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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Muhammad Shoaib Ralf Dragon Jorn Ostermann Institut für Informationsverarbeitung Leibniz Universität Hannover Germany

Cast shadows cause serious problems in the functionality of vision-based applications, such as video surveillance, traffic monitoring and various other applications. Accurate detection and removal of cast shadows is a challenging task. Common shadow detection techniques normally use color information, which is not a reliable base in every scenario. This paper presents a novel scheme for real time detection of cast shadows using contour like structures of objects, which are obtained by gradient-based background subtraction. The scheme does not use any color information. Two basic rules are followed for shadow detection. The first rule is that shadows do not change the texture of the background. The second rule is a cast shadow lies outside the boundary of an object and has a relatively small common boundary with the object. Experimental results show the performance of the proposed scheme. Objective evaluation shows that the algorithm classifies 90 percent of the pixels of the objects and their shadow correctly.

关键词： Humans Object detection Video surveillance Optical distortion detection algorithms Monitoring Change detection algorithms Video sequences Image motion analysis Merging

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Chinese multi-document summarization based on Topic detection technology

Chinese multi-document summarization based on Topic Detectio...

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Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA

作者： Mei-ling Liu Tie-jun Zhao Dept. of Comput. Sci. & Technol. Harbin Inst. of Technol. Harbin China

This paper reports an initial study that aims to Chinese multi-document summarization. We introduce and compare different dynamic threshold model which TDT (Topic detection and Tracking), and get document sets based on topic, then focus on Chinese multi-document summarization generation. Our approaches are based on the combination TDT temporal properties and multi-document summarization. Results show that using different dynamic threshold in TDT influence output summary representation. For our future work, we will continue to study the temporal multi-document summarization base on the web.

关键词： Natural language processing Paper technology Computer science Abstracts Computational intelligence Computer industry Application software Broadcast technology Broadcasting detection algorithms

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A New Reduced Complexity ML detection Scheme for MIMO Systems

A New Reduced Complexity ML Detection Scheme for MIMO System...

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IEEE International Conference on Communications (ICC)

作者： J.-S. Kim S.-H. Moon I. Lee School of Electrical Engineering Korea University Seoul South Korea

For multiple-input multiple-output (MIMO) systems, the optimum maximum likelihood (ML) detection requires tremendous complexity as the number of antennas or modulation level increases. This paper proposes a new algorithm which attains the ML performance with significantly reduced complexity. Based on the minimum mean square error (MMSE) criterion, the proposed scheme reduces the search space by excluding unreliable candidate symbols in data streams. Utilizing the probability metric which evaluates the reliability with the normalized likelihood functions of each symbol candidate, near optimal ML detection is made possible. A threshold parameter is introduced to balance a tradeoff between complexity and performance. Besides, we propose an efficient way of generating the log likelihood ratio (LLR) values which can be used for coded systems.

关键词： MIMO Maximum likelihood detection Transmitters detection algorithms Mean square error methods Maximum likelihood decoding Vectors Detectors Information technology Receiving antennas

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ALANDA for configuration-free analysis of process data

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IFAC Proceedings Volumes 2009年第10期42卷 504-509页

作者： M. Soemers F. Alsmeyer AixCAPE e.V. D-52072 Aachen Germany

For the long-term storage of measured data from production processes, process information management systems (PIMS) have been established in the last years. The use of these measurement data offers optimization potential if the relevant process information can be extracted. This contribution gives an overview of the innovative algorithms the software platform ALANDA provides for online and offline analysis of process data. On the basis of configuration-free algorithms, the effort for data analysis and model building can be reduced significantly. An introduction to the methods for PIMS configuration, basic preprocessing, and trend detection is given. These methods, which are predominantly based on wavelet analysis are used for the identification of a soft sensor in an industrial application. Finally, we present a tutorial demonstration of ALANDA in terms of a trend detection in a separation process.

关键词： Data compression Preprocessing Trends detection algorithms Modelling

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Cognitive Radio Spectrum Sensing Based on Index Belief Degree Function

Cognitive Radio Spectrum Sensing Based on Index Belief Degre...

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

作者： Meng-yun Liu Shi-yin Li Qiong Liu School of Information and Electrical Engineering CUMT Xuzhou Jiangsu China

Nowadays, cognitive radio technology is an intelligent spectrum sharing technology. In order to ensure the licensed users will not be interfered, cognitive(unlicensed) users must be able to detect the spectrum vacancy fast and reliably. For several existing multi-node detection schemes didn't take the test results' reliability of the cognitive users in different locations into consideration, this paper Combines the real environment of wireless communication and proposes an optimized spectrum sensing algorithm based on the index belief degree function after improving on the distributed spectrum sensing algorithm. The simulation results show that the novel algorithm has improved a lot in the detection performance than the existing technology. It has a good anti-jamming performance, a low false alarm probability and a high detection probability, which improves the spectrum utilization rate.

关键词： Cognitive radio Resource management Wireless communication Wireless sensor networks FCC detection algorithms Signal detection Fading Logic Testing

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Fast Detecting Outliers over Online Data Streams

Fast Detecting Outliers over Online Data Streams

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International Conference on Information Engineering and Computer Science (ICIECS)

作者： Xianghong Tang Guohui Li Gang Chen School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424449934

How to mine outliers of online data streams in a short time is an unsolved problem. We propose a new outlier factor metric whose name is the frequent pattern contradiction outlier factor called FPCOF for short. FPCOF can easily measure the degree to which each data instance in data streams is considered as an outlier. In order to compute FPCOF, we construct an outlier detection tree (or OD-tree in short) and design a set of algorithms (ODFP-SW). These algorithms can fast compute FPCOF of new incoming elements by incrementally updating them on the OD-tree, and dynamically maintain the candidate outlier sets and FPCOF of the candidate outliers. The results of experiments show that the proposed method not only can efficiently and accurately mine the outliers in online data streams, but also is more scalable than other existing algorithms.

关键词： Computer science Algorithm design and analysis Data mining detection algorithms

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Unsupervised Class Separation of Multivariate Data through Cumulative Variance-Based Ranking

Unsupervised Class Separation of Multivariate Data through C...

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IEEE International Conference on Data Mining (ICDM)

作者： Andrew Foss Osmar R. Zaïane Sandra Zilles Department of Computing Science University of Alberta Edmonton Canada Department of Computing Science University of Regina Regina Canada

This paper introduces a new extension of outlier detection approaches and a new concept, class separation through variance. We show that accumulating information about the outlierness of points in multiple subspaces leads to a ranking in which classes with differing variance naturally tend to separate. Exploiting this leads to a highly effective and efficient unsupervised class separation approach, especially useful in the difficult case of heavily overlapping distributions. Unlike typical outlier detection algorithms, this method can be applied beyond the `rare classes' case with great success. Two novel algorithms that implement this approach are provided. Additionally, experiments show that the novel methods typically outperform other state-of-the-art outlier detection methods on high dimensional data such as Feature Bagging, SOE1, LOF, ORCA and Robust Mahalanobis Distance and competes even with the leading supervised classification methods.

关键词： detection algorithms Bagging Robustness

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Reconfigurable real-time MIMO detector on GPU

Reconfigurable real-time MIMO detector on GPU

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Asilomar Conference on Signals, Systems & Computers

作者： Michael Wu Yang Sun Joseph R. Cavallaro Electrical and Computer Engineering Rice University Houston TX USA

ISBN: (纸本)9781424458257

In a high performance multiple-input multiple-output (MIMO) system, a soft output MIMO detector combined with a channel decoder is often used at the receiver to maximize performance gain. Graphic processor unit (GPU) is a low-cost parallel programmable co-processor that can deliver extremely high computation throughput and is well suited for signal processing applications. We propose and implement a novel soft MIMO detection algorithm and show we meet real-time performance while maintaining flexibility using GPU.

关键词： MIMO Detectors Decoding Performance gain Graphics Coprocessors Concurrent computing Throughput Signal processing algorithms detection algorithms

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Intelligent Workload Factoring for a Hybrid Cloud Computing Model

Intelligent Workload Factoring for a Hybrid Cloud Computing ...

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IEEE World Congress on Services (SERVICES)

作者： Hui Zhang Guofei Jiang Kenji Yoshihira Haifeng Chen Akhilesh Saxena NEC Laboratories of America Inc. Princeton NJ USA

We present an intelligent workload factoring service for enterprise customers to make the best use of public cloud services along with their privately-owned (legacy) data centers. It enables federation between on- and off-premise infrastructures for hosting Internet-based applications, and the intelligence lies in the explicit segregation of base workload and trespassing workload, the two naturally different components composing the application workload. The core technology of the intelligent workload factoring service is a fast frequent data item detection algorithm, which enables factoring incoming requests not only on volume but also on data content, upon changing application data popularity.

关键词： Cloud computing Internet Load management Computer architecture Streaming media Resource management Technology management National electric code Laboratories detection algorithms

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Towards Continuous Asset Tracking: Low-Power Communication and Fail-Safe Presence Assurance

Towards Continuous Asset Tracking: Low-Power Communication a...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Bernhard Firner Prashant Jadhav Yanyong Zhang Richard Howard Wade Trappe Eitan Fenson WINLAB Rutgers University North Brunswick NJ USA Inpoint New York NY USA

Asset tracking is an important application domain for wireless sensor networks. However, continuous tracking of a large number of items at the individual item level over a significant period of time is still not feasible. There are two main obstacles. The first is the need for efficient, low-power communication protocols. Many current protocols employ energy-expensive methods to achieve reliable communication for arbitrary traffic situations. Such protocols are not suitable for continuous asset tracking applications. The second challenge is the lack of a robust presence detection algorithm that can differentiate packet losses caused by a missing item from packet losses caused by the ambient radio environment. In this paper, we designed a simple communication protocol, Uni-HB, and demonstrated it can lead to longer system lifetime and higher communication reliability than several popular protocols. We also devised two robust detection algorithms that can yield low false alarm rates while achieving timely loss notification. We took an experimental approach, and evaluated protocols on a generic embedded hardware platform that has an similar architecture to motes. We also derived analytical models to validate our experimental measurements.

关键词： Protocols Robustness Hardware Sensor systems Wireless sensor networks detection algorithms Sensor systems and applications Communications Society USA Councils Telecommunication network reliability

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