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Breather dynamics in an optomechanical array with staggered coupling

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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Energy Minimization with Secrecy Provisioning in Federated Learning-Assisted Marine Digital Twin Networks

Energy Minimization with Secrecy Provisioning in Federated L...

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IEEE International Conference on communications (ICC)

作者： Li Ping Qian Mingqing Li Xinyu Dong 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

Digital twin has been emerging as a promising paradigm that connects the physical entities and digital space, and continuously evolves and optimizes the physical systems. In this paper, we focus on studying the efficient data communication and computation when constructing the marine digital twin network with secrecy provisioning. Specifically, we leverage the federated learning (FL) to train the digital twin model. In the process of FL, all unmanned surface vehicles (USVs) deliver the trained models with non-orthogonal multiple access (NOMA) to the high altitude platform (HAP) for the global model aggregation. Considering the possible eavesdropping on the HAP, we utilize the chaotic sequences to spread the model information during the global model broadcasting. In this framework, we further want to minimize the total energy consumption of completing the digital twin training by jointly optimizing the global accuracy, local accuracy, HAP's transmission power, and model uploading duration subject to the secrecy provisioning and latency constraint. Despite the non-convexity, we propose a low-complexity search algorithm (LCS-Algorithm) to solve this joint optimization problem. Finally, the numerical results validate the performance of the proposed algorithm in terms of optimality and time efficiency.

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Automatic Detection of Morse with Wideband Receiver 21

Automatic Detection of Morse with Wideband Receiver

引用

21st IEEE International Conference on communication technology, ICCT 2021

作者： Cheng, Hui Wang, Feng Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing314033 China

ISBN: (纸本)9781665432061

Morse communication is widely used in the field of land, water and air transportation and military communication, and especially plays a great role in military communication as a necessary means. Morse signals have noise interference inevitably in the process of HF channel transmission. Because of the noise has greatly harmed to human body, it increases the Labor intensity of copy personnel, especially for that long time engaged in HF Morse copy work. To solve the problem that receiving of Morse signals requires manual operation, this paper presents a solution to automatically detect Morse signals with a wideband receiver. The solution features equipment miniaturization, enables Morse signals to be received in full frequency and time domain and gets rid of manual work. It also allows real-time access to the situation of HF Morse signals. © 2021 IEEE.

关键词： Air transportation

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A USRP-Based Testbed for Dual Mode Passive Radar 21

A USRP-Based Testbed for Dual Mode Passive Radar

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21st IEEE International Conference on communication technology, ICCT 2021

作者： Zhu, Huijie Zhang, Lan Wang, Yiteng Shen, Libo Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing314033 China

ISBN: (纸本)9781665432061

This paper presents a dual mode passive radar with alternative localization mechanisms of time difference of arrival (TDOA)-direction of arrival (DOA) mode and multi-TDOA mode. For demonstration and validation, a high-performance testbed is needed with the characteristics of wide dynamic range, multichannel, time-frequency synchronization and flexible local oscillator mechanism. To realize these features, an Ettus X310 universal software radio peripheral (USRP) is utilized for integration in the passive radar hardware platform. Processing algorithms for the multi-channel signals acquired by the X310 is implemented in real-time with the graphics processing unit (GPU) embedded in a server computer. Outdoor detections of small unmanned aerial vehicle with the dual mode operation of the testbed show a meter-level accuracy. © 2021 IEEE.

关键词： Graphics processing unit

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Performance Analysis of Underwater Acoustic Automatic Modulation Classification System with Machine Learning 21

Performance Analysis of Underwater Acoustic Automatic Modula...

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21st IEEE International Conference on communication technology, ICCT 2021

作者： Zhang, Lan Zhu, Huijie Sun, Wei Zhou, Qing Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China The 36th Research Institution of China Electronics Technology Group Corporation Jiaxing341000 China

ISBN: (纸本)9781665432061

Underwater acoustic (UWA) channel is a triple-selective fading channel in time, space and frequency as a result of its lower sound speed and its complicated boundaries in the ocean. It is very difficult to realize a successful classification of the communication signal modulation within a single feature in such harsh channel. The feature extraction is much more sensitive to the environmental parameters and signals. In this paper, an automatic modulation classification (AMC) system based on machine learning is established in order to improve the correction rate of AMC for the several common modulation types. In the case of extracting the feature, the machine learning algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Classification and Regression Tree (CART) are adopted. The performance comparison of these three algorithms is implemented through the simulation data analysis, which would denote how the UWA multipath channel, the signal noise ratio (SNR) affects the performance of the proposed system. © 2021 IEEE.

关键词： Underwater acoustic communication

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The Multi-Dimensional Scaling Algorithm with Centroid as Reference Point for Location in Wireless Senso Network 21

The Multi-Dimensional Scaling Algorithm with Centroid as Ref...

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21st IEEE International Conference on communication technology, ICCT 2021

作者： Bin, Jiang An'Nan, Lu Mingyi, You Yunxia, Ye Kai, Huang Binhua, Shi Science and Technology on Communication Information Security Control Laboratory Zhejiang Jiaxing China The 36th Research Institute of China Electronics Technology Group Corporation Zhejiang Jiaxing China

ISBN: (纸本)9781665432061

The multi-dimensional scaling(MDS) method is a kind of method used to analyze distance matrix or dissimilarity matrix to form data points in low-dimensional space. MDS algorithm uses the distance information between nodes in wireless sensor networks to estimate the relative location of nodes. Classical multi-dimensional scaling algorithm chooses any node in the network as reference point to estimate node location. With the increase of distance measurement error, the node position error estimated by this method also increases significantly. In order to improve the location performance, this paper proposes an MDS algorithm based on the centroid of all nodes in the network as a reference point, and derives the calculation formula of MDS algorithm based on centroid as reference point. Compared with the classical MDS algorithm, the simulation results show that the MDS algorithm base on centroid as the reference point has better positioning results. © 2021 IEEE.

关键词： Location

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