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检索条件"任意字段=Sensor Fusion: Architectures, Algorithms, and Applications VI"

共 485 条记录，以下是71-80 订阅

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A new hyperspectral image compression paradigm based on fusion 6

A new hyperspectral image compression paradigm based on fusi...

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Conference on High-Performance Computing in Geoscience and Remote Sensing vi

作者： Guerra, Raul Melian, Jose Lopez, Sebastian Sarmiento, Roberto IUMA Inst Appl Microelect Campus Tafira Las Palmas Of Gran Canar Spain

ISBN: (纸本)9781510604186;9781510604193

The on-board compression of remote sensed hyperspectral images is an important task nowadays. One of the main difficulties is that the compression of these images must be performed in the satellite which carries the hyperspectral sensor. Hence, this process must be performed by space qualified hardware, having area, power and speed limitations. Moreover, it is important to achieve high compression ratios without compromising the quality of the decompress image. In this manuscript we proposed a new methodology for compressing hyperspectral images based on hyperspectral image fusion concepts. The proposed compression process has two independent steps. The first one is to spatially degrade the remote sensed hyperspectral image to obtain a low resolution hyperspectral image. The second step is to spectrally degrade the remote sensed hyperspectral image to obtain a high resolution multispectral image. These two degraded images are then send to the earth surface, where they must be fused using a fusion algorithm for hyperspectral and multispectral image, in order to recover the remote sensed hyperspectral image. The main advantage of the proposed methodology for compressing remote sensed hyperspectral images is that the compression process, which must be performed on-board, becomes very simple, being the fusion process used to reconstruct image the more complex one. An extra advantage is that the compression ratio can be fixed in advanced. Many simulations have been performed using different fusion algorithms and different methodologies for degrading the hyperspectral image. The results obtained in the simulations performed corroborate the benefits of the proposed methodology.

关键词： Hyperspectral images compression Hyperspectral data fusion real time applications

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Trajectory Optimisation for Avionics-Based GNSS Integrity Augmentation System 35

Trajectory Optimisation for Avionics-Based GNSS Integrity Au...

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35th IEEE/AIAA Digital Avionics Systems Conference (DASC)

作者： Sabatini, Roberto Moore, Terry Hill, Chris Gardi, Alessandro Ramasamy, Subramanian Gilgien, Michael RMIT Univ Sch Engn Melbourne Vic 3000 Australia Univ Nottingham Nottingham Geospatial Inst Nottingham NG7 2TU England Cranfield Univ Dept Aerosp Engn Cranfield MK43 0AL Beds England

ISBN: (纸本)9781509025237

In this paper, trajectory optimisation algorithms developed specifically for the Global Navigation satellite System (GNSS) Avionics-Based Integrity Augmentation (ABIA) system are presented. The ABIA system is designed to increase the levels of integrity and accuracy (as well as continuity in multi-sensor data fusion architectures) of GNSS in a variety of mission-and safety-critical aviation applications. The trajectory optimisation algorithms can be employed both for flight planning as well as real-time optimisation of manned and unmanned aircraft for all flight phases. Three and six Degrees-of-Freedom (3-DoF/6-DoF) aircraft dynamics models are adopted to generate a number of feasible flight trajectories that also satisfy the GNSS constraints. A detailed simulation case study is presented to evaluate the performance of trajectory optimisation algorithms for GNSS integrity augmentation using an AIRBUS A320 3-DoF aircraft dynamics model. Results confirm that the employed trajectory optimisation algorithms are capable of supporting high-integrity tasks when GNSS is used as the primary source of navigation data.

关键词： Avionics Based Integrity Augmentation Trajectory Optimisation Caution and Wanirng Flags GNSS Integrity Mission-and Safety-Critical GNSS applications

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Performance evaluation on contour extraction using Hough transform and RANSAC for multi-sensor data fusion applications in industrial food inspection

Performance evaluation on contour extraction using Hough tra...

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IEEE Signal Processing algorithms, architectures, Arrangements, and applications Conference

作者： M. Kroger W. Sauer-Greff R. Urbansky M. Lorang M. Siegrist Chair for Communications Engeneering University of Kaiserslautern WIPOTEC GmbH

ISBN: (纸本)9781509026616

For multi-sensor data fusion applications the accurate alignment of different sensor data is essential for the proper combination of matching features. In food inspection system the boxing often is in a rectangular shape. This knowledge can be used to rectify the image data, an important step in the alignment stage. In case of low contrast between boxing and background, the detected contour may differ significantly from the actual values. In this paper the performance of the Hough transform and the RANdom SAmple Consensus (RANSAC)-algorithm are evaluated relating to the correct extraction of the boxing contour out of contour data distorted by position errors of the outer shape. The evaluation results indicate the superiority of the RANSAC algorithm with respect to scalability, robustness and execution time.

关键词： Hough transform Food inspection Edge detection boxing multi-sensor data fusion sensor Data Performance Evaluation location error contour image data execution time

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Collaborative Wireless sensor Networks: architectures, algorithms and applications

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INFORMATION fusion 2015年 22卷 1-2页

作者： Fortino, Giancarlo Bal, Mert Li, Wenfeng Shen, Weiming Univ Calabria DIMES I-87036 Arcavacata Di Rende CS Italy Miami Univ Dept Engn Technol Hamilton OH 45011 USA Wuhan Univ Technol Sch Logist Engn Wuhan 430063 Peoples R China Natl Res Council Canada London ON N6G 4X8 Canada

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STAC: A Comprehensive sensor fusion Model for Scene Characterization

STAC: A Comprehensive Sensor Fusion Model for Scene Characte...

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Conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Kira, Zsolt Wagner, Alan R. Kennedy, Chris Zutty, Jason Tuell, Grady Georgia Tech Res Inst Atlanta GA 30332 USA

ISBN: (纸本)9781628416145

We are interested in data fusion strategies for Intelligence, Surveillance, and Reconnaissance (ISR) missions. Advances in theory, algorithms, and computational power have made it possible to extract rich semantic information from a wide variety of sensors, but these advances have raised new challenges in fusing the data. For example, in developing fusion algorithms for moving target identification (MTI) applications, what is the best way to combine image data having different temporal frequencies, and how should we introduce contextual information acquired from monitoring cell phones or from human intelligence? In addressing these questions we have found that existing data fusion models do not readily facilitate comparison of fusion algorithms performing such complex information extraction, so we developed a new model that does. Here, we present the Spatial, Temporal, Algorithm, and Cognition (STAC) model. STAC allows for describing the progression of multi-sensor raw data through increasing levels of abstraction, and provides a way to easily compare fusion strategies. It provides for unambiguous description of how multi-sensor data are combined, the computational algorithms being used, and how scene understanding is ultimately achieved. In this paper, we describe and illustrate the STAC model, and compare it to other existing models.

关键词： multisensor fusion data fusion scene characterization scene understanding

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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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Data fusion Mathematics: Theory and Practice

引用

2015年

作者： Raol, Jiten R

ISBN: (数字)9781498721028

ISBN: (纸本)9781498721028;1498721028

Fills the Existing Gap of Mathematics for Data fusion Data fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI). Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in various applications that include the monitoring of vehicles, aerospace systems, large-scale structures, and large industrial automation plants. Data fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and provides a proper and adequate understanding of the basic mathematics directly related to DF. The material covered can be used for evaluation of the performances of any designed and developed DF systems. It tries to answer whether unified data fusion mathematics can evolve from various disparate mathematical concepts, and highlights mathematics that can add credibility to the data fusion process. Focuses on Mathematical Tools That Use Data fusion This text explores the use of statistical/probabilistic signal/image processing, filtering, component analysis, image algebra, decision making, and neuro-FLGA paradigms in studying, developing and validating data fusion processes (DFP). It covers major mathematical expressions, and formulae and equations as well as, where feasible, their derivations. It also discusses SDF concepts, DF models and architectures, aspects and methods of type 1 and 2 fuzzy logics, and related practical applications. In addition, the author covers soft computing paradigms that are finding increasing applications in multisensory DF approaches and applications. This book: Explores the use of interval type 2 fuzzy logic and ANFIS in DF Covers the mathematical treatment of many types of filtering algorithms, target-tracking methods, and kinematic DF methods Presents single and multi-sensor tracking and fusion mathematics Considers specific DF architectures in the context

关键词： practice theory mathematics fusion data

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Real-Time Power-Efficient Integration of Multi-sensor Occupancy Grid on Many-Core

Real-Time Power-Efficient Integration of Multi-Sensor Occupa...

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IEEE International Workshop on Advanced Robotics and its Social Impacts (ARSO)

作者： Rakotovao, Tiana Mottin, Julien Puschini, Diego Laugier, Christian CEA LETI MINATEC Campus17 Rue Martyrs F-38000 Grenoble France INRIA Grenoble Rhone Alpes F-38334 Saint Ismier France

ISBN: (纸本)9781467380294

Safe Autonomous Vehicles (AVs) will emerge when comprehensive perception systems will be successfully integrated into vehicles. Advanced perception algorithms based on occupancy grids were developed for AV prototypes. These algorithms estimate the position and speed of every obstacle in the environment by using data fusion from multiple sensors. Computational requirements of such fusion prevent their integration into AVs on current low-power embedded hardware. However, recent emerging many-core architectures offer opportunities to fulfill the automotive market constraints and efficiently support advanced perception applications. This paper explores the integration of the occupancy grid multi-sensor fusion algorithm into low power many-core architectures. The parallel properties of this function are used to achieve real-time performance with a power consumption less than 1W. The proposed implementation produces an occupancy grid of 500 x 300 cells within 6.26ms. The execution time is 6x faster than typical sensor output rates and 9x faster than previous embedded prototypes.

关键词： Integration

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Reliable sources and uncertain decisions in multisensor systems

Reliable sources and uncertain decisions in multisensor syst...

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Conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Minor, Christian Johnson, Kevin Nova Res Inc Alexandria VA 22308 USA Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9781628416145

Conflict among information sources is a feature of fused multisource and multisensor systems. Accordingly, the subject of conflict resolution has a long history in the literature of data fusion algorithms such as that of Dempster-Shafer theory (DS). Most conflict resolution strategies focus on distributing the conflict among the elements of the frame of discernment (the set of hypotheses that describe the possible decisions for which evidence is obtained) through rescaling of the evidence. These "closed-world" strategies imply that conflict is due to the uncertainty in evidence sources stemming from their reliability. An alternative approach is the "open-world" hypothesis, which allows for the presence of "unknown" elements not included in the original frame of discernment. Here, conflict must be considered as a result of uncertainty in the frame of the discernment, rather than solely the province of evidence sources. Uncertainty in the operating environment of a fused system is likely to appear as an open-world scenario. Understanding the origin of conflict (source versus frame of discernment uncertainty) is a challenging area for research in fused systems. Determining the ratio of these uncertainties provides useful insights into the operation of fused systems and confidence in their decisions for a variety of operating environments. Results and discussion for the computation of these uncertainties are presented for several combination rules with simulated data sets.

关键词： multisensor systems data fusion sensor reliability interferants open-world sensing Dempster-Shafer theory combination rules

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An asset valuation approach using fuzzy logic

An asset valuation approach using fuzzy logic

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Conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Leung, Henry Univ Calgary Calgary AB Canada

ISBN: (纸本)9781628416145

Organisations that own operational networks have many networked assets to support daily business. For meaningful decision support on these assets and their services, network defenders need to know the values of their assets and services. Unfortunately, there is no easy way of knowing or determining these values, and no universally recognised approach to asset valuation exists. Proprietary and published approaches, mostly in risk analysis, tend to assume values whose significance may be hard to justify in practice. Fortunately, experienced computer security experts can give intuitive guidance on the relative importance of network assets in operational networks. Such experiential knowledge, though difficult to quantify through classical relational mathematics, can be generally effective in assigning relative values to assets. In this work, we propose to capitalise on this expertise by combining asset attribute factors with expert and experiential knowledge about assets to determine their values. We exploit the mathematical theory of fuzzy logic that can be used to model and quantify human expertise and experiential knowledge. Our approach starts by modeling experts' experiential knowledge about assets and their properties as fuzzy variables. Then we use a fuzzy inference system to translate that knowledge into an asset value. Our results show asset values that are a close match with what an experienced expert would infer local asset values to be.

关键词： Alert Correlation Detection Multi-sensor

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