adoption, both internal and external to companies in safety-critical domains, and identify gaps in the existing literature. Our approach addresses these gaps and focuses on asset-driven methodology and quality assurance. We provide an illustrative example and evaluate CASCADE's suitability and scalability in an automotive OEM. We evaluate the generalizability of CASCADE in the medical domain, highlighting its benefits and necessary adaptations. Furthermore, we support the creation and management of SACs by developing a machine-learning model to classify security-related requirements and investigating the management of security evidence. We identify deficiencies in evidence management practices and propose potential areas for automation. Finally, our work contributes to the advancement of security assurance practices and provides practical support for practitioners in creating and managing SACs. Copyright © 2025, The Authors. All rights reserved.

关键词： Automotive industry

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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Predicting compressive strength of sustainable concrete using advanced AI models: DLNN, RF, and MARS

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Asian Journal of Civil engineering 2025年第5期26卷 1939-1954页

作者： Wagh, Monali Waghmare, Charuta Gudadhe, Amit Thakur, Nisha Mohammed, Salah J. Algburi, Sameer Majdi, Hasan Sh. Ansari, Khalid Department of Civil Engineering Yeshwantrao Chavan College of Engineering Nagpur India Computer Science and Design Division Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha India Department of Civil Engineering Bajaj Institute of Technology Wardha India Civil Engineering Department Dijlah University College Baghdad Iraq Al-Kitab University Kirkuk 36015 Iraq Department of Chemical Engineering and Petroleum Industries Al-Mustaqbal University College Babylon 51001 Iraq

Recycled aggregate is becoming a sustainable construction resource that minimizes the carbon footprint in concrete structures. To predict the behavior of environmentally friendly (EnF) concrete in sustainable construction, it is necessary to predict the compressive strength using recycled materials accurately. The current research highlights the performance of the Deep Learning Neural Network (DLNN), Random Forests (RFs), and Multivariate Adaptive Regression Splines (MARS) are evaluated and extensive analysis of data segmentation was performed by splitting the dataset used in this study into 75–25% as well as 80–20% training/testing scenarios using Extreme Gradient Boosting (XG Boost), a quantitative measurement of the effect of data segmentation on model efficiency. The combination of AI models with Extreme Gradient Boosting (XG Boost) was employed to ascertain the governing variables on the CS prediction. Numerous statistical models developed were used to compare the effectiveness of these given models showing the best performance of the DLNN model based on the least RMSE (2.93). The results found that more variables should be added to the prediction problem for better prediction accuracy and the data split of 80–20% was the best choice. Based on the high accuracy of models, the results demonstrated that over the other established models, the DLNN model surpasses them in the analysis of concrete behavior and is useful for future applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Compressive strength prediction Deep learning neural network Multivariate adaptive regression splines Random forests Recycled aggregate XG Boost

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Identifying Critical Points of Departure for the design of Self-Fashioning Technologies 25

Identifying Critical Points of Departure for the Design of S...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Rebeca Blanco Cardozo Rachael Garrett Mafalda Samuelsson-Gamboa Kashyap Haresamudram Dominika Lisy Maria Rogg Claudia Núñez-Pacheco Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Interaction Design CSE Chalmers University of Technology University of Gothenburg Gothenburg Sweden Department of Technology and Society Lund University Lund Sweden Department of Thematic Studies Division of Gender Studies Linköping University Linköping Sweden Department for Informatics and Media Uppsala University Uppsala Sweden Department of Computer Science and Media Technology Malmö University Malmö Sweden

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Enhanced Glaucoma Detection Using U-Net and U-Net+ Architectures Using Deep Learning Techniques

SSRN

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SSRN 2025年

作者： Kumar, B.P. Pradeep Rangaiah, Pramod K.B. Augustine, Robin Department of Computer Science and Design Atria Institute of Technology Bengaluru560024 India Microwaves in Medical Engineering Group Division of Solid State Electronics Department of Electrical Engineering Uppsala University Box 65 UppsalaSE-751 03 Sweden

This study compares multiple image processing and deep learning methods to demonstrate an enhanced approach to glaucoma diagnosis. The approach focuses on noise reduction using median filtering and optic disc segmentation utilizing the U-Net and U-Net+ architectures. Capsule Networks were utilized for feature extraction and Extreme Learning Machines (ELM) for diagnostic classification. Three datasets were evaluated, including DRISHTI-GS, DRIONS-DB, and HRF, utilizing important parameters such as accuracy, sensitivity, and specificity. The findings revealed that median filtering reduced noise by 97.88\%, with a peak signal-to-noise ratio of 44.99. U-Net beat U-Net+ in optic disc in the process of segmentation with a Dice coefficient of 0.8557, a Jaccard index of 0.7307, and higher segmentation accuracy. The suggested model has great diagnostic accuracy, scoring 99\% for DRISHTI-GS, 99.5\% for DRIONS-DB, and 98.5\% for HRF. These findings show that using deep learning approaches can increase glaucoma diagnosis accuracy and reliability, with important implications for healthcare applications and patient outcomes. © 2025, The Authors. All rights reserved.

关键词： Median filters

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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Secure-by-Construction Synthesis for Control Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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Challenging Futures: Using Chatbots to Reflect on Aging and Dementia 25

Challenging Futures: Using Chatbots to Reflect on Aging and ...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Rucha Khot Teis Arets Joel Wester Franziska Burger Niels van Berkel Rens Brankaert Wijnand IJsselsteijn Minha Lee Industrial Design Eindhoven University of Technology Eindhoven Netherlands Human-Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Department of Computer Science Aalborg University Aalborg Denmark Industrial Design Engineering University of Ghent Ghent Belgium Industrial Design Eindhoven University of Technology Eindhoven Netherlands Tranzo Tilburg University Tilburg Netherlands and Fontys School of Applied Sciences Eindhoven Netherlands Human Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Industrial Design Eindhoven University of Technology Eindhoven Netherlands and Centrum Wiskunde & Informatica Amsterdam Netherlands

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