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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Jifu Liang Xinyao Tang S. I. Hariharan Arjuna Madanayake Soumyajit Mandal Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland OH Department of Electrical and Computer Engineering Florida International University Miami FL Instrumentation Division Brookhaven National Laboratory Upton NY

This paper describes an analog CMOS IC for fast and fully-parallel finite-difference time-domain (FDTD) simulations of 2D electromagnetic (EM) problems. The chip uses discrete-time switched-current (SI) networks to model Maxwell's equations in 2D while minimizing the effects of device mismatch on solver accuracy. A prototype design in 180 nm technology implements a $16\times 16$ solver grid within an active area of 44.5 mm 2 while consuming 345 mW at a clock frequency of 20 MHz.

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FATUnetr:fully attention Transformer for 3D medical image segmentation

FATUnetr:fully attention Transformer for 3D medical image se...

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IEEE International Conference on Mechatronics and Automation

作者： QingFeng Li Jigang Tong Sen Yang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Tianjin China Department of Electrical Engineering Tshwane University of Technology South Africa

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Recent studies have explored the applicability of the Transformer model in 3D medical segmentation tasks. The core attention mechanism of transformer, has the property of capturing remote dependencies, which is highly robust when segmenting medical images. Inspired by the emerging visual groupings in Vision Transformers (ViTs), self-attention can be made more robust by improving the intermediate representation. We propose a novel framework called FATUnetr that aims to take advantage of the full attention module to improve accuracy. We also reduce the number of parameters by applying deeply separable convolution at the bottleneck layer. We have verified the effectiveness in the extracranial vault multiple atlas labelling (BTCV) dataset for multi-organ segmentation.

关键词： Image segmentation Visualization Three-dimensional displays Attention mechanisms Accuracy Convolution Transformer cores

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Performance evaluation of communication networks for networked microgrids

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e-Prime - Advances in electrical engineering, Electronics and Energy 2024年 8卷

作者： Ali, Ola Aghmadi, Ahmed Mohammed, Osama A. Energy Systems Research Laboratory Department of Electrical and Computer Engineering Florida International University Miami 33174 FL United States

The main goal of cyber-physical power systems (CPPSs) is to provide seamless collaboration between the physical and cyber systems, bridging disciplines, such as communication networks and control systems. controlling the operation of Microgrids (MGs) as a cyber-physical power system must include both the physical and cyber layers. Generally, the microgrid control center MGCC is employed for reliable operation management between the different local controllers within the networked microgrids (NMGs). Therefore, an excessive number of communication channels between the MGCC and the local controllers will exist, affecting the control performance of the NMGs. This paper focuses on developing an innovative communication network emulation model for standalone DC networked microgrids using a Network Simulator (NS3), miming the communication infrastructure to control the networked microgrid system. Through the real-time simulation, the performance of the proposed communication network model was evaluated by employing two transmission protocols, User Datagram Protocol (UDP) and Transmission control Protocol (TCP), to facilitate the data exchange between the MGCC and the local controllers in each MG while operating under different traffic loads. The comparative results of using these protocols in terms of the key performance indicators (KPIs) of the communication performance metrics, such as the mean delay, percentage of packet loss, and transmitted and received bitrate, reveal notable insights into their suitability in data transmission for controlling and managing the operation of the NMGs. © 2024 The Author(s)

关键词： Cyber-physical power systems (CPPSs) KPIs Microgrid control center (MGCC) Network Simulator (NS3) Networked microgrids (NMGs) TCP UDP

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Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion

Synthesizing Robust Walking Gaits via Discrete-Time Barrier ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Maegan Tucker Kejun Li Aaron D. Ames School of Electrical and Computer Engineering and the Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA Department of Computation and Neural Systems California Institute of Technology Pasadena CA Department of Mechanical and Civil Engineering and the Department of Control and Dynamical Systems California Institute of Technology Pasadena CA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness – the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation-in-the-loop learning approach. Further, we leverage discrete-time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multi-contact walking on the Atalante lower-body exoskeleton.

关键词： Legged locomotion Measurement Uncertainty Exoskeletons Robustness State estimation Robots

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Named Entity Recognition in Chinese Medical Texts Based on RoBERTa-WWM-IDCNN-CRF

Named Entity Recognition in Chinese Medical Texts Based on R...

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System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Lan Tang Jie Kong Liang Xu School of Computer Xi‘an Shiyou University Xi‘an China Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Electronic medical records and doctor-patient conversations contain a wealth of useful information, such as disease symptoms, drug names, and cure cycles. Traditional deep learning approaches utilize bidirectional recurrent neural networks to encode text, which cannot effectively capture long-distance dependencies in text. In order to identify medical entity types in various texts more accurately, a named entity recognition approach based on RoBERTa-WWM-IDCNN-CRF is proposed. This approach introduces the RoBERTa-WWM pre-trained model, in order to improve the overall understanding of long Chinese texts for the first time. The model generates semantic representations containing prior knowledge. Then, the generated semantic representations are feed to the IDCNN to extract sentence level characteristics. Finally, the global optimal solution is obtained and the results of the named entity are produced through the CRF. The experiment indicates that the improvement of $F_{1}$ score, accuracy, and recall of RoBERTa-WWM-IDCNN-CRF model are 7.94%, 4.52% and 10.02% comparing with the classical BiLSTM-CRF model.

关键词： Drugs Recurrent neural networks Semantics Named entity recognition Oral communication Medical services systems simulation Feeds Electronic medical records Diseases

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Open Source Heterogeneous SoCs for Artificial Intelligence: The PULP Platform experience

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IEEE Solid-State Circuits Magazine 2025年第2期17卷 49-60页

作者： Francesco Conti Angelo Garofalo Davide Rossi Giuseppe Tagliavini Luca Benini Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy Integrated Systems Laboratory ETH Zürich Zurich Switzerland Department of Computer Science and Engineering University of Bologna Bologna Italy ETH Zürich Zurich Switzerland University of Bologna Bologna Italy

The exponential growth in AI algorithm complexity creates significant challenges for designing heterogeneous AI SoCs, requiring rapid and cost-effective development cycles. Open-source hardware offers a potential solution by enabling reuse of high-quality, non-differentiating IPs, allowing SoC designers to focus on innovative, differentiating features. Since 2013, the PULP (Parallel Ultra-Low Power) Platform project has been active in designing research IPs and releasing them as opensource. In this article, we focus on the PULP experience designing heterogeneous AI acceleration SoCs, centered on the PULP cluster equipped with a combination of augmented RISC-V processors and cooperative hardware accelerators called HWPEs (Hardware Processing Engines). We detail the evolution of AIdedicated PULP SoCs both in terms of silicon prototypes and of software tools to enable the deployment of end-to-end AI models.

关键词： Program processors Prototypes Low power electronics Open source hardware Artificial intelligence Software tools Integrated circuit modeling Hardware acceleration Engines Algorithm design and analysis System-on-chip

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computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

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computers, Materials & Continua 2022年第11期73卷 3983-4002页

作者： Abdullah AAsiri Tariq Ali Ahmad Shaf Muhammad Aamir Muhammad Shoaib Muhammad Irfan Hassan A.Alshamrani Fawaz F.Alqahtani Osama M.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia

Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to *** resonance imaging methods capture skull images that contain healthy and affected *** checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic ***,auto segmentation of the affected part is needed to facilitate ***,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor *** CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region *** proposed method was evaluated on a publicly available BraTS2020 *** statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice ***,our method achieved an accuracy value of 0.98,which is most prominent than existing ***,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

关键词： Brain tumor support vector machine convolutional neural network BraTS classification

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Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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