text:
@ARTICLE{Barone2017,
author={Barone, S. and Paoli, A. and Razionale, A.V.},
title={Optical tracking of a tactile probe for the reverse engineering of industrial impellers},
journal={Journal of Computing and Information Science in Engineering},
year={2017},
volume={17},
number={4},
doi={10.1115/1.4036119},
art_number={041003},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019370244&doi=10.1115%2f1.4036119&partnerID=40&md5=0ec0f823086c59792cff45b9301d38ac},
affiliation={Department of Civil and Industrial Engineering, University of Pisa, Largo Lucio Lazzarino, n.1, Pisa, Italy},
abstract={Different sensor technologies are available for dimensional metrology and reverse engineering processes. Tactile systems, optical sensors, and computed tomography (CT) are being used to an increasing extent in various industrial contexts. However, each technique has its own peculiarities, which may limit its usability in demanding applications. The measurement of complex shapes, such as those including hidden and twisted geometries, could be better afforded by multisensor systems combining the advantages of two or more data acquisition technologies. In this paper, a fully automatic multisensor methodology has been developed with the aim at performing accurate and reliable measurements of both external and internal geometries of industrial components. The methodology is based on tracking a customized hand-held tactile probe by a passive stereo vision system. The imaging system automatically tracks the probe by means of photogrammetric measurements of markers distributed over a plate rigidly assembled to the tactile frame. Moreover, the passive stereo system is activated with a structured light projector in order to provide full-field scanning data, which integrate the point-by-point measurements. The use of the same stereo vision system for both tactile probe tracking and structured light scanning allows the two different sensors to express measurement data in the same reference system, thus preventing inaccuracies due to misalignment errors occurring in the registration phase. The tactile methodology has been validated by measuring primitive shapes. Moreover, the effectiveness of the integration between tactile probing and optical scanning has been experienced by reconstructing twisted and internal shapes of industrial impellers. Copyright © 2017 by ASME.},
author_keywords={industrial impeller;  multisensor system;  structured light scanning;  tactile-optical measuring system},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sun2017122,
author={Sun, S. and Lin, H. and Ma, J. and Li, X.},
title={Multi-sensor distributed fusion estimation with applications in networked systems: A review paper},
journal={Information Fusion},
year={2017},
volume={38},
pages={122-134},
doi={10.1016/j.inffus.2017.03.006},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017025985&doi=10.1016%2fj.inffus.2017.03.006&partnerID=40&md5=7493c659a31cbbdc278b05379f90270a},
affiliation={School of Electronics Engineering, Heilongjiang University, China},
abstract={In this paper, the advances of distributed fusion estimation (DFE) algorithms for multi-sensor networked systems (NSs) are reviewed. First, several general DFE algorithms are reviewed. Then, some random phenomena in NSs are discussed, including data quantization, random transmission delays, packet dropouts, fading measurements and communication disturbances. Finally, the advances of DFE algorithms for NSs are summarized. © 2017 Elsevier B.V.},
author_keywords={Distributed fusion estimation;  Multisensor;  Network-induced phenomenon;  Networked system;  Random uncertainty},
document_type={Article},
source={Scopus},
}

@ARTICLE{AlHage201761,
author={Al Hage, J. and E. El Najjar, M. and Pomorski, D.},
title={Multi-sensor fusion approach with fault detection and exclusion based on the Kullback–Leibler Divergence: Application on collaborative multi-robot system},
journal={Information Fusion},
year={2017},
volume={37},
pages={61-76},
doi={10.1016/j.inffus.2017.01.005},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010471343&doi=10.1016%2fj.inffus.2017.01.005&partnerID=40&md5=40e4f3bdc54beddda85e7e9151631e0c},
affiliation={CRIStAL laboratory (Research Center in Computer Science, Signal and Automatic Control of Lille) CNRS UMR 9189, University of Lille, Villeneuve d'Ascq, France},
abstract={This paper presents a multi-sensor fusion strategy able to detect the spurious sensors data that must be eliminated from the fusion procedure. The used estimator is the informational form of the Kalman Filter (KF) namely Information Filter (IF). In order to detect the erroneous sensors measurements, the Kullback–Leibler Divergence (KLD) between the a priori and a posteriori distributions of the IF is computed. It is generated from two tests: One acts on the means and the other deals with the covariance matrices. Optimal thresholding method based on a Kullback–Leibler Criterion (KLC) is developed and discussed in order to replace classical approaches that fix heuristically the false alarm probability. Multi-robot systems became one of the major fields of study in the indoor environment where the environmental monitoring and the response to crisis must be ensured. Consequently, the robots required to know precisely their positions and orientations in order to successfully perform their mission. Fault detection and exclusion (FDE) play a crucial role in enhancing the integrity of localization of the multi-robot team. The main contributions of this paper are: - developing a new method of sensors data fusion that tackle the erroneous data issues, - developing a Kullback–Leibler based criterion for the threshold optimization, - Validation with real experimental data from a group of robots. © 2017 Elsevier B.V.},
author_keywords={Collaborative Localization;  Fault detection and exclusion;  Information filter;  Kullback–Leibler Divergence;  Multi-sensor data fusion;  Threshold optimization},
document_type={Article},
source={Scopus},
}

@ARTICLE{Moselhi2017,
author={Moselhi, O. and Ahmed, M. and Bhowmick, A.},
title={Multisensor Data Fusion for Bridge Condition Assessment},
journal={Journal of Performance of Constructed Facilities},
year={2017},
volume={31},
number={4},
doi={10.1061/(ASCE)CF.1943-5509.0001000},
art_number={04017008},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018425363&doi=10.1061%2f%28ASCE%29CF.1943-5509.0001000&partnerID=40&md5=05f0b27780cbe273b29c239aa59f0fb6},
affiliation={Dept. of Building, Civil and Environmental Engineering, Concordia Univ., 1455 de Maisonneuve Blvd., West Montréal, QC, Canada},
abstract={Considerable work has been carried out using a number of sensing technologies for condition assessment of civil infrastructure. Fewer efforts, however, have been directed for integrating the use of these technologies. This paper presents a newly developed method for automated condition assessment and rating of concrete bridge decks. The method integrates the use of ground-penetrating radar (GPR) and infrared thermography (IR) technologies. It utilizes a data-fusion technique to improve the accuracy of detecting defects and, accordingly, that of condition assessment. In this research, pixel-level image fusion is applied to assess bridge condition using these two technologies. GPR data are displayed as three-dimensional (3D) from 24 scans equally spaced by 0.33 m to interpret a section of a bridge deck surface in Montréal, Canada. The analysis of reflected amplitude waves is used to detect subsurface changed conditions. The GPR data are fused with infrared thermography images using a wavelet transform technique. Four scenarios based on image processing are studied and their application before and after data fusion is assessed in relation to accuracy of the employed fusion process. The concrete deck's condition is assessed based on deteriorated areas that are extracted from the new fused image. Analysis of the results showed that bridge condition assessment can be improved with image fusion and this can enhance the capabilities of inspectors in interpretation of the results obtained. © 2017 American Society of Civil Engineers.},
author_keywords={Bridges;  Condition assessment;  Data fusion;  Image processing;  Infrared thermography (IR);  Three-dimensional ground-penetrating radar (3D GPR)},
document_type={Article},
source={Scopus},
}

@ARTICLE{Deilamsalehy2017,
author={Deilamsalehy, H. and Havens, T.C. and Manela, J.},
title={Heterogeneous Multisensor Fusion for Mobile Platform Three-Dimensional Pose Estimation},
journal={Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME},
year={2017},
volume={139},
number={7},
doi={10.1115/1.4035452},
art_number={071002},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018501138&doi=10.1115%2f1.4035452&partnerID=40&md5=8f11e5aa9f5ef3f6c274990798f6a068},
affiliation={Department of Electrical and Computer Engineering, Department of Computer Science, Michigan Technological University, Houghton, MI, United States},
abstract={Precise, robust, and consistent localization is an important subject in many areas of science such as vision-based control, path planning, and simultaneous localization and mapping (SLAM). To estimate the pose of a platform, sensors such as inertial measurement units (IMUs), global positioning system (GPS), and cameras are commonly employed. Each of these sensors has their strengths and weaknesses. Sensor fusion is a known approach that combines the data measured by different sensors to achieve a more accurate or complete pose estimation and to cope with sensor outages. In this paper, a three-dimensional (3D) pose estimation algorithm is presented for a unmanned aerial vehicle (UAV) in an unknown GPS-denied environment. A UAV can be fully localized by three position coordinates and three orientation angles. The proposed algorithm fuses the data from an IMU, a camera, and a two-dimensional (2D) light detection and ranging (LiDAR) using extended Kalman filter (EKF) to achieve accurate localization. Among the employed sensors, LiDAR has not received proper attention in the past; mostly because a two-dimensional (2D) LiDAR can only provide pose estimation in its scanning plane, and thus, it cannot obtain a full pose estimation in a 3D environment. A novel method is introduced in this paper that employs a 2D LiDAR to improve the full 3D pose estimation accuracy acquired from an IMU and a camera, and it is shown that this method can significantly improve the precision of the localization algorithm. The proposed approach is evaluated and justified by simulation and real world experiments. Copyright © 2017 by ASME.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Vines-Cavanaugh2017,
author={Vines-Cavanaugh, D.a  and Shamsabadi, S.S.a  and Zhao, Y.a  and Huang, G.b  c  and Wadia-Fascetti, S.d  and Schirner, G.e  and Birken, R.b  c  and Wang, M.a },
title={City-wide application of the affordable and rapid streetscan pavement-management system},
journal={Journal of Infrastructure Systems},
year={2017},
volume={23},
number={2},
doi={10.1061/(ASCE)IS.1943-555X.0000333},
art_number={B4016010},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017520111&doi=10.1061%2f%28ASCE%29IS.1943-555X.0000333&partnerID=40&md5=e1b64cb14a51bd168da8f475dde2a777},
affiliation={Dept. of Civil and Environmental Engineering, Northeastern Univ., Boston, MA, United States; StreetScan, Inc., 151 South Bedford St., Burlington, MA, United States; Northeastern Univ., Boston, MA, United States; Dept. of Civil Engineering, Northeastern Univ., Boston, MA, United States; Dept. of Electrical and Computer Engineering, Northeastern Univ., Boston, MA, United States},
abstract={The StreetScan pavement-management system (PMS) is introduced. It empowers cities to make more cost-effective road repair decisions. At the heart of the system is a vehicle outfitted with a patented, multimodal sensor array that rapidly collects data for every street in the city. A combination of sensor-specific algorithms and multisensor data fusion are applied to convert the raw data into more actionable condition indices. Decision trees and mathematical models are then applied to determine what repairs are needed and how to prioritize them in the most cost effective manner. Users are able to visualize and interact with the repair suggestions and condition indices through an innovative web-based geographic information system (GIS) application. At less than half the cost, the StreetScan PMS is considerably more affordable than conventional PMSs. This paper validates its effectiveness in the real world by taking a look at two recent applications: (1) a city-wide application in Beverly, Massachusetts and (2) an application to several streets in Boston, Massachusetts. © 2016 American Society of Civil Engineers.},
author_keywords={Automated pavement inspection;  Infrastructure monitoring;  Pavement condition index;  Pavement management system},
document_type={Article},
source={Scopus},
}

@ARTICLE{DePaola20171502,
author={De Paola, A.a  and Ferraro, P.a  and Gaglio, S.a  and Lo Re, G.a  and Das, S.K.b },
title={An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments},
journal={IEEE Transactions on Mobile Computing},
year={2017},
volume={16},
number={6},
pages={1502-1515},
doi={10.1109/TMC.2016.2599158},
art_number={7539530},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019225011&doi=10.1109%2fTMC.2016.2599158&partnerID=40&md5=c69683e8559a149ea1fd7f4b31e8391d},
affiliation={DICGIM Department, University of Palermo, Palermo, Italy; Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, United States},
abstract={The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual information in the fusion process, to increase the accuracy of inference and hence decision making in a dynamically changing environment. In this paper, we propose a context-aware, self-optimizing, adaptive system for sensor data fusion, based on a three-tier architecture. Heterogeneous data collected by sensors at the lowest tier are combined by a dynamic Bayesian network at the intermediate tier, which also integrates contextual information to refine the inference process. At the highest tier, a self-optimization process dynamically reconfigures the sensory infrastructure, by sampling a subset of sensors in order to minimize energy consumption and maximize inference accuracy. A Bayesian approach allows to deal with the imprecision of sensory measurements, due to environmental noise and possible hardware malfunctions. The effectiveness of our approach is demonstrated with the application scenario of the user activity recognition in an Ambient Intelligence system managing a smart home environment. Experimental results show that the proposed solution outperforms static approaches for context-aware multi-sensor fusion, achieving substantial energy savings whilst maintaining a high degree of inference accuracy. © 2016 IEEE.},
author_keywords={Learning;  wireless sensor networks},
document_type={Article},
source={Scopus},
}

@ARTICLE{Doudou2017621,
author={Doudou, F.Z.a  and Djebbari, A.b },
title={Particle Swarm Optimization Aided Serial Acquisition in Distributed OSCFAR and CMLD for DS-CDMA Systems in Fading Channels},
journal={Wireless Personal Communications},
year={2017},
volume={94},
number={3},
pages={621-640},
doi={10.1007/s11277-016-3640-y},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983792624&doi=10.1007%2fs11277-016-3640-y&partnerID=40&md5=bb5b9f6b25a34e55ee017926740e1e5c},
affiliation={Aeronautical Institute, Saad Dahlab University of Blida, Blida, Algeria; Faculty of Technology, University of Djillali Liabes of Sidi Bel Abbès, Sidi Bel Abbes, Algeria},
abstract={In this paper, adaptive serial acquisition schemes are proposed using threshold optimization in Multisensor data fusion for mobile communication systems in frequency selective Rayleigh fading channel. Two distributed adaptive detectors based on ordered statistics constant false alarm rate (OSCFAR) and censored mean level detector (CMLD) are used. For threshold optimization, the Particle Swarm Optimization (PSO) with linearly decreasing strategy is suggested, employing two logical fusion rules to enhance the serial-search in a multipath environment. The acquisition and detection performances are investigated under different system parameters and channel condition, namely the number of distributed sensors, the partial correlation length, the number of resolvable path and the effect of fading. Simulation results shown the robustness of the proposed distributed adaptive acquisition CMLD processor using PSO over the one based on OSCFAR. Moreover, the benefit of proposed schemes over adaptive acquisition ones using one antenna is also investigated by simulation. © 2016, Springer Science+Business Media New York.},
author_keywords={CMLD;  Distributed adaptive detector;  Particle swarm optimization;  Serial code acquisition;  Threshold optimization},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gravina20171339,
author={Gravina, R.a  and Alinia, P.b  and Ghasemzadeh, H.b  and Fortino, G.a },
title={Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges},
journal={Information Fusion},
year={2017},
volume={35},
pages={1339-1351},
doi={10.1016/j.inffus.2016.09.005},
note={cited By 22},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988027649&doi=10.1016%2fj.inffus.2016.09.005&partnerID=40&md5=0e05c590b432df690432493f5d0b807e},
affiliation={Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, Via P. Bucci, 87036 Rende (CS), Italy; School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164-2752, United States},
abstract={Body Sensor Networks (BSNs) have emerged as a revolutionary technology in many application domains in health-care, fitness, smart cities, and many other compelling Internet of Things (IoT) applications. Most commercially available systems assume that a single device monitors a plethora of user information. In reality, BSN technology is transitioning to multi-device synchronous measurement environments; fusion of the data from multiple, potentially heterogeneous, sensor sources is therefore becoming a fundamental yet non-trivial task that directly impacts application performance. Nevertheless, only recently researchers have started developing technical solutions for effective fusion of BSN data. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on multi-sensor fusion in the area of BSN. This survey discusses clear motivations and advantages of multi-sensor data fusion and particularly focuses on physical activity recognition, aiming at providing a systematic categorization and common comparison framework of the literature, by identifying distinctive properties and parameters affecting data fusion design choices at different levels (data, feature, and decision). The survey also covers data fusion in the domains of emotion recognition and general-health and introduce relevant directions and challenges of future research on multi-sensor fusion in the BSN domain. © 2016 Elsevier B.V.},
author_keywords={Data-level fusion;  Decision-level fusion;  Feature-level fusion;  Human activity recognition;  Multi-sensor data fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Karimi2017768,
author={Karimi, D.a  and Akbarizadeh, G.b  and Rangzan, K.a  and Kabolizadeh, M.a },
title={Effective supervised multiple-feature learning for fused radar and optical data classification},
journal={IET Radar, Sonar and Navigation},
year={2017},
volume={11},
number={5},
pages={768-777},
doi={10.1049/iet-rsn.2016.0346},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018257055&doi=10.1049%2fiet-rsn.2016.0346&partnerID=40&md5=67e6be123b4f46f1c5909d2cbf0a1749},
affiliation={Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran; Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran},
abstract={In multi-sensor data fusion based on multiple features, the high dimensionality of feature space increases the runtime and computational complexity. The present study proposes a new algorithm based on the combination of random subspace (RS), linear discriminant analysis and sparse regularisation (LDASR), namely RS-LDASR for feature space dimensionality reduction, supervised feature selection, and learning. The use of RSs can effectively solve the problem of high dimensionality and high feature-to-instance ratio. The extraction of multiple features from the images raises the possibility of a correlation between features which reduce classification accuracy. In this study, after the construction of several RSs, supervised feature selection and learning based on LDASR were applied with very high accuracy. Classification and image fusion for remote sensing data analysis were tested by the implementation of feature-based fusion on two pairs of fused synthetic aperture radar and optical data. Four feature matrices were constructed using attribute profiles (APs), multi-APs (MAPs), non-negative matrix factorisation (NMF), and textural features. Support vector machine and rotation forest were applied as the base classifiers. The results show that use of RS-LDASR significantly improved the classification accuracy based on NMF plus texture features and even NMF alone. © 2016, The Institution of Engineering and Technology.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yokoya20172842,
author={Yokoya, N.a  b  c  and Zhu, X.X.b  c  and Plaza, A.d },
title={Multisensor coupled spectral unmixing for time-series analysis},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2017},
volume={55},
number={5},
pages={2842-2857},
doi={10.1109/TGRS.2017.2655115},
art_number={7862183},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013628188&doi=10.1109%2fTGRS.2017.2655115&partnerID=40&md5=ec25bec1bb2c3ba2f36c912cb53e58cf},
affiliation={Department of Advanced Interdisciplinary Studies, University of Tokyo, Tokyo, Japan; Remote Sensing Technology Institute, German Aerospace Center, Wessling, Germany; Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany; Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, Cáceres, Spain},
abstract={We present a new framework, called multisensor coupled spectral unmixing (MuCSUn), that solves unmixing problems involving a set of multisensor time-series spectral images in order to understand dynamic changes of the surface at a subpixel scale. The proposed methodology couples multiple unmixing problems based on regularization on graphs between the time-series data to obtain robust and stable unmixing solutions beyond data modalities due to different sensor characteristics and the effects of nonoptimal atmospheric correction. Atmospheric normalization and cross calibration of spectral response functions are integrated into the framework as a preprocessing step. The proposed methodology is quantitatively validated using a synthetic data set that includes seasonal and trend changes on the surface and the residuals of nonoptimal atmospheric correction. The experiments on the synthetic data set clearly demonstrate the efficacy of MuCSUn and the importance of the preprocessing step. We further apply our methodology to a real time-series data set composed of 11 Hyperion and 22 Landsat-8 images taken over Fukushima, Japan, from 2011 to 2015. The proposed methodology successfully obtains robust and stable unmixing results and clearly visualizes class-specific changes at a subpixel scale in the considered study area. © 1980-2012 IEEE.},
author_keywords={Change detection;  coupled spectral unmixing;  multisensor data fusion;  time-series analysis},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tang2017,
author={Tang, Y. and Zhou, D. and Xu, S. and He, Z.},
title={A weighted belief entropy-based uncertainty measure for multi-sensor data fusion},
journal={Sensors (Switzerland)},
year={2017},
volume={17},
number={4},
doi={10.3390/s17040928},
art_number={928},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018272675&doi=10.3390%2fs17040928&partnerID=40&md5=38d68286686a838af769e03269932d98},
affiliation={School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China},
abstract={In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster–Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster–Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.},
author_keywords={Dempster–Shafer evidence theory;  Deng entropy;  Sensor data fusion;  Uncertainty measure;  Weighted belief entropy},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Saadabadi2017818,
author={Saadabadi, M.S.E. and Yousefi, E. and Khaloozadeh, K. and Khaloozadeh, H.},
title={Airflow estimation of a laboratory Anemometer via fisher statistical technique},
journal={2017 10th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2017},
year={2017},
pages={818-822},
doi={10.1109/ATEE.2017.7905173},
art_number={7905173},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019260434&doi=10.1109%2fATEE.2017.7905173&partnerID=40&md5=a5037e5ec318ce8faa8e4f750e4d36f6},
affiliation={Industrial Control Center of Excellence, Systems and Control Engineering Department, K.N. Toosi University of Technology, Tehran, Iran},
abstract={Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation and other areas. In this paper a method for measuring airflow is presented using three different sensors, Windmill Anemometer, Thermal Anemometer, and Venturi tube with the help of Data Fusion technique. It is necessary and critical to obtain the most accurate measurements in many industrial applications, or as an example in medical centers which utilize ventilators to help patients breathe. The aim is to demonstrate that by combining data gained from different sensors using Data Fusion, the results are more accurate and reliable compared to single-sensor measurements. To gather all data gained by sensors and transferring them to computer, an interface circuit consisting a microcontroller is put in use and for sampling data we use MATLAB software. Then using Fisher method we estimate the airflow from different data obtained from the sensors. To conclude, we show that the results from data fusion is more reliable and accurate. © 2017 IEEE.},
author_keywords={Airflow;  Multisensor Data Fusion;  Static Estimation;  Thermal;  Venturi;  Windmill},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Becker2017154,
author={Becker, F. and Paul, O.},
title={Efficient cross-sensitivity compensation in multisensor systems by half-blind calibration},
journal={Sensors and Actuators, A: Physical},
year={2017},
volume={257},
pages={154-164},
doi={10.1016/j.sna.2016.08.027},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013993554&doi=10.1016%2fj.sna.2016.08.027&partnerID=40&md5=a34cc15c4d7f0d37588f0f1435ae9ebf},
affiliation={Microsystem Materials Laboratory, Department of Microsystems Engineering (IMTEK), University of Freiburg, Georges-Koehler-Allee 103, Freiburg, Germany},
abstract={Sensor systems designed to determine external measurands often show parasitic sensitivities to other influences, so-called disturbances. Calibration of such a system aims for the extraction of disturbance-compensated measurand values from the system output signals. Often this goal is achieved by exposing the system to a sufficient number of load conditions involving well-controlled values not only of the measurands but also of the disturbances. In this work we show that the calibration effort focused exclusively on the extraction of disturbance-compensated measurand values can be considerably reduced in the case of systems with linear response. The conclusions therefore apply to Hall, piezoresistive stress, and temperature sensors, among others. During the calibration procedure, well-controlled measurand values need indeed to be applied to the system; however, while all disturbance parameters need to be varied during calibration, accurate knowledge of their values is not needed. By making use exclusively of the calibration measurand values and the concurrently extracted sensor signals, it is possible to determine a reduced calibration matrix which ensure the successful extraction of disturbance-compensated measurand values from sensor signals. The effectiveness of the method is demonstrated using a six-degree-of freedom linear force-moment microtransducer with redundant sensors. The method may save cost by simpler calibration setups and the time-saving procedure it proposes. © 2016 Elsevier B.V.},
author_keywords={Calibration;  Compensation;  Cross-sensitivity;  Elimination;  Multisensor systems;  Parasitic sensitivity},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Pombo2017,
author={Pombo, N.a  b  c  and Garcia, N.a  b  c  d  and Bousson, K.b  e },
title={A data fusion model to evaluate computerized pain diaries on anxiety and depression assessment},
journal={2016 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2016},
year={2017},
doi={10.1109/BlackSeaCom.2016.7901575},
art_number={7901575},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019018654&doi=10.1109%2fBlackSeaCom.2016.7901575&partnerID=40&md5=ef136d64914b50235627dfcba00cbad1},
affiliation={Instituto de Telecomunicações, Covilhã, Portugal; Department of Aerospace Science, Universidade da Beira Interior, Covilhã, Portugal; ALLab - Assisted Living Computing and Telecommunications Laboratory, Covilhã, Portugal; Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal; LAETA/UBI-AEROG, Covilhã, Portugal},
abstract={As self-reporting of pain complaints is considered the most accurate assessment method, the evaluation of the adopted technology on it, raises several challenges. In addition, as pain could be assessed based on different dimensions such as pain intensity, symptoms of anxiety, catasthrophizing, and depression, then are promising new methods to evaluate the effect of the adoption of this technology. In this paper, a case study based on a data fusion model using a quantitative combined with qualitative assessment aims the evaluation of electronic pain diaries on anxiety and depression assessment. The proposed method revealed it suitability to compare the use of pen-and-paper versus computerized systems for pain assessment. © 2016 IEEE.},
author_keywords={data fusion;  electronic pain diary;  multisensor;  pain assessment},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ramji2017193,
author={Ramji, V.a  and Hssayeni, M.a  and Burack, M.A.b  and Ghoraani, B.c },
title={Parkinson's disease medication state management using data fusion of wearable sensors},
journal={2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017},
year={2017},
pages={193-196},
doi={10.1109/BHI.2017.7897238},
art_number={7897238},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018399609&doi=10.1109%2fBHI.2017.7897238&partnerID=40&md5=3f753ca3d8951b14041ca71223ded707},
affiliation={Electrical Engineering Department, Rochester Institute of Technology, Rochester, NY, United States; Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States; Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States},
abstract={Parkinson disease (PD) cure remains one of the greatest challenges in chronic neurological disorder therapy, motivating efforts to provide actionable information to guide self-managed therapy adjustments. In this paper, we develop a data fusion approach to combine multi-dimensional data from body-worn inertial sensors to automatically identify different medication states of patients with PD. The proposed approach employs several signal processing algorithms including time-frequency analysis and tensor decomposition to extract features that can represent spectral, temporal, and spatial behavior of body motion data. A Support Vector Machines (SVM) classifier is trained and tested using a data set of 12 patients, which resulted in an overall average accuracy of 78%. © 2017 IEEE.},
author_keywords={Multi-sensor data fusion;  Parkinson's disease;  Treatment management;  Wearable sensors},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Zimmermann2017,
author={Zimmermann, C.a  and Zeilfelder, J.a  and Bloecher, T.a  and Diehl, M.a  and Essig, S.a  and Stork, W.b },
title={Evaluation of a smart drink monitoring device},
journal={SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings},
year={2017},
doi={10.1109/SAS.2017.7894061},
art_number={7894061},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018255590&doi=10.1109%2fSAS.2017.7894061&partnerID=40&md5=7f40394e17d1344459580c98564537fb},
affiliation={Dept. of Embedded Systems and Sensors Engineering (ESS), FZI Research Center for Information Technology, Karlsruhe, Germany; Institute of Information Processing Technologies, Karlsruhe Institute of Technology KIT, Karlsruhe, Germany},
abstract={Liquid supply risk management done for inpatient care of the elderly or persons suffering from diseases is currently a time intensive procedure. The paper describes the design and evaluation of an automatic beverage monitoring system. An electronic device is presented, which is able to measure the fluid intake of the patient and provide an automated documentation and connection to existing software architecture. In a user-oriented study the functionality and acceptance were evaluated and the results show that drinking motivation can verifiably result in temporarily increased hydration. © 2017 IEEE.},
author_keywords={ambient assisted living;  behavior monitoring;  beverage monitoring;  hydration;  multisensor system;  nutrition},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ando2017,
author={Ando, B.a  and Baglio, S.a  and Marietta, V.a  and Pistorio, A.a  and Dibilio, V.b  and Mostile, G.b  and Nicoletti, A.b  and Zappia, M.b },
title={A multisensor architecture for the assessment of postural sway in elderly and people with neurological disease},
journal={SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings},
year={2017},
doi={10.1109/SAS.2017.7894085},
art_number={7894085},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018272732&doi=10.1109%2fSAS.2017.7894085&partnerID=40&md5=6bfcf555b94bf3b1da5e3498fc010840},
affiliation={Department of Electric Electronic and Information Engineering, DIEEI, University of Catania, Catania, Italy; Clinica Neurologica, AOU Policlinico Vittorio Emanuele of Catania, Catania, Italy},
abstract={Postural instability is one of the symptoms of Parkinson's disease which can cause falls with possible unpleasant consequences. Two main tests, the Timed Up and Go and the Pull Test, are typically performed by physicians to assess the postural instability of patients. Such tests require on purpose structured laboratory equipped with expensive monitoring systems. Moreover, in the case of Pull test, the validity of the tests can be compromised by the nonstandard backward tug provided to the patient. The solution proposed in this paper consists of a low cost multisensor system and a methodology which allows to implement an objective estimation of the input solicitation as well as the assessment and classification of the user response. Different parameters (10) presented in the literature for the sake of the postural instability evaluation were investigated, in case of Pull Test, with a large set of experiments (59) in order to extract a set of features useful for the classification between stable and unstable behavior. Results obtained demonstrated the feasibility of the methodology proposed. © 2017 IEEE.},
author_keywords={inertial sensors;  multisensor system;  Parkinson's disease;  postural instability;  Pull Test;  stabilogram},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ramgopal2017,
author={Ramgopal, I.A. and Manivannan, P.V.},
title={Development of multi-sensor data fusion technique for the automated Bus Rapid Transport System},
journal={Proceedings of 2016 International Conference on Robotics: Current Trends and Future Challenges (RCTFC)},
year={2017},
doi={10.1109/RCTFC.2016.7893409},
art_number={7893409},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018372097&doi=10.1109%2fRCTFC.2016.7893409&partnerID=40&md5=0e46c5057b8a214337e02e0a87ddc403},
affiliation={Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, India},
abstract={This paper focuses on the development of a multi-sensor data-fusion algorithm, which plays a vital role in vehicle localization using continuous landmarks in an automated Bus Rapid Transport (BRT) System. The model vehicle is instrumented with odometry-based wheel encoders and Infra Red (IR) distance sensors. Vehicle localization is done using an Extended Kalman Filter (EKF), which fuses data from these sensors. The path error is corrected discretely by using hall-effect sensors and magnets. The system has been modeled and simulated in MATLAB for straight line and curved paths to test the efficacy of the algorithm. Finally, the implementation of the sensor system has been done on a laboratory scale model integrated with the sensors to verify the integrity of the system. Tests were performed to calculate the error in detection of the actual co-ordinates, as well as to test out the control system being developed. © 2016 IEEE.},
author_keywords={Autonomous vehicle control;  Extended Kalman Filter;  Sensor data fusion;  Vehicle localization component},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Wang20172005,
author={Wang, F.a  and Hu, L.a  and Zhou, J.b  and Hu, J.a  and Zhao, K.a },
title={A semantics-based approach to multi-source heterogeneous information fusion in the internet of things},
journal={Soft Computing},
year={2017},
volume={21},
number={8},
pages={2005-2013},
doi={10.1007/s00500-015-1899-7},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945218970&doi=10.1007%2fs00500-015-1899-7&partnerID=40&md5=f280efcd925519e2a06ff97f6530990c},
affiliation={College of Computer Science and Technology, Jilin University, Changchun, China; College of Information Science and Technology, Bohai University, Jinzhou, China},
abstract={Multi-source heterogeneous information fusion in the Internet of Things (IoT) is a type of information processing that attempts to provide a comprehensive, timely and accurate perception and feedback relative to physical things. Traditional multi-sensor data fusion can deal with the same type of data effectively. However, as new characteristics emerge in the IoT, interoperable service-oriented technologies are required to share real-world data among heterogeneous devices to integrate and fuse the multi-source heterogeneous IoT data. To address these issues, an architecture that can provide guidance for the development of IoT information fusion is required. We compare features of IoT data and information with an existing wireless sensor network and the Internet, which, to the best of our knowledge, is the first comparison of this kind. Then, we design a framework for multi-source heterogeneous information fusion in the IoT and use an experimental simulation platform to build an environmental monitoring system to assess the framework. © 2015, Springer-Verlag Berlin Heidelberg.},
author_keywords={Heterogeneous information fusion;  Internet of things;  Semantic sensor network},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ren2017144,
author={Ren, M.J.a  and Sun, L.J.a  and Liu, M.Y.b  and Cheung, C.F.b  and Yin, Y.H.a  and Cao, Y.L.c },
title={A weighted least square based data fusion method for precision measurement of freeform surfaces},
journal={Precision Engineering},
year={2017},
volume={48},
pages={144-151},
doi={10.1016/j.precisioneng.2016.11.014},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007320061&doi=10.1016%2fj.precisioneng.2016.11.014&partnerID=40&md5=ea8273c95a627e8b59deb11151ab32c9},
affiliation={Robotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Partner State Key Laboratory of Ultra-Precision Machining Technology, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, China; Institute of Advanced Manufacturing Engineering, Zhejiang University, Hangzhou, China},
abstract={The trend towards product miniaturisation and multi-functionality constitutes a driving force for the application of complex surfaces in many fields such as advanced optics. The precision measurement of these surfaces should be carried out at multiple scales, of which process commonly involves several datasets obtained from different sensors. This paper presents a weighted least square based multi-sensor data fusion method for such measurement. The method starts from unifying the coordinate frames of the measured datasets using an intrinsic feature based surface registration method. B-spline surface is used to fit linear surface model to each identified overlapping area of the registered datasets, respectively. By forming a common basis function, the fitted surface models and the corresponding residuals are then combined to construct a weighted least square based data fusion system which is used to generate a fused surface model. An analysis of the uncertainty propagation in data fusion process is also given. Both computer simulation and actual measurement on various freeform surfaces are conducted to verify the validity of proposed method. The results indicate that the proposed method is capable of fusing multi-sensor measured datasets with notable reduction of the measurement uncertainty. © 2016},
author_keywords={B-spline;  Data fusion;  Freeform surfaces;  Precision surface measurement;  Weighted least square},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yang2017385,
author={Yang, L.a  b  and Zhang, J.a  b  and Guo, Y.c  and Wang, Q.a  b },
title={Bayesian-based information extraction and aggregation approach for multilevel systems with multi-source data},
journal={Journal of Systems Engineering and Electronics},
year={2017},
volume={28},
number={2},
pages={385-400},
doi={10.21629/JSEE.2017.02.19},
art_number={7921810},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019242806&doi=10.21629%2fJSEE.2017.02.19&partnerID=40&md5=77766be8212508f8e40b8773b8c2551c},
affiliation={School of Reliability and Systems Engineering, Beihang University, Beijing, China; Science and Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Beijing, China; School of Automation Science and Electrical Engineering, Beihang University, Beijing, China},
abstract={The ever-increasing complexity of industry facilities has made the reliability analysis and assessment an imperative yet tough work. Motivated by practical engineering requirement, this paper develops a Bayesian-based information extraction and aggregation (BIEA) approach for system level reliability estimation of a complex system. It takes both subjective judgments and objective field outputs into consideration. Novel features of this approach is a unique information content based aggregation process, which allows a flexible application of this framework in separated modules on account for purpose. The coherency of which is guaranteed by the objective information content calculation. This work goes beyond the alternatives that deal with solely attributed data under ideal information circumstance, and investigates a more generic tool for real engineering application. Limitations embedded in traditional statistical modeling methods have been eliminated in a nature manner by information transition and integration. In addition, a double axis driving mechanism (DADM) for erecting the antenna of a satellite is demonstrated as case study for benefit illustration and effectiveness verification. © 2017 Beijing Institute of Aerospace Information.},
author_keywords={imbalanced information;  multi-level and multi-source data fusion;  system reliability},
document_type={Article},
source={Scopus},
}

@ARTICLE{Medjkoune2017259,
author={Medjkoune, S.a  and Mouchere, H.b  and Petitrenaud, S.c  and Viard-Gaudin, C.b },
title={Combining Speech and Handwriting Modalities for Mathematical Expression Recognition},
journal={IEEE Transactions on Human-Machine Systems},
year={2017},
volume={47},
number={2},
pages={259-272},
doi={10.1109/THMS.2017.2647850},
art_number={7829301},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010666390&doi=10.1109%2fTHMS.2017.2647850&partnerID=40&md5=8daef10b22d980c427d7c032d0680f97},
affiliation={Department of Automatic and Computer Science, Ecole des Mines de Douai, Douai, France; UBL, Universite de Nantes, IRCCyN UMR CNRS 6597, Nantes, France; UBL, Universite du Maine, LIUM-EA 4023, Le Mans, France},
abstract={In this paper, we open new perspectives for mathematical expression recognition by introducing an original bimodal system. Since handwritten mathematical expression recognition is a very challenging task prone to many ambiguities, we use speech as an additional modality to circumvent limitations that are inherent to the written form. A use case scenario corresponds to lectures given in classrooms where the teacher would write and read aloud any mathematical expressions to allow a better interpretation. In addition to state-of-the-art solutions for recognizing handwriting and speech, we introduce a multilayer architecture for the merger of modalities. Specifically, the Dempster-Shafer theory is used to process the information at the symbol level. This bimodal system is evaluated on real bimodal data, the HAMEX dataset. Large improvements are observed when speech and handwriting are combined when compared to the single handwriting modality. © 2013 IEEE.},
author_keywords={Belief functions;  handwriting;  information alignment;  mathematical expression (ME);  multilevel data fusion;  speech},
document_type={Article},
source={Scopus},
}

@ARTICLE{Li20173205,
author={Li, X. and Xu, Q.},
title={A Reliable Fusion Positioning Strategy for Land Vehicles in GPS-Denied Environments Based on Low-Cost Sensors},
journal={IEEE Transactions on Industrial Electronics},
year={2017},
volume={64},
number={4},
pages={3205-3215},
doi={10.1109/TIE.2016.2637306},
art_number={7776890},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015321271&doi=10.1109%2fTIE.2016.2637306&partnerID=40&md5=42035ddef20604877e8bda4a2566050c},
affiliation={School of Instrument Science and Engineering, Southeast University, Nanjing, China},
abstract={How to achieve reliable and accurate positioning performance using low-cost sensors is one of the main challenges for land vehicles. This paper proposes a novel fusion positioning strategy for land vehicles in GPS-denied environments, which enhances the positioning performance simultaneously from the sensor and methodology levels. It integrates multiple complementary low-cost sensors not only incorporating GPS and microelectromechanical-based inertial measurement unit, but also a "virtual" sensor, i.e., a sliding-mode observer (SMO). The SMO is first synthesized based on nonlinear vehicle dynamics model to estimate vehicle state information robustly. Then, a federated Kalman filter (FKF) is designed to fuse all sensor information, which can easily isolate and accommodate such sensor failures as GPS ones due to its decentralized filtering architecture. Further, a hybrid global estimator (HGE) is constructed by augmenting the FKF with a grey predictor, which has the advantages of dealing with the systems with uncertain or insufficient information. The HGE works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outage to realize accurate and reliable positioning. The experimental results validate the effectiveness and reliability of the proposed strategy. © 2017 IEEE.},
author_keywords={Federated Kalman filter (FKF);  grey predictor (GP);  multisensor fusion;  sliding-mode observer (SMO);  vehicle positioning},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Mousavi2017477,
author={Mousavi, M.a  and Ghanbari, M.a  and Nasr, A.a  and Moosavian, S.A.A.a  and Zarafshan, P.b },
title={Sensory feedback performance improvement on RoboCab: An experimental approach to wire-driven parallel manipulator},
journal={4th RSI International Conference on Robotics and Mechatronics, ICRoM 2016},
year={2017},
pages={477-482},
doi={10.1109/ICRoM.2016.7886787},
art_number={7886787},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017524621&doi=10.1109%2fICRoM.2016.7886787&partnerID=40&md5=e8d50cff14450ef1a002a0d226db5970},
affiliation={Center of Excellence in Robotics and Control, Advanced Robotics and Automated Systems (ARAS) Lab., Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran; Department of Agro-Technology, College of Aburaihan, University of Tehran, Pakdasht, Tehran, Iran},
abstract={This paper introduces a method to improve the feedback accuracy using multisensor data fusion. Flexibility in cables and unmodeled rotation of feeder pulley in the spatial workspace cause unreliability in the encoder data as well as sagging in cables. The purpose of this work is to provide an effective scheme to combine other sensors' information from multiple sources to increase the precision and reliability of feedback data. In this study, sensor level fusion algorithm is used to integrate and fuse together the camera and Inertial Measurement Unit (IMU) data. The main application of this method is in the implementation of various control methodologies where more accurate feedback data leads to an improved control performance. It is assumed that the cable mass is negligible and hence it insignificantly contributes to the robot dynamics. Experimental results show the merits of the proposed approach which will be discussed. © 2016 IEEE.},
author_keywords={Cable Robot;  Dynamic Analysis;  Multisensor Data Fusion;  Parallel Manipulator},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Madany2017366,
author={Madany, Y.M. and El-Badawy, E.-S.A. and Soliman, A.M.},
title={Fault Detection Prediction Analysis of Multi-sensor Data Fusion Architecture and Isolation Using Pseudo Sensor Enhancement Method (PSEM)},
journal={Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS},
year={2017},
pages={366-371},
doi={10.1109/ISMS.2016.68},
art_number={7877240},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017262240&doi=10.1109%2fISMS.2016.68&partnerID=40&md5=de8b3240dea54a411f0e0eb40a36856c},
affiliation={Communications and Electronics Department, Alexandria University, Alexandria, Egypt},
abstract={The recent technology of intelligent systems is using multiple sensors to assess real-time information of the internal system states and the environmental operating conditions. In the case of sensor (s) transient faults, the disconnection of the readings causes inappropriate actions. So the need for sensor fault detecting and estimating multiple sensor faults becomes an important issue to optimize and enhance the system performance. In this paper, fault detection prediction analysis of multi-sensor architecture is introduced to study different number of sensors with different arrangement architectures and to detect transient faults in each sensor. Then, a new proposed pseudo sensor enhancement method (PSEM) is presented and investigated to isolate and compensate for the transient faults. The simulation results of different sensor architecture configurations without and with applying the PSEM have been obtained and analyzed to demonstrate the enhancement in operational performance of the proposed method to meet the requirements for multiple sensor intelligent systems. © 2016 IEEE.},
author_keywords={fault detection prediction;  fault isolation;  intelligent system applications;  Multiple sensors;  pseudo sensor enhancement method (PSEM)},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Ren2017414,
author={Ren, M.J.a  and Sun, L.J.a  and Liu, M.Y.b  and Cheung, C.F.b  and Yin, Y.H.a },
title={A Reconstruction-Registration Integrated Data Fusion Method for Measurement of Multiscaled Complex Surfaces},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2017},
volume={66},
number={3},
pages={414-423},
doi={10.1109/TIM.2016.2636538},
art_number={7801891},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008462113&doi=10.1109%2fTIM.2016.2636538&partnerID=40&md5=1dc9ade3877a7edadbac4f4b298091c0},
affiliation={School of Mechanical Engineering, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China; Partner State Key Laboratory of Ultra-precision Machining Technology, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong},
abstract={The combined use of multiple measurement sensors is considered as a promising solution in surface metrology. Such hybrid instruments require sophisticated data fusion process to achieve overall better measurement results. This paper presents a reconstruction-registration integrated data fusion method to address the difficulty in modeling and fusing multiscaled complex data sets. The method decomposes the data sets into different scales by fitting a common surface via reconstruction and registration process so that the modeling and fusion process are also decomposed, and are only performed among the fitting and matching residuals of the data sets. The quality of the fused results is improved based on weighted mean method with the aid of Gaussian process model by taking into account the associated errors of each data set. The validity of the proposed method is verified through a series of comparison tests with existing methods by both computer simulation and actual measurement. It is shown that both enhanced registration accuracy and fusion quality are achieved by the proposed method with acceptable computation cost. The method should improve the metrological performance of the multisensor instruments in measuring complex surfaces. © 2016 IEEE.},
author_keywords={Data fusion;  multiscaled surfaces;  precision surface measurement;  surface modeling;  surface registration},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Li20171973,
author={Li, Z. and Fei, L. and Yongyi, Z.},
title={Based on the Particle Swarm Optimization-Neural Network integration algorithm in Internet of Vehicles application},
journal={Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016},
year={2017},
pages={1973-1977},
doi={10.1109/IMCEC.2016.7867562},
art_number={7867562},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016800794&doi=10.1109%2fIMCEC.2016.7867562&partnerID=40&md5=71bd4b71d4fdea4bfab1df3ed0036369},
affiliation={Shenyang Normol University Software College, China},
abstract={Internet of Vehicles, an emerging industry, is being gradually popularized and applied. This paper is to propose a new construction method to neural network ensembles that based on the key technology to multi-sensor data fusion of Internet of Vehicles system, which is an effective solution to the problems such as the information collection about vehicle traffic. Training a group of neural networks Independently, and then use discrete particle swarm optimization (PSO) algorithm and describe all possible neural network ensemble when the value of particles is 0 or 1 in multi-dimensional space. The estimated value to predict error of the network integration is expressed by correlation degree among individual networks that made up of integration, and it will be used as the fitness function during the optimization process. We will select the individual networks which has much differences among the parts that get involved in neural network ensemble. © 2016 IEEE.},
author_keywords={Internet of Vehicles;  Multi-sensor data fusion;  Neural network ensemble;  Particle swarm optimization},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Jing2017,
author={Jing, L.a  and Wang, T.a  and Zhao, M.b  and Wang, P.a },
title={An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox},
journal={Sensors (Switzerland)},
year={2017},
volume={17},
number={2},
doi={10.3390/s17020414},
art_number={414},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013831942&doi=10.3390%2fs17020414&partnerID=40&md5=174cb226eaaa0aeff55a4247d44c1254},
affiliation={School of Mechanical Engineering, Tianjin University, Tianjin, China; School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China},
abstract={A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.},
author_keywords={Deep convolutional neural networks;  Fault diagnosis;  Feature learning;  Multi-sensor data fusion;  Planetary gearbox},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Shkvarko2017,
author={Shkvarko, Y.V.a  and Lopez, J.A.a  and Santos, S.R.b },
title={Maximum entropy neural networks for feature enhanced imaging with collaborative microwave multi-sensor data fusion},
journal={LAMC 2016 - IEEE MTT-S Latin America Microwave Conference},
year={2017},
doi={10.1109/LAMC.2016.7851241},
art_number={7851241},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015918971&doi=10.1109%2fLAMC.2016.7851241&partnerID=40&md5=68c67668fde4fbea724566c5db82f46e},
affiliation={Centre for Advanced Research and Education (CINVESTAV), National Polytechnic Institute, Guadalajara, Jalisco, Mexico; University Centre for Exact Sciences and Engineering (CUCEI), University of Guadalajara, Jalisco, Mexico},
abstract={We present a collaborative neural network (NN) computing-oriented approach for feature enhanced reconstruction of microwave remote sensing (RS) imagery via sensor data fusion. Two reconstruction/fusion frameworks are proposed and featured. Both unify the maximum entropy and descriptive experiment design regularization (DEDR) paradigms but employ different NN-based fusion (NNF) strategies. The first one addressed as RS-NNF(1) aggregates the adaptively weighted DEDR-structured individual sensor image recovery objective functions, while the second one addressed as RS-NNF(2) performs cooperative multi-sensor statistical recovery performances enhancement-oriented fusion. The simulations corroborate superiority of both proposed technics over the conventional non-collaborative RS image fusion with RS-NNF(2) on top. © 2016 IEEE.},
author_keywords={Experiment design;  imaging radar;  maximum entropy;  neural network;  regularization;  sensor fusion},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Wang20171,
author={Wang, X.a  and Ahmet Sekercioglu, Y.b  and Drummond, T.a  and Natalizio, E.b  and Fantoni, I.b  and Fremont, V.b },
title={Collaborative multi-sensor image transmission and data fusion in mobile visual sensor networks equipped with RGB-D cameras},
journal={IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems},
year={2017},
pages={1-8},
doi={10.1109/MFI.2016.7849458},
art_number={7849458},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015188157&doi=10.1109%2fMFI.2016.7849458&partnerID=40&md5=02d67185ad5fb7562847b095601d47d2},
affiliation={ARC Centre of Excellence for Robotic Vision, Monash University, Victoria, Australia; Sorbonne Universités, Université de Technologie de Compiègne, CNRS, UMR 7253 Heudiasyc, Compiègne, France},
abstract={We present a scheme for multi-sensor data fusion applications, called Relative Pose based Redundancy Removal (RPRR), that efficiently enhances the wireless channel utilization in bandwidth-constrained operational scenarios for RGB-D camera equipped visual sensor networks. Pairs of nodes cooperatively determine their own relative pose, and by using this knowledge they identify the correlated data related to the common regions of the captured color and depth images. Then, they only transmit the non-redundant information present in these images. As an additional benefit, the scheme also extends the battery life through reduced number of packet transmissions. Experimental results confirm that significant gains in terms of wireless channel utilization and energy consumption would be achieved when the RPRR scheme is used in visual sensor network operations. © 2016 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Bouvry2017424,
author={Bouvry, P.a  and Chaumette, S.b  and Danoy, G.a  and Guerrini, G.c  and Jurquet, G.c  and Kuwertz, A.d  and Muller, W.d  and Rosalie, M.a  b  and Sander, J.d },
title={Using heterogeneous multilevel swarms of UAVs and high-level data fusion to support situation management in surveillance scenarios},
journal={IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems},
year={2017},
pages={424-429},
doi={10.1109/MFI.2016.7849525},
art_number={7849525},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015259126&doi=10.1109%2fMFI.2016.7849525&partnerID=40&md5=975258efe445ae9b72b4efab17aa04dc},
affiliation={University of Luxembourg, SnT/FSTC-CSC, Luxembourg, Luxembourg; Univ. Bordeaux, LaBRI, UMR 5800, Talence, France; Thales Airborne Systems, Pessac, France; Fraunhofer IOSB, Karlsruhe, Germany},
abstract={The development and usage of Unmanned Aerial Vehicles (UAVs) quickly increased in the last decades, mainly for military purposes. This technology is also now of high interest in non-military contexts like logistics, environmental studies and different areas of civil protection. While the technology for operating a single UAV is rather mature, additional efforts are still necessary for using UAVs in fleets (or swarms). The Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques (ASIMUT) project which is supported by the European Defence Agency (EDA) aims at investigating and demonstrating dedicated surveillance services based on fleets of UAVs. The aim is to enhance the situation awareness of an operator and to decrease his workload by providing support for the detection of threats based on multi-sensor multi-source data fusion. The operator is also supported by the combination of information delivered by the heterogeneous swarms of UAVs and by additional information extracted from intelligence databases. As a result, a distributed surveillance system increasing detection, high-level data fusion capabilities and UAV autonomy is proposed. © 2016 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Simon2017259,
author={Simon, C. and Ludwig, T. and Kruse, M.},
title={Extracting sensor models from a scene based simulation},
journal={IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems},
year={2017},
pages={259-264},
doi={10.1109/MFI.2016.7849498},
art_number={7849498},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015243518&doi=10.1109%2fMFI.2016.7849498&partnerID=40&md5=73e1c7780bf8247781ba09cf8acd6182},
affiliation={IAV GmbH, Germany},
abstract={A safety critical system like an autonomous car has to rely on an excellent perception, both in accuracy and reliability to allow a correct functional behaviour of the system. It is not conceivable that one sensor or even a set of sensors will be able to provide this demanded quality on its own. Current approaches are aiming for car external solutions like smart infrastructure or Vehicle2X communication to achieve the desired trust into the perception. However this paper is focusing on the enhancement of the internal sensor set and the corresponding Bayesian multi sensor data fusion. This kind of fusion is dependent on sufficient sensor models to weight the measurements correctly. In the paper at hand a method is shown how these models can be derived from real world measurement data and simulation results using a scene based pattern recognition. © 2016 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Yang2017279,
author={Yang, Y.a  b },
title={Non-rigid image registration for visible color and thermal IR face},
journal={ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings},
year={2017},
pages={279-284},
doi={10.1109/ICALIP.2016.7846571},
art_number={7846571},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016106024&doi=10.1109%2fICALIP.2016.7846571&partnerID=40&md5=dfbf5c33b95576543e20a088024e7976},
affiliation={School of Information Science and Technology, Yunnan Normal University, Kunming, China; Engineering Research Center of GIS Technology in Western China, Ministry of Education of China, Yunnan Normal University, Kunming, China},
abstract={Multi-sensor data fusion between visible sensors and infrared (IR) sensors plays an important role in computer vision and pattern recognition. However, the current methods using single feature based correspondence estimators with a rigid or affine transformation are unable to build accurate alignments when scenes are not planar. In this work, we present a non-rigid image registration method for visible color and thermal IR face. We evaluated the performance of the proposed method on a public database, and compared with state-of-the-art methods where our method shows the best alignments in most scenarios. © 2016 IEEE.},
author_keywords={Color;  Face images;  Image registration;  Non-rigid;  Thermal IR;  Visible},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ilyas2017,
author={Ilyas, K. and Ullah, I.},
title={A state estimation and fusion algorithm for high-speed low-Altitude targets},
journal={Proceedings of the 2016 19th International Multi-Topic Conference, INMIC 2016},
year={2017},
doi={10.1109/INMIC.2016.7840127},
art_number={7840127},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015205896&doi=10.1109%2fINMIC.2016.7840127&partnerID=40&md5=e2a08a0cfba2b43083b8b605b9ce10ef},
affiliation={Comsats Institute of Information and Technology, Abbottabad, Pakistan},
abstract={Autonomous systems require situational awareness that is resilient to unusual but realistic conditions. Modern maritime defense systems are equipped with highly intelligent tracking sensors to detect and track high-speed incoming threats. Such systems with single sensor are not reliable and accurate. The paper suggests multi-sensor data fusion approach to overcome the limitations of a single sensor. The sensors used in this study are Laser Detection And Ranging (LADAR) and infrared (IR). The information obtained from these sensors is fused to achieve 3-D localization of high-speed incoming threats. The Kalman and extended Kalman filter are employed for optimum state estimates and data fusion. Computer simulations clearly demonstrate the efficiency of the proposed algorithm. Performance of the presented fusion approach in comparison with other existing approaches is also presented. Computational time of each technique is also computed for comparison. © 2016 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Bader201711,
author={Bader, K. and Lussier, B. and Schön, W.},
title={A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localization},
journal={Robotics and Autonomous Systems},
year={2017},
volume={88},
pages={11-23},
doi={10.1016/j.robot.2016.11.015},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007049085&doi=10.1016%2fj.robot.2016.11.015&partnerID=40&md5=ae87a493043933c6e25c23be0a75f4a0},
affiliation={Sorbonne Universités, Université de Technologie de Compiègne, CNRS, UMR 7253, Heudiasyc, CS 60 319, Compiégne, France},
abstract={Multisensor perception has an important role in robotics and autonomous systems, providing inputs for critical functions including obstacle detection and localization. It is starting to appear in critical applications such as drones and ADASs (Advanced Driver Assistance Systems). However, this kind of complex system is difficult to validate comprehensively. In this paper we look at multisensor perception systems in relation to an alternative dependability method, namely fault tolerance. We propose an approach for tolerating faults in multisensor data fusion that is based on the more traditional method of duplication–comparison, and that offers detection and recovery services. We detail an example implementation using Kalman filter data fusion for mobile robot localization. We demonstrate its effectiveness in this case study using real data and fault injection. © 2016 Elsevier B.V.},
author_keywords={Data fusion;  Dependability;  Fault tolerance;  Multi-sensor perception},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Nur2017,
author={Nur, D. and Dharmiza, S. and Salleh, A. and Seignez, E.},
title={Monocular visual odometry with road probability distribution factor for lane-level vehicle localization},
journal={2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016},
year={2017},
doi={10.1109/ICARCV.2016.7838777},
art_number={7838777},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015221370&doi=10.1109%2fICARCV.2016.7838777&partnerID=40&md5=9b2d768b37b8b9e7ac82d9712f28c6b1},
affiliation={SATIE, Université Paris-Sud, ENS Cachan, CNRS, Université Paris-Saclay, Orsay, France},
abstract={Towards achieving lane-level localization, precision and accuracy plays an important role in vehicle localization efficiency. While Global Positioning System (GPS) is usually used for localization, it has low accuracy caused by signal degradation due to several reasons such as lack of well-positioned satellites, signal obstruction or multipath error. Thus, multi-sensor data fusion has been widely studied to improve vehicle localization. By utilizing the existing techniques for monocular visual odometry and particle filter localization, this paper presents how road information available in OpenStreetMap contributes to accurate and precise vehicle localization by exploiting road probability distribution factor in particle filter implementation. This approach was verified in two datasets with different road features and it has shown better performance compared with the established particle filter localization. As our results indicate, this approach is feasible for lane-level localization for intelligent vehicles. © 2016 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Zhang2017,
author={Zhang, L. and Gao, H.},
title={A deep learning-based multi-sensor data fusion method for degradation monitoring of ball screws},
journal={Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016},
year={2017},
doi={10.1109/PHM.2016.7819792},
art_number={7819792},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015646537&doi=10.1109%2fPHM.2016.7819792&partnerID=40&md5=5d0c5e2789b8f6cc043b631227b241c0},
affiliation={Department of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China},
abstract={As ball screw has complex structure and long range of distribution, single signal collected by one sensor is difficult to express its condition fully and accurately. Multi-sensor data fusion usually has a better effect compared with single signal. Multi-sensor data fusion based on neural network(BP) is a commonly used multi-sensor data fusion method, but its application is limited by local optimum problem. Aiming at this problem, a multi-sensor data fusion method based on deep learning for ball screw is proposed in this paper. Deep learning, which consists of unsupervised learning and supervised learning, is the development and evolution of traditional neural network. It can effectively alleviate the optimization difficulty. Parallel superposition on frequency spectra of signals is directly done in the proposed deep learning-based multi-sensor data fusion method, and deep belief networks(DBN) are established by using fused data to adaptively mine available fault characteristics and automatically identify the degradation condition of ball screw. Test is designed to collect vibration signals of ball screw in 7 different degradation conditions by using 5 acceleration sensors installed on different places. The proposed fusion method is applied in identifying the degradation degree of ball screw in the test to demonstrate its efficacy. Finally, the multi-sensor data fusion based on neural network is also applied in degradation degree monitoring. The monitoring accuracy of deep learning-based multi-sensor data fusion is higher compared with that of neural network-based multi-sensor data fusion, which means the proposed method has more superiority. © 2016 IEEE.},
author_keywords={Ball screw;  Deep learning;  Degradation monitoring;  Multi-sensor data fusion},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kumar20171,
author={Kumar, P.a  and Gauba, H.a  and Roy, P.P.a  and Dogra, D.P.b },
title={Coupled HMM-based multi-sensor data fusion for sign language recognition},
journal={Pattern Recognition Letters},
year={2017},
volume={86},
pages={1-8},
doi={10.1016/j.patrec.2016.12.004},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006507532&doi=10.1016%2fj.patrec.2016.12.004&partnerID=40&md5=81d734afbbc9ad63e9fbd7136a6e8a6f},
affiliation={Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India; School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, India},
abstract={Recent development of low cost depth sensors such as Leap motion controller and Microsoft kinect sensor has opened up new opportunities for Human-Computer-Interaction (HCI). In this paper, we propose a novel multi-sensor fusion framework for Sign Language Recognition (SLR) using Coupled Hidden Markov Model (CHMM). CHMM provides interaction in state-space instead of observation states as used in classical HMM that fails to model correlation between inter-modal dependencies. The framework has been used to recognize dynamic isolated sign gestures performed by hearing impaired persons. The dataset has been tested using existing data fusion approaches. The best recognition accuracy has been achieved as high as 90.80% with CHMM. Our CHMM-based approach shows improvement in recognition performance over popular existing data fusion techniques. © 2016 Elsevier B.V.},
author_keywords={Bayesian classification;  Depth sensors;  Hidden Markov model (Coupled HMM, HMM);  Sign language recognition},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Lee2017,
author={Lee, S. and Bakr, M.A.},
title={An optimal data fusion for distributed multisensor systems: Covariance extension method},
journal={Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, IMCOM 2017},
year={2017},
doi={10.1145/3022227.3022268},
art_number={42},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015207521&doi=10.1145%2f3022227.3022268&partnerID=40&md5=753bc47922c853a6db63529675dde76d},
affiliation={Intelligent Systems Research Institute, Sungkyunkwan University, South Korea},
abstract={A general data fusion framework for distributed sensor systems of arbitrary redundancies is presented. By a general framework, we mean that the proposed method, referred to here as Covariance Extension (CE) Method, is able to directly fuse different dimensions of state vectors as well as constraints without additional processing. For instance, well-known Bar-Shalom Campo and Millman's formulae for the fusion of 2 and N correlated sensors, respectively, cannot properly handle state vectors of unequal dimensions. The proposed CE Method aggregates all the state vectors into a single vector in an extended space and transforms data fusion as a problem of satisfying the constraints among state vectors in the extended space. Specifically, the method orthogonally projects the mean and covariance of the aggregated state vectors on the equality constraint manifold in the extended space. The proposed CE Method is proven to be optimal in the sense of Minimum Mean Square Error (MMSE), while providing computational efficiency over Millman's formula. Simulation results shows the robust nature and effectiveness of the proposed method. © 2017 ACM.},
author_keywords={Bar-Shalom Campo;  Covariance extension;  Data fusion;  Distributed architecture;  Kalman filter;  Millman's formula;  Multisensor},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zebin2017306,
author={Zebin, T.a  and Scully, P.J.b  and Ozanyan, K.B.a  b },
title={Inertial sensor based modelling of human activity classes: Feature extraction and multi-sensor data fusion using machine learning algorithms},
journal={Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
year={2017},
volume={181 LNICST},
pages={306-314},
doi={10.1007/978-3-319-49655-9_38},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009437486&doi=10.1007%2f978-3-319-49655-9_38&partnerID=40&md5=cf8dd06097bf4519c5fbf61299804cc3},
affiliation={School of Electrical and Electronic Engineering, University of Manchester, Manchester, United Kingdom; Photon Science Institute, University of Manchester, Manchester, United Kingdom},
abstract={Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.},
author_keywords={Accelerometer data;  Data-fusion;  Feature extraction;  Human activity recognition;  Inertial measurement unit;  Machine learning algorithms},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Hao2017563,
author={Hao, P. and Jia, D.-Y. and Yin, Z.},
title={Leak detection and location of seals in spacecraft structures based on a multi-sensor data fusion method},
journal={Astrophysics and Space Science Proceedings},
year={2017},
volume={47},
pages={563-572},
doi={10.1007/978-3-319-19309-0_56},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019748437&doi=10.1007%2f978-3-319-19309-0_56&partnerID=40&md5=77992d67f668c68c4ed567d4a3f08e36},
affiliation={Institute of Manned Space System Engineering, CAST, Beijing, China},
abstract={During manned long-term missions in orbit, sealed structures have to meet the challenges of the space environment, such as meteoroids and space debris impacts, charged particles radiation, temperature cycling, vacuum environment, etc. Sealed structures and rubber sealing may be damaged by these factors. A sealed module faces the risk of leaks that can affect the pressure inside the module and even threaten the safety of the astronauts. Therefore, when performing leak-proof welding, it is an important precondition to know how to repair the failed structure and how to detect and locate the leak rapidly. General leak monitoring methods were surveyed and analyzed and it was demonstrated that any method, when used alone, can’t be used effectively for rapid detection and location of leaks in a sealed structure. A multi-sensor data fusion method is presented that allows diagnosing the leaks and the leakage rate thus allowing for effective leak detection and location in sealed spacecraft structures. The leak detection and location system consists of two subsystems: pressure leak monitor and meteoroid and space debris impact monitor. These two subsystems work continuously to monitor the spacecraft leakage rate and impact damage, respectively. In addition, an information interface, estimation strategy, and transfer flow of five subsystems were designed. This method is substantiated by a ground experiment to confirm that the sealed structure leak reason, location and leakage rate can be detected accurately and can be applied in welding operations directly. © Springer International Publishing AG 2017.},
author_keywords={Leak detection and location;  Manned spacecraft;  Multi-sensor data fusion;  Seal structure;  Structure health estimation;  Structure protection},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Gong2017918,
author={Gong, X. and Ding, L. and Du, W. and Wang, H.},
title={Gear Fault Diagnosis Using Dual Channel Data Fusion and EEMD Method},
journal={Procedia Engineering},
year={2017},
volume={174},
pages={918-926},
doi={10.1016/j.proeng.2017.01.242},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016928922&doi=10.1016%2fj.proeng.2017.01.242&partnerID=40&md5=137ec9891482655049ceb01393f41a89},
affiliation={School of Mechanical and Electronic Engineering, Zhengzhou University of Light Industry, Zhengzhou, China},
abstract={When a gear fault is presented, the ensemble empirical decomposition (EEMD) method has been widely used in the detection of gear fault and has given good results. However, under signals from two vibration transducers which are installed in different directions, the vibration data from every sensor becomes necessary to obtain much more precise results. This paper suggests a new feature frequency extraction approach for gear malfunctions diagnosis using the full vector spectrum (FCS) and EEMD method. The approach embodies model building of multi-sensor data fusion and main intrinsic mode functions(IMFs) analysing of merged data from two vibration sensors. By using EEMD the data from single channel can be decomposed into a number of IMFs. To alleviate the diversity problem of EEMD decomposition results occurring in two channels, full vector spectrum (FVS) is introduced. With full vector spectrum, information obtained from each sensor can be merged a piece of vibration evidence in frequency domain, and then we use Hilbert transform to calculate the envelope spectrum of each IMF to evaluate the fault feature frequency. In this work, simulated signal and gear experiment were conducted to validate this strategy. The results have proven that the proposed method can enhance the effectiveness of feature frequency extraction in diagnosing gear faults. © 2017 The Authors.},
author_keywords={demodulation;  ensemble empirical decomposition;  full vector spectrum;  gear fault diagnosis;  Vibration analysis},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Yu201727,
author={Yu, J.-J.a  and Chen, X.-B.b },
title={Integrated application research about the necklace type wearable health sensing system under the internet of things},
journal={Lecture Notes in Electrical Engineering},
year={2017},
volume={399},
pages={27-47},
doi={10.1007/978-981-10-2404-7_3},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992650026&doi=10.1007%2f978-981-10-2404-7_3&partnerID=40&md5=fc96d7984cc097e2a8a92e169a642964},
affiliation={Department of Industrial Engineering, South China University of Technology, Guangzhou, China; School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China},
abstract={The Wearable Health Sensing System (WHSS) is to integrate the various sensors on the necklace, which is convenient for the real-time detection of human life and health, meanwhile to use the data processing ability of portable handheld terminal and coordination with the application of APP interaction to make the health assessment, diet care information pushing, and the emergency medical treatment true. This paper puts forward a scheme that integrating the multiple health sensors on the necklace, which includes the movement frequency, heart rate, sweat, skin tissue fluid, ECG, blood oxygen saturation, skin moisture, body fat, etc., objecting to setup a dynamic and collaborative monitoring physiological data under the daily life and the complicated working environment by means of a lower physiological and psychological load. Three modules are designed for WHSS: sensor integration module, power supply and power module, and signal integration and network transmission module. Corresponding sensors are chosen for WHSS according to various application requirements. Existing disturbances affect the accuracy and reliability of the judgment about the health feature points, accordingly the anti-interference processing is carried out. WHSS has only integrated multi-sensor without setting powerful data processing unit, so the captured signal is extended to handheld terminals to display and control in accordance with the processing ability of formalities terminal. The function model of data fusion system is setup, the level of the data fusion system is analyzed, the data fusion algorithm is realized, and the Multi-sensor system identification technology is studied. Due to the automatic comprehensive analysis capabilities of the multi-sensor system identification technology, it could be more effective and accurate to reflect the physical status of the object than any single sensor in the system. Under the environment of the Internet of things, the fusion of multiple data display control from WHSS, can accurately obtain the health information, and sets up the health system security network to a certain extent. © Zhejiang University Press and Springer Science+Business Media Singapore 2017.},
author_keywords={Data fusion processing;  Internet of things;  Multisensor system identification;  Wearable health sensing system},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kashanian2017493,
author={Kashanian, H. and Dabaghi, E.},
title={Feature dimension reduction of multisensor data fusion using principal component fuzzy analysis},
journal={International Journal of Engineering, Transactions A: Basics},
year={2017},
volume={30},
number={4},
pages={493-499},
doi={10.5829/idosi.ije.2017.30.04a.06},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019133946&doi=10.5829%2fidosi.ije.2017.30.04a.06&partnerID=40&md5=3af0e5f8ab2c32efb3392bde29bbf061},
affiliation={Department of Computer Science and Software Engineering, Ferdows Islamic Azad University, Ferdows, Iran},
abstract={These days, the most important areas of research in many different applications, with different tools, are focused on how to get awareness. One of the serious applications is the awareness of the behavior and activities of patients. The importance is due to the need of ubiquitous medical care for individuals. That the doctor knows the patient's physical condition, sometimes is very important. Of course, there are other important applications for this information. There are a variety of methods and tools for measurement, gathering, and analysis of the physical behaviors and activities' information. One of the most successful tools for this aim are ubiquitous intelligent electronic devices, specifically smartphones, and smart watches. There are many sensors in these devices, some of which can be used to understand the activities of daily living. As an output result, these sensors produce many raw data. Thus, it is needed to process these information and recognize the individual behavior of the output of this processing. In this paper, the basic components of the analysis phase for this process have been proposed. Simulations validate the benefits and superiority of this method. © 2017, Materials and Energy Research Center. All rights reserved.},
author_keywords={Health care;  Intelligent devices;  Multisensor data fusion;  Principle components fuzzy analysis;  Ubiquitous activities monitoring},
document_type={Article},
source={Scopus},
}

@ARTICLE{Duda201729,
author={Duda, Z.},
title={State estimation in a decentralized discrete time LQG control for a multisensor system},
journal={Archives of Control Sciences},
year={2017},
volume={27},
number={1},
pages={29-39},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016208709&partnerID=40&md5=38d2adff269d2f350f9391554719e32f},
affiliation={Silesian University of Technology, Gliwice, Poland},
abstract={In the paper a state filtration in a decentralized discrete time Linear Quadratic Gaussian problem formulated for a multisensor system is considered. Local optimal control laws depend on global state estimates and are calculated by each node. In a classical centralized information pattern the global state estimators use measurements data from all nodes. In a decentralized system the global state estimates are computed at each node using local state estimates based on local measurements and values of previous controls, from other nodes. In the paper, contrary to this, the controls are not transmitted between nodes. It leads to nonconventional filtration because the controls from other nodes are treated as random variables for each node. The cost for the additional reduced transmission is an increased filter computation at each node. Copyright ©Silesian University of Technology 2017.},
author_keywords={Kalman filter;  LQG problem;  Multisensor system},
document_type={Article},
source={Scopus},
}

@ARTICLE{Huang201784,
author={Huang, H. and Chen, X. and Zhou, Z. and Liu, H. and Lv, C.},
title={Study On INS/DR integration navigation system using EKF/RK4 Algorithm For Underwater Gliders},
journal={Journal of Marine Science and Technology (Taiwan)},
year={2017},
volume={25},
number={1},
pages={84-95},
doi={10.6119/JMST-016-0913-3},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018819146&doi=10.6119%2fJMST-016-0913-3&partnerID=40&md5=9e0d582ecca7cf5fea697ef79c11957c},
affiliation={School of Instrument Science and Engineering, Southeast University, Nanjing, China},
abstract={The underwater glider has the advantages of low power, long endurance and high accuracy. Micro-Electro-Mechanical- System (MEMS) grade inertial sensors are more suitable for an underwater glider because of their low cost and small size. Models of MEMS sensor noises which include not only the white noises and random walk terms but also the bias instabilities of the sensor noises are analyzed. The integrated navigation system for the underwater glider is composed of a dead reckoning (DR) module, an inertial navigation system (INS) based on MEMS sensors aided by a tri-Axis magnetic sensor. Due to the inherent error characteristics, MEMS grade devices suffer from the non-stationary stochastic sensor errors and nonlinear inertial errors which cannot be well handled by the conventional filter algorithms, this paper proposes extended Kalman filter (EKF) fusing Runge-Kutta (RK4) algorithm (EKF/RK4) which can implement the data fusion of multisensor. The proposed EKF/RK4 can take advantage of the EKF to achieve the optimal estimation of attitude and position and then make better use of the RK4 to further improve the estimation accuracy. In order to evaluate the effectiveness of the proposed algorithm, the EKF/RK4 algorithm is applied to the underwater navigation system designed in our lab and a series of land experiments are performed. The performance of the proposed EKF/RK4 algorithm based on our navigation system is analyzed and compared with the traditional algorithms. The experiment results show that the proposed algorithm is more effective in reducing the attitude and position errors than KF/RK4 and EKF.},
author_keywords={Dead reckoning (DR);  Extended kalman filter (EKF);  Inertial navigation system (INS);  Runge-kutta (RK4)},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sowah2017559,
author={Sowah, R.A.a  and Ofoli, A.R.b  and Krakani, S.N.a  and Fiawoo, S.Y.a },
title={Hardware design and web-based communication modules of a real-time multisensor fire detection and notification system using fuzzy logic},
journal={IEEE Transactions on Industry Applications},
year={2017},
volume={53},
number={1},
pages={559-566},
doi={10.1109/TIA.2016.2613075},
art_number={7575685},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010297228&doi=10.1109%2fTIA.2016.2613075&partnerID=40&md5=9bf3f3b7b8cae8db7a756329a28c432c},
affiliation={University of Ghana, PMB 25, Accra, Ghana; University of Tennessee, Knoxville, TN, United States},
abstract={This paper presents the design and development of a fuzzy logic based multisensor fire detection system and a web-based notification system. Until recently, most consumer grade fire detection systems relied solely on smoke detectors. The protection provided by these has been established to be limited by the type of fire present and the detection technology at use. The problem is further compounded by the lack of adequate alert and notification mechanisms. A typical system relies on the physical presence of a human being to act on the alert. In developing countries, poor planning and addressing negatively affects the fire and rescue crew's response time. To address this problem, a fuzzy logic system was implemented using an Arduino development board with inputs from an MQ2 smoke sensor, a TMP102 temperature sensor, and a DFRobot flame sensor. The output of the detection system is sent over short message service (SMS) using a SIM900 global system for mobile communication (GSM) module to the web-based system and the house owner or caretaker in real-time. With access granted to the web-based system, the fire and rescue crew also get notified in real-time with location information. A comparison between the efficiency of the notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements in the form of timely detection, alerting, and response. © 1972-2012 IEEE.},
author_keywords={Data fusion;  Fire detection;  Fuzzy logic;  Microcontroller-based;  Real-time;  Web-based},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Chen2017,
author={Chen, J. and Dou, Y. and Li, Y. and Li, J. and Li, G.},
title={A transient fault recognition method for an AC-DC hybrid transmission system based on MMC information fusion},
journal={Energies},
year={2017},
volume={10},
number={1},
doi={10.3390/en10010023},
art_number={23},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009288258&doi=10.3390%2fen10010023&partnerID=40&md5=9ed795a3aee1f35ce73b53625f291ace},
affiliation={School of Electrical Engineering, Northeast Electric Power University, Jilin, China},
abstract={At present, the research is still in the primary stage in the process of fault disturbance energy transfer in the multilevel modular converter based high voltage direct current (HVDC-MMC). An urgent problem is how to extract and analyze the fault features hidden in MMC electrical information in further studies on the HVDC system. Aiming at the above, this article analyzes the influence of AC transient disturbance on electrical signals of MMC. At the same time, it is found that the energy distribution of electrical signals in MMC is different for different arms in the same frequency bands after the discrete wavelet packet transformation (DWPT). Renyi wavelet packet energy entropy (RWPEE) and Renyi wavelet packet time entropy (RWPTE) are proposed and applied to AC transient fault feature extraction from electrical signals in MMC. Using the feature extraction results of Renyi wavelet packet entropy (RWPE), a novel recognition method is put forward to recognize AC transient faults using the information fusion technology. Theoretical analysis and experimental results show that the proposed method is available to recognize transient AC faults.},
author_keywords={Discrete wavelet packet transformation;  Fault recognition;  Feature extraction;  MMC;  Renyi entropy;  Transient faults},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Shen2016,
author={Shen, F. and Yan, R.},
title={A thermostatic control strategy based on multi-sensor data fusion and fuzzy-PID method},
journal={Proceedings of the International Conference on Sensing Technology, ICST},
year={2016},
doi={10.1109/ICSensT.2016.7796326},
art_number={7796326},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010039010&doi=10.1109%2fICSensT.2016.7796326&partnerID=40&md5=be1030fc8e59ecc329d27299c1229225},
affiliation={School of Instrument Science and Engineering, Southeast University, Nanjing, China},
abstract={The steady temperature is vital to organ-saving out of body in a hypothermic machine perfusion (HMP) system. A thermostatic control strategy based on multi-sensor data fusion and fuzzy-PID method is proposed in this paper to improve the accuracy. Firstly, the basic frame of HMP system and the installation of sensors are expounded. Then the data fusion based on modified Bayes estimation is carried out to weaken the possible measurement error, resulting from the sensor faults and noise interference. Specially, the better error-recovery ability is proved in the cascaded Bayes algorithm. Secondly, the fuzzy and the fuzzy-PID (Proportion Integration Differentiation) controller are adopted respectively according to the difference of temperature-deviation. Here the former is designed to offer the control variation of compressor needed while the latter is to gain three control coefficients of PID algorithm. The dynamic and static tests indicate that the thermostatic control result meets the need of patients although it is also affected by some extra factors, such as the external temperature, flow speed of solution and working modes. © 2016 IEEE.},
author_keywords={data fusion;  fuzzy-PID controller;  HMP system;  modified Bayes estimation;  thermostatic control},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Fawaz201621,
author={Fawaz, A.a  and Bohara, A.b  and Cheh, C.b  and Sanders, W.H.a },
title={Lateral Movement Detection Using Distributed Data Fusion},
journal={Proceedings of the IEEE Symposium on Reliable Distributed Systems},
year={2016},
pages={21-30},
doi={10.1109/SRDS.2016.014},
art_number={7794326},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010219972&doi=10.1109%2fSRDS.2016.014&partnerID=40&md5=f203fbcbffa4a2645e77fd741efb7a67},
affiliation={Department of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, United States; Department of Computer Science, University of Illinois, Urbana-Champaign, United States},
abstract={Attackers often attempt to move laterally from host to host, infecting them until an overall goal is achieved. One possible defense against this strategy is to detect such coordinated and sequential actions by fusing data from multiple sources. In this paper, we propose a framework for distributed data fusion that specifies the communication architecture and data transformation functions. Then, we use this framework to specify an approach for lateral movement detection that uses host-level process communication graphs to infer network connection causations. The connection causations are then aggregated into system-wide host-communication graphs that expose possible lateral movement in the system. In order to provide a balance between the resource usage and the robustness of the fusion architecture, we propose a multilevel fusion hierarchy that uses different clustering techniques. We evaluate the scalability of the hierarchical fusion scheme in terms of storage overhead, number of message updates sent, fairness of resource sharing among clusters, and quality of local graphs. Finally, we implement a host-level monitor prototype to collect connection causations, and evaluate its overhead. The results show that our approach provides an effective method to detect lateral movement between hosts, and can be implemented with acceptable overhead. © 2016 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Wang2016156,
author={Wang, Z.a  b  c  and Wu, X.a  c  and Cai, B.a  c  and Tao, C.d  and Zhang, Z.d  and Wang, Y.d },
title={A fusion-based localization method of mobile robot with equality geometric constraint},
journal={2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016},
year={2016},
pages={156-162},
doi={10.1109/RCAR.2016.7784018},
art_number={7784018},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010070528&doi=10.1109%2fRCAR.2016.7784018&partnerID=40&md5=0220595c37c8cd78312d7fc201c0c470},
affiliation={School of Electronic Information and Engineering, Beijing Jiao Tong University, China; State Key Laboratory of Robotics and System, Harbin Institute of Technology, China; Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, Beijing, China; CSR Qindao Sifang Co. Ltd., Qindao, China},
abstract={GPS/ODO fusion-based localization is always used for mobile robot in outdoor environment. This approach takes advantage of short-term accuracy of odometry measurement and the long-term correction by the GPS unit. Generally, to improve the localization accuracy, precise system model and sensor noise model are probed. In this paper, by taking the linear and nonlinear equality state constraints into the EKF-based fusion framework with the method of perfect measurement and estimate projection, the performance of fusion results can be greatly improved. The modeling process is introduced and two simulation cases have been conducted, which show the positioning accuracy of the mobile robot can be significantly improved after the introduction of the equivalent constraint. Theoretically, this implementation allows us to define additional adjustments to improve the overall behavior of the filter. © 2016 IEEE.},
author_keywords={equality constraint;  extended Kalman filter;  Localization;  multi-sensor data fusion},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Salavasidis2016115,
author={Salavasidis, G.a  and Harris, C.a  and Mcphail, S.a  and Phillips, A.B.a  and Rogers, E.b },
title={Terrain aided navigation for long range AUV operations at arctic latitudes},
journal={Autonomous Underwater Vehicles 2016, AUV 2016},
year={2016},
pages={115-123},
doi={10.1109/AUV.2016.7778658},
art_number={7778658},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010366592&doi=10.1109%2fAUV.2016.7778658&partnerID=40&md5=1f8be7db40c235c07a712e29050dfc07},
affiliation={National Ocenography Centre, Southampton, United Kingdom; Department of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom},
abstract={This paper investigates the potential of use of Terrain Aided Navigation (TAN) methods to exploit the extended endurance capabilities of an enhanced 1500m depth rated variant of the Autosub Long Range (ALR), the ALR1500 Autonomous Underwater Vehicle (AUV), for operations under the Arctic ice. A simulator for the TAN system using a low resolution Arctic bathymetric map is developed to study the capability to reduce/bound the positioning error growth due to the Dead Reckoning (DR) drift over time. Bathymetric measurements are obtained using a low sample rated single beam echo sounder and a Jittered Bootsrap Particle Filter (JBPF) is used for the multi-sensor data fusion problem. Sensitivity analysis of JBPF has been performed for a range of values of number of particles, jittering variances and measurement frequencies and a discussion over the results is given. Results from the simulation demonstrate that JBPF is capable of providing a robust and accurate solution to the localization problem provided that the AUV follows a trajectory with a sufficiently variant terrain. Finally, a brief discussion regarding the future extension of this work is also provided.},
author_keywords={Autonomous underwater vehicles;  Localisation;  Long range arctic auv operations;  Particle filter;  Terrain- Aided navigation},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Sabatini2016,
author={Sabatini, R.a  and Moore, T.b  and Hill, C.b  and Gardi, A.a  and Ramasamy, S.a  and Gilgien, M.c },
title={Trajectory optimisation for avionics-based GNSS integrity augmentation system},
journal={AIAA/IEEE Digital Avionics Systems Conference - Proceedings},
year={2016},
volume={2016-December},
doi={10.1109/DASC.2016.7778054},
art_number={7778054},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009507947&doi=10.1109%2fDASC.2016.7778054&partnerID=40&md5=9a0360a880d4b4afdcb6a0d30b063dc6},
affiliation={School of Engineering, RMIT University, Melbourne, VIC, Australia; Nottingham Geospatial Institute, University of Nottingham, Nottingham, United Kingdom; Department of Aerospace Engineering, Cranfield University, Cranfield, United Kingdom},
abstract={In this paper, trajectory optimisation algorithms developed specifically for the Global Navigation satellite System (GNSS) Avionics-Based Integrity Augmentation (ABIA) system are presented. The ABIA system is designed to increase the levels of integrity and accuracy (as well as continuity in multi-sensor data fusion architectures) of GNSS in a variety of mission- and safety-critical aviation applications. The trajectory optimisation algorithms can be employed both for flight planning as well as real-time optimisation of manned and unmanned aircraft for all flight phases. Three and six Degrees-of-Freedom (3-DoF/6-DoF) aircraft dynamics models are adopted to generate a number of feasible flight trajectories that also satisfy the GNSS constraints. A detailed simulation case study is presented to evaluate the performance of trajectory optimisation algorithms for GNSS integrity augmentation using an AIRBUS A320 3-DoF aircraft dynamics model. Results confirm that the employed trajectory optimisation algorithms are capable of supporting high-integrity tasks when GNSS is used as the primary source of navigation data. © 2016 IEEE.},
author_keywords={Avionics Based Integrity Augmentation;  Caution and Wanirng Flags;  GNSS Integrity;  Mission-and Safety-Critical GNSS Applications;  Trajectory Optimisation},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Parmehr20167032,
author={Parmehr, E.G.a  and Fraser, C.S.b  and Zhang, C.c },
title={Automatic Parameter Selection for Intensity-Based Registration of Imagery to LiDAR Data},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2016},
volume={54},
number={12},
pages={7032-7043},
doi={10.1109/TGRS.2016.2594294},
art_number={7542534},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981745180&doi=10.1109%2fTGRS.2016.2594294&partnerID=40&md5=100e86da09009cb5b65f8c1c1d24ab60},
affiliation={Center for Urban Research, RMIT University, Melbourne, VIC, Australia; Cooperative Research Center for Spatial Information, University of Melbourne, Melbourne, VIC, Australia; RMIT University, Melbourne, VIC, Australia},
abstract={Automatic registration of multisensor data, for example, imagery and Light Detection And Ranging (LiDAR), is a basic step in data fusion in the field of geospatial information processing. Mutual information (MI) has recently attracted research attention as a statistical similarity measure for intensity-based registration of multisensor images in the related fields of computer vision and remote sensing. Since MI-based registration methods rely on joint probability density functions (pdfs) for the data sets, errors in pdf estimation can affect the MI value, causing registration failure due to the presence of nonmonotonic surfaces of similarity measure. The quality of the estimated pdf is highly dependent upon both the bin size and the smoothing technique used in the pdf estimation procedure. The lack of a general approach to assign an appropriate bin size value for the pdf of multisensor data reduces both the level of automation and the robustness of the registration. In this paper, a novel bin size selection approach is proposed to improve registration reliability. The proposed method determines the best (uniform or variable) bin size for the pdf estimation via an analysis of the relationship between the similarity measure values of the data and the adopted geometric transformation. This highlights the role of the component of MI sensitive to the transformation, rather than the MI component that is unrelated to the transformation, such as noise. The performance of the proposed method for the registration of aerial imagery to LiDAR point clouds is investigated, and experimental results are compared with those achieved through a feature-based registration method. © 2016 IEEE.},
author_keywords={Image registration;  multisensor data;  mutual information (MI);  probability density function (pdf) parameter estimation},
document_type={Article},
source={Scopus},
}

@ARTICLE{Amanatiadis20167597,
author={Amanatiadis, A.},
title={A Multisensor Indoor Localization System for Biped Robots Operating in Industrial Environments},
journal={IEEE Transactions on Industrial Electronics},
year={2016},
volume={63},
number={12},
pages={7597-7606},
doi={10.1109/TIE.2016.2590380},
art_number={7508948},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996937985&doi=10.1109%2fTIE.2016.2590380&partnerID=40&md5=1089fb0a0afea782b9e8b032ed0369ef},
affiliation={Department of Production and Management Engineering, School of Engineering, Democritus University of Thrace, Xanthi, Greece},
abstract={This paper exploits three heterogeneous but complementary technologies along with a cellular automata particle filtering technique for enhancing localization positioning performance. The system is designed to meet challenging indoor operational conditions, thus different types of humanoid motions and industrial conditions were studied and evaluated according to qualitative attributes. In order to assess the proportional contribution of each navigation subsystem based on their accuracy, the extracted qualitative components are introduced to a particle filtering inference system. The final location estimate is calculated by applying different weights in the resampling stage depending on their accuracy performance for each time step. Experimental and simulation results have shown not only a reduced positioning error in terms of localization effectiveness but also a superior performance in several challenging industrial operational conditions. More accurate and real-time positioning is offered by the proposed method which can be also applied to any biped robot even in challenging operational conditions. © 1982-2012 IEEE.},
author_keywords={Cellular automata (CA);  humanoid robots;  localization;  multisensor fusion;  particle filter},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Kröger2016234,
author={Kröger, M.a  and Sauer-Greff, W.a  and Urbansky, R.a  and Lorang, M.b  and Siegrist, M.b },
title={Performance evaluation on contour extraction using Hough transform and RANSAC for multi-sensor data fusion applications in industrial food inspection},
journal={Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA},
year={2016},
pages={234-237},
doi={10.1109/SPA.2016.7763619},
art_number={7763619},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014177823&doi=10.1109%2fSPA.2016.7763619&partnerID=40&md5=39f43223e3cdb57ac7ac0952bcc9d558},
affiliation={Department of Communications Engeneering, University of Kaiserslautern, Kaiserslautern, Germany; WIPOTEC GmbH, Kaiserslautern, Germany},
abstract={For multi-sensor data fusion applications the accurate alignment of different sensor data is essential for the proper combination of matching features. In food inspection system the boxing often is in a rectangular shape. This knowledge can be used to rectify the image data, an important step in the alignment stage. In case of low contrast between boxing and background, the detected contour may differ significantly from the actual values. In this paper the performance of the Hough transform and the RANdom SAmple Consensus (RANSAC)-Algorithm are evaluated relating to the correct extraction of the boxing contour out of contour data distorted by position errors of the outer shape. The evaluation results indicate the superiority of the RANSAC algorithm with respect to scalability, robustness and execution time.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Panicker2016507,
author={Panicker, M.a  and Mitha, T.a  and Oak, K.a  and Deshpande, A.M.a  and Ganguly, C.b },
title={Multisensor data fusion for an autonomous ground vehicle},
journal={Conference on Advances in Signal Processing, CASP 2016},
year={2016},
pages={507-512},
doi={10.1109/CASP.2016.7746225},
art_number={7746225},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85003845665&doi=10.1109%2fCASP.2016.7746225&partnerID=40&md5=4c175ed2c83e0ae11996bfa4f2576207},
affiliation={Cummins College of Engineering for Women, Pune, India; Defence Research and Development Organization, Dighi, Pune, India},
abstract={Multisensor data fusion is an emerging technology applied to military areas such as automated target recognition, battlefield surveillance, guidance and control of autonomous vehicles and also to non-military applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion include artificial intelligence, pattern recognition, statistical estimation, and other areas. In this work, we have used multisensor data fusion for navigation and to control an autonomous vehicle. Various sensors such as GPS, digital camera, digital compass, laser scanner and ultrasound sensor have been used to gather data from structured surroundings. This data is then preprocessed and selectively fused to make reliable decisions. The sensors are interfaced with an onboard vehicle controller and the vehicle navigates a certain path on the basis of a robust multisensory data fusion algorithm. © 2016 IEEE.},
author_keywords={Autonomous vehicle;  Data fusion;  Statistical estimation},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Habib2016221,
author={Habib, C.a  and Makhoul, A.a  and Darazi, R.b  and Couturier, R.a },
title={Multisensor data fusion for patient risk level determination and decision-support in wireless body sensor networks},
journal={MSWiM 2016 - Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems},
year={2016},
pages={221-224},
doi={10.1145/2988287.2989173},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007016801&doi=10.1145%2f2988287.2989173&partnerID=40&md5=24097a39c5997f93005a9e037abd16e3},
affiliation={Univ. Bourgogne Franche-Comté (UBFC), FEMTO-ST Institute, Belfort, France; Antonine University, TICKET Lab, Hadat-Baabda, Lebanon},
abstract={Wireless Body Sensor Networks (WBSNs) are a low-cost solution for healthcare applications allowing continuous and remote monitoring. However, many challenges are addressed in WBSNs such as limited energy resources, early detection of emergencies and fusion of large amount of heterogeneous data in order to take decisions. In this paper, we propose a multisensor data fusion approach enabling one to determine the patient risk level based on vital signs scores. Consequently, a corresponding decision is taken routinely and each time an emergency is detected. This approach is based on early warning score systems, a fuzzy inference system and a technique determining the score of a vital sign given its past and current value. We evaluate our approach on real healthcare datasets. © 2016 ACM.},
author_keywords={Decision-making;  Early warning system;  Fuzzy theory;  Multisensor data fusion;  Patient risk level;  WBSN},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Pohl20162556,
author={Pohl, C.a  and Kanniah, K.D.b  and Loong, C.K.b },
title={Monitoring oil palm plantations in Malaysia},
journal={International Geoscience and Remote Sensing Symposium (IGARSS)},
year={2016},
volume={2016-November},
pages={2556-2559},
doi={10.1109/IGARSS.2016.7729660},
art_number={7729660},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007489112&doi=10.1109%2fIGARSS.2016.7729660&partnerID=40&md5=ab72e7ea1fcaf2fcfec0172028a5f525},
affiliation={Institute of Computer Sciences, University of Osnabrueck, Barbarastr. 22B, Osnabrueck, Germany; Faculty of Geoinformation and Real Estate (FGHT), Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia},
abstract={Oil palm is recognized as the golden crop. It produces the highest oil yield among oil seed crops. Malaysia, as the world's second largest producer of palm oil, has 16 % of its lands planted with oil palms. Multisensor remote sensing plays an important role by providing relevant, timely and accurate information that can be developed into a plantation monitoring system to optimize production and sustainability. Synthetic aperture radar sensors deliver 'cloud-free' images. Optical remote sensing provides important physical parameters of the plantation using multispectral data acquisition. Both types of data are complementary and need to be exploited simultaneously to obtain a holistic view on the plantation. The research described in this paper anticipates the development of a multisensor image and data fusion system for oil palm plantation management. © 2016 IEEE.},
author_keywords={data fusion;  GIS;  Palm oil;  remote sensing},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Li20162637,
author={Li, J. and Jia, L. and Liu, G.},
title={Multisensor Time Synchronization Error Modeling and Compensation Method for Distributed POS},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2016},
volume={65},
number={11},
pages={2637-2645},
doi={10.1109/TIM.2016.2598020},
art_number={7547391},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983059137&doi=10.1109%2fTIM.2016.2598020&partnerID=40&md5=db516f0e9c0af9ea257811728cb77363},
affiliation={Science and Technology on Inertial Laboratory, Key Lab. of Fundamental Sci. for Natl. Def.-Novel Inertial Instrum. and Navigation System Technology, Beihang University, Beijing

repository: nidiascampos/smartgreen

repo_name: nidiascampos/smartgreen

branch_name: refs/heads/master

gha_language: Python