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International Conference on Intelligent Transportation

作者： Zhipeng Li Yun Sun Fuqiang Liu Wenhuan Shi Key Laboratory of Embedded System and Service Computing supported by Ministry of Education University of Tongji Shanghai China School of Electronics and Information Engineering University of Tongji Shanghai China Department of Control Science and Technology Shanghai Jiaotong University Shanghai China

In this paper, we present a pedestrian detection approach using spatial histograms of oriented gradients feature. As spatial histograms of oriented gradients consist of marginal distributions of an image over local and global patches, they can preserve shape and contour of a pedestrian simultaneously. There are two main contributions in this paper. First of all, we expand the histograms of oriented gradients features from single-size to variable-size which can capture local and global feature of pedestrian automatically. We call theses feature as the "spatial histograms of oriented gradients". Secondly, we employ histogram similarity and Fisher criterion to measure discriminability of features and select some discriminative features to identify the pedestrian. SVM classifier is constructed to train the selected features from target and surrounding background. The proposed algorithm is tested on some public database. Experimental results show that the proposed approach is efficient and rapid in pedestrian detection.

关键词： Robustness Conferences Histograms Support vector machines Shape Intelligent transportation systems Sun Support vector machine classification Computer vision Image edge detection

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A distributed SVM ensemble for image classification and annotation

A distributed SVM ensemble for image classification and anno...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Nasullah Khalid Alham Maozhen Li Yang Liu Mahesh Ponraj Man Qi School of Engineering and Design Brunel University Uxbridge UK The Key Laboratory of Embedded Systems and Service Computing Ministry of Education University of Tongji China Department of Computing Canterbury Christ Church University Canterbury Kent UK

Combination of classifiers leads to a substantial reduction of classification errors in a wide range of applications. Among them SVM ensembles with bagging have shown better performance in classification than a single SVM. However, the training process of SVM ensembles is notably computationally intensive especially when the number of replicated training datasets is large. This paper presents MRESVM, a MapReduce based distributed SVM ensemble algorithm for image annotation which re-samples the training dataset based on bootstrapping and trains SVM on each dataset in parallel using a cluster of computers. MRESVM is evaluated in a experimental environment and the results show that the MRESVM algorithm reduces the training time significantly while achieves high level of accuracy in classifications.

关键词： Support vector machines Training Bagging Classification algorithms Accuracy Algorithm design and analysis Clustering algorithms

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Robust Adaptive Control of Nonlinear systems with Asymmetric Non-smooth Saturation

Robust Adaptive Control of Nonlinear Systems with Asymmetric...

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第28届中国控制与决策会议

作者： Xuehai Tu Junfeng Lai Kai Zhao Institute of Smart System and Renewable Energy School of AutomationChongqing University The Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing Universit

ISBN: (纸本)9781467397155

In this paper,the robust adaptive control is proposed for a class of parametric uncertain nonhnear system with asymmetric non-smooth input *** introducing a well-defined smooth function,a robust adaptive control algorithms are developed to deal with the problem of asymmetric non-smooth *** most of the existing control schemes for the uncertain nonhnear system,which requires an assumption that the control gain is an unknown constant,the developed controller only needs that the control gain is a bounded and unknown time-varying *** this work,the robust adaptive controller can guarantee global uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis.A simulation results is presented to illustrate the effectiveness of the proposed adaptive control technique.

关键词： Robust Adaptive Control Asymmetric Non-Smooth Saturation Uniformly Bounded

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Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking

arXiv

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arXiv 2018年

作者： Huang, Yewei Zhao, Junqiao He, Xudong Zhang, Shaoming Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University 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

In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. High-level landmarks, the parking slots, are extracted and enriched with labels to avoid the aliasing of low-level visual features. We then proposed a robust method for detecting incorrect data associations between parking slots and further extended the optimization framework by dynamically eliminating suboptimal data associations. Visual fiducial markers are introduced to improve the overall precision. As a result, a semantic map of the parking lot can be established fully automatically and robustly. We experimented the performance of real-time localization based on the map using our autonomous driving platform TiEV, and the average accuracy of 0.3m track tracing can be achieved at a speed of 10kph. Copyright © 2018, The Authors. All rights reserved.

关键词： Indoor positioning systems

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Low-rank subspace representation from optimal coded-aperture for unsupervised classification of hyperspectral imagery

arXiv

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arXiv 2020年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China

This paper aims at developing a clustering approach with spectral images directly from the compressive measurements of coded aperture snapshot spectral imager (CASSI). Assuming that compressed measurements often lie approximately in low dimensional subspaces corresponding to multiple classes, state of the art methods generally obtains optimal solution for each step separately but cannot guarantee that it will achieve the globally optimal clustering results. In this paper, a low-rank subspace representation (LRSR) algorithm is proposed to perform clustering on the compressed measurements. In addition, a subspace structured norm is added into the objective of low-rank representation problem exploiting the fact that each point in a union of subspaces can be expressed as a sparse linear combination of all other points and that the matrix of the points within each subspace is low rank. Simulation with real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectroscopy

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Design and Implementation of DSP Cache

Design and Implementation of DSP Cache

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International Conference on Communication Technology (ICCT)

作者： Kunkun Liang Jun Wu Haoqi Ren Zhifeng Zhang Bin Tan Electronics and Information Engineering College Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China School of Computer Science Fudan University Shanghai China College of Electronics and Information Engineering Jinggangshan University Ji'an China

ISBN: (纸本)9781665432078

The cache can improve the DSP processor's access speed to external memory and solve the “Storage Wall” problem. Designing an efficient and flexible cache module plays an vital role in improving the memory access efficiency and overall performance of the DSP. Based on the analysis of DSP storage level design requirements, combined with the actual pipeline structure of the DSP processor SWIFT independently developed by our laboratory, in this paper, we design and implement first- level instruction and data cache. For L1 D-Cache, the relevant conflict detection and processing module is designed to support four parallel Load/Store access requests. The size of the cache can be flexibly configured according to actual application requirements to achieve a balance between power consumption and latency. To write back the modified data left in the cache before the cache size is changed, the Cacheclean instruction is designed. Finally, module-level functional verification and logic synthesis are carried out. The results show that the cache function meets expectations, and the critical path after logic synthesis optimization meets the requirement of 1GHz frenuency.

关键词： Frequency synthesizers Power demand Instruction sets Conferences Pipelines Memory management Process control

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Distributed Computation Offloading using Deep Reinforcement Learning in Internet of Vehicles

Distributed Computation Offloading using Deep Reinforcement ...

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IEEE International Conference on Communications in China (ICCC)

作者： Chen Chen Zheng Wang Qingqi Pei Ci He Zhibin Dou State Key Laboratory of Integrated Services Networks Xidian University Xi'an P.R.China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing China The Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

In this paper, we first take the moving vehicles as a RP (resource pool), by which we proposed a distributed computation offloading scheme to fully utilize the available resources and reduce task execution time in I0V (Internet of Vehicles). After that, we divide a complex task into many sub-tasks and indicate that how to assign these small tasks to satisfy the task execution time in RP is a NP problem. The executing time of a task is modeled as the longest calculation time among all small tasks, which is actually a min-max problem. For a dynamically vehicular environment, a distributed computing offloading strategy based on deep reinforcement learning is proposed to find the best offloading scheme to minimize the execution time of a task. Numerical results demonstrate that our scheme is better than Partial Flooding Algorithm and can make full use of the available computing resources of surrounding vehicles by considering the mobility of vehicles, the delay of communication transmission, and the separability of the tasks, thus greatly reducing the execution time of the computing tasks.

关键词： Computational modeling Simulation Reinforcement learning User experience Delays Internet Task analysis

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MapReduce-based parallel GEP algorithm for efficient function mining in big data applications

MapReduce-based parallel GEP algorithm for efficient functio...

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作者： Liu, Yang Ma, Chenxiao Xu, Lixiong Shen, Xiaodong Li, Maozhen Li, Pengcheng School of Electrical Engineering and Information Sichuan University Chengdu610065 China Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University Shanghai200092 China State Grid Shanxi Electric Power Company Xi'an710048 China

Gene expression programming (GEP) algorithm is one of the most effective function mining algorithms in enabling the mathematical equation fitting for the input dataset. However, GEP algorithm encounters low efficiency issue in big data processing due to large overhead in its evolution when it handles the large-scale data. In order to solve the issue, this paper presents two parallelized GEP algorithms using MapReduce. Based on data separation, the first algorithm aims at speeding up the large-scale classification. However, it is lack of ability to output the mined equation explicitly. Therefore, based on the further improvements of the first algorithm, the second parallelized GEP algorithm aims at mining the equation efficiently and also outputs the equation explicitly and directly. The experimental results show that both algorithms are effective for processing large volume of data. © 2017 John Wiley & Sons, Ltd.

关键词： MapReduce

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Maximally permissive control of flexible manufacturing systems with fewest disjunctive constraints

Maximally permissive control of flexible manufacturing syste...

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2012 International Conference onAdvanced Mechatronic systems, ICAMechS 2012

作者： Chen, YuFeng Zhou, MengChu Li, XiaoOu School of Electro-Mechanical Engineering Xidian University Xi'an 710071 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States Departamento de Computación CINVESTAV-IPN Av. IPN 2508 Col. Zacatenco México DF C.P. 07360 Mexico

ISBN: (纸本)9780955529382

For Petri net models whose legal reachability spaces are non-convex, one cannot optimally control them by the conjunctions of linear constraints. This work proposes a method to find a set of linear constraints such that each constraint can forbid all first-met bad markings and every legal marking satisfies at least one constraint. Then, the disjunctions of the obtained constraints can make all legal markings reachable and forbid all first-met bad markings, i.e., the controlled net is live and maximally permissive. The work also develops an integer linear programming model to minimize the number of the constraints. Finally, an example is provided to illustrate the proposed method. © 2012 Intl Jrnal Advanced Mech.

关键词： Flexible manufacturing systems

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DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

arXiv

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arXiv 2022年

作者： Tian, Xuebo Zhao, Junqiao Ye, Chen 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

Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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