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Autocorrelation Invariance Property of Chaos for Wireless Communication

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arXiv 2022年

作者： Yin, Hui-Ping Ren, Hai-Peng Grebogi, Celso Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'An710048 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AB24 3UE United Kingdom

A new feature of the chaotic signal generated by chaotic shape-forming filter (CSF) is uncovered in this work. We find that, the autocorrelation function (ACF) of the transmitting signal generated by CSF keeps the same as that of the base function of CSF, no matter what information is encoded. We derive the analytical equation to describe the relation between the ACF of the received signal and the wireless channel parameters using the ACF of the transmitted signal as prior knowledge revealed by the finding in this work. This new property can be utilized together with different wireless communication systems to improve the system performance. Specially, to demonstrate the improvement, channel state information (CSI) is identified using the chaotic baseband wireless communication as a paradigm. Two significant benefits by using the new property are 1) the CSI can be identified without the probe information known to the receiver as done in the conventional wireless communication systems, which improves the bandwidth efficiency, especially in the time-varying channel;2) the correlation operation is insensitive to the channel noise, which improves the identification accuracy as compared to the commonly used methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel state information

MM-Pyramid: Multimodal Pyramid Attentional Network for Audio-Visual Event Localization and Video Parsing

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arXiv 2021年

作者： Yu, Jiashuo Cheng, Ying Zhao, Rui-Wei Feng, Rui Zhang, Yuejie School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

Recognizing and localizing events in videos is a fundamental task for video understanding. Since events may occur in auditory and visual modalities, multimodal detailed perception is essential for complete scene comprehension. Most previous works attempted to analyze videos from a holistic perspective. However, they do not consider semantic information at multiple scales, which makes the model difficult to localize events in different lengths. In this paper, we present a Multimodal Pyramid Attentional Network (MM-Pyramid) for event localization. Specifically, we first propose the attentive feature pyramid module. This module captures temporal pyramid features via several stacking pyramid units, each of them is composed of a fixed-size attention block and dilated convolution block. We also design an adaptive semantic fusion module, which leverages a unit-level attention block and a selective fusion block to integrate pyramid features interactively. Extensive experiments on audio-visual event localization and weakly-supervised audio-visual video parsing tasks verify the effectiveness of our approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schrödinger Bridges

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arXiv 2025年

作者： Qin, Shixi Yang, Zhiyong Bao, Shilong Wang, Shi Xu, Qianqian Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper focuses on implanting multiple heterogeneous backdoor triggers in bridge-based diffusion models designed for complex and arbitrary input distributions. Existing backdoor formulations mainly address single-attack scenarios and are limited to Gaussian noise input models. To fill this gap, we propose MixBridge, a novel diffusion Schrödinger bridge (DSB) framework to cater to arbitrary input distributions (taking I2I tasks as special cases). Beyond this trait, we demonstrate that backdoor triggers can be injected into MixBridge by directly training with poisoned image pairs. This eliminates the need for the cumbersome modifications to stochastic differential equations required in previous studies, providing a flexible tool to study backdoor behavior for bridge models. However, a key question arises: can a single DSB model train multiple backdoor triggers? Unfortunately, our theory shows that when attempting this, the model ends up following the geometric mean of benign and backdoored distributions, leading to performance conflict across backdoor tasks. To overcome this, we propose a Divide-and-Merge strategy to mix different bridges, where models are independently pre-trained for each specific objective (Divide) and then integrated into a unified model (Merge). In addition, a Weight Reallocation Scheme (WRS) is also designed to enhance the stealthiness of MixBridge. Empirical studies across diverse generation tasks speak to the efficacy of MixBridge. The code is available at: https://***/qsx830/ MixBridge. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential equations

Explore Contextual information for 3D Scene Graph Generation

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arXiv 2022年

作者： Liu, Yuanyuan Long, Chengjiang Zhang, Zhaoxuan Liu, Bokai Zhang, Qiang Yin, Baocai Yang, Xin The Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Meta Reality Labs BurlingameCA94010 United States The Key Lab of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China The Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing University of Technology Beijing100124 China

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

Recurrent Attentional Reinforcement Learning for Fault Diagnosis of Hydraulic System

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Recurrent Attentional Reinforcement Learning for Fault Diagn...

International Conference on Control, Decision and information Technologies (CoDIT)

作者： Zhenhui Tang Ru Liu Kang Lou Chaojian Gao Jingcheng Wang SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Marine Equipment Research Institute Shanghai China Department of Automation the Key Laboratory of System Control and Information Processing Ministry of Education of China and the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an China

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although data-driven fault diagnosis method attains remarkable progress by learning fault features automatically, the existing data-driven fault diagnosis pipelines learn non-discriminative feature. To deal with these problems, a new data-driven fault diagnosis method with recurrent attentional module is proposed to find a sequence of informative features by localizing distinguishing time fragments about each individual faults iteratively. The contextual sequence dependency between the localized time fragments is enhanced. Then, the fault types are further predicted on localized time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on hydraulic system show that the proposed framework achieves superior diagnosis performance and computation efficiency.

关键词： Fault diagnosis Location awareness Accuracy Computational modeling Pipelines Reinforcement learning Hydraulic systems Computational efficiency information technology

Camo: Capturing the Modularity by End-to-End Models for Symbolic Regression

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SSRN

SSRN 2024年

作者： Liu, Jingyi Wu, Min Yu, Lina Li, Weijun Li, Wenqiang Li, Yanjie Hao, Meilan Deng, Yusong Wei, Shu Annlab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China

Modularity serves as an omnipresent paradigm across the spectrum of natural phenomena, societal constructs, and human pursuits, spanning from biological systems to corporate hierarchies and further. Within the realm of Symbolic Regression, which seeks to deduce explicit formulas from empirical data, modularity is seen as a strategic asset for capturing essential substructures to augment fitting accuracy. Symbolic Regression inherently addresses a problem of compositional optimization;hence, preserving beneficial sub-structures enhances the efficiency of future explorations. In this study, we introduce a methodology to integrate modularity into the search mechanism, designating the term "module" to reflect invaluable sub-structures. We select an end-to-end model to assimilate the module into the search process, chosen for its scalability and ability to generalize. Modules function as higher-order knowledge and serve as primary operators, thereby enriching the search directory for Symbolic Regression. Our innovative algorithm fosters autonomous learning or evolution of modules within the learning framework. Furthermore, we introduce a hierarchical module extraction strategy from the expression tree and a module refinement system aimed at discarding redundant elements while assimilating new advantageous ones. We carried out ablation studies, the highest average R2 across several datasets was achieved by the model with module extraction and update mechanism. Furthermore, our method reached comparable R2 against state-of-the-art with half of the expression complexity. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

Analysis of river morphology change in Zhengzhou reach of the Yellow River based on long time series remote sensing images

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Analysis of river morphology change in Zhengzhou reach of th...

2024 International Conference on Remote Sensing, Mapping, and Geographic information Systems, RSMG 2024

作者： Li, Ziwei Liang, Bingjie Chen, Yi Ge, Wen Deng, Xingran Jianzhong, Guo The College of Geography and Environmental Science Henan University No. 379 North Mingli Road Henan Province Zhengzhou450001 China Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Henan Province Zhengzhou450001 China Key Laboratory of Spatiotemporal Perception and Intelligent processing Ministry of Natural Resources Henan Province Zhengzhou450001 China Yellow River Institute of Hydraulic Research Yellow River Conservancy Commission Zhengzhou450003 China China Construction Eighth Engineering Division Rall Transit Construction Co Ltd Zhongjian Building No.6 Wenlan Road Xianlin University Town Qixia District Jiangsu Province Nanjing City210023 China

ISBN: (纸本)9781510685826

The Zhengzhou reach of the Yellow River is a typical meandering section with frequent changes in river morphology. In this paper, using satellite remote sensing technology and Landsat 8 satellite imagery data, the morphological changes of the Yellow River in Zhengzhou (Taohuayu - Huayuankou) in the lower reaches from 2013 to 2022 were quantitatively studied and analyzed. The overall water surface area of the study segment changes significantly (61.8 square kilometers), while the length remains stable. The variations in the area and length of the river segment exhibit a pattern of phased changes. Regarding the main meanders, the first and third meanders showed a trend of initial strengthening followed by weakening, while the second meander showed an upward trend. The intensity of the river's morphological changes followed a pattern of stability, followed by a period of drastic alteration, and then a return to stability. Overall, the tendency for the river segment in the study area to meander has diminished over the study period. © 2024 SPIE.

关键词： Remote sensing Satellites Satellite imaging Floods Earth observing sensors Feature extraction Landsat Clouds Statistical analysis Roads

Large AI Model-Based Semantic Communications

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arXiv 2023年

作者： Jiang, Feibo Peng, Yubo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua You, Xiaohu Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed reality, and the Internet of Everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation, frequent knowledge updates, and insecure knowledge sharing. Fortunately, the development of the large AI model (LAM) provides new solutions to overcome the above issues. Here, we propose a LAM-based SC framework (LAM-SC) specifically designed for image data, where we first apply the segment anything model (SAM)-based KB (SKB) that can split the original image into different semantic segments by universal semantic knowledge. Then, we present an attention-based semantic integration (ASI) to weigh the semantic segments generated by SKB without human participation and integrate them as the semanticaware image. Additionally, we propose an adaptive semantic compression (ASC) encoding to remove redundant information in semantic features, thereby reducing communication overhead. Finally, through simulations, we demonstrate the effectiveness of the LAM-SC framework and the possibility of applying the LAM-based KB in future SC paradigms. Copyright © 2023, The Authors. All rights reserved.

关键词： Latent semantic analysis

Steady-state Analysis of Bipolar Transistor

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Steady-state Analysis of Bipolar Transistor

Progress in Electromagnetic Research Symposium (PIERS)

作者： Yeqiang Yan Xingang Ren Shuping He Xiaotao Huang Zhixiang Huang Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan China Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Lingnan Normal University Zhanjiang China

ISBN: (纸本)9781665409889

Numerical simulation of semiconductor devices enable the quick obtain of the device physical characteristics. However, the trial productions of the semiconductor devices have the disadvantages of long cycle and high cost. Therefore, it is necessary to carry out numerical simulation investigating semiconductor devices. We propose the coupling approach and the decoupling-Gummel method to solve the semiconductor equations. The results of the two methods are compared with the commercial software. Numerical experiment indicates that the results obtained by our method are accurate. When the transistor is at a low voltage bias, the decoupling method converges faster than the coupling method. In the case of high voltage bias, the decoupling method has a slower convergence speed than the coupling method, even difficult to converge for higher voltage bias. However, the coupling method has higher memory requirements than the Gummel method. Therefore, we need to balance the advantages of the coupling method and the Gummel method when conduct the numerical simulation.

关键词： Couplings Bipolar transistors Voltage Production Numerical simulation Mathematical models Software