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Proceedings of the 2020 2nd International Conference on Big-data Service and Intelligent Computation

作者： Junhui Wang Shuangyin Ren Jiezhong He Xiaolan Ji Diqing Huang College of Computer National University of Defense Technology and State Key Laboratory of Mathematical Engineering and Advanced Computing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Academy of Military Science College of Computer National University of Defense Technology

ISBN: (纸本)9781450388399

As more embedded environments need license plate recognition systems, how to recognize car plates with high speed/accuracy and low energy has become an important and challenging problem. In this paper, we propose a ultra-Fast miNi (FaNi) license plate recognition (LPR) system. The FaNi system are divided into one training sub-system and one inference sub-system. The former are used to get some offline features; then, the latter is deployed online to recognize license numbers with nearly real-time speed. The inference system is comprised of the vision processing unit (VPU) and the display unit. These two parts are both implemented with hardware logic. Experiments show that the FaNi system can obtain high accuracy and high speed with low resource cost.

关键词： VPU Plate Recognition System FPGA

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Practical Software Diversification Tool Chain for Dissimilar Redundancy Architecture

Practical Software Diversification Tool Chain for Dissimilar...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Gaofei Zhang Zheng Zhang Jiang Wang Xinsheng Ji State key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Engineering University Zhengzhou China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

With the rapid development of the information society, there are corresponding tool solutions for various daily problems. Aiming at the complex generation process and inconvenient operation of heterogeneous components in a security defense system constructed with dissimilar redundancy, in this paper, we propose a practical basic software application isomerization tool chain, which integrates a series of key technologies of heterogeneous structures. At the same time, corresponding automated tests are provided for users to conduct comprehensive evaluation. Experimental evaluation shows that the tool chain will be able to provide heterogeneous component support for security defense technologies based on dissimilarity redundancy construction and improve the security of software systems.

关键词： Redundancy Computer architecture Tools Software Hardware Security Testing

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Virasoro generators in the Fibonacci model tensor network: Tackling finite-size effects

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Physical Review B 2023年第24期107卷 245146-245146页

作者： Xiangdong Zeng Ruoshui Wang Ce Shen Ling-Yan Hung State Key Laboratory of Surface Physics Fudan University Shanghai 200433 China Department of Physics and Center for Field Theory and Particle Physics Fudan University Shanghai 200433 China Department of Physics Cornell University Ithaca New York 14853 USA Institute for Advanced Study Tsinghua University Beijing 100084 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Yau Mathematical Sciences Center Tsinghua University Beijing 100084 China

In this paper, we extend the method implementing Virasoro operators in a tensor network we proposed in Wang et al. [Phys. Rev. B 106, 115116 (2022)] and test it on the Fibonacci model, which is known to suffer from far more finite-size effects. To pick up the “seed” state that would flow to the stress tensor in the thermodynamic limit, we make use of the topological idempotent that projects the transfer matrix to the trivial sector. Combined with an optimization method, the seed state can be identified. We demonstrate that the descendant states in the Fibonacci model can be correctly generated with this approximate stress tensor, giving further evidence that the method applies more generally.

关键词： Conformal field theory Quantum criticality Lattice models in statistical physics Finite-size scaling Tensor network methods

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Medical Image Registration and Its Application in Retinal Images: A Review

arXiv

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arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

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An landmark evaluation algorithm based on router identification and delay measurement 5th

An landmark evaluation algorithm based on router identificat...

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Ma, Te Liu, Fenlin Zhang, Fan Luo, Xiangyang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Science and Technology Institute Zhengzhou China

ISBN: (纸本)9783030242701

City-level landmarks serve as an important foundation for achieving city-level and higher-precision IP geolocation. On the basis of the feature that router host names in network-developed areas often imply geographical location information, this paper proposes a city-level landmark evaluation algorithm based on router identification. Firstly, router host name matching rules are formulated by using plenty of network topology information obtained through probing. Secondly, the matching rules are used to extract router host names;the geographical locations corresponding to routers are queried through the established geographical location dictionary;and the nearest router that can obtain a city-level location in the probing path is reserved. Finally, candidate landmarks are evaluated based on the rule that the physical distance between network entities is less than the delay conversion distance. The experimental results show that the proposed algorithm can effectively evaluate the reliability of landmarks: among the experimental results on 8000 network landmarks in 4 cities in the United states, compared with the typical database query-based landmark obtaining, the city-level accuracy rates of landmarks are increased by 4.4%, 6.85%, 4.35% and 7.05% respectively. © Springer Nature Switzerland AG 2019.

关键词： Location

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Ultrabright Multiplexed Energy-Time-Entangled Photon Generation from Lithium Niobate on Insulator Chip

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Physical Review Applied 2021年第6期15卷 064059-064059页

作者： Guang-Tai Xue Yun-Fei Niu Xiaoyue Liu Jia-Chen Duan Wenjun Chen Ying Pan Kunpeng Jia Xiaohan Wang Hua-Ying Liu Yong Zhang Ping Xu Gang Zhao Xinlun Cai Yan-Xiao Gong Xiaopeng Hu Zhenda Xie Shining Zhu National Laboratory of Solid State Microstructures School of Physics School of Electronic Science and Engineering College of Engineering and Applied Sciences and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China State Key Laboratory of Optoelectronic Materials and Technologies and School of Physics and Engineering Sun Yat-sen University Guangzhou 510275 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computing National University of Defense Technology Changsha 410073 China

A high-flux entangled-photon source is a key resource for quantum optical study and application. Here, it is realized in a lithium niobate on isolator (LNOI) chip, with 2.79 × 1011 Hz/mW photon-pair rate and 1.53 × 109 Hz/(nm mW) spectral brightness. These data are boosted by over 2 orders of magnitude compared with existing technologies. A 160-nm-broad bandwidth is engineered for eight-channel multiplexed energy-time entanglement. Harnessed by high-extinction-frequency correlation and Franson interferences up to 99.17% visibility, such energy-time-entanglement multiplexing further enhances the high-flux data rate and warrants broad application in quantum-information processing on a chip.

关键词： Integrated optics Optical quantum information processing Optoelectronics Photon pairs & parametric down-conversion Quantum state engineering Second order nonlinear optical processes Optical sources & detectors Waveguides

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Keliang Zhao Jovial Niyogisubizo Linxia Xiao Yi Pan Dongqing Wei Didi Rosiyadi Yanjie Wei Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China Research Center for Artificial Intelligence and Cybersecurity National Research and Innovation Agency Bandung Indonesia

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs.

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Pre-ictal causal connectivity reveals the epileptic network characteristics for deep brain stimulation

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Neuroscience 2025年 579卷 329-343页

作者： Yifan Wang Tao Yu Yulan Ma Weigang Cui Xueyuan Wang Liankun Ren Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Beijing Institute of Functional Neurosurgery Department of Functional Neurosurgery Xuanwu Hospital Capital Medical University Beijing 100053 China School of Engineering Medicine Beihang University Beijing 100191 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing 100053 China Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Department of Automation Science and Electrical Engineering Beihang University Beijing 100191 China

Deep brain stimulation of anterior nucleus of the thalamus (ANT-DBS) is an effective clinical treatment for drug-resistant focal epilepsy. However, the complex epileptic network characteristics underlying ANT-stimulation effectiveness remain unknown, owing to currently unclear connectivity between ANT and seizure-related cortex in pre-ictal periods. Here, we developed a novel individualized pre-ictal ANT-cortical tripartite connectivity network (PANT-CTCNet), aiming to reveal epileptic network characteristics using intracranial stereo-electroencephalography (sEEG) recordings in five patients with focal epilepsy for 90 trials. Each trial represented a pre-ictal or post-stimulus sEEG duration, which was used to construct the epileptic network. By employing conditional Granger causality, we constructed individualized ANT-cortical connectivity networks and found a common epileptic network centred on ANT closely connected with seizure-related cortex in pre-ictal periods. After ANT stimulation for clinical validation, strengthened pre-ictal connectivity between ANT and epileptogenic zones led to significant decline in the causal intensity of epileptic networks. The PANT-CTCNet can give a quantitative reference for clinical preoperative evaluation of patient suitability for ANT-DBS treatment. These findings regarding epileptic network characteristics provide theoretical basis in the selection of optimal surgical candidates for personalized ANT-DBS.

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge computing Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal state update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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Opcode sequence analysis of Android malware by a convolutional neural network

Opcode sequence analysis of Android malware by a convolution...

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作者： Li, Dan Zhao, Lichao Cheng, Qingfeng Lu, Ning Shi, Wenbo School of Computer Science and Engineering Northeastern University Shenyang China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China School of Computer Science and Technology Xidian University Xi'an China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China

The number of malware has exploded due to the openness of the Android platform, and the endless stream of malware poses a threat to the privacy, tariffs, and device of mobile phone users. A novel Android mobile malware detection system is proposed, which employs an optimized deep convolutional neural network to learn from opcode sequences. The optimized convolutional neural network is trained multiple times by the raw opcode sequences extracted from the decompiled Android file, so that the feature information can be effectively learned and the malicious program can be detected more accurately. More critically, the k-max pooling method with better results is adopted in the pooling operation phase, which improves the detection effect of the proposed method. The experimental results show that the detection system achieved the accuracy of 99%, which is 2%-11% higher than the accuracy of the machine learning detection algorithms when using the same data set. It also ensures that the indicators, such as F1-score, recall, and precision, are maintained above 97%. Based on the detection system, a multi–data set comparison experiment is carried out. The introduced k-max pooling is deeply studied, and the effect of k of k-max pooling on the overall detection effect is observed. © 2019 John Wiley & Sons, Ltd.

关键词： Android malware

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