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检索条件"任意字段=Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009"

共 283 条记录，以下是71-80 订阅

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Leveraging Provenance to Improve Data fusion in Sensor Networks

Leveraging Provenance to Improve Data Fusion in Sensor Netwo...

引用

Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Dogan, Gulustan Seo, Eunsoo Brown, Theodore Abdelzaher, Tarek F. CUNY New York NY 10021 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9780819490858

Provenance is the information about the origin of the data inputs and the data manipulations to a obtain a final result. With the huge amount of information input and potential processing available in sensor networks, provenance is crucial for understanding the creation, manipulation and quality of data and processes. Thus maintaining provenance in a sensor network has substantial advantages. In our paper, we will concentrate on showing how provenance improves the outcome of a multi-modal sensor network with fusion. To make the ideas more concrete and to show what maintaining provenance provides, we will use a sensor network composed of binary proximity sensors and cameras to monitor intrusions as an example. Provenance provides improvements in many aspects such as sensing energy consumption, network lifetime, result accuracy, node failure rate. We will illustrate the improvements in accuracy of the position of the intruder in a target localization network by simulations.

关键词： Provenance Data fusion Sensor Networks

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Songs of cyberspace: an update on sonifications of network traffic to support situational awareness

Songs of cyberspace: an update on sonifications of network t...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Ballora, Mark Giacobe, Nicklaus A. Hall, David L. Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

ISBN: (纸本)9780819486387

Building on our previous work, we extend sonification techniques to common network security data. In this current work, we examine packet flow and the creation of socket connections between a requestor's IP address and port number with the server's IP address and port number. Our goals for the aural rendering are twofold: to make certain conditions immediately apparent to untrained listeners, and to create a sound model capable of enough nuance that there is the possibility of unexpected patterns becoming apparent to a seasoned listener. This system could be used to potentially provide better cognitive refinement capabilities for data fusion systems, especially when multiple sources of data at various levels of refinement are presented to the human analyst.

关键词： Sonification auditory display SuperCollider network security data fusion JDL process model

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Real-time tracking and fast retrieval of persons in multiple surveillance cameras of a shopping mall

Real-time tracking and fast retrieval of persons in multiple...

引用

Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Bouma, Henri Baan, Jan Landsmeer, Sander Kruszynski, Chris van Antwerpen, Gert Dijk, Judith TNO POB 96864 NL-2509 JG The Hague Netherlands

ISBN: (纸本)9780819495471

The capability to track individuals in CCTV cameras is important for e.g. surveillance applications at large areas such as train stations, airports and shopping centers. However, it is laborious to track and trace people over multiple cameras. In this paper, we present a system for real-time tracking and fast interactive retrieval of persons in video streams from multiple static surveillance cameras. This system is demonstrated in a shopping mall, where the cameras are positioned without overlapping fields-of-view and have different lighting conditions. The results show that the system allows an operator to find the origin or destination of a person more efficiently. The misses are reduced with 37%, which is a significant improvement.

关键词： Surveillance tracking CCTV person re-identification multi-sensor fusion

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Spatial Voting For Automatic Feature Selection, fusion, and Visualization

Spatial Voting For Automatic Feature Selection, Fusion, and ...

引用

Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Jaenisch, Holger Handley, James Licht Strahl Engineering Inc. (United States) Johns Hopkins Univ. (United States) Alabama A&M Univ. (United States)

ISBN: (纸本)9780819495471

We present a novel feature selection, fusion, and visualization utility using Spatial Voting (SV). This SV feature optimization utility is designed to be an off-line stand-alone utility to help an investigator find useful feature pairs for cluster analysis and lineage identification. The analysis can be used to enable the analyst to vary parameters manually and explore the best combination that yields visually appealing or significant groups or spreading of data points depending on the planned use of the analysis downstream. Several different criteria are available to the user in order to determine the best SV grid size and feature pair including minimizing zeros, minimizing covariance, balanced minimum covariance, or the maximization of one of eight different scoring metrics: Containment, Rand Index, Purity, Precision, Recall, F-Score, Normalized Mutual information (NMI), and Adjusted Rand Index (ARI). The tool that is described in this work facilitates this analysis and makes it simple, efficient, and interactive if the analyst so desires.

关键词： Spatial Voting Automatic Feature Selection fusion Visualization NMI Rand Index ARI

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The application of Dempster-Shafer theory for landmine detection

The application of Dempster-Shafer theory for landmine detec...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Mudigonda, NR Kacelenga, R Erickson, D Gen Dynam Canada Ltd Calgary AB T2E 8P2 Canada

ISBN: (纸本)0819449598

This paper explores the concepts of multisensor data fusion based on the Dempster-Shafer (DS) evidential theory in order to achieve a mine versus false-alarm (FA) classification of landmine targets. Initially, a decision-level DS algorithm is proposed to combine the evidence from multiple sensors of the landmine detection system developed by the General Dynamics Canada Limited (GD Canada). Subsequently, a feature-level DS fusion algorithm is employed to operate on a set of features reported by the ground penetrating radar (GPR) sensor of the system. The data used in the present study was acquired from the Aberdeen Proving Grounds (APG) test site in USA as part of the Ground Standoff Mine Detection System (GSTAMIDS) trials. The proposed decision-level DS algorithm yielded a probability of detection (Pd) of 92.53% at a false-alarm rate (FAR) value of 0.0697 FAs/m(2). The Pd and the FAR performance results achieved by using the decision-level DS algorithm are comparable with the results obtained using three other decision-level fusion algorithms that were previously developed by GD, Canada based on heuristic, Bayesian inference, and Voting fusion concepts. On the other hand, the feature-level DS fusion, when tested with the information presented by the GPR sensor only, resulted in a higher Pd value of 78.54% as compared to the corresponding result of 61.43% obtained by using the heuristic algorithm. The GPR sensor is one of the three scanning sensors present on the system.

关键词： landmine detection multisensor data fusion Dempster-Shafer theory Bayesian theory voting fusion

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Probabilistic Multi-source Multi-INT Intel fusion Benefit Analysis

Probabilistic Multi-source Multi-INT Intel Fusion Benefit An...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Wisniewski, Ski (David) Hershey, Paul Raytheon 302 Sci Pk Rd State Coll PA 16802 USA Raytheon Dulles VA 20166 USA

ISBN: (纸本)9781628410587

The Process for Probabilistic Multi-Source Multi-INT Intel fusion Benefit Analysis** (PIFBA) provides a methodology for statistically computing the probability of detecting, locating, and tracking objects in order to assess current capabilities or the benefit of adding or removing ISR assets in order to obtain the statistically optimal result. The PIFBA process defines the approach to calculate the probabilistic benefits or rewards associated with integrating or fusing multisource multi-INT products across a wide range of platforms, sensors, environmental conditions and target objects. This process applies to analyzing ISR capabilities, effectiveness, and gaps, as well as the benefits of applying existing or new technology and tactics. The PIFBA process was designed to answer the following questions: Based on the defined ISR assets - what is the probability that we know a piece of Intel with sufficient accuracy and timeliness to be of value to the analyst or warfighter? What is the benefit of adding new data via additional platforms, sensors, or processing? What is the benefit of adding new systems or technology and to what degree of performance must they exhibit in order to affect the statistical outcome?

关键词： Multi-Source Multi-INT fusion Automated Decision Aides ISR Statistical Computation Probability of Detecting, Locating, and Tracking Processing, Exploitation, Dissemination (PED)

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Novel classification fusion techniques for military target classification

Novel classification fusion techniques for military target c...

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Conference on multisensor, multisource information fusion

作者： Liu, Ting Lampropoulos, George A. AUG Signals 73 Richmond St W Toronto ON Canada

ISBN: (纸本)9780819466938

The objective of this paper is to develop novel classification structures for military targets detection and recognition by employing different fusion techniques. In real applications, the great diversity of materials in the background areas and the similarity between the background and target signatures result in high false alarm rates and large miss classification errors. In this paper, three new systems are proposed using different fusion techniques: pixel level fusion, decision fusion, and classification fusion employing confidence vectors. These new developed systems are tested using an experimental data to show its effectiveness.

关键词： hyperspectral detection classification fusion confusion matrix false alarm

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PASES: Combining radar and video sensing for improved pedestrian safety

PASES: Combining radar and video sensing for improved pedest...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Chenault, David B. Edmondson, Richard P. Polaris Sensor Technol Inc 200 West Side SqSuite 320 Huntsville AL 35801 USA

ISBN: (纸本)9781510601130

Polaris Sensor Technologies reports on the development of Pedestrian Automated System for Enforcement and Safety (PASES), a radar and video based system used to monitor vehicle and pedestrian traffic with the intent of improving pedestrian safety. Data is fused from a system of multiple sensors and multiple sensor modalities to identify vehicular violations of pedestrian right of way. A focus was placed on the selection of low cost COTS sensors to make the system more widely available to state and local DOTs with limited budgets. applications include automated enforcement, adaptive traffic control, and improved intersection and crosswalk design based on high quality data available for traffic engineering. We discuss early results with high fidelity sensors, and the performance trades made in order to make the system affordable. A discussion of the system processing architecture is included which highlights the treatment of each sensor data type, and the means of combining the processed data products into state information related to traffic incidents involving vehicles and pedestrians.

关键词： Pedestrian radar video tracking sensor fusion

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Hybrid methods for multisource information fusion and decision support

Hybrid methods for multisource information fusion and decisi...

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Conference on multisensor, multisource information fusion

作者： Braun, Jerome J. Glina, Yan MIT Lincoln Lab 244 Wood St Lexington MA 02173 USA

ISBN: (纸本)0819462985

This paper presents the progress of an ongoing research effort in multisource information fusion for biodefense decision support. The effort concentrates on a novel machine-intelligence hybrid-of-hybrids decision support architecture termed FLASH (fusion, Learning, Adaptive Super-Hybrid) we proposed. The highlights of FLASH discussed in the paper include its cognitive-processing orientation and the hybrid nature involving heterogeneous multiclassifier machine learning and approximate reasoning paradigms. Selected specifics of the FLASH internals, such as its feature selection techniques, supervised learning, clustering, recognition and reasoning methods, and their integration, are discussed. The results to date are presented, including the background type determination and bioattack detection computational experiments using data obtained with a multisensor fusion testbed we have also developed. The processing of imprecise information originating from sources other than sensors is considered. Finally, the paper discusses applicability of FLASH and its methods to complex battlespace management problems such as course-of-action decision support.

关键词： multisource information fusion machine intelligence cognitive processing decision support biodefense

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Better-than-the-Best fusion Algorithm with Application in Human Activity Recognition

Better-than-the-Best Fusion Algorithm with Application in Hu...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Najjar, Nayeff Gupta, Shalabh Univ Connecticut Storrs CT 06269 USA

ISBN: (纸本)9781628416145

This paper introduces the Better-than-the-Best fusion (BB-Fus) algorithm. The BB-Fus algorithm is a simple and effective information fusion algorithm that combines the information from different sources (be it sensors, features or classifiers) to improve the Correct Classification Rate (CCR). It can be observed that in most classification problems, different sensors or features might have different classification accuracies in separating different classes. Therefore, this paper constructs an optimal decision tree that isolates one class at a time with the best sensor to separate that particular class. The paper shows that the decision tree improves the overall CCR as compared to the use of any single sensor or feature for any 3-class classification problem. The efficiency of the BB-Fus algorithm is validated on the Opportunity data set to solve the human activity recognition problem where a set of 56 sensors (including a localization system, accelerometers, inertial measurement units and magnetic sensors mounted on various body parts;besides, accelerometers and gyroscopes mounted on different objects) are used. The CCR resulting from the BB-Fus algorithm is 96% while the best sensor achieved 94% CCR.

关键词： Sensor fusion information fusion Human Activity Recognition BB-Fus Decision Trees

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