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Chinese Automation Congress (CAC)

作者： Shijie Wang Xiaodong Deng Wancheng Liu Yuhai Li Shitao Chen Longjun Liu Institute of Artificial Intelligence and Robotics (IAIR) Xi’an Jiaotong University Xi’an China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

Stereo depth estimation is widely applied in computer vision and intelligent robotic systems. For real-time mobile stereo vision applications, how to efficiently design and implement stereo matching on resource-limited hardware platforms is still a grand challenge. In this paper, we propose and implement a low-power, high-resolution stereo matching hardware architecture on field-programmable gate array (FPGA) with the input of structured light camera. In order to obtain high-quality point cloud data, we present an image preprocessing module and an improved cost function for stereo matching algorithm. Furthermore, we leverage hardware description language to implement our stereo matching computing architecture and all algorithm models are evaluated on the Xilinx ZCU102 development board, which only takes about 61147 LUTs and 786 BRAMs. Compared with high-performance CPUs, the computing speed is significantly improved, 1.84 times faster than Intel i7-10700@2.90GHz, and the power consumption is only 3.915W.

关键词： Costs Power demand Robot vision systems Estimation Computer architecture Hardware Real-time systems

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Design an Efficient DNN Inference Framework with PS-PL Synergies in FPGA for Edge Computing

Design an Efficient DNN Inference Framework with PS-PL Syner...

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Chinese Automation Congress (CAC)

作者： Wancheng Liu Yuhai Li Yining Yang Jinyu Zhu Longjun Liu Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China Institute of Artificial Intelligence and Robotics (IAIR) Xi’an Jiaotong University Xi’an China

in recent years, Deep Neural Network (DNN) based methods have achieved great success for changing inflexible machine to intelligent and live system. Internet of Things (IoT) applications equipped with DNN in domains such as self-driving, speech processing and video surveillance particular challenges. Since DNNs are memory-/computing- intensive applications, FPGAs are customized to accelerate DNN on edge system due to their low latency and high energy efficiency. However, embedded CPU is usually essential for IoT system and single accelerator of FPGA is hard to gain maximized performance of the whole IoT system. Therefore, we design an efficient DNN inference framework with programming system (PS, e.g., ARM CPU) and programming logic (PL, e.g. FPGA) synergies for intelligent IoT system. Compared with the common IoT system only separately leverages FPGA for DNN accelerating and ARM for other tasks, our framework can make ARM and FPGA jointly accelerate DNN inference from the beginning. Our PS-PL co-design approach can take full advantage of the merits of ARM (control operations) and FPGA (parallel computing operations) for various DNN model accelerating. We implemented our co-design framework on Zynq SoCs. The experimental results show that our framework can achieve maximal 205.9 GOPs/s DNN inference accelerating under 6.2W while it would not affect other CPU tasks running on IoT system.

关键词： Program processors Computational modeling Neural networks Programming Parallel processing Video surveillance Internet of Things

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Factored Trace Lasso Based Linear Regression Methods: Optimizations and Applications 5th

Factored Trace Lasso Based Linear Regression Methods: Optimi...

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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Zhang, Hengmin Du, Wenli Liu, Xiaoqian Zhang, Bob Qian, Feng School of Information Science and Engineering Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Department of Computer and Information Science University of Macau 999078 China

ISBN: (纸本)9789811623356

Consider that matrix trace lasso regularized convex p -norm with p= 1, 2 regression methods usually have the higher computational complexity due to the singular value decomposition (SVD) of larger size matrix in big data and information processing. By factoring the matrix trace lasso into the squared sum of two Frobenius-norm, this work studies the solutions of both adaptive sparse representation (ASR) and correlation adaptive subspace segmentation (CASS), respectively. Meanwhile, the derived models involve multi-variable nonconvex functions with at least two equality constraints. To solve them efficiently, we devise the nonconvex alternating direction multiplier methods (NADMM) with convergence analysis satisfying the Karush-Kuhn-Tucher (KKT) conditions. Finally, numerical experiments to the subspace clustering can show the less timing consumptions than CASS and the nearby performance of our proposed method when compared with the existing segmentation methods like SSC, LRR, LSR and CASS. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Singular value decomposition

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Twitter Bot Detection with Multi-Head Attention and Supervised Contrastive Learning

Twitter Bot Detection with Multi-Head Attention and Supervis...

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International Conference on Wavelet Active Media technology and information Processing (ICWAMTIP)

作者： Liu Qiang Lu Jiazhong Huang Yuanyuan Zhang Weisha Lv Jun School of Cybersecurity (Xin Gu Industrial College) Chengdu University of Information Technology Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu China SUGON Industrial Control and Security Center Chengdu China School of Foreign Languages University of Electronic Science and Technology of China Chengdu China MetaX Integrated Circuits (Shanghai) Co. Ltd

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

The task of detecting bots on Twitter is an indispensable task for combating social network disinformation, curbing rumors, and managing online public opinion. However, today's social bots often disguise themselves by mimicking various aspects of real user characteristics to escape detection, which poses an unprecedented challenge to the existing Twitter bot detection task. To address this, an innovative detection method is presented in this study that improves the accuracy of Twitter bot detection by realizing the effective fusion of multi-dimensional features through the multi-head attention mechanism and applying it to a relational graph convolutional network. It also combines supervised contrastive learning to strengthen the differentiation ability of the high-latitude features. Experiments conducted on the publicly available dataset Twibot-20, compared to the baseline, the proposed method achieves approximately 1% better results in both Accuracy and the Matthews Correlation Coefficient, proving its effectiveness.

关键词： Training Attention mechanisms Accuracy Social networking (online) Graph convolutional networks Blogs Contrastive learning information processing Feature extraction Fake news

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Secure Computation Offloading via Cooperative Jamming in Marine IoT Networks

Secure Computation Offloading via Cooperative Jamming in Mar...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Li Ping Qian Mingqing Li Xinyu Dong Yuan Wu Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China Department of Computer and Information Science University of Macau Macao SAR China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9781665435413

Edge computing has been envisioned as a promising approach to enable the computation-intensive yet latencysensitive marine mobile services in the fifth generation and beyond wireless networks. In this paper, we investigate the edge computing in Marine Internet of Things (M-IoT) via the assistance of unmanned surface vehicles (USVs) subject to the eavesdropping attack. In particular, we consider a scenario in which USVs are exploited to provide cooperative jamming for the communication security at the physical layer when the high altitude platform (HAP) is performing task offloading transmission. We jointly optimize the workload offloaded by HAP, the HAP's transmission power as well as each USV's interfering signal power with the objective of minimizing the total energy consumption for completing the total workloads under the latency constraint. The bisection search method is first adopted to obtain the optimal solutions to the offloaded workload and each USV's interfering signal power. Further, by exploiting the monotonicity, the polyblock outer approximation based algorithm (POA-Algorithm) is designed to obtain the HAP's optimal transmission power. Finally, numerical results validate the optimality and effectiveness of our proposed algorithm by comparing it with the results of LINGO and different jamming schemes.

关键词： Energy consumption Wireless networks Search methods Physical layer Approximation algorithms security Jamming

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Deep transfer clustering of radio signals

arXiv

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arXiv 2021年

作者： Xuan, Qi Li, Xiaohui Chen, Zhuangzhi Xu, Dongwei Zheng, Shilian Yang, Xiaoniu The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Modulation recognition is an important task in radio signal processing. Most of the current researches focus on supervised learning. However, in many real scenarios, it is difficult and cost to obtain the labels of signals. In this letter, we turn to the more challenging problem: can we cluster the modulation types just based on a large number of unlabeled radio signals? If this problem can be solved, we then can also recognize modulation types by manually labeling a very small number of samples. To answer this problem, we propose a deep transfer clustering (DTC) model. DTC naturally integrates feature learning and deep clustering, and further adopts a transfer learning mechanism to improve the feature extraction ability of an embedded convolutional neural network (CNN) model. The experiments validate that our DTC significantly outperforms a number of baselines, achieving the state-of-the-art performance in clustering radio signals for modulation recognition. © 2021, CC BY.

关键词： Modulation

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Adaptive visibility graph neural network and it's application in modulation classification

arXiv

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arXiv 2021年

作者： Xuan, Qi Qiu, Kunfeng Zhou, Jinchao Chen, Zhuangzhi Xu, Dongwei Zheng, Shilian Yang, Xiaoniu The Institute of Cyberspace Security The College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Our digital world is full of time series and graphs which capture the various aspects of many complex systems. Traditionally, there are respective methods in processing these two different types of data, e.g., Recurrent Neural Network (RNN) and Graph Neural Network (GNN), while in recent years, time series could be mapped to graphs by using the techniques such as Visibility Graph (VG), so that researchers can use graph algorithms to mine the knowledge in time series. Such mapping methods establish a bridge between time series and graphs, and have high potential to facilitate the analysis of various real-world time series. However, the VG method and its variants are just based on fixed rules and thus lack of flexibility, largely limiting their application in reality. In this paper, we propose an Adaptive Visibility Graph (AVG) algorithm that can adaptively map time series into graphs, based on which we further establish an end-to-end classification framework AVGNet, by utilizing GNN model DiffPool as the classifier. We then adopt AVGNet for radio signal modulation classification which is an important task in the field of wireless communication. The simulations validate that AVGNet outperforms a series of advanced deep learning methods, achieving the state-of-the-art performance in this task. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series

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Analysis of Frequency Offset for Satellite Navigation Receiver Using Carrier-Aided Code Tracking Loop 20

Analysis of Frequency Offset for Satellite Navigation Receiv...

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20th IEEE International Conference on Communication technology, ICCT 2020

作者： Gao, Teng Wang, Lijun Jin, Xiaofan No.36 Research Institute of Cetc Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781728181417

Carrier-aided code tracking loop is widely used in satellite navigation receivers. This kind of loop structure can reduce code tracking noise by narrowing the bandwidth of code tracking loop. The performance of carrier-aided code tracking loop in receivers is affected by frequency deviation of reference clock source. This paper analyzes the influence of carrier frequency offset and sampling frequency offset on carrier-aided code tracking loop due to reference clock offset. The results show that large frequency offset can cause code tracking loop lose lock, code tracking loop is more sensitive to sampling frequency deviation and increasing the loop bandwidth can reduce the effects of frequency offset. This analysis provides reference for receiver tracking loop design. © 2020 IEEE.

关键词： Bandwidth

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Multi Topological Charges Vortex Beam Generator Based on Nonlinear Bound States in the Continuum with Improvement of Nonlinear Conversion Efficiency

SSRN

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SSRN 2023年

作者： Xu, Hang Li, Hui Tan, Qi Song, Chunyu Li, Mengyao Liu, Yanyan Zhang, Yating Wang, Bo Li, Jie Yao, Jianquan Ministry of Education School of Precision Instruments and Opto-Electronics Engineering Tianjin University Tianjin300072 China Function laboratory for Marine Fisheries Science and FoocProduction Processes Qingdao National LaboratoryTechnology Qingdao China Key Laboratory of Quantum Matt Science Henan Key Laboratory of Photovoltaic Materials Henan University KaifengHenan475004 China Information Materials and Device Applications Key Laboratory of Sichuan ProvincialUniversities Chengdu University of Information Technology 610225 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

In the momentum space of bound states in the continuum (BIC), vortex configurations around the Γ-point are generated by the selection rules. The electric field distribution close to the BIC satisfies twofold rotational symmetry (C2). Since the periodic structure of 2D photonic crystals (PhCs) supports the generation of odd harmonics, this characteristic provides an attractive approach for generating high-harmonic optical vortex (OV) beams with both left and right handedness. Also, the topological charge of OV beams generated by C4v-symmetric PhCs can be further improved by an increase of structural symmetry. Moreover, since the nonlinear conversion efficiency of directly used PhCs is low, their practicability is limited. Here, a hexagonal arrangement of C6v-symmetric 2D PhCs slab is selected. Through energy band selection, higher order fundamental and nonlinear OV beams can be generated, which can not only cover the topological charge of the C4v-symmetric structure, but also generate more abundant topological charge. In addition, we improve the third-order nonlinear conversion efficiency by the mirror approach, which is 22 times greater than with the direct use of PhCs. This method can improve the channel capacity of OV generators based on BICs and make them more practical. © 2023, The Authors. All rights reserved.

关键词： Conversion efficiency

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

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