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Financialized methods for market-based multi-sensor fusion

Financialized methods for market-based multi-sensor fusion

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Abernethy, Jacob Johnson-Roberson, Matthew Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

ISBN: (纸本)9781479999941

Autonomous systems rely on an increasing number of input sensors of various modalities, and the problem of sensor fusion has received attention for many years. Autonomous system architectures are becoming more complex with time, and the number and placement of sensors will be modified regularly, sensors will fail for many reasons, information will arrive asynchronously, and the system will need to adjust to rapidly changing environments. To address these issues we propose a new paradigm for fusing information from multiple sources that draws from the rich of field pertaining to financial markets, particularly recent research on prediction market design. Among the many benefits of this financialized approach is that, both in theory and in practice, markets are well-equipped to robustly synthesize information from diverse sources in a decentralized fashion. Our framework poses sensor processing algorithms as profit-seeking market participants, data is incorporated via financial transactions, and the joint estimation is represented as a price equilibrium. We use pedestrian detection as a motivating application. Pedestrian detection is a well studied field and essential to autonomous driving. Real world fusion results are presented on RGB and LIDAR data from the KITTI Vision Benchmark Suite. We demonstrate we can achieve comparable performance to state-of-the-art hand designed fusion techniques using the proposed approach.

关键词： Commerce

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Proposition of a Bayesian model for the propagation of the information in a wireless sensor network 12

Proposition of a Bayesian model for the propagation of the i...

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12th IEEE/ACS International conference of Computer Systems and applications (AICCSA)

作者： Lehmann, Frederic Chaal, Dina Lyhyaoui, Abdelouahid Abdelmalek Essaadi Univ ENSA Tangier Natl Sch Appl Sci LTI Lab Tetouan Morocco Telecom SudParis Inst Mines Telecom CNRS Dept CITIUMR 5157SAMOVAR Evry France

ISBN: (纸本)9781509004782

Wireless sensor networks (WSN) are specially distributed sensors deployed for the purpose of sensing and monitoring physical or environmental conditions. sensor measurements in WSN usually suffer from both random errors (noise) and systematic errors (bias). This poses a major problem in the end application, as the data from the network become progressively uninformative. This work proposes a model of spatial and temporal variations of the state of any scalar phenomenon under study. We present a novel estimation approach of the bias based on a message passing algorithm, in order to calibrate the network. The algorithm is designed to solve inference problems in a Bayesian network, which is a probabilistic graphical model that represents a set of random variables and their conditional dependencies. Therefore it is a technique for representing the dependencies between the hidden variables and noisy observations as a graphical model. In our case, the hidden variables that we want to estimate are formed by the state of the scalar phenomenon to be studied, and the measurements taken by the sensors act as noisy observations. So we seek to develop message passing algorithms representing the exchange of information between sensors in a distributed manner.

关键词： Data fusion Bayesians models learning methods nonlinear signal processing detection

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i-TEAMS: Cooperative GNC for small UAV formations 2

i-TEAMS: Cooperative GNC for small UAV formations

引用

2nd IEEE International Workshop on Metrology for Aerospace

作者： Vetrella, Amedeo Rodi Fasano, Giancarmine Accardo, Domenico Univ Naples Federico II Dept Ind Engn Naples Italy

ISBN: (纸本)9781479975693

The research project named "i-TEAMS" (innovative Techniques for Autonomous Micro-UAV Swarms) aims at developing new architectures and technologies for distributed guidance, navigation, and control of mini and/or micro Unmanned Aircraft Systems. Main research topics and the project strategy are described. Then, the basic logical steps of innovative cooperative navigation algorithms, and measurement uncertainty budgets, are provided. Experimental results demonstrate effective vision-based tracking and promising performance in terms of attitude estimation based on differential GPS and relative sensing.

关键词： Unmanned Aircraft Systems Cooperative Guidance Navigation and Control sensor fusion Vision-based Tracking Cooperative GNSS applications

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Cross-modal face recognition using multi-matcher face scores 24

Cross-modal face recognition using multi-matcher face scores

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conference on Signal Processing, sensor/Information fusion, and Target Recognition XXIV

作者： Zheng, Yufeng Blasch, Erik Alcorn State Univ Lorman MS 39096 USA US Air Force Res Lab Rome NY USA

ISBN: (纸本)9781628415902

The performance of face recognition can be improved using information fusion of multimodal images and/or multiple algorithms. When multimodal face images are available, cross-modal recognition is meaningful for security and surveillance applications. For example, a probe face is a thermal image (especially at nighttime), while only visible face images are available in the gallery database. Matching a thermal probe face onto the visible gallery faces requires cross-modal matching approaches. A few such studies were implemented in facial feature space with medium recognition performance. In this paper, we propose a cross-modal recognition approach, where multimodal faces are cross-matched in feature space and the recognition performance is enhanced with stereo fusion at image, feature and/or score level. In the proposed scenario, there are two cameras for stereo imaging, two face imagers (visible and thermal images) in each camera, and three recognition algorithms (circular Gaussian filter, face pattern byte, linear discriminant analysis). A score vector is formed with three cross-matched face scores from the aforementioned three algorithms. A classifier (e.g., k-nearest neighbor, support vector machine, binomial logical regression [BLR]) is trained then tested with the score vectors by using 10-fold cross validations. The proposed approach was validated with a multispectral stereo face dataset from 105 subjects. Our experiments show very promising results: ACR (accuracy rate) = 97.84%, FAR (false accept rate) = 0.84% when cross-matching the fused thermal faces onto the fused visible faces by using three face scores and the BLR classifier.

关键词： Cross-modal face recognition stereo fusion face pattern byte (FPB) multispectral face recognition score fusion binomial logical regression

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Platform-dependent optimization considerations for mHealth applications

Platform-dependent optimization considerations for mHealth a...

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conference on Mobile Devices and Multimedia: Enabling Technologies, algorithms, and applications

作者： Kaghyan, Sahak Akopian, David Sarukhanyan, Hakob NAS RA Inst Informat & Automat Problems Yerevan Armenia Univ Texas San Antonio San Antonio TX USA

ISBN: (纸本)9781628415018

Modern mobile devices contain integrated sensors that enable multitude of applications in such fields as mobile health (mHealth), entertainment, sports, etc. Human physical activity monitoring is one of such the emerging applications. There exists a range of challenges that relate to activity monitoring tasks, and, particularly, exploiting optimal solutions and architectures for respective mobile software application development. This work addresses mobile computations related to integrated inertial sensors for activity monitoring, such as accelerometers, gyroscopes, integrated global positioning system (GPS) and WLAN-based positioning, that can be used for activity monitoring. Some of the aspects will be discussed in this paper. Each of the sensing data sources has its own characteristics such as specific data formats, data rates, signal acquisition durations etc., and these specifications affect energy consumption. Energy consumption significantly varies as sensor data acquisition is followed by data analysis including various transformations and signal processing algorithms. This paper will address several aspects of more optimal activity monitoring implementations exploiting state-of-the-art capabilities of modern platforms.

关键词： human activity classification energy consumption wearable sensors parallel tasks smartphones

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Comparison of Pixel-Level and Feature Level Image fusion Methods 2

Comparison of Pixel-Level and Feature Level Image Fusion Met...

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2nd International conference on Computing for Sustainable Global Development (INDIACom)

作者： Nirmala, D. Egfin Vaidehi, V. Madras Inst Technol Dept Elect Madras Tamil Nadu India Madras Inst Technol AU KBC Res Ctr Madras Tamil Nadu India

ISBN: (纸本)9789380544168

In recent times multiple imaging sensors are employed in several applications such as surveillance, medical imaging and machine vision. In these multi-sensor systems there is a need for image fusion techniques to effectively combine the information from disparate imaging sensors into a single composite image which enables a good understanding of the scene. The prevailing fusion algorithms employ either the mean or choose-max fusion rule for selecting the best coefficients for fusion at each pixel location. The choose-max rule distorts constants background information whereas the mean rule blurs the edges. Hence, in this proposed paper, the fusion rule is replaced by a soft computing technique that makes intelligent decisions to improve the accuracy of the fusion process in both pixel and feature based image fusion. Non Sub-sampled Contourlet Transform (NSCT) is employed for multi-resolution decomposition as it is demonstrated to capture the intrinsic geometric structures in images effectively. Experiments demonstrate that the proposed pixel and feature level image fusion methods provides better visual quality with clear edge information and objective quality indexes than individual multiresolution-based methods such as discrete wavelet transform and NSCT.

关键词： image fusion fuzzy logic non-subsampled contourlet transform segmentation fusion performance metrics multi resolution

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The Locus analytical framework for indoor localization and tracking applications 24

The Locus analytical framework for indoor localization and t...

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conference on Signal Processing, sensor/Information fusion, and Target Recognition XXIV

作者： Segou, Olga E. Thomopoulos, Stelios C. A. Natl Ctr Sci Res Demokritos Inst Informat & Telecommun Integrated Syst Lab GR-15310 Athens Greece

ISBN: (纸本)9781628415902

Obtaining location information can be of paramount importance in the context of pervasive and context-aware computing applications. Many systems have been proposed to date, e.g. GPS that has been proven to offer satisfying results in outdoor areas. The increased effect of large and small scale fading in indoor environments, however, makes localization a challenge. This is particularly reflected in the multitude of different systems that have been proposed in the context of indoor localization (e.g. RADAR, Cricket etc). The performance of such systems is often validated on vastly different test beds and conditions, making performance comparisons difficult and often irrelevant. The Locus analytical framework incorporates algorithms from multiple disciplines such as channel modeling, non-uniform random number generation, computational geometry, localization, tracking and probabilistic modeling etc. in order to provide: (a) fast and accurate signal propagation simulation, (b) fast experimentation with localization and tracking algorithms and (c) an in-depth analysis methodology for estimating the performance limits of any Received Signal Strength localization system. Simulation results for the well-known Fingerprinting and Trilateration algorithms are herein presented and validated with experimental data collected in real conditions using IEEE 802.15.4 ZigBee modules. The analysis shows that the Locus framework accurately predicts the underlying distribution of the localization error and produces further estimates of the system's performance limitations (in a best-case/worst-case scenario basis).

关键词： Localization Fingerprinting Trilateration Probabilistic Modeling Received Signal Strength Wireless Channel Computational Geometry

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An Efficient Spatial Query Processing Algorithm in Multi-sink Directional sensor Network 15th

An Efficient Spatial Query Processing Algorithm in Multi-sin...

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15th International conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Ma, Zheng Zheng, Jin Jia, Weijia Wang, Guojun Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Hunan Univ Sch Informat Sci & Engn Changsha 410082 Hunan Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Guangzhou Univ Sch Comp Sci & Educ Software Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783319271613;9783319271606

In order to address the problem of energy- and time-efficient execution of spatial queries in directional sensor networks, an efficient hybrid spatial query processing algorithm called SQPDSN is proposed in this paper. In the majority of studies on query processing using wireless sensor networks, sensors are assumed to have an isotropic sensing and transmission model. However, in certain applications the sensors have directional sensing and directional transmission model. SQPDSN only requires each node within the query region send data message once which reduces the data messages. For achieving minimal energy consumption and minimal response time, our query processing model ensures that only the relevant nodes for the correct execution of a query are involved in the query execution. Each sector has a node which collects the sensory data in it, aggregates the data to derive partial query result and send it to the next sector. Compared with other techniques, the experimental results demonstrated an improvement of the proposed technique in terms of energy efficient query cover with lower communication cost.

关键词： Multi-Sink wireless sensor networks Directional transmission Spatial query Parallel processing

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fusion of Inertial and Visual Measurements for RGB-D SLAM on Mobile Devices 15

Fusion of Inertial and Visual Measurements for RGB-D SLAM on...

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IEEE International conference on Computer Vision (ICCV)

作者： Brunetto, Nicholas Salti, Samuele Fioraio, Nicola Cavallari, Tommaso Di Stefano, Luigi Univ Bologna Dept Comp Sci & Engn Bologna Italy

ISBN: (纸本)9780769557205

Simultaneous Localization and Mapping (SLAM) algorithms have been recently deployed on mobile devices, where they can enable a broad range of novel applications. Nevertheless, pure visual SLAM is inherently weak at operating in environments with a reduced number of visual features. Indeed, even many recent proposals based on RGB-D sensors cannot handle properly such scenarios, as several steps of the algorithms are based on matching visual features. In this work we propose a framework suitable for mobile platforms to fuse pose estimations attained from visual and inertial measurements, with the aim of extending the range of scenarios addressable by mobile visual SLAM. The framework deploys an array of Kalman filters where the careful selection of the state variables and the preprocessing of the inertial sensor measurements result in a simple and effective data fusion process. We present qualitative and quantitative experiments to show the improved SLAM performance delivered by the proposed approach.

关键词： Cameras Kalman filters Mobile communication Mobile handsets Optimization Simultaneous localization and mapping Visualization

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Error Diagnosis for Energy Efficient Data Aggregation in Wireless sensor Networks 9

Error Diagnosis for Energy Efficient Data Aggregation in Wir...

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IEEE 9th International conference on Intelligent Systems and Control (ISCO)

作者： Ambekar, Chetan Shinde, Chirag Betkar, Swapnil Indulkar, Aniket Univ Mumbai KJ Somaiya COE Bombay Maharashtra India Univ Mumbai AC Patil COE Bombay Maharashtra India Univ Mumbai MHSCOE Bombay Maharashtra India

ISBN: (纸本)9781479964802

sensor networks are collection of sensor nodes which co-operatively send sensed data to base station. As sensor nodes are battery driven, an efficient utilization of power is essential in order to use networks for long duration hence it is needed to reduce data traffic inside sensor networks, also reduced amount of data need to send to base station. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. Wireless sensor networks (WSN) offer an increasingly sensor nodes need less power for processing as compared to transmitting data. It is preferable to do in network processing inside network and reduce packet size. One such approach is data aggregation which attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity. Wireless sensor networks have limited computational power and limited memory and battery power, this leads to increased complexity for application developers and often results in applications that are closely coupled with network protocols. In this paper, a data aggregation framework along with the algorithm on wireless sensor networks is presented. The aim of the proposed work consist a detailed study regarding the error which can occur by the data aggregation and also the circuit complexity which is generated for reduction of these errors.

关键词： cluster aggregation data energy efficient

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