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Multiview Integration Network for Multitask Robotic Surgical Scene Analysis

IEEE Transactions on Instrumentation and Measurement 2025年

作者： Shen, Wenting Wang, Yaonan Liu, Min Wang, Jiazheng Ding, Renjie Zhang, Zhe Meijering, Erik Hunan University College of Electrical and Information Engineering National Engineering Research Center of Robot Visual Perception and Control Technology International Scientific and Technological Innovation Cooperation Base for Biomedical Image Processing Hunan Changsha410082 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

Surgical scene analysis holds a pivotal role in robot-assisted surgery. However, existing methods often suffer from single or little views, leading to erroneous scene analysis conclusions. To address these issues, a novel Multiview Integration Network (MVINet) is proposed, which comprehensively analyzes the surgical scene by integrating effective information from multiple views, including global, local, dynamic, and static views. As a multitask scene analysis network, MVINet could simultaneously perform semantic segmentation of surgical instruments and detection of instrument-tissue interaction. By designing a unique Global-Local Spatial Feature Memory module (G-LSFM), the joint information from globally and locally analyzed views following graph analysis simultaneously enhances the accuracy of multitask scene analysis. The Dynamic-Static Visual Feature Memory module (D-SVFM) introduces an innovative approach by simultaneously incorporating temporal feature from consecutive frames and static feature from single frame into the node features of the interaction reasoning network. The multiscale perspective of both features further enhances the ability of the module to analyze the complex scenes. Experimental results on a public and a private dataset demonstrate that our method achieves superior performance compared to other state-of-the-art methods for two crucial tasks in surgical scene analysis. In the instrument segmentation task, MVINet outperforms the second-best method by 1.73% and 0.55% in terms of mIoU scores. For the interaction detection task, MVINet surpasses the second-best method by 10.14% and 5.47% in mAP scores. © 1963-2012 IEEE.

关键词： Semantic Segmentation

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Enhanced ferromagnetism in an artificially stretched lattice in quasi-two-dimensional Cr2Ge2Te6

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Physical Review B 2025年第2期111卷 L020402-L020402页

作者： Hiroshi Idzuchi Andres E. Llacsahuanga Allcca Anh Khoa Augustin Lu Mitsuhiro Saito Subhadip Das John F. Ribeiro Michel Houssa Ruishen Meng Kazutoshi Inoue WPI Advanced Institute for Materials Research (AIMR) Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (CSIS) Tohoku University Sendai 980-8577 Japan Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Mathematics for Advanced Materials Open Innovation Laboratory National Institute of Advanced Industrial Science and Technology Sendai 980-8577 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Institute of Engineering Innovation The University of Tokyo Tokyo 113-8656 Japan Institute of Physics and Astronomy and Villum Centers for Dirac Materials and for Hybrid Quantum Materials and Devices Aarhus University 8000 Aarhus-C Denmark School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Department of Physics and Astronomy Katholieke Universiteit Leuven 3001 Leuven Belgium

Crystal lattice is key in understanding magnetic interactions between atoms in solids. As the effective control of the lattice via tensile stress is limited, there are only a few demonstrations of controlling magnetic properties by expanding the lattice. This study reveals a clear correlation between enhanced magnetism, where Curie temperature (TC) is doubled from ∼60 to ∼120 K, and lattice expansion in prototypical van der Waals magnet Cr2Ge2Te6 with sputtered dielectric overlayers NiO and MgO. We ascribe the mechanism of this TC increase to a change in exchange interactions induced by strain, studied by several experimental probes and computational approaches.

关键词： Van der Waals forces

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Arch-LLM: Taming LLMs for Neural Architecture Generation via Unsupervised Discrete Representation Learning

arXiv

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arXiv 2025年

作者： Poddenige, Deshani Geethika Seneviratne, Sachith Senanayake, Damith Niranjan, Mahesan Suganthan, P.N. Halgamuge, Saman School of Electrical Mechanical and Infrastructure Engineering University of Melbourne Australia School of Electronics and Computer Science University of Southampton Southampton United Kingdom KINDI Computing Research Center College of Engineering Qatar University Doha Qatar

Unsupervised representation learning has been widely explored across various modalities, including neural architectures, where it plays a key role in downstream applications like Neural Architecture Search (NAS). These methods typically learn an unsupervised representation space before generating/ sampling architectures for the downstream search. A common approach involves the use of Variational Autoencoders (VAEs) to map discrete architectures onto a continuous representation space, however, sampling from these spaces often leads to a high percentage of invalid or duplicate neural architectures. This could be due to the unnatural mapping of inherently discrete architectural space onto a continuous space, which emphasizes the need for a robust discrete representation of these architectures. To address this, we introduce a Vector Quantized Variational Autoencoder (VQ-VAE) to learn a discrete latent space more naturally aligned with the discrete neural architectures. In contrast to VAEs, VQ-VAEs (i) map each architecture into a discrete code sequence and (ii) allow the prior to be learned by any generative model rather than assuming a normal distribution. We then represent these architecture latent codes as numerical sequences and train a text-to-text model leveraging a Large Language Model to learn and generate sequences representing architectures. We experiment our method with Inception/ ResNet-like cell-based search spaces, namely NAS-Bench-101 and NAS-Bench-201. Compared to VAE-based methods, our approach improves the generation of valid and unique architectures by over 80% on NASBench-101 and over 8% on NASBench-201. Finally, we demonstrate the applicability of our method in NAS employing a sequence-modeling-based NAS algorithm. © 2025, CC BY.

关键词： Normal distribution

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General and Offset-Resistant Physical-Layer Acknowledgement Approach for Cross-Technology Communication

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IEEE Transactions on Communications 2025年

作者： Yao, Shumin Zhao, Qinglin Zhou, MengChu Feng, Li Zhang, Peiyun Albeshri, Aiiad Macau University of Science and Technology School of Computer Science and Engineering Avenida Wei Long Taipa China Pengcheng Laboratory Department of Broadband Communication Shenzhen518066 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Nanjing University of Information Science & Technology Engineering Research Center of Digital Forensics of Ministry of Education School of Computer Science Nanjing210044 China King Abdulaziz University Department of Computer Science Jeddah21481 Saudi Arabia

Cross-technology communication (CTC) enables direct communications among devices with heterogeneous wireless technologies (e.g., Bluetooth, WiFi, ZigBee, etc.), thereby reducing the cost and complexity of their interconnections. However, CTC is essentially unreliable due to the technology heterogeneity, and most existing CTC designs do not provide acknowledgment (ACK) feedback to ensure reliable data transmission. Few ACK designs are only applicable to feedback for ZigBee-WiFi pair and are vulnerable to sampling offsets that inherently exist in CTC. In this work, we propose a General and Offset-resistant Physical-layer ACK approach, called GOP-ACK, to support reliable feedback with practical considerations. Its core idea lies in encoding ACK messages with offset-resistant signal that has two benefits: 1) it can be adapted to a wide range of CTC scenarios with minimal adjustment, and 2) it can be effortlessly and robustly detected even in the presence of sampling offsets. We offer practical guidelines to tackle key deployment challenges related to signal construction, efficient and robust transmission, and effective firmware module reuse, enabling the application of GOP-ACK to specific CTC scenarios. Based on these guidelines, we implement two design cases: ZigBee-to-BLE and ZigBee-to-WiFi feedback, and propose a theoretical model to analyze their performance. We then conduct real-world experiments and extensive simulations to verify its feasibility and superiority over the state of the art, thereby enhancing the practicality of CTC greatly. © 1972-2012 IEEE.

关键词： Firmware

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Design of Nonlinear Delay Differential System for Analyzing Vulnerabilities in Nanoscale Hardware Implants: A Deep Dive into Intelligent Computing Networks

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Nano 2025年第3期20卷

作者： Asif Raja, Muhammad Junaid Ali Masood, Zaheer Hussain, Ijaz Zameer, Aneela Mehmood, Ammara Raja, Muhammad Asif Zahoor Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Department of Electrical Engineering Capital University of Science and Technology Islamabad Pakistan Department of Computer and Information Sciences Pakistan Institute of Engineering & Applied Sciences Islamabad Pakistan School of Electrical and Data Engineering University of Technology Sydney Australia Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan

In the annals of contemporary innovation, the study of miniature marvels nanoscale hardware implants emerged as a pivotal instrument, scarcely perceptible to the naked eye, embodies a prowess of cutting-edge technologies and clandestine intrigue. The objective of this research is to introduce a time delay nonlinear system for nanoscale hardware implants vulnerabilities that portray the exploitation of a system by utilizing the stupendous knacks of advanced deep bidirectional long short-term memory (LSTM) networks for time series predictions. Firmware-level bugs present the potential for escalating privileges and executing of code remotely beneath the operating system, allowing for infiltration or complete intervention within a computer system. The designed deep bidirectional LSTM is configured to precisely predict and forecast the dynamic states of time delay differential system, offering a robust framework for mimicking delays that commonly manifest in the real cyber-physical systems. To orchestrate the system compromises in real scenarios through the activation of bugged hardware, time delay factors τ1 and τ2 are introduced to account the time delays necessary for exploiting the bugged and patched nodes, respectively. Synthetic data is generated to train the LSTM network for all scenarios of the model for the dynamics of bugged, compromised, patched nodes and these acquired information is used for training, testing and validation purposes regarding exploitation of the bugged hardware. Comparative analysis on exhaustive simulations revealed a minimal difference between the LSTM's predictions and those from the numerical outcomes with MSE in the range of 10-7, underscoring the network's effectiveness, robustness and stability in modeling complex system dynamics of hardware vulnerabilities. © 2025 World Scientific Publishing Company.

关键词： Firmware

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Inner Rotor Shaping Method for Torque Ripple Suppression in Hollow-Cup Machines

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IEEE Transactions on Industry Applications 2025年

作者： Cui, Zhanpeng Zhou, Jinxiang Le, Yun Zhang, Liu Wang, Kun Liu, Ximing Zheng, Shiqiang Beihang University School of Instrumentation and Optoelectronic Engineering Beijing100191 China Beihang University Zhejiang Engineering Research Center of Precision Electromagnetic Control Technology and Equipment Ningbo Institute of Technology Ningbo315800 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China

The ultra-precise stabilization spacecraft (UPSS) requires the use of hollow-cup machines (HCMs) with extremely low output torque ripple to achieve accurate attitude control. Conventional permanent magnet (PM) shaping methods are effective in suppressing torque ripple. However, they pose challenges such as magnetization difficulty and mechanical strength reduction. This paper proposes an inner rotor harmonic shaping (IRHS) method to reduce the torque ripple and avoid the defects of shaped PMs. This method is based on the adjustment effect of air-gap permeance harmonic injection on the air-gap flux density harmonics. To further explore the advantages of this method, this paper compares it with several PM shaping methods. A total of five machine topologies are considered, and their electromagnetic characteristics are comprehensively analyzed and compared. The proposed method reduces the torque ripple by 87% compared to conventional HCMs. Meanwhile, the comparison with several PM shaping methods shows that the IRHS achieves a 3% to 6% increase in the average torque and a 10% to 12% reduction in the PM loss while obtaining a comparable torque ripple. Finally, two prototype machines are manufactured and experimentally validated. © 1972-2012 IEEE.

关键词： Electromagnetic characteristics hollow-cup machines (HCMs) inner rotor harmonic shaping (IRHS) permanent magnet (PM) shaping torque ripple suppression

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

引用

17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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H control for General Conformable Polynomial Fuzzy Models

引用

International Journal of Fuzzy Systems 2025年 1-10页

作者： Laajimi, Malek Gassara, Hamdi Rhaima, Mohamed Mchiri, Lassaad Makhlouf, Abdellatif Ben Laboratory of Sciences and Techniques of Automatic control & Computer Engineering National School of Engineering of Sfax Sfax Tunisia Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Panthéon-Assas University Paris II Paris France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

This research focuses on exploring the exponential $$H_\infty $$ stability of general conformable nonlinear system. In order to address the nonlinearity inherent in the system, a polynomial fuzzy (PF) method is employed. Modeling a general conformable nonlinear system within the polynomial framework reduces the number of fuzzy rules compared to the classical Takagi–Sugeno fuzzy (TSF). Furthermore, controlling such a complex system, which accounts for perturbations, employs a PF model instead TSF model to describe its nonlinear dynamics, and incorporates a general conformable derivative instead of an integer-order one, is significantly more challenging, and remains unaddressed in previous studies. In this paper, a PF controller is designed in the form of sum of squares (SOS) to enhance the resilience against perturbations and ensure the exponential stability of the proposed model. The proposed SOS can be solved numerically, and partially symbolically, using the recently developed SOSTOOLS. In order to ensure the $$H_\infty $$ performance, a generalized criterion is defined for the general conformable nonlinear system. To demonstrate the effectiveness of the proposed method, a numerical example is provided.

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Potential of Vision Transformer in Leukemia Detection

Potential of Vision Transformer in Leukemia Detection

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Rayan Ahmed Laraib Nadeem Maryam Shafique Zameera Saleem Ayesha Iqbal Hafsa Iqbal School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Shaukat Khanum Memorial Cancer Hospital & Research Center Lahore Pakistan

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Leukemia, a bone marrow-related disease, arises from the abnormal proliferation of cells. It is the predominant form of childhood cancer, representing about one-third of cases annually. This study focuses on incorporating AI models to enhance the early detection of Leukemia. A public dataset is employed for testing purposes which comprises of 15,114 images in total. Advancing the field, this research employs vision transformer techniques, previously unexplored in leukemia detection, showing promising efficiency for such applications. Emphasizing the critical role of precision in medical imaging, particularly in the context of Leukemia detection, this study contributes to the ongoing efforts to improve the diagnostic accuracy and patient outcomes.

关键词： computer vision Machine learning algorithms Image analysis Accuracy Leukemia Transformers Medical diagnosis Intelligent systems Medical diagnostic imaging Testing

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Accelerating Stable Matching between Workers and Spatial-Temporal Tasks for Dynamic MCS: A Stagewise Service Trading Approach

arXiv

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arXiv 2025年

作者： Qi, Houyi Liwang, Minghui Wang, Xianbin Fu, Liqun Hong, Yiguang Li, Li Cheng, Zhipeng School of Informatics Xiamen University Fujian China Department of Control Science and Engineering Canada Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Jiangsu China

Designing proper incentives in mobile crowdsensing (MCS) networks represents a critical mechanism in engaging distributed mobile users (workers) to contribute heterogeneous data for diverse applications (tasks). We develop a novel stagewise trading framework to reach efficient and stable matching between tasks and workers, upon considering the diversity of tasks and the dynamism of MCS networks. This framework integrates futures and spot trading stages, where in the former, we propose futures trading-driven stable matching and pre-path-planning (FT-SMP3) for long-term task-worker assignment and pre-planning of workers’ paths based on historical statistics and risk analysis. While in the latter, we investigate spot trading-driven DQN path planning and onsite worker recruitment (ST-DP2WR) mechanism to enhance workers’ and tasks’ practical utilities by facilitating temporary worker recruitment. We prove that our proposed mechanisms support crucial properties such as stability, individual rationality, competitive equilibrium, and weak Pareto optimality theoretically. Also, comprehensive evaluations confirm the satisfaction of these properties in practical network settings, demonstrating our commendable performance in terms of service quality, running time, and decision-making overheads. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk analysis

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