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Absorption bias: An ideal descriptor for radiation tolerance of nanocrystalline BCC metals

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arXiv 2022年

作者： Wei, Liuming Zhao, Zhe Li, Yonggang Zheng, Qirong Zhang, Chuanguo Li, Jingyu Zhao, Gaofeng Da, Bo Zeng, Zhi Key Laboratory of Materials Physics Institute of Solid State Physics HFIPS Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Institute for Computational Materials Science School of Physics and Electronics Henan University Kaifeng475004 China Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Namiki 1-1 Ibaraki Tsukuba305-0044 Japan

To evaluate the radiation tolerance of nanocrystalline (NC) materials, the damage effects of Fe and W as typical body-centered cubic (BCC) metals under uniform irradiation are studied by a sequential multi-scale modelling framework. An ideal descriptor, the absorption bias (the ratio of the absorption abilities of grain boundaries (GBs) to interstitials (I) and vacancies (V)), is proposed to characterize the radiation tolerance of materials with different grain sizes. Low absorption bias promotes defects annihilation through enhancing I-V recombination and optimally tuning its competition with GB absorption. Thus, the lower absorption bias, the higher anti-irradiation performance of NC BCC metals is. Furthermore, by comprehensively considering the mechanical property, thermal stability and radiation resistance, nano-crystals are recommended for Fe-based structural materials but coarse crystals for W-based plasma-facing materials. This work reevaluates the radiation resistance of NC materials, resulting in new strategies for designing structural materials of nuclear devices through manipulating grain sizes. Copyright © 2022, The Authors. All rights reserved.

关键词： Grain size and shape

A 128kb Stochastic computing Chip based on RRAM Flicker Noise with High Noise Density and Nearly Zero Autocorrelation on 28-nm CMOS Platform

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A 128kb Stochastic Computing Chip based on RRAM Flicker Nois...

International Electron Devices Meeting (IEDM)

作者： Tiancheng Gong Qiao Hu Danian Dong Haijun Jiang Jianguo Yang Xiaoxin Xu Xiaoming Chen Qing Luo Qi Liu Steve S. Chung Hangbing Lyu Ming Liu Key Laboratory of Microelectronics Devices and Integrated Technology Institute of Microelectronics of Chinese Academy of Sciences Beijing China Zhejiang Lab HangzhouChina Zhejiang Lab Hangzhou China Institute of Computing Technology of Chinese Academy of Sciences Beijing China Frontier Institute of Chip and System Fudan University China National Yang Ming Chiao Tung University Taiwan

ISBN: (纸本)9781665425735

In this work, a 128kb RRAM flicker noise based Stochastic computing (SC) chip is demonstrated on 28-nm HKMG CMOS platform for the first time. Flicker noise in RRAM is selected as the entropy source owing to the higher noise density compared with the conventional logic devices, even the advanced 14-nm FinFET. The reliability of the flicker noise in RRAM is further improved by an optimized weak forming scheme at the array level towards accurate and stable SC applications. Moreover, a probability modulated true random number generator (PM-TRNG) circuit is proposed to generate stochastic bit-streams (SBS) with the desired probability and eliminated correlation. The autocorrelation function (ACF) and NIST testing results show that the correlation of output SBS is nearly zero. Finally, this RRAM-flicker noise based SC chip is successfully implemented for edge detection in image processing system and extremely low error rate (3.13%@128 BSL) can be achieved.

关键词： Error analysis Image processing Image edge detection NIST FinFETs Generators Autocorrelation

DSC: Deep scan context descriptor for large-scale place recognition

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arXiv 2021年

作者： Cui, Jiafeng Huang, Tengfei Cai, Yingfeng Zhao, Junqiao Xiong, Lu Yu, Zhuoping School of Automobile Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that captures the relationship among segments of a point cloud. Unlike previous methods that utilize either semantics or a sequence of adjacent point clouds for better place recognition, we only use raw point clouds to get competitive results. Concretely, we first segment the point cloud egocentrically to acquire centroids and eigenvalues of the segments. Then, we introduce a graph neural network to aggregate these features into an embedding representation. Extensive experiments conducted on the KITTI dataset show that DSC is robust to scene variants and outperforms existing methods. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

BCE-PPDS: Blockchain-based cloud–edge collaborative privacy-preserving data sharing scheme for IoT

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Future Generation Computer systems 2026年 174卷

作者： Guijuan Wang Qi Liu Zhongyuan Yu Hongliang Zhang Anming Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan Shandong 250353 China China University of Petroleum Qingdao Shandong 266000 China

Internet of Things (IoT) devices generate large amounts of data every day that can be combined with intelligent platforms for predictive analytics and scientific research. However, concerns about privacy and security hinder the willingness of individuals to share data. Blockchain emerged as a promising infrastructure for facilitating secure data sharing due to its decentralized, immutability, and auditable benefits. In this paper, we propose a blockchain-based cloud–edge collaborative privacy protection data sharing scheme (BCE-PPDS), which is decentralized and enables data requesters (DRs) to search data resources using smart contracts to efficiently obtain target data. To protect the identity privacy of data owners (DOs), we propose a novel certificateless linkable ring signature algorithm with efficient performance. This algorithm is not only suitable for deployment on resource-limited IoT devices, so that DOs can realize anonymous identity authentication, but also can aggregate the generated ring signatures for batch verification, so as to improve the efficiency of signature verification. In addition, we designed a key distribution algorithm using the Asmuth–Bloom secret sharing scheme to ensure the security of the key. Under the random oracle model, BCE-PPDS is provably secure. The experimental results verify that BCE-PPDS is efficient and practical.

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Adaptive Interference-Aware VNF Placement for Service-Customized 5G Network Slices

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Adaptive Interference-Aware VNF Placement for Service-Custom...

IEEE Conference on Computer Communications

作者： Qixia Zhang Fangming Liu Chaobing Zeng Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781728105161

Based on network function virtualization (NFV) and software defined network (SDN), network slicing is proposed as a new paradigm for building service-customized 5G network. In each network slice, service-required virtual network functions (VNFs) can be flexibly deployed in an on-demand manner, which will support a variety of 5G use cases. However, due to the diverse performance requirements among different 5G scenarios, an adaptive VNF placement approach is needed to automatically accommodate to service-specific requirements. In this paper, we tackle the VNF placement problem by first proposing a general 5G network slice framework, which jointly contains both edge cloud and core cloud servers. Specially, based on the fact that VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantity the VNF interference. With an aim to maximize the total throughput of accepted requests, we propose an Adaptive Interference-Aware (AIA) heuristic approach to automatically place VNFs in 5G service-customized network slices. Through simulations on two typical 5G scenarios, we demonstrate that AIA can efficiently handle traffic variation especially caused by VNF interference and improve the total throughput by 20.11% and 24.21% in autonomous driving and 4K/8K HD video network slices as compared with the state-of-the-art methods.

关键词： slicing degradation of performance AUTOMATIC INCIDENT ACTIONS Network placement Self tuning AUTOMATIC

Anomaly Detection of Vehicle CAN Network Based on Message Content 2nd

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Anomaly Detection of Vehicle CAN Network Based on Message Co...

2nd EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2019

作者： Mo, Xiuliang Chen, Pengyuan Wang, Jianing Wang, Chundong Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China Sichuan University ChengduSichuan610207 China

ISBN: (纸本)9783030213725

With the rapid advance of intelligent vehicles, auxiliary driving and automatic driving have been paid more attention to. While vehicle security has become increasingly prominent, which is seriously related to the property and personal safety. The attacker can send abnormal information to the controller through internal CAN bus. Because of the particularity of the vehicle CAN network information communication protocol, the encryption authentication technology cannot effectively solve the safety problem of the vehicle network. In the paper, a novel anomaly detection method based on CAN packet content is proposed. The scheme is effective in preventing in-vehicle ECU attacks caused by malicious modifications. Statistical thinking is adopted to analyze the characteristics of normal message content. Then a confidence interval based on normal features is defined for detecting abnormal network messages. Its detection performance has been demonstrated through experiments carried out on real CAN traffic gathered from an unmodified licensed vehicle. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Anomaly detection

A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication system

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arXiv 2022年

作者： Chen, Zhimin Chen, Peng Guo, Ziyu Zhang, Yudong Wang, Xianbin The School of Electronic and Information Shanghai Dianji University Shanghai201306 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China The School of Information Science and Technology Fudan University Shanghai200433 China The State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai200433 China The School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada

With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle’s direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS. Moreover, different from the existing estimation methods, we also consider the interference signals introduced by wireless communication in the ISAC system. Then, we propose a novel atomic norm-based method to remove the interference signals and reconstruct the sparse signal. Additionally, a novel Hankel-based multiple signal classification (MUSIC) method is formulated to obtain the DOA information after the interference removal. To reduce the interference signals more efficiently and improve the performance of the sparse reconstruction, we optimize the measurement matrix to improve the signal-to-interference-plus-noise ratio (SINR). Finally, the theoretical Cram’er-Rao lower bound (CRLB) is derived for the ISAC system on the vehicle DOA estimation. Simulation results show that the proposed method can achieve better performance in the DOA estimation, and the corresponding CRLB with different distributions of the sensing nodes are shown. The code for the proposed method is available online https://***/chenpengseu/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Vehicles

Mining Robust Frequent Items in Data Streams

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Mining Robust Frequent Items in Data Streams

IEEE International Conference on Joint Cloud computing (JCC)

作者： Rui Xia Haipeng Dai Zhanchao Du Meng Li Alex X. Liu Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing CHINA Institute of Manned Space System Engineering China Academy of Space Technology Beijing CHINA Shanghai key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai CHINA

ISBN: (数字)9781728169811

ISBN: (纸本)9781728169828

This paper studies the problem of robust frequent items mining in data streams that generalizes the traditional frequent items mining by considering the noise of datasets. That is, different items may correspond to the same entity because of noise; examples include different images of the same object and fluctuated data in the same setting measured by sensors. Our objective is to identify those items that correspond to the same entity and have an aggregated frequency exceeding a given threshold, which named as robust frequent items. To the best of our knowledge, there is no existing works on mining robust frequent items in a data stream. In this paper, we first propose a scheme by applying sampling and spatial partition to address the problem in low dimensional spaces. Furthermore, we extend the above algorithmic framework to high dimensional spaces by incorporating the locality sensitive hashing scheme to deal with the approximate nearest neighbor problem. We conduct evaluations using synthetic datasets and compare our scheme with two prior adapted schemes. Our results demonstrate that the efficiency of our algorithms outperforms the adaptive Space Saving by 14.8% and 9.8% in terms of precision and recall, respectively.

关键词： Noise measurement Robustness Data mining Partitioning algorithms Approximation algorithms Two dimensional displays Sensors

Unsupervised joint learning of depth, optical flow, ego-motion from video

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arXiv 2021年

作者： Li, Jianfeng Zhao, Junqiao Song, Shuangfu Feng, Tiantian The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China School of Surveying and Geoinfomatics Tongji University Shanghai China Insititute of Intelligent Vehicle Tongji University Shanghai China

Estimating geometric elements such as depth, camera motion, and optical flow from images is an important part of the robot's visual perception. We use a joint selfsupervised method to estimate the three geometric elements. Depth network, optical flow network and camera motion network are independent of each other but are jointly optimized during training phase. Compared with independent training, joint training can make full use of the geometric relationship between geometric elements and provide dynamic and static information of the scene. In this paper, we improve the joint self-supervision method from three aspects: network structure, dynamic object segmentation, and geometric constraints. In terms of network structure, we apply the attention mechanism to the camera motion network, which helps to take advantage of the similarity of camera movement between frames. And according to attention mechanism in Transformer, we propose a plug-and-play convolutional attention module. In terms of dynamic object, according to the different influences of dynamic objects in the optical flow self-supervised framework and the depth-pose self-supervised framework, we propose a threshold algorithm to detect dynamic regions, and mask that in the loss function respectively. In terms of geometric constraints, we use traditional methods to estimate the fundamental matrix from the corresponding points to constrain the camera motion network. We demonstrate the effectiveness of our method on the KITTI dataset. Compared with other joint self-supervised methods, our method achieves state-of-the-art performance in the estimation of pose and optical flow, and the depth estimation has also achieved competitive *** Codes 65Dxx © 2021, CC BY-NC-ND.

关键词： Geometry