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Toward Energy Efficient Multiuser IRS-Assisted URLLC systems: A Novel Rank Relaxation Method

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arXiv 2023年

作者： Jalali, Jalal Lemic, Filip Tabassum, Hina Berkvens, Rafael Famaey, Jeroen IDLab University of Antwerp - imec Sint-Pietersvliet 7 Antwerp2000 Belgium AI-Driven Systems Lab i2Cat Foundation Barcelona Spain Department of Electrical Engineering and Computer Science York University TorontoON Canada

This paper proposes an energy efficient resource allocation design algorithm for an intelligent reflecting surface (IRS)-assisted downlink ultra-reliable low-latency communication (URLLC) network. This setup features a multi-antenna base station (BS) transmitting data traffic to a group of URLLC users with short packet lengths. We maximize the total network’s energy efficiency (EE) through the optimization of active beamformers at the BS and passive beamformers (a.k.a phase shifts) at the IRS. The main non-convex problem is divided into two sub-problems. An alternating optimization (AO) approach is then used to solve the problem. Through the use of the successive convex approximation (SCA) with a novel iterative rank relaxation method, we construct a concave-convex objective function for each sub-problem. The first sub-problem is a fractional program that is solved using the Dinkelbach method and a penalty-based approach. The second sub-problem is then solved based on semi-definite programming (SDP) and the penalty-based approach. The iterative solution gradually approaches the rank-one for both the active beamforming and unit modulus IRS phase-shift sub-problems. Our results demonstrate the efficacy of the proposed solution compared to existing benchmarks. © 2023, CC BY.

关键词： Beamforming

A Comprehensive Survey of Spectrum Sharing Schemes from a Standardization and Implementation Perspective

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arXiv 2022年

作者： Parvini, Mohammad Zarif, Amir Hossein Nouruzi, Ali Mokari, Nader Javan, Mohammad Reza Abbasi, Bijan Ghasemi, Amir Yanikomeroglu, Halim The Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran The Department of Electrical and Computer Engineering Shahrood University of Technology Iran The Commutations Research Centre Innovation Science and Economic Development Canada OttawaONK2H 8S2 Canada The Department of Systems and Computer Engineering Carleton University Ottawa Canada

As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use of the exclusively allocated bands on a shared basis through spectrum sharing mechanisms. However, due to the technical complexities of these mechanisms, their design presents challenges, as it requires coordination among multiple entities. To address this challenge, in this paper, we begin with a detailed review of the recent literature on spectrum sharing methods, classifying them on the basis of their operational frequency regime-that is, whether they are implemented to operate in licensed bands (e.g., licensed shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing (DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to narrow the gap between the standardization and vendor-specific implementations, we provide a detailed review of the potential implementation scenarios and necessary amendments to legacy cellular networks from the perspective of telecom vendors and regulatory bodies. Next, we analyze applications of artificial intelligence (AI) and machine learning (ML) techniques for facilitating spectrum sharing mechanisms and leveraging the full potential of autonomous sharing scenarios. Finally, we conclude the paper by presenting open research challenges, which aim to provide insights into prospective research endeavors. © 2022, CC BY.

关键词： Standardization

Non-convex potential games for finding global solutions to sensor network localization

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arXiv 2023年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Sensor networks

Performance evaluation of a solar-powered membrane capacitive deionization system considering energy recovery

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Results in Engineering 2025年 26卷

作者： Bahrami, HamidReza Gorjian, Shiva Mokhtarzadeh, Hamed Ghobadian, Barat Kuriqi, Alban Gheisari, Jafar Fraunhofer Institute for Solar Energy Systems ISE Heidenhofstraße 2 Freiburg im Breisgau79110 Germany P.O. Box: 14115-111 Tehran Iran Faculty of Environmental Engineering Wroclaw University of Science and Technology Wroclaw50-370 Poland Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran

Freshwater scarcity remains a global challenge, necessitating sustainable and energy-efficient desalination technologies. Membrane capacitive deionization (MCDI) offers a promising alternative to conventional methods due to its lower energy consumption and potential for energy recovery, making it particularly suitable for off-grid and rural applications. This study investigates the performance of a compact MCDI unit powered by an off-grid photovoltaic (PV) system with battery storage. An experimental approach was adopted to assess the impact of charging current and feed water flow rate on desalination performance and energy recovery. Results indicate that increasing the supplied current from 2 A to 8 A enhances the desalination rate by 49 %, while reducing the flow rate from 1.4 l/min to 0.2 l/min improves desalination efficiency by 25 %. The maximum energy recovery from the MCDI module reaches 17.55 % under a 4 A charging current and 1300 ppm feedwater salinity, with an energy consumption of 0.962 kJ/l of treated saline water. Economic analysis reveals that the system achieves a payback period of less than one year when the treated water price exceeds 0.034 USD/l. The results confirm that the proposed PV-MCDI system is an energy-efficient and cost-effective solution for desalination, particularly in remote areas. Additionally, the integration of renewable energy reduces fossil fuel reliance and minimizes environmental impact, making it a sustainable solution for brackish water treatment. © 2025 The Author(s)

关键词： Desalination

Knowledge Graph Completion with Low-Dimensional Gated Hierarchical Hyperbolic Embedding

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SSRN

SSRN 2024年

作者： Fang, Yan Liu, Xiaodong Lu, Wei Pedrycz, Witold Lang, Qi Yang, Jianhua School of Control Science and Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6R 2V4 AB Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia School of Computer Science and Information Technology Northeast Normal University Changchun130117 China

Knowledge Graph Embedding aims to advance the learning performance of knowledge graphs by deliberating the complexity of hierarchical data structures in a finite-dimensional space. Most traditional Euclidean models struggle to accurately describe and navigate the intricate hierarchical relationships between nodes. The hyperbolic embedding methods, particularly in conjunction with graph neural networks, offer promise in effectively modeling complex hierarchies in a limited-dimensional space. These methods can accommodate more information about nodes under the same conditions but are also more susceptible to interference from complex neighborhood features. This paper introduces HyperGatE, which novelty combines a hyperbolic graph hierarchical neighbor structure based on a gated filter and a hyperbolic isometric rotation model for knowledge graph completion. By analyzing the hierarchical structure of the knowledge base and using a gated mechanism to form multi-layer embedding networks of entity-relational paths, the gated filter selects diverse uneven multi-hop relations within the entity neighborhood and measures them through graph attention networks. Furthermore, the simplified hyperbolic isometric model allows for uncovering hierarchical structures in the data. HyperGatE experimentally surpasses the state-of-the-art Euclidean and hyperbolic space models in high and low dimensions across four well-known data, namely WN18RR, FB15k-237, NELL-995, and Kinship. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images

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Cancer Imaging 2024年第1期24卷 1页

作者： Lee, I-Cheng Tsai, Yung-Ping Lin, Yen-Cheng Chen, Ting-Chun Yen, Chia-Heng Chiu, Nai-Chi Hwang, Hsuen-En Liu, Chien-An Huang, Jia-Guan Lee, Rheun-Chuan Chao, Yee Ho, Shinn-Ying Huang, Yi-Hsiang Division of Gastroenterology and Hepatology Department of Medicine Taipei Veterans General Hospital Taipei Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Computer Science and Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Radiology Taipei Veterans General Hospital Taipei Taiwan National Taiwan University School of Medicine Taipei Taiwan Cancer Center Taipei Veterans General Hospital Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS 2 B) National Yang Ming Chiao Tung University Hsinchu Taiwan College of Health Sciences Kaohsiung Medical University Kaohsiung Taiwan Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan Healthcare and Service Center Taipei Veterans General Hospital Taipei Taiwan

Background: Automatic segmentation of hepatocellular carcinoma (HCC) on computed tomography (CT) scans is in urgent need to assist diagnosis and radiomics analysis. The aim of this study is to develop a deep learning based network to detect HCC from dynamic CT images. Methods: Dynamic CT images of 595 patients with HCC were used. Tumors in dynamic CT images were labeled by radiologists. Patients were randomly divided into training, validation and test sets in a ratio of 5:2:3, respectively. We developed a hierarchical fusion strategy of deep learning networks (HFS-Net). Global dice, sensitivity, precision and F1-score were used to measure performance of the HFS-Net model. Results: The 2D DenseU-Net using dynamic CT images was more effective for segmenting small tumors, whereas the 2D U-Net using portal venous phase images was more effective for segmenting large tumors. The HFS-Net model performed better, compared with the single-strategy deep learning models in segmenting small and large tumors. In the test set, the HFS-Net model achieved good performance in identifying HCC on dynamic CT images with global dice of 82.8%. The overall sensitivity, precision and F1-score were 84.3%, 75.5% and 79.6% per slice, respectively, and 92.2%, 93.2% and 92.7% per patient, respectively. The sensitivity in tumors < 2 cm, 2–3, 3–5 cm and > 5 cm were 72.7%, 92.9%, 94.2% and 100% per patient, respectively. Conclusions: The HFS-Net model achieved good performance in the detection and segmentation of HCC from dynamic CT images, which may support radiologic diagnosis and facilitate automatic radiomics analysis. © The Author(s) 2024.

关键词： Computed tomography Deep learning Detection Hepatocellular carcinoma Segmentation

Experience-Based Evolutionary Algorithms for Expensive Optimization

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arXiv 2023年

作者： Yu, Xunzhao Wang, Yan Zhu, Ling Filev, Dimitar Yao, Xin The CERCIA School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom The Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Ford Motor Company 2101 Village Rd DearbornMI48124 United States

Optimization algorithms are very different from human optimizers. A human being would gain more experiences through problem-solving, which helps her/him in solving a new unseen problem. Yet an optimization algorithm never gains any experiences by solving more problems. In recent years, efforts have been made towards endowing optimization algorithms with some abilities of experience learning, which is regarded as experience-based optimization. In this paper, we argue that hard optimization problems could be tackled efficiently by making better use of experiences gained in related problems. We demonstrate our ideas in the context of expensive optimization, where we aim to find a near-optimal solution to an expensive optimization problem with as few fitness evaluations as possible. To achieve this, we propose an experience-based surrogate-assisted evolutionary algorithm (SAEA) framework to enhance the optimization efficiency of expensive problems, where experiences are gained across related expensive tasks via a novel meta-learning method. These experiences serve as the task-independent parameters of a deep kernel learning surrogate, then the solutions sampled from the target task are used to adapt task-specific parameters for the surrogate. With the help of experience learning, competitive regression-based surrogates can be initialized using only 1d solutions from the target task (d is the dimension of the decision space). Our experimental results on expensive multi-objective and constrained optimization problems demonstrate that experiences gained from related tasks are beneficial for the saving of evaluation budgets on the target problem. Copyright © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training

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Journal of Neuroscience Research 2023年第6期101卷 901-915页

作者： Ng, Hei-Yin Hydra Wu, Changwei W. Huang, Feng-Ying Huang, Chih-Mao Hsu, Chia-Fen Chao, Yi-Ping Jung, Tzyy-Ping Chuang, Chun-Hsiang Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Educational Psychology and Counseling College of Education National Tsing Hua University Hsinchu Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University Taipei Taiwan Brain and Consciousness Research Center Taipei Medical University-Shuang Ho Hospital New Taipei Taiwan Department of Education National Taipei University of Education Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Institute of Behavioral Sciences Chang Gung University Taoyuan Taiwan Department of Child Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San Diego La Jolla CA United States

Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation. © 2023 Wiley Periodicals LLC.

关键词： direct directed transfer function effective connectivity emotional regulation MBSR mindfulness

Standard vs. non-standard cross-validation: evaluation of performance in a space with structured distribution of datapoints

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Procedia computer science 2021年 192卷 1245-1254页

作者： Grzegorz Baron Urszula Stańczyk Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Cross-validation is a popularly used approach to evaluation of performance for classifiers. It relies on random selection of independent samples for training and testing, and assumes that if any similarities among samples exist, they do not lead to known grouping of datapoints in the input space. If these conditions are violated, as it may happen for datasets with some structure of samples included, standard cross-validation can return biased results even for many folds. In the paper the research on cross-validation was reported for application to stylometric datasets, describing a task of authorship attribution. The comparison of standard and non-standard processing was presented. In the latter case, selected subsets of examples were swapped over between training and test sets several times. The experiments with three popular classifiers showed that standard cross-validation tended to give over-optimistic results, whereas non-standard processing was more guarded, and by that more reliable. To avoid high computational costs involved, evaluation based on averaged predictions for limited numbers of test sets can be considered as a reasonable compromise.

关键词： Cross-validation Fold Evaluation of Performance Classification Distribution Stylometry