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MTS in False Positive Reduction for Multi-sensor fusion

MTS in False Positive Reduction for Multi-Sensor Fusion

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

作者： Woodley, Robert Gosnell, Michael Cudney, Elizabeth 21st Century Syst Inc Omaha NE 68106 USA

ISBN: (纸本)9781628410587

The Mahalanobis Taguchi System (MTS) is a relatively new tool in the vehicle health maintenance domain, but has some distinct advantages in current multi-sensor implementations. The use of Mahalanobis Spaces (MS) allows the algorithm to identify characteristics of sensor signals to identify behaviors in machines. MTS is extremely powerful with the caveat that the correct variables are selected to form the MS. In this research work, 56 sensors monitor various aspects of the vehicles. Typically, using the MTS process, identification of useful variables is preceded by validation of the measurements scale. However, the MTS approach doesn't directly include any mitigating steps should the measurement scale not be validated. Existing work has performed outlier removal in construction of the MS, which can lead to better validation. In our approach, we modify the outlier removal process with more liberal definitions of outliers to better identify variables' impact prior to identification of useful variables. This subtle change substantially lowered the false positive rate due to the fact that additional variables were retained. Traditional MTS approaches identify useful variables only to the extent they provide usefulness in identifying the positive (abnormal) condition. The impact of removing false negatives is not included. Initial results show our approach can reduce false positive values while still maintaining complete fault identification for this vehicle data set.

关键词： Mahalanobis-Taguchi System Multi-sensor fusion Variable Selection Threshold Selection Vehicle Health Maintenance Fault Detection False Positive Reduction Comparison of Methods

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Human-autonomy sensor fusion for rapid object detection

Human-autonomy sensor fusion for rapid object detection

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IEEE/RSJ International conference on Intelligent Robots and Systems

作者： R. M. Robinson Hyungtae Lee M. J. McCourt A. R. Marathe Heesung Kwon Chau Ton W. D. Nothwang Sensors & Electron Devices Directorate Army Research Laboratory Adelphi MD USA Research and Engineering Education Facility University of Florida Shalimar FL USA Human Research & Engineering Directorate Army Research Laboratory Aberdeen Proving Ground MD USA

Human-autonomy sensor fusion is an emerging technology with a wide range of applications, including object detection/recognition, surveillance, collaborative control, and prosthetics. For object detection, humans and computer-vision-based systems employ different strategies to locate targets, likely providing complementary information. However, little effort has been made in combining the outputs of multiple autonomous detectors and multiple human-generated responses. This paper presents a method for integrating several sources of human- and autonomy-generated information for rapid object detection tasks. Human electroencephalography (EEG) and button-press responses from rapid serial visual presentation (RSVP) experiments are fused with outputs from trained object detection algorithms. Three fusion methods-Bayesian, Dempster-Shafer, and Dynamic Dempster-Shafer-are implemented for comparison. Results demonstrate that fusion of these human classifiers with computer-vision-based detectors improves object detection accuracy over purely computer-vision-based detection (5% relative increase in mean average precision) and the best individual computer vision algorithm (28% relative increase in mean average precision). Computer vision fused with button press response and/or the XDAWN + Bayesian Linear Discriminant Analysis neural classifier provides considerable improvement, while computer vision fused with other neural classifiers provides little or no improvement. Of the three fusion methods, Dynamic Dempster-Shafer Theory (DDST) fusion exhibits the greatest performance in this application.

关键词： Detectors Object detection Presses Training Electroencephalography Computer vision Heuristic algorithms

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Implementation of Fog computing for reliable E-health applications

Implementation of Fog computing for reliable E-health applic...

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Asilomar conference on Signals, Systems & Computers

作者： Razvan Craciunescu Albena Mihovska Mihail Mihaylov Sofoklis Kyriazakos Ramjee Prasad Simona Halunga Center for TeleInfrastruktur Aalborg University Telecommunication Department University Politehnica of Bucharest

ISBN: (纸本)9781467385770

This paper addresses the current technical challenge of an impedance mismatch between the requirements of smart connected object applications within the sensing environment and the characteristics of today's cloud infrastructure. We investigate the possibility to offload cloud tasks, such as storage and data signal processing to the edge of the network, thus decreasing the latency associated with performing those tasks within the cloud. The research scenario is an e-Health laboratory implementation where the real-time processing is performed by the home PC, while the extracted metadata is sent to the cloud for further processing.

关键词： Real-time systems Metadata Detection algorithms Gas detectors sensor fusion Biomedical monitoring

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Towards a unified architecture for mapping static environments 17

Towards a unified architecture for mapping static environmen...

引用

17th International conference on Information fusion (fusion)

作者： Kubertschak, Tim Maehlisch, Mirko Wuensche, Hans-Joachim AUDI AG I EE 31 D-85045 Ingolstadt Germany Univ Bundeswehr Munchen TAS D-85577 Neubiberg Germany

ISBN: (纸本)9781479916344

sensor fusion systems are becoming more and more important in automotive applications, especially for future driver assistance systems and autonomous driving. While it was sufficient to use single sensors in early driving assistance systems, future systems will rely on several sensors with different measurement principles. The probably conflicting measurements need to be fused to retrieve a complete picture of the current surroundings. However, an increased number of sensors leads to difficulties while incorporating their measurements, since every sensor provides its data via special interfaces. This inconvenience of current sensor fusion architectures has been solved for moving objects. A fusion architecture based on a generic interface the object list - has been developed over the last years. It allows an easy use of additional sensors as long as they provide their measurements as object lists. Similar solutions for static environments are rare, but the recent proposal of the Fences approach [1] is promising. This work shows, how sensors and occupancy grids are represented in the Fences-architecture. Steps and algorithms are presented to transform sensor reading into Fences as well as extracting Fences from common grid-based strategies for mapping static environments. The algorithms are applied to the conversion of ultrasonic sensor readings and LiDAR measurements to Fences. Furthermore, the construction of Fences from Bayesian occupancy grids is presented.

关键词： Advanced Driving Assistance Systems Multisensor Data fusion Static Environment Unified Architecture

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Multi-sensor Data fusion in Wireless sensor Networks for Planetary Exploration

Multi-Sensor Data Fusion in Wireless Sensor Networks for Pla...

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NASA/ESA conference on Adaptive Hardware and Systems (AHS)

作者： Zhai, Xiaojun Jing, Hongyuan Vladimirova, Tanya Univ Leicester Dept Engn Leicester LE1 7RH Leics England

ISBN: (纸本)9781479953561

The SWIPE (Space Wireless sensor Networks for Planetary Exploration) project uses Wireless sensor Networks (WSN) to characterise the surface of the Moon. The envisaged scenario is that hundreds of small wireless sensor nodes dropped onto the Moon surface will collect scientific measurements. An ad-hoc WSN connecting these nodes will propagate the measurement data to sink nodes for uploading to a lunar orbiter and a subsequent transmission to Earth. The data gathered from the sensors will be processed using state-of-the-art data fusion techniques to overcome the restricted energy and bandwidth resources. In this paper, we first provide a short survey of classical data fusion techniques for WSNs. We then introduce data fusion architectures for the SWIPE project. Building on this, we propose data processing algorithms that enable energy conservation and processing efficiency in the proposed SWIPE architectures. The proposed algorithms are evaluated via a series of simulation models. The results show that the proposed algorithms can efficiently reduce the amount of the transmitted scientific data providing a good level of accuracy in the data reconstruction. Furthermore, they are able to correctly evaluate the node health status.

关键词： wireless sensor network data fusion planetary exploration

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Comparison of Pixel-level and feature level image fusion methods

Comparison of Pixel-level and feature level image fusion met...

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International conference on Computing for Sustainable Global Development (INDIACom)

作者： D. Egfin Nirmala V. Vaidehi Department of Electronics Madras Institute of technology Chennai India AU-KBC Research Centre Madras Institute of technology Chennai India

ISBN: (纸本)9781479968329

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： Support vector machines Image fusion Feature extraction Discrete wavelet transforms Fuzzy logic Entropy

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

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

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International conference on Computer Vision Workshops (ICCV Workshops)

作者： Nicholas Brunetto Samuele Salti Nicola Fioraio Tommaso Cavallari Luigi Di Stefano Luigi Di Stefano Department of Computer Science and Engineering University of Bologna Bologna Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste Trieste Friuli-Venezia Giulia IT

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

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TDA2X, A SOC OPTIMIZED FOR ADVANCED DRIVER ASSISTANCE SYSTEMS

TDA2X, A SOC OPTIMIZED FOR ADVANCED DRIVER ASSISTANCE SYSTEM...

引用

IEEE International conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Sankaran, Jagadeesh Zoran, Nikolic Texas Instruments Inc Dallas TX 13532 USA

ISBN: (纸本)9781479928934

TDA2X is an optimized scalable system on chip (SoC) solution from Texas Instruments that spans various application areas of ADAS such as front-camera, surround-view and the emerging area of sensor fusion. It accomplishes this through a focused set of heterogeneous processors, brought together in a scalable architecture with a rich set of integrated peripherals, providing an optimal mix of performance in a low power footprint for Advanced Driver Assistance Systems (ADAS) vision analytics. Computer vision algorithms across the various ADAS application systems have a rich variation and diversity in processing requirements along with the need to run them concurrently within challenging thermal budgets. A heterogeneous architecture with various programmable elements allows system developers to map various portions of the algorithms to the architectures that are best suited for the underlying task allowing maximizing system performance and reducing development time and effort in developing these complex systems. Scalability of the architecture by varying the number of cores and clock speeds of these heterogeneous architectures, allows for scalability in performance and power across low, mid and high end products with one software investment. A critical focus on functional safety across the cores and various memories is particularly essential given the mission critical nature of ADAS applications.

关键词： ADAS SoC Front Camera Surround View sensor fusion and Programmable Vision Accelerators

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Genetic Algorithm Based Cluster Head Optimization Using Topology Control For Hazardous Environment Using WSN

Genetic Algorithm Based Cluster Head Optimization Using Topo...

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International conference on Innovations in Information, Embedded and Communication Systems

作者： S. Emalda Roslin Department of Electronics and Control Engineering Sathyabama University

ISBN: (纸本)9781479968190

Wireless sensor Network (WSN) is widely used in recent years for the applications where human intervention is impossible. In case of nuclear power plant if any small delay occurs for data forwarding due to any node failure may results in severe disaster. Hence effective Topology Control is required to obtain an energy efficient sensor network even if any node fails. An efficient topology control using genetic algorithm based cluster head selection is presented in this paper. In this work, three tier sensor network architecture is developed and is comprised of Super Head nodes, Cluster Head nodes and Cluster Slave nodes. The results obtained for the developed three tier architecture is compared with two tier and one tier architectures. Residual Energy, Bandwidth and Memory Capacity are used as selection criteria. Quantitative analysis is also carried out to study the impact of N-tiers on the performance of the proposed algorithms. From the quantitative analysis of the proposed methodologies on the N-tier architecture with various node densities, it has been proved that two tier architecture provides reduced energy consumption compared to others.

关键词： Wireless sensor Network Genetic algorithm Topology control N tier architecture

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

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

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IEEE/ACS International conference on Computer Systems and applications

作者： Frederic Lehmann Dina Chaal Abdelouahid Lyhyaoui LTI Laboratory National School of Applied Sciences ENSA-Tangier Abdelmalek Essaadi Universy Institut Mines-Telecom Telecom SudParis department CITI UMR-CNRS 5157 SAMOVAR France

ISBN: (纸本)9781509004799

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 eirors (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： Bayesians models Data fusion detection learning methods nonlinear signal processing

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