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On Computing Multi-agent Itinerary Planning in Distributed Wireless sensor Networks 10th

On Computing Multi-agent Itinerary Planning in Distributed W...

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10th International conference on Wireless algorithms, Systems, and applications (WASA)

作者： Liu, Bo Cao, Jiuxin Yin, Jie Yu, Wei Liu, Benyuan Fu, Xinwen Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Towson Univ Dept Comp & Informat Sci Towson MD USA Univ Massachusetts Dept Comp Sci Lowell MA USA

ISBN: (纸本)9783319218373;9783319218366

The agent technology has been widely used in wireless sensor networks to perform data fusion and energy balancing. Existing multi-agent itinerary algorithms are either time-consuming, or too complicated to realize in reality. In this paper, we design a routing itinerary planning scheme for the multi-agent itinerary problem by constructing the spanning tree of WSN nodes. First, we build a multi-agent based distributed WSN (DWSN) model. Second, we present a novel routing itinerary algorithm named DMAIP, which can group all the sensor nodes into multiple itineraries for agents. We also extend DMAIP and design DMAIP-E, which can avoid long distance transmission in DMAIP. Our evaluation results demonstrate that our algorithms are better in the aspect of life cycle and energy consumption than the existing DWSN data collecting schemes.

关键词： Distributed wireless sensor network Mobile agent Itinerary planning Energy efficiency

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Fusing Social Network Data with Hard Data 18

Fusing Social Network Data with Hard Data

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18th International conference on Information fusion (fusion)

作者： Abirami, T. Taghavi, Ehsan Tharmarasa, R. Kirubarajan, T. Boury-Brisset, Anne-Claire McMaster Univ Hamilton ON Canada Def Res & Dev Canada Valcartier PQ Canada

ISBN: (纸本)9780982443866

Social networking sites such as Twitter, Facebook and Flickr play an important role in disseminating breaking news about natural disasters, terrorist attacks and other events. They serve as sources of first-hand information to deliver instantaneous news to the masses since millions of users visit these sites to post and read news items regularly. Hence, by exploring efficient mathematical techniques like Dempster-Shafer theory and Modified Dempster's rule of combination, we can process large amounts of data from these sites to extract useful information in a timely manner. In surveillance related applications, the objective of processing voluminous social network data is to predict events like revolutions and terrorist attacks before they unfold. By fusing the soft and often unreliable data from these sites with hard and more reliable data from sensors like radar and the Automatic Identification System (AIS), we can improve our event prediction capability. In this paper, we present a class of algorithms to fuse hard sensor data with soft social network data (tweets) in an effective manner. Preliminary results using real AIS data are also presented.

关键词： Dempster-Shafer belief theory Random finite set theory Modified Dempster's rule of combination soft and hard data fusion airborne surveillance of surface targets event prediction social data analysis

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Non-Subsampled Contourlet based Feature level fusion using Fuzzy Logic and Golden Section Algorithm for Multisensor Imaging Systems

Non-Subsampled Contourlet based Feature level Fusion using F...

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IEEE International conference on Computer Graphics, Vision and Information Security (CGVIS)

作者： Nirmala, D. Egfin Vaidehi, V. Loyola ICAM Coll Engn & Technol Madras 600034 Tamil Nadu India Anna Univ Madras Inst Technol Madras 600044 Tamil Nadu India

ISBN: (纸本)9781467374378

With the recent developments in the field of visual sensor technology, multiple imaging sensors are used in several applications such as surveillance, medical imaging and machine vision, in order to improve their capabilities. The goal of any efficient image fusion algorithm is to combine the visual information, obtained from a number of disparate imaging sensors, into a single fused image without the introduction of distortion or loss of information. The existing fusion algorithms employ either the mean or choose-max fusion rule for selecting the best features for fusion. The choose-max rule distorts constants background information whereas the mean rule blurs the edges. In this paper, Non-Subsampled Contourlet Transform (NSCT) based two feature-level fusion schemes are proposed and compared. In the first method Fuzzy logic is applied to determine the weights to be assigned to each segmented region using the salient region feature values computed. The second method employs Golden Section Algorithm (GSA) to achieve the optimal fusion weights of each region based on its Petrovic metric. The regions are merged adaptively using the weights determined. Experiments show that the proposed feature-level fusion methods provide better visual quality with clear edge information and objective quality metrics than individual multi-resolution-based methods such as Dual Tree Complex Wavelet Transform and NSCT.

关键词： Feature-level fusion Fuzzy logic NSCT and Golden Section Algorithm Petrovic metric

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Regional Image fusion with Genetic Algorithm Optimization 9

Regional Image Fusion with Genetic Algorithm Optimization

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9th Asia International conference on Mathematical Modelling and Computer Simulation - Asia Modelling Symposium, AMS 2015

作者： Ahmed, Attiq Khurshid, Hasnat Riaz, Muhammad Mohsin Ghafoor, Abdul Zaidi, Tahir Electrical Engineering Department National University of Sciences and Technology Pakistan Center of Advanced Studies Technology COMSATS Pakistan College of Electrical and Mechanical Engineering Islamabad Pakistan

ISBN: (纸本)9781467383233

Image fusion merges the complementary information of different sensors and wavelengths. Images from multiple sensors such as visible and infrared (IR) are of particular interest in many applications. We present here a multi-stage image fusion scheme for multi-sensor images. At first stage, the proposed method segments the image into homogeneous regions and generates segmentation maps. At second stage, the segmentation maps are combined by an adaptive weight adjustment procedure. The third stage fuses the input images and segmentation maps via genetic algorithm based multi-objective optimization strategy. The results indicated that our proposed fusion scheme yields good quality fused images when compared against the existing image fusion method. © 2015 IEEE.

关键词： Genetic algorithms

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Real-time arm tracking for HMI applications 12

Real-time arm tracking for HMI applications

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12th IEEE International conference on Wearable and Implantable Body sensor Networks, BSN 2015

作者： Masters, Matthew Osborn, Luke Thakor, Nitish Soares, Alcimar Department of Biomedical Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Electrical Engineering Federal University of Uberlandia Uberlandia Brazil

ISBN: (纸本)9781467372015

Limb tracking is an important aspect of human-machine interfaces (HMI). These systems, however, can often be limited by complex algorithms requiring significant processing power, obtrusive and immobile sensing techniques, and high costs. In this work, we utilize a sensor fusion algorithm implemented in commercial inertial measurement units (IMU) to combine accelerometer and gyroscope measurements in an effort to minimize computational requirements of the limb tracking system. In addition, previously developed methods were implemented to eliminate sensor drift by including information from a magnetometer. We tested the accuracy of our system by computing the root mean squared error (RMSE) of the true angle between the headings of two sensors and the estimate of that angle through quaternion-vector manipulations. An average RMSE of approximately 2.9° was achieved. Our limb tracking system is wearable, minimally complex, low-cost, and simple to use which has proven useful in multiple HMI applications discussed herein. © 2015 IEEE.

关键词： Mean square error

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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 (IROS)

作者： Robinson, Ryan M. Lee, Hyungtae McCourt, Michael J. Marathe, Amar R. Kwon, Heesung Tone, Chau Nothwang, William D. Army Res Lab Sensors & Electron Devices Directorate Adelphi MD USA Univ Florida Res & Engn Educ Facil Shalimar FL USA Army Res Lab Human Res & Engn Directorate Aberdeen Proving Ground MD USA

ISBN: (纸本)9781479999941

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： Object detection

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A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

A motor imagery based brain-computer interface system via sw...

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IEEE International conference on Fuzzy Systems (FUZZ-IEEE)

作者： Shang-Lin Wu Yu-Ting Liu Kuang-Pen Chou Yang-Yin Lin Jie Lu Guangquan Zhang Chun-Hsiang Chuang Wen-Chieh Lin Chin-Teng Lin Brain Research Center Institute of Electrical Control Engineering Institute of Computer Science and Engineering Electronic Systems Research Division National Chung-Shan Institute of Science and Technology (NCSIST) Taoyuan Taiwan Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Department of Computer Science National Chiao-Tung University Hsinchu Taiwan

ISBN: (纸本)9781509006274

A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.

关键词： Electroencephalography Feature extraction Wireless sensor networks Wireless communication Electrodes Particle swarm optimization Microcontrollers

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PerSEE: a Central sensors fusion Electronic Control Unit for the development of perception-based ADAS 14

PerSEE: a Central Sensors Fusion Electronic Control Unit for...

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14th IAPR International conference on Machine Vision applications (MVA)

作者： Gruyer, Dominique Belaroussi, Rachid Li, Xuanpeng Lusetti, Benoit Revilloud, Marc Glaser, Sebastien IFSTTAR COSYS LIVIC French Inst Sci & Technol Transport Dev & Network Component & Syst Dept Lab Vehicle Infrastruct Driver Interact 77 Rue Chantiers F-78000 Versailles France

ISBN: (纸本)9784901122146

Automated vehicles and Advanced Driver Assistance Systems (ADAS) face a variety of complex situations that are dealt with numerous sensors for the perception of the local driving area. Going forward, we see an increasing use of multiple, different sensors inputs with radar, camera and inertial measurement the most common sensor types. Each system has its own purpose and either displays information or performs an activity without consideration for any other ADAS systems, which does not make the best use of the systems. This paper presents an embedded real-time system to combine the attributes of obstacles, roadway and ego-vehicle features in order to build a collaborative local map. This embedded architecture is called PerSEE: a library of vision-based state-of-the-art algorithms was implemented and distributed in processors of a main fusion electronic board and on smart-cameras board. The embedded hardware architecture of the full PerSEE platform is detailed, with block diagrams to illustrate the partition of the algorithm on the different processors and electronic boards. The communications interfaces as well as the development environment are described.

关键词： Advanced driver assistance systems

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IMU Drift Reduction for Augmented Reality applications 2nd

IMU Drift Reduction for Augmented Reality Applications

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2nd International conference on Augmented and Virtual Reality (SALENTO AVR)

作者： Sekar, Lakshmi Prabha Nattamai Santos, Alexander Beltramello, Olga CERN European Org Nucl Res CH-1211 Geneva Switzerland Univ Roma Tor Vergata Rome Italy

ISBN: (纸本)9783319228884;9783319228877

Pose estimation is a major task in any augmented reality (AR) application. sensors like inertial measurement units (IMUs) and cameras are used for this purpose. IMU provides fast data, from which a pose can be determined with fewer calculations. In most cases, this data is influenced by drift. This paper investigates a drift reduction technique on positions calculated from accelerometer data and the result shows that this technique significantly reduces drift in fast dynamic movements. Furthermore, this drift reduced IMU pose can be fused with low pose updates from vision algorithms to improve the accuracy and speed suitable for AR applications.

关键词： Augmented reality Pose estimation Inertial measurement unit Marker tracking sensor fusion

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A survey of sensor fusion methods in wearable robotics

A survey of sensor fusion methods in wearable robotics

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作者： Novak, Domen Riener, Robert Sensory-Motor Systems Lab ETH Zurich Tannenstrasse 1 ZurichCH-8092 Switzerland

Modern wearable robots are not yet intelligent enough to fully satisfy the demands of end-users, as they lack the sensor fusion algorithms needed to provide optimal assistance and react quickly to perturbations or changes in user intentions. sensor fusion applications such as intention detection have been emphasized as a major challenge for both robotic orthoses and prostheses. In order to better examine the strengths and shortcomings of the field, this paper presents a review of existing sensor fusion methods for wearable robots, both stationary ones such as rehabilitation exoskeletons and portable ones such as active prostheses and full-body exoskeletons. fusion methods are first presented as applied to individual sensing modalities (primarily electromyography, electroencephalography and mechanical sensors), and then four approaches to combining multiple modalities are presented. The strengths and weaknesses of the different methods are compared, and recommendations are made for future sensor fusion research. © 2014 Elsevier B.V. All rights reserved.

关键词： Electroencephalography

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