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Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems

Science China(information Sciences) 2021年第5期64卷 151-162页

作者： Xiang ZHANG Shuping HE Vladimir STOJANOVIC Xiaoli LUAN Fei LIU Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil EngineeringUniversity of Kragujevac Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied in the system modes and the filter modes reasonably. Moreover, a suitable LyapunovKrasovskii function is utilized and linear matrix inequalities are applied to obtain adequate conditions. These techniques guarantee the finite-time boundedness and strict dissipativity of the filtering error dynamic system. Furthermore, the design problems of the passive filter and the H∞ filter are studied by adjusting the three parameters U, G and V. Finally, the filter gains and the optimal index α*are obtained and the correctness and feasibility of the designed approach are verified by a simulation example.

关键词： dissipative filtering hidden Markov model finite-time boundedness Markov jump systems conic-type nonlinearity

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Examining the Fourier Spectrum of Speech signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective Computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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A Model-free False Data Injection Attack Strategy in Networked Control Systems

A Model-free False Data Injection Attack Strategy in Network...

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IEEE Conference on Decision and Control

作者： Xiaoyu Luo Chongrong Fang Chengcheng Zhao Jianping He The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The State Key Laboratory of Industrial Control Technology and Institute of Cyberspace Research Zhejiang University China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The data-driven attack strategies recently have received much attention when the full knowledge of the system model is unknown or difficult to be obtained for the adversary. Note that despite the critical parameters of the system model being unavailable for the adversary, the existing data-driven attack methods still depend on the linearity of the unknown system model. In this paper, we design a completely model-free attack strategy where the adversary with limited capability aims to compromise state variables such that the output value follows the expected trajectory. Specifically, we first construct a zeroth-order feedback optimization framework and uninterruptedly use probing signals for real-time measurements. Then, we iteratively update the attack signals along the composite direction of the gradient estimates of the objective function evaluations and the projected gradients. These objective function evaluations can be obtained only by real-time measurements. Furthermore, we characterize the optimality of these solutions via the optimality gap, which is affected by the dimensions of the attack signal, the iterations of solutions, and the convergence rate of the system. Extensive simulations are conducted to show the effectiveness of the proposed attack strategy.

关键词： Networked control systems Computational modeling Current measurement Linear programming Real-time systems Data models Time measurement

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Fast Candidate Region Extraction for SAR Ship Target

Fast Candidate Region Extraction for SAR Ship Target

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Panpan Zhang Haibo Luo Zheng Xu Miao He Shenyang Institute of Automation Shenyang China Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China

ISBN: (纸本)9781665465373

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset.

关键词： Deep learning Training Redundancy radar polarimetry Inference algorithms Indexes Marine vehicles

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Autocorrelation Invariance Property of Chaos for Wireless Communication

arXiv

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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

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SAR Target Recognition Using Improved Monogenic-Based Feature Extraction Framework

SAR Target Recognition Using Improved Monogenic-Based Featur...

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IEEE International Conference on radar

作者： Feng Li Weijun Yao Yang Li Wei Chen Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Beijing China Radar Research Lab Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665468893

Applying computer vision methods to synthetic aperture radar (SAR) image recognition is a research trend in recent years, and a series of valuable results have been achieved. In order to use machine learning classifiers for recognition, it is necessary to extract effective features, and most of these features are directly extracted based on grayscale SAR images. SAR data is usually rare, and more difficult to collect than optical images. Therefore, the problem of recognition using a small-size training set for SAR is more challenging to practical pattern recognition methods. The monogenic signal is an extended version of analytic signals in high-dimensional space which has attracted attention. In this paper, a new recognition framework which is based on feature dimensionality augmentation using combined multi-scale monogenic components and histogram of oriented gradient (HOG) feature is proposed. Proposed feature is named as MONO-HOG. This letter focuses on recognition under both standard operating condition (SOC) and small-sample scene. Experiments on moving and stationary target automatic recognition (MSTAR) data set show that our proposed framework has satisfying performance.

关键词： Training Computer vision Image recognition Target recognition Feature extraction Optical imaging radar polarimetry

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Moiré cavity quantum electrodynamics

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Science Advances 2025年第21期11卷 eadv8115页

作者： Wang, Yu-Tong Ye, Qi-Hang Yan, Jun-Yong Qiao, Yufei Liu, Yu-Xin Ye, Yong-Zheng Chen, Chen Cheng, Xiao-Tian Li, Chen-Hui Zhang, Zi-Jian Huang, Cheng-Nian Meng, Yun Zou, Kai Zhan, Wen-Kang Zhao, Chao Hu, Xiaolong Tee, Clarence Augustine T.H. Sha, Wei E.I. Huang, Zhixiang Liu, Huiyun Jin, Chao-Yuan Ying, Lei Liu, Feng State Key Laboratory of Extreme Photonics and Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Physics Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China School of Precision Instrument and Optoelectronic Engineering Tianjin University Tianjin300072 China Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education Tianjin300072 China Laboratory of Solid State Optoelectronics Information Technology Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Science Beijing101804 China College of Physics and Electrical Information Engineering Zhejiang Normal University Hangzhou310058 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Zhejiang Hangzhou311200 China

Quantum emitters are a key component in photonic quantum technologies. Enhancing single-photon emission by engineering their photonic environment is essential for improving overall efficiency in quantum information processing. However, this enhancement is often limited by the need for ultraprecise emitter placement within conventional photonic cavities. Inspired by the fascinating physics of moiré pattern, we propose a multilayer moiré photonic crystal with a robust isolated flatband. Theoretical analysis reveals that, with nearly infinite photonic density of states, the moiré cavity simultaneously has a high Purcell factor and large tolerance over the emitter’s position, breaking the constraints of conventional cavities. We then experimentally demonstrate various cavity quantum electrodynamic phenomena with a quantum dot in moiré cavity. A large tuning range (up to 40-fold) of quantum dot’s radiative lifetime is achieved through strong Purcell enhancement and inhibition effects. Our findings open the door for moiré flatband cavity–enhanced quantum light sources and quantum nodes for the quantum internet. Copyright © 2025 The Authors, some rights reserved.

关键词： Quantum electronics

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Interactive Prototype Learning and Self-Learning for Few-Shot Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Song, Yuhui Xu, Chenchu Wang, Boyan Du, Xiuquan Chen, Jie Zhang, Yanping Li, Shuo School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230051 China Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Engineering Case Western Reserve University Cleveland44106 United States

Few-shot learning alleviates the heavy dependence of medical image segmentation tasks on large-scale labeled data, but it shows a strong performance gap when dealing with new targets and new tasks compared with traditional deep learning. Existing few-shot medical image segmentation models mainly realize segmentation by learning the semantic class knowledge of a few known (support) samples and extending it to unknown (query) samples. However, the neglect of the differences between known and unknown samples leads to a certain deviation in the transfer of class knowledge, and the trained model tends to overfit the class knowledge of training classes, which greatly reduces the segmentation and generalization performance of few-shot model. In this work, we propose an interactive prototype learning and self-learning network to alleviate the above problems. Specifically, we propose an interactive prototype learning network to promote feature consistency between support images and query images to improve the accuracy of prototype metric learning. In addition, to avoid overfitting the training class knowledge and reduce the over-dependence of query segmentation on the class knowledge, we introduce a query features-guided self-learning network to achieve the finer segmentation of query images through its rich feature information. Our model obtained the competitive segmentation results on three multi-organ and multi-modal public datasets that show a substantial improvement in generalization ability. © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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A Fast Method for Shortest-Path Cover Identification in Large Complex Networks

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Computers, Materials & Continua 2020年第5期63卷 705-724页

作者： Qiang Wei Guangmin Hu Chao Shen Yunfei Yin School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041China MOE Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong UniversityXi’an710049China The Electronic Engineering Department Universidad de SevillaSeville41004Spain School of Astronautics Harbin Institute of TechnologyHarbin150001China

Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and ***,the existing methods are time-consuming for even moderate-scale *** this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance *** effectiveness of the proposed method is validated through synthetic and real-world *** experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.

关键词： Network discovery shortest-path cover shortest-path distance query large complex networks

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SFANet: Efficient Detection of Vehicle Targets in SAR Images Based on SAR-specialized Feature Aggregation

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhou, Peng Wang, Peng Zhu, Daiyin Zhou, Bilian Lv, Jiming Ye, Zheng Nanjing University of Aeronautics and Astronautics Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing 211106 China Ministry of Natural Resources Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA Guangdong 510062 China Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute Shenzhen 518057 China Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou 450052 China Ministry of Natural Resources Key Laboratory of Spatiotemporal Perception and Intelligent Processing Zhengzhou 450052 China Land and Resources Technical Center of Guangdong Province Guangdong 510062 China

With the swift progress of deep learning technologies, the detection of ground vehicles in synthetic aperture radar (SAR) images has emerged as a prominent research focus. In this article, research is conducted on the detection of ground vehicles using MiniSAR mounted on unmanned aerial vehicles. However, the substantial background interference and high similarity between ground vehicles make it challenging for object detection. To address these issues, an efficient object detection network based on SAR-specialized feature attention (SFA) and lightweight global detector (LGD) is proposed in this article. First, the SFA is proposed, which introduces proportional attention to different detection layers. By analyzing and focusing on spatial and channel information, it alleviates the classification challenges when dealing with highly similar ground vehicles and limited information. Second, a novel detector LGD is proposed, which combines Swin Transformer and lightweight convolutions. LGD captures deeper semantic information through global attention, enabling the network to detect vehicles in complex scenes. Finally, transfer learning is utilized to load pre-classification weights of the backbone. To verify the effectiveness of the proposed algorithm, comprehensive experiments were performed across multiple datasets. The results indicate that the proposed method offers substantial performance enhancements. © 2025 IEEE. All rights reserved.

关键词： Feature attention global attention lightweight MiniSAR object detection

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