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

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Leveraging Provenance to Improve Data fusion in sensor Networks

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

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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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Practical Considerations in Bayesian fusion of Point sensors

Practical Considerations in Bayesian Fusion of Point Sensors

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

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

ISBN: (纸本)9780819490858

sensor data fusion is and has been a topic of considerable research, but rigorous and quantitative understanding of the benefits of fusing specific types of sensor data remains elusive. Often, sensor fusion is performed on an ad hoc basis with the assumption that overall detection capabilities will improve, only to discover later, after expensive and time consuming laboratory and/or field testing that little advantage was gained. The work presented here will discuss these issues with theoretical and practical considerations in the context of fusing chemical sensors with binary outputs. Results are given for the potential performance gains one could expect with such systems, as well as the practical difficulties involved in implementing an optimal Bayesian fusion strategy with realistic scenarios. Finally, a discussion of the biases that inaccurate statistical estimates introduce into the results and their consequences is presented.

关键词： sensor fusion Bayes decision theory

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Spectral and Spatial Algorithm Architecture for Classification of Hyperspectral and LIDAR

Spectral and Spatial Algorithm Architecture for Classificati...

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

作者： Rand, Robert S. Khuon, Timothy S. Natl Geospatial Intelligence Agcy Off Innovis Bas & Appl Res Springfield VA 22153 USA

ISBN: (纸本)9780819490858

Architecture for neural net multi-sensor data fusion is introduced and analyzed. This architecture consists of a set of independent sensor neural nets, one for each sensor, coupled to a fusion net. The neural net of each sensor is trained (from a representative data set of the particular sensor) to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. To begin the processing, the 3D point cloud LIDAR data is classified based on a multi-dimensional mean-shift segmentation and classification into clustered objects. Similarly, the multi-band HSI data is spectrally classified by the Stochastic Expectation-Maximization (SEM) into a classification map containing pixel classes. For sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The first layer is the sensor level consisting of two neural nets: spatial neural net and spectral neural net. The second level consists of a single neural net, that is the fusion neural net. The success of the system in utilizing sensor synergism for an enhanced classification is clearly demonstrated by applying this architecture for classifying on November 2010 airborne data collection of LIDAR and HSI over the Gulfport, MS, area.

关键词： sensor fusion Architecture Neural Net HSI LIDAR Classification Mean-shift Stochastic Expectation-Maximization Imagery Back-propagation Perceptron Probability of detection and false alarm

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Multirobot autonomous landmine detection using distributed multisensor information aggregation

Multirobot autonomous landmine detection using distributed m...

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

作者： Jumadinova, Janyl Dasgupta, Prithviraj Univ Nebraska Omaha NE 68182 USA

ISBN: (纸本)9780819490858

We consider the problem of distributed sensor information fusion by multiple autonomous robots within the context of landmine detection. We assume that different landmines can be composed of different types of material and robots are equipped with different types of sensors, while each robot has only one type of landmine detection sensor on it. We introduce a novel technique that uses a market-based information aggregation mechanism called a prediction market. Each robot is provided with a software agent that uses sensory input of the robot and performs calculations of the prediction market technique. The result of the agent's calculations is a 'belief' representing the confidence of the agent in identifying the object as a landmine. The beliefs from different robots are aggregated by the market mechanism and passed on to a decision maker agent. The decision maker agent uses this aggregate belief information about a potential landmine and makes decisions about which other robots should be deployed to its location, so that the landmine can be confirmed rapidly and accurately. Our experimental results show that, for identical data distributions and settings, using our prediction market-based information aggregation technique increases the accuracy of object classification favorably as compared to two other commonly used techniques.

关键词： Prediction market information fusion multi-sensor aggregation landmine detection

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Agent-based analysis of trustworthiness in wireless sensor networks

Agent-based analysis of trustworthiness in wireless sensor n...

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

作者： Fernandes, Ronald Li, Biyan Vadakkeveedu, Kalyan Verma, Ajay Gustafson, Paul Hwang, Jong Knowledge Based Syst Inc 1408 Univ Dr E College Stn TX 77840 USA Air Force Lab Dayton OH 45433 USA

ISBN: (纸本)9780819490858

Information assurance is a critical component of any organization's data network. Trustworthiness of the sensor data, especially in the case of wireless sensor networks (WSNs), is an important metric for any application that requires situational awareness. In a WSN, information packets are typically not encrypted and the nodes themselves could be located in the open, leaving them susceptible to tampering and physical degradation. In order to develop a method to assess trustworthiness in WSNs, we have utilized statistical trustworthiness metrics and have implemented an agent-based simulation platform that can perform various trustworthiness measurement experiments for various WSN operating scenarios. Different trust metrics are used against multiple vulnerabilities to detect anomalous behavior and node failure as well as malicious attacks. The simulation platform simulates WSNs with various topologies, routing algorithms, battery and power consumption models, and various types of attacks and defense mechanisms. Additionally, we adopt information entropy based techniques to detect anomalous behavior. Finally, detection techniques are fused to provide various metrics, and various trustworthiness metrics are fused to provide aggregate trustworthiness for the purpose of situational awareness.

关键词： agent-based simulation trust and trustworthiness wireless sensor networks NetLogo simulation

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Colour-the-INSight: combining a direct view rifle sight with fused intensified and thermal imagery

Colour-the-INSight: combining a direct view rifle sight with...

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

作者： Hogervorst, M. A. Jansen, C. Toet, A. Bijl, P. Bakker, P. Hiddema, A. C. van Vliet, S. F. TNO Human Factors POB 23 NL-3769 GZ Soesterberg Netherlands Thales Netherland BV Huizen Netherlands Photonis Netherlands BV NL-9300 AB Groningen Netherlands Minist Def Joint Soldier & Equipment Ctr Expertise The Hague Netherlands

ISBN: (纸本)9780819490858

We present the design and evaluation of a new demonstrator rifle sight viewing system containing direct view, red aim point and fusion of an (uncooled, LWIR) thermal sensor with a digital image intensifier. Our goal is to create a system that performs well under a wide variety of (weather) conditions during daytime and nighttime and combines the advantages of the various sensor systems. A real-time colour image with salient hot targets is obtained from the night vision sensors by implementing the Colour-the-Night fusion method (Hogervorst & Toet, 2010) on the on-board processor. The prototype system was evaluated in a series of field trials with military observers performing detection and identification tasks. The tests showed that during daytime the addition of a thermal image to direct vision is advantageous, e. g. for the detection of hot targets. At nighttime, the fusion of thermal and image intensified imagery results in increased situational awareness and improved detection of (hot) targets. For identification of small (handheld) objects, the technology needs to be further refined

关键词： image fusion false colour natural colour mapping real-time fusion night vision

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Human cognitive and perceptual factors in JDL level 4 hard/soft data fusion

Human cognitive and perceptual factors in JDL level 4 hard/s...

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

作者： Rimland, Jeffrey C. Hall, David L. Graham, Jacob L. Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

ISBN: (纸本)9780819490858

Utilization of human participants as "soft sensors" is becoming increasingly important for gathering information related to a wide range of phenomena including natural and man-made disasters, environmental changes over time, crime prevention, and other roles of the "citizen scientist." The ubiquity of advanced mobile devices is facilitating the role of humans as "hybrid sensor platforms", allowing them to gather data (e. g. video, still images, GPS coordinates), annotate it based on their intuitive human understanding, and upload it using existing infrastructure and social networks. However, this new paradigm presents many challenges related to source characterization, effective tasking, and utilization of massive quantities of physical sensor, human-based, and hybrid hard/soft data in a manner that facilitates decision making instead of simply amplifying information overload. In the Joint Directors of Laboratories (JDL) data fusion process model, "level 4" fusion is a meta-process that attempts to improve performance of the entire fusion system through effective source utilization. While there are well-defined approaches for tasking and categorizing physical sensors, these methods fall short when attempting to effectively utilize a hybrid group of physical sensors and human observers. While physical sensor characterization can rely on statistical models of performance (e. g. accuracy, reliability, specificity, etc.) under given conditions, "soft" sensors add the additional challenges of characterizing human performance, tasking without inducing bias, and effectively balancing strengths and weaknesses of both human and physical sensors. This paper addresses the challenges of the evolving human-centric fusion paradigm and presents cognitive, perceptual, and other human factors that help to understand, categorize, and augment the roles and capabilities of humans as observers in hybrid systems.

关键词： data fusion soft data fusion participatory sensing tasking cognition perception human factors

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Using arm and hand gestures to command robots during stealth operations

Using arm and hand gestures to command robots during stealth...

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

作者： Stoica, Adrian Assad, Chris Wolf, Michael You, Ki Sung Pavone, Marco Huntsberger, Terry Iwashita, Yumi CALTECH Jet Prop Lab Pasadena CA 91109 USA

ISBN: (纸本)9780819490858

Command of support robots by the warfighter requires intuitive interfaces to quickly communicate high degree-of-freedom (DOF) information while leaving the hands unencumbered. Stealth operations rule out voice commands and vision-based gesture interpretation techniques, as they often entail silent operations at night or in other low visibility conditions. Targeted at using bio-signal inputs to set navigation and manipulation goals for the robot (say, simply by pointing), we developed a system based on an electromyography (EMG) "BioSleeve", a high density sensor array for robust, practical signal collection from forearm muscles. The EMG sensor array data is fused with inertial measurement unit (IMU) data. This paper describes the BioSleeve system and presents initial results of decoding robot commands from the EMG and IMU data using a BioSleeve prototype with up to sixteen bipolar surface EMG sensors. The BioSleeve is demonstrated on the recognition of static hand positions (e. g. palm facing front, fingers upwards) and on dynamic gestures (e. g. hand wave). In preliminary experiments, over 90% correct recognition was achieved on five static and nine dynamic gestures. We use the BioSleeve to control a team of five LANdroid robots in individual and group/squad behaviors. We define a gesture composition mechanism that allows the specification of complex robot behaviors with only a small vocabulary of gestures/commands, and we illustrate it with a set of complex orders.

关键词： Human-robot interfaces gesture recognition electromyography EMG sensor arrays stealth operations

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Allothetic and Idiothetic sensor fusion in Rat-inspired Robot Localization

Allothetic and Idiothetic Sensor Fusion in Rat-inspired Robo...

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

作者： Weitzenfeld, Alfredo Fellous, Jean-Marc Barrera, Alejandra Tejera, Gonzalo Univ South Florida Polytech Div Informat Technol 3433 Winter Lake Rd Lakeland FL 33803 USA Univ Arizona Dept Psychol & Applied Mat Tucson AZ 85721 USA Dept Comp Engn Inst Technol Autonomo Mexico Mexico City 01080 DF Mexico Univ Republica Inst Comp Montevideo 11300 Uruguay

ISBN: (纸本)9780819490858

We describe a spatial cognition model based on the rat's brain neurophysiology as a basis for new robotic navigation architectures. The model integrates allothetic (external visual landmarks) and idiothetic (internal kinesthetic information) cues to train either rat or robot to learn a path enabling it to reach a goal from multiple starting positions. It stands in contrast to most robotic architectures based on SLAM, where a map of the environment is built to provide probabilistic localization information computed from robot odometry and landmark perception. Allothetic cues suffer in general from perceptual ambiguity when trying to distinguish between places with equivalent visual patterns, while idiothetic cues suffer from imprecise motions and limited memory recalls. We experiment with both types of cues in different maze configurations by training rats and robots to find the goal starting from a fixed location, and then testing them to reach the same target from new starting locations. We show that the robot, after having pre-explored a maze, can find a goal with improved efficiency, and is able to (1) learn the correct route to reach the goal, (2) recognize places already visited, and (3) exploit allothetic and idiothetic cues to improve on its performance. We finally contrast our biologically-inspired approach to more traditional robotic approaches and discuss current work in progress.

关键词： robotics biologically-inspired spatial cognition navigation localization SLAM

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Advances in data representation for hard/soft information fusion

Advances in data representation for hard/soft information fu...

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

作者： Rimland, Jeffrey C. Coughlin, Dan Hall, David L. Graham, Jacob L. Penn State Univ Coll IST 101IST Bldg University Pk PA 16802 USA Penn State Univ Paterno Lib University Pk PA 16802 USA

ISBN: (纸本)9780819490858

Information fusion is becoming increasingly human-centric. While past systems typically relegated humans to the role of analyzing a finished fusion product, current systems are exploring the role of humans as integral elements in a modular and extensible distributed framework where many tasks can be accomplished by either human or machine performers. For example, "participatory sensing" campaigns give humans the role of "soft sensors" by uploading their direct observations or as "soft sensor platforms" by using mobile devices to record human-annotated, GPS-encoded high quality photographs, video, or audio. Additionally, the role of "human-in-the-loop", in which individuals or teams using advanced human computer interface (HCI) tools such as stereoscopic 3D visualization, haptic interfaces, or aural "sonification" interfaces can help to effectively engage the innate human capability to perform pattern matching, anomaly identification, and semantic-based contextual reasoning to interpret an evolving situation. The Pennsylvania State University is participating in a Multi-disciplinary University Research Initiative (MURI) program funded by the U. S. Army Research Office to investigate fusion of hard and soft data in counterinsurgency (COIN) situations. In addition to the importance of this research for Intelligence Preparation of the Battlefield (IPB), many of the same challenges and techniques apply to health and medical informatics, crisis management, crowd-sourced "citizen science", and monitoring environmental concerns. One of the key challenges that we have encountered is the development of data formats, protocols, and methodologies to establish an information architecture and framework for the effective capture, representation, transmission, and storage of the vastly heterogeneous data and accompanying metadata -- including capabilities and characteristics of human observers, uncertainty of human observations, "soft" contextual data, and information pedigree. Thi

关键词： hard and soft information fusion hybrid cognition service oriented architecture SOA ESB

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