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Fast double-parallel image processing based on FPGA

Fast double-parallel image processing based on FPGA

引用

IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Ye Li Qingming Yao Bin Tian Wencong Xu State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy and Sciences Beijing China School of Mechanical Electrical and Information Engineering Shandong University Weihai China

Due to FPGA's flexibility and parallelism, it is popular for accelerating image processing. In this paper, a double-parallel architecture based on FPGA has been exploited to speed up median filter and edge detection tasks, which are essential steps during image processing. The double-parallel scheme includes an image-level parallel and an operation-level parallel. The image-level parallel is a high-level parallel which divides one image into different parts and processes them concurrently. The operation-level parallel, which is embedded in each image-level parallel thread, fully explores every parallel part inside the concrete algorithms. The corresponding design is based on a DE2 Development Board which contains a CYCLONE II FPGA device. Meanwhile, the same task has also been implemented on PC and DSP for performance comparison. Despite the fact that operating frequencies of used PC and DSP are much higher than FPGA's, FPGA costs less time per computed image than both of them. By taking advantage of the double-parallel technique, the speed/frequency ratio of FPGA is 202 times faster than PC and 147 times faster than DSP. Finally, a detailed discussion about different platforms is conducted, which analyzes advantages and disadvantages of used computing platforms. This paper reveals that the proposed double-parallel scheme can dramatically speed up image processing methods even on a low-cost FPGA platform with low frequency and limited resources, which is very meaningful for practical applications.

关键词： Image edge detection Field programmable gate arrays Digital signal processing Hardware Kernel Detectors

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Impulsive control method based on improved differential evolution algorithm

Impulsive control method based on improved differential evol...

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作者： Zhao, Juan Cai, Tao Deng, Fang Yang, Hongwei School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex Systems Intelligent Control and Decision Ministry of Education BIT Beijing 100081 China

ISBN: (纸本)9789881725592

In this paper, a new strategy based on impulsive control model of high speed roller is proposed. To make the roller hit the specified target, the strategy is summarized as an optimal control model calculating required number of impulses, the igniting time and phase of each impulse, and also an improved Differential Evolution (DE) Algorithm is adopted to resolve. It is too difficult to solve the orderliness of igniting time by Basic DE, so a Mapping Differential Evolution Algorithm (MDE) is proposed, which maps the sorting time variables to random coefficient range [0,1]. In addition, a DE with self-adapting parameter is used to accelerate the convergence rate. Simulation results show the high accuracy of high speed roller with the method. © 2011 Chinese Assoc of Automati.

关键词： Mapping

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Improved differential evolution-based particle filter algorithm for target tracking

Improved differential evolution-based particle filter algori...

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作者： Wang, Yanan Chen, Jie Gan, Minggang School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex Systems Intelligent Control and Decision Ministry of Education BIT Beijing 100081 China

ISBN: (纸本)9789881725592

This paper presents an improved target tracking algorithm based on the differential evolution particle filter (DEPF) in order to solve the problem of particle degeneracy. In this method, the mutation, crossover and selection steps of the differential evolution are introduced into the particle filter, which optimizes the particle sampling process. Particles are moved towards regions where they are close to the value of the posterior density function through DEPF algorithm, and it improves the estimation precision, inhibits the particle degeneracy and enhances the diversity of the particles. The target observation model based on the color feature is constructed simultaneously in the algorithm, and the method of the target model update in different states is also studied. The experimental results show that the target even in attitude change and occlusion can be tracked accurately and real-timely with the DEPF algorithm. The proposed algorithm has higher tracking accuracy and is robust. © 2011 Chinese Assoc of automation.

关键词： Target tracking

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Agent-based traffic simulation and traffic signal timing optimization with GPU

Agent-based traffic simulation and traffic signal timing opt...

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International Conference on intelligent Transportation

作者： Zhen Shen Kai Wang Fenghua Zhu State Key Laboratory of Intelligent Control and Management of Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences (CAS) Beijing China Center of Military Computational Experiments and Parallel Systems Technology and College of Mechatronics Engineering and Automation National University of Defense Technology Changsha Hunan China

With the advantage of simulating the details of a transportation system, the “microsimulation” of a traffic system has long been a hot topic in the intelligent Transportation systems (ITS) research. The Cellular Automata (CA) and the Multi-Agent System (MAS) modeling are two typical methods for the traffic microsimulation. However, the computing burden for the microsimulation and the optimization based on it is usually very heavy. In recent years the Graphics Processing Units (GPUs) have been applied successfully in many areas for parallel computing. Compared with the traditional CPU cluster, GPU has an obvious advantage of low cost of hardware and electricity consumption. In this paper we build an MAS model for a road network of four signalized intersections and we use a Genetic Algorithm (GA) to optimize the traffic signal timing with the objective of maximizing the number of the vehicles leaving the network in a given period of time. Both the simulation and the optimization are accelerated by GPU and a speedup by a factor of 195 is obtained. In the future we will extend the work to large scale road networks.

关键词： Graphics processing unit Vehicles Roads Optimization Computational modeling Genetic algorithms Timing

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An optimized modeling method based on BPEL4WS

An optimized modeling method based on BPEL4WS

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Sheng Liu Gang Xiong Dong Fan Jianshi Yao State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Science Beijing 100190 China Unit 61541 The Chinese People's Liberation Army Beijing 100094 China

As an efficient business process execution language which supports web services, BPEL4WS is widely supported by the academic and the industrial circles. According to the shortcomings such as number of computer terms, abstract model definition and the complex description of people activity, this paper presents easy-to-use BPEL4WS modeling method and tool which encapsulates computer terms and convert business models to BPEL4WS models directly. In comparison to the other modeling methods, our method cut down the number of modeling elements by more than 85 percent and save the modeling time by 80 percent and accelerate model running by more than 40 percent. Thus it is more appropriate for popularization.

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Modeling and analyzing transportation systems based on ACP approach

Modeling and analyzing transportation systems based on ACP a...

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International Conference on intelligent Transportation

作者： Fenghua Zhu Fei-Yue Wang Runmei Li Yisheng Lv Songhang Chen State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences (CAS) Beijing China School of Electronics and Information Engineering Beijing Jiaotong University BJTU Beijing China

The ACP (Artificial societies, Computational experiments and Parallel execution) approach has provided us an opportunity to look into new methods in addressing transportation problems from new perspectives. In this paper, we present our works and results of applying ACP approach in modeling and analyzing transportation system, especially carrying out computational experiments based on artificial transportation systems. Two aspects in the modeling process are analyzed. The first is growing artificial transportation system from bottom up using agent-based technologies. The second is modeling environment impacts in simple-is-consistent principle. Finally, two computational experiments are carried out on one specific ATS, Jinan ATS, and numerical results are presented to illustrate the applications of our method.

关键词： Analytical models Rain Computational modeling Vehicles Object oriented modeling

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Neural network based online traffic signal controller design with reinforcement training

Neural network based online traffic signal controller design...

引用

International Conference on intelligent Transportation

作者： Yujie Dai Jinzong Hu Dongbin Zhao Fenghua Zhu State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences (CAS) Beijing China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences (CAS) China

Traffic congestion leads to problems like delays, decreasing flow rate, and higher fuel consumption. Consequently, keeping traffic moving as efficiently as possible is not only important to economy but also important to environment. Traffic system is a large complex nonlinear stochastic system. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus, computational intelligence (CI) technologies gain more and more attentions. Neural Networks (NNs) is a well developed CI technology with lots of promising applications in traffic signal control (TSC). In this paper, a neural network (NN) based signal controller is designed to control the traffic lights in an urban traffic road network. Scenarios of simulation are conducted under a microscopic traffic simulation software. Several criterions are collected. Results demonstrate that through online reinforcement training the controllers obtain better control effects than the widely used pre-time and actuated methods under various traffic conditions.

关键词： Artificial neural networks Training Vehicles Roads Software Real time systems Learning systems

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Video vehicle detection through multiple background-based features and statistical learning

Video vehicle detection through multiple background-based fe...

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International Conference on intelligent Transportation

作者： Mingxia Sun Kunfeng Wang Ming Tang Fei-Yue Wang Jinfeng Yang Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences (CAS) Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences (CAS) Beijing China

This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.

关键词： Vehicles Feature extraction Training Image edge detection Vehicle detection Meteorology Radio frequency

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A delay system method to design of event-triggered control of networked control systems

A delay system method to design of event-triggered control o...

引用

IEEE Conference on Decision and control

作者： Dong Yue Engang Tian Qing-Long Han Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Hubei 430074 China School of Electrical and Automation Engineering Nanjing Normal University 78 Bancang Street Nanjing 210042 China Center for Intelligent and Networked Systems and the School of Information and Communication Technology Central Queensland University Rockhampton Qld 4702 Australia

ISBN: (纸本)9781612848006

The event-triggered H_(infinity) control design is investigated for networked control systems with uncertainties and transmission delays. A novel event-triggering scheme is proposed, which has some advantages over traditional ones with a continuous detector. Considering the effect of the transmission delay, a delay system model for the analysis is firstly constructed. Then, based on the model and Lyapunov functional method, criteria for the stability with an H_(infinity) norm bound and criteria for the co-design of both the feedback gain and the trigger parameters are derived. In order to solve the feedback gain and the trigger parameters, the linear matrix inequality technique is employed. From the simulation example, it can be concluded that the proposed event-triggering scheme is superior to some other event-triggering schemes in some existing literature.

关键词： feedback gain Networked control systems Event trigger Delay systems Scheme Co-design control systems design control

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Algorithm design of traffic incident automatic detection based on mobile detection

Algorithm design of traffic incident automatic detection bas...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Zi Zhang Xiaoli Lin Bin Hu Communications Commission of Guangzhou Municipality Guangzhou Communication Information Construction Investment and Operation Company Limited Guangzhou China Transportation Management General Station of Guangzhou Haizhu Guangzhou China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy and Sciences Beijing China

Most researches of traffic incident auto-detection are based on the data from fixed detectors, which are limited by costs and position. In order to resolve this problem, existing algorithms of traffic incident automatic detection are analyzed and compared, and an algorithm of traffic incident auto-detection are provided based on mobile-detection technology. The traffic data are grouped in 5-min intervals, analyzed by a three-layer BP neural network, and utilized for traffic incident detection. 16 traffic incidents of different locations and different levels are modeled in the simulation experiment based on VISSIM, and detection rate, false alarm rate and average detection time are adopted as indicators to evaluate the algorithm. Finally, the algorithm is proved to be effective and applicable in practice.

关键词： Three dimensional displays Bayesian methods Detectors

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