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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Rediscovering BCE Loss for Uniform Classification

arXiv

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arXiv 2024年

作者： Li, Qiufu Jia, Xi Zhou, Jiancan Shen, Linlin Duan, Jinming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Computer Vision Institute Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China School of Computer Science University of Birmingham United Kingdom Aqara Lumi United Technology Co. Ltd China

This paper introduces the concept of uniform classification, which employs a unified threshold to classify all samples rather than adaptive threshold classifying each individual sample. We also propose the uniform classification accuracy as a metric to measure the model’s performance in uniform classification. Furthermore, begin with a naive loss, we mathematically derive a loss function suitable for the uniform classification, which is the BCE function integrated with a unified bias. We demonstrate the unified threshold could be learned via the bias. The extensive experiments on six classification datasets and three feature extraction models show that, compared to the SoftMax loss, the models trained with the BCE loss not only exhibit higher uniform classification accuracy but also higher sample-wise classification accuracy. In addition, the learned bias from BCE loss is very close to the unified threshold used in the uniform classification. The features extracted by the models trained with BCE loss not only possess uniformity but also demonstrate better intra-class compactness and inter-class distinctiveness, yielding superior performance on open-set tasks such as face recognition. © 2024, CC BY.

关键词： Classification (of information)

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GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement

GeoReF: Geometric Alignment Across Shape Variation for Categ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Linfang Zheng Tze Ho Elden Tse Chen Wang Yinghan Sun Hua Chen Ales Leonardis Wei Zhang Hyung Jin Chang Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology China School of Computer Science. University of Birmigsham UK Department of Computer Science the University of Hong Kong China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress to-wards instance-level object pose refinement. Yet, category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrep-ancies between the target object and the shape prior. To address these challenges, we introduce a novel architecture for category-level object pose refinement. Our approach in-tegrates an HS-Iayer and learnable affine transformations, which aims to enhance the extraction and alignment of Geometric information. Additionally, we introduce a cross-cloud transformation mechanism that efficiently merges di-verse data sources. Finally, we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive ex-periments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments, we demonstrate significant improvement over the baseline method by a large margin across all metrics. 1 1 Project page: https://***/***

关键词： Point cloud compression computer vision Shape Soft sensors Pose estimation computer architecture Predictive models

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Chinese named entity recognition method for the field of network security based on RoBERTa

Chinese named entity recognition method for the field of net...

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2021 International Conference on Networking and Network Applications, NaNA 2021

作者： Zhu, Xiaoyan Zhang, Yu Zhu, Lei Hei, Xinhong Wang, Yichuan Hu, Feixiong Yao, Yanni Xi'an Jiaotong University School of Computer Science and Engineering Xi'an710049 China School of Computer Science and Technology Xi'an University of technology Shaanxi Xi'an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Shaanxi Xi'an710048 China Shenzhen Tencent Computer System Co. Ltd Shenzhen518054 China

ISBN: (纸本)9781665441582

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively. © 2021 IEEE.

关键词： Network security

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Tensor Dynamic Fusion Based Modality-Imbalanced Multimodal Federated Learning in Mobile Edge computing for Consumer Applications

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IEEE Transactions on Consumer Electronics 2024年

作者： Nie, Xin Yi, Lingzhi Yang, Laurence T. Deng, Xianjun Fan, Fulan Zhang, Hongtao Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei EngineeringResearch Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Zhongnan University of Economics and Law School of Information and Safety Engineering Wuhan430074 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China St. Francis Xavier University Department of Computer Science Antigonish Canada South-Central Minzu University School of Education Wuhan430074 China

Mobile edge computing extends the capabilities of computation and storage from the cloud to the edge, enabling local data storage and processing. This computing paradigm complements federated learning (FL), as it satisfies the need for privacy protection while providing efficiency and quick response. In consumer applications, multimodal data is experiencing explosive growth. Therefore, multimodal FL has emerged as a crucial research area. However, applying traditional FL methods directly to multimodal data poses certain challenges. Additionally, issues like sensor failures result in modality-imbalanced data among clients, i.e., the number and type of data modalities between client devices vary. Hence, it is imperative to design specialized FL frameworks and models specifically tailored for analyzing multimodal data. To address these challenges, we propose a tensor fusion-based multimodal federated learning (TenMFL). First, a tensor dynamic fusion network is designed for addressing the challenge of multimodal data fusion. This approach separates the parameter forward computation and the modal fusion computation, effectively resolving issues related to client-side modality imbalance. Second, we introduce a hybrid fusion method that combines feature fusion and decision fusion to further exploit the complementarity of multimodal data. We evaluate TenMFL on three multimodal datasets, experimental results demonstrate our superior performances. © 1975-2011 IEEE.

关键词： Federated learning

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Application of SNNS Model Based On Multi-Dimensional Attention In Drone Radio Frequency Signal Classification

Application of SNNS Model Based On Multi-Dimensional Attenti...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zheng Si Chao Liu Jianyu Liu Yinhao Zhou Henan Institute of Advanced Technology Zhengzhou University Zhengzhou Zhongke Institute of Integrated Circuit and System Application School of Computer Science Shanghai Key Laboratory of Data Science Fudan University

Spiking Neural Networks (SNNs) are attracting attention due to their energy efficiency and importance in neuromorphic computing. Therefore, we propose an SNN-based method for classifying drone RF signals in complex electromagnetic environments. Specifically, we designed a new SNNs model called Spiking-EfficientNet based on EfficientNetV2 and improved its performance with a multidimensional attention mechanism. Experimental results demonstrate that Spiking-EfficientNet achieved classification accuracy of 99.13% and 96.02% on the ZK RF and DroneDetectV2 datasets. Importantly, Spiking-EfficientNet not only outperforms traditional Artificial Neural Networks (ANNs) in performance, but also exhibits significantly lower energy consumption. The energy consumption is only 20.1% of EfficientNetV2, 2.56% of VGG11, 10.71% of ResNet18, and 61.15% of MobileNetV2. This study demonstrates the significant potential of SNNs in drone RF signal classification and provides a low-power solution.

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Spatio-Temporal Transformer-Based Reinforcement Learning for Robot Crowd Navigation

arXiv

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arXiv 2023年

作者： He, Haodong Fu, Hao Wang, Qiang Zhou, Shuai Liu, Wei The School of Computer Science and Technology Wuhan University of Science and Technology China The Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430081 China

The social robot navigation is an open and challenging problem. In existing work, separate modules are used to capture spatial and temporal features, respectively. However, such methods lead to extra difficulties in improving the utilization of spatio-temporal features and reducing the conservative nature of navigation policy. In light of this, we present a spatio-temporal transformer-based policy optimization algorithm to enhance the utilization of spatio-temporal features, thereby facilitating the capture of human-robot interactions. Specifically, this paper introduces a gated embedding mechanism that effectively aligns the spatial and temporal representations by integrating both modalities at the feature level. Then Transformer is leveraged to encode the spatio-temporal semantic information, with hope of finding the optimal navigation policy. Finally, a combination of spatio-temporal Transformer and self-adjusting policy entropy significantly reduces the conservatism of navigation policies. Experimental results demonstrate the effectiveness of the proposed framework, where our method shows superior performance. © 2023, CC BY.

关键词： Navigation

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Defending against a laser-seeding attack on continuous-variable quantum key distribution using an improved optical power limiter

引用

Physical Review A 2023年第5期108卷 052616-052616页

作者： Qingquan Peng Binwu Gao Dongyang Wang Qin Liao Zhiyue Zuo Hai Zhong Anqi Huang Ying Guo School of Automation Central South University Changsha 410083 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China School of Computer Science Beijing University of Posts and Telecommunications Beijing 100876 China

Laser-seeding attack enables an attacker to control the source output and eavesdrops on the key information, compromising the security of the continuous-variable (CV) quantum key distribution (QKD) system. In this paper, we suggest an improved optical power limiter (OPL) to defend against a laser-seeding attack on CV QKD systems. The proposed OPL utilizes a temperature monitor to detect Eve's attacks through temperature changes, providing an effective optical component for improving the security and reliability of the CV QKD system in practical environments, which is called an OPL-T scheme. In particular, we consider the security threats of a laser-seeding attack on two common CV QKD systems and prove their theoretical security degradation. Experiments are conducted to verify the effective OPL-T, and the results show that this defense scheme has an advantage in resisting the laser-seeding attack. Numerical simulations demonstrate the security analysis on CV QKD equipped with an OPL-T, which further confirms the effectiveness of our scheme. This work provides a practical solution for the security enhancements of continuous-variable quantum communication in practical applications.

关键词： Quantum communication Quantum communication, protocols & technology

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Electrical resistivity-temperature characteristics enhancement of insulating cross-linked polyethylene composites by incorporating positive temperature coefficient particles with different Curie temperatures

引用

High Voltage 2023年第1期8卷 1-11页

作者： Guimin Jiang Yuanxiang Zhou Chenyuan Teng Yunxiao Zhang Xin Huang Lei Xue Luming Zhou The Wind Solar Storage Division of State Key Laboratory of Control and Simulation of Power System and Generation Equipment Xinjiang UniversityUrumqiChina State Key Laboratory of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina College of Information Engineering Zhejiang University of TechnologyHangzhouChina School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Incorporating positive temperature coefficient(PTC)ceramic particles into polymers provides a prospective alternative solution for suppressing severe electric field distortions caused by negative temperature coefficient(NTC)of electrical resistivity within polymer insulation in high voltage direct current *** effect of the Curie temperature of PTC particles on the inhibition of the NTC effect of cross-linked polyethylene(XLPE)is investigated in this *** temperature coefficient particles with varied Curie temperatures are surface-modified and melt blending with *** modified particles are dispersed on averagely in the *** electrical resistivity,space charge behaviour,and direct current(DC)electrical breakdown strength of the samples are investigated at different temperatures,exploring the effect of the Curie temperature of PTC particles on suppressing the NTC effect of *** is demonstrated that the reduction in Curie temperature can further suppress the NTC effect of XLPE,enhance the DC strength at elevated temperatures,and inhibit the internal space charge *** reduction in the Curie temperatures means that the initiation of the PTC effect advances,exhibiting a higher potential barrier and inhibiting the NTC effect more *** work may give a reference for improving the temperature stability of DC properties for XLPE cable insulation.

关键词： coefficient resistivity insulation

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On the Impact of Interlayer Misalignment for Dual-Layer Data Detection in Three Dimensional Magnetic Recording

SSRN

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SSRN 2024年

作者： Luo, Ke Liao, Yanzhe Zhang, Kezheng Jian, Yugen Wang, Shaobing Chen, Jincai Lu, Ping Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China School of Public Health Hubei University of Medicine Shiyan442000 China

Three-dimensional magnetic recording (3DMR) is a crucial technology for significantly increasing the storage capacity of hard disk drives (HDDs). However, the presence of inter-symbol interference (ISI), inter-track interference (ITI), and inter-layer interference (ILI) poses significant challenges to the accurate detection of data stored on multiple layers. This study addresses the impact of interlayer misalignment on the bit error rate (BER) performance in 3DMR systems. We evaluate the performance based on a neural network estimator for reconstructing the top-layer read response signal using feedback from a Viterbi detector, enabling the separation of bottom-layer signals by subtraction from the mixed readback signal. We introduce a dual-layer partial response maximum likelihood (PRML) detector for simultaneous bit retrieval from both layers. We investigate methods of a per-layer binary classifier and a dual-layer four-class classifier based on neural networks. Our study demonstrates the BER performance of these detection schemes influenced by the interlayer misalignment, especially when the offset is 0, 10%, 50%, and 90% of the bit dimensions between the two layers. The results show that the neural network-based reconstruction and separation method achieves better bottom-layer BER performance under a slight interlayer misalignment. The BER performance benefits more from a mild downtrack offset compared with the crosstrack offset. Both the separation detection based on a neural network and the dual-layer PRML achieve the lowest top-layer BER and the worst bottom-layer BER when the interlayer misalignment is 50% of a bit size. © 2024, The Authors. All rights reserved.

关键词： Bit error rate

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