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International Conference on Image and Graphics (ICIG)

作者： Xianbin Cao Renjun Lin Pingkun Yan Xuelong Li Anhui Province Key Laboratory of Software in Computing and Communication School of Computer Science and Technology University of Science and Technology Hefei Anhui China Center of OPTical IMagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy and Sciences Xi'an Shaanxi China

The limitation of the existing methods of traffic data collection is that they rely on techniques that are strictly local in nature. The airborne system in unmanned aircrafts provides the advantages of wider view angle and higher mobility. However, detecting vehicles in airborne videos is a challenging task because of the scene complexity and platform movement. Most of the techniques used in stationary platforms cannot perform well in this situation. A new and efficient method based on Bayes model is proposed in this paper. This method can be divided into two stages, attention focus extraction and vehicle classification. Experimental results demonstrated that, compared with other representative algorithms, our method obtained better performance with higher detection rate, lower false positive rate and faster detection speed.

关键词： Vehicles Feature extraction Vehicle detection Road transportation Cameras Atmospheric modeling Classification algorithms

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IPRAR: A DFG-based approach to instruction clustering for multicluster VLIW DSP processor with SIMD structure

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Journal of Computational Information Systems 2014年第3期10卷 1257-1269页

作者： Yang, Yangzhao Gu, Naijie Zhao, Zeng Ren, Kaixin School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Computing and Communication Software Hefei 230027 China USTC and Loongson Technology Associated Laboratory Institute of Advanced Technology University of Science and Technology of China Hefei 230027 China

Modern Very Long Instruction (VLIW) DSP can improve performance by using instruction-level parallelism (ILP) and obtain better parallelism through instruction clustering. The essence of clustering is resource allocation. Traditional clustering method assumes that one instruction can only be assigned to a certain cluster, which is inapplicable to the architecture with SIMD structure. To address this issue, this paper presents IPRAR, a novel instruction clustering algorithm for the features of SIMD structure based on DFG. Due to considering the features of SIMD structure, IPRAR can be implemented on BWDSP100 with satisfactory. Besides, IPRAR can make the load of cluster more balanced and minimize the intercluster communication overload. Finally, numerous experiment results show that the IPRAR has an average of 7-10% performance improvement compared to the traditional methods. © 2014 Binary Information Press.

关键词： Clustering algorithms

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KLT Feature Based Vehicle Detection and Tracking in Airborne Videos

KLT Feature Based Vehicle Detection and Tracking in Airborne...

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International Conference on Image and Graphics (ICIG)

作者： Xianbin Cao Jinhe Lan Pingkun Yan Xuelong Li Anhui Province Key Laboratory of Software in Computing and Communication School of Computer Science and Technology University of Science and Technology Hefei Anhui China Center of OPTical IMagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy and Sciences Xi'an Shaanxi China

Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are difficult to develop because of factors like UAV motion, scene complexity and so on. In this paper, we propose a new framework of multi-motion layer analysis to detect and track moving vehicles in airborne platform. Moving vehicles are firstly detected by registration and temporal differencing to establish motion layers. After motion layers are constructed, they are maintained over time for tracking vehicles. All vehicles are tracked by maintaining their corresponding motion layers. Our experimental results showed that compared with other previous algorithms, our method can achieve better results in terms of detection and tracking performance.

关键词： Vehicles Tracking Videos Feature extraction Vehicle detection Maintenance engineering Motion segmentation

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Optimizing BLAS2 for a decoupled access/execute architecture processor

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Journal of Computational Information Systems 2014年第3期10卷 1231-1241页

作者： Zhao, Zeng Gu, Naijie Yang, Yangzhao Ren, Kaixin School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China USTC and Loongson Technology Associated Laboratory Institute of Advanced Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Computing and Communication Software Hefei 230027 China

Basic Linear Algebra Subroutines (BLAS) is a widely used basic mathematical library for high-performance computing. It has a great impact on the performance of supercomputers. This paper focuses on memory accessing optimization for the level-2 library of BLAS (BLAS2) based on Godson-3B chips. The key contribution of this work is proposing several methods to make full use of the Godson-3B's hardware features, as well as to improve the performance of memory access. GEMV, the kernel function of BLAS2, is selected to demonstrate the optimization methods for Godson-3B. Using Single Instruction Multiple Data (SIMD) instructions, Direct Register Access (DRA) and Direct Cache Access (DCA) of Godson-3B, two optimization algorithms for GEMV are introduced in this paper. Experiments show that the best computing performance of the optimized GEMVs on Godson-3B is more than 1.8Gops, which is far superior to that of other versions. While the test scale of GEMVs is greater than the size of L2-cache, its average performance exceeds UBLAS and OpenBlas, the two most famous optimized BLAS libraries for GODSON, by more than 60% and 20% respectively. © 2014 Binary Information Press.

关键词： Supercomputers

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A low-cost pedestrian-detection system with a single optical camera

A low-cost pedestrian-detection system with a single optical...

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作者： Cao, Xian-Bin Qiao, Hong Keane, John IEEE Department of Computer Science and Technology University of Science and Technology of China Hefei 230026 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230026 China Laboratory of Complex Systems and Intelligent Science Institute of Automation Chinese Academy of Sciences Beijing 100080 China School of Informatics University of Manchester M60 1QD Manchester United Kingdom University of Manchester M13 9PL Manchester United Kingdom

The ultimate purpose of a pedestrian-detection system (PDS) is to reduce pedestrian-vehicle-related injury. Most such systems tend to adopt expensive sensors, such as infrared devices, in expectation of better performance. In comparison, a low-cost optical-camera-based system has much potential practical value, including a greater detection range, and can easily be trained to detect other objects. However, such low-cost systems are difficult to design (e.g., little original information can be collected, and the scene is very complex). To address these problems, an effective and reliable classifier is needed. The classifier should have a proper structure, its features need to be well selected, and a large number of high-quality samples are necessary for training. In this paper, we present a low-cost PDS which only uses a single optical camera. We design a cascade classifier to achieve an effective and reliable detection. First, our system scans two sequential frames at each zoom scale with a sliding window. Second, with each window, both appearance and motion features are extracted. A well-trained cascade classifier, combining statistical learning with a decomposed support-vector-machine classifier, then determines whether the window contains a human body. At the same time, to provide as much information as possible about the pedestrian, a small-scale weighted template tree trained by a coevolutionary algorithm is adopted to identify each pedestrian's direction, and the distance of each from the vehicle is also provided using an estimation algorithm. During the training procedure, we select key features by using the AdaBoost algorithm and a large number of high-quality samples. Experimental results demonstrate that the system is suitable for pedestrian detection in city traffic: The detection speed is more than 10 ft/s, the detection rate reaches 80%, and the false positive rate is no more than 0.3‰. © 2008 IEEE.

关键词： Pedestrian safety

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A Parallel NURBS-based Isogeometric Analysis Framework on Multi-core CPUs

A Parallel NURBS-based Isogeometric Analysis Framework on Mu...

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2012年全国理论计算机科学学术年会(NCTCS2012)

作者： Licai Guo Zhangjin Huang School of Computer Science and Technology University of Science and Technology of China PR China USTC-SICT Joint Laboratory on Network and Key Laboratory of Software in Computing and Communication Anhui Province PR China USTC-SICT Joint Laboratory on Network and Communication PR China School of Computer Science and Technology University of Science and Technology of China PR China

parallel framework for NURBS-based isogeometric analysis on multi-core cpus is presented in this *** framework is composed of two parts:(1) construction of the stiffness matrix and right hand side vector; (2) a parall...

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Smoothness in noise reduction of chaotic time series

Smoothness in noise reduction of chaotic time series

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2011 International Conference on computers, communications, Control and Automation

作者： Chenxi Shao Jiangong Ji Jingyuan Shao Lipeng Xiao Department of Computer Science and Technology University of Science and Technology of China Department of Modern Physics and Nonlinear Science Center University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Information center Jianghuai Automobile Co.Ltd.

Noise is an omnipresent phenomenon. It obscures the real behavior of dynamical system. Lots of methods are proposed to remove the noise contaminating time series. However, almost all the methods consider the noise reduction in the phase space and often sharp points are kept. Different with these methods, this paper proposes a method directly on the time series itself, considering the gauss noise feature and the smoothness of the real data, uses curve-fitting way to eliminate the sharp points. The numeral results verify the effectiveness of our method.

关键词： smoothness,noise reduction,curve-fitting,Local-Geometric-Projection

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Dynamic protocol conformance test method based on local error diagnostic information

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Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications 2007年第1期30卷 140-144页

作者： Lin, Hua-Hui Zhou, Hao Pan, Quan-Ke Zhao, Bao-Hua Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

To enhance the reliability and the fault coverage of the test results, a local error diagnostic mechanism was introduced into dynamic protocol conformance test, a new dynamic test method was proposed, whose principle and correctness was analyzed on determinate FSM model. The new method took full advantage of the dynamic information generated during test to avoid the incorrect locating of local error. Also a local error diagnostic method was presented on error sub-sequence, which was not restricted to single error assumption and more practical. The experimental results showed that the method can effectively avoid the descending of the test reliability and give better fault coverage.

关键词： Transmission control protocol

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Secrecy Analysis of UAV Control Information Transmission via NOMA

Secrecy Analysis of UAV Control Information Transmission via...

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IEEE Conference on Wireless communications and Networking

作者： Zhaoxin Feng Huabing Lu Nan Zhao Zhaoyuan Shi Yunfei Chen Xianbin Wang School of Information and Communication Engineering Dalian University of Technology Dalian China Key Lab. of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China Department of Engineering University of Durham Durham UK Department of Electrical and Computer Engineering Western University London ON Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Unmanned aerial vehicle (UAV) assisted wireless communication is essential for the next-generation mobile networks. In coping with the increased dynamics in UAV networks, the design of control information transmission is essential, requiring ultra reliability, low latency, and high security. In this paper, considering both the large-scale path loss and the Nakagami-m small-scale fading, we investigate the secrecy performance of UAV control information transmission in a NOMA ground-air network with an external flying eavesdropper. A spherical secrecy protection zone is set, and the closed-form expressions for average secure BLER and average achievable secrecy throughput are derived. After that, the asymptotic performance in the high SNR regime is analyzed to get more insights. Ultimately, simulation results verify the accuracy of analysis.

关键词： Wireless communication NOMA Accuracy Simulation Information processing Autonomous aerial vehicles Throughput

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An Efficient and Slight Convolutional Neural Network for Vehicle Type Classification

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Journal of Physics: Conference Series 2018年第1期1069卷

作者： Haibo Zheng Naijie Gu Xiaoci Zhang Anhui Province Key Laboratory of Computing and Communication Software School of Computer and Technology University of Science and Technology of China Hefei China

With the popularity of small storage embedded devices, recent researches on deep convolutional neural networks no longer only focus on improving the classification accuracy but start to attach great importance to the size of convolutional neural networks. This paper proposes a small deep convolutional neural network for vehicle type classification, based on the Fire module and the residual module. The Fire module is applied to maintain a small number of parameters. The residual module is employed to accelerate the training process and improves the classification accuracy. Experimental results show that our model achieves 95.57% accuracy of vehicle type classification on an open source dataset BIT-Vehicle. This outperforms previous researches and some classical convolutional neural networks such as AlexNet, ResNet-32 and SqueezeNet. What is more, the size of our model is only 5.3 MB, which has 40X fewer parameters than AlexNet and has 15X fewer parameters than ResNet-32.

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