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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Xi-Yuan Gong, Xiao-Feng Wang, Lei Feng, Wei Lin, Qiu-Hua School of Information and Communication Engineering Dalian University of Technology China

ISBN: (纸本)9798350368741

Recently, coupled tensor decomposition has been widely used in data fusion of a hyperspectral image (HSI) and a multispectral image (MSI) for hyperspectral super-resolution (HSR). However, exsiting works often ignore the inherent non-negative (NN) property of the image data, or impose the NN constraint via hard-thresholding which may interfere with the optimization procedure and cause the method to be sub-optimal. As such, we propose a novel NN coupled canonical polyadic decomposition (NN-C-CPD) algorithm, which makes use of the parametric method and nonlinear least squares (NLS) framework to impose the NN constraint into the C-CPD computation. More exactly, the NN constraint is converted into the squared relationship between the NN entries of the factor matrices and a set of latent parameters. Based on the chain rule for deriving the derivatives, the key entities such as gradient and Jacobian with regards to the latent parameters can be derived, thus the NN constraint is naturally integrated without interfering with the optimization procedure. Experimental results are provided to demonstrate the performance of the proposed NN-C-CPD algorithm in HSR applications. © 2025 IEEE.

关键词： Coupled canonical polyadic decomposition hyperspectral super-resolution non-negative tensor

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Robust Fusion of Bone and Air-Conducted Sensors for Speech Enhancement with Adaptive Temporal-Frequency Attention

Robust Fusion of Bone and Air-Conducted Sensors for Speech E...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Zhenglong Chen, Zhe Yin, Fuliang School of Information and Communication Engineering Dalian University of Technology China

ISBN: (纸本)9798350368741

The multi-modal speech enhancement method has improved performance due to the diverse sources of its input data, which includes low-distortion air-conducted (AC) signals and low-noise bone-conducted (BC) signals. In light of these considerations, a novel complex-domain deep learning-based BC and AC speech fusion enhancement method is proposed. Specifically, the BC and AC spectrum is fed into a pre-fusion module firstly. Then the preliminary fused feature is concatenated with the original signals and fed into encoder, followed by adaptive time-frequency attention modules, in which the self-attention mechanism is employed to reconstruct the encoded features in both the time and frequency axes. After that, two masks are generated by decoders and multiplied with the original BC and AC spectrum. Finally, the two signals are summed and converted to the time-domain. The disabled situation when only one sensor works is considered to improve the robustness by introducing a probability of failure in the process of feeding the data to train the model. Experiments demonstrate that the proposed method outperforms existing fusion approaches, especially in the aspect of robustness against one invalid input channel. © 2025 IEEE.

关键词： attention mechanism bone-conduction feature fusion multi-modal speech enhancement

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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1458-1475页

作者： Jing Liang Hongyu Lin Caitong Yue Ponnuthurai Nagaratnam Suganthan Yaonan Wang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001 School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China KINDI Center for Computing Research Qatar UniversityDohaQatarand also with the Nanyang Technology UniversitySingapore College of Electrical and Information Engineering and also with the National Engineering Research Center for Robot Visual Perception and Control TechnologyHunan UniversityChangsha 410082China IEEE

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective *** paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision *** to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal *** proposed algorithm adopts a dual-population framework and an improved environmental selection *** utilizes a convergence archive to help the first population improve the quality of *** improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first *** combination of these two strategies helps to effectively balance and enhance conver-gence and diversity *** addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is *** proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

关键词： Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.

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Search Algorithm Based on Permutation Group by Quantum Walk on Hypergraphes

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Chinese Journal of Electronics 2022年第4期31卷 626-634页

作者： JIANG Yaoyao CHU Pengcheng MA Yulin MA Hongyang School of Science Qingdao University of Technological School of Information and Control Engineering Qingdao University of Technology

Because a significant number of algorithms in computational science include search challenges and a large number of algorithms that can be transformed into search problems have garnered significant attention, especially the time rate and accuracy of search, a quantum walk search algorithm on hypergraphs,whose aim is to reduce time consumption and increase the readiness and controllability of search, is proposed in this paper. First, the data points are divided into groups and then isomorphic to the permutation set. Second, the element coordinates in the permutation set are adopted to mark the position of the data points. Search the target data by the controllable quantum walk with multiparticle on the ring. By controlling the coin operator of quantum walk, it is determined that search algorithm can increase the accuracy and controllability of search. It is determined that search algorithm can reduce time consumption by increasing the number of search particles. It also provides a new direction for the design of quantum walk algorithms, which may eventually lead to entirely new algorithms.

关键词： Search algorithm Quantum walk Permutation groups

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Non-uniform electron density estimation based on electromagnetic wave attenuation in plasma

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Chinese Physics B 2025年第1期34卷 381-386页

作者： Zhaoying Wang Lixin Guo Maixia Fu Shaoshuai Guo Yinsheng Li Key Laboratory of Grain Information Processing and Control(Henan University of Technology) Ministry of EducationZhengzhou 450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001China School of Physics Xidian UniversityXi’an 710071China

The surface of a high-speed vehicle reentering the atmosphere is surrounded by plasma *** to the influence of the inhomogeneous flow field around the vehicle,understanding the electromagnetic properties of the plasma sheath can be *** the electron density of the plasma sheath is crucial for understanding and achieving plasma stealth of *** this work,the relationship between electromagnetic wave attenuation and electron density is deduced *** attenuation distribution along the propagation path is found to be proportional to the integral of the plasma electron *** result is used to predict the electron density ***,the average electron density is obtained using a back-propagation neural network ***,the spatial distribution of the electron density can be determined from the average electron density and the normalized derivative of attenuation with respect to the propagation *** to traditional probe measurement methods,the proposed approach not only improves efficiency but also preserves the integrity of the plasma environment.

关键词： attenuation electromagnetic propagation plasma electron density

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Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

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China communications 2024年第3期21卷 230-246页

作者： Tong Minglei Li Song Han Wanjiang Wang Xiaoxiang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing ***,the network state information is uncertain or *** deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this *** problem of minimizing the average sum task completion delay of all IoT devices over all time periods is *** decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier *** results validate that the proposed scheme performs better than other baseline schemes.

关键词： computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision

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information Freshness-Oriented Trajectory Planning and Resource Allocation for UAV-Assisted Vehicular Networks

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China communications 2023年第5期20卷 244-262页

作者： Hao Gai Haixia Zhang Shuaishuai Guo Dongfeng Yuan School of Control Science and Engineering Shandong UniversityJinan 250061China Shandong Key Laboratory of Wireless Communication Technologies Shandong UniversityJinan 250061China China Academy of Information and Communication Technology(CAICT) Beijing 100191China

In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum *** adopt the expected sum age of information(ESAoI)to measure the network-wide information *** is jointly affected by both the UAVs trajectory and the resource allocation,which are coupled with each other and make the analysis of ESAoI *** tackle this challenge,we introduce a joint trajectory planning and resource allocation procedure,where the UAVs firstly fly to their destinations and then hover to allocate resource blocks(RBs)during a *** on this procedure,we formulate a trajectory planning and resource allocation problem for ESAoI *** solve the mixed integer nonlinear programming(MINLP)problem with hybrid decision variables,we propose a TD3 trajectory planning and Round-robin resource allocation(TTPRRA).Specifically,we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm(TD3)for UAVs trajectory planning,and utilize Round Robin rule for the optimal resource *** TTP-RRA,the UAVs obtain their flight velocities by sensing the locations and the age of information(AoI)of the vehicles,then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful *** results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI(AAoI).

关键词： information freshness for vehicular networks multi-UAV trajectory planning resource allocation deep reinforcement learning

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Taxonomy of User-Centric Errors Leading to Cyber Attacks in LoRaWAN 17

Taxonomy of User-Centric Errors Leading to Cyber Attacks in ...

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17th International Conference on Security of information and Networks, SIN 2024

作者： Parajuli, Indu Ray, Biplob Rakshit Hassan, Jahan Cowling, Michael College of Information and Communication Technology School of Engineering and Technology Central Queensland University Australia

ISBN: (纸本)9798331509736

In recent years, the use of the Internet of Things (IoT) has grown rapidly in smart cities, households, and smart industries environments due to the benefits of convenience and easy implementation. The Long-Range Wide Area Network (LoRaWAN) is a key technology driving this growth, which has seen significant adoption due to its scalability, long-range capabilities, low power consumption, lost cost and wide area coverage. As the use of LoRaWAN-enabled IoT environment has increased, the cyber-attacks related to it have also increased. About 95 % of the cyber-attacks in IoT environment occurs due to the security vulnerabilities caused by the user's errors. However, much research in the LoRaWAN environ-ment is highly focused on the security vulnerabilities due to technical errors, leaving gaps in identifying the security vulnerabilities that occur due to user errors. Therefore, this paper addresses these gaps by identifying user-centric errors which may lead to cyber-attacks in an LoRAWAN IoT environment. It builds a taxonomy based on a comprehensive literature review and maps these user errors to potential security vulnerabilities within LoRa WAN- enabled IoT environments. © 2024 IEEE.

关键词： Cyber attacks

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Model-Based Cross-Scale Reinforcement Learning Optimal control 6

Model-Based Cross-Scale Reinforcement Learning Optimal Contr...

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6th International Conference on Electronic engineering and Informatics, EEI 2024

作者： Li, Gonghe Zhou, Linna Liu, Xiaomin Yang, Chunyu School of Information and Control Engineering China University of Mining and Technology Xuzhou China China University of Mining and Technology Faculty of School of Information and Control Engineering Xuzhou China

ISBN: (纸本)9798350353594

A model-based cross-scale reinforcement learning optimal control method is proposed for a class of nonlinear singularly perturbed systems. Initially, according to the singular perturbation theory, the original singularly perturbed system is decomposed into fast and slow subsystems, thereby transforming the original optimal control problem into optimal control problems for the fast and slow subsystems. A cross-scale information interaction mechanism is proposed, where full-order system state data is sampled to update the initial values of the fast and slow subsystems. This approach helps overcome the limitations associated with the online solving of rigid equations and reduce the cumulative errors. To approximate the solution of the Hamilton-Jacobi-Bellman (HJB) equations for the fast and slow subsystems, neural networks (NNs) are employed to approximate the cost functions within iterative processes. Simulation resultst validated the effectiveness of the proposed method. © 2024 IEEE.

关键词： cross-scale information interaction Nonlinear singularly perturbed systems rein-forcement learning

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Two Algorithms Based on Iterative Optimization for UAV Path Planning

Two Algorithms Based on Iterative Optimization for UAV Path ...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Chen, Biao Li, Shengming Lv, Zongyang School of Control Science and Engineering Dalian University of Technology Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350316308

In this paper, we propose two algorithms based on iterative optimization for UAV path planning. In the first one, a nonlinear programming (NLP) problem of unmanned aerial vehicle (UAV) path planning is transformed into a general quadratically constrained quadratic programming (QCQP) problem by a numerical optimization method and solved through iteration. This method can converge to the optimal solution without giving the initial value. It overcomes the problems that NLP solvers are dependent on the initial guess, slow to converge, and sometimes can't converge to a feasible solution. In the second one, we improve the optimized rapidly exploring random tree algorithm (RRT∗) and add UAV flight constraints to it. The improved RRT∗ algorithm reduces the length of the path and makes the path smoother. © 2023 IEEE.

关键词： Motion planning

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