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SSRN 2022年

作者： Es-Sabry, Mohammed El Akkad, Nabil Merras, Mostafa Satori, Khalid LISAC Computer Science Signals Automation and Cognitivism Laboratory Faculty of Sciences Dhar-Mahraz Sidi Mohamed Ben Abdellah University B.P 1796 Atlas Fez Morocco LESA Laboratory of Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco Department of Computer Science High School of Technology Moulay Ismail University Meknes Morocco

In this paper, we introduce an improved 32-bit color image encryption method using Four 1D chaotic maps that are Logistic map, Chebyshev map, Tent map, and Sine map. these maps were used to fill the four matrices of our encryption system with unique integers that are between 0 and 255. Each matrix has a size of arranged in a square. The four channels of a 32-bit color image are represented by these matrices (red, green, blue, and alpha). The upper-left and lower-right matrices are used to encrypt respectively the red and the alpha channels. The upper-right and lower-left matrices are used to encrypt respectively the green and the blue channels. For the confusion section, we apply the Arnold Cat Map transformation to shuffle all the pixels and change their position. Several safety criteria were used to evaluate the performance of our algorithm against common attacks and have shown excellent results. © 2022, The Authors. All rights reserved.

关键词： Cryptography

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Adaptive weighted image fusion algorithm based on NSCT multi-scale decomposition

Adaptive weighted image fusion algorithm based on NSCT multi...

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2020 International Conference on System science and engineering, ICSSE 2020

作者： Liu, Jia Duan, Miyi Chen, Wen-Bai Shi, Haobin Beihang University Smart City College Beijing Union University Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beijing China Beihang University Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beijing China Beijing Information Science and Technology University School of Automation Beijing China Northwestern Polytechnical University School of Computer Science Xi' an China

ISBN: (纸本)9781728159607

The purpose of infrared image and visible light image fusion is to preserve as much as possible the target information in the infrared image and the detailed information in the visible light image. For this purpose, this paper first extracts the infrared image saliency map based on guided filtering, uses NSCT to decompose infrared and visible light images at multiple scales, adds the infrared image saliency map to the low-frequency component, and applies weighted average fusion through the adaptive infrared weight map. In the high frequency component, the absolute value of the maximum coefficient is taken to be the maximum for fusion. This method takes into account the generally high brightness of human targets in infrared images, maintains the edge information of the image, and fully considers the anti-noise performance of the fusion method, and can adaptively take into account the complex and changeable environment. © 2020 IEEE.

关键词： Image fusion

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BIG-FUSION: Brain-Inspired Global-Local Context Fusion Framework for Multimodal Emotion Recognition in Conversations 39

BIG-FUSION: Brain-Inspired Global-Local Context Fusion Frame...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yusong Fang, Xuanye Yin, Huifeng Li, Dongyuan Li, Guoqi Xu, Qi Xu, Yi Zhong, Shuai Xu, Mingkun Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai China Department of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan School of Computer Science and Technology Dalian University of Technology Dalian China Department of Precision Instrument Tsinghua University Beijing China Institute of Automation Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)157735897X

Considering the importance of capturing both global conversational topics and local speaker dependencies for multimodal emotion recognition in conversations, current approaches first utilize sequence models like Transformer to extract global context information, then apply Graph Neural Networks to model local speaker dependencies for local context information extraction, coupled with Graph Contrastive Learning (GCL) to enhance node representation learning. However, this sequential design introduces potential biases: the extracted global context information inevitably influences subsequent processing, compromising the independence and diversity of the original local features;current graph augmentation methods in GCL cannot consider both global and local context information in conversations to evaluate the node importance, hindering the learning of key information. Inspired by the human brain excels at handling complex tasks by efficiently integrating local and global information processing mechanisms, we propose an aligned global-local context fusion framework for sequence-based design to address these problems. This design includes a dual-attention Transformer and a dual-evaluation method for graph augmentation in GCL. The dual-attention Transformer combines global attention for overall context extraction with sliding-window attention for local context capture. Meanwhile, spiking neuron dynamics are introduced to enhance the representation capability of the extracted features, supporting the effective the interaction between different modalities from these global and local cues. The dual-evaluation method in GCL comprises global importance evaluation to identify nodes crucial for overall conversation context, and local importance evaluation to detect nodes significant for local semantics, generating augmented graph views that preserve both global and local information. This approach ensures balanced information processing throughout the pipeline, enhancing biologica

关键词： Graph neural networks

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Three-dimensional C-scan-based generation adversarial network with synthetic input to improve optical coherence tomography angiography

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Journal of Biomedical Optics 2025年第5期30卷 056006页

作者： Xu, Jingjiang Feng, Zhongwu Qiu, Haixia Tang, Peijun Gao, Kai Huang, Yanping Lan, Gongpu Qin, Jia An, Lin Jia, Gangyong Wu, Qing Foshan University Guangdong-Hong Kong-Macao Intelligent Micro-Nano Optoelectronic Technology Joint Laboratory Foshan China Guangdong Weiren Meditech Co. Ltd Innovation and Entrepreneurship Teams Project of Guangdong Pearl River Talents Program Foshan China Foshan University School of Mechatronic Engineering and Automation Foshan China Chinese PLA General Hospital The First Medical Centre Department of Laser Medicine Beijing China South China Normal University College of Biophotonics Guangzhou China Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science State Key Laboratory of Ophthalmology Guangdong Provincial Clinical Research Center for Ocular Diseases Zhongshan Ophthalmic Center Guangzhou China Hangzhou Dianzi University School of Computer Science Hangzhou China

Significance: Optical coherence tomography angiography (OCTA) usually suffers from the inherent random fluctuations of noise and speckles in the imaging system. Previous deep learning methods have mainly focused on improving the quality of B-scan blood flow images or en face projection images. We propose a deep learning method to reconstruct high-quality 3D vasculature, which fully utilizes the volumetric OCTA data and the topological features of the vascular network. Aim: We propose a deep learning method called the three-dimensional C-scanbased generation adversarial network (3DCS-GAN) to improve vascular visualization for volumetric OCTA data. Approach: To train the network, we superimposed the single-shot en face OCTA images on avascular noisy C-scan images to synthesize the input data and used the multiple averaged en face OCTA images as the reference labels. The deep learning algorithm is based on Pix2Pix architecture and consists of a generator model and a discriminator model. A perceptual loss function was utilized by combining content loss and adversarial loss. The proposed algorithm is applied to the C-scan images depth-by-depth to suppress the background noise and enhance vascular visualization in the 3D OCTA data. Results: The proposed method has improved the contrast-to-noise ratio of crosssectional OCTA images by ∼2 times. It greatly enhances the visualization of blood vessels in the deep layer and offers much clearer blood vessel topology in the 3D volume-rendering of OCTA. 3DCS-GAN has exhibited superior image enhancement compared with alternative methods. It has been used to enhance the OCTA images of port wine stain disease for clinical investigation. Conclusions: It demonstrates that the proposed 3DCS-GAN can greatly improve vascular visualization in the deep layer, provide better image quality than the multiple averaged OCTA images, and achieve superior image enhancement for volumetric OCTA data. © 2025 SPIE. All rights reserved.

关键词： Image enhancement

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Modulated-Virtual-Vector-Based Predictive Current Control for Dual Three-Phase PMSM With Enhanced Steady-State Performance

Modulated-Virtual-Vector-Based Predictive Current Control fo...

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Annual Conference of Industrial Electronics Society

作者： Ze Li Xingyu Zheng Jinhui Xia Liling Wang Yuanbo Guo Xiaonan Gao Zhiheng Liu Xiaohua Zhang College of Electronic and Information Engineering Hebei University Baoding China School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education Southeast University Nanjing China School of Electrical Engineering Dalian University of Technology Dalian China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The School of Electrical Engineering Dalian University of Technology Dalian China

Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has attracted great attention due to its high reliability and high-power output capacities. However, the conventional single-voltage-vector-based predictive current control (SV-PCC) for DTP-PMSM presents high torque ripple and current harmonics, and high computational burden. To solve those issues, a modulated-virtual-vector-based PCC (MVV-PCC) for DTP-PMSM is proposed in this paper. Wherein, twenty-four VVs are synthesized by the inherent voltage vectors, and two VVs and one zero voltage vector with optimal duty cycles are determined and applied in each sampling period to improve the steady-state performance. The selection of optimal VVs and the calculation of the optimal duty cycles are simplified by integrating the deadbeat control and modulation scheme. Various comparisons are carried out to validate the effectiveness and superiority of the proposed MVV-PCC strategy.

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On input-to-state stability verification of identified models obtained by Koopman operator

arXiv

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arXiv 2023年

作者： Mei, Wenjie Zheng, Dongzhe Zhou, Yu Taha, Ahmad Zhao, Chengyan School of Automation Key Laboratory of MCCSE the Ministry of Education Southeast University Nanjing210096 China Department of Computer Science Shanghai Jiao Tong University Shanghai200240 China Inria Centrale Lille CRIStAL UMR 9189 CNRS Lille59000 France Civil and Environmental Engineering and Electrical Department Vanderbilt University Nashville37235 United States Graduate School of Science and Engineering Ritsumeikan University Shiga525-8577 Japan

This paper proposes a class of basis functions for realizing the input-to-state stability verification of identified models obtained from the true system (assumed to be input-to-state stable) using the Koopman operator. The formulated input-to-state stability conditions are in the form of linear matrix inequalities. Two extensions are presented to relax the imposed restrictions on the basis functions. Several numerical examples are provided to demonstrate the efficacy of the proposed results. © 2023, CC BY.

关键词： Linear matrix inequalities

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ruijie Xu Shichao Chen Wenqiao Sun Yisheng Lv Jialiang Luo Ying Tang The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems The Center of National Railway Intelligent Transportation System Engineering and Technology Institute of Automation Chinese Academy of Sciences China Academy of Railway Sciences Corporation Limited Beijing China China Academy of Railway Sciences Corporation Limited Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing Beijing China Department of Electrical and Computer Engineering Rowan University Glassboro USA

Global Navigation Satellite Systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance.

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STD-TR: End-to-End Spatio-Temporal Action Detection with Transformers

STD-TR: End-to-End Spatio-Temporal Action Detection with Tra...

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2021 China automation Congress, CAC 2021

作者： Li, Zexian Wang, Tian Zhu, Aichun Liu, Kexin Shi, Peng Snoussi, Hichem School of Automation Science and Electrical Engineering Beihang University Beijing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China Institute of Artificial Intelligence Beihang University Beijing China School of Computer Science and Technology Nanjing Tech University Nanjing China School of Mathematics and Computer Science Fujian Normal University Fujian China Institute Charles Delaunay-LM2S University of Technology of Troyes Troyes France

ISBN: (纸本)9781665426473

Spatio-temporal action detection methods locate human actions in both spatial and temporal dimension, which usually follow a two-stage structure. In this paper, We propose STD-TR, a novel spatio-temporal action detection framework with an end-to-end transformer structure. STD-TR employs two branches to extract feature from video clip and key frame concurrently, then sends the aggregated feature to the transformer encoder-decoder. View spatio-temporal action detection as a set matching and prediction problem, STD-TR employs learned object queries to model the relation of feature context, and directly outputs all predictions at one inference time. Our method remove all hand-designed and can be optimized by a joint loss. Besides, a Hungarian algorithm and a upgraded linking strategy are used for bipartite set matching and action tube generation respectively. Convincing experiment result on challenging dataset demonstrates the superiority of our method. © 2021 IEEE

关键词： computer vision

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S$^{\text{4}}$TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

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Event-Triggered Hybrid Impulsive Control on Lag Synchronization of Delayed Memristor-Based Bidirectional Associative Memory Neural Networks for Image Hiding

SSRN

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SSRN 2022年

作者： Yuan, Manman Luo, Xiong Mao, Xue Han, Zhen Sun, Lei Zhu, Peican School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Shunde Graduate School University of Science and Technology Beijing Guangdong Foshan528399 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Shannxi Xi’an710072 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Shaanxi Xi’an710072 China

Memristor-based bidirectional associative memory neural networks (MBAMNNs) analysis can help to reveal the dynamic basis of secure communication. Effectively representing the synchronization of MBAMNNs is the principal task of applying brain-inspired neural networks to image hiding. Previous research has typically utilized continuous control methods to represent the synchronization of MBAMNNs, but these are not well coordinated under limited network bandwidth. Besides, MBAMNNs provide special communication features and uncertain factors to describe the image hiding, but few studies have explored such elements. To address these two issues, we propose an event-triggered hybrid impulsive control on lag synchronization of MBAMNNs for the image hiding. Specifically, we incorporate both time-varying uncertain factors and topology structure into a designed event-triggered controller. The frequency of the controller can be automatically updated from two novel triggered conditions, and Zeno-behavior can be avoided effectively. Experimental results demonstrate that our scheme not only outperforms several exciting approaches in synchronization but also can effectively realize the image hiding under low energy consumption. © 2022, The Authors. All rights reserved.

关键词： Memristors

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