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2023 IEEE International Conference on Communications, ICC 2023

作者： Wang, Fei Zhang, Xi Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&m University College StationTX77843 United States

ISBN: (纸本)9781538674628

Intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Verification 14

LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Ver...

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14th International Symposium on Chinese Spoken Language processing, ISCSLP 2024

作者： Liu, Shuanghong Song, Zhida Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi China School of Intelligence Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798331516826

Due to its superior performance and fewer parameters, CAM++ has become the state-of-the-art model for speaker verification tasks. This model uses 2D convolutional blocks to extract front-end features, which are then fed into a densely connected time-delay neural network backbone to extract deep features. However, the simple stacking of 2D convolutions may lead to the generation of a significant amount of redundant features, which is detrimental to efficient feature extraction. Furthermore, although CAM++ already has a relatively small number of parameters, there is still room for further optimization. To address these issues, this paper first employs depthwise separable convolutions to replace the dilated convolutions in the backend network of CAM++, making the model more lightweight. Next, we introduce spatial and channel reconstruction convolution (SCConv) in the ResBlock module of CAM++ to reduce redundant features and optimize the feature extraction process. Finally, after SCConv, we apply squeeze and excitation attention mechanism to model the interdependencies between channels and recalibrate each channel, further enhancing the model’s representational capacity. We name the resulting model LE-CAM++. Our proposed model achieves an EER of 0.686 and a minDCF of 0.084 on the VoxCeleb1-O dataset. Compared to the baseline model CAM++, the EER is reduced by 11%, and the minDCF is reduced by 28%. Additionally, the model parameters are reduced by 8%. ©2024 IEEE.

关键词： Deep neural networks

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Data Association for Maneuvering Targets Through a Combined Siamese Network and Xgboost Model

SSRN

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SSRN 2023年

作者： Gao, Chang Yan, Junkun Chen, Bo Varshney, Pramod K. Jia, Tianyi Liu, Hongwei National Laboratory of Radar Signal Processing Xidian University Xian710071 China Department of Electrical Engineering and Computer Science Syracuse University NY13244 United States

Data association plays an important role in forming target tracks when false alarms exist. Its accuracy is key to reducing the computational burden of the combinatorial explosion problem inherent to target tracking in environments where false alarms are densely distributed. Traditional methods are usually developed according to some assumed motion models and suffer performance degradation when there is mismatch between assumed models and the actual motion, which may often be the case for maneuvering targets. To cope with this problem, we propose a data-driven method that learns the association criteria directly from data. By use of the domain knowledge and a convolutional Siamese network, features of different attributes present in the observations are extracted. Then the features are fused through the use of XGBoost. Simulation results show that the proposed method performs better than the traditional model-based methods in correlating observations of maneuvering targets and avoiding correlations of false alarms. By visualizing the decision process of the feature fusion model, it is found that the proposed method learns from the data to make comprehensive use of multiple features and does not simply set hard thresholds for the features. The computational complexity is also analyzed both theoretically and experimentally. © 2023, The Authors. All rights reserved.

关键词： Target tracking

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Adaptive IMM Algorithm Based on Variational Inference for Multiple Maneuvering Extended Targets Tracking

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Advances in Astronautics 2025年 1-15页

作者： Wang, Shenghua Li, Renxian Men, Chenkai Cao, Yunhe Yeo, Tat-Soon School of Communications and Information Engineering and School of Artificial Intelligence Xi’an University of Posts and Telecommunications Xi’an China National Lab of Radar Signal Processing Xidian University Xi’an China The Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

In order to track multiple maneuvering extended targets accurately, an adaptive interacting multiple model algorithm based on variational inference (AIMM-VI) is proposed. An augmented state is constructed to cater for time-varying orientation angle and track realistic shape changes, resulting in better elliptical shape estimation and tracking accuracy. Multiple measurements from multiple extended targets are effectively assigned to corresponding targets through the marginal association probability distribution criterion, and the variational inference is used to accurately estimate the augmented state and shape information, which greatly improves the parameters estimation performance. The residual and likelihood functions are updated in real-time according to the results of variational inference, allowing for the updating of the model probability in real-time. The Markov probability transfer matrix is subsequently adaptively updated by the compression ratio, which makes the algorithm more adaptable to maneuvering target and significantly improves the adaptability and robustness of the algorithm. The final simulation and experiment results show that the proposed algorithm can effectively improve the tracking performance of multiple maneuvering extended targets.

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Efficient Fault Detection in Nonlinear Industrial Process: A Reduced Kernel PCA-Based Spectral Clustering Approach

Efficient Fault Detection in Nonlinear Industrial Process: A...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Kais Bouzrara Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The challenges posed by nonlinearities in industrial systems necessitate innovative techniques that outperform the limitations of traditional methods such as principal component analysis (PCA). While Kernel Principal Component Analysis (KPCA) offers a robust solution to handle nonlinear data, its computational requirements are a significant issue, especially for large-sized datasets. In this work, we propose a novel technique, namely, reduced kernel principal component analysis-based spectral clustering (RKPCA SpC ), to monitor and detect faults in the benchmark Tennessee Eastman process. The suggested approach addresses the complexity associated with KPCA by reducing data size during the model training phase. This reduction involves retaining only the principal components, preserving informative features, and selecting pertinent samples without compromising the original data's content. The efficacy of the proposed method is evaluated through key performance metrics, including false alarm rate (FAR), missed detection rate (MDR), detection time delay (DTD), and computation time (CT). Additionally, gained execution time (GET), gained storage space (GSP), and loss function (LF) are considered, providing a comprehensive assessment of the developed paradigms' effectiveness. The results demonstrate the promising capabilities of our proposed scheme.

关键词： Training Measurement Technological innovation Fault detection Delay effects Computational modeling Complexity theory Kernel Monitoring Principal component analysis

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Analysis of Waves and Human Interaction Using Beach Webcams

Analysis of Waves and Human Interaction Using Beach Webcams

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OCEANS

作者： Harel Mendelman Tomer Masssas Yair Moshe Signal and Image Processing Laboratory (SIPL) Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion – Israel Institute of Technology Haifa Israel

Automatic monitoring of beach video streams is important for improving safety, environmental monitoring, research, and education related to beach activities. This paper introduces a novel approach for monitoring water bodies by analyzing beach video streams. In contrast to earlier works, in the proposed approach we analyze not only the behavior of water bodies, or of humans on beach videos, but also the interactions between them. By integrating human activity and water behavior analysis, the approach provides new insights that are unattainable by analyzing each separately. To accomplish this objective, deep neural networks are utilized to analyze video streams from existing beach webcams. The analysis includes the detection and tracking of people and waves, as well as higher level analysis. We use the task of characterizing surfing conditions as a case study and demonstrate our ability to estimate the values of key parameters that can help determine the quality of a surf spot at a given time.

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On the Service Rate Region of Reed-Muller Codes

arXiv

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arXiv 2025年

作者： Ly, Hoang Soljanin, Emina Lalitha, V. Department of Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States Signal Processing and Communications Research Center IIIT Hyderabad India

We study the Service Rate Region (SRR) of Reed-Muller (RM) codes in the context of distributed storage systems. The SRR is a convex polytope comprising all achievable data access request rates under a given coding scheme. It represents a critical metric for evaluating system efficiency and scalability. Using the geometric properties of RM codes, we characterize recovery sets for data objects, including their existence, uniqueness, and enumeration. This analysis reveals a connection between recovery sets and minimum-weight codewords in the dual RM code, providing a framework for identifying small recovery sets. Using these results, we derive explicit and tight bounds for the maximal achievable demand for individual data objects, which define the maximal simplex within the service rate region. © 2025, CC BY.

关键词： Reed-Solomon codes

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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An External Denoising Framework for Magnetic Resonance Imaging: Leveraging Anatomical Similarities Across Subjects with Fast Searches 8

An External Denoising Framework for Magnetic Resonance Imagi...

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8th International Conference on signal and Image processing, ICSIP 2023

作者： Mei, Lifeng Liu, Sixing Tang, Chenhui Cai, Jiaqia Wang, Jingli Liu, Yilong Wu, Ed X. Lyu, Mengye Shenzhen Technology University College of Health Science and Environmental Engineering Shenzhen China Shenzhen Technology University College of Big Data and Internet Shenzhen China Guangzhou China The University of Hong Kong Laboratory of Biomedical Imaging and Signal Processing Department of Electrical and Electronic Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350397932

External denoising, also known as reference-based denoising, utilizes information from clean reference images, yielding more robust results than internal denoising, especially in situations with high noise levels. The effectiveness of reference-based denoising relies on the similarity between reference and noisy images. In this paper, we introduce a novel external denoising framework for magnetic resonance imaging (MRI) that takes advantage of the inherent anatomical structure similarities across subjects. Our framework employs noise-resistant neural networks to extract deep features, which facilitate searches for high-quality reference images from a large external database. This versatile framework is compatible with all reference-based denoising algorithms, obviating the need for acquiring additional reference images from the subject under examination. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Evaluation of Learning Management Systems Based on Usability and User Experience: A Systematic Literature Review

Evaluation of Learning Management Systems Based on Usability...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Emil Agusalim Habi Talib Paulus Insap Santosa Sunu Wibirama Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Signal Processing Laboratory Universitas Gadjah Mada Indonesia

The use of a learning management system (LMS) in the world of education is encouraged to support and to optimize the teaching and learning process. Using the LMS, the learning process that has previously been performed in schools can be performed anywhere and anytime. A good LMS provides convenience and comfort to its users. However, to the best of our knowledge, there is no review study that focusses on the usability and user experience of LMS. Therefore, in this literature study, we surveyed several methods used to measure usability and user experience in the LMS. We managed to collect 40 papers discussing the evaluation of usability and user experience in LMS. Furthermore, we successfully answered four formulated research questions. Based on our study, we found that there are 9 methods used to measure usability and user experience, 5 stages carried out in evaluating usability and user experience, 25 characteristics measured, and there are 13 data analysis methods that can be used to analyze usability and user experience in LMS. These findings are important for academics and practitioners working on information technology to support the educational process.

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