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3rd EAI International Conference on Artificial Intelligence for communications and Networks, AICON 2021

作者： Zhu, Huijie Liu, Mingqian Li, Ming Gao, Xiuhui Science and Technology on Communication Information Security Control Laboratory The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing314033 China State Key Laboratory of Integrated Service Networks Xidian University Shaanxi Xi’an710071 China State 722 Factory Guangxi Guilin541001 China

ISBN: (纸本)9783030901950

This paper proposes a novel passive location parameter estimator using multiple satellites for moving aerial targets. Specifically, we consider target detection in the ground dual receiver system, which is divided into reference channel and monitoring channel. The reference channel receives the direct wave signals from multiple external emitters, and the monitoring channel receives the echo signals reflected by the target. We propose a target detection method based on a generalized likelihood ratio (GLRT). This detection algorithm is robust to the reference channel noise. Extensive simulations are conducted to evaluate the performance of the algorithm under various network settings. © 2021, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Satellites

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Passive Target Detection Based on Rao Using Multi-satellite Illumination 3rd

Passive Target Detection Based on Rao Using Multi-satellite ...

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3rd EAI International Conference on Artificial Intelligence for communications and Networks, AICON 2021

ISBN: (纸本)9783030901950

In this paper, hypotheses of target detection features is proposed when constructing the signal model. Under this condition, the Rao detection method is extended to weak echo signal detection using multi-satellite illumination. Furthermore, a multi-satellite weak echo signal detection algorithm is proposed based on Rao detection. When the noise variance is unknown, the Rao detector is designed according to the existence of an interfering target. Moreover, the detector’ detection thresholds are obtained given the false alarm probability, respectively. Finally, we evaluate the Rao detection performance in different situations with extensive simulations. © 2021, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Satellites

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Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Li, Minghui Liu, Wei Hu, Shengshan Zhang, Yechao Wan, Wei Xue, Lulu Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. China Cluster and Grid Computing Lab. China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six

关键词： Topology

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Small Object Detection Algorithm Based on RPANet and Positional Convolution Attention Mechanism 5

Small Object Detection Algorithm Based on RPANet and Positio...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Tang, Zongbing Yang, Dan Qu, Junsuo School of Communication and Information Engineering Xi'an University of Posts and Telecommunications China School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an Robertic Intelligent Systems International Science and Technology Cooperation Base Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

With the development of deep learning, small object detection has a significant role in application fields such as smart factories and remote sensing images. In order to address the problem of difficult and low accuracy detection of small objects due to small pixel scale and little feature information. In this paper, we present a path aggregation network with residual characteristic RPANet on YOLOv3 algorithm, which can twice use the feature information of the backbone network to enhance the small object feature information, and also offer a positional convolution attention mechanism module PCAM to thoroughly learn and extract the small object feature information as well as reduce the unnecessary feature information in the background, so as to further enhance the detection capability of the model for small objects. The experimental results demonstrate that the improved YOLOv3 algorithm is more effective for small object detection. © 2022 ACM.

关键词： Convolution

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Heptagonal core anti-resonant hollow-core fiber for laser beam mode shapers

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Chinese Optics Letters 2025年第04期 54-60页

作者：王洋张小贝陈伟张琦杨勇李茂春颜苗王廷云 Key Laboratory of Specialty Fiber Optics and Optical Access Networks' Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication' Shanghai Institute for Advanced Communication and Data Science School of Communication and Information EngineeringShanghai University Laboratory of Science and Technology on Marine Navigation and Control' China State Shipbuilding Corporation

We report a novel anti-resonant hollow-core fiber（ARF） with a heptagonal core surrounded by connected cladding tubes,which is employed as a laser beam mode shaper. Cladding nodes are formed between each pair of adjacent cladding tubes,which efficiently enhances the mode coupling, thus significantly suppressing higher-order modes（HOMs） within a short fiber length. Numerical investigation and simulation for variations in the curvature of each node are conducted, through which an optimized structure of cladding nodes is obtained. Experimental results demonstrate that for a 1-m-long ARFbased mode shaper, the output beam quality M2is enabled, and the near-diffraction-limit beam quality is 1.03. The ARF presents a maximum average transmission power of 600 mW, achieving a total coupling efficiency of approximately85%. Moreover, it is maintained for one hour without inducing any damage while maintaining beam quality. These results highlight the great potential of the mode shaper in applications such as laser surgery, precision welding, and laser writing.

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Real-Time Liquid Crystal Atmospheric Turbulence Simulator Using Gpu Calculation

SSRN

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SSRN 2023年

作者： Huang, RongHui Ni, XiaoLong Yu, Xin ChunYi, Chen The School of Opto-Electronic Engineering Changchun University of Science and Technology Changchun130022 China Fundamental Science on Space-Ground Laser Communication Technology Laboratory Changchun University of Science and Technology Changchun130022 China Key Laboratory of Photoelectric Measurement & Control and Optical Information Transfer Technology Changchun University of Science and Technology 7089 Weixing Road Changchun130022 China

Simulated turbulence phase screens are widely used in various optical experiment scenarios, but there is no satisfactory solution in terms of speed and accuracy so far. For this purpose, we use the numerical simulation method of turbulence phase screen based on the sparse spectral model to generate a large amount of data and make the Liquid Crystal Atmospheric Turbulence Simulator (LC-ATS) have the ability to simulate the atmosphere in real-time by using the Computational Unified Device Architecture (CUDA) on GPU for parallel computation. Experiments were conducted on a Liquid Crystal Spatial Light Modulator (LC-SLM) using a wavefront phase screen that generates an atmospheric turbulence model. The experimental results yielded up to about 1.36ms per frame at the resolution of 512x512, and up to about 423Hz at the liquid crystal on silicon (LC-OS). To our knowledge, this is the highest speed used to generate turbulent phases in real-time. © 2023, The Authors. All rights reserved.

关键词： Atmospheric turbulence

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ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion Purification

arXiv

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arXiv 2024年

作者： Wang, Xianlong Hu, Shengshan Zhang, Yechao Zhou, Ziqi Zhang, Leo Yu Xu, Peng Wan, Wei Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Information and Communication Technology Griffith University SouthportQLD4215 Australia

Clean-label indiscriminate poisoning attacks add invisible perturbations to correctly labeled training images, thus dramatically reducing the generalization capability of the victim models. Recently, defense mechanisms such as adversarial training, image transformation techniques, and image purification have been proposed. However, these schemes are either susceptible to adaptive attacks, built on unrealistic assumptions, or only effective against specific poison types, limiting their universal applicability. In this research, we propose a more universally effective, practical, and robust defense scheme called ECLIPSE. We first investigate the impact of Gaussian noise on the poisons and theoretically prove that any kind of poison will be largely assimilated when imposing sufficient random noise. In light of this, we assume the victim has access to an extremely limited number of clean images (a more practical scene) and subsequently enlarge this sparse set for training a denoising probabilistic model (a universal denoising tool). We then introduce Gaussian noise to absorb the poisons and apply the model for denoising, resulting in a roughly purified dataset. Finally, to address the trade-off of the inconsistency in the assimilation sensitivity of different poisons by Gaussian noise, we propose a lightweight corruption compensation module to effectively eliminate residual poisons, providing a more universal defense approach. Extensive experiments demonstrate that our defense approach outperforms 10 state-of-the-art defenses. We also propose an adaptive attack against ECLIPSE and verify the robustness of our defense scheme. Our code is available at https://***/CGCL-codes/ECLIPSE. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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SMCOS: Fast and Parallel Modular Multiplication on ARM NEON Architecture for ECC 17th

SMCOS: Fast and Parallel Modular Multiplication on ARM NEON ...

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17th International Conference on information security and Cryptology, Inscrypt 2021

作者： Wang, Wenjie Wang, Wei Lin, Jingqiang Fu, Yu Meng, Lingjia Wang, Qiongxiao State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100089 China School of Cyber Security University of Chinese Academy of Sciences Beijing100089 China Data Assurance and Communication Security Research Center CAS Beijing100089 China School of Cyber Security University of Science and Technology of China Anhui Hefei230027 China Beijing Institute University of Science and Technology of China Beijing China

ISBN: (纸本)9783030883225

Elliptic Curve Cryptography (ECC) is considered a more effective public-key cryptographic algorithm in some scenarios, because it uses shorter key sizes while providing a considerable level of security. Modular multiplication constitutes the "arithmetic foundation" of modern public-key cryptography such as ECC. In this paper, we propose the Cascade Operand Scanning for Specific Modulus (SMCOS) vectorization method to speed up the prime field multiplication of ECC on Single Instruction Multiple Data (SIMD) architecture. Two key features of our design sharply reduce the number of instructions. 1) SMCOS uses operands based on non-redundant representation to perform a "trimmed" Cascade Operand Scanning (COS) multiplication, which minimizes the cost of multiplication and other instructions. 2) One round of fast vector reduction is designed to replace the conventional Montgomery reduction, which consumes less instructions for reducing intermediate results of multiplication. Further more, we offer a general method for pipelining vector instructions on ARM NEON platforms. By this means, the prime field multiplication of ECC using the SMCOS method reaches an ever-fastest execution speed on 32-bit ARM NEON platforms. Detailed benchmark results show that the proposed SMCOS method performs modular multiplication of NIST P192, Secp256k1, and Numsp256d1 within only 205, 310 and 306 clock cycles respectively, which are roughly 32% faster than the Multiplicand Reduction method, and about 47% faster than the Coarsely Integrated Cascade Operand Scanning method. © 2021, Springer Nature Switzerland AG.

关键词： Public key cryptography

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2023年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Hybrid Shaping for Bit-Interleaved Coded Modulation with Iterative Decoding

Hybrid Shaping for Bit-Interleaved Coded Modulation with Ite...

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IEEE Conference on Vehicular technology (VTC)

作者： Jiayi Yang Qianfan Wang Congduan Li Xiao Ma School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this paper, we integrate the 5G low-density parity-check (LDPC) coded modulation systems with hybrid shaping, where the centroid-based geometric shaping is implemented to remedy the performance loss of the many-to-one probabilistic shaping. Taking into account the fact that the 5G parity-check matrices have an uneven density in different parts, we elaborately design a simple row-column interleaver for the bit-interleaved coded modulation with iterative decoding (BICM-ID) system to allocate the ambiguous bits caused by the many-to-one mapping to the sparser parity part, resulting in the hybrid shaping for BICM-ID (HS-BICM-ID) system. Numerical results have shown that the HS-BICM-ID can obtain shaping gains of about 0.5 dB and 1.4 dB compared to the constant composition distribution matching (CCDM) shaping and the geometric shaping, respectively, while it can obtain a shaping gain of about 1.7 dB compared to the scheme with uniform input. Our work has also shown that, at low and moderate spectral efficiency, the presented hybrid shaping can achieve a significant shaping gain and effectively remedy the performance loss of dyadic many-to-one probabilistic shaping.

关键词： Vehicular and wireless technologies 5G mobile communication Spectral efficiency Modulation Probabilistic logic Interleaved codes Gain

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