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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Zehui Zhang Wenqiang Pu Rui Zhou Mingyi You Wei Wang Junkun Yan Shenzhen Research Institute of Big Data Shenzhen China National Key Laboratory of Electromagnetic Space Security Jiaxing China National Key Laboratory of Radar Signal Processing Hangzhou Institute of Technology Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In increasingly complex electromagnetic environments, distributed systems are crucial for passive target localization. These systems, consisting of spatially dispersed sensing nodes, collaboratively enhance the localization of target signals. Particularly in blind localization tasks within low signal-to-noise ratio (SNR) settings, distributed passive localization offers improved positioning performance. Traditional passive localization methods typically follow a two-step process: initially extracting parameters, such as the direction of arrivals (DOA), from raw data, followed by localizing the target. This approach often requires a high SNR. In contrast, Direct Position Determination (DPD) methods directly leverage all the received raw data, thus overcoming the constraints of the two-step process. However, DPD methods involve a time-consuming grid search within the area of interest. To address this challenge, we propose a radiation source localization method that utilizes random sampling. This method capitalizes on the low-rank properties of the grid search matrix used in DPD methods. By integrating random sampling with low-rank matrix completion algorithms, our approach efficiently localizes the radiation source. Simulation results demonstrate that this random sampling-based method significantly reduces computational demands while preserving high localization accuracy.

关键词： Location awareness Accuracy Direction-of-arrival estimation Simulation Data processing Sensors Computational efficiency Data mining Electromagnetics signal to noise ratio

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Model Switching Detection-Aided Adaptive Transition Probability Matrix IMM Algorithm 2

Model Switching Detection-Aided Adaptive Transition Probabil...

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2nd IEEE International Conference on signal, information and Data processing, ICSIDP 2024

作者： Chen, Youwang Deng, Yunkai Tian, Weiming Radar Technology Research Institute Beijing Institute of Technology Beijing China Beijing Institute of Technology Yangtze Delta Region Academy Zhejiang Jiaxing China Beijing Institute of Technology Advanced Technology Research Institute Jinan China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Institute of Technology School of Information and Electronic Beijing China

ISBN: (纸本)9798331515669

In conventional interactive multiple model (IMM) algorithm, the fixed transition probability matrix results in slow model switching and low tracking accuracy. Existing adaptive transition probability IMM algorithms still suffer from large errors during model switching. This paper proposes a model switching detection-aided adaptive transition probability matrix IMM algorithm. To deal with model switch time delay, a transition probability correction function is established, capable of dynamically and rapidly adjusting the transition probabilities. And a model switching detection method is proposed to improve tracking accuracy during model switching. By the numerical simulation, the method's efficacy is substantiated and confirmed. © 2024 IEEE.

关键词： Adaptive Transition Probability Matrix IMM Model Switching Detection

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A Dual-Branch Network Based on ViT and Mamba for Semantic Segmentation of Remote Sensing Image

A Dual-Branch Network Based on ViT and Mamba for Semantic Se...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Ke An Ying Wang Liang Chen Yupie Wang National Key Laboratory for Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Shanghai Academy of Spaceflight Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Semantic segmentation of remote sensing images has significant applications across various scenarios. The prevailing frameworks include Convolutional Neural Network (CNN) and Transformer. However, CNN is limited by the receptive field of convolutions, while the Transformer is constrained by computational complexity, which restricts attention calculations to local windows and fails to effectively address long-range dependency modeling. The efficient Mamba architecture, characterized by linear complexity, offers a promising solution to these challenges. Inspired by Mamba, we propose a dual-branch network based on ViT and Mamba. The Vision Transformer (ViT) branch incorporates the Swin Transformer to model spatial details while maintaining computational complexity within acceptable bounds. Complementarily, the Mamba branch efficiently captures global context and long-range dependencies. Additionally, to suppress noise and conflicting information arising from the fusion of features from different frameworks, we design the Cross-Model Fusion Module (CMFM) and the Cross-Model Relevance Loss (CMRLoss) to achieve semantic consistency in the fusion process. The comprehensive experimental findings on the commonly utilized GaoFen-2 and iSAID datasets clearly illustrate the advantages of our proposed approach compared to the leading methods in the field.

关键词： Semantic segmentation Computational modeling Semantics Noise Transformers Data processing Sensors Convolutional neural networks Computational complexity Remote sensing

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Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection 23

Hierarchical vector quantized transformer for multi-class un...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Ruiying Lu YuJie Wu Long Tian Dongsheng Wang Bo Chen Xiyang Liu Ruimin Hu School of Cyber Engineering Xidian University Software Engineering Institute Xidian University National Key Laboratory of Radar Signal Processing Xidian University

Unsupervised image Anomaly Detection (UAD) aims to learn robust and discriminative representations of normal samples. While separate solutions per class endow expensive computation and limited generalizability, this paper focuses on building a unified framework for multiple classes. Under such a challenging setting, popular reconstruction-based networks with continuous latent representation assumption always suffer from the "identical shortcut" issue, where both normal and abnormal samples can be well recovered and difficult to distinguish. To address this pivotal issue, we propose a hierarchical vector quantized prototype-oriented Transformer under a probabilistic framework. First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut. The vector quantized iconic prototypes are integrated into the Transformer for reconstruction, such that the abnormal data point is flipped to a normal data point. Second, we investigate an exquisite hierarchical framework to relieve the codebook collapse issue and replenish frail normal patterns. Third, a prototype-oriented optimal transport method is proposed to better regulate the prototypes and hierarchically evaluate the abnormal score. By evaluating on MVTec-AD and VisA datasets, our model surpasses the state-of-the-art alternatives and possesses good interpretability. The code is available at https://***/RuiyingLu/HVQ-Trans.

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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An End-to-End Autoencoder-based Probabilistic Shaping Method for Optical Communication 7

An End-to-End Autoencoder-based Probabilistic Shaping Method...

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7th Optics Young Scientist Summit, OYSS 2024

作者： Teng, Ziqiang Guo, Dong Gao, Ran Kong, Leyi Han, Wentao Chang, Huan Li, Zhipei Xin, Xiangjun School of Information and Electronics Beijing Institute of Technology 5 Zhongguancun South Street Haidian District Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology 5 Zhongguancun South Street Haidian District Beijing100081 China Institute of Science and Technology Research Beijing Institute of Technology 5 Zhongguancun South Street Haidian District Beijing100081 China

ISBN: (数字)9781510688377

ISBN: (纸本)9781510688360

The advent of services such as big data and cloud computing has driven a continuous increase in the required transmission rate and capacity of optical fiber communication systems, which now support over 90% of global data traffic. Probability shaping (PS), which adjusts the probability distribution of constellation points, enhances both data transmission rates and the resilience of signals to channel nonlinearity. However, traditional PS methods, such as those using constant composition distribution matching (CCDM), cannot dynamically adjust the probability distribution of constellation points based on channel conditions. To address this limitation, we propose an end-to-end Autoencoder (AE)-based PS technique for optical communication. Simulation results show that compared to traditional 16/64/256QAM, this scheme achieves a maximum improvement of 0.356 bit/symbol in mutual information over the AWGN channel. In the optical fiber channel, under varying signal power and fiber length conditions, the mutual information is improved in different degrees and the influence of channel nonlinearity on the signal is reduced. Innovatively, residual connections are added to the decoder to enhance the model's generalization performance, enabling a deeper network structure and improving its ability to handle complex optical fiber channels. © 2025 SPIE.

关键词： Optical fiber communication

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AN MULTILAYER FUSION STRATEGY BASED ON IMPROVED YOLOV5 FOR SHIP DETECTION IN SAR IMAGES

AN MULTILAYER FUSION STRATEGY BASED ON IMPROVED YOLOV5 FOR S...

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IET International radar Conference 2023, IRC 2023

作者： Chen, Fan Shi, Hao Zhao, Liangbo Mao, Yongfei Pan, Hongxin Chen, Liang Radar Research Lab Beijing Institute of Technology Beijing China General Department of Remote Sensing Satellites China Academy of Space Technology China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Since entering the era of deep learning, the single-stage detection algorithm represented by YOLO has achieved some progress in the detection of ship targets by synthetic aperture radar (SAR). However, the accuracy of single-stage detection is lower than that of two-stage detection, especially for small target detection. To this end, this paper proposes an improved YOLOv5 detection method based on convolutional bolck attention module CBAM) and WPAN. At the same time, the focal efficient intersection over union (Focal EIoU) is introduced to optimize the calculation of bounding box regression loss. The experiment is verified on SAR ship detection dataset(SSDD). The results show that the proposed improved YOLOv5 algorithm can improve the false alarm and missing detection problems in multi-scale target detection, and improve the detection accuracy consequently. © The Institution of Engineering & Technology 2023.

关键词： Synthetic aperture radar

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Image Moment Invariants to Rotational Motion Blur

arXiv

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arXiv 2023年

作者： Mo, Hanlin Hao, Hongxiang Zhao, Guoying Center for Machine Vision and Signal Analysis University of Oulu Finland Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Rotational motion blur caused by the circular motion of the camera or/and object is common in life. Identifying objects from images affected by rotational motion blur is challenging because this image degradation severely impacts image quality. Therefore, it is meaningful to develop image invariant features under rotational motion blur and then use them in practical tasks, such as object classification and template matching. This paper proposes a novel method to generate image moment invariants under general rotational motion blur and provides some instances. Further, we achieve their invariance to similarity transform. To the best of our knowledge, this is the first time that moment invariants for rotational motion blur have been proposed in the literature. We conduct extensive experiments on various image datasets disturbed by similarity transform and rotational motion blur to test these invariants’ numerical stability and robustness to image noise. We also demonstrate their performance in image classification and handwritten digit recognition. Current state-of-the-art blur moment invariants and deep neural networks are chosen for comparison. Our results show that the moment invariants proposed in this paper significantly outperform other features in various tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Design of Control Circuit for Active Metasurface Based on Varactor Diode

Design of Control Circuit for Active Metasurface Based on Va...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yue Zhang Yuefei Liu Guohui Yang Kuang Zhang Yingsong Li School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

A control circuit for active metasurface based on varactor diode is designed in this paper. By varying the reverse bias voltage on the varactor diode, the designed circuit can regulate the additional phase of the active meta-atoms. The circuit adopts a form of one master and multiple slaves, which can simultaneously control the bias voltage of 960 meta-atoms. Each slave can control up to 64 channels. The control accuracy is 0.04V. The power consumption of a single slave is 160mW. Though experiment verification, the circuit has a good effect in controlling the beam deflection of the metasurface and generating OAM beams.

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

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9th International Conference on intelligent Computing and signal processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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