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Toward Optimal Cost-Energy Management Green Framework for Sustainable Future Wireless Networks

computers, Materials & Continua 2021年第7期68卷 1321-1339页

作者： Mohammed H.Alsharif Abu Jahid Mahmoud A.Albreem Peerapong Uthansakul Jamel Nebhen Khalid Yahya Department of Electrical Engineering College of Electronics and Information EngineeringSejong UniversitySeoul05006Korea Department of Electrical and Computer Engineering University of OttawaOttawaK1N 6N5ONCanada Department of Electronics and Communications Engineering A’Sharqiyah UniversityIbra400Oman School of Telecommunication Engineering Suranaree University of TechnologyNakhon RatchasimaThailand College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Mechatronics Faculty of Engineering and ArchitectureIstanbul Gelisim UniversityIstanbul34310Turkey

The design of green cellular networking according to the trafc arrivals has the capability to reduce the overall energy consumption to a cluster in a cost-effective *** cell zooming approach has appealed much attention that adaptively ofoads the BS load demands adjusting the transmit power based on the trafc intensity and green energy ***,the researchers are focused on implementing renewable energy resources,which are considered the most attractive practices in designing energy-efcient wireless networks over the long term in a cost-efcient way in the existing *** utilization of available solar can be adapted to acquire cost-effective and reliable power supply to the BSs,especially that sunlight is free,available everywhere,and a good alternative energy option for the remote ***,planning a photovoltaic scheme necessitates viability assessment to avoid poor power supply,particularly for ***,cellular operators need to consider both technical and economic factors before the implementation of solar-powered *** paper proposed the usercentric cell zooming policy of solar-powered cellular base stations taking into account the optimal technical criteria obtained by the HOMER software *** results have shown that the proposed system can provide operational expenditure(OPEX)savings of up to 47%.In addition,the efcient allocation of resource blocks(RBs)under the cell zooming technique attain remarkable energy-saving performance yielding up to 27%.

关键词： Wireless networks beyond 5G green communications sustainability OPEX communication systems

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Non-Gaussian States Generation Using a High-Speed Single-Pixel Superconducting Nano-Strip Photon-Number-Resolving Detector

Non-Gaussian States Generation Using a High-Speed Single-Pix...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Takahashi, Kazuma Endo, Mamoru Nomura, Takefumi Sonoyama, Tatsuki Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Yabuno, Masahiro China, Fumihiro Terai, Hirotaka Miki, Shigehito Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan NTT Device Technology Labs. NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Hyogo Kobe651-2492 Japan Graduate School of Engineering Kobe University 1-1 Rokkodai-cho Nada-ku Hyogo Kobe657-0013 Japan Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States

ISBN: (纸本)9781957171395

We performed the first non-Gaussian state generation through two photon subtraction with a recently developed single-pixel superconducting nano-strip photon-number-resolving detector. We observed negative values of Wigner function of W(0,1.0) = -0.0072 ± 0.006 without loss correction. © 2024 The Author(s) © Optica Publishing Group 2024.

关键词： Photons

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Guest Editorial: Sentiment Analysis as a Multidisciplinary Research Area

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第5期3卷 638-641页

作者： Cambria, Erik Xing, Frank Thelwall, Mike Welsch, Roy Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore National University of Singapore Department of Information Systems and Analytics School of Computing 117418 Singapore University of Wolverhampton School of Mathematics and Computing WolverhamptonWV1 1LY United Kingdom Massachusetts Institute of Technology Sloan School of Management CambridgeMA02139 United States

The papers in this special section focus on bringing multidisciplinary knowledge into sentiment analysis. The last two decades have witnessed an enormous amount of research works on sentiment analysis and significant progress has been made. In terms of depth, finer-grained semantic schemas are defined, such as aspect, target, and category. In terms of broadness, multimodal sentiment analysis touches audio and video channels. Conversational sentiment analysis considers contextual and time-dependent relations and domain-specific sentiment analysis settings tackles real-life challenges, including but not limited to user profiling, financial prediction, abusive language detection, and mental health.

关键词： Special Issues And Sections Sentiment Analysis Computational Modeling Semantics Neural Networks Market Research Recommender Systems Knowledge Based Systems Fuzzy Logic

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MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery

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Fundamental Research 2025年

作者： Lan, Wei Tang, Zhentao Liao, Haibo Chen, Qingfeng Chen, Yi-Ping Phoebe Zhang, Zhaolei Wang, Jianxin Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Guangxi 530004 China Department of Computer Science and Information Technology La Trobe University 3086 VIC Australia Donnelly Centre Department of Computer Science and Department of Molecular Genetics University of Toronto M5S 1A1 ON Canada School of Computer Science and Engineering Central South University Hunan 410083 China

Multi-omics cancer data provides complementary views of tumorigenesis and progression. Technical challenges exist in integrating these heterogeneous data into deep learning models to better understand tumorigenesis and predict cancer recurrence. We herein propose a novel end-to-end deep learning method (MULGONET) for cancer recurrence prediction and biomarker discovery. First, MULGONET can effectively solve the curse of dimensionality and the lack of model interpretability in multi-omics data integration. Second, it explores interactions and regulatory relationships between genes and GO terms, thus providing biological insights. Benchmark results show that MULGONET outperforms other contemporary classification methods. It achieves AUPRs of 0.774 ± 0.015, 0.873 ± 0.003 and 0.702 ± 0.011 on the bladder, pancreatic and stomach cancer datasets, respectively. We also show that MULGONET can effectively identify prognostic genes and GO terms associated with cancer recurrence. © 2025

关键词： Biomarker discovery Cancer recurrence Deep learning Interpretability Multi-omics data

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Microengineered platforms for characterizing the contractile function of in vitro cardiac models

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Microsystems & Nanoengineering 2022年第1期8卷 29-50页

作者： Wenkun Dou Manpreet Malhi Qili Zhao Li Wang Zongjie Huang Junhui Law Na Liu Craig A.Simmons Jason T.Maynes Yu Sun Department of Mechanical and Industrial Engineering University of TorontoTorontoON M5S 3G8Canada Program in Molecular Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Biochemistry University of TorontoTorontoON M5S 1A8Canada Institute of Robotics and Automatic Information System and the Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China School of Mechanical&Automotive Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai 200444China Institute of Biomedical Engineering University of TorontoTorontoON M5S 3G9Canada Translational Biology&Engineering Program Ted Rogers Centre for Heart ResearchTorontoON M5G 1M1Canada Department of Anesthesiology and Pain Medicine University of TorontoTorontoON M5S 1A8Canada Department of Anesthesia and Pain Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada Department of Computer Science University of TorontoTorontoON M5T 3A1Canada

Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology,disease modeling and drug cardiotoxicity as well as for therapeutic *** still exist in obtaining the complete capability of in situ sensing to fully evaluate the complex functional properties of cardiac cell/tissue *** to contractile strength(contractility)and beating regularity(rhythm)are particularly important to generate accurate,predictive *** new platforms and technologies to assess the contractile functions of in vitro cardiac models is essential to provide information on cell/tissue physiologies,drug-induced inotropic responses,and the mechanisms of cardiac *** this review,we discuss recent advances in biosensing platforms for the measurement of contractile functions of in vitro cardiac models,including single cardiomyocytes,2D monolayers of cardiomyocytes,and 3D cardiac *** characteristics and performance of current platforms are reviewed in terms of sensing principles,measured parameters,performance,cell sources,cell/tissue model configurations,advantages,and *** addition,we highlight applications of these platforms and relevant discoveries in fundamental investigations,drug testing,and disease ***,challenges and future outlooks of heart-on-a-chip platforms for in vitro measurement of cardiac functional properties are discussed.

关键词： cardiac contractile regularity

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A Utility-frequency Skyline Itemsets Pattern Mining Algorithm with Threshold Restrictions

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1183-1202页

作者： Liu, Yadong Tayeb, Shahab Wu, Jimmy Ming-Tai College of Computer Science and Engineering Shandong University of Science and Technology Qianwangang Road 579 Qingdao China Department of Electrical and Computer Engineering California State University Engineering East 274 Fresno United States Department of Information Management National Kaohsiung University of Science and Technology No1 University Rd. Yanchao Dist Kaohsiung City824 Taiwan

Skyline frequent utility patterns have been extensively studied. However, the existing skyline pattern mining algorithms are inefficiency and time consuming, and can be inadequate in practical applications, as sometimes traditional skyline pattern may obtain points that are relatively extreme in one dimension. These one-sided extreme points are not the points that the user would like to see in many applications, and the user prefer to see the skyline itemsets with relatively balanced two dimensions. Therefore, this paper proposes a new pattern of skyline mining with threshold settings. In the process of new skyline pattern mining, multiple thresholds are used to exclude extreme points that do not satisfy the user’s needs in certain dimensions, while also greatly reducing the number of search size and improving the speed of searching the target itemsets. In addition, the intelligent recommendation system of an online shopping website is taken as an example to demonstrate the application potential of the new pattern in a service AI system. In this paper, the UFmax array structure is applied so that it can be used not only to get the itemsets with the highest utility at the same frequency, but also to exclude the set of items whose utility and frequency both do not satisfy the threshold. The SFUTPMiner algorithm is introduced to make skyline pattern mining more accurate and efficient. Experiments were conducted on six databases respectively, and the proposed algorithm was compared with the current popular mining algorithm. The results show that the designed new pattern of skyline mining with threshold settings not only provides a streamlined and optimised set of result items, but also surpasses the previous traditional pattern in the aspect of runtime, search space and memory consumption. © 2023, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Data mining

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QoE-Guaranteed Optimization in MEC-Enabled Metaverse: An Active Inference Deep Reinforcement Learning Approach

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Du, Jianbo Gong, Jie Chu, Xiaoli Xiong, Zehui Chen, Xianfu Dong, Mianxiong Yu, F. Richard Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an710121 China The University of Sheffield Mappin Street Department of Electronic and Electrical Engineering SheffieldS1 3JD United Kingdom Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore Shenzhen CyberAray Network Technology Company Ltd Shenzhen518000 China Muroran Institute of Technology Department of Sciences and Informatics Muroran050-8585 Japan Carleton University Systems and Computer Engineering OttawaON Canada

In this paper, we consider a MEC-enabled metaverse scenario which consists of a remote metaverse server and an edge server that cooperates to provide services to mobile users. The edge server is deployed at the base station (BS), serves a dual role: augmenting computational capabilities for user equipment (UE) and pre-caching a portion of the metaverse service contents before each time slot. Moreover, the foreground information and the requested contents generated by the UEs can also be cached to the BS. We formulate a problem to maximize the cache hit number by jointly optimizing contents pre-caching and resource allocation at the BS while considering UEs preference and reducing the UEs total energy consumption, essential for the efficient delivery of services in dynamic MEC environments. To solve this problem, we reformulate it as a partially observable markov decision process and propose an active inference enabled deep reinforcement learning algorithm, which combines active inference with deep reinforcement learning to select the optimal strategy by minimizing the expected free energy. Simulations show that the proposed algorithm can effectively improve the total quality of experience and the cache hit number of UEs, while minimizing the UEs total energy consumption compared with other baseline algorithms © 2015 IEEE.

关键词： Active learning

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Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts

arXiv

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arXiv 2024年

作者： Zhao, Changyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin Letaief, Khaled B. CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

AI technologies have become more widely adopted in wireless communications. As an emerging type of AI technologies, the generative artificial intelligence (GAI) gains lots of attention in communication security. Due to its powerful learning ability, GAI models have demonstrated superiority over conventional AI methods. However, GAI still has several limitations, including high computational complexity and limited adaptability. Mixture of Experts (MoE), which uses multiple expert models for prediction through a gate mechanism, proposes possible solutions. Firstly, we review GAI model’s applications in physical layer communication security, discuss limitations, and explore how MoE can help GAI overcome these limitations. Furthermore, we propose an MoE-enabled GAI framework for network optimization problems for communication security. To demonstrate the framework’s effectiveness, we provide a case study in a cooperative friendly jamming scenario. The experimental results show that the MoE-enabled framework effectively assists the GAI algorithm, solves its limitations, and enhances communication security. © 2024, CC BY.

关键词： Network security

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A Review and Benchmark on State-of-the-Art Steel Defects Detection

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SN computer Science 2024年第1期5卷 114页

作者： Chazhoor, Anthony Ashwin Peter Ho, Edmond S. L. Gao, Bin Woo, Wai Lok Computer and Information Science Department Northumbria University ELB 108 Ellison Pl Newcastle upon Tyne NE1 8ST United Kingdom School of Computing Science University of Glasgow 18 Lilybank Gardens Glasgow G12 8RZ United Kingdom School of Automation Engineering University of Electronic Science and Technology of China 4 1st Ring Rd East 2 Section Chenghua District Sichuan Chengdu 610056 China

Steel, a critical material in construction, automobile, and railroad manufacturing industries, often presents defects that can lead to equipment failure, significant safety risks, and costly downtime. This research aims to evaluate the performance of state-of-the-art object detection models in detecting defects on steel surfaces, a critical task in industries such as railroad and automobile manufacturing. The study addresses the challenges of limited defect data and lengthy model training times. Five existing state-of-the-art object detection models (faster R-CNN, deformable DETR, double head R-CNN, Retinanet, and deformable convolutional network) were benchmarked on the Northeastern University (NEU) steel dataset. The selection of models covers a broad spectrum of methodologies, including two-stage detectors, single-stage detectors, transformers, and a model incorporating deformable convolutions. The deformable convolutional network achieved the highest accuracy of 77.28% on the NEU dataset following a fivefold cross-validation method. Other models also demonstrated notable performance, with accuracies within the 70–75% range. Certain models exhibited particular strengths in detecting specific defects, indicating potential areas for future research and model improvement. The findings provide a comprehensive foundation for future research in steel defect detection and have significant implications for practical applications. The research could improve quality control processes in the steel industry by automating the defect detection task, leading to safer and more reliable steel products and protecting workers by removing the human factor from hazardous environments. © 2023, The Author(s).

关键词： Automated defect detection Deep learning models Steel defect detection Steel surface

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A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第5期3卷 833-842页

作者： Hussain, Tassadaq Wang, Wei-Chien Gogate, Mandar Dashtipour, Kia Tsao, Yu Lu, Xugang Ahsan, Adeel Hussain, Amir Edinburgh Napier University School of Computing Edinburgh EH11 4BN United Kingdom National Cheng Kung University Institute of Computer and Communication Engineering Tainan 701 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei 11529 Taiwan Chung Yuan Christian University Department of Electrical Engineering Taoyuan 32023 Taiwan National Institute of Information and Communications Technology Tokyo 187-0021 Japan University of Wolverhampton School of Mathematics and Computer Science Wolverhampton WV1 1LY United Kingdom

Removing background noise from acoustic observations to obtain clean signals is an important research topic regarding numerous real acoustic applications. Owing to their strong model capacity in function mapping, deep neural network-based algorithms have been successfully applied in target signal enhancement in acoustic applications. As most target signals carry semantic information encoded in a hierarchal structure in short-and long-term contexts, noise may distort such structures nonuniformly. In most deep neural network-based algorithms, such local and global effects are not explicitly considered in a modeling architecture for signal enhancement. In this article, we propose a temporal attentive pooling (TAP) mechanism combined with a conventional convolutional recurrent neural network (CRNN) model, called TAP-CRNN, which explicitly considers both global and local information for acoustic signal enhancement (ASE). In the TAP-CRNN model, we first use a convolution layer to extract local information from acoustic signals and a recurrent neural network (RNN) architecture to characterize temporal contextual information. Second, we exploit a novel attention mechanism to contextually process salient regions of noisy signals. We evaluate the proposed ASE system using an infant cry dataset. The experimental results confirm the effectiveness of the proposed TAP-CRNN, compared with related deep neural network models, and demonstrate that the proposed TAP-CRNN can more effectively reduce noise components from infant cry signals with unseen background noises at different signal-to-noise levels. We further tested the TAP-CRNN ASE system on a downstream infant cry detection (ICD) system, which determines whether a sound segment is involved in an infant cry event. Experimental results show that TAP-CRNN ASE can effectively reduce the noise components, thereby improving the performance of ICD under noisy conditions. © 2020 IEEE.

关键词： Acoustic signal enhancement (ASE) bidirectional long-short term memory convolu-tional neural networks recurrent neural networks (RNN)

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