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2019 International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2019

作者： Junchao, Wang Zhenwu, Liu Hao, Liu Jianmin, Pang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Software diversity has been proven to be an effective approach to enhance system security. To make the best of the advantage brought by software diversity, a multi-variant execution environment is needed. However, although some MVEEs have been proposed, most of them are either too simple or only focusing one aspect which limits their widely adoption in industry. In this paper we propose a framework for multi-variant execution environment to enhance the security of software systems. The framework addresses different aspects when implementing a MVEE and can help to make the best of software diversity to enhance the system's security. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Artificial intelligence

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A CGAN-LSTM-Based Framework for Time-Varying Non-Stationary Channel Modeling

arXiv

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arXiv 2025年

作者： Guo, keying He, Ruisi Yang, Mi Zhang, Yuxin Ai, Bo Zhang, Haoxiang Han, Jiahui Chen, Ruifeng School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China State Key Laboratory of Advanced Rail Autonomous Operation Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China China Academy of Industrial Internet Ministry of Industry and Information Technology Beijing China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing100081 China

Time-varying non-stationary channels, with complex dynamic variations and temporal evolution characteristics, have significant challenges in channel modeling and communication system performance evaluation. Most existing methods of time-varying channel modeling focus on predicting channel state at a given moment or simulating short-term channel fluctuations, which are unable to capture the long-term evolution of the channel. This paper emphasizes the generation of long-term dynamic channel to fully capture evolution of non-stationary channel properties. The generated channel not only reflects temporal dynamics but also ensures consistent stationarity. We propose a hybrid deep learning framework that combines conditional generative adversarial networks (CGAN) with long short-term memory (LSTM) networks. A stationarity-constrained approach is designed to ensure temporal correlation of the generated time-series channel. This method can generate channel with required temporal non-stationarity. The model is validated by comparing channel statistical features, and the results show that the generated channel is in good agreement with raw channel and provides good performance in terms of non-stationarity. Copyright © 2025, The Authors. All rights reserved.

关键词： Long Term Evolution (LTE)

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DSA-NIE: Double Static Attributes Based Node Importance Evaluation of Inter-Domain Routing System 5

DSA-NIE: Double Static Attributes Based Node Importance Eval...

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5th IEEE International Conference on Computer and Communications, ICCC 2019

作者： Zhu, Huihu Qiu, Han Zhu, Junhu Wang, Qingxian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728147437

Assessing the importance of AS nodes is important for the protection of inter-domain routing system security. However, the existing literature on node importance evaluation methods based on static attributes are difficult to accurately characterize nodes. And which based on dynamic attributes have high time complexity. To address these problems, we analyze the relationship between static structural attributes and dynamic attributes after node failure. It is found that the degree can indirectly reflect the impact of the UPDATE packet propagation and the number of routing paths can reflect the impact caused by load redistribution. And then, we build static attribute characterization model of node failure effect. After that an evaluation method DSA-NIE is put forward. Given the two static attributes, DSA-NIE could effectively assessment of the importance of nodes. When compared with SD-KNI algorithm, the time complexity has been reduced to O(lmn). And the experiment result shows that the failure effect of the key nodes assessed by the DSA-NIE is 7.4% higher than the average, and up to 12.9% higher. © 2019 IEEE.

关键词： evaluation Inter-domain Routing System static attribute

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Neural Manifold Learning Based on 40 nm Dual-Mode PCM Compute-in-Memory Chip with Hardware Adaptive Drift Compensation

Neural Manifold Learning Based on 40 nm Dual-Mode PCM Comput...

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International Electron Devices Meeting (IEDM)

作者： Longhao Yan Yuqi Li Xi Li Zelun Pan Zeyu Wang Xile Wang Bowen Wang Zhe Zhan Xiyuan Tang Yaoyu Tao Woo-Ping Ge Zhitang Song Ru Huang Yuchao Yang Beijing Advanced Innovation Center for Integrated Circuits School of Integrate Circuits Peking University Beijing China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Center for Brain Inspired Intelligence Chinese Institute for Brain Research (CIBR) Beijing China Center for Brain Inspired Chips Institute for Artificial Intelligence Peking University Beijing China Guangdong Provincial Key Laboratory of In-Memory Computing Chips School of Electronic and Computer Engineering Peking University Shenzhen China

ISBN: (数字)9798350365429

ISBN: (纸本)9798350365436

Neural manifold learning (NML), as a significant research topic in the field of neuroscience, plays a vital role in the realization of intelligent brain-computer interface (BCI) at the edge. However, traditional edge computing platforms fall short in meeting the demands for the huge computing power needed to process massive neural data. Here, we design a 288Kb dual-mode phase change memory (PCM) compute-in-memory chip in 40nm technology and utilize its two cores for two fundamental kernels in NML, vector matrix multiplication (VMM) and true random number generation, and develop a PCM-based NML system for the first time. In particular, in order to mitigate the performance degradation caused by PCM conductance drift, we propose a novel adaptive-PCM-drift-compensation scheme that significantly improves the accuracy of VMM results. Compared with CPU platforms running the same tasks, the resulting chip improves the computing energy efficiency and throughput by $476\times$ and $264\times$ , respectively.

关键词： Phase change materials Virtual machine monitors In-memory computing Throughput Hardware Energy efficiency Common Information Model (computing) Vectors Manifold learning System-on-chip

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Ultraclean Surface of Garnet-Type Electrolyte Enables Low Interfacial Resistance in Solid-state Lithium Batteries

SSRN

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SSRN 2022年

作者： Qin, Zhiwei Xie, Yuming Meng, Xiangchen Qian, Delai Shan, Cheng Mao, Dongxin He, Gang Zheng, Zhen Wan, Long Huang, Yongxian X. State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin150001 China School of Materials Science and Engineering Harbin Institute of Technology Harbin150001 China Center for Analysis Measurement and Computing Harbin Institute of Technology Harbin150001 China

Solid-state lithium batteries (SSLBs) with garnet-type Li6.5La3Zr1.5Ta 0.5O12 (LLZTO) are one of the most promising candidates for next-generation energy storage system due to their high safety and high stability against Li metal. However, the poor interfacial contact of Li/LLZTO interface induced by Li2CO3 surface impurity hinders its practical application. Herein, we propose an environmental friendliness and convenient high-speed mechanical polishing (HMP) method to remove impurities on porous LLZTO surface and retrieve its lithiophilicity. The strong centrifugal force provided by the high rotation speed promotes the complete removal of Li2CO3 impurity and accelerates the Li-ion transport at Li/LLZTO interface, reducing the interfacial resistance to 28.15 Ω·cm 2 under ultraclean interface, which is comparable to the state-of-the-art surface treatments. Symmetric Li cells assembled with high-speed (5000 rpm) polished LLZTO exhibit a critical current density of 1.91 mA·cm-2 and excellent cycling stability for 1200 h at 0.1 mA·cm-2. SSLBs with LiFePO 4 cathode achieve a superior stable cycling performance with a high discharge capacity of 138.6 mA·h·g-1 and a capacity retention of 89.5% after 500 cycles (0.2 C). This work guides a new understanding for obtaining lithiophilic Li/LLZTO interface through a simple, low-cost and high-efficient polishing strategy, which is potentially utilized for large-scale industrial production of SSLBs. © 2022, The Authors. All rights reserved.

关键词： Solid electrolytes

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Interface engineering for Garnet-Type Electrolyte Enables Low Interfacial Resistance in Solid-state Lithium Batteries

SSRN

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SSRN 2022年

作者： Qin, Zhiwei Xie, Yuming Meng, Xiangchen Qian, Delai Shan, Cheng Mao, Dongxin He, Gang Zheng, Zhen Wan, Long Huang, Yongxian State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin150001 China School of Materials Science and Engineering Harbin Institute of Technology Harbin150001 China Center for Analysis Measurement and Computing Harbin Institute of Technology Harbin150001 China

Solid-state lithium batteries (SSLBs) with garnet-type Li6.5La3Zr1.5Ta0.5O12 (LLZTO) are one of the most promising candidates for next-generation energy storage system due to their high safety and high stability against Li metal. However, the poor interfacial contact of Li/LLZTO interface induced by Li2CO3 surface impurity hinders its practical application. Herein, interface engineering enabled by high-speed mechanical polishing (HMP) method is proposed to remove impurities on porous LLZTO surface and retrieve its lithiophilicity. The strong centrifugal force provided by the high rotation speed promotes the complete removal of Li2CO3 impurity and accelerates the Li-ion transport at Li/LLZTO interface, reducing the interfacial resistance to 28.15 Ω·cm2 under ultraclean interface, which is comparable to the state-of-the-art surface treatments. Symmetric Li cells assembled with high-speed (5000 rpm) polished LLZTO exhibit a critical current density of 1.91 mA·cm-2 and excellent cycling stability for 1200 h at 0.1 mA·cm-2. SSLBs with LiFePO4 cathode achieve a superior stable cycling performance with a high discharge capacity of 138.6 mA·h·g-1 and a capacity retention of 89.5% after 500 cycles (0.2 C). This work guides a new understanding for obtaining lithiophilic Li/LLZTO interface through a simple, low-cost and high-efficient polishing strategy, which is potentially utilized for large-scale industrial production of SSLBs. © 2022, The Authors. All rights reserved.

关键词： Solid electrolytes

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Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system

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Digital Communications and Networks 2025年第3期11卷 634-641页

作者： Adeel Akram Muhammad Bilal Khan Najah Abed Abu Ali Qixing Zhang Awais Ahmad Muhammad Shahid Iqbal Syed Atif Moqurrab Laboratory for Intelligent Sensing and Computing Institute of Advanced Technology University of Science and Technology of China Hefei 230031 Anhui China State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230026 Anhui China College of IT United Arab Emirates University Al-Ain 15551 United Arab Emirates Department of Electrical and Computer Engineering COMSATS University Islamabad Attock 43600 Pakistan College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Kingdom of Saudi Arabia Department of Computer Science and Information Technology Women University of AJ&K Bagh Pakistan School of Computing Gachon University 1342 Seongnam-daero Sujeong-gu Seongnam-si 13120 Korea

The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timely intervention can mitigate their adverse effects. In this context, the need for non-invasive, efficient monitoring systems becomes paramount. Although wearable sensors have gained traction for monitoring health activities, they may cause discomfort during prolonged use, especially for the elderly. To address this issue, we present an intelligent, non-invasive Software-Defined Radio Frequency (SDRF) sensing system, tailored red for monitoring elderly people's falls during routine activities. Harnessing the power of deep learning and machine learning, our system processes the Wireless Channel state Information (WCSI) generated during regular and fall activities. By employing sophisticated signal processing techniques, the system captures unique patterns that distinguish falls from normal activities. In addition, we use statistical features to streamline data processing, thereby optimizing the computational efficiency of the system. Our experiments, conducted for a typical home environment while using treadmill, demonstrate the robustness of the system. The results show high classification accuracies of 92.5%, 95.1%, and 99.8% for three Artificial Intelligence (AI) algorithms. Notably, the SDRF-based approach offers flexibility, cost-effectiveness, and adaptability through software modifications, circumventing the need for hardware overhaul. This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring, providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.

关键词： AI Elderly falls Intelligent learning SDRF WCSI

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An efficient matrix multiplication with enhanced privacy protection in cloud computing and its applications

arXiv

引用

arXiv 2021年

作者： Liu, Chun Hu, Xuexian Chen, Xiaofeng Wei, Jianghong Liu, Wenfen State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou450001 China Xidian University China State Key Laboratory of Cryptology P.O. Box 5159 Beijing100878 China Xidian University China and State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou450001 China Guangxi Key Laboratory of Cryptogpraphy and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

As one of the most important basic operations, matrix multiplication computation (MMC) has varieties of applications in the scientific and engineering community such as linear regression, k-nearest neighbor classification and biometric identification. However handling these tasks with large-scale datasets will lead to huge computation beyond resource-constrained client’s computation power. With the rapid development of cloud computing, outsourcing intensive tasks to cloud server has become a promising method. While the cloud server is generally out of the control of clients, there are still many challenges concerned with the privacy security of clients’ sensitive data. Motivated by this, Lei et al. presented an efficient encryption scheme based on random permutation to protect the privacy of client’s data in outsourcing MMC task. Nevertheless, there exists inherent security flaws in their scheme, revealing the statistic information of zero elements in the original data thus not satisfying the computational indistinguishability (IND-ZEA). Aiming to enhance the security of the outsourcing MMC task, we propose a new encryption scheme based on subtly designed invertible matrix where the additive perturbation is introduced besides the multiplicative perturbation. Furthermore, we show that the proposed encryption scheme can be applied to not only MMC task but also other kinds of outsourced tasks such as linear regression and principal component analysis. Theoretical analyses and experiments indicate that our methods are more secure in terms of data privacy, with comparable performance to the state-of-the-art scheme based on matrix transformation. Copyright © 2021, The Authors. All rights reserved.

关键词： Principal component analysis

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Stabilization of Continuous-Time Markov/Semi-Markov Jump Linear Systems Via Finite Data-Rate Feedback

SSRN

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SSRN 2022年

作者： Wang, Jingyi Feng, Jianwen Xu, Chen Wu, Xiaoqun Lü, Jinhu College of Mathematics and Statistics Shenzhen University Shenzhen518060 China School of Mathematics and Statistics Hubei Key Laboratory of Computational Science Wuhan University Wuhan China State Key Laboratory of Software Development Environment School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

This letter investigates almost sure exponential stabilization of continuous-time Markov jump linear systems (MJLSs) under communication data-rate constraints by introducing sampling and quantization into the feedback control. Different from previous works, the sampling times and the jump times are independent of each other in this letter. The quantization is recursively adjusted on the sampling time, and its updating strategy does not depend on the switching in a sampling interval. In other words, the explicit value of the switching signal in a sampling interval is not necessary. The numerically testable condition is developed to ensure almost sure exponential stabilization of MJLSs under the proposed communication and control protocols. We also drop the assumption of stabilizability of all individual modes required in previous works about the switched systems. Moreover, we extend the result to the case of continuous-time semi-Markov jump linear systems (semi-MJLSs) via the semi-Markov kernel approach. Finally, some numerical examples are presented to illustrate the effectiveness on stabilization of the proposed communication and control protocols. © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Image Electronic Evidence Screening Based on Improved SSD

Image Electronic Evidence Screening Based on Improved SSD

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Yafei Liu Liehui Jiang Tieming Liu Youwei Zhang Zhongyuan Network Security Research institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

ISBN: (纸本)9781665447300

With the development of information technology, electronic evidence plays an increasingly important role in the judicial trial. In Judicial Forensics, it is difficult to extract effective electronic evidence accurately and quickly from a large number of electronic data. Traditional means generally take artificial identification to collect, which takes a long time and is not efficient. Using SSD target detection and recognition algorithm instead of traditional means can effectively reduce the time of screening evidence, but the basic SSD neural network is prone to miss detection for small targets. To solve the above problems, an improved SSD-based image electronic evidence screening method is proposed. This method optimizes the SSD neural network adaptively, introduces the attention mechanism module in the shallow convolution layer of the network to improve the representation ability of the feature map, and fuses the image features obtained from different convolution layers with multi-scale features to increase the shallow feature information. The experimental data are used to test the improved algorithm and analyze the experimental results. It is found that compared with the original SSD neural network algorithm, the detection mean average precision of the improved algorithm is increased by 4.7%, reaching 84.3%, which shows the feasibility and effectiveness of the improved algorithm.

关键词： Image recognition Convolution Target recognition Fuses Neural networks Stacking Signal processing algorithms

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