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arXiv 2025年

作者： Ryzhii, V. Tang, C. Ryzhii, M. Shur, M.S. Frontier Research Institute for Interdisciplinary Sciences Tohoku University Sendai980-8578 Japan Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We investigate the carrier transport characteristics of perforated graphene layer (PGL) composed of arrays of interdigital coplanar graphene microribbons (GMRs) connected by graphene nanoribbon (GNR) bridges. We analyze their operation at room-temperature. Under an applied bias voltage, two-dimensional electron and hole systems (2DES and 2DHS) form in adjacent GMRs. The terminal current in these PGL structures is primarily governed by thermionic transport across the GNR bridges. As electrons and holes traverse the GNRs, they induce heating in the 2DES and 2DHS, creating a positive feedback loop between carrier heating and thermionic emission. This phenomenon, characterized as hot-carrier thermal breakdown, can give rise to S-shaped inter-GMR current-voltage characteristics. These unique transport properties make PGLs promising candidates for fast, voltage-controlled room-temperature switches and electromagnetic radiation detectors. © 2025, CC BY.

关键词： Thermionic emission

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Towards functional verifying a family of SystemC TLMs

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Frontiers of computer science 2020年第1期14卷 53-66页

作者： Tun LI Jun YE Qingping TAN College of Computer National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems Changsha 410073China Jiangnan Institute of Computing Technology Wuxi 214083China

It is often the case that in the development of a system-on-a-chip(SoC)design,a family of SystemC transaction level models(TLM)is *** in the same family often share common functionalities but differ in their timing,implementation,configuration and performance in various SoC developing *** most cases,all the TLMs in a family must be verified for the follow-up design *** our previous work,we proposed to call such family TLM product line(TPL),and proposed feature-oriented(FO)design methodology for efficient TPL ***,developers can only verify TLM in a family one by one,which causes large portion of duplicated verification ***,in our proposed methodology,functional verification of TPL has become a *** this paper,we proposed a novel TPL verification method for FO *** our method,for the given property,we can exponentially reduce the number of TLMs to be verified by identifying mutefeature-modules(MFM),which will avoid duplicated *** proposed method is presented in informal and formal way,and the correctness of it is *** theoretical analysis and experimental results on a real design show the correctness and efficiency of the proposed method.

关键词： system-on-a-chip transaction level model SystemC feature-oriented functional verification

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Optimizing Cloud IoT Applications with Machine Learning Driven Hybrid Key Management Protocols for WSN Performance

Optimizing Cloud IoT Applications with Machine Learning Driv...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Simhadri Mallikarjuna Rao B. Adinarayana Kasani Vaddikasulu Satya Prasad Dontamsetti Peesapati Surya Venkata Ranganadh Research Scholar Department of Computer Science and Engineering Aacharya Nagarjuna University Guntur Andhra Pradesh India R &D Department of EEE & CSE Ravi Technology Vishakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Eluru college of engineering and technology Eluru Andhra Pradesh India Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Bhimavaram Andhra Pradesh India Software Engineer USA

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

AI-IoT integration is significant, changing the future of networking, particularly in terms of performance and security enhancements. The collection and transmission of sensitive data across secure networks is crucial for the IoT, which combines embedded systems and Networks of Wireless Sensors (WSNs). Ensuring the efficacy and security of these networks is crucial. In this research, a novel hybrid key Hybrid management protocol for Internet of Things applications using WSNs is proposed. The maximizes network performance of efficiency and security by utilizing cloud services and machine learning, specifically XGBoost. The suggested protocol creates a secure communication framework for WSNs by combining symmetric and asymmetric key management strategies. Furthermore, the centralized functions and scalability of cloud-based services enable network-wide updates and effective key distribution. The protocol offers anomaly detection, intelligent network load balancing, dynamic key management, predictive key distribution, and intelligent network load balancing by employing XGBoost-based machine learning algorithms. By using network behavior modelling and historical data analysis, the system forecasts the optimal times and locations for important updates, reducing overhead and enhancing security. Additionally, anomaly detection based on XGBoost improves the protocol's ability to recognize anomalies and potential security risks. This hybrid architecture creates a highly flexible and effective protocol for IoT-enabled WSNs by combining cloud integration, centralized key management, and AI-driven intelligence. The proposed approach performs better on the network due to proven methods and algorithms.

关键词： Support vector machines Wireless sensor networks Cloud computing Protocols Accuracy Machine learning algorithms Scalability Internet of Things Security Anomaly detection

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Metaverse: A Vision, Architectural Elements, and Future Directions for Scalable and Realtime Virtual Worlds

arXiv

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arXiv 2023年

作者： Ismail, Leila Buyya, Rajkumar Lab School of Computing and Information Systems The University of Melbourne Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

With the emergence of Cloud computing, Internet of Things-enabled Human-computer Interfaces, Generative Artificial Intelligence, and high-accurate Machine and Deep-learning recognition and predictive models, along with the Post Covid-19 proliferation of social networking, and remote communications, the Metaverse gained a lot of popularity. Metaverse has the prospective to extend the physical world using virtual and augmented reality so the users can interact seamlessly with the real and virtual worlds using avatars and holograms. It has the potential to impact people in the way they interact on social media, collaborate in their work, perform marketing and business, teach, learn, and even access personalized healthcare. Several works in the literature examine Metaverse in terms of hardware wearable devices, and virtual reality gaming applications. However, the requirements of realizing the Metaverse in realtime and at a large-scale need yet to be examined for the technology to be usable. To address this limitation, this paper presents the temporal evolution of Metaverse definitions and captures its evolving requirements. Consequently, we provide insights into Metaverse requirements. In addition to enabling technologies, we lay out architectural elements for scalable, reliable, and efficient Metaverse systems, and a classification of existing Metaverse applications along with proposing required future research *** Codes ***, *** © 2023, CC BY.

关键词： Energy efficiency

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Optimal Pricing for Service Caching and Task Offloading in Edge Computing

Optimal Pricing for Service Caching and Task Offloading in E...

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Annual Conference on Wireless On Demand Network systems and Services (WONS)

作者： Feridun Tütüncüoğlu György Dán Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Motivated by the emergence of function-as-a-service (FaaS) as a programming abstraction for edge computing, we consider the problem of caching and pricing applications for edge computation offloading. We model the problem as a multiple-follower Stackelberg game, where the operator is the leader and decides what applications to cache and how much to charge for their use, while the wireless devices (WDs) are the followers and decide whether or not to offload their computations. We show that the WDs’ interaction can be modeled as a player-specific congestion game and show the existence and computability of equilibria. We then show that the equilibrium price of the operator can be computed in polynomial time for any cache placement, and propose a greedy algorithm for computing the applications to be cached. We use extensive simulations to show that the proposed heuristic performs close to optimal at negligible computational overhead.

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Exploring the Potential of LLM-based Chatbots for Task Scheduling in Robot Operations

Exploring the Potential of LLM-based Chatbots for Task Sched...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Catarina Rema Armando Sousa Heber Sobreira Pedro Costa Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

The rise of Industry 4.0 has revolutionized manufacturing by integrating real-time data analysis, artificial intelligence (AI), automation, and interconnected systems, enabling adaptive and resilient smart factories. Autonomous Mobile Robots (AMRs), with their advanced mobility and navigation capabilities, are a pillar of this transformation. However, their deployment in job shop environments adds complexity to the already challenging Job Shop Scheduling Problem (JSSP), expanding it to include task allocation, robot scheduling, and travel time optimization, creating a multi-faceted, non-deterministic polynomial-time hardness (NP-hard) problem. Traditional approaches such as heuristics, meta-heuristics, and mixed integer linear programming (MILP) are commonly used. Recent AI advancements, particularly large language models (LLM), have shown potential in addressing these scheduling challenges due to significant improvements in reasoning and decision-making from textual data. This paper examines the application of LLM to tackle scheduling complexities in smart job shops with mobile robots. Guided by tailored prompts inserted manually, LLM are employed to generate scheduling solutions, being these compared to an heuristic-based method. The results indicate that LLM currently have limitations in solving complex combinatorial problems, such as task scheduling with mobile robots. Due to issues with consistency and repeatability, they are not yet reliable enough for practical implementation in industrial environments. However, they offer a promising foundation for augmenting traditional approaches in the future.

关键词： Job shop scheduling Service robots Large language models Chatbots Real-time systems Mobile robots Resource management Reliability Autonomous robots Smart manufacturing

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Searching for an Accurate Robot Calibration via Improved Levenberg–Marquardt and Radial Basis Function System

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Journal of Field Robotics 2025年第0期

作者： Li, Zhibin Deng, Xun Chen, Tinghui Yang, Yuhang Chen, Linlin Yang, Xiwen Hu, Zhenzhen Hu, Lun Hu, Pengwei Li, Shuai Luo, Xin Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences Urumqi China School of Software Engineering Chengdu University of Information Technology Chengdu China Dazhou Key Laboratory of Government Data Security Sichuan University of Arts and Science Dazhou China School of Software Engineering Xinjiang University Urumqi China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland VTT-Technical Research Centre of Finland Oulu Finland College of Computer and Information Science Southwest University Chongqing China

Robots are frequently utilized in manufacturing, aviation, and other industries, which enhance industrial production efficiency and quality. Specifically, robots perform high-precision tasks like welding, assembly and material handling, which reduce the intensity of manual labor in factories. In the logistics field, robots automatically sort and deliver goods, thereby speeding up supply chain operations. However, the prolonged operation of robots suffers from a decline in positioning accuracy, which makes them unable to satisfy task requirements. To address this challenging issue, this study designs an efficient calibration system integrating the Levenberg–Marquardt algorithm with fuzzy proportion integration differentiation controller and radial basis function neural network. The innovations of this method include: (1) integrating the fuzzy proportion integration differentiation controller into the updating rules of Levenberg–Marquardt algorithm, which further enhances the identification performance of kinematic errors;(2) adopting the radial basis function neural network to handle the robot dynamic errors, which addresses the complexity of dynamic error sources. Extensively experimental robot positioning points are gathered on an HSR JR680 robot, and then experimental validations are conducted by using the designed calibration system. The experiments indicate that the developed algorithm outperforms these existing advanced algorithms. © 2025 Wiley Periodicals LLC.

关键词： Radial basis function networks

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on computer-Supported Cooperative Work and Social Computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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CLOUD load balancing for storing the internet of things using deep load balancer with enhanced security

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Measurement: Sensors 2023年 28卷

作者： Sree Devi, K. Dhana Sumathi, D. Vignesh, V. Anilkumar, Chunduru Kataraki, Kirankumar Balakrishnan, S. Telangana Hyderabad India Department of CSE Kuppam Engineering College AP Kuppam India Department of Computer Science and Engineering Myleripalayam Village Tamil Nadu Othakkal Mandapam India Department of Information Technology GMR Institute of Technology Andhra Pradesh 532127 India Department of Information Science and Engineering RV Institute of Technology and Management Bengaluru India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India

The traditional load balancing some disadvantages are (1) reduction in the wait times, (2) Storing encrypted data in a location is difficult. To solve this issues proposed work introduced a Deep Load Balancer (DLB) use of cloud load balancing for storing the Internet of Things (IoT). The work analyzed the drawbacks of traditional load balancing algorithms and proposes the use of DLB as an improved approach for balancing the load of IoT devices in a cloud environment. Proposed DLB strategy is the process of following steps are (i) normalizing and standardizing the attributes of the resources that are being managed. This step involves (ii) Allocating and optimizing constrained resources using a DLB. (iii) Succeeding in minimizing delay. The advantages of DLB for IoT storage are scalability, cost efficiency, and security is effectively discussed. Finally, DLB approach is which leverages the large amounts of data generated by IoT devices. Furthermore, a deep learning model is proposed for predicting the load balancing efficiency of DLB in a cloud environment. Finally, the experimental results are analyzed and the implications of the proposed approach are clearly discussed. The performance evaluation metrics are Response Time (RT), Makespan (MS), Associated Overhead (AO) and Migration Time (MT) of the proposed work DLB compared with existing techniques are TA, ESCE and TA + ESCE. The results demonstrated that DLB is a promising technique for cloud load balancing of IoT devices. © 2023 The Authors

关键词： Internet of things

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Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling

arXiv

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arXiv 2025年

作者： Kluger, Dan M. Lu, Kerri Zrnic, Tijana Wang, Sherrie Bates, Stephen Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Stanford Data Science Department of Statistics Stanford University StanfordCA94305 United States

Machine learning models are increasingly used to produce predictions that serve as input data in subsequent statistical analyses. For example, computer vision predictions of economic and environmental indicators based on satellite imagery are used in downstream regressions;similarly, language models are widely used to approximate human ratings and opinions in social science research. However, failure to properly account for errors in the machine learning predictions renders standard statistical procedures invalid. Prior work uses what we call the Predict-Then-Debias estimator to give valid confidence intervals when machine learning algorithms impute missing variables, assuming a small complete sample from the population of interest. We expand the scope by introducing bootstrap confidence intervals that apply when the complete data is a nonuniform (i.e., weighted, stratified, or clustered) sample and to settings where an arbitrary subset of features is imputed. Importantly, the method can be applied to many settings without requiring additional calculations. We prove that these confidence intervals are valid under no assumptions on the quality of the machine learning model and are no wider than the intervals obtained by methods that do not use machine learning predictions. Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

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