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检索条件"机构=The MoE Key Laboratory of Embedded System and Service Computing"

共 547 条记录，以下是161-170 订阅

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Local-directed network coding in vehicular ad-hoc networks

Local-directed network coding in vehicular ad-hoc networks

引用

IEEE International Symposium on Intelligent Signal Processing and Communication systems (ISPACS)

作者： Haojie Luo Dongdong Zhang Chen Ye The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

In this paper, we proposed a novel network coding method called local-directed network coding to improve the throughput of the vehicular ad-hoc network. Different from other vehicular network coding methods, we consider the direction of packet transmission. In detail, the intermediate node selects one main packet and some sub-packets from forwards and backwards sending queues, and then encodes these packets into one packet to broadcast. Simulation results show that our approach can efficiently improve the throughput of application layer in vehicular networks.

关键词： Encoding Buffer storage Accidents Vehicles Streaming media Artificial neural networks Performance evaluation

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Counting Pedestrian in Crowded Subway Scene

Counting Pedestrian in Crowded Subway Scene

引用

Congress on Image and Signal Processing, CISP

作者： Keju Zu Fuqiang Liu Zhipeng Li Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China

When the high occlusion occurs in crowded scene, face detection is a better substitute for detecting pedestrian. In this paper, we present a novel crowd analysis method based on discriminative descriptor of faces and support vector machine (SVM) ensemble. Through manipulating the input features in the same sample set, the different input features of faces are extracted to train two SVM classifiers. The classification scores of two generated classifiers are combined adaptively to make a collective decision. The first SVM, as the principal classifier gives out most of face hypotheses, while the second SVM serves as secondary one to rejecting the false positive. We present experiment to test the proposed method in crowded subway video, and the result shows that the SVM ensemble outperforms the single SVM in counting the pedestrian.

关键词： Layout Support vector machines Support vector machine classification Face detection Histograms Surveillance Humans Lighting Cameras Feature extraction

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Image Retrieval Based on Manifold Learning and Incorporate Clustering

Image Retrieval Based on Manifold Learning and Incorporate C...

引用

GCIS, WRI Global Congress on Intelligent systems

作者： Jianzhu Cui Fuqiang Liu Zhipeng Li Jing Li The Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China

This paper presents a novel unsupervised learning framework named image retrieval based on manifold learning and incorporate clustering. The dimensionality of image descriptors used in image retrieval applications is quite high. Given a query image, our algorithm first makes use of manifold learning (LPP) for dimensionality reduction and manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly. Then we use the similarities among target images for improving the performance of the image retrieval systems by cluster-based retrieval of images by unsupervised learning. Our algorithm retrieves image clusters as retrieval results by applying K-means clustering algorithm to a collection of images collected by manifold ranking algorithm. Experimental results on a general-purpose image database show that our algorithm attains a significant improvement over existing systems.

关键词： Image retrieval Clustering algorithms Feedback Unsupervised learning Support vector machines Support vector machine classification Image databases Content based retrieval Intelligent systems Laboratories

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Accelerating Support Vector Machine Learning with GPU-Based MapReduce

Accelerating Support Vector Machine Learning with GPU-Based ...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Tianyao Sun Hanli Wang Yun Shen Jun Wu Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781479986989

With the exploding growth of data, the computational complexity required by learning Support Vector Machine (SVM) lays a heavy burden on real-world applications. To address this issue, parallel computational techniques can be employed such as the Graphics Processing Units (GPUs) and MapReduce model. As it is well known, GPUs are microprocessors on a multi-core architecture which reveal high performance in mass data parallel computing, and MapReduce allows computational tasks to be divided into a plurality of parts, distributed to various computing nodes and combined on a single node. In this paper, we propose a GPU-based MapReduce framework to accelerate SVM learning by jointly utilizing the parallel computing power of GPU and MapReduce. Extensive experimental results have verified the effectiveness and efficiency of the proposed approach.

关键词： Graphics processing units Support vector machines Kernel Training Optimization Acceleration Parallel processing

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An Efficient Webpage Classification Algorithm Based on LSH

An Efficient Webpage Classification Algorithm Based on LSH

引用

International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Junjun Liu Haichun Sun Zhijun Ding The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

With the explosive growth of Internet information,it is more and more important to fetch real-time and related *** it puts forward higher requirement on the speed of webpage classification which is one of common methods to retrieve and manage *** get a more efficient classifier,this paper proposes a webpage classification method based on locality sensitive hash *** which,three innovative modules including building feature dictionary,mapping feature vectors to fingerprints using Localitysensitive hashing,and extending webpage features are *** compare results show that the proposed algorithm has better performance in lower time than the na?ve bayes one.

关键词： Explosive growth Webpage classification Locality-sensitive hashing Fingerprint Extending webpage features

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Real-Time Communication Method for mHealth Base on Extended XMPP Protocol

Real-Time Communication Method for mHealth Base on Extended ...

引用

The 9th International Conference on Wireless Communications,Networking and Mobile computing(WICOM 2013)

作者： Chenjie Shi Yu Fang The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

Considering characteristic of mHealth communication and problems of existing methods, this paper presents a real-time communication method for mHealth based on extended XMPP protocol. The method can maintain the role status efficiently and reduce data latency during the communication process. Meanwhile, it can be extended flexibly to meet increasing communication demands of mHealth services. Furthermore, a system framework is presented to support telemonitoring scene. Finally, system implementation and feasibility tests verify the effectiveness of the method and framework.

关键词： mHealth XMPP Protocol Real-Time Communication

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Vehicle classification based on the fusion of deep network features and traditional features

Vehicle classification based on the fusion of deep network f...

引用

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Hua Qian Yaying Zhang Chunmei Liu Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Consider to the defect of traditional features which have high empirical components. A vehicle classification algorithm based on the fusion of higher-layer features of a deep network and traditional features was proposed. Firstly, the traditional features of PHOG and LBP-EOH were extracted. Secondly, the higher-layer features excavated from the vehicle pictures by deep belief networks were added, making these three kinds of features together by feature fusion. Finally, support vector machine is used to train and classify the vehicle. When the number of training samples is large enough, the algorithm has a significant effect compared to those with traditional features. It can achieve the accuracy of 95% in the six categories of vehicles.

关键词： Vehicles Support vector machines Feature extraction Algorithm design and analysis

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Vehicle to Vehicle and Roadside Sensor Communication for Intelligent Navigation

Vehicle to Vehicle and Roadside Sensor Communication for Int...

引用

International Conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： Rongyan Xia Chen Ye Dongdong Zhang Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China

Hybrid sensor network technology is a key component of future ITS applications. In this paper, we propose a hybrid architecture that combined VANETs and roadside WSNs for intelligent navigation. Different from traditional ITS application, Sensors in WSNs and VANETs are used to perceive and exchange roadside and vehicular information to support the intelligent navigating decision process. We firstly give the system and protocol architecture, and then we discuss the scenarios and use cases of our system in intelligent navigation. After that, we describe the software and hardware implementation of our prototype, conduct a simulation on the discussed scenarios, and present a detailed data communication experimental result to prove the feasibility of our prototype.

关键词： Vehicles Wireless sensor networks Navigation Data communication Roads Prototypes Path planning

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A Traffic Flow Prediction Approach: LSTM with Detrending

A Traffic Flow Prediction Approach: LSTM with Detrending

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IEEE International Conference on Progress in Informatics and computing (PIC)

作者： Zheng Zhao Yaying Zhang The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781538676738;9781538676721

Traffic flow prediction plays a key role in many Intelligent Transportation system research and applications. It aims to forecast the forthcoming traffic conditions with the help of historical data. Urban traffic always has its morning and afternoon peak hours. We also observed that the urban traffic flow can always be divided into main trend data and its residual part. The main trend data presents a similar trend on different days. The residual data is time-variant part which reflects the short-term fluctuation of traffic condition over each day. Enlighted by detrending, Principal Component Analysis (PCA) method is applied to extract the main trend data in this paper. The residual data is obtained by subtracting the main trend data from the overall traffic flow data. Then Long Short-Term Memory (LSTM) model is proposed to predict the residual data. With main trend data and predicted residual data, the urban traffic flow can be predicted by the joint PCA and LSTM approach. Finally, the empirical study demonstrates the propose method outperforms similar traffic prediction models.

关键词： Market research Predictive models Principal component analysis Logic gates Roads Feature extraction Artificial neural networks

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Adopting WirelessHART for In-Vehicle-Networking

Adopting WirelessHART for In-Vehicle-Networking

引用

IEEE International Symposium on Cyberspace Safety and Security

作者： Zhuo Bi Deji Chen Cheng Wang Changjun Jiang Ming Chen Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University

ISBN: (纸本)9781479989386

It is estimated that the breakthrough in the broad deployment of Internet of Things (IoT) could come from smart cars. Indeed, we have seen multi-facet advances around cars: new material, in vehicle infotainment, driverless cars, smart transportation, electrical vehicles, etc. However, in-vehicle-networking has been mainly by wire;the wiring for a car is largely prebuilt during the design phase. With more and more things networked within a car, wiring has taken up 1-2 percent of the total weight. This translates into burning up to 0.1 kilogram fuel over 100 kilometers. On the other hand, the advances in wireless technology, especially the broad acceptance of WirelssHART in the industrial settings, has proved its capability in harsh environments. This paper studies what could happen if we use WirelessHART mesh network for in-vehicle communication. While new wireless network protocols are needed to perform the task of CAN, the dominant in-vehicle fieldbus, WirelessHART could take on the work performed by LIN, the fieldbus for peripheral devices. A detailed study is provided to compare these buses. Road tests were performed, in which a WirelessHART network keeps running for the whole 20 minute period.

关键词： Reliable Wireless Sensor Network Smart Car CAN LIN WirelessHART

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