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Efficiently Removing Dissolved Organic Pollutants in Landfill Leachate Concentrate Using Dual-Anode Fe2+/Hclo System: The Significance of Insolubilization Based On Oxidative Coupling of Organics

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SSRN 2023年

作者： Liang, Sheng Wang, Jinling Shen, Zhizhi Yan, Wei Nengzi, Lichao Feng, Chunhua Yu, Li Hu, Junqing College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen518118 China Faculty of Chemical Engineering Kunming University of Science and Technology Kunming650500 China State Environmental Protection Key Laboratory of Managing Technology of Drinking Water Source Shenzhen Academy of Environmental Science Shenzhen518001 China Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters Ministry of Education School of Environment and Energy South China University of Technology Guangzhou510006 China

The strategy to mineralize dissolved organics for the decontamination of landfill leachate concentrate (LLC) is expensive. Because mineralization requires high stoichiometric ratio of oxidants (e.g., •OH) or electron transfer (i.e., energy) to the pollutants molecular. Whereas, transferring the dissolved organic pollutants into separable insoluble matters is an energy-saving strategy. Herein, we developed a dual-anode Fe2+/HClO system for removing dissolved organic pollutants in LLC through insolubilization based on oxidative coupling. In this system, the majority of dissolved organic pollutants were transferred into insoluble substances, which could be removed easily. 89.8% of the dissolved organic carbon (DOC) in LLC was fast removed in 3 hours with a low specific energy consumption (SEC, 4.0 kWh kg-1 COD and 17.6 kWh kg-1 DOC), and the formation of toxic by-products can be well controlled. Interestingly, the formation of insoluble substances was predominantly attributed to the oxidative coupling of organic small molecules into the macromolecular ones. This was evidenced by the generation of insoluble compounds with the larger molecular mass (evidenced by mass spectrums), and the formation of coupling or polymerization products of the model pollutant (guaiacol). Specifically, the macromolecular matters were derived from the organic radicals (evidenced by EPR tests) coupling with each other. It was reported that Fe2+/HClO reaction produced reactive Fe(IV) under acid condition. According to the investigations in this work, we propose it is possibly because Fe(IV) was generated in the dual-anode Fe2+/HClO system, and Fe(IV) oxidized pollutants to produce the organic radicals. This work offers a novel electrochemical method for efficient removal of organic pollutants in LLC with minimal toxic by-products generated, and revealed the significance of oxidative coupling process in the electrochemical Fe2+/HClO system. © 2023, The Authors. All rights reserved.

关键词： Organic pollutants

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Design Method for Series-Fed Omnidirectional Filtenna Based on Full-Metal 3-D Printing

Design Method for Series-Fed Omnidirectional Filtenna Based ...

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Cross Strait Quad-Regional Radio science and Wireless technology Conference (CSRWTC)

作者： Shu-Qing Zhang Shu-Ting Wu Sai-Wai Wong Yejun He Zhonghe Zhang Xuzhou Yu State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen Key Laboratory of Antennas and Propagation Guangdong Engineering Research Center of Base Station Antennas and Propagation College of Electronics and Information Engineering Shenzhen University China The Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau SAR China

ISBN: (数字)9798331507794

ISBN: (纸本)9798331507800

This paper proposed a design approach for a series-fed omnidirectional antenna based on 3D full-metal printing. A major issue when designing series-fed antennas is the radiation pattern lobing caused by the unique feeding mechanism. In this paper, necessary adjustments are made based on the phase relationships between the currents and electric fields of each radiating element, resulting in improved radiation characteristics for the series-fed antenna. To validate this idea, the paper designs a rod-shaped full-metal omnidirectional antenna, achieving a wide operating bandwidth and high gain. Additionally, a filtering antenna with omnidirectional radiation is designed, which achieves a high level of out-of-band suppression as well as good omnidirectional radiation performance.

关键词： Wireless communication Phased arrays Filtering Design methodology Omnidirectional antennas Bandwidth Three-dimensional printing Antenna feeds Electric fields Antenna radiation patterns

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Fedmed-Gan: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis

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SSRN 2023年

作者： Xie, Guoyang Wang, Jinbao Huang, Yawen Lyu, Jiayi Zheng, Feng Zheng, Yefeng Jin, Yaochu Department of Computer Science and Engineering Southern University of Science and Technology 518055 China Department of Computer Science University of Surrey Surrey GuildfordGU2 7XH United Kingdom Tencent Jarvis Lab Shenzhen518040 China Faculty of Technology Bielefeld University Bielefeld33619 Germany Shenzhen-Shanwei Central Hospital China

Utilizing multi-modal neuroimaging data has been proved to be effective in investigating human cognitive activities and certain pathologies. However, it is not practical to obtain the full set of paired neuroimaging data centrally since the collection faces several constraints, \textit{e.g.}, high examination cost, long acquisition time, and image corruption. In addition, these data are dispersed into different medical institutions and thus cannot be aggregated for centralized training considering the privacy issues. There is a clear need to launch federated learning and facilitate the integration of dispersed data from different institutions. In this paper, we propose a new benchmark for federated domain translation on unsupervised brain image synthesis (FedMed-GAN) to bridge the gap between federated learning and medical GAN. FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators, and is widely applied to different proportions of unpaired and paired data with variation adaptation properties. We treat the gradient penalties using the federated averaging algorithm and then leverage the differential privacy gradient descent to regularize the training dynamics. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods, demonstrating that the proposed algorithm outperforms the state-of-the-art. © 2023, The Authors. All rights reserved.

关键词： Gradient methods

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CasCenter: A Cascaded Center Network for Visual Tracking

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Qun Zhang, Haijun Yang, Kai Shi, Yong-Guo Zeng, Deqiang Yap, Wun-She Xili University Town Department of Computer Science Harbin Institute of Technology Shenzhen518055 China WuHan Textile University School of Computer Science and Artificial Intelligence Wuhan430200 China Neijiang Normal University School of Mathematics and Information Science Data Recovery Key Laboratory of Sichuan Province Neijiang641100 China Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering and Science Sungai Long43000 Malaysia

Object tracking has advanced significantly with Transformer-based architectures in recent years. However, replacing convolutional layers with global cross-attention in the tracking head of these architectures results in a loss of object-centric inductive bias. Consequently, existing Transformer-based methods often struggle with complex real-life scenarios, such as low resolution, background clutter, and scale variation. To address this issue, we propose a new Vision Transformer-based anchor-free tracking framework named CasCenter. Specifically, the framework features a cascade attention module in the decoder that propagates tracking cues from the previous tracking head to refine object features in a coarse-to-fine manner, enabling the tracker to focus more effectively on the target. Additionally, to further improve tracking stability and accuracy, we incorporate SIoU loss, a multi-scale tracking head, and a Gaussian mask-constrained cross-attention mechanism that emphasizes target regions while suppressing background interference. Extensive experiments demonstrate the superiority of our proposed CasCenter. © 1975-2011 IEEE.

关键词： Object detection

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Sensing With Random Communication Signals

arXiv

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arXiv 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany Southern University of Science and Technology Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security and also The Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025, CC BY-SA.

关键词： Signaling

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Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

arXiv

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arXiv 2024年

作者： Jin, Lyudong Tang, Ming Pan, Jiayu Zhang, Meng Wang, Hao Zhejiang University University of Illinois at Urbana–Champaign Institute Zhejiang University Haining314400 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China School of Software Technology Zhejiang University Ningbo315048 China Department of Data Science and AI Faculty of Information Technology Monash University MelbourneVIC3800 Australia

In the realm of emerging real-time networked applications such as cyber-physical systems (CPS), the Age of Information (AoI) has emerged as a pivotal metric for evaluating the timeliness. To meet the high computational demands, such as those in smart manufacturing within CPS, mobile edge computing (MEC) presents a promising solution for optimizing computing and reducing AoI. In this work, we study the timeliness of computational-intensive updates and explore jointly optimizing the task updating (when to generate a task) and offloading (where to process a task) policies to minimize AoI. Specifically, we consider edge load dynamics and formulate a task scheduling problem to minimize the expected time-average AoI. Solving this problem is challenging due to the fractional objective introduced by AoI and asynchronous decision-making of the semi-Markov game (SMG). To this end, we propose a fractional reinforcement learning (RL) framework. We begin by introducing a fractional single-agent RL framework and establish its linear convergence rate. Building on this, we develop a fractional multi-agent RL framework, extending Dinkelbach’s method and demonstrating its equivalence to the inexact Newton’s method. Furthermore, we provide the conditions under which the framework achieves linear convergence to the Nash equilibrium (NE). To tackle the challenge of asynchronous decision-making in SMG, we further design an asynchronous model-free fractional multiagent RL algorithm, where each mobile device can determine the task updating and offloading decisions without knowing the real-time system dynamics and decisions of other devices. Experimental results show that when compared with the best baseline algorithm, our proposed algorithms reduce the average AoI by up to 52.6%. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Semantic Feature Division Multiple Access for Digital Semantic Multiple Access Channels

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Shen, Bin Ma, Shuai Chen, Rui Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi'an710071 China Peng cheng Laboratory Shenzhen518066 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

To solve the multi-user semantic communication network capacity bottlenecks, this paper proposes a semantic feature domain multiple access (SFDMA) scheme. By extracting and encoding semantic features and jointly processing multi-user data, the scheme can realize the encoding and transmission of multi-user semantic information in the separated feature subspace. Based on this scheme, a semantic digital multiple access network model is proposed to perform multi-user inference tasks, and an efficient semantic encoding and decoding scheme is developed by using variational information bottleneck (VIB) theory to ensure both the orthogonality in the feature domain and the classification accuracy. Furthermore, a SFDMA model for image reconstruction is designed based on the Swin Transformer. The modified model also considers the reconstruction quality and orthogonality of semantic feature domain. In addition, we design an optimization target for multiple access to implement an adaptive power allocation method for multi-user semantic communications. Simulation results show that our proposed SFDMA scheme exhibits semantic feature domain orthogonality in different applications, and the adaptive power allocation method shows excellent performance in the classification task, and effectively guarantee the quality of service (QoS) of the semantic communications. © 2015 IEEE.

关键词： Image reconstruction

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain technology for Al-Najaf/Iraq Oil Refinery 19

Secure and Privacy-preserving Data-sharing Framework based o...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling

Probabilistic Constellation Shaping for OFDM-Based ISAC Sign...

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IEEE Globecom Workshops

作者： Zhen Du Fan Liu Yifeng Xiong Tony Xiao Han Weijie Yuan Yuanhao Cui Changhua Yao Yonina C. Eldar School of Electronic and Information Engineering Nanjing University of Information Science & Technology Nanjing China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China School of Information and Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Huawei Technologies Ltd. Shenzhen China Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G). In pursuit of this goal, a common strategy is that a unified waveform, such as Orthogonal Frequency Division Multiplexing (OFDM), should serve dual-functional roles by enabling simultaneous sensing and communications (S&C) operations. However, the sensing performance of an OFDM communication signal is substantially affected by the randomness of the data symbols mapped from bit streams. Therefore, achieving a balance between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, in this paper we analyze the ambiguity function of the OFDM communication signal modulated by random data. Subsequently, a probabilistic constellation shaping (PCS) method is proposed to devise the probability distributions of constellation points, which is able to strike a scalable S&C tradeoff of the random transmitted signal. Finally, the superiority of the proposed PCS method over conventional uniformly distributed constellations is validated through numerical simulations.

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An artificial intelligence reconstruction of global gridded surface winds

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science Bulletin 2022年第20期67卷 2060-2063,M0003页

作者： Lihong Zhou Haofeng Liu Xin Jiang Alan D.Ziegler Cesar Azorin-Molina Jiang Liu Zhenzhong Zeng School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Faculty of Fisheries and Aquatic Resources Maejo UniversityChiang Mai 50290Thailand Centro de Investigaciones sobre Desertificación Consejo Superior de Investigaciones Científicas(CIDECSIC-UV-Generalitat Valenciana)ClimateAtmosphere and Ocean Laboratory(Climatoc-Lab)MoncadaValencia 46113Spain

Gridded wind speed data products with global coverage and continuous long-term time series are widely used in many applica-tions,such as evaluating wind energy potential[1]and drought processes[2]However,some available products do not accurately reproduce observed wind speed trends on land[3.4].leading to biased or inaccurate conclusions in studies on wind-related ***-situ weather stations involve direct measurements that accurately preserve wind speed ***,the uneven distribu-tion and incomplete time series have constrained their widespread applications in regional and global *** limitations,which we have encountered firsthand in investigating the global wind silling and reversal phenomena[4].have inspired us to cre-ate a new global gridded surface wind product that preserves observed wind patterns and trends.

关键词：卷积神经网络人工智能缺失值可再生能源平均风速 CNN 气候变化连续性

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