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arXiv 2025年

作者： Bocus, Mohammud J. Häkkinen, Juha Fontes, Hélder Drzewiecki, Marcin Qiu, Senhui Eder, Kerstin Piechocki, Robert School of Computer Science University of Bristol Bristol United Kingdom Circuits and Systems Research Unit University of Oulu Oulu Finland MpicoSys Embedded Pico Systems Sp. z o. o. Gdynia Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdansk Poland INESC TEC Porto Portugal Faculty of Engineering University of Porto Porto Portugal

his paper presents a comprehensive analysis of the energy consumption characteristics of a Silicon (Si)-based Reconfigurable IoT (RIoT) node developed in the initial phase of the SUPERIOT project, focusing on key operating states, including Bluetooth Low Energy (BLE) communication, Narrow-Band Visible Light Communication (NBVLC), sensing, and E-ink display. Extensive measurements were conducted to establish a detailed energy profile, which serves as a benchmark for evaluating the effectiveness of subsequent optimizations and future node iterations. To minimize the energy consumption, multiple optimizations were implemented at both the software and hardware levels, achieving a reduction of over 60% in total energy usage through software modifications alone. Further improvements were realized by optimizing the E-ink display driving waveform and implementing a very low-power mode for non-communication activities. Based on the measured data, three measurement-based energy consumption models were developed to characterize the energy behavior of the node under: (i) normal, unoptimized operation, (ii) low-power, software-optimized operation, and (iii) very low-power, hardware-optimized operation. These models, validated with new measurement data, achieved an accuracy exceeding 97%, confirming their reliability for predicting energy consumption in diverse configurations. © 2025, CC BY.

关键词： Digital storage

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Silver-atom catalysts boosted nitrogen-doped MoS2 for chemiresistive NO2 gas sensor

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Sensors and Actuators B: Chemical 2025年 439卷

作者： Kumar, Ashok Barala, Suraj Gutal, Akash Popat Chakkar, Atul G. Kumar, Pradeep Paranjothy, Manikandan Kumar, Mahesh Department of Electrical Engineering Indian Institute of Technology Jodhpur342030 India Interdisciplinary Department of Space Science and Technology Indian Institute of Technology Jodhpur342030 India Department of Chemistry Indian Institute of Technology Jodhpur342030 India School of Physical Sciences Indian Institute of Technology Himachal Pradesh Mandi175005 India Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

MoS2 has elicited notable interest as a promising material for gas sensing applications. The pristine MoS2 is still encumbered by drawbacks such as low response, large response time, and a propensity for weak adsorption of target gases, which can impede its effectiveness in practical applications. To address these challenges, this study investigates the functionalization of nitrogen-doped MoS2 with silver nanoparticles to improve its sensing performance for NO2 gas. MoS2 nanosheets were synthesized through chemical vapor deposition and subsequently subjected to nitrogen plasma treatment to facilitate doping. We evaluated the gas sensing performance of pristine MoS2, Ag-decorated MoS2, nitrogen-doped MoS2, and Ag-decorated nitrogen-doped MoS2 (Ag-N-MoS2) specifically for NO2 gas sensing. The Ag-N-MoS2 configuration demonstrated a response that was nearly double that of pristine MoS2 at 100 °C, demonstrating the benefits of this dual enhancement strategy. Additionally, selectivity tests revealed the sensor's capacity to distinguish NO2 from other gases. To reinforce our experimental results, density functional theory (DFT) calculations were conducted, confirming the improved electronic properties achieved through nitrogen doping and Ag NP functionalization. This research underscores the potential of Ag-N-MoS2 as a formidable platform for sophisticated gas sensors, addressing crucial environmental monitoring requirements while surmounting the intrinsic limitations of pristine MoS2. © 2025 Elsevier B.V.

关键词： Plasma CVD

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An efficient schedulability analysis based on worst-case interference time for real-time systems

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science China(Information sciences) 2024年第9期67卷 88-104页

作者： Hongbiao LIU Mengfei YANG Lei QIAO Xi CHEN Jian GONG School of Computer Science and Technology Xidian University China Academy of Space Technology Beijing Institute of Control Engineering

Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.

关键词： five-tuple real-time task model real-time system spacecraft Internet of Things exact schedulability analysis worst-case interference time

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

arXiv

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arXiv 2025年

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C.L. Philip The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of New Network Pengcheng Laboratory Shenzhen518000 China The China Tobacco Guangdong Industrial Corporation Guangzhou510385 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contextual information among image patches. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. Copyright © 2025, The Authors. All rights reserved.

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A direct torque intelligent control strategy for induction motor based on matrix converter

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Australian Journal of Electrical and Electronics engineering 2015年第3期12卷 175-182页

作者： Zhao, Junmei Zhang, Zhijie Ren, Yifeng Li, Xiao School of Computer Science and Control Engineering North University of China Taiyuan China

Direct torque control (DTC) technology as a high-performance control method uses stator flux to determine the direction of magnetic field and controls converter switch state through discrete two-point regulator to obtain high torque dynamic performance. But none of switch states can generate exact voltage vector to produce desired torque and flux changes in most of switch instances. This causes a high torque ripple. To solve this problem, a novel DTC strategy using fuzzy neural network (FNN) instead of hysteresis controller is proposed. Flux error, torque error and flux angle are taken as FNN variables. In order to solve nonlinear problem of parameter variation in DTC, a fuzzy adaptive speed PI regulator of analysing optimizing PI control speed response and combining experts experience is proposed. The simulation results show that control system obtains rapid speed response, stronger robustness, higher precision of speed control and small torque ripple. © 2015 © 2015 Engineers Australia.

关键词： Fuzzy neural networks

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Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

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International Journal of Automation and computing 2014年第5期11卷 480-488页

作者： Sheng-Ye Yan Xin-Xing Xu Qing-Shan Liu School of Information and Control Nanjing University of Information Science and Technology School of Computer Engineering Nanyang Technological University

This paper proposes a new two-phase approach to robust text detection by integrating the visual appearance and the geometric reasoning rules. In the first phase, geometric rules are used to achieve a higher recall rate. Specifically, a robust stroke width transform(RSWT) feature is proposed to better recover the stroke width by additionally considering the cross of two strokes and the continuousness of the letter border. In the second phase, a classification scheme based on visual appearance features is used to reject the false alarms while keeping the recall rate. To learn a better classifier from multiple visual appearance features, a novel classification method called double soft multiple kernel learning(DS-MKL) is proposed. DS-MKL is motivated by a novel kernel margin perspective for multiple kernel learning and can effectively suppress the influence of noisy base kernels. Comprehensive experiments on the benchmark ICDAR2005 competition dataset demonstrate the effectiveness of the proposed two-phase text detection approach over the state-of-the-art approaches by a performance gain up to 4.4% in terms of F-measure.

关键词： Text detection geometric rule stroke width transform (SWT) support vector machine (SVM) multiple kernel learning (MKL)

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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

arXiv

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Deep learning for hyperspectral image classification: A comprehensive review and future predictions

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Information Fusion 2025年 123卷

作者： Yongchao Song Junhao Zhang Zhaowei Liu Yang Xu Siwen Quan Lijun Sun Jiping Bi Xuan Wang School of Computer and Control Engineering Yantai University Yantai 264005 China Science and Technology Office Yantai University Yantai 264005 China School of Electronics and Control Engineering Chang’an University Xi’an 710064 China

Hyperspectral image classification (HSIC) is an important research direction in the field of remote sensing image analysis and computer vision, which is of great practical significance. Hyperspectral imaging (HSI) is widely used in a variety of scenarios with its rich spectral and spatial information, but problems such as high-dimensional data characteristics and scarcity of labeled samples challenge the classification accuracy. Deep learning (DL), with its powerful feature extraction and modeling capabilities, provides an effective means to solve the nonlinear problems in HSIC. In this survey, we systematically review the research progress and applications of DL in HSIC. Firstly, we outline the importance of accurate classification, analyze the features of HSI and the challenges faced by DL in this area. Secondly, we introduce different feature representations of HSI and provide a comprehensive describe of the application of various DL models in HSIC. Meanwhile, we also explore DL methods that can effectively improve the classification performance in the case of insufficient training samples. Finally, we summarize the current research situation, and put forward the future development direction and suggestions.

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Robust H-infinity control for constrained uncertain systems and its application to active suspension

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控制理论与应用（英文版） 2012年第4期10卷 470-476页

作者： Miaomiao MA Hong CHEN Xiangjie LIU School of Control and Computer Engineering North China Electric Power University Department of Control Science and Engineering Jilin University

This paper presents an LMI based robust H-infinity control scheme for constrained systems with norm- bounded uncertainties. The uncertainties are incorporated in the evaluation of the H-infinity norm and the time-domain constraints. The robust closed-loop properties inclusive of stability, H-infinity performance and the satisfaction of the time- domain constraints are discussed. Analysis and simulation results for a 2 DOF quarter-car model show possible improve- ments on ride comfort, while robustly respecting safety related constraints such as good road holding, limited suspension strokes and actuator saturation.

关键词： Constraints H-infinity performance LMI optimization Norm-bounded uncertainties Active suspension

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