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arXiv 2023年

作者： Li, Xuefei Chen, Mingzhe Liu, Yuchen Zhang, Zhilong Liu, Danpu Mao, Shiwen The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Computer Science North Carolina State University RaleighNC27695 United States The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

In this paper, the problem of joint communication and sensing is studied in the context of terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles (SPVs) provide either communication service or sensing service to target vehicles, where it is essential to determine 1) the service mode (i.e., providing either communication or sensing service) for each SPV and 2) the subset of target vehicles that each SPV will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of the communication target vehicles, while satisfying the sensing service requirements of the sensing target vehicles, by determining the service mode and the target vehicle association for each SPV. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle’s graph information related to its location, connection, and communication interference. Using this extracted graph information, a joint service mode selection and target vehicle association strategy is then determined to adapt to the dynamic vehicle topology with various vehicle types (e.g., target vehicles and service provider vehicles). Simulation results show that the proposed GNN-based scheme can achieve 93.66% of the sum rate achieved by the optimal solution, and yield up to 3.16% and 31.86% improvements in sum rate, respectively, over a homogeneous GNN-based algorithm and a conventional optimization algorithm without using GNNs. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Low-Complexity Joint Radar-Communication Beamforming: From Optimization to Deep Unfolding

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IEEE Journal on Selected Topics in Signal Processing 2025年

作者： Zhang, Jianjun Masouros, Christos Liu, Fan Huang, Yongming Swindlehurst, A. Lee Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China University College London Department of Electronic & Electrical Engineering LondonWC1E7JE United Kingdom Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of California at Irvine Center for Pervasive Communications and Computing IrvineCA92697 United States

By sharing the same hardware platform, spectral resource as well as transmit waveform, dual-functional radar-communication (DFRC) systems have been envisioned as a key technology for the future wireless networks. However, advanced signal processing algorithms for DFRC, which can achieve better performance, tradeoff or other design goals, often suffer from prohibitive computational complexity. This motivates us to design low-complexity joint radar sensing and communication beamforming algorithms in this paper, so as to achieve better energy efficiency, communication-sensing tradeoff, and so on. First, we formulate the problem of joint radar-communication beamforming based on symbol-level precoding (SLP) by incorporating constructive interference so as to improve the energy efficiency. To address the formulated problem, we tailor highly parallelizable iterative optimization algorithms that are shown to converge to stationary (or locally optimal) points. To achieve better performance, we propose efficient recursive optimizations that monotonically improve the performance metric of interest. Simulation results indicate that the proposed iterative algorithms outperform the previous approaches. Finally, to further reduce the complexity, we employ deep unfolding to design efficient learning-based algorithms. Besides parallelizability, the learning-based algorithms also enjoy appealing advantages of scalability in the number of served users, the number of transmit antennas and the length of the radar pulse. © 2007-2012 IEEE.

关键词： Radar interference

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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On-chip beam rotators,adiabatic mode converters,and waveplates through low-loss waveguides with variable cross-sections

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Light(science & Applications) 2022年第8期11卷 1876-1890页

作者： Bangshan Sun Fyodor Morozko Patrick S.Salter Simon Moser Zhikai Pong Raj B.Patel Ian A.Walmsley Mohan Wang Adir Hazan Nicolas Barré Alexander Jesacher Julian Fells Chao He Aviad Katiyi Zhen-Nan Tian Alina Karabchevsky Martin J.Booth Department of Engineering Science University of OxfordOxfordOX13PJUK School of Electrical and Computer Engineering Ben-Gurion University of the NegevP.O.B.653Beer-Sheva8410501Israel Institute of Biomedical Physics Medical University of InnsbruckMüllerstraße 446020InnsbruckAustria Ultrafast Quantum Optics group Department of PhysicsImperial College LondonLondonUK Department of Physics University of OxfordOxfordUK Erlangen Graduate School in Advanced Optical Technologies(SAOT) Friedrich-Alexander-University Erlangen-NürnbergPaul-Gordan-Straße 691052ErlangenGermany State Key Laboratory of Integrated Optoelectronics College of Electronic Science and EngineeringJilin UniversityChangchun130012China

Photonics integrated circuitry would benefit considerably from the ability to arbitrarily control waveguide cross-sections with high precision and low loss,in order to provide more degrees of freedom in manipulating propagating ***,we report a new method for femtosecond laser writing of optical-fiber-compatible glass waveguides,namely spherical phase-induced multicore waveguide(SPIM-WG),which addresses this challenging task with three-dimensional on-chip light *** in the heating regime with high scanning speed,precise deformation of cross-sections is still achievable along the waveguide,with shapes and sizes finely controllable of high resolution in both horizontal and vertical transversal *** observed that these waveguides have high refractive index contrast of 0.017,low propagation loss of 0.14 dB/cm,and very low coupling loss of 0.19 dB coupled from a single-mode ***-WG devices were easily fabricated that were able to perform on-chip beam rotation through varying angles,or manipulate the polarization state of propagating light for target *** also demonstrated SPIM-WG mode converters that provide arbitrary adiabatic mode conversion with high efficiency between symmetric and asymmetric nonuniform modes;examples include circular,elliptical modes,and asymmetric modes from ppKTP(periodically poled potassium titanyl phosphate)waveguides which are generally applied in frequency conversion and quantum light *** inside optical glass,these waveguides and devices have the capability to operate across ultra-broad bands from visible to infrared *** compatibility with optical fiber also paves the way toward packaged photonic integrated circuitry,which usually needs input and output fiber connections.

关键词： waveguide waveguides polarization

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A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks

arXiv

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arXiv 2025年

作者： Li, Dong Wan, Guihong Wu, Xintao Wu, Xinyu Nirmal, Ajit J. Lian, Christine G. Sorger, Peter K. Semenov, Yevgeniy R. Zhao, Chen Department of Computer Science Baylor University TX United States Department of Dermatology Massachusetts General Hospital Harvard Medical School MA United States Laboratory of Systems Pharmacology Harvard Medical School MA United States Department of Electrical Engineering and Computer Science University of Arkansas AR United States Department of Dermatology Brigham and Women’s Hospital Harvard Medical School MA United States Department of Pathology Brigham and Women’s Hospital Harvard Medical School MA United States

Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, such as limited data accessibility, high variability across datasets, the necessity for domain-specific adaptation, and the lack of standardized evaluation benchmarks. This survey provides a comprehensive review of CPathFMs in computational pathology, focusing on datasets, adaptation strategies, and evaluation tasks. We analyze key techniques, such as contrastive learning and multi-modal integration, and highlight existing gaps in current research. Finally, we explore future directions from four perspectives for advancing CPathFMs. This survey serves as a valuable resource for researchers, clinicians, and AI practitioners, guiding the advancement of CPathFMs toward robust and clinically applicable AI-driven pathology solutions. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

Collaborative Trolley Transportation System with Autonomous ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Bingyi Xia Hao Luan Ziqi Zhao Xuheng Gao Peijia Xie Anxing Xiao Jiankun Wang Max Q.-H. Meng Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China School of Computing National University of Singapore Singapore Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering Department of Electrical and Computer Engineering Chinese University of Hong Kong in Hong Kong University of Alberta Canada

Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments. (Video 1 1 Video demonstration: https://***/efnPERm0Rco.)

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RECO-LFSR: Reconfigurable Low-power Cryptographic processor based on LFSR for Trusted IoT platforms

RECO-LFSR: Reconfigurable Low-power Cryptographic processor ...

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IEEE International Symposium on Quality Electronic Design

作者： Mohamed El-Hadedy Russell Hua Kazutomo Yoshii Wen-Mei Hwu Martin Margala Department of Electrical and Computer Engineering California State Polytechnic University Pomona USA Coordinated Science Laboratory University of Illinois at Urbana-Champaign USA College of Computing University of Louisiana at Lafayette Louisiana USA Argonne National Laboratory Lemont Illinois USA

Today we see lightweight computer hardware utilized in large volumes, especially with the growing use of IoT devices in homes. However, such devices often ignore security until it is too late and sensitive data breaches have *** here, the importance of finding lightweight cryptographic primitives to secure IoT devices is exponentially increasing, while not impacting the limited resources and limitation of the battery lifetime. In the search for a lightweight cryptographic standard, one must consider how to implement such algorithms optimally. For example, certain parts of an algorithm might be faster in hardware than in software and vice *** paper presents a hardware extension supporting the MicroBlaze softcore processor to efficiently implement one of the Lightweight Cryptography (LWC) finalists (TinyJAMBU) on Digilent Nexys A7-100T. The proposed hardware extension consists of a reconfigurable Non-Linear Feedback Shift Register (NLFSR), the central computing part for the authenticated encryption with associated data (AEAD) TinyJAMBU. The proposed NLFSR can run different variants of TinyJAMBU while only consuming 186 mWh in just ten minutes at 100 MHz. The total resources needed to host the proposed NLFSR on the FPGA are 610 LUT and 505 Flip-Flops while executable the binary size is 352 bytes smaller. Therefore, the proposed solution based on the hardware extension is x2.17 times faster than the pure software implementation of the whole TinyJAMBU using MicroBlaze while consuming six mWh more. To our knowledge, this is the first implementation of TinyJAMBU using software/hardware partitioning on FPGA with the softcore processor MicroBlaze.

关键词： Microprocessors Software algorithms Shift registers Hardware Software Table lookup Internet of Things

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BLTESTI: Benchmarking Lightweight TinyJAMBU on Embedded Systems for Trusted IoT

BLTESTI: Benchmarking Lightweight TinyJAMBU on Embedded Syst...

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IEEE International SOC Conference

作者： Mohamed El-Hadedy Russell Hua Shahzman Saqib Kazutomo Yoshii Wen-Mei Hwu Martin Margala Department of Electrical and Computer Engineering California State Polytechnic University Pomona USA Coordinated Science Laboratory University of Illinois at Urbana-Champaign USA College of Computing University of Louisiana at Lafayette Louisiana USA Argonne National Laboratory Lemont Illinois USA

Embedded systems face an increasing number of attacks, necessitating improved security measures. However, the physical constraints of these devices often limit the use of traditional cryptographic techniques. To address this challenge, NIST organized a competition focused on lightweight cryptographic algorithms (LWC) tailored for embedded systems, aiming to minimize resource usage in low-power devices. This paper benchmarks the Rust, C, and Golang programming languages, considering their advantages in memory safety, portability, and concurrency, to implement the LWC algorithm finalist named TinyJAMBU. TinyJAMBU utilizes the non-linear feedback shift register (NLFSR) model as a core security element. The evaluation is conducted on embedded systems and FPGAs to determine the optimal strategy for mapping LWC cryptographic algorithms onto resource-constrained devices. Among the three languages, the C-based implementation achieves the highest runtime efficiency, while TinyGo demonstrates the lowest power consumption. Rust falls between the two, indicating trade-offs associated with each language. Despite its larger memory footprint of 73kB, the C-based implementation of TinyJAMBU outperforms other language implementations, achieving a faster runtime of ≈ 81 seconds for the same algorithm and 4-byte message size. Golang exhibits the smallest memory footprint of 19kB, while Rust’s memory footprint falls between the other two at 66kB. Furthermore, implementing TinyJAMBU on an FPGA using software-hardware partitioning with MicroBlaze takes ≈ 6.30 seconds to complete 10,000 encryption and decryption cycles, consuming around 1.09W of power. Although employing a proposed NLFSR co-processor with MicroBlaze for the 128-bit version of TinyJAMBU may not be the fastest approach, the reconfigurable nature of FPGAs allows for replicating the co-processor multiple times, offering significant advantages over the current state-of-the-art architecture of embedded systems.

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Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

arXiv

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arXiv 2023年

作者： Xia, Bingyi Luan, Hao Zhao, Ziqi Gao, Xuheng Xie, Peijia Xiao, Anxing Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada School of Computing National University of Singapore Singapore

— Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments. (Video1) Copyright © 2023, The Authors. All rights reserved.

关键词： Multipurpose robots

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Dual Bidirectional Copy-Paste with Shape Constraint for Semi-Supervised Image Segmentation

Dual Bidirectional Copy-Paste with Shape Constraint for Semi...

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IEEE International Symposium on Biomedical Imaging

作者： Bowei Shen Xiaoquan Huang Yonghong Shi Shiyao Chen School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Department of Gastroenterology and Hepatology Zhongshan Hospital Fudan University Shanghai China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Semi-supervised learning utilizes unlabeled data and a small amount of pixel-level labeled data to achieve image semantic segmentation. However, its performance largely depends on the semantic learning of the boundary details of the object in the image, as the uncertainty of the boundary region can reduce the segmentation accuracy. To this end, this paper proposes a novel semi-supervised learning method based on the student-teacher model, which is constrained by the dual bidirectional copy-paste and the signed distance map of the object shape. The method is carried out through two stages: fully supervised pre-training and semi-supervised re-training. The former uses labeled data to train and learn the initial student model, and its parameters are used to initialize and update the latter’s teacher model, which is used to segment unlabeled data to obtain its pseudo labels. At each stage, the dual bidirectional copy-paste between ‘labeled data’ enhances the data, while the similar copy-paste performed on the corresponding ‘label’ supervises the student model. Copy-paste allows the network to focus on the entire object, while the signed distance map on the label data helps to learn the complex shape changes of the object boundary. Especially in the re-training stage, the above calculation mainly occurs between labeled data and unlabeled data with 'pseudo label', which effectively bridges the empirical distribution mismatch between labeled data and unlabeled data, enabling the network to learn a more generalized data distribution. Our method was validated on two datasets, LA Heart and ACDC. Compared with other semi-supervised learning methods, our method outperforms previous works at 5% and 10% labeled data volumes, achieving the SOTA results.

关键词： Bridges Heart Uncertainty Shape Biological system modeling Semantic segmentation Semantics

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