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IEEE International Conference on Communications (ICC)

作者： Qianqian Wang Quansheng Guan Julian Cheng Xiangdong Jia Mangang Xie Yuankun Tang College of Computer Science and Engineering Northwest Normal University Lanzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou China School of Engineering The University of British Columbia Kelowna BC Canada

Ultrasonic intra-body communication (IBC) is an emerging technique to enable revolutionary healthcare applications. This paper proposes an ultrasonic index modulation with spread spectrum (UsIM-SS) scheme for IBCs. The information bits consist of modulation bits and index bits. The core idea of UsIM-SS is that the modulation bits are spread by spreading codes and the index bits are leveraged to select the time-hoping sequence to transmit the spreading bits. We also find that the existing ultrasonic wideband (UsWB) and UsIM can be regarded as special cases of the proposed UsIM-SS. Furthermore, the soft decision-based maximum likelihood (S-ML) receiver and hard decision-based ML (H-ML) receiver are proposed for UsIM-SS, providing a trade-off between bit-error rate (BER) and complexity. Theoretical spectrum efficiency (SE) and BER expressions for UsIM-SS using S-ML and H-ML are also derived and verified by extensive Monte Carlo simulations. Both theoretical and simulation results show that UsIM-SS can achieve lower BER and higher SE than existing UsWB and Us 1M techniques.

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GNSS Interference Classification Using Federated Reservoir Computing

GNSS Interference Classification Using Federated Reservoir C...

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International Conference on Communication Technology (ICCT)

作者： Ziqiang Ye Yulan Gao Xinyue Liu Yue Xiao Ming Xiao Saviour Zammit National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu China Division of Information Science and Engineering KTH Royal Institute of Technology Stockholm Sweden SWJTU-Leeds Joint School Southwest Jiaotong University Chengdu China Department of Communications and Computer Engineering University of Malta Malta

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The expanding use of Unmanned Aerial Vehicles (UAVs) in vital areas like traffic management, surveillance, and environmental monitoring highlights the need for robust communication and navigation systems. Particularly vulnerable are Global Navigation Satellite Systems (GNSS), which face a spectrum of interference and jamming threats that can significantly undermine their performance. While traditional deep learning approaches are adept at mitigating these issues, they often fall short for UAV applications due to significant computational demands and the complexities of managing large, centralized datasets. In response, this paper introduces Federated Reservoir Computing (FedRC) as a potent and efficient solution tailored to enhance interference classification in GNSS systems used by UAVs. Our experimental results demonstrate that FedRC not only achieves faster convergence but also sustains lower loss levels than traditional models, highlighting its exceptional adaptability and operational efficiency.

关键词： Deep learning Global navigation satellite system Adaptation models Computational modeling Reservoir computing Interference Traffic control Autonomous aerial vehicles Jamming Convergence

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Write Summary Step-by-Step: A Pilot Study of Stepwise Summarization

arXiv

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arXiv 2024年

作者： Chen, Xiuying Gao, Shen Li, Mingzhe Zhu, Qingqing Gao, Xin Zhang, Xiangliang The Computer Electrical and Mathematical Sciences and Engineering division KAUST Saudi Arabia The University of Electronic Science and Technology of China China Ant Group China Peking university China University of Notre Dame American Samoa KAUST Saudi Arabia

Nowadays, neural text generation has made tremendous progress in abstractive summarization tasks. However, most of the existing summarization models take in the whole document all at once, which sometimes cannot meet the needs in practice. Practically, social text streams such as news events and tweets keep growing from time to time, and can only be fed to the summarization system step by step. Hence, in this paper, we propose the task of Stepwise Summarization, which aims to generate a new appended summary each time a new document is proposed. The appended summary should not only summarizes the newly added content but is also coherent with the previous summary, to form an up-to-date complete summary. To tackle this challenge, we design an adversarial learning model, named Stepwise Summary Generator (SSG). First, SSG selectively processes the new document under the guidance of the previous summary, obtaining polished document representation. Next, SSG generates the summary considering both the previous summary and the document. Finally, a convolutional-based discriminator is employed to determine whether the newly generated summary is coherent with the previous summary. For the experiment, we extend the traditional two-step update summarization setting to multi-step stepwise setting, and re-propose a large-scale stepwise summarization dataset based on a public story generation dataset. Extensive experiments on this dataset show that SSG achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Ablation studies demonstrate the effectiveness of each module in our framework. We also discuss the benefits and limitations of recent large language models on this task. © 2024, CC BY.

关键词： Large datasets

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Progress on the Journey to Put Field Electron Emission Onto a Better Scientific Basis

Progress on the Journey to Put Field Electron Emission Onto ...

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International Conference on Vacuum Nanoelectronics (IVNC)

作者： Richard G. Forbes Sergey V. Filippov Anatoly G. Kolosko Eugeni O. Popov University of Surrey Advanced Technology Institute & School of Computer Science and Electronic Engineering Guildford Surrey UK Division of Plasma Physics Atomic Physics and Astrophysics Ioffe Institute St Petersburg Russia

This presentation is part of a long-term project to put field electron emission (FE) onto a better scientific basis, by seeking reliable quantitative agreement between theory and experiment, especially as regards emission-current values. The main paper aims are: (1) to respond to remarks made in recent papers [1], [2]; (2) to restate the thinking behind our 2022 methodology [3] for choosing between different FE models using experiments; (3) to assess progress; and (4) to make further suggestions about improved approaches.

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GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction

arXiv

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arXiv 2023年

作者： Sun, Ge Wang, Sheng Zhu, Lei Liu, Ming Ma, Jun Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Accurate prediction of pedestrian trajectories is crucial for improving the safety of autonomous driving. However, this task is generally nontrivial due to the inherent stochasticity of human motion, which naturally requires the predictor to generate multi-modal prediction. Previous works leverage various generative methods, such as GAN and VAE, for pedestrian trajectory prediction. Nevertheless, these methods may suffer from mode collapse and relatively low-quality results. The denoising diffusion probabilistic model (DDPM) has recently been applied to trajectory prediction due to its simple training process and powerful reconstruction ability. However, current diffusion-based methods do not fully utilize input information and usually require many denoising iterations that lead to a long inference time or an additional network for initialization. To address these challenges and facilitate the use of diffusion models in multi-modal trajectory prediction, we propose GDTS, a novel Goal-Guided Diffusion Model with Tree Sampling for multi-modal trajectory prediction. Considering the "goal-driven" characteristics of human motion, GDTS leverages goal estimation to guide the generation of the diffusion network. A two-stage tree sampling algorithm is presented, which leverages common features to reduce the inference time and improve accuracy for multi-modal prediction. Experimental results demonstrate that our proposed framework achieves comparable state-of-the-art performance with real-time inference speed in public datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Artificial Intelligence based Models to Support Water Quality Prediction using Machine Learning Approach

Artificial Intelligence based Models to Support Water Qualit...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： P. William Oluwadare Joshua Oyebode Gandikota Ramu Manish Gupta Dibyhash Bordoloi Anurag Shrivastava Department of Information Technology Sanjivani College of Engineering Savitribai Phule Pune University Pune India Civil and Environmental Engineering Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Division of Research and Development Lovely Professional University Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

The purpose of this study is to simulate the propagation of bio-contamination risk in Water Distribution System (WDS). This will be accomplished by putting an emphasis on source identification and response modelling. This will be accomplished in the WDS via the process of simulating the bio-contamination risk propagation under real environmental circumstances. This will be done in order to accomplish this goal. Due to the failure to take into consideration the ability for the bulk of the pollutants to undergo chemical degradation and the neglecting of dynamic responses, an overestimation of the population that is exposed has resulted. The study presented here takes into account the connected change in a variety of water quality indicators that are caused by the mixing of the pollutants. These measurements include the total organic carbon content, pH, and alkalinity. In practise, the baseline requirements for each of the aforementioned metrics are set at the beginning of the regular WDS monitoring. Contamination is then indicated by considerable deviations from the base line. The study's goal was to determine whether it is feasible to develop a smart monitoring system powered by artificial intelligence (AI) that would successfully enable water operators to ensure near real-time quality control for the early detection of chemical and/or biological pollution and risk management. The study's goal was to see whether it was possible to develop an artificial intelligence (AI)-based smart monitoring system. The study's purpose was to investigate the viability of building a sophisticated monitoring system that would allow water operators to perform quality control in near real time. Artificial Neural Networks (ANN) and other sophisticated pattern recognizers like Support Vector Machines (SVMs) are examples of cutting-edge sensor technologies that have been implemented as solutions to identify anomalies and estimate the severity of such irregularities.

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Spectral Characterization of a Terahertz Vortex Beam Generated Via a Two-Photon Lithography Printed Spiral Phase Plate

Spectral Characterization of a Terahertz Vortex Beam Generat...

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Photonics North

作者： Andreea Aura Paraipan Diana Gonzalez-Hernandez Innem V. A. K. Reddy Giacomo Balistreri Luca Zanotto Mostafa Shalaby Roberto Morandotti Carlo Liberale Luca Razzari Centre Energie Matériaux Telecommunications (INRS-EMT) Institut National de la Recherche Scientifique Varennes QC Canada Biologcal and Environmental Science Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwai Kingdom of Saudi Arabia Deparment of Electrical Engineering University of Buffalo Buffalo New York USA Swiss Terahertz Research-Zürich Zürich Switzerland Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) ThuwaI Kingdom of Saudi Arabia

ISBN: (数字)9798350366365

ISBN: (纸本)9798350366372

A terahertz (THz) vortex beam is generated through a spiral phase plate fabricated via two-photon polymerization lithography (TPL). TPL is a three-dimensional (3D) printing technique that exhibits high resolution and low surface roughness enabling the fabrication of high-frequency THz devices. The SPP is characterized via a scanless THz time-domain imaging (TDI) technique that allows to obtain a coherent hyperspectral analysis of the generated vortex beam. Numerical simulations are also conducted for comparison with experiments, revealing a good agreement.

关键词： Terahertz wave imaging Spirals Lithography Three-dimensional printing Numerical simulation Surface roughness Rough surfaces

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GAN-enabled framework for fire risk assessment and mitigation of building blueprints 30

GAN-enabled framework for fire risk assessment and mitigatio...

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30th International Conference on Intelligent Computing in engineering 2023, EG-ICE 2023

作者： Ye, Mao Chen, Long Milne, Alistair Hillier, John Sølvsten, Simon Wuhan Zhongyuan Electronic Group Co. Ltd. China Department of Computer Science Loughborough University United Kingdom School of Architecture Building and Civil Engineering Loughborough University United Kingdom School of Business and Economics Loughborough University United Kingdom Department of Geography and Environment Loughborough University United Kingdom Department of Sociology Environmental and Business Economics University of Southern Denmark Denmark

Fire risk posed by residential buildings endangers individuals' lives and safety but also severely impacts economic activities of a society. Consequently, minimising the risk of fire and reduce losses through the development of effective solutions for fire risk assessment and mitigation was, is and will remain crucial. This paper proposes a systematic framework for assessing and mitigating fire risk in buildings that incorporates hybrid of computer vision and artificial intelligence techniques. This framework relies on the automatic blueprint interpretation and can handle both manual and digital blueprints relying on the connectivity analysis of room layout. A well-trained connectivity-constrained generative adversarial network (cc-GAN) has been developed to redesign the original building blueprints to meet fire safety criteria. Overall, our work aims to offer building designers and engineers a practical tool for enhancing the fire safety performance of building layouts. © 2023 30th EG-ICE: International Conference on Intelligent Computing in engineering 2023. All rights reserved.

关键词： Generative adversarial networks

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Battle Against Fluctuating Quantum Noise: Compression-Aided Framework to Enable Robust Quantum Neural Network

Battle Against Fluctuating Quantum Noise: Compression-Aided ...

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Design Automation Conference

作者： Zhirui Hu Youzuo Lin Qiang Guan Weiwen Jiang Electrical and Computer Engineering Department George Mason University Fairfax VA US Quantum Science and Engineering Center George Mason University Fairfax VA US Earth and Environmental Sciences Division Los Alamos National Laboratory NM US Department of Computer Science Kent State University Kent OH

Recently, we have been witnessing the scale-up of superconducting quantum computers; however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to leveraging the power of quantum computing. Although there exist error mitigation or error-aware designs for quantum applications, the inherent fluctuation of noise (a.k.a., instability) can easily collapse the performance of error-aware designs. What’s worse, users can even not be aware of the performance degradation caused by the change in noise. To address both issues, in this paper we use Quantum Neural Network (QNN) as a vehicle to present a novel compression-aided framework, namely QuCAD, which will adapt a trained QNN to fluctuating quantum noise. In addition, with the historical calibration (noise) data, our framework will build a model repository offline, which will significantly reduce the optimization time in the online adaption process. Emulation results on an earthquake detection dataset show that QuCAD can achieve 14.91% accuracy gain on average in 146 days over a noise-aware training approach. For the execution on a 7-qubit IBM quantum processor, ibm-jakarta, QuCAD can consistently achieve 12.52% accuracy gain on earthquake detection.

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Fused Fuzzy Deep Learning and Information Steganography for Privacy Preservation in Medical Consumer electronics

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IEEE Transactions on Consumer electronics 2025年

作者： Chen, Jingxue Zhao, Pengbiao Deng, Erqiang Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Xiong, Hu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China the School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China the KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India the Division of Research and Development Lovely Professional University Phagwara India the University Centre of Research and Development Chandigarh University Mohali India the Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia the Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

—Medical Consumer electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be processed using segmentation, diagnosis, recognition to obtain useful information. Deep learning plays an important role in medical image segmentation and recognition. However, other personal information about the patient, such as previous cases, is often required to determine the final diagnosis. However, sending personal information and medical images generated through MCE to the clinic directly exposes patient privacy. Therefore, in this paper, we propose a digital medical image privacy protection framework based on information steganography with fuzzy deep learning. The proposed framework transforms patient privacy information into invisible noise and hides it in the deep covert space of the image. This method plays a crucial role in ensuring patient privacy and data security. Furthermore, this method can ensure a high level of image segmentation accuracy that supports the development of personalized medical services. The experimental results show that the proposed framework can embed patient information in medical images without affecting the segmentation task, and effectively protect privacy. Finally, the proposed framework provides new possibilities and impetus for the popularization of MCE. © 2025 IEEE.

关键词： Medical imaging

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