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arXiv 2025年

作者： Ghosh, Subhadip Gebru, Endalk Y. Kashyap, Chandramouli V. Harjani, Ramesh Sapatnekar, Sachin S. Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN United States Cadence Design Systems PortlandOR United States

Device sizing is crucial for meeting performance specifications in operational transconductance amplifiers (OTAs), and this work proposes an automated sizing framework based on a transformer model. The approach first leverages the driving-point signal flow graph (DPSFG) to map an OTA circuit and its specifications into transformer-friendly sequential data. A specialized tokenization approach is applied to the sequential data to expedite the training of the transformer on a diverse range of OTA topologies, under multiple specifications. Under specific performance constraints, the trained transformer model is used to accurately predict DP-SFG parameters in the inference phase. The predicted DP-SFG parameters are then translated to transistor sizes using a precomputed look-up table-based approach inspired by the gm/Id methodology. In contrast to previous conventional or machine-learning-based methods, the proposed framework achieves significant improvements in both speed and computational efficiency by reducing the need for expensive SPICE simulations within the optimization loop;instead, almost all SPICE simulations are confined to the onetime training phase. The method is validated on a variety of unseen specifications, and the sizing solution demonstrates over 90% success in meeting specifications with just one SPICE simulation for validation, and 100% success with 3–5 additional SPICE simulations. © 2025, CC BY.

关键词： Table lookup

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Development of a Hybrid Measurement System for Surgical Instrument Motion of Laparoscopic Surgery

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年第2期7卷 550-561页

作者： Ebina, Koki Abe, Takashige Yan, Lingbo Hotta, Kiyohiko Kamijo, Chihiro Higuchi, Madoka Kon, Masafumi Kikuchi, Hiroshi Miyata, Haruka Matsumoto, Ryuji Osawa, Takahiro Murai, Sachiyo Kurashima, Yo Shichinohe, Toshiaki Watanabe, Masahiko Komizunai, Shunsuke Tsujita, Teppei Sase, Kazuya Chen, Xiaoshuai Senoo, Taku Shinohara, Nobuo Konno, Atsushi Hokkaido University Graduate School of Information Science and Technology Sapporo Japan Hokkaido University Faculty of Medicine Department of Renal and Genitourinary surgery Sapporo Japan Hokkaido University Clinical simulation center Faculty of Medicine Sapporo Japan Hokkaido University Faculty of Medicine Department of Gastroenterological Surgery II Sapporo Japan Hokkaido University Faculty of Medicine Department of Anatomy Sapporo Japan Kagawa University Faculty of Eenginerring and Design Takamatsu Japan National Defense Academy of Japan Department of Mechanical Engineering Yokosuka Japan Tohoku Gakuin University Department of Mechanical Engineering and Intelligent Systems Sendai Japan Hirosaki University Graduate School of Science and Technology Hirosaki Japan

Laparoscopic surgery has become a common surgical technique owing to its minimal invasiveness. However, surgeons require advanced techniques, and several studies have evaluated surgical skills through motion measurements to improve skill proficiency. However, existing measurement systems have a low tolerance for occlusion and are difficult to use in operating rooms with many obstacles. Therefore, a hybrid measurement system was developed for laparoscopic surgery. This system consists of an inertial measurement unit (IMU), a distance sensor, and an optical motion capture (MoCap). When MoCap data are unavailable, surgical instrument motion is calculated using the IMU and distance sensor data, and when it is available, the IMU drift is corrected using MoCap data. The MoCap markers were arranged individually, thus facilitating the measurement of multiple instruments simultaneously. The validation experiment in the wet-lab training confirmed that the error was smaller than that measured using MoCap alone, and the subjects expressed that the subjective disturbance caused by the sensors was very small during the procedure. The measurement experiment was conducted in cadaver surgical training, and 15 cases of nephrectomy were successfully recorded. This system facilitated highly accurate measurements during practical surgical training and surgical skills analysis. © 2018 IEEE.

关键词： Laparoscopy

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5DGWO-GAN:A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT systems

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Computers, Materials & Continua 2025年第1期82卷 881-911页

作者： Sarvenaz Sadat Khatami Mehrdad Shoeibi Anita Ershadi Oskouei Diego Martín Maral Keramat Dashliboroun Department of Data Science Engineering University of HoustonHoustonTX 77204USA The WPI Business School Worcester Polytechnic InstituteWorcesterMA 01609USA School of Systems and Enterprises Stevens Institute of TechnologyHobokenNJ 07030USA Department of Computer Science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Ernest G.Welch School of Art&Design Georgia State UniversityAtlantaGA 30303USA

The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control *** the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are *** detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate *** paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT ***,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss *** address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN *** 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local *** proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model *** generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded *** evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and *** conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false *** 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence *** proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and

关键词： Internet of things intrusion detection generative adversarial networks five-dimensional binary gray wolf optimizer deep learning

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Exploring Users' Perspectives on a Solid-Enabled Personal Data Store Enhanced Streaming Service 25

Exploring Users' Perspectives on a Solid-Enabled Personal Da...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing systems

作者： Tim Theys Stephanie Van Hove Peter Mechant Gill Van Impe Alexander Heerinckx Jelle Saldien imec-mict-UGent Department of Industrial Systems Engineering and Product Design Ghent University Ghent Belgium imec-mict-UGent Department of Communication Sciences Ghent University Ghent Belgium imec-mict-UGent Department of Industrial Systems Engineering and Product Design Ghent University Ghent Belgium and Department of Product Development University of Antwerp Antwerpen Belgium

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BatteryCDE: A Transferable Future Capacity Estimation Method for Battery Degradation with Irregular Sampling and Missing Data

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IEEE Transactions on Transportation Electrification 2025年

作者： Wang, Tianjing Ren, Chao Xiong, Houbo Song, Qun Dong, ZhaoYang City University of Hong Kong Department of Electrical Engineering Hong Kong KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Zhejiang University School of Electrical Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

To overcome critical limitations in existing methods for battery capacity estimation, including long-term estimation, handling of irregular sampling and missing data, and transferability across different battery types, this study proposes a novel estimation method, termed batteryCDE, by integrating neural controlled differential equations (CDEs) with attention mechanisms. It first constructs a continuous data path using cubic spline interpolation, followed by neural CDEs that generate feature-wise and cycle-wise attention features to capture essential battery characteristics. These attention-weighted features are processed by another neural CDE to model differential relationships, leading to precise capacity estimations. This study also provides a theoretical analysis of the advantages of batteryCDE over conventional discrete models in terms of long-term estimation, handling of irregular sampling and missing data, and transferability. Extensive experiments evaluate batteryCDE’s performance, utilizing three scenarios: varying estimation horizons, missing data, and cross-battery transfer. Results show that batteryCDE outperforms traditional models like LSTM, Transformer and neural CDE networks. Even with 50% missing data, an estimation horizon of 100 cycles, and application to different batteries, batteryCDE maintains an estimation error below 0.05. Compared to other methods, batteryCDE reduces estimation errors by 6.59% for long-term predictions, 15.09% for handling missing data, and 17.01% for cross-battery transferability. © 2025 IEEE. All rights reserved.

关键词： Battery degradation capacity irregular and missing data neural controlled differential equations transferability

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Numerical Study on Natural Circulation System under Various Cooling Mediums

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Frontiers in Heat and Mass Transfer 2025年第2期23卷 397-420页

作者： Yumei Lv Wei Dai Shupeng Xie Peng Hu Fei He CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Thermal Science and Energy EngineeringUniversity of Science and Technology of ChinaHefei230027China Beijing Institute of Astronautical Systems Engineering Beijing100076China

Aiming at the global design issue of transpiration cooling thermal protection system,a self-driven circulation loop is proposed as the internal coolant flow passage for the transpiration cooling structure to achieve adaptive *** enhance the universality of this internal cooling pipe design and facilitate its application,numerical studies are conducted on this systemwith four commonly used cooling mediums as ***,the accuracy of the numerical method is verified through an established experimental ***,transient numerical simulations are performed on the flow states of different cooling mediums in the new self-circulation *** on the numerical result,the flow,phase change,and heat transfer characteristics of different cooling mediums are *** in fluid velocity and latent heat of phase change result in significant variation in heat exchange capacity among different coolingmediums,with the maximumdifference reaching up to 3 ***,faster circulation speed leads to greater heat transfer capacity,with a maximum of 7600 W/m^(2).Consequently,the operating mechanism and cooling laws of the natural circulation system is further investigated,providing a reference for the practical application of this system.

关键词： Transpiration cooling natural circulation loop phase change heat transfer capability flow state

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Additive manufacturing in space food: A bibliometric review

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Procedia Computer Science 2025年 253卷 2498-2507页

作者： Marco Scatto Virginia Dolci Karen Venturini Benedetta Pini Alberto Petroni Barbara Bigliardi Department of Economics Science Engineering and Design University of San Marino via Consiglio dei Sessanta 99 Dogana (Republic of San Marino) Department of Engineering for Industrial Systems and Technologies University of Parma Parco Area delle Scienze 181/A Parma (Italy)

Additive Manufacturing (AM) is a technique that is gaining momentum in various manufacturing fields. The possibilities that 3D printing as AM technology offers, such as shortening the supply chain, decreasing production time and costs, enabling production in harsh environments, and solving environmental issues, have aroused the interest of the scientific community and companies themselves. One of the possible applications of AM is for food production in spacecrafts or space stations for Space Mission. Bibliometric analysis offers significant opportunities for understanding and advancing the field of additive manufacturing (AM) in the context of space missions. This analysis will provide insights into research trends, technological maturity, and collaborative networks, which are crucial for effective decision-making in this rapidly evolving field. A bibliometric analysis of the literature on the topic will identify4 clusters of investigation and verify the state of the art of this innovation in the food for Aerospace Missions. This analysis of publication trends will help to identify emerging areas of interest and potential gaps in research, guiding future investigations.

关键词： Additive manufacturing space food 3d printing food products bibliometric analysis space mission

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Artificial Intelligence as a Facilitator for Public Administration Procedures: A Literature Review

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Procedia Computer Science 2025年 253卷 2537-2546页

作者： Benedetta Pini Virginia Dolci Emilio Gianatti Alberto Petroni Barbara Bigliardi Azio Barani Department of Engineering for Industrial Systems and Technologies University of Parma Parco Area delle Scienze 181/A Parma (Italy) Department of Economics Science Engineering and Design University of San Marino via Consiglio dei Sessanta 99 Dogana (Republic of San Marino)

The concept of Artificial Intelligence (AI) has emerged as an interdisciplinary research field encompassing a wide range of advanced technologies. These technologies are designed to gather information from the surrounding environment and act autonomously in order to achieve specific goals. The scientific literature emphasizes how AI has become an essential component in multiple fields, with a particularly significant impact in areas of public interest. In the context of the public sector, AI offers extraordinary potential for improving operational efficiency, increasing the quality of services and supporting decision-making processes. This study aims to examine the adoption of AI in public administrations through a review of available scientific literature. The research focuses on identifying the main themes and key relationships in the application of AI in the public sector. The aim is to draw a clear picture of current trends, challenges and opportunities associated with the use of AI in public institutions.

关键词： Artficial Intelligence (AI) Public Sector Public Administration Review

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Artificial intelligence in consumer preferences: implications for health and well-being

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Procedia Computer Science 2025年 253卷 2869-2878页

作者： Barbara Bigliardi Eleonora Bottani Virginia Dolci Laura Monferdini Benedetta Pini Department of Engineering for Industrial Systems and Technologies University of Parma Parco Area delle Scienze 181/A Parma (Italy) Department of Economics Science Engineering and Design University of San Marino via Consiglio dei Sessanta 99 Dogana (Republic of San Marino)

The article explores how AI influences consumer behavior and wellbeing, addressing both the opportunities and challenges posed by AI technologies. It acknowledges the exponential growth of AI capabilities facilitated by advancements in computing power, IoT, and big data, which have revolutionized industries such as manufacturing and services. This evolution underscores the necessity for understanding AI’s impacts on consumer experiences, ethical dimensions, and societal implications. The research’s timely focus on AI’s role in enhancing or hindering consumer health and wellbeing directly responds to current economic contexts shaped by technological advancements.

关键词： Artificial Intelligence Consumers experience health well-being

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Impact of EMI on the Reliability of Crossbar Architecture-based Inference in CMOS Technology

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Shivdeep Boyapati, Subrahmanyam Das, Devarshi Mrinal Indian Institute of Technology Ropar Department of Electrical Engineering Punjab Rupnagar140001 India Carinthia University of Applied Sciences Center for Integrated Systems and Circuits Design Austria

Electromagnetic Interference (EMI) can compromise the accuracy of inference and, hence, the reliable decision-making of an AI-based autonomous circuit. The summing amplifier at the crossbar’s output converts the weighted sum of currents into voltage. Amplifiers are inherently susceptible to EMI, which induces a DC offset at the output, leading to incorrect summation. This can potentially compromise the reliability of crossbar-based hardware implementing artificial intelligence for mission-critical applications. This paper investigates the EMI susceptibility of amplifiers and crossbar circuits. We have designed the crossbar circuit of size 2×2 using 180 nm CMOS technology to show the EMI-induced offset, considering resistive weights. The effect of weights of the crossbar circuit on the EMI susceptibility is discussed. The EMI-immune amplifier and crossbar circuit design to achieve immunity without trading off the area, power, and complexity is proposed, as these are critical parameters for large crossbar circuits. The problem is illustrated by implementing the 2×2 crossbar circuit with commercial OpAmp ICs. A 7-segment digit recognition application is implemented on a 7×10 crossbar to illustrate the incorrect inference due to EMI susceptibility and made EMI-immune. © 2011-2012 IEEE.

关键词： CMOS integrated circuits

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