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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Ruxin Zhi Yuanyuan Ma Zhan Xu Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing

In this paper, we propose an economical-efficient downlink resource allocation scheme for the low-earth orbit (LEO) satellite communication system with cache-enabled terminals. The broadcast channel model with the incorporation of shadowed Rice fading is adopted and subsequently transformed into a finite-state Markov channel model. Primarily, we deduce the formulation for the attainable channel capacity pertaining to discrete-rate transmission, wherein adaptive modulation and coding are employed. Second, we define an economical efficiency (ECE) metering based on the system revenue and cost and derive the closed-form ECE. Finally, an optimization problem with maximizing the ECE is developed in the terms of transmit power of the satellite, allocation of caching memory, file grouping, and probability falling into each range of the channel state. This problem is nondeterministic polynomial hard (NP-hard) in essence and is thus difficult to solve. For solving it, we first optimize the allocation of caching memory and file grouping. Then, we propose a gradient strategy based heuristic algorithm to acquire the allocation of power and probability in each channel state. The numerical findings substantiate that the proposed scheme presents a substantial enhancement to the ECE of the LEO satellite communication system.

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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Transmission Schedule for Remote State Estimation in CPSs with Energy Harvesting and Sharing Two-Hop Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green communications and Networking 2025年

作者： Xing, Wei Zhang, Junfeng Zong, Guangdeng Wang, Yuhan Zhao, Xudong Hainan University School of Information and Communication Engineering China Tiangong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology China

This paper investigates the problem of optimizing the transmission schedule between a sensor and a relay in cyber-physical systems to achieve accurate remote state estimation. The considered system model represents the plant as a linear time-invariant system, wherein the sensor measurements are transmitted to the remote estimator using an intelligent relay. Both the sensor and relay nodes are capable of harvesting energy from the external environment, and they are required to make transmission decisions at each time step to ensure the stability of system's operation. In addition, energy sharing between each other can yield significant advantages by capitalizing on the available battery power and maximizing energy harvesting from the external environment. We describe the problem of finding the optimal transmission schedule as a dynamic programming model, and establish structural properties on the optimal schedule of the sensor and relay nodes, more precisely, (i) for given battery levels and harvested energy, the optimal transmission strategy is to transmit the measurements when the error covariances at the relay and the remote estimator meet certain conditions, (ii) for given harvested energy and error covariances at the relay and the remote estimator, the optimal transmission strategy is a threshold-based strategy on the battery levels. © 2025 IEEE.

关键词： Dynamic programming

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Self-Interference Cancellation Circuit With Improved Transmission Power Efficiency Based On Unequal Power Divider 13

Self-Interference Cancellation Circuit With Improved Transmi...

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13th International Conference on Microwave and Millimeter Wave technology, ICMMT 2021

作者： Li, Huimin Xiang, Qianyin Huang, Xiaoguo Jia, Dinghong Feng, Quanyuan Southwest Jiaotong University School Of Information Science And Technology Chengdu611756 China Science And Technology On Communication Information Security Control Laboratory Jiaxing314033 China 41st Institute Of CETC Qingdao266555 China

ISBN: (纸本)9781665434379

The paper proposes a self-interference cancellation (SIC) architecture based on unequal power divider. The transmission efficiency of SIC is investigated. A power divider with unequal power dividing ratio is used in the SIC for improving transmission efficiency of circulator isolated transceiver. A four-tap SIC circuit was designed and fabricated. Measurement results show that 60dB isolation is achieved in the frequency band of 915-926MHz, which is an increase of 37dB compared to that of the original circulator. The transmission efficiency (TE) increases from 42.5% (power-dividing ratio is 1:1) to 69.1% (power-dividing ratio is 1:4). © 2021 IEEE.

关键词： Timing circuits

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Breather dynamics in an optomechanical array with staggered coupling

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Physical Review A 2025年第3期111卷 033502-033502页

作者： Yuan Zhao Yiheng Hou Yu Wang Zhuo Fan Wei Peng Qin Zhou Siliu Xu Shihua Chen Key Laboratory of Optoelectronic Sensing and Intelligent Control Hubei University of Science and Technology Xianning 437100 China School of Biomedical Engineering and Imaging Xianning Medical College Hubei University of Science and Technology Xianning 437100 China School of Electronic and Information Engineering Hubei University of Science and Technology Xianning 437100 China Research Center of Nonlinear Science School of Mathematics and Physical Sciences Wuhan Textile University Wuhan 430200 China School of Physics and Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing 211189 China

We present an approach toward generating highly controllable breathers, using an array of staggered optomechanical cavities that feature alternating coupling strengths. Four different types of breather states as well as stable two-soliton states are demonstrated to form in such an optomechanical array, thanks to the interplay between the photon-hopping induced tunneling effect and the photon-phonon coupling in the optomechanical cell. It is seen that both their photonic and phononic wave components manifest quasi-periodic oscillatory patterns and coherent localization. We unveil that such breather dynamics can be well controlled by adjusting the coupling strength, the cavity field decay, and the number of array cells. As a result, we observe an unusual odd-even lattice effect in that stable breathers are only available when the cell number is even. We anticipate that these findings from the staggered optomechanical configuration may expand the realm of cavity optomechanics, yielding promising applications such as on-chip manipulation of light propagation.

关键词： Solitons

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Defend Against Jamming Attacks Using Deep Reinforcement Learning 13

Defend Against Jamming Attacks Using Deep Reinforcement Lear...

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13th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2021

作者： Shen, Weiguo Wang, Wei Jin, Hu Zhang, Weifeng Science and Technology on Communication Information Security Control Laboratory China National University of Defense Technology China Jiaxing University China Jiaxing Deep Intelligence Technology Co. Ltd China

ISBN: (纸本)9781665418126

Cognitive radio network has the learning ability to adjust transmission behavior to adapt to dynamic electromagnetic environment, and it has become a research hotspot in the field of the new generation of intelligent wireless communication systems. However, recent research results show that cognitive radio networks are vulnerable to interference attacks, resulting in a serious decline in spectrum utilization. In this paper, an intelligent anti-jamming scheme based on deep reinforcement learning is proposed, and the anti-jamming decision of the communication party is modeled as a Markov decision chain. This method can help the communication transmitter to learn a combination of communication parameters that can maximize the probability of communication success. In the specific implementation, we use Double DQN as the communication decision-making brain, and specially designed a convolutional neural network suitable for the communication spectrum as input to accurately estimate the action value function. The simulation results show that our method has better performance than the traditional anti-jamming method based on reinforcement learning. © 2021 IEEE.

关键词： Reinforcement learning

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Learning-Driven Transmission Latency Minimization in EH-Relay Assisted IoT Networks

Learning-Driven Transmission Latency Minimization in EH-Rela...

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IEEE Conference on Global communications (GLOBECOM)

作者： Qianru Wang Li Ping Qian Mingqing Li Wei Jiang Yuan Wu Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

Internet of Things (IoT) is one of the key applications of 5G, and the data transmission is the basis of IoT networks. In this paper, we investigate the data transmission scheme in non-orthogonal multiple access (NOMA) for IoT networks to minimize the transmission latency. In order to improve the communication efficiency between devices and the base station (BS) without more energy consumption, we deploy an energy harvesting (EH) relay node between devices and the BS for data transmitting and forwarding. Based on this networking model, we first aim at minimizing the transmission latency by jointly optimizing the transmit power of devices and the relay, forwarding ratios among devices, and forwarding time fraction when transmitting a fixed data bits from devices to the BS via the relay under the constraints of energy buffer and data buffer. Noted that the formulated problem is discrete-continuous mixed and non-convex, we apply the deep deterministic policy gradient (DDPG) algorithm in the framework of bisection searching to obtain the optimal solution. Specifically, the bisection searching is used to seek the possible transmission latency, and the DDPG is to check the feasibility of the chosen transmission latency. Finally, the effectiveness of the proposed model-data-driven algorithm is verified by comparing it with other benchmark algorithms, such as LINGO.

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A Fast Framework for Efficiently Constructing Valuable Cubes 26th

A Fast Framework for Efficiently Constructing Valuable Cubes

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26th International Conference on information and communications security, ICICS 2024

作者： Li, Jingtao Gao, Bo Wan, Jianxiong Li, Leixiao Zhang, Jiaxiang School of Data Science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Hohhot China School of Information Engineering Beijing Institute of Graphic Communication Beijing China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People’s Republic of China Beijing China

ISBN: (纸本)9789819787975

The cube attack is a powerful technique for attacking symmetric ciphers, of which one of the core parts is the search for good cubes. The construction of candidate cubes targeting linear superpolies, proposed at Asiacrypt 2021 [20], is the dominant strategy for searching good cubes. However, the time spent on constructing candidate cubes increases significantly with initialization rounds on Trivium. To address this issue, we propose a novel framework called fast framework (The source code and data for this paper are available at https://***/Elpsys/Fast-Framework.), which enhances the efficiency of constructing candidates and discovers better cubes. The integration of numeric mapping and division property is the core of our framework;they can improve efficiency while guaranteeing accuracy. The proposed method is successfully applied to 830-and 832-round Trivium, both identifying more than 20 valuable cubes whose superpolies contain linear-independent terms and have the potential to be used in further practical key-recovery attacks. The experimental results demonstrated that the efficiency of constructing the mother cube is improved by at least a factor of 9 compared to conventional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Geometry

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Subgraph Networks Based Contrastive Learning

arXiv

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arXiv 2023年

作者： Wang, Jinhuan Shao, Jiafei Wang, Zeyu Yu, Shanqing Xuan, Qi Yang, Xiaoniu The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Graph contrastive learning (GCL), as a self-supervised learning method, can solve the problem of annotated data scarcity. It mines explicit features in unannotated graphs to generate favorable graph representations for downstream tasks. Most existing GCL methods focus on the design of graph augmentation strategies and mutual information estimation operations. Graph augmentation produces augmented views by graph perturbations. These views preserve a locally similar structure and exploit explicit features. However, these methods have not considered the interaction existing in subgraphs. To explore the impact of substructure interactions on graph representations, we propose a novel framework called subgraph network-based contrastive learning (SGNCL). SGNCL applies a subgraph network generation strategy to produce augmented views. This strategy converts the original graph into an Edge-to-Node mapping network with both topological and attribute features. The single-shot augmented view is a first-order subgraph network that mines the interaction between nodes, node-edge, and edges. In addition, we also investigate the impact of the second-order subgraph augmentation on mining graph structure interactions, and further, propose a contrastive objective that fuses the first-order and second-order subgraph information. We compare SGNCL with classical and state-of-the-art graph contrastive learning methods on multiple benchmark datasets of different domains. Extensive experiments show that SGNCL achieves competitive or better performance (top three) on all datasets in unsupervised learning settings. Furthermore, SGNCL achieves the best average gain of 6.9% in transfer learning compared to the best method. Finally, experiments also demonstrate that mining substructure interactions have positive implications for graph contrastive learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

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