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American Control Conference (ACC)

作者： Shivam Bajaj Shaunak D. Bopardikar Alexander Von Moll Eric Torng David W. Casbeer Department of Electrical and Computer Engineering Michigan State University Control Science Center Air Force Research Laboratory Department of Computer Science and Engineering Michigan State University

We consider a perimeter defense problem in a planar conical environment comprising a turret that has a finite range and non-zero startup time. The turret seeks to defend a concentric perimeter against N ≥ 2 intruders. Upon release, each intruder moves radially towards the perimeter with a fixed speed. To capture an intruder, the turret's angle must be aligned with that of the intruder's angle and must spend a specified startup time at that orientation. We address offline and online versions of this optimization problem. Specifically, in the offline version, we establish that in general parameter regimes, this problem is equivalent to solving a Travelling Repairperson Problem with Time Windows (TRP-TW). We then identify specific parameter regimes in which there is a polynomial time algorithm that maximizes the number of intruders captured. In the online version, we present a competitive analysis technique in which we establish a fundamental guarantee on the existence of at best (N – 1)-competitive algorithms. We also design two online algorithms that are provably 1 and 2-competitive in specific parameter regimes.

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RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection

arXiv

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arXiv 2025年

作者： Shi, Jianrui Zhao, Yong Cui, Zeyang Shen, Xiaoming Zeng, Minhang Liu, Xiaojie Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computing and Data Science The University of Hong Kong Hong Kong Pengcheng Laboratory Shenzhen China

Object detection plays a crucial role in smart video analysis, with applications ranging from autonomous driving and security to smart cities. However, achieving real-time object detection on edge devices presents significant challenges due to their limited computational resources and the high demands of deep neural network (DNN)-based detection models, particularly when processing high-resolution video. Conventional strategies, such as input down-sampling and network up-scaling, often compromise detection accuracy for faster performance or lead to higher inference latency. To address these issues, this paper introduces RE-POSE, a Reinforcement Learning (RL)Driven Partitioning and Edge Offloading framework designed to optimize the accuracy-latency trade-off in resource-constrained edge environments. Our approach features an RL-Based Dynamic Clustering Algorithm (RL-DCA) that partitions video frames into non-uniform blocks based on object distribution and the computational characteristics of DNNs. Furthermore, a parallel edge offloading scheme is implemented to distribute these blocks across multiple edge servers for concurrent processing. Experimental evaluations show that RE-POSE significantly enhances detection accuracy and reduces inference latency, surpassing existing methods. © 2025, CC BY.

关键词： Computation offloading

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An Architecture for Virtual Tethering of ROVs

An Architecture for Virtual Tethering of ROVs

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OCEANS

作者： Rajat Mishra Mandar Chitre Bharath Kalyan Yuen Min Too Hari Vishnu Luyuan Peng Acoustic Research Laboratory Tropical Marine Science Institute National University of Singapore Department of Electrical & Computer Engineering National University of Singapore

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

We introduce an architecture designed for wireless Remote Operated Vehicle (ROV) operations during intervention surveys. Using the recent advancements in 3D modeling, visual odometry, video compression, and underwater communication, our approach aims to facilitate fully wireless ROV operations. The proposed architecture comprises several modular blocks, each with versatile applications spanning diverse domains, including multiplayer gaming, remote rover control and video transmission over a low-bandwidth connection. This comprehensive framework signifies progress in ROV operations, replacing the traditional physical tether with a software-defined virtual tether.

关键词： Wireless communication Surveys Underwater communication Three-dimensional displays Oceans computer architecture Video compression

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A Feasibility Study on Novel View Synthesis of Underwater Structures using Neural Radiance Fields

A Feasibility Study on Novel View Synthesis of Underwater St...

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OCEANS

作者： Yuen Min Too Hari Vishnu Mandar Chitre Bharath Kalyan Luyuan Peng Rajat Mishra Acoustic Research Laboratory Tropical Marine Science Institute National University of Singapore Department of Electrical & Computer Engineering National University of Singapore

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

We explore Neural Radiance Fields (NeRFs) for synthesizing novel views of underwater structures. This learning-based approach relies on a sparse set of camera views to model the 3D geometry of underwater structures and scenes. Real-world underwater scenes exhibit significant temporal variations, introducing challenges in maintaining visual consistency. We investigate three NeRF implementations: 1) nerfacto, which rep-resents the baseline; 2) nerfacto with transient embed dings and 3) nerfacto with a robust loss, which are designed to deal with scene inconsistencies. We evaluate these implementations using datasets collected in 1) a controlled environment and 2) a real underwater setting. The modified implementations consistently outperform the original nerfacto across both datasets. The performance im-provements are particularly pronounced in the dataset obtained from the real underwater setting where scene inconsistencies are more prevalent. This underscores the importance of robustifying NeRF implementations to ensure consistent performance in the challenging underwater environments.

关键词： Underwater structures Visualization Solid modeling Three-dimensional displays Oceans Neural radiance field Cameras

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Robotic fish:Opening a new era of underwater detection

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The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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Advances and Open Challenges in Federated Foundation Models

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IEEE Communications Surveys and Tutorials 2025年

作者： Ren, Chao Yu, Han Peng, Hongyi Tang, Xiaoli Zhao, Bo Yi, Liping Tan, Alysa Ziying Gao, Yulan Li, Anran Li, Xiaoxiao Li, Zengxiang Yang, Qiang School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Sweden College of Computing and Data Science Nanyang Technological University Singapore College of Computer Nankai University China School of Medicine Yale University United States Digital Research Institute of Enn Group Langfang China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong WeBank Shenzhen China

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabilities, while addressing concerns of privacy, data decentralization and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of FMs. A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. Key challenges, including how to enable FL to deal with high complexity of computational demands, privacy considerations, contribution evaluation, and communication efficiency, are thoroughly discussed. Moreover, this paper explores the intricate challenges of communication, scalability and security inherent in training/fine-tuning FMs via FL. It highlights the potential of quantum computing to revolutionize the processes of training, inference, optimization and security. This survey also introduces the implementation requirement of FedFM and some practical FedFM applications. It highlights lessons learned with a clear understanding of our findings for FedFM. Finally, this survey not only provides insights into the current state and challenges of FedFM, but also offers a blueprint for future research directions, emphasizing the need for developing trustworthy solutions. It serves as a foundational guide for researchers and practitioners interested in contributing to this interdisciplinary and rapidly advancing field. © 2025 IEEE.

关键词： Federated learning

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Experimental demonstration of a flexible-grid 1×(2×3)mode-and wavelength-selective switch using silicon microring resonators and counter-tapered couplers

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Chinese Optics Letters 2024年第1期22卷 52-58页

作者：孔德军鲁昊汪鹏君符强戴世勋陈伟伟王栎沣张波豪马凌霄李军戴庭舸杨建义 Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou 325035China College of Science and Technology Ningbo UniversityNingbo 315300China Laboratory of Infrared Materials and Devices The Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211China Department of Information Science and Electronics Engineering and Cyrus Tang Center for Sensor Materials and Applications Zhejiang UniversityHangzhou 310027China

A flexible-grid 1×(2×3)mode-and wavelength-selective switch which comprises counter-tapered couplers and silicon microring resonators has been proposed,optimized,and demonstrated experimentally in this *** carefully thermally tuning phase shifters and silicon microring resonators,mode and wavelength signals can be independently and flexibly conveyed to any one of the output ports,and different bandwidths can be generated as *** particle swarm optimization algorithm and finite difference time-domain method are employed to optimize structural parameters of the twomode(de)multiplexer and crossing *** bandwidth-tunable wavelength-selective optical router composed of12 microring resonators is studied by taking advantage of the transfer matrix *** results show that,for the fabricated module,cross talk less than-10.18 dB,an extinction ratio larger than 17.41 d B,an in-band ripple lower than0.79 dB,and a 3-dB bandwidth changing from 0.38 to 1.05 nm are obtained,as the wavelength-channel spacing is 0.40 *** corresponding response time is measured to be 13.64μs.

关键词： integrated optics optical waveguide mode-and wavelength-selective switch

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Computational Model Based on CNN to Identify Masses from Liver Images 2

Computational Model Based on CNN to Identify Masses from Liv...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Jyothirmai, Ch. N. V. Hemalakshmi, G.R. Prakash, N.B. Upadhye, Vijay Jagdish Patil, Harshal Taqui, Syed Noeman Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering and Technology Department of CSE Andhra Pradesh 521 225 India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh 466114 India National Engineering College Department of Electrical and Electronics Engineering Kovilpatti 628503 India Parul Institute of Applied Sciences Parul University Research and Development Cell Department of Microbiology Gujarat 391760 India Department of Computer Science and Engineering Maharashtra 412 115 India Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Saveetha School of Engineering Chennai602105 India

ISBN: (纸本)9798350327533

Therefore, Convolutional Neural Networks (CNNs) are capable of predicting or extrapolating from identified patterns to unknown data, enabling the analysis and extraction of knowledge from large images, in order to produce consistent outputs for inputs not previously presented. On the downside, the training process takes a long time as it is computationally expensive. Aiming to reduce the computational time to perform the training of the neural network (NN), and the analysis of images during the testing process, as well as taking advantage of the natural distributed characteristic of ANN, it was decided to transfer the processing-intensive parts of the tool from CPU (Central Processing Unit) to a GPU (Graphics Processing Unit). Finally, simulator tests were carried out, with consequent verification of the effectiveness of diagnoses on ultrasound images of the liver. The accuracy and total processing time for carrying out the analyses proposed during this research were then evaluated, making a comparison between the network training and testing times obtained on the CPU and GPU. It can also be seen-in both Tr&Tt cases-that GPU had a higher gain than CPU (about 10.6 times faster), even when dealing with a small database, with 120 images of 202500 pixels each. © 2024 IEEE.

关键词： Central Processing Unit Convolutional Neural Networks Graphics Processing Unit Masses Ultrasound liver images

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Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge 38

Once Read is Enough: Domain-specific Pretraining-free Langua...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dong, Fang Chen, Mengyi Zhou, Jixian Shi, Yubin Chen, Yixuan Dong, Mingzhi Wang, Yujiang Li, Dongsheng Yang, Xiaochen Zhu, Rui Dick, Robert Lv, Qin Yang, Fan Lu, Tun Gu, Ning Shang, Li China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath United Kingdom Microsoft Research Asia Shanghai China School of Mathematics & Statistics University of Glasgow United Kingdom Bayes Business School City St George's University of London United Kingdom Department of Electrical Engineering and Computer Science University of Michigan United States Department of Computer Science University of Colorado Boulder United States School of Microelectronics Fudan University China

Language models (LMs) only pretrained on a general and massive corpus usually cannot attain satisfying performance on domain-specific downstream tasks, and hence, applying domain-specific pretraining to LMs is a common and indispensable practice. However, domain-specific pretraining can be costly and time-consuming, hindering LMs' deployment in real-world applications. In this work, we consider the incapability to memorize domain-specific knowledge embedded in the general corpus with rare occurrences and "long-tail" distributions as the leading cause for pretrained LMs' inferior downstream performance. Analysis of Neural Tangent Kernels (NTKs) reveals that those long-tail data are commonly overlooked in the model's gradient updates and, consequently, are not effectively memorized, leading to poor domain-specific downstream performance. Based on the intuition that data with similar semantic meaning are closer in the embedding space, we devise a Cluster-guided Sparse Expert (CSE) layer to actively learn long-tail domain knowledge typically neglected in previous pretrained LMs. During pretraining, a CSE layer efficiently clusters domain knowledge together and assigns long-tail knowledge to designate extra experts. CSE is also a lightweight structure that only needs to be incorporated in several deep layers. With our training strategy, we found that during pretraining, data of long-tail knowledge gradually formulate isolated, "outlier" clusters in an LM's representation spaces, especially in deeper layers. Our experimental results show that only pretraining CSE-based LMs is enough to achieve superior performance than regularly pretrained-finetuned LMs on various downstream tasks, implying the prospects of domain-specific-pretraining-free language models. © 2024 Neural information processing systems foundation. All rights reserved.

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Spectrally-Efficient Beamforming Design for STAR-RIS-Aided URLLC NOMA Systems

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IEEE Transactions on Communications 2024年第7期72卷 4414-4431页

作者： Katwe, Mayur Vitthalrao Deshpande, Rasika Singh, Keshav Pan, Cunhua Ghare, Pradnya H. Duong, Trung Q. National Institute of Technology Raipur Department of Electronics and Communication Engineering Chattisgarh Raipur492010 India Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804201 Taiwan Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Visvesvaraya National Institute of Technology Department of Electronics and Communication Engineering Nagpur440027 India Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Next-generation wireless applications are expected to enable extended ultra-reliable low-latency communication (URLLC) to support high data rates along with ultra-reliability and low-latency features beyond the capabilities of existing core services. There is a need to transition from conventional architectures to more efficient and robust multiple-access schemes to meet these consolidated requirements in resource-constrained systems. This study explores the utilization of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in non-orthogonal multiple access (NOMA) systems to enable spectrally efficient URLLC, even under the imperfect channel state information. In particular, we focus on maximizing spectral efficiency by jointly designing robust beamforming at the base station and STAR-RIS subject to given URLLC requirements. Due to the non-convexity of the formulated problem, we propose an alternating optimization framework that obtains sub-optimal solutions to the problems of beamforming design at the BS and STAR-RIS, respectively by exploiting S-procedure and successive convex approximation. Simulation results confirm that the STAR-RIS-NOMA system can significantly boost the spectral efficiency by 10-15% compared to conventional reflecting-only RIS while guaranteeing the strict URLLC requirements. Specifically, among all the possible modes of STAR-RIS, the time-splitting mode provides better spectral efficiency than other modes owing to its better interference management. 0090-6778 © 2024 IEEE.

关键词： Beamforming

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