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

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Hardware-aware performance evaluation for the co-design of image sensors and vision algorithms 13

Hardware-aware performance evaluation for the co-design of i...

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13th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and applications to Circuit Design, SMACD 2016

作者： villegas-Pachón, C. Carmona-Galán, R. Fernández-Berni, J. Rodríguez-vázquez, Á. CSIC-University of Seville Spain

ISBN: (纸本)9781509004904

The top-down approach to system design allows obtaining separate specifications for each subsystem. In the case of vision systems, this means propagating system-level specifications down to particular specifications for e. g. the image sensor, the image processor, etc. This permits to adopt different design strategies for each one of them, as long as they meet their own specifications. This approach can lead to over-design, which is not always affordable. Conversely, if higher-level specifications are too tight, they can lead to impossible specifications at the lower levels. This is certainly the case for embedded vision systems in which high-performance needs to be paired with a very restricted power budget. In order to explore alternative architectures, we need tools that allow for simultaneous optimization of different blocks. However, the link between low-level non-idealities and high-level performance is missing. CAD tools for the design and verification of analog and mixed-signal integrated circuits are not well suited for the simulation of higher-level functionalities. Our approach is to extract relevant data from circuit-level simulation and to build an OpenCv model to be employed in the design of the algorithm. The utility of this approach is illustrated by the evaluation of the effect of column-wise and pixel-wise FPN at the sensor on the performance of viola-Jones face detection. © 2016 IEEE.

关键词： Specifications

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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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Real Time Cooperative Localization for Autonomous vehicles

Real Time Cooperative Localization for Autonomous Vehicles

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IEEE International Conference on Intelligent Transportation Systems

作者： Farid Bounini Denis Gingras Herve Pollart Dominique Gruyer LIV - Université de Sherbrooke Canada OPAL-RT Technologies Inc Montreal Canada LIVIC IFSTTAR France

ISBN: (纸本)9781509018901

This paper describes a new strategy for real-time cooperative localization of autonomous vehicles. The strategy aims to improve the vehicles localization accuracy and reduce the impact of computing time of multi-sensor data fusion algorithms and vehicle-to-vehicle communication on parallel architectures. The method aims to solve localization issues in a cluster of autonomous vehicles, equipped with low-cost navigation systems in an unknown environment. It stands on multiple forms of the Kalman filter derivatives to estimate the vehicles' nonlinear model vector state, named local fusion node. The vehicles exchange their local state estimate and Covariance Intersection algorithm for merging the local vehicles' state estimate in the second node (named global data fusion node). This strategy simultaneously exploits the proprioceptive and sensors -a Global Positioning System, and a vehicle-to-vehicle transmitter and receiver - and an exteroceptive sensor, range finder, to sense their surroundings for more accurate and reliable collaborative localization.

关键词： cooperative localization Data fusion extended Kalman filter covariance intersection ADAS Transportation System autonomous vehicles v2v communication

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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

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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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Cooperative fusion for Road obstacles detection using Laser Scanner and Camera

Cooperative Fusion for Road obstacles detection using Laser ...

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World Congress on Intelligent Control and Automation

作者： Shashibushan Yenkanchi Q. M. Jonathan Wu Department of Electrical and Computer Engineering University of Windsor Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781467384155

In order to account for robustness of Automotive safety applications, fusion of data from multiple sensors is of remarkable importance to know the position of road obstacles. Challenges arise in Multi sensor Data fusion (MSDF) due to sensor uncertainty, multiple occluding targets and clutter by changing weather conditions. The proposed architecture address the problem by fusing information cooperatively from Laser scanner and monocular camera for robust detection of scene objects in the vehicle environment. The fusion steps in the proposed method involve the application of the Mestimator Sample Consensus (MSAC) algorithm for ground plane removal and density based clustering of laser data. Then the filtered laser objects are projected on the image plane and the corresponding region of interest (ROI) is extracted to localize the potential targets. Experimental results on challenging scene sequences of benchmark data sets prove the robustness of proposed fusion architecture for detecting vehicles on the road.

关键词： vehicles Clustering algorithms Roads sensors Laser fusion Cameras

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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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Real Time Transducer Signal Features Extraction: A Standard Approach 13

Real Time Transducer Signal Features Extraction: A Standard ...

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13th IEEE International Conference on Industrial Informatics (INDIN)

作者： Monte, G. Huang, v. Abate, F. Paciello, v. Pietrosanto, A. Univ Tecnol Nacl Fac Reg Neuquen Plaza Huincul Argentina Better World IES Stand Mishawaka IN USA Univ Salerno Dept Ind Engn I-84084 Fisciano SA Italy

ISBN: (纸本)9781479966493

This paper presents a real time algorithm, based on a segmentation and labelling technique, for analog transducer signals, which extracts information from the signal. The process of sampling is extremely important because it is the only one that connects the real world with the digital world. In addition, the proposed technique can be embedded into sensors, allowing applications in sensor networks and sensors data fusion architectures like Internet of Things. A simplified version of these algorithms has been proposed as a standard for transducer signal feature extraction. These algorithms are analysed and experimental results are tackled for representative signals, in different fields.

关键词： sampling sensor networks IoT standards real time smart sensors

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