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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

Science China(Information Sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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Obtaining Electromagnetic Properties of Multi-Type Media in Realistic Environments:state-of-the-Art and Prospects

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China Communications 2025年第1期22卷 25-40页

作者： Guo Lantu Guan Ke Liu Ting He Danping Zhang Haixia Zhu Qiuming Lu Jun Zhang Minggao China Research Institute of Radiowave Propagation Qingdao 266107China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Control Science and Engineering Shandong UniversityJinan 250061China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Jinan 250061China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Jiaxing University Jiaxing 314001China

To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current ***,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic *** a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic ***,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.

关键词： electromagnetic properties of media multi-type media parameter inversion ray tracing realistic environment

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Cooperative structure of Li/Ni mixing and stacking faults for achieving high-capacity Co-free Li-rich oxides

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Journal of Energy Chemistry 2024年第8期95卷 315-324,I0007页

作者： Zhen Wu Yu-Han Zhang Hao Wang Zewen Liu Xudong Zhang Xin Dai Kunyang Zou Xiaoming Lou Xuechen Hu Lijing Ma Yan Liu Yongning Liu State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101ShandongChina Hefei Advanced Computing Center Operation Management Corp Ltd Hefei 230088AnhuiChina Center for High Performance Computing Network Information CenterXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Wuhan Dongfeng Motor Corporation Ltd Wuhan 430056HubeiChina School of Materials Science and Intelligent Engineering Nanjing UniversitySuzhou 215163JiangsuChina Key Laboratory of MEMS of the Ministry of Education Southeast UniversityNanjing 210096JiangsuChina International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina

Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high ***,they commonly face severe structural instability and poor electrochemical activity,leading to diminished capacity and voltage ***,we introduce a Co-free LLO,Li_(1.167)Ni_(0.222)Mn_(0.611)O_(2)(Cf-L1),which features a cooperative structure of Li/Ni mixing and stacking *** structure regulates the crystal and electronic structures,resulting in a higher discharge capacity of 300.6 mA h g^(-1)and enhanced rate capability compared to the typical Co-free LLO,Li_(1.2)Ni_(0.2)Mn_(0.6)O_(2)(Cf-Ls).Density functional theory(DFT)indicates that Li/Ni mixing in LLOs leads to increased Li-O-Li configurations and higher anionic redox activities,while stacking faults further optimize the electronic interactions of transition metal(TM)3d and non-bonding O 2p ***,stacking faults accommodate lattice strain,improving electrochemical reversibility during charge/discharge cycles,as demonstrated by the in situ XRD of Cf-L1 showing less lattice evolution than *** study offers a structured approach to developing Co-free LLOs with enhanced capacity,voltage,rate capability,and cyclability,significantly impacting the advancement of the next-generation Li-ion batteries.

关键词： Co-free Li-rich oxides Li/Ni mixing Stacking faults Electronic structure

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Research on Fuzzing Technology for JavaScript Engines 21

Research on Fuzzing Technology for JavaScript Engines

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Tian, Ye Qin, Xiaojun Gan, Shuitao State Key Laboratory of Mathematical Engineering and Advanced Computing Zheng zhou Wuxi China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781450389853

JavaScript engine is the core component of web browsers, whose security issues are one of the critical aspects of the overall Web Eco-Security. Fuzzing technology, as an efficient software testing approach, has been widely applied to detecting vulnerabilities in different JavaScript engines, which is a security research hotspot at present. Based on systematical dissection of existing fuzzing methods, this paper reviews the development and technical ideas of JavaScript Engine Fuzzing combined with taxonomy, proposes a general framework of JavaScript Engine Fuzzing and analyzes the key techniques involved. Finally, we discuss the core issues that restrict efficiency in current research and present an outlook on the future trends of JavaScript Engine Fuzzing.. © 2021 Association for computing Machinery. All rights reserved.

关键词： Engines

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Critical Variable Guided Mutation for Directed Greybox Fuzzing

Critical Variable Guided Mutation for Directed Greybox Fuzzi...

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Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Yubo He Long Liu The State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Directed greybox fuzzing directs fuzzers to specified code areas and has gained great achievements in 1-day vulnerability detection. However, existing directed graybox fuzzers fail to generate the crash sample even if they found a testcase reaching the target site. There are mainly two questions that affect the effectiveness of directed greybox fuzzing: basic block-level target is coarse enough for 1-day vulnerability detection and the fuzzers follow a specific rule to select operators regardless of the vulnerability itself. This paper points out that only a few vulnerability-related variables are related to the vulnerability triggering. Based on the vulnerability-related variables, this paper proposes critical variable guided mutation, a mutation scheduling method to enhance the crash reproduction capability of directed greybox fuzzing. We implemented a prototype MDGF based on the critical variable guided mutation and evaluate it on real world programs. Evaluation of MDGF on various real-world programs showed that MDGF found vulnerabilities faster than the mainstream directed greybox fuzzers. The experimental results showed that the speed of MDGF is 6.18 times faster than that of AFLGo and 1.40 times faster than Beacon, and MDGF can find 1.71x more bugs than AFLGo.

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An Intrusion Detection Feature Selection Method Based on Improved Mutual Information

An Intrusion Detection Feature Selection Method Based on Imp...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Zan Qi Jinlong Fei Jue Wang Xue Li State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Feature selection methods for classification are crucial for intrusion detection techniques using machine learning. High-dimensional features in intrusion detection data affect computational complexity, consume more used resources and more time for data analysis, and the irrelevant and redundant features among them often hinder the performance of classifiers and mislead the classification task. Therefore, it is challenging to select more relevant features from intrusion detection data containing many such features. In this paper, we propose an efficient feature selection algorithm that first considers the correlation between features and the redundancy of pairs of features with respect to class labels based on an improved Pearson correlation coefficient, and later improves the evaluation function based on conditional mutual information to obtain a final subset of features with the goal of improving the classification rate and accuracy. The proposed feature selection method based on improved conditional mutual information is compared with three existing feature selection methods on the frequently studied public benchmark intrusion detection dataset NSL-KDD. The experimental results show that the features selected by the proposed method in this paper lead to a significant reduction in execution time while resulting in higher classification accuracy.

关键词： Correlation coefficient Correlation Redundancy Intrusion detection Machine learning Telecommunication traffic Feature extraction

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TS-GGNN: Combining Graph and Sequence Features for Vulnerability Detection in Source Code

TS-GGNN: Combining Graph and Sequence Features for Vulnerabi...

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International Conference on Communications, Information System and Computer engineering (CISCE)

作者： Xin Zhou Jianmin Pang Chunyan Zhang Feng Yue Junchao Wang Guangming Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Software vulnerability detection is crucial for maintaining the security and stability of software systems. In this paper, we propose a novel neural network model called TS-GGNN to address the problem of vulnerability detection in source code slices. The TS-GGNN model effectively captures both local and global features of vulnerable code by fusing sequence features with graph features. To achieve this, we utilize graph structure and sequence structure learning approaches to comprehensively extract valuable information from the source code slices. Our experiments are conducted on the SARD dataset, which consists of 61,638 code samples annotated for the presence or absence of vulnerabilities. The results demonstrate that TS-GGNN has the best vulnerability detection performance, with an accuracy of 99.4%, a precision of 98.81%, and an F1 score as high as 99.4% thereby validating the effectiveness of the TS-GGNN model in capturing features relevant to software vulnerabilities.

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Research on Cross-site Scripting Attack Detection Technology Based on Few-shot Learning

Research on Cross-site Scripting Attack Detection Technology...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Dongzhe Lu Long Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

With the intensification of informatization and mobility, various web security threats are emerging. Cross-site scripting (XSS) attack is the most common type of web attack. Most traditional detection methods have been difficult to adapt to the existing confusion variants of XSS attacks. In this paper, we extract features based on big data collected from 2017 to 2022. In order to improve the XSS detection effect of detection tools, we build machine learning models based on more than 210,000 positive and negative samples, among which CNN has the best performance. Furthermore, we propose a new algorithm that improves the traditional virtual sample generation technology based on prior knowledge in order to improve the generalization of the models. Experimental results show that in most cases, the performance of the algorithm in this paper is better than other VSG methods, and the ability to detect and discover unknown attacks is improved to a certain extent.

关键词： Machine learning algorithms Automation Cross-site scripting Perturbation methods Neural networks Machine learning Big Data

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An Improved key Mismatch Attack on Kyber

An Improved Key Mismatch Attack on Kyber

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Cryptography, Security and Privacy (CSP), IEEE International Conference on

作者： Yaru Wang Haodong Jiang Zhi Ma State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Recently, American National Institute of Standards and Technology (NIST) announced Kyber as the first KEM candidate to be standardized. The security of Kyber is based on the modular learning with errors (MLWE) problem, which achieves excellent efficiency and size. This work proposes an improved key mismatch on Kyber, which can reduce the number of queries required to recover the secret key. We first transform the problem of finding a certain parameter of ciphertexts into a quantum ordered search problem. Then we give the procedure of finding the value of a parameter in the ciphertexts by the quantum method. Finally, we instantiate this attack method on Kyber512, Kyber768 and Kyber1024. Compared with the existing attack algorithm, our improved attack reduces the number of queries for Kyber512, Kyber768 and Kyber1024 by 63%, 59% and 45%, respectively.

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Crop Enclave Interface for SGX Programs

Crop Enclave Interface for SGX Programs

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IEEE International Conference on Joint Cloud computing (JCC)

作者： Yaqi Fan Zhifeng Chen Qingbao Li Wenbo Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Existing memory attacks against SGX use the enclave interface, such as ECALLs and OCALLs, to inject malicious data into the enclave’s trusted memory to trigger memory corruption vulnerabilities therein. Therefore, enclave interface security becomes a key issue in defending against such attacks. However, a comprehensive static analysis of source SGX programs is currently lacking to obtain sufficient a priori knowledge to provide effective runtime interface protection for the enclave. In view of this, we identify 8 types of unsafe input data of enclave and design a new interface cropping method, SGXCrop. This method extracts critical interface information from source SGX programs, including ECALLs in use and unsafe input data, which are cropped at runtime of SGX programs. Tests in real SGX environment verify that the proposed method can effectively crop illegal ECALLs and unsafe input data.

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