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IEEE Conference on Wireless Communications and Networking

作者： Yusi Long Gary C.F. Lee Lanhua Li Shimin Gong Sumei Sun Dusit Niyato School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China Institute for Infocomm Research (IR) A*STAR Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic communication network. The energy-limited ground users (GUs) provide semantic services to periodically generated raw data and a UAV relays the extracted semantic information to a base station (BS). Semantic extraction enhances data responsiveness and reduces the age-of-information (AoI) by transmitting only the most essential information. However, more complex semantic extraction increases energy consumption, making it easier for the GUs to deplete their energy. Therefore, we introduce a novel energy-efficient AoI (EAoI) metric to capture both information freshness and energy consumption of the GUs. We formulate a time-averaged EAoI minimization problem by jointly optimizing the GUs' scheduling, pre-extraction strategy, semantic control, computing resource allocation, and the UAV's trajectory. We further propose a semantic-aware joint pre-extraction and trajectory planning (Sem-JPT) algorithm to decompose the complex optimization problem into three subproblems, which are solved by a series of approximation methods. Simulation results demonstrate that semantic communication can reduce the overall EAoI by more than 18% compared with conventional bit-based communication. Moreover, the proposed Sem-JPT algorithm can maintain information freshness and prolong the GUs' lifetimes, outperforming existing baselines.

关键词： Energy consumption Trajectory planning Processor scheduling Wireless networks Semantic communication Minimization Information age Energy efficiency Trajectory Data mining

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TivNe-SLAM: Dynamic Mapping and Tracking via Time-Varying Neural Radiance Fields

arXiv

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arXiv 2023年

作者： Duan, Chengyao Yang, Zhiliu School of Information Science and Engineering Yunnan University Yunnan Kunming650500 China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Yunnan Kunming650500 China

Previous attempts to integrate Neural Radiance Fields (NeRF) into the Simultaneous Localization and Mapping (SLAM) framework either rely on the assumption of static scenes or require the ground truth camera poses, which impedes their application in real-world scenarios. This paper proposes a time-varying representation to track and reconstruct the dynamic scenes. Firstly, two processes, a tracking process and a mapping process, are maintained simultaneously in our framework. In the tracking process, all input images are uniformly sampled and then progressively trained in a self-supervised paradigm. In the mapping process, we leverage motion masks to distinguish dynamic objects from the static background, and sample more pixels from dynamic areas. Secondly, the parameter optimization for both processes is comprised of two stages: the first stage associates time with 3D positions to convert the deformation field to the canonical field. The second stage associates time with the embeddings of the canonical field to obtain colors and a Signed Distance Function (SDF). Lastly, we propose a novel keyframe selection strategy based on the overlapping rate. Our approach is evaluated on two synthetic datasets and one real-world dataset, and the experiments validate that our method achieves competitive results in both tracking and mapping when compared to existing state-of-the-art NeRF-based dynamic SLAM systems. Copyright © 2023, The Authors. All rights reserved.

关键词： SLAM robotics

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Pre-Chirp-Domain Index Modulation for Affine Frequency Division Multiplexing

arXiv

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arXiv 2024年

作者： Liu, Guangyao Mao, Tianqi Liu, Ruiqi Xiao, Zhenyu Beihang University Beijing100191 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China Wireless and Computing Research Institute ZTE Corporation Beijing100029 China

Affine frequency division multiplexing (AFDM), tailored as a novel multicarrier technique utilizing chirp signals for high-mobility communications, exhibits marked advantages compared to traditional orthogonal frequency division multiplexing (OFDM). AFDM is based on the discrete affine Fourier transform (DAFT) with two modifiable parameters of the chirp signals, termed as the pre-chirp parameter and post-chirp parameter, respectively. These parameters can be fine-tuned to avoid overlapping channel paths with different delays or Doppler shifts, leading to performance enhancement especially for doubly dispersive channel. In this paper, we propose a novel AFDM structure with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can embed additional information bits into the pre-chirp parameter design for both spectral and energy efficiency enhancement. Specifically, we first demonstrate that the application of distinct pre-chirp parameters to various subcarriers in the AFDM modulation process maintains the orthogonality among these subcarriers. Then, different pre-chirp parameters are flexibly assigned to each AFDM subcarrier according to the incoming bits. By such arrangement, aside from classical phase/amplitude modulation, extra binary bits can be implicitly conveyed by the indices of selected pre-chirping parameters realizations without additional energy consumption. At the receiver, both a maximum likelihood (ML) detector and a reduced-complexity ML-minimum mean square error (ML-MMSE) detector are employed to recover the information bits. It has been shown via simulations that the proposed AFDM-PIM exhibits superior bit error rate (BER) performance compared to classical AFDM, OFDM and IM-aided OFDM algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Lyapunov-guided Deep Reinforcement Learning for Semantic-aware AoI Minimization in UAV-assisted Wireless Networks

arXiv

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arXiv 2024年

作者： Long, Yusi Gong, Shimin Sun, Sumei Lee, Gary Li, Lanhua Niyato, Dusit The School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen Campus Shenzhen518000 China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China A*STAR Singapore The College of Computing and Data Science Nanyang Technological University Singapore

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic network where the ground users (GUs) periodically capture and upload the sensing information to a base station (BS) via UAVs’ relaying. Both the GUs and the UAVs can extract semantic information from large-size raw data and transmit it to the BS for recovery. Smaller-size semantic information reduces latency and improves information freshness, while larger-size semantic information enables more accurate data reconstruction at the BS, preserving the value of original information. We introduce a novel semantic-aware age-of-information (SAoI) metric to capture both information freshness and semantic importance, and then formulate a time-averaged SAoI minimization problem by jointly optimizing the UAV-GU association, the semantic extraction, and the UAVs’ trajectories. We decouple the original problem into a series of subproblems via the Lyapunov framework and then use hierarchical deep reinforcement learning (DRL) to solve each subproblem. Specifically, the UAV-GU association is determined by DRL, followed by the optimization module updating the semantic extraction strategy and UAVs’ deployment. Simulation results show that the hierarchical structure improves learning efficiency. Moreover, it achieves low AoI through semantic extraction while ensuring minimal loss of original information, outperforming the existing baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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Short-term power load forecasting during the COVID-19 pandemic using XGBoost and Copula theory

Short-term power load forecasting during the COVID-19 pandem...

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2021 China Automation Congress, CAC 2021

作者： Liu, Bo-Wen Xu, Da He, Yong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665426473

The global coronavirus disease (COVID-19) has brought great challenges to the power systems due to its limitations on social, economic and productive activities. This paper proposes a short-term load forecasting method during COVID-19 pandemic based on copula theory and eXtreme Gradient Boosting (XGBoost). In this method, the coupling relationship among the cross-domain meteorological, public health, and mobility time-series data are fully analyzed based on copula theory, which is used for the short-term power load forecasting based on multi-factor fusion XGBoost algorithm. The proposed method has been fully evaluated and benchmarked on available cross-domain open-access United States data to demonstrate its effectiveness and superiority on short-term load forecasting of COVID-19. © 2021 IEEE

关键词： COVID-19

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Phase behavior of symmetric diblock copolymers under 3D soft confinement

arXiv

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arXiv 2024年

作者： He, Zhijuan Huang, Jin Jiang, Kai Shi, An-Chang School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education 411105 China Laboratory of Mathematics and Complex Systems Ministry of Education School of Mathematical Sciences Beijing Normal University Beijing100875 China Department of Physics and Astronomy McMaster University HamiltonONL8S 4M1 Canada

The phase behavior of symmetric diblock copolymers under three-dimensional (3D) soft confinement is investigated using the self-consistent field theory. The soft confinement is realized in binary blends composed AB diblock copolymers and C homopolymers, where the copolymers self-assemble to form a droplet embedded in the homopolymer matrix. The phase behavior of the confined block copolymers is regulated by the degree of confinement and the selectivity of the homopolymers, resulting in a rich variety of novel structures. When the C homopolymers are neutral to the A- and B-blocks, stacked lamellae (SL) are formed where the number of layers increases with the droplet volume, resulting in a morphological transition sequence from Janus particle to square SL. When the C homopolymers are strongly selective to the B-blocks, a series of non-lamellar morphologies, including onion-, hamburger-, cross-, ring-, and cookie-like structures, are observed. A detailed free energy analysis reveals a first-order reversible transformation between SL and onion-like (OL) structures when the selectivity of the homopolymers is changed. Our results provide a comprehensive understanding of how various factors, such as the copolymer concentration, homopolymer chain length, degree of confinement, homopolymer selectivity, affect the self-assembled structures of diblock copolymers under soft 3D confinement. Copyright © 2024, The Authors. All rights reserved.

关键词： Free energy

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Radar Vital Signs Detection Method Based on Variational Mode Decomposition and Wavelet Transform

Radar Vital Signs Detection Method Based on Variational Mode...

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2021 China Automation Congress, CAC 2021

作者： Ma, Shaopeng Xue, Wei Chen, Kehui Wang, Zexi School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665426473

To deal with noise interference in frequency modulated continuous wave (FMCW) radar vital signs and the interference of breathing harmonics on the heartbeat signal, a vital signs detection method based on variational mode decomposition (VMD) and wavelet transform is proposed. First, VMD is applied to decompose the vital signs into a series of intrinsic mode function (IMF) components. Then, the IMF components with spectral peaks in the frequency range of breathing and heartbeat are selected to reconstruct the breathing and heartbeat signals respectively. Finally, the wavelet transform threshold method is used to remove the noise in the breathing and heartbeat signals. The experimental results show the proposed method can overcome the influence of breathing harmonics, accurately extract the breathing and heartbeat signals, and effectively improve the signal-to-noise ratio and detection accuracy of heart rate. © 2021 IEEE

关键词： Variational mode decomposition

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Survival prediction of LGG patients based on deep learning of MRI data and genomic data integration

Survival prediction of LGG patients based on deep learning o...

引用

2021 China Automation Congress, CAC 2021

作者： Sun, Lingzhi He, Yong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665426473

Low-grade gliomas (LGG) is the most common primary intracranial tumor, with high incidence rate, high recurrence rate, high mortality rate and low cure rate. Therefore, it is necessary to predict the survival of LGG patients in diagnosis. Considering the complementarity of information, the new proposed algorithm in this study is designed to integrate Magnetic Resonance Image (MRI) data and gene expression data using deep learning method to predict the Disease Specific Survival (DSS) of LGG patients. Firstly, MRI data of 44 patients is screened from TCIA database, and then the corresponding gene expression data of 44 patients is searched from TCGA database. Then, 724 image feature data extracted from MRI data are filtered and extracted by deep learning method, and DSS tags are used to train the model;deep learning method is used to extract 20530 features of gene expression data, and DSS tags are also used for training. As a contrast, the deep learning method is used to integrate the two features to train the model. Experiments are evaluated on MRI data, gene expression data and the integration data of MRI and gene expression data, respectively. The results show that by using the integration of MRI data and gene expression data performs better than using single data in terms of the time-dependent receiver operating characteristic(ROC) and the area under the curve (AUC) of the ROC curve criteria. © 2021 IEEE

关键词： Magnetic resonance imaging

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Freespace Optical Flow Modeling for Automated Driving

arXiv

引用

arXiv 2023年

作者： Feng, Yi Zhang, Ruge Du, Jiayuan Chen, Qijun Fan, Rui College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China High-Performance Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

— Optical flow and disparity are two informative visual features for autonomous driving perception. They have been used for a variety of applications, such as obstacle and lane detection. The concept of "U-V-Disparity" has been widely explored in the literature, while its counterpart in optical flow has received relatively little attention. Traditional motion analysis algorithms estimate optical flow by matching correspondences between two successive video frames, which limits the full utilization of environmental information and geometric constraints. Therefore, we propose a novel strategy to model optical flow in the collision-free space (also referred to as drivable area or simply freespace) for intelligent vehicles, with the full utilization of geometry information in a 3D driving environment. We provide explicit representations of optical flow and deduce the quadratic relationship between the optical flow component and the vertical coordinate. Through extensive experiments on several public datasets, we demonstrate the high accuracy and robustness of our model. Additionally, our proposed freespace optical flow model boasts a diverse array of applications within the realm of automated driving, providing a geometric constraint in freespace detection, vehicle localization, and more. We have made our source code publicly available at https://***/FSOF. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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