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arXiv 2018年

作者： He, Xudong Zhao, Junqiao Sun, Lu Huang, Yewei Zhang, Xinglian Li, Jun Ye, Chen Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China School of Automotive Studies Tongji University Shanghai China

In this paper, we studied a SLAM method for vector-based road structure mapping using multi-beam LiDAR. We propose to use the polyline as the primary mapping element instead of grid cell or point cloud, because the vector-based representation is precise and lightweight, and it can directly generate vector-based High-Definition (HD) driving map as demanded by autonomous driving systems. We explored: 1) the extraction and vectorization of road structures based on local probabilistic fusion. 2) the efficient vector-based matching between frames of road structures. 3) the loop closure and optimization based on the pose-graph. In this study, we took a specific road structure, the road boundary, as an example. We applied the proposed matching method in three different scenes and achieved the average absolute matching error of 0.07 m. We further applied the mapping system to the urban road with the length of 860 meters and achieved an average global accuracy of 0.466 m without the help of high precision GPS. Copyright © 2018, The Authors. All rights reserved.

关键词： Mapping

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HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detection in High-Speed Networks

HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detect...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Jiakun Yu Yu-E Sun He Huang Yang Du Guoju Gao Hongli Xu Shigang Chen School of Computer Science and Technology Soochow University Suzhou China School of Rail Transportation Soochow University Suzhou China Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China School of Computer Science and Technology University of Science and Technology of China Hefei China

Detecting heavy hitters that constitute the majority of network traffic is a critical task in network measurement. However, the highly skewed network traffic and the size-limited on-chip memory pose great challenges for heavy-hitter detection. The prior arts either use sketches to track all flows or maintain a fix-sized tracking list for recording elephant flows, resulting in limited detection precision or time-consuming tracking list exchanges. This paper presents HeavyTracker, an efficient algorithm that detects heavy hitters based on a count-with-threshold strategy. A two-dimensional tracker unit array is employed in our design to capture the heavy hitters, where each flow is randomly mapped to one unit in all rows. The tracker unit is designed to accept many flows and precisely report the largest two with frequencies that reach a predefined threshold. This design eliminates the frequent and time-consuming exchanges for maintaining the tracking list, allowing us to use a hash table to track the reported elephant flows. Experimental results based on real Internet traces show that HeavyTracker achieves a high $\mathbf{F}_{\beta}$-Score of threshold-t detection (0.97) and over 99% precision of top-k detection under a tight memory size. Besides, it reduces the frequency estimation error by 97.4% compared to the state-of-the-art.

关键词： Art High-speed networks Measurement uncertainty Telecommunication traffic Size measurement Frequency estimation Internet

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Software services protection security protocol based on software watermarking in cloud environment

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Tongxin Xuebao/Journal on Communications 2012年第SUPPL.2期33卷 176-181页

作者： Xu, Jin-Chao Zeng, Guo-Sun Wang, Wei Department of Computer Science and Technology Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China

Aimed to resist the infringement of software services in cloud environment, a software services protection security protocol based on software watermarking in cloud environment was presented. On the consideration of actual needs in cloud computing, the protocol introduced the software watermarking as a service cloud which was responsible for software watermark embedding and extraction. The protocol not only met the basic requirements of software copyright protection, but also gived the detailed strategy to solve the infringement, and hardened the ability of the software watermarking to resist various attacks. The analysis shows that the proposed protocol has higher security.

关键词： Copyrights

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TiEV: The tongji intelligent electric vehicle in the intelligent vehicle future challenge of China

arXiv

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arXiv 2018年

作者： Zhao, Junqiao Ye, Chen Wu, Yan Guan, Linting Cai, Lewen Sun, Lu Yang, Tao He, Xudong Li, Jun Ding, Yongchao Zhang, Xinglian Wang, Xinchen Huang, Jinlin Zhang, Enwei Huang, Yewei Jiang, Wei Zhang, Shaoming Xiong, Lu Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Department of Computer Science and Technology School of electrics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China School of Automotive Studies Tongji University Shanghai China

TiEV is an autonomous driving platform implemented by the Tongji University of China. The vehicle is drive-by-wire and is fully powered by electricity. We devised the software system of TiEV from scratch, which is capable of driving the vehicle autonomously in urban paths as well as on fast express roads. We describe our whole system, especially novel modules of probabilistic perception fusion, large-scale mapping and updating, the 1st and the 2nd planning and the overall safety concern. TiEV finished 2016, and 2017 Intelligent Vehicle Future Challenge of China held at Changshu. We show our experiences on the development of autonomous vehicles and discuss the future trends. Copyright © 2018, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Optimal scheduling of single-arm multi-cluster tools with two-space buffering modules

Optimal scheduling of single-arm multi-cluster tools with tw...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： FaJun Yang NaiQi Wu Yan Qiao MengChu Zhou Department of Industrial Engineering Guangdong University of Technology Guangzhou China Institute of Systems Engineering Macau University of Science and Technology Macau Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

A multi-cluster tool is composed of a number of single-cluster tools linked by buffering modules. The capacity of a buffering module can be one or two. Aiming at finding an optimal one-wafer cyclic schedule, this work explores the effect of two-space buffering modules on the performance of a multi-cluster tool. The tool is modeled by a kind of Petri nets. The dynamic behavior of robot waiting and tasks, process modules, and buffers is well captured by the net model. With the model, this work shows that there is always a one-wafer cyclic schedule that reaches the lower bound of the cycle time of a process-dominant tool. Furthermore, a constant-time algorithm is revealed to find such a schedule for the first time for such multi-cluster tools. An illustrative example is given to show the application and power of this new method.

关键词： Schedules Robot kinematics Firing Semiconductor device modeling Fires Load modeling

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

arXiv

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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

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RETRACTED ARTICLE: Stability analysis of dynamic collaboration model under two-lane case

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Nonlinear Dynamics 2014年第1期81卷 1021-1021页

作者： Li, Zhipeng Zhang, Run Xu, Shangzhi Qian, Yeqing The Key Laboratory of Embedded System and Service Computing Supported by Ministry of Education Tongji University Shanghai China

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Research on BP imaging algorithm parallelization using C6678 DSPS

Research on BP imaging algorithm parallelization using C6678...

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IET International Radar Conference 2015

作者： Jiayun Zhao Yuan Yuan Jinghua Wang Ye Jin Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China North China Institute of Computing Technology Beijing China Beijing institute of spacecraft system engineering China academy of space technology Beijing China

ISBN: (纸本)9781510822023

Compared to the traditional SAR imaging algorithm, Back Projection(BP) algorithm is an accurate point-by-point imaging radar algorithm based on time-domain, with simple principle and without any approximation error in the imaging process. However, because of intensive computation and low efficiency, it's a new challenge to storage to capacity, throughput and processing ability of DSPs, a single DSP is not enough to meet these demands. So a parallel implementation method of BP algorithm based on TMS320C6678 DSP is proposed in this *** put forward a large point FFT multi-core parallel processing method on 2/4/8 cores what is frequently used in BP algorithm, and a multi-core synchronization method based on distributed memory. Finally using the measured data, we verify the parallel method can greatly enhance the multi-core parallelism, and the real-time performance of BP algorithm has been significantly improved.

关键词： BP imaging algorithm large point FFT multi-core parallel processing multi-core synchronization

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

arXiv

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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

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New trust model based on recommendation evidence for P2P networks

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Jisuanji Xuebao/Chinese Journal of Computers 2008年第2期31卷 270-281页

作者： Tian, Chun-Qi Zou, Shi-Hong Wang, Wen-Dong Cheng, Shi-Duan Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China Broadband Net-work Research Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China

A novel trust model for P2P networks, namely RETM, a Recommendation Evidence based Trust Model, is presented in this paper. It solves some problems, for instance, not invalidly aggregating incompatible recommendation information and dealing with uncertainty of information in the reputation-based P2P trust model. Before combining the evidences, RETM will filter out noisy recommendation information, and moreover the method makes RETM more robust. In addition a feedback-based probabilistic searching algorithm is proposed to find the recommendation information, which improves the searching success rate and lowers the network traffic. Theoretical analyses and experimental results show that, compared to the current some trust models, the proposed model RETM has advantages in modeling dynamic trust relationship and aggregating recommendation information, moreover, is more robust on trust security problems and more advanced in successful transaction rate.

关键词： Internet

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