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arXiv 2023年

作者： Zhao, Boqun Ouyang, Chongjun Zhang, Xingqi Liu, Yuanwei Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The School of Electrical and Electronic Engineering University College Dublin DublinD04 V1W8 Ireland School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

Integrated Sensing and Communications (ISAC) surpasses the conventional frequency-division sensing and communications (FDSAC) in terms of spectrum, energy, and hardware efficiency, with potential for greater enhancement through integration of non-orthogonal multiple access (NOMA). Leveraging these advantages, a multiple-input multiple-output NOMA-ISAC framework is proposed in this paper, in which the technique of signal alignment is adopted. The performance of the proposed framework for both downlink and uplink is analyzed. 1) The downlink ISAC is investigated under three different precoding designs: a sensing-centric (S-C) design, a communications-centric (C-C) design, and a Pareto optimal design. 2) For the uplink case, two scenarios are investigated: a S-C design and a C-C design, which vary based on the order of interference cancellation between the communication and sensing signals. In each of these scenarios, key performance metrics including sensing rate (SR), communication rate (CR), and outage probability are investigated. For a deeper understanding, the asymptotic performance of the system in the high signal-to-noise ratio (SNR) region is also explored, with a focus on the high-SNR slope and diversity order. Finally, the SR-CR rate regions achieved by ISAC and FDSAC are studied. Numerical results reveal that in both downlink and uplink cases, ISAC outperforms FDSAC in terms of sensing and communications performance and is capable of achieving a broader rate region, clearly showcasing its superiority. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Superabelian logics

arXiv

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arXiv 2024年

作者： Cintula, Petr Jankovec, Filip Noguera, Carles Institute of Computer Science Czech Academy of Sciences Pod Vodárenskou věží 271/2 Prague 8182 07 Czech Republic Department of Information Engineering and Mathematics University of Siena San Niccolò via Roma 56 Siena53100 Italy

This paper presents an algebraic study of a family of logics related to Abelian logic (Ab), the logic of Abelian lattice-ordeded groups. Ab is treated as the basic, weakest logic, and its expansions are referred to as superabelian logics. To account for the language differences in Lukasiewicz logics, we introduced the expansion of Ab with an additional constant and call it pointed Abelian logic (pAb), with semantics based on pointed Abelian -groups. We take the first steps in the study of infinitary extensions of Ab, give a completeness theorem for pAb, and obtain axiomatizations of Lukasiewicz unbound logics relative to pAb. The paper concludes with an exploration of other significant extensions of pAb and their algebraic characterizations. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Intelligent and Self- Reconfigurable Manufacturing System

SSRN

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SSRN 2023年

作者： Arnarson, Halldor Tahir, Syed Abdur Rahman Shu, Beibei Bremdal, Bernt Arild Solvang, Bjørn Department of Industrial Engineering UiT The Arctic University of Norway Lodve Langesgate 2 Nordland Narvik8514 Norway Department of Computer Science and Computational Engineering UiT The Arctic University of Norway and Smart Innovation Norway Lodve Langesgate 2 and Håkon Melbergs vei 16 Nordland Halden Narvik8514 and 1783 Norway

Customer demand and profit potential have pushed companies to offer diverse customization options in their products and move towards mass customization. To move towards mass customization reconfigurable manufacturing systems (RMS) have been prosed as a solution. However, it comes with challenges, including labor, costs, time, and complexity. Introducing intelligence, smartness and state-of-the-art industry 4.0 technologies into reconfigurable manufacturing systems (RMS) can be a solution to these challenges. Combining these concepts can be used to develop an intelligent RMS that can be automatically reconfigured without any human intervention. In this paper, a novel framework has been proposed that explains all the details from the idealization phase to design and physical implementation. The framework covers individual elements of the RMS, introduces the technologies that enable intelligence and follows through the steps that add up to the complete functionality of the system. In addition, we propose an intelligent reconfiguration process that automates the reconfiguration of an RMS reconfiguration. Based on this framework and intelligent reconfiguration, a physical demonstration is also presented that proves the efficacy of the system, validates, and verifies the framework. The constructed system is demonstrated as an intelligent RMS capable of mass customization without human effort. © 2023, The Authors. All rights reserved.

关键词： Industry 4.0

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Application of a novel generative adversarial network to wind power forecasting 8

Application of a novel generative adversarial network to win...

引用

8th International Conference of Precision Machinery and Manufacturing Technology, ICPMMT 2023

作者： Liao, G.C. Wu, R.C. Wu, T.T. Liao, B.T. Department of Electrical Engineering Lunghwa University of Science and Technology No. 300 Sec.1 Wanshou Rd. Guishan District Taoyuan City333326 Taiwan Department of Electrical Engineering I-Shou University No. 1 Sec. 1 Syuecheng Rd. Dashu District Kaohsiung City84001 Taiwan Department of Computer Science & Information Engineering Chang Gung University No. 259 Wenhua 1st Rd. Guishan Dist. Taoyuan City33302 Taiwan HQQA Johnson Health Tech. Co. Ltd. No. 999 Dongda Road Daya District Section 2 Taichung City428314 Taiwan

As the global economy rapidly develops, energy consumption and carbon dioxide emissions have increased annually, prompting countries to strive for carbon neutrality by 2050. Accurate wind power forecasting can aid power system dispatch departments to obtain wind farms' output and improve the power system's new energy absorption capacity by coordinating multiple power generation resources. To this end, this study proposes a novel method for wind power forecasting: the Generative Adversarial Network method-based Deep Q Neural Network (GDQN). Wind power is a nonlinear model with random characteristics like dynamics and uncertainty. The GDQN generates wind power data similar to historical wind power data, solving the problem of insufficient wind power data samples by developing adversarial networks. The deep Q-learning network is then utilized to predict future wind power data. The experimental results based on the actual test of the total power generated by all wind turbines in a complete wind farm indicate that the proposed GDQN method can significantly reduce the Mean Absolute Percentage Error (MAPE %) of wind power forecasting, as compared to other commonly used methods in wind power forecasting. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

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A Tutorial on Six-Dimensional Movable Antenna Enhanced Wireless Networks: Synergizing Positionable and Rotatable Antennas

arXiv

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arXiv 2025年

作者： Shao, Xiaodan Mei, Weidong You, Changsheng Wu, Qingqing Zheng, Beixiong Wang, Cheng-Xiang Li, Junling Zhang, Rui Schober, Robert Zhu, Lipeng Zhuang, Weihua Shen, Xuemin Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Chengdu611731 China Shenzhen518055 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Microelectronics South China University of Technology Guangzhou511442 China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing211189 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Erlangen91054 Germany

Six-dimensional movable antenna (6DMA) is a new and revolutionary technique that fully exploits the wireless channel spatial variations at the transmitter/receiver by flexibly adjusting the three-dimensional (3D) positions and/or 3D rotations of antennas/antenna surfaces (sub-arrays), thereby improving the performance of wireless networks cost-effectively without the need to deploy additional antennas. It is thus expected that the integration of new 6DMAs into future sixth-generation (6G) wireless networks will fundamentally enhance antenna agility and adaptability, and introduce new degrees of freedom (DoFs) for system design. Despite its great potential, 6DMA faces new challenges to be efficiently implemented in wireless networks, including corresponding architectures, antenna position and rotation optimization, channel estimation, and system design from both communication and sensing perspectives. In this paper, we provide a tutorial on 6DMA-enhanced wireless networks to address the above issues by unveiling associated new channel models, hardware implementations and practical position/rotation constraints, as well as various appealing applications in wireless networks. Moreover, we discuss two special cases of 6DMA, namely, rotatable 6DMA with fixed antenna position and positionable 6DMA with fixed antenna rotation, and highlight their respective design challenges and applications. We further present prototypes developed for 6DMA-enhanced communication along with experimental results obtained with these prototypes. Finally, we outline promising directions for further investigation. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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On Sequential Bayesian Inference for Continual Learning

arXiv

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arXiv 2023年

作者： Kessler, Samuel Cobb, Adam Rudner, Tim G.J. Zohren, Stefan Roberts, Stephen J. University of Oxford Department of Engineering Science OxfordOX2 6ED United Kingdom University of Oxford Department of Computer Science OxfordOX1 3QG United Kingdom SRI International ArlingtonVA22209 United States

Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revisit sequential Bayesian inference and assess whether using the previous task’s posterior as a prior for a new task can prevent catastrophic forgetting in Bayesian neural networks. Our first contribution is to perform sequential Bayesian inference using Hamiltonian Monte Carlo. We propagate the posterior as a prior for new tasks by approximating the posterior via fitting a density estimator on Hamiltonian Monte Carlo samples. We find that this approach fails to prevent catastrophic forgetting demonstrating the difficulty in performing sequential Bayesian inference in neural networks. Furthermore, we study simple analytical examples of sequential Bayesian inference and CL and highlight the issue of model misspecification which can lead to sub-optimal continual learning performance despite exact inference. Furthermore, we discuss how task data imbalances can cause forgetting. From these limitations, we argue that we need probabilistic models of the continual learning generative process rather than relying on sequential Bayesian inference over Bayesian neural network weights. Our final contribution is to propose a simple baseline called Prototypical Bayesian Continual Learning, which is competitive with the best performing Bayesian continual learning methods on class incremental continual learning computer vision benchmarks. © 2023, CC BY.

关键词： Contrastive Learning

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Distributed Task-Oriented Communication Networks with Multimodal Semantic Relay and Edge Intelligence

arXiv

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arXiv 2024年

作者： Guo, Jie Chen, Hao Song, Bin Chi, Yuhao Yuen, Chau Yu, Fei Richard Li, Geoffrey Ye Niyato, Dusit The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The Hangzhou Institute of Technology Xidian University Hangzhou311231 China The School of Electrical and Electronics Engineering The School of Computer Science and Engineering Respectively Nanyang Technological University 639798 Singapore The Department of Systems and Computer Engineering Carleton University OttawaK1S 5B6 Canada The Faculty of Engineering Imperial College London LondonSW7 2AZ United Kingdom

In this article, we present a novel framework, named distributed task-oriented communication networks (DTCN), based on recent advances in multimodal semantic transmission and edge intelligence. In DTCN, the multimodal knowledge of semantic relays and the adaptive adjustment capability of edge intelligence can be integrated to improve task performance. Specifically, we propose the key techniques in the framework, such as semantic alignment and complement, a semantic relay scheme for deep joint source-channel relay coding, and collaborative device-server optimization and inference. Furthermore, a multimodal classification task is used as an example to demonstrate the benefits of the proposed DTCN over existing methods. Numerical results validate that DTCN can significantly improve the accuracy of classification tasks, even in harsh communication scenarios (e.g., low signal-to-noise regime), thanks to multimodal semantic relay and edge intelligence. © 2024, CC BY-NC-SA.

关键词： Signal to noise ratio

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An Application of Quadrature-Inspired Generalized Choquet Integral to Anomaly Detection and Multi-Class Problems

SSRN

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SSRN 2023年

作者： Karczmarek, Pawel Dolecki, Michal Powroznik, Pawel Lagodowski, Zbigniew A. Gregosiewicz, Adam Galka, Lukasz Pedrycz, Witold Czerwinski, Dariusz Department of Computer Science Lublin University of Technology ul. Nadbystrzycka 36B Lublin20-618 Poland Department of Mathematics Lublin University of Technology ul. Nadbystrzycka 36B Lublin20-618 Poland Department of Electrical & Computer Engineering University of Alberta EdmontonT6R 2V4 AB Canada Department of Electrical and Computer Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw Poland

In this study, we thoroughly analyze and evaluate a novel approach to derive an aggregate classification score. The aggregation process applied to various classifiers is realized on a basis of Choquet integral enhancements. These new expressions are inspired by Newton-Cotes quadratures and other formulae well-known from numerical analysis. Unlike previous approaches where to find the Choquet integral generalization one has to use two or three adjacent values associated to the membership of a given element to individual classes, here, one can use more efficient enhancement. Namely, a t-norm appearing under the symbol of integral can be effectively replaced by mathematical expressions used in carrying out numerical integration formulae. It yields more precise results and fully reflects the concept of numerical integration. Moreover, in a series of experiments, we thoroughly analyze the performance of the proposed approach in terms of accuracy in the classification tasks. We analyze the pros and cons of the new approach and establish the experimental settings which can be used in similar tasks. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection

arXiv

引用

arXiv 2023年

作者： Zhou, Wei Yue, Guanghui Zhang, Ruizeng Qin, Yipeng Liu, Hantao Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada School of Biomedical Engineering Shenzhen University Shenzhen518060 China Beijing Institute of Technology Beijing100081 China School of Computer Science and Informatics Cardiff University CardiffCF24 4AX United Kingdom

Many dense 3D point clouds have been exploited to represent visual objects instead of traditional images or videos. To evaluate the perceptual quality of various point clouds, in this letter, we propose a novel and efficient Reduced-Reference quality metric for point clouds, which is based on Content-oriented sAliency Projection (RR-CAP). Specifically, we make the first attempt to simplify reference and distorted point clouds into projected saliency maps with a downsampling operation. Through this process, we tackle the issue of transmitting large-volume original point clouds to user-ends for quality assessment. Then, motivated by the characteristics of the human visual system (HVS), the objective quality scores of distorted point clouds are produced by combining content-oriented similarity and statistical correlation measurements. Finally, extensive experiments are conducted on SJTU-PCQA and WPC databases. The experimental results demonstrate that our proposed algorithm outperforms existing reduced-reference and no-reference quality metrics, and significantly reduces the performance gap between state-of-the-art full-reference quality assessment methods. In addition, we show the performance variation of each proposed technical component by ablation tests. © 2023, CC BY.

关键词： Luminance

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Adversarial Attacks and Defenses in Deep Learning

引用

engineering 2020年第3期6卷 346-360页

作者： Kui Ren Tianhang Zheng Zhan Qin Xue Liu Institute of Cyberspace Research Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 2E8Canada School of Computer Science McGill UniversityMontrealQC H3A 0E9Canada

With the rapid developments of artificial intelligence(AI)and deep learning(DL)techniques,it is critical to ensure the security and robustness of the deployed ***,the security vulnerability of DL algorithms to adversarial samples has been widely *** fabricated samples can lead to various misbehaviors of the DL models while being perceived as benign by *** implementations of adversarial attacks in real physical-world scenarios further demonstrate their ***,adversarial attack and defense techniques have attracted increasing attention from both machine learning and security communities and have become a hot research topic in recent *** this paper,we first introduce the theoretical foundations,algorithms,and applications of adversarial attack *** then describe a few research efforts on the defense techniques,which cover the broad frontier in the *** open problems and challenges are subsequently discussed,which we hope will provoke further research efforts in this critical area.

关键词： Machine learning Deep neural network Adversarial example Adversarial attack Adversarial defense

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