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arXiv 2022年

作者： Wang, Wenxuan Chen, Chen Wang, Jing Zha, Sen Zhang, Yan Li, Jiangyun School of Automation and Electrical Engineering University of Science and Technology Beijing China Center for Research in Computer Vision University of Central Florida United States

For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly. Previous research on volumetric medical image segmentation in a slice-by-slice manner conventionally use the identical 2D deep neural network to segment all the slices of the same case, ignoring the data heterogeneity among image slices. In this paper, we focus on multi-modal 3D MRI brain tumor segmentation and propose a dynamic architecture network named Med-DANet based on adaptive model selection to achieve effective accuracy and efficiency trade-off. For each slice of the input 3D MRI volume, our proposed method learns a slice-specific decision by the Decision Network to dynamically select a suitable model from the predefined Model Bank for the subsequent 2D segmentation task. Extensive experimental results on both BraTS 2019 and 2020 datasets show that our proposed method achieves comparable or better results than previous state-of-the-art methods for 3D MRI brain tumor segmentation with much less model complexity. Compared with the state-of-the-art 3D method TransBTS, the proposed framework improves the model efficiency by up to 3.5× without sacrificing the accuracy. Our code will be publicly available at https://***/Wenxuan-1119/Med-DANet. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Integrated Software Model for Design, Optimization, and Reverse engineering of High-Power Laser Systems

Integrated Software Model for Design, Optimization, and Reve...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Hirschman, Jack Lemons, Randy Wang, Minyang Kroetz, Peter Carbajo, Sergio Stanford and SLAC National Accelerator Laboratory 348 Via Pueblo StanfordCA94305 United States Electrical and Computer Engineering and Physics and Astronomy Department UCLA 420 Westwood Los AngelesCA90095 United States Max Planck Research Department for Structural Dynamics University of Hamburg Hamburg20146 Germany

Modeling chirped-pulse amplification (CPA)- and nonlinear optics (NLO)-based systems is crucial to advance CPA+NLO design and optimization. We present a modular and expandable start-to-end model for engineering and re... 详细信息

ISBN: (纸本)9781957171258

关键词： Nonlinear optics

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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2023 China Automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： Decision trees

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Bidirectional RoF 5G Fronthaul Transmission Links with an InAs/InP Quantum Dash Laser

Bidirectional RoF 5G Fronthaul Transmission Links with an In...

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2023 Photonics North, PN 2023

作者： Xie, Xiaoran Liu, Guocheng Zeb, Khan Liu, Jiaren Mao, Youxin Poole, Philip J. Barrios, Pedro Weber, John Vachon, Martin Sabourin, Nicaulas Zhang, Xiupu Huang, Yu Yao, Jianping Wu, Ke Lu, Zhenguo Advanced Electronics and Photonics Research Centre National Research Council Canada OttawaON Canada Concordia University IPhotonics Labs Department of Electrical and Computer Engineering MontrealQC Canada University of Ottawa Microwave Photonics Research Laboratory OttawaON Canada Poly-Grames Research Center Polytechnique Montreal MontrealQC Canada

ISBN: (纸本)9798350326734

We have experimentally demonstrated a bidirectional radio-over-fiber (RoF) 5G new radio (NR) fronthaul transmission link based on an InAs/InP quantum dash mode-locked laser (QD-MLL), which provide low noise and highly coherent optical carriers. Two of the optical carriers are used for heterodyne based millimeter wave (MMW) RoF downlink transmission. The signal of the downlink is a 2-GBaud 16-QAM signal located at 28.951 GHz after heterodyne detection. The third optical carrier is used in uplink. The signal used in the uplink is a 2-GBaud 16-QAM signal located at 4 GHz. The result is verified by error-vector-magnitude (EVM) in real-time. The EVM of the downlink and uplink is up to 9.5% and 7.9% respectively in back-to-back setup. © 2023 IEEE.

关键词： Quantum dash lasers

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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VNF-Based Service Provision in Software Defined LEO Satellite Networks

VNF-Based Service Provision in Software Defined LEO Satellit...

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作者： Jia, Ziye Sheng, Min Li, Jiandong Zhou, Di Han, Zhu State Key Laboratory of ISN Xidian University Xi'an China Department of Electrical and Computer Engineering University of Houston HoustonTX United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Low earth orbit (LEO) satellite networks will play important roles in the sixth generation (6G) communication system. Software defined network technique is a novel approach introduced to the LEO satellite networks to improve the resource flexibility and efficiency, forming the software defined LEO satellite networks (SDLSNs). How to efficiently allocate the resources of SDLSN to provide services for the terrestrial users is a key issue. Hence, in this work, we explore the service provision for SDLSN via virtual network functions (VNFs) orchestration on the software defined time-evolving graph. In view of the scarce, intermittent and unstable satellite-to-satellite (S2S) links, the problem is formulated to minimize the S2S resource consumption while satisfying the terrestrial tasks, which is in the form of integer linear programming. Since the problem is intractable by exhaustive search, we design a branch-and-price algorithm based on the coupling of Dantzig-Wolfe decomposition, column generation, and branch-and-bound to efficiently acquire the optimal solution. Further, to obtain a faster solution for practical usage, we further design an approximation algorithm for the subproblem and leverage the beam search to accelerate the pruning for the search tree. Finally, extensive simulations are conducted and the numerical results validate the effectiveness of the proposed schemes. © 2002-2012 IEEE.

关键词： Approximation algorithms

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Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach

arXiv

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arXiv 2024年

作者： Wang, Bin Fang, Jun Li, Hongbin Eldar, Yonina C. The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data dispersed over various data sources. Standard FL, which employs a single server, can only support a limited number of users, leading to degraded learning capability. In this work, we consider a multi-server FL framework, referred to as Confederated Learning (CFL), in order to accommodate a larger number of users. A CFL system is composed of multiple networked edge servers, with each server connected to an individual set of users. Decentralized collaboration among servers is leveraged to harness all users’ data for model training. Due to the potentially massive number of users involved, it is crucial to reduce the communication overhead of the CFL system. We propose a stochastic gradient method for distributed learning in the CFL framework. The proposed method incorporates a conditionally-triggered user selection (CTUS) mechanism as the central component to effectively reduce communication overhead. Relying on a delicately designed triggering condition, the CTUS mechanism allows each server to select only a small number of users to upload their gradients, without significantly jeopardizing the convergence performance of the algorithm. Our theoretical analysis reveals that the proposed algorithm enjoys a linear convergence rate. Simulation results show that it achieves substantial improvement over state-of-the-art algorithms in terms of communication efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning systems

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

MetaGCD: Learning to Continually Learn in Generalized Catego...

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International Conference on computer vision (ICCV)

作者： Yanan Wu Zhixiang Chi Yang Wang Songhe Feng Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.

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CMOS-based Single-Cycle In-Memory XOR/XNOR

arXiv

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arXiv 2023年

作者： Alam, Shamiul Hutchins, Jack Shukla, Nikhil Asifuzzaman, Kazi Aziz, Ahmedullah Dept. of Electrical Eng. and Computer Sci. University of Tennessee KnoxvilleTN37996 United States Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States Oak Ridge National Laboratory Oak RidgeTN United States

Big data applications are on the rise, and so is the number of data centers. The ever-increasing massive data pool needs to be periodically backed up in a secure environment. Moreover, a massive amount of securely backed-up data is required for training binary convolutional neural networks for image classification. XOR and XNOR operations are essential for large-scale data copy verification, encryption, and classification algorithms. The disproportionate speed of existing compute and memory units makes the von Neumann architecture inefficient to perform these Boolean operations. Compute-in-memory (CiM) has proved to be an optimum approach for such bulk computations. The existing CiM-based XOR/XNOR techniques either require multiple cycles for computing or add to the complexity of the fabrication process. Here, we propose a CMOS-based hardware topology for single-cycle in-memory XOR/XNOR operations. Our design provides at least 2× improvement in the latency compared with other existing CMOS-compatible solutions. We verify the proposed system through circuit/system-level simulations and evaluate its robustness using a 5000-point Monte Carlo variation analysis. This all-CMOS design paves the way for practical implementation of CiM XOR/XNOR at scaled technology nodes. © 2023, CC BY.

关键词： Cryptography

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A Secure Bayesian Compressive Spectrum Sensing Technique Based Chaotic Matrix for Cognitive Radio Networks 12th

A Secure Bayesian Compressive Spectrum Sensing Technique Bas...

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12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020

作者： Benazzouza, Salma Ridouani, Mohammed Salahdine, Fatima Hayar, Aawatif RITM Laboratory CED Engineering Sciences Hassan II University Casablanca Morocco Department of Electrical and Computer Engineering The University of North Carolina at Charlotte Charlotte United States

ISBN: (纸本)9783030736880

Compressive spectrum sensing is an advanced technique to improve the detection process in cognitive radio networks. It aims at reconstructing sparse signals from only few measurements and then achieving spectrum sensing process using only the estimated signal. In cooperative cognitive radio networks, security, time, and uncertainty issues can affect the detection performance when external malicious users try to join the network. Thus, enhanced compressive spectrum sensing techniques are extremely needed. We proposed a novel combination based on the efficiency of the Bayesian recovery inference and the strengths of the Chaotic sensing matrices to accelerate the spectrum sensing, deal with uncertainty in measurements, and provide inherent security of the system. The proposed technique is implemented and tested using several performance metrics. The simulation results prove that our proposed technique is highly efficient over the acquisition and the recovery processes. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network security

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