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IEEE Conference on Wireless Communications and Networking

作者： Caijuan Chen Chaofeng Zhang Gaolei Li Yuchen Liu The Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science NC State University North Carolina US

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints.

关键词： NOMA Interference cancellation Heuristic algorithms Disasters Quality of service Throughput Dynamic scheduling Internet of Things Resource management Optimization

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Digital Twins for Dynamic Management of Blockchain Systems 22

Digital Twins for Dynamic Management of Blockchain Systems

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Proceedings of the Winter Simulation Conference

作者： Georgios Diamantopoulos Nikos Tziritas Rami Bahsoon Georgios Theodoropoulos University of Birmingham Birmingham UNITED KINGDOM and Southern University of Science and Technology Shenzhen CHINA Department of Informatics and Telecommunications University of Thessaly Lamia GREECE School of Computer Science University of Birmingham Edgbaston Birmingham UNITED KINGDOM Department of Computer Science and Engineering Research Institute for Trustworthy Southern University of Science and Technology Shenzhen CHINA

Blockchain systems are challenged by the so-called Trilemma tradeoff: decentralization, scalability, and security. Infrastructure and node configuration, choice of the Consensus Protocol, and complexity of the application transactions are cited among the factors that affect the tradeoff balance. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This paper introduces the utilization of DigitalTwins for this purpose. The novel contribution of the paper is the design of a framework and conceptual architecture of a Digital Twin that can assist in maintaining the Trilemma tradeoffs of time-critical systems. The proposed Digital Twin is illustrated via an innovative approach to the dynamic selection of Consensus Protocols. Simulation results show that the proposed framework can effectively support the dynamic adaptation and management of the Blockchain.

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Hepatic Vessel Roadmap Prediction Using Adaptive Tracking and Bending Energy Modeling in X-Ray Fluoroscopy

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Yang, Shuo Xiao, Deqiang Geng, Haixiao Ai, Danni Fan, Jingfan Fu, Tianyu Song, Hong Duan, Feng Yang, Jian Beijing Institute of Technology Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing100081 China Beijing Institute of Technology Zhengzhou Research Institute Zhengzhou450003 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China First Medical Center of Chinese PLA General Hospital Department of Interventional Radiology Beijing100853 China

Dynamic visualization of the hepatic vessel is crucial in X-ray image-guided transjugular intrahepatic portosystemic shunt (TIPS) procedures. However, intraoperative breathing and the presence of guidewires complicate the prediction of the vessel position and posture without contrast agents. The respiration compensation technique aims to utilize the intraoperative respiration modeling to deform the initial vessel roadmap, thereby achieving the dynamic vessel prediction in the X-ray image sequence for the interventional guidance. Therefore, we propose a novel respiration compensation framework utilizing the adaptive tracking and bending energy modeling to achieve the stable vessel roadmap prediction under free breathing. First, we introduce the inter-frame rigid displacement compensation module based on the domain adaptation and adaptive centroid tracking. This module fits the respiratory curve from the X-ray images, providing the temporal motion priors for aligning roadmaps across frames. Second, we propose the novel deformation compensation module based on the bending energy modeling to correct the respiratory motion, wherein we utilize the energy features of the guidewires to drive the non-rigid registration. The control points sampled by the bending energy guide the local image to form the deformation field, facilitating the dynamic overlap of the deformed vessel roadmaps in X-ray images. Experimental results on simulated and clinical datasets show an average tracking error of 0.95 ± 0.26 mm and 1.49 ± 0.40 mm, respectively. The effective and fast (mean 57 ms per frame) compensation achieved by our framework has the potential for improving the outcome of liver intervention and reducing the reliance on contrast agents. © 2013 IEEE.

关键词： Alcohol Abuse Alcohol Dependence Alcohol Use Disorder Electroencephalogram Graph Convolutional Network Multiscale

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Time-dependent alternative route planning 20

Time-dependent alternative route planning

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20th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2020

作者： Kontogiannis, Spyros Paraskevopoulos, Andreas Zaroliagis, Christos D. Department of Computer Science and Engineering University of Ioannina Greece Computer Technology Institute and Press "Diophantus" Patras Greece Department of Computer Engineering and Informatics University of Patras Greece Department of Computer Engineering and Informatics University of Patras Greece Computer Technology Institute and Press "Diophantus" Patras Greece

ISBN: (纸本)9783959771702

We present a new method for computing a set of alternative origin-to-destination routes in road networks with an underlying time-dependent metric. The resulting set is aggregated in the form of a time-dependent alternative graph and is characterized by minimum route overlap, small stretch factor, small size and low complexity. To our knowledge, this is the first work that deals with the time-dependent setting in the framework of alternative routes. Based on preprocessed minimum travel-time information between a small set of nodes and all other nodes in the graph, our algorithm carries out a collection phase for candidate alternative routes, followed by a pruning phase that cautiously discards uninteresting or low-quality routes from the candidate set. Our experimental evaluation on real time-dependent road networks demonstrates that the new algorithm performs much better (by one or two orders of magnitude) than existing baseline approaches. In particular, the entire alternative graph can be computed in less than 0.384sec for the road network of Germany, and in less than 1.24sec for that of Europe. Our approach provides also "quick-and-dirty" results of decent quality, in about 1/300 of the above mentioned query times for continental-size instances. © Spyros Kontogiannis, Andreas Paraskevopoulos, and Christos D. Zaroliagis

关键词： Travel time

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New Paradigms in Manifold Learning for Knowledge Discovery and Inverse Design of Photonic Nanostructures 19

New Paradigms in Manifold Learning for Knowledge Discovery a...

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Quantum Sensing and Nano Electronics and Photonics XIX 2023

作者： Zandehshahvar, Mohammadreza Javani, Mohammad Hadigheh Kiarashi, Yashar Zhu, Muliang Brown, Tyler Bao, Daqian Marzban, Mahmoodreza Pourabolghasem, Reza Adibi, Ali School of Electrical and Computer Engineering Georgia Institute of Technology 778 Atlantic Dr NW GA30332 United States Clark Atlanta University 223 James P Brawley Dr SW AtlantaGA30314 United States Department of Biomedical Informatics Emory School of Medicine 100 Woodruff Circle AtlantaGA30322 United States

ISBN: (纸本)9781510659650

This talk is focused on using the intelligent aspects of machine learning (ML) for both the understanding of the subtle properties of nanophotonic devices and their inverse design to achieve a desired response. It will be shown that by reducing the dimensionality of the problem using manifold learning techniques and simplifying the resulting networks using pruning, the computation complexity of the underlying artificial intelligence (AI) algorithms will be considerably reduced. Furthermore, by optimally defining the loss function (or the metric) for AI algorithms, priceless information about the properties of photonic nanostructures can be uncovered while facilitating the better visualization of the input-output relationship in these nanostructures. In addition, the resulting manifold-learning algorithms can be optimally trained to facilitate the inverse design of such nanostructures while minimizing the structural complexity. This talk will provide the foundation for both knowledge discovery and design in photonic nanostructures using manifold learning and metric learning and their application to the highly desired metaphotonic structures as an example platform. © 2023 SPIE.

关键词： Nanostructures

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Electrically Programmable Non-Volatile Silicon Photonic Content Addressable Memory (CAM) cell

Electrically Programmable Non-Volatile Silicon Photonic Cont...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Lian, Chuanyu Sun, Hongyi Huang, Yi-Siou Vitale, Steven A. Hu, Juejun Takeuchi, Ichiro Youngblood, Nathan Vagionas, Christos Ríos Ocampo, Carlos A. Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Advanced Materials and Microsystems Group MIT Lincoln Laboratory LexingtonMA02139 United States Department of Materials Science & Engineering MIT CambridgeMA02139 United States Quantum Materials Center Department of Physics University of Maryland College ParkMD20742 United States Electrical & Computer Engineering Department The University of Pittsburgh PittsburghPA15213 United States Department of Informatics Center for Interdisciplinary Research and Innovation Aristotle University of Thessaloniki Thessaloniki Greece

We experimentally demonstrate the first electrically programmable, non-volatile silicon photonic content addressable memory cell using Sb2Se3 phase change material on microring resonators, opening the path for light-based search operations in zero-power look-up tables. © Optica Publishing Group 2024

关键词： Silicon photonics

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Dual-Band Planar Microstrip Monopole Antenna Design Using Multi-Objective Hybrid Optimization Algorithm

Dual-Band Planar Microstrip Monopole Antenna Design Using Mu...

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Progress in Electromagnetic research Symposium (PIERS)

作者： V. Hosseini F. Shapour P. Pinho Y. Farhang K. Majidzadeh Ch. Ghobadi J. Nourinia S. Barshandeh M. Shokri Zh. Amiri M. Jalilirad K. Kaboutari Department of Computer Engineering and Information Technology Payame Noor University Tehran Iran Computer Engineering Department Urmia Branch Islamic Azad University Urmia Iran Instituto de Telecomunicações Aveiro Portugal Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Department of Computer Engineering Khoy Branch Islamic Azad University Khoy Iran Department of Electrical Engineering Urmia University Urmia Iran Department of Computer Science School of Engineering Afagh Higher Education Institute Urmia Iran

This research uses a new proposed multi-objective optimization algorithm that utilizes an untested combined chaotic map to integrate Customized Mutated PSO (CM-PSO) with a Modified Genetic Algorithm (MGA) to design an antenna with specific electromagnetic characteristics. The hybrid approach achieves preferred outcomes faster than PSO, CM-PSO, GA, and MGA by avoiding being trapped in local minimums. The proposed Metaheuristic Algorithm (MA) functionality has been authenticated successfully using Benchmark Functions (BFs) like Rastrigin Function (RaF), Ackley Function (AF), Rosenbrock Function (RoF), and Booth Function (BoF). Finally, the validity of the offered approach for electromagnetic applications is demonstrated by optimizing a dual-band planar microstrip monopole antenna with a simple structure, such that its optimized $S_{11}$ be less than -10 dB at two frequency bands encompasses 2.4 to 2.484 and 5.15 to 5.825 GHz for Wireless Local Area Network (WLAN) with IEEE 802.11 standards. The proposed algorithm allows the optimization criteria to be customized. The optimization algorithm developed in MATLAB is used to determine the necessary parameter adjustments in order to achieve expected frequency bands using CM-PSO or an innovative MGA, while high-frequency and electromagnetic simulations are performed using computer Simulation technology (CST) Studio Suite. The dimensions of the proposed antenna's elements are critical input parameters of the algorithm, named decision variables.

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Artificial intelligence-assisted generative pretrained transformers for applications of ChatGPT in higher education among graduates

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SN Social Sciences 2024年第2期4卷 1-23页

作者： Prajapati, Jigna B. Kumar, Ashwini Singh, Sudarshan Prajapati, Bhupendra Thakar, Yash Tambe, Prashant R. Ved, Amit Acharya Motibhai Patel Institute of Computer Studies Ganpat University Gujarat Kherva 384012 India Department of Mechanical Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies (Deemed to Be University) Haryana Faridabad 121004 India Department of Pharmaceutical Sciences Faculty of Pharmacy Chiang Mai University Chiang Mai 50200 Thailand Office of Research Administration Chiang Mai University Chiang Mai 50200 Thailand Shree S. K. Patel College of Pharmaceutical Education and Research Ganpat University Gujarat Kherva 384012 India Assistant Manager at Incubation Centre Ganpat University Gujarat Kherva 384012 India Technical Campus Akole Maharashtra Nawalewadi 422601 India Department of Electrical Engineering Marwadi University Gujarat Rajkot 360003 India

Education that aims at sustainable development is a growing field, notably over the last few decades. Measuring the level of technology-based educational development has become a necessity for well-rounded educational experience. The availability of numerous artificial intelligence-based software is leading education in dynamic way. technology based on artificial intelligence (AI) has been advancing constantly and becoming increasingly pervasive in various facets of our lives. AI in education is facilitating bit-sized learning with maintaining a human touch by digital courses and other low-storage study materials. Chatbots and other AI-enabled technologies assisting in enhancing education learning experience;however, their major drawbacks are algorithmic bias. The present study uses a qualitative case study to analyze chat Generative Pre-trained Transformer (ChatGPT) in education among youngsters and explore its potential opportunities in the fields of higher education. Also, the present survey-based analysis highlights the difficulties, risks, limitations of AI in higher education, and recent developments in AI among the graduates. The survey-based analysis indicated that AI-assisted ChatGPT has gained tremendous popularity among researchers of varied fields. The discussion has led to the conclusion that AI holds a significant promise to be the best solution for all future educational developments in a variety of ways. However, safe usage with ethics is required while working with AI from an academic perspective. Furthermore, a broad investigation is required among various users of different streams and age groups to strengthen utilization of AI-enabled technologies for enhance learning process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Artificial intelligence Case study Chatbots ChatGPT GPT Higher education

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Electrically Programmable Non-Volatile Silicon Photonic Content Addressable Memory (CAM) cell

Electrically Programmable Non-Volatile Silicon Photonic Cont...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Silicon photonics

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User-experience oriented POI recommendation with pseudo rating

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Multimedia Tools and Applications 2025年第16期84卷 16583-16603页

作者： Yi, Kun Yamagishi, Ryu Li, Jiyi Li, Taishan Bai, Zhengyang Ma, Qiang Kyoto Institute of Economic Research Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Graduate School of Informatics Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Department of Computer Science and Engineering University of Yamanashi Takeda 4-3-11 Kofu 400- 8511 Yamanashi Japan Research Institute of Non-Timber Forestry Chinese Academy of Forestry Key Laboratory of Non-Timber Forest Germplasm Enhancement & Utilization of State Administration of Forestry and Grassland No. 3 Weiwu Road Jinshui District Zhengzhou450003 China RIKEN Center for Computational Science RIKEN Nihonbashi 1-chome Mitsubishi Bld. 15th floor 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Information Science Kyoto Institute of Technology Matsugasaki Sakyo-ku Kyoto Kyoto606-8585 Japan

Point-of-interest (POI) recommendation faces challenges in managing data sparsity and cold-start problems, particularly in terms of personalized recommendation. Firstly, most existing users only visit a small number of POIs, which results in severe data sparsity and makes most conventional methods unsuitable because they require adequate historical data from each user to learn the model. In addition, new users have little or no historical record. Existing ask-to-rate methods for cold start user scenarios treat user answers as historical data, which is inappropriate and causes difficulty in capturing user preferences. Therefore, in this paper, to tackle the data sparsity problem, we propose a user-experience model (UEM) to uncover user behaviors from several aspects. The proposed UEM involves four factors: Who (does it), What (the user does), Where (the user does it), and When (the user does it). In contrast to other methods that model user behavior from individual records, our model introduces a latent variable of user experience to extract similarity among users. This feature makes our model suitable for mitigating the severe sparse-data issue in POI recommendations. Furthermore, to handle the cold-start problem for new users, we propose a pseudo-rating mechanism (PRM) to capture new users’ preferences and recommend POIs. For new users, photos are provided for rating, and these ratings are then used to capture their preferences and estimate the probability of their visits. Extensive experimental results demonstrate that the proposed method achieves better recommendation performance and fairness than the state-of-the-art methods. Moreover, user study results validates that the users’ preferences captured via PRM are satisfactory, and UEM provides satisfactory POI recommendations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Behavioral research

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