WorldWideScience

Sample records for adaptive sensor fusion

  1. Adaptive sensor fusion using genetic algorithms

    International Nuclear Information System (INIS)

    Fitzgerald, D.S.; Adams, D.G.

    1994-01-01

    Past attempts at sensor fusion have used some form of Boolean logic to combine the sensor information. As an alteniative, an adaptive ''fuzzy'' sensor fusion technique is described in this paper. This technique exploits the robust capabilities of fuzzy logic in the decision process as well as the optimization features of the genetic algorithm. This paper presents a brief background on fuzzy logic and genetic algorithms and how they are used in an online implementation of adaptive sensor fusion

  2. Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

    Science.gov (United States)

    Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

    2018-02-06

    This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

  3. Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter

    Directory of Open Access Journals (Sweden)

    Bingbing Gao

    2018-02-01

    Full Text Available This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system integrated navigation.

  4. Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

    Science.gov (United States)

    Olivares, A.; Górriz, J. M.; Ramírez, J.; Olivares, G.

    2011-02-01

    Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed.

  5. Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

    International Nuclear Information System (INIS)

    Olivares, A; Olivares, G; Górriz, J M; Ramírez, J

    2011-01-01

    Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed

  6. Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.

    Science.gov (United States)

    Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing

    2011-01-01

    In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.

  7. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

    Science.gov (United States)

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-02-21

    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.

  8. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    Directory of Open Access Journals (Sweden)

    Luyang Jing

    2017-02-01

    Full Text Available 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.

  9. Sensor Data Fusion

    DEFF Research Database (Denmark)

    Plascencia, Alfredo; Stepán, Petr

    2006-01-01

    The main contribution of this paper is to present a sensor fusion approach to scene environment mapping as part of a Sensor Data Fusion (SDF) architecture. This approach involves combined sonar array with stereo vision readings.  Sonar readings are interpreted using probability density functions...

  10. An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors.

    Science.gov (United States)

    Foussier, Jerome; Teichmann, Daniel; Jia, Jing; Misgeld, Berno; Leonhardt, Steffen

    2014-05-09

    Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed

  11. Multi-sensor fusion using an adaptive multi-hypothesis tracking algorithm

    NARCIS (Netherlands)

    Kester, L.J.H.M.

    2003-01-01

    The purpose of a tracking algorithm is to associate data measured by one or more (moving) sensors to moving objects in the environment. The state of these objects that can be estimated with the tracking process depends on the type of data that is provided by these sensors. It is discussed how the

  12. Sensor fusion for mobile robot navigation

    International Nuclear Information System (INIS)

    Kam, M.; Zhu, X.; Kalata, P.

    1997-01-01

    The authors review techniques for sensor fusion in robot navigation, emphasizing algorithms for self-location. These find use when the sensor suite of a mobile robot comprises several different sensors, some complementary and some redundant. Integrating the sensor readings, the robot seeks to accomplish tasks such as constructing a map of its environment, locating itself in that map, and recognizing objects that should be avoided or sought. The review describes integration techniques in two categories: low-level fusion is used for direct integration of sensory data, resulting in parameter and state estimates; high-level fusion is used for indirect integration of sensory data in hierarchical architectures, through command arbitration and integration of control signals suggested by different modules. The review provides an arsenal of tools for addressing this (rather ill-posed) problem in machine intelligence, including Kalman filtering, rule-based techniques, behavior based algorithms and approaches that borrow from information theory, Dempster-Shafer reasoning, fuzzy logic and neural networks. It points to several further-research needs, including: robustness of decision rules; simultaneous consideration of self-location, motion planning, motion control and vehicle dynamics; the effect of sensor placement and attention focusing on sensor fusion; and adaptation of techniques from biological sensor fusion

  13. Fusion of Images from Dissimilar Sensor Systems

    National Research Council Canada - National Science Library

    Chow, Khin

    2004-01-01

    Different sensors exploit different regions of the electromagnetic spectrum; therefore a multi-sensor image fusion system can take full advantage of the complementary capabilities of individual sensors in the suit...

  14. The Algorithm of CFNN Image Data Fusion in Multi-sensor Data Fusion

    Directory of Open Access Journals (Sweden)

    Xiaohong ZENG

    2014-03-01

    Full Text Available CFNN hybrid system in Multi-sensor data fusion introduced fuzzy logic reasoning and neural network adaptive, self-learning ability, and using fuzzy neurons, so networking skills appropriate to adjust the input and output fuzzy membership function, and can dynamically optimize fuzzy reasoning in global by means of compensated logic algorithm, to make the network more fault tolerance, stability and speed up training. This paper introduces a mathematical model of the image data fusion, and elaborates CFNN image data fusion algorithms, simulation results show that this method can significantly improve the quality of the image data fusion, data fusion with other existing algorithms have a very significant effect.

  15. Adaptive multisensor fusion for planetary exploration rovers

    Science.gov (United States)

    Collin, Marie-France; Kumar, Krishen; Pampagnin, Luc-Henri

    1992-01-01

    The purpose of the adaptive multisensor fusion system currently being designed at NASA/Johnson Space Center is to provide a robotic rover with assured vision and safe navigation capabilities during robotic missions on planetary surfaces. Our approach consists of using multispectral sensing devices ranging from visible to microwave wavelengths to fulfill the needs of perception for space robotics. Based on the illumination conditions and the sensors capabilities knowledge, the designed perception system should automatically select the best subset of sensors and their sensing modalities that will allow the perception and interpretation of the environment. Then, based on reflectance and emittance theoretical models, the sensor data are fused to extract the physical and geometrical surface properties of the environment surface slope, dielectric constant, temperature and roughness. The theoretical concepts, the design and first results of the multisensor perception system are presented.

  16. Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

    Science.gov (United States)

    Mendes, José Jair Alves; Vieira, Mário Elias Marinho; Pires, Marcelo Bissi; Stevan, Sergio Luiz

    2016-01-01

    The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some physical variables associated with the human body. These techniques are presented in various biomedical and sports applications, which cover areas related to diagnostics, rehabilitation, physical monitoring, and the development of performance in athletes, among others. Although some applications are described in only one of two fields of study (biomedicine and sports), it is very likely that the same application fits in both, with small peculiarities or adaptations. To illustrate the contemporaneity of applications, an analysis of specialized papers published in the last six years has been made. In this context, the main characteristic of this review is to present the largest quantity of relevant examples of sensor fusion and smart sensors focusing on their utilization and proposals, without deeply addressing one specific system or technique, to the detriment of the others. PMID:27669260

  17. Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

    Directory of Open Access Journals (Sweden)

    José Jair Alves Mendes Jr.

    2016-09-01

    Full Text Available The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some physical variables associated with the human body. These techniques are presented in various biomedical and sports applications, which cover areas related to diagnostics, rehabilitation, physical monitoring, and the development of performance in athletes, among others. Although some applications are described in only one of two fields of study (biomedicine and sports, it is very likely that the same application fits in both, with small peculiarities or adaptations. To illustrate the contemporaneity of applications, an analysis of specialized papers published in the last six years has been made. In this context, the main characteristic of this review is to present the largest quantity of relevant examples of sensor fusion and smart sensors focusing on their utilization and proposals, without deeply addressing one specific system or technique, to the detriment of the others.

  18. Multispectral Image Enhancement Through Adaptive Wavelet Fusion

    Science.gov (United States)

    2016-09-14

    AFRL-AFOSR-UK-TR-2017-0005 Multispectral image enhancement through adaptive wavelet fusion Alexander Toet Nederlandse Organisatie voor Toegepast...image enhancement through adaptive wavelet fusion 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-15-1-0433 5c.  PROGRAM ELEMENT NUMBER 61102F 6. AUTHOR(S...efficient. 15. SUBJECT TERMS adaptive wavelet fusion, Multispectral image enhancement, Multispectral image fusion, multiband image interpolation

  19. Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning.

    Science.gov (United States)

    Begum, Shahina; Barua, Shaibal; Ahmed, Mobyen Uddin

    2014-07-03

    Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

  20. Sensor fusion for intelligent alarm analysis

    International Nuclear Information System (INIS)

    Nelson, C.L.; Fitzgerald, D.S.

    1996-01-01

    The purpose of an intelligent alarm analysis system is to provide complete and manageable information to a central alarm station operator by applying alarm processing and fusion techniques to sensor information. This paper discusses the sensor fusion approach taken to perform intelligent alarm analysis for the Advanced Exterior Sensor (AES). The AES is an intrusion detection and assessment system designed for wide-area coverage, quick deployment, low false/nuisance alarm operation, and immediate visual assessment. It combines three sensor technologies (visible, infrared, and millimeter wave radar) collocated on a compact and portable remote sensor module. The remote sensor module rotates at a rate of 1 revolution per second to detect and track motion and provide assessment in a continuous 360 degree field-of-regard. Sensor fusion techniques are used to correlate and integrate the track data from these three sensors into a single track for operator observation. Additional inputs to the fusion process include environmental data, knowledge of sensor performance under certain weather conditions, sensor priority, and recent operator feedback. A confidence value is assigned to the track as a result of the fusion process. This helps to reduce nuisance alarms and to increase operator confidence in the system while reducing the workload of the operator

  1. Enhanced chemical weapon warning via sensor fusion

    Science.gov (United States)

    Flaherty, Michael; Pritchett, Daniel; Cothren, Brian; Schwaiger, James

    2011-05-01

    Torch Technologies Inc., is actively involved in chemical sensor networking and data fusion via multi-year efforts with Dugway Proving Ground (DPG) and the Defense Threat Reduction Agency (DTRA). The objective of these efforts is to develop innovative concepts and advanced algorithms that enhance our national Chemical Warfare (CW) test and warning capabilities via the fusion of traditional and non-traditional CW sensor data. Under Phase I, II, and III Small Business Innovative Research (SBIR) contracts with DPG, Torch developed the Advanced Chemical Release Evaluation System (ACRES) software to support non real-time CW sensor data fusion. Under Phase I and II SBIRs with DTRA in conjunction with the Edgewood Chemical Biological Center (ECBC), Torch is using the DPG ACRES CW sensor data fuser as a framework from which to develop the Cloud state Estimation in a Networked Sensor Environment (CENSE) data fusion system. Torch is currently developing CENSE to implement and test innovative real-time sensor network based data fusion concepts using CW and non-CW ancillary sensor data to improve CW warning and detection in tactical scenarios.

  2. Decentralized Sensor Fusion for Ubiquitous Networking Robotics in Urban Areas

    Science.gov (United States)

    Sanfeliu, Alberto; Andrade-Cetto, Juan; Barbosa, Marco; Bowden, Richard; Capitán, Jesús; Corominas, Andreu; Gilbert, Andrew; Illingworth, John; Merino, Luis; Mirats, Josep M.; Moreno, Plínio; Ollero, Aníbal; Sequeira, João; Spaan, Matthijs T.J.

    2010-01-01

    In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted. PMID:22294927

  3. Decentralized Sensor Fusion for Ubiquitous Networking Robotics in Urban Areas

    Directory of Open Access Journals (Sweden)

    Aníbal Ollero

    2010-03-01

    Full Text Available In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites, a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted.

  4. Decentralized sensor fusion for Ubiquitous Networking Robotics in Urban Areas.

    Science.gov (United States)

    Sanfeliu, Alberto; Andrade-Cetto, Juan; Barbosa, Marco; Bowden, Richard; Capitán, Jesús; Corominas, Andreu; Gilbert, Andrew; Illingworth, John; Merino, Luis; Mirats, Josep M; Moreno, Plínio; Ollero, Aníbal; Sequeira, João; Spaan, Matthijs T J

    2010-01-01

    In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted.

  5. HEAT Sensor: Harsh Environment Adaptable Thermionic Sensor

    Energy Technology Data Exchange (ETDEWEB)

    Limb, Scott J. [Palo Alto Research Center, Palo Alto, CA (United States)

    2016-05-31

    This document is the final report for the “HARSH ENVIRONMENT ADAPTABLE THERMIONIC SENSOR” project under NETL’s Crosscutting contract DE-FE0013062. This report addresses sensors that can be made with thermionic thin films along with the required high temperature hermetic packaging process. These sensors can be placed in harsh high temperature environments and potentially be wireless and self-powered.

  6. Sensor Fusion for Autonomous Mobile Robot Navigation

    DEFF Research Database (Denmark)

    Plascencia, Alfredo

    Multi-sensor data fusion is a broad area of constant research which is applied to a wide variety of fields such as the field of mobile robots. Mobile robots are complex systems where the design and implementation of sensor fusion is a complex task. But research applications are explored constantly.......  The main objective of a multi-sensor system is to improve the capabilities of a single sensor when translating different sensory inputs into the construction of a map that can be used for navigation. In this context, it is important to find novel solutions based on the state of the art of this field....... The scope of the thesis is limited to building a map for a laboratory robot by fusing range readings from a sonar array with landmarks extracted from stereo vision images using the (Scale Invariant Feature Transform) SIFT algorithm....

  7. Driver drowsiness detection using multimodal sensor fusion

    Science.gov (United States)

    Andreeva, Elena O.; Aarabi, Parham; Philiastides, Marios G.; Mohajer, Keyvan; Emami, Majid

    2004-04-01

    This paper proposes a multi-modal sensor fusion algorithm for the estimation of driver drowsiness. Driver sleepiness is believed to be responsible for more than 30% of passenger car accidents and for 4% of all accident fatalities. In commercial vehicles, drowsiness is blamed for 58% of single truck accidents and 31% of commercial truck driver fatalities. This work proposes an innovative automatic sleep-onset detection system. Using multiple sensors, the driver"s body is studied as a mechanical structure of springs and dampeners. The sleep-detection system consists of highly sensitive triple-axial accelerometers to monitor the driver"s upper body in 3-D. The subject is modeled as a linear time-variant (LTV) system. An LMS adaptive filter estimation algorithm generates the transfer function (i.e. weight coefficients) for this LTV system. Separate coefficients are generated for the awake and asleep states of the subject. These coefficients are then used to train a neural network. Once trained, the neural network classifies the condition of the driver as either awake or asleep. The system has been tested on a total of 8 subjects. The tests were conducted on sleep-deprived individuals for the sleep state and on fully awake individuals for the awake state. When trained and tested on the same subject, the system detected sleep and awake states of the driver with a success rate of 95%. When the system was trained on three subjects and then retested on a fourth "unseen" subject, the classification rate dropped to 90%. Furthermore, it was attempted to correlate driver posture and sleepiness by observing how car vibrations propagate through a person"s body. Eight additional subjects were studied for this purpose. The results obtained in this experiment proved inconclusive which was attributed to significant differences in the individual habitual postures.

  8. Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

    Directory of Open Access Journals (Sweden)

    Huadong Wang

    2015-01-01

    Full Text Available In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF, as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.

  9. Performance evaluation of multi-sensor data fusion technique for ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    sive algorithm suitable for real-time application using digital computers. Among different approaches for Kalman Filter-based sensor fusion, two commonly employed ... Multi-sensor data fusion strategy at Test Range. The fusion scheme adopted at Integrated Test Range is shown in figure 2. The scheme is evolved based on ...

  10. Non-verbal communication through sensor fusion

    Science.gov (United States)

    Tairych, Andreas; Xu, Daniel; O'Brien, Benjamin M.; Anderson, Iain A.

    2016-04-01

    When we communicate face to face, we subconsciously engage our whole body to convey our message. In telecommunication, e.g. during phone calls, this powerful information channel cannot be used. Capturing nonverbal information from body motion and transmitting it to the receiver parallel to speech would make these conversations feel much more natural. This requires a sensing device that is capable of capturing different types of movements, such as the flexion and extension of joints, and the rotation of limbs. In a first embodiment, we developed a sensing glove that is used to control a computer game. Capacitive dielectric elastomer (DE) sensors measure finger positions, and an inertial measurement unit (IMU) detects hand roll. These two sensor technologies complement each other, with the IMU allowing the player to move an avatar through a three-dimensional maze, and the DE sensors detecting finger flexion to fire weapons or open doors. After demonstrating the potential of sensor fusion in human-computer interaction, we take this concept to the next level and apply it in nonverbal communication between humans. The current fingerspelling glove prototype uses capacitive DE sensors to detect finger gestures performed by the sending person. These gestures are mapped to corresponding messages and transmitted wirelessly to another person. A concept for integrating an IMU into this system is presented. The fusion of the DE sensor and the IMU combines the strengths of both sensor types, and therefore enables very comprehensive body motion sensing, which makes a large repertoire of gestures available to nonverbal communication over distances.

  11. Sensor data fusion to predict multiple soil properties

    NARCIS (Netherlands)

    Mahmood, H.S.; Hoogmoed, W.B.; Henten, van E.J.

    2012-01-01

    The accuracy of a single sensor is often low because all proximal soil sensors respond to more than one soil property of interest. Sensor data fusion can potentially overcome this inability of a single sensor and can best extract useful and complementary information from multiple sensors or sources.

  12. The ABC Adaptive Fusion Architecture

    DEFF Research Database (Denmark)

    Bunde-Pedersen, Jonathan; Mogensen, Martin; Bardram, Jakob Eyvind

    2006-01-01

    and early implementation of a systemcapable of adapting to its operating environment, choosingthe best fit combination of the client-server and peerto-peer architectures. The architecture creates a seamlessintegration between a centralized hybrid architecture and adecentralized architecture, relying on what...

  13. Desensitized Optimal Filtering and Sensor Fusion Tool Kit, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — Research on desensitized optimal filtering techniques and a navigation and sensor fusion tool kit using advanced filtering techniques is proposed. Research focuses...

  14. A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation

    Directory of Open Access Journals (Sweden)

    Yu Gu

    2016-01-01

    Full Text Available A novel sensor fusion design framework is presented with the objective of improving the overall multisensor measurement system performance and achieving graceful degradation following individual sensor failures. The Unscented Information Filter (UIF is used to provide a useful tool for combining information from multiple sources. A two-step off-line and on-line calibration procedure refines sensor error models and improves the measurement performance. A Fault Detection and Identification (FDI scheme crosschecks sensor measurements and simultaneously monitors sensor biases. Low-quality or faulty sensor readings are then rejected from the final sensor fusion process. The attitude estimation problem is used as a case study for the multiple sensor fusion algorithm design, with information provided by a set of low-cost rate gyroscopes, accelerometers, magnetometers, and a single-frequency GPS receiver’s position and velocity solution. Flight data collected with an Unmanned Aerial Vehicle (UAV research test bed verifies the sensor fusion, adaptation, and fault-tolerance capabilities of the designed sensor fusion algorithm.

  15. Statistical modeling and data fusion of automotive sensors for object detection applications in a driving environment

    Science.gov (United States)

    Hurtado, Miguel A.

    In this work, we consider the application of classical statistical inference to the fusion of data from different sensing technologies for object detection applications in order to increase the overall performance for a given active safety automotive system. Research evolved mainly around a centralized sensor fusion architecture assuming that three non-identical sensors, modeled by corresponding probability density functions (pdfs), provide discrete information of target being present or absent with associated probabilities of detection and false alarm for the sensor fusion engine. The underlying sensing technologies are the following standard automotive sensors: 24.5 GHz radar, high dynamic range infrared camera and a laser-radar. A complete mathematical framework was developed to select the optimal decision rule based on a generalized multinomial distribution resulting from a sum of weighted Bernoulli random variables from the Neyman-Pearson lemma and the likelihood ratio test. Moreover, to better understand the model and to obtain upper bounds on the performance of the fusion rules, we assumed exponential pdfs for each sensor and a parallel mathematical expression was obtained based on a generalized gamma distribution resulting from a sum of weighted exponential random variables for the situation when the continuous random vector of information is available. Mathematical expressions and results were obtained for modeling the following case scenarios: (i) non-identical sensors, (ii) identical sensors, (iii) combination of nonidentical and identical sensors, (iv) faulty sensor operation, (v) dominant sensor operation, (vi) negative sensor operation, and (vii) distributed sensor fusion. The second and final part of this research focused on: (a) simulation of statistical models for each sensing technology, (b) comparisons with distributed fusion, (c) overview of dynamic sensor fusion and adaptive decision rules.

  16. Multi-Source Multi-Sensor Information Fusion

    Indian Academy of Sciences (India)

    M. Senthilkumar (Newgen Imaging) 1461 1996 Oct 15 13:05:22

    Multi-Source Multi-Sensor Information Fusion. FOREWORD. The human brain routinely carries out information processing and fusion. The objective is to collect observations from various similar or dissimilar sources and sensors, extract the required information (inferences) and combine/fuse these with a view to obtaining ...

  17. Tracking and sensor data fusion methodological framework and selected applications

    CERN Document Server

    Koch, Wolfgang

    2013-01-01

    Sensor Data Fusion is the process of combining incomplete and imperfect pieces of mutually complementary sensor information in such a way that a better understanding of an underlying real-world phenomenon is achieved. Typically, this insight is either unobtainable otherwise or a fusion result exceeds what can be produced from a single sensor output in accuracy, reliability, or cost. This book provides an introduction Sensor Data Fusion, as an information technology as well as a branch of engineering science and informatics. Part I presents a coherent methodological framework, thus providing th

  18. PERSON AUTHENTICATION USING MULTIPLE SENSOR DATA FUSION

    Directory of Open Access Journals (Sweden)

    S. Vasuhi

    2011-04-01

    Full Text Available This paper proposes a real-time system for face authentication, obtained through fusion of Infra Red (IR and visible images. In order to identify the unknown person authentication in highly secured areas, multiple algorithms are needed. The four well known algorithms for face recognition, Block Independent Component Analysis(BICA, Kalman Filtering(KF method, Discrete Cosine Transform(DCT and Orthogonal Locality Preserving Projections (OLPP are used to extract the features. If the data base size is very large and the features are not distinct then ambiguity will exists in face recognition. Hence more than one sensor is needed for critical and/or highly secured areas. This paper deals with multiple fusion methodology using weighted average and Fuzzy Logic. The visible sensor output depends on the environmental condition namely lighting conditions, illumination etc., to overcome this problem use histogram technique to choose appropriate algorithm. DCT and Kalman filtering are holistic approaches, BICA follows feature based approach and OLPP preserves the Euclidean structure of face space. These recognizers are capable of considering the problem of dimensionality reduction by eliminating redundant features and reducing the feature space. The system can handle variations like illumination, pose, orientation, occlusion, etc. up to a significant level. The integrated system overcomes the drawbacks of individual recognizers. The proposed system is aimed at increasing the accuracy of the person authentication system and at the same time reducing the limitations of individual algorithms. It is tested on real time database and the results are found to be 96% accurate.

  19. Advances in Multi-Sensor Data Fusion: Algorithms and Applications

    Directory of Open Access Journals (Sweden)

    Jingying Fu

    2009-09-01

    Full Text Available With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1 Improvements of fusion algorithms; (2 Development of “algorithm fusion” methods; (3 Establishment of an automatic quality assessment scheme.

  20. Target Engageability Improvement Through Adaptive Data Fusion and Sensor Management: An Approach Based on the Fire Control Radar Search to Lock-On Time

    Science.gov (United States)

    2008-05-01

    de fusion de données et de gestion des ressources. La Section des systèmes d’aide à la décision (SAD), à Recherche et développement...exemple pratique d’un système adaptif de fusion de données et de gestion de ressources appliqué au problème d’engagement des cibles dans la défense...NUMÉRO DU PROJET OU DE LA SUBVENTION (Spécifier si c’est un projet ou une subvention)

  1. Freeway Multisensor Data Fusion Approach Integrating Data from Cellphone Probes and Fixed Sensors

    Directory of Open Access Journals (Sweden)

    Shanglu He

    2016-01-01

    Full Text Available Freeway traffic state information from multiple sources provides sufficient support to the traffic surveillance but also brings challenges. This paper made an investigation into the fusion of a new data combination from cellular handoff probe system and microwave sensors. And a fusion method based on the neural network technique was proposed. To identify the factors influencing the accuracy of fusion results, we analyzed the sensitivity of those factors by changing the inputs of neural-network-based fusion model. The results showed that handoff link length and sample size were identified as the most influential parameters to the precision of fusion. Then, the effectiveness and capability of proposed fusion method under various traffic conditions were evaluated. And a comparative analysis between the proposed method and other fusion approaches was conducted. The results of simulation test and evaluation showed that the fusion method could complement the drawback of each collection method, improve the overall estimation accuracy, adapt to the variable traffic condition (free flow or incident state, suit the fusion of data from cellphone probes and fixed sensors, and outperform other fusion methods.

  2. Data fusion system architecture for unattended ground sensors

    Science.gov (United States)

    Zhang, Junliang; Zhao, Yuming

    1998-03-01

    In this paper, it develops an artificial intelligence method that uses object-oriented approach to construct the blackboard of data fusion for unattended ground sensors including geophone sensor, acoustic sensor, pressure sensor, infra-red sensor, magnetic sensor, image sensor etc.. It can perform detection, correlation, association and estimation to the sensors' output and obtain the exact recognition of targets, the number of target groups and the estimation for both the states of targets and the situation and threat. The whole blackboard is divided into three regions, including: single sensor fusion region, multisensor fusion region and threat estimation region. The three regions are expressed in classes. Knowledges of each domain in the three regions are also expressed by classes and encapsulated in class hierarchy structure. Thus the whole blackboard can be viewed as object forest, the distributed knowledge inference can be realized by object reference. Both statistics and hierarchy inference approaches are used in the blackboard structure so as to efficiently perform fusion and inference. Furthermore, the method is realized in C++ language and demonstrated by the simulation of sensor alarming datum under battlefield environment.

  3. Sensor Fusion and Model Verification for a Mobile Robot

    DEFF Research Database (Denmark)

    Bisgaard, Morten; Vinther, Dennis; Østergaard, Kasper Zinck

    2005-01-01

    This paper presents the results of modeling, sensor fusion and model verification for a four-wheel driven, four-wheel steered mobile robot moving in outdoor terrain. The model derived for the robot describes the actuator and wheel dynamics and the vehicle kinematics, and includes friction terms...... as well as slip. An Unscented Kalman Filter (UKF) based on the dynamic model is used for sensor fusion, feeding sensor measurements back to the robot controller in an intelligent manner. Through practical experiments with the robot, the UKF is demonstrated to improve the reliability of the sensor signals...

  4. Desensitized Optimal Filtering and Sensor Fusion Tool Kit, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — It is proposed to develop desensitized optimal filtering techniques and to implement these algorithms in a navigation and sensor fusion tool kit. These proposed...

  5. Performance evaluation of multi-sensor data fusion technique for ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    In the context of target tracking and estimation, multi-sensor data fusion (MSDF) is the pro- cess of combining ... the entire target scenario from each of the pieces of evidence alone, due to the inherent limi- tations of ... state-vector fusion has lower computational cost and possesses the advantage of parallel imple- mentation ...

  6. Application of a sensor fusion algorithm for improving grasping stability

    International Nuclear Information System (INIS)

    Kim, Jae Hyeon; Yoon, Hyun Suck; Moon, Hyung Pil; Choi, Hyouk Ryeol; Koo Ja Choon

    2015-01-01

    A robot hand normally employees various sensors that are packaged in small form factor, perform with delicately accurate, and cost mostly very expensive. Grasping operation of the hand relies especially on accuracy of those sensors. Even with a set of advanced sensory systems embedded in a robot hand, securing a stable grasping is still challenging task. The present work makes an attempt to improve force sensor accuracy by applying sensor fusion method. An optimal weight value sensor fusion method formulated with Kalman filters is presented and tested in the work. Using a set of inexpensive sensors, the work achieves a reliable force sensing and applies the enhanced sensor stability to an object pinch grasping.

  7. Multi-sensor image fusion and its applications

    CERN Document Server

    Blum, Rick S

    2005-01-01

    Taking another lesson from nature, the latest advances in image processing technology seek to combine image data from several diverse types of sensors in order to obtain a more accurate view of the scene: very much the same as we rely on our five senses. Multi-Sensor Image Fusion and Its Applications is the first text dedicated to the theory and practice of the registration and fusion of image data, covering such approaches as statistical methods, color-related techniques, model-based methods, and visual information display strategies.After a review of state-of-the-art image fusion techniques,

  8. Sensor fusion for antipersonnel landmine detection, a case study

    NARCIS (Netherlands)

    Breejen, E. den; Schutte, K.; Cremer, F.

    1999-01-01

    In this paper the multi sensor fusion results obtained within the European research project GEODE (Ground Explosive Ordnance Detection system) are presented. The lay out of the test lane and the individual sensors used are described. The implementation of the SCOOP algorithm improves the ROC curves,

  9. Multi-Sensor Data Fusion Technique

    Science.gov (United States)

    Rodier, S. D.; Hu, Y.; Vaughan, M.; Hlavka, D.; Arnold, T.

    2006-12-01

    We describe a data fusion technique for combining lidar measurements with correlative observations made by passive sensors. Simultaneous measurements obtained by the Cloud Physics Lidar (CPL)1 and the MODIS Airborne Simulator (MAS)2 serve as inputs to a Kohonen self-organizing map (SOM)3 algorithm, which in turn classifies the collocated MAS+CPL pixels according to scene type; i.e., according to the number, the type, and the vertical locations of the cloud and aerosol layers present. Tests conducted using the MAS data alone show that the SOM algorithm recognizes a much greater percentage of the pixels containing high, thin clouds than does the standard MAS cloud mask algorithm. Results obtained when using the combined measurements identify a greater number of distinct classes of data (i.e., scene types) within individual MAS pixels, and thus will allow the selection of more accurate physical models for the retrievals of radiatively significant properties. The wealth of information gained by including the lidar profile in the SOM algorithm clustering study is, however, limited by (to) the instruments nadir only footprint. To fully utilize this information a method for extending or transferring the classification to the full passive swath was developed. We will describe our technique for extending the classifications derived from the nadir track analysis, where we have coincident measurements from both instruments, to the full passive sensor swath, for which we have only MAS measurements. Preliminary validation studies show that we can expect a classification success rate of better than 70 percent when applying this method. The recent CALIPSO-CloudSat validation campaign will provide additional datasets to validate our technique. REFERENCES M. J. McGill, D. L. Hlavka, W. D. Hart, V. S. Scott, J. D. Spinhirne, and B. Schmid, "The cloud physics lidar: Instrument description and initial measurement results", Applied Optics, 41, pp. 3725 3734, 2002. King, M. D., Y. J

  10. Pulse-coupled neural network sensor fusion

    Science.gov (United States)

    Johnson, John L.; Schamschula, Marius P.; Inguva, Ramarao; Caulfield, H. John

    1998-03-01

    Perception is assisted by sensed impressions of the outside world but not determined by them. The primary organ of perception is the brain and, in particular, the cortex. With that in mind, we have sought to see how a computer-modeled cortex--the PCNN or Pulse Coupled Neural Network--performs as a sensor fusing element. In essence, the PCNN is comprised of an array of integrate-and-fire neurons with one neuron for each input pixel. In such a system, the neurons corresponding to bright pixels reach firing threshold faster than the neurons corresponding to duller pixels. Thus, firing rate is proportional to brightness. In PCNNs, when a neuron fires it sends some of the resulting signal to its neighbors. This linking can cause a near-threshold neuron to fire earlier than it would have otherwise. This leads to synchronization of the pulses across large regions of the image. We can simplify the 3D PCNN output by integrating out the time dimension. Over a long enough time interval, the resulting 2D (x,y) pattern IS the input image. The PCNN has taken it apart and put it back together again. The shorter- term time integrals are interesting in themselves and will be commented upon in the paper. The main thrust of this paper is the use of multiple PCNNs mutually coupled in various ways to assemble a single 2D pattern or fused image. Results of experiments on PCNN image fusion and an evaluation of its advantages are our primary objectives.

  11. Multiratio fusion change detection with adaptive thresholding

    Science.gov (United States)

    Hytla, Patrick C.; Balster, Eric J.; Vasquez, Juan R.; Neuroth, Robert M.

    2017-04-01

    A ratio-based change detection method known as multiratio fusion (MRF) is proposed and tested. The MRF framework builds on other change detection components proposed in this work: dual ratio (DR) and multiratio (MR). The DR method involves two ratios coupled with adaptive thresholds to maximize detected changes and minimize false alarms. The use of two ratios is shown to outperform the single ratio case when the means of the image pairs are not equal. MR change detection builds on the DR method by including negative imagery to produce four total ratios with adaptive thresholds. Inclusion of negative imagery is shown to improve detection sensitivity and to boost detection performance in certain target and background cases. MRF further expands this concept by fusing together the ratio outputs using a routine in which detections must be verified by two or more ratios to be classified as a true changed pixel. The proposed method is tested with synthetically generated test imagery and real datasets with results compared to other methods found in the literature. DR is shown to significantly outperform the standard single ratio method. MRF produces excellent change detection results that exhibit up to a 22% performance improvement over other methods from the literature at low false-alarm rates.

  12. Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning

    OpenAIRE

    Begum, Shahina; Barua, Shaibal; Ahmed, Mobyen Uddin

    2014-01-01

    Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classificati...

  13. Projective Method for Generic Sensor Fusion Problem

    International Nuclear Information System (INIS)

    Rao, N.S.V.

    1999-01-01

    In a multiple sensor system, each sensor produces an output which is related to the desired feature according to a certain probability distribution. We propose a fuser that combines the sensor outputs to more accurately predict the desired feature. The fuser utilizes the lower envelope of regression curves of sensors to project the sensor with the least error at each point of the feature space. This fuser is optimal among all projective fusers and also satisfies the isolation property that ensures a performance at least as good as the best sensor. In the case the sensor distributions are not known, we show that a consistent estimator of this fuser can be computed entirely based on a training sample. Compared to linear fusers, the projective fusers provide a complementary performance. We propose two classes of metafusers that utilize both linear and projectives fusers to perform at least as good as the best sensor as well as the best fuser

  14. Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

    Science.gov (United States)

    Almasri, Marwah; Elleithy, Khaled; Alajlan, Abrar

    2015-01-01

    Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot’s wheels, and 24 fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes. PMID:26712766

  15. Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

    Directory of Open Access Journals (Sweden)

    Marwah Almasri

    2015-12-01

    Full Text Available Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot’s wheels, and 24 fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes.

  16. Soft Thermal Sensor with Mechanical Adaptability.

    Science.gov (United States)

    Yang, Hui; Qi, Dianpeng; Liu, Zhiyuan; Chandran, Bevita K; Wang, Ting; Yu, Jiancan; Chen, Xiaodong

    2016-11-01

    A soft thermal sensor with mechanical adaptability is fabricated by the combination of single-wall carbon nanotubes with carboxyl groups and self-healing polymers. This study demonstrates that this soft sensor has excellent thermal response and mechanical adaptability. It shows tremendous promise for improving the service life of soft artificial-intelligence robots and protecting thermally sensitive electronics from the risk of damage by high temperature. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. A hierarchical structure approach to MultiSensor Information Fusion

    Energy Technology Data Exchange (ETDEWEB)

    Maren, A.J. (Tennessee Univ., Tullahoma, TN (United States). Space Inst.); Pap, R.M.; Harston, C.T. (Accurate Automation Corp., Chattanooga, TN (United States))

    1989-01-01

    A major problem with image-based MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the pixel, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Pixel-level fusion has problems with coregistration of the images or data. Attempts to fuse information using the features of segmented images or data relies an a presumed similarity between the segmentation characteristics of each image or data stream. Symbolic-level fusion requires too much advance processing to be useful, as we have seen in automatic target recognition tasks. Image-based MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Scene Structure (HSS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The MSS is intermediate between a pixel-based representation and a scene interpretation representation, and represents the perceptual organization of an image. Fused HSSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based,region interpretation.

  18. A hierarchical structure approach to MultiSensor Information Fusion

    Energy Technology Data Exchange (ETDEWEB)

    Maren, A.J. [Tennessee Univ., Tullahoma, TN (United States). Space Inst.; Pap, R.M.; Harston, C.T. [Accurate Automation Corp., Chattanooga, TN (United States)

    1989-12-31

    A major problem with image-based MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the pixel, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Pixel-level fusion has problems with coregistration of the images or data. Attempts to fuse information using the features of segmented images or data relies an a presumed similarity between the segmentation characteristics of each image or data stream. Symbolic-level fusion requires too much advance processing to be useful, as we have seen in automatic target recognition tasks. Image-based MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Scene Structure (HSS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The MSS is intermediate between a pixel-based representation and a scene interpretation representation, and represents the perceptual organization of an image. Fused HSSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based,region interpretation.

  19. Sensor and information fusion for improved hostile fire situational awareness

    Science.gov (United States)

    Scanlon, Michael V.; Ludwig, William D.

    2010-04-01

    A research-oriented Army Technology Objective (ATO) named Sensor and Information Fusion for Improved Hostile Fire Situational Awareness uniquely focuses on the underpinning technologies to detect and defeat any hostile threat; before, during, and after its occurrence. This is a joint effort led by the Army Research Laboratory, with the Armaments and the Communications and Electronics Research, Development, and Engineering Centers (CERDEC and ARDEC) partners. It addresses distributed sensor fusion and collaborative situational awareness enhancements, focusing on the underpinning technologies to detect/identify potential hostile shooters prior to firing a shot and to detect/classify/locate the firing point of hostile small arms, mortars, rockets, RPGs, and missiles after the first shot. A field experiment conducted addressed not only diverse modality sensor performance and sensor fusion benefits, but gathered useful data to develop and demonstrate the ad hoc networking and dissemination of relevant data and actionable intelligence. Represented at this field experiment were various sensor platforms such as UGS, soldier-worn, manned ground vehicles, UGVs, UAVs, and helicopters. This ATO continues to evaluate applicable technologies to include retro-reflection, UV, IR, visible, glint, LADAR, radar, acoustic, seismic, E-field, narrow-band emission and image processing techniques to detect the threats with very high confidence. Networked fusion of multi-modal data will reduce false alarms and improve actionable intelligence by distributing grid coordinates, detection report features, and imagery of threats.

  20. Sensor Fusion-based Event Detection in Wireless Sensor Networks

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Havinga, Paul J.M.

    2009-01-01

    Recently, Wireless Sensor Networks (WSN) community has witnessed an application focus shift. Although, monitoring was the initial application of wireless sensor networks, in-network data processing and (near) real-time actuation capability have made wireless sensor networks suitable candidate for

  1. A Multi-Sensor Data Fusion Method for Navigation

    Directory of Open Access Journals (Sweden)

    Ruyun Sun

    2014-09-01

    Full Text Available A data fusion method based on navigation observations from multiple sensors with asynchronous sampling rates is presented aiming at the accomplishment of an optimal navigation result. Firstly, the observation estimations of navigation sensors at the same designated time are obtained through the fitting or interpolation algorithms based on the multi-sensors’ measurements at different times, which solves the observation inconsistency resulting from asynchronous sampling frequencies of multi-sensors. Secondly, the optimal navigation result is achieved through assigning appropriate weights to the above observations or estimations based on the relationships among them. Finally, the data fusion method is validated by simulation, and the results demonstrate that it can effectively reduce the observation noises of multi-sensors and improve the accuracy of navigation.

  2. Fuzzy-Based Sensor Fusion for Cognitive Radio-Based Vehicular Ad Hoc and Sensor Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Jalil Piran

    2015-01-01

    Full Text Available In wireless sensor networks, sensor fusion is employed to integrate the acquired data from diverse sensors to provide a unified interpretation. The best and most salient advantage of sensor fusion is to obtain high-level information in both statistical and definitive aspects, which cannot be attained by a single sensor. In this paper, we propose a novel sensor fusion technique based on fuzzy theory for our earlier proposed Cognitive Radio-based Vehicular Ad Hoc and Sensor Networks (CR-VASNET. In the proposed technique, we considered four input sensor readings (antecedents and one output (consequent. The employed mobile nodes in CR-VASNET are supposed to be equipped with diverse sensors, which cater to our antecedent variables, for example, The Jerk, Collision Intensity, and Temperature and Inclination Degree. Crash_Severity is considered as the consequent variable. The processing and fusion of the diverse sensory signals are carried out by fuzzy logic scenario. Accuracy and reliability of the proposed protocol, demonstrated by the simulation results, introduce it as an applicable system to be employed to reduce the causalities rate of the vehicles’ crashes.

  3. Oil exploration oriented multi-sensor image fusion algorithm

    Directory of Open Access Journals (Sweden)

    Xiaobing Zhang

    2017-04-01

    Full Text Available In order to accurately forecast the fracture and fracture dominance direction in oil exploration, in this paper, we propose a novel multi-sensor image fusion algorithm. The main innovations of this paper lie in that we introduce Dual-tree complex wavelet transform (DTCWT in data fusion and divide an image to several regions before image fusion. DTCWT refers to a new type of wavelet transform, and it is designed to solve the problem of signal decomposition and reconstruction based on two parallel transforms of real wavelet. We utilize DTCWT to segment the features of the input images and generate a region map, and then exploit normalized Shannon entropy of a region to design the priority function. To test the effectiveness of our proposed multi-sensor image fusion algorithm, four standard pairs of images are used to construct the dataset. Experimental results demonstrate that the proposed algorithm can achieve high accuracy in multi-sensor image fusion, especially for images of oil exploration.

  4. Bi-channel Sensor Fusion for Automatic Sign Language Recognition

    DEFF Research Database (Denmark)

    Kim, Jonghwa; Wagner, Johannes; Rehm, Matthias

    2008-01-01

    In this paper, we investigate the mutual-complementary functionality of accelerometer (ACC) and electromyogram (EMG) for recognizing seven word-level sign vocabularies in German sign language (GSL). Results are discussed for the single channels and for feature-level fusion for the bichannel sensor...

  5. Multiple image sensor data fusion through artificial neural networks

    Science.gov (United States)

    With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...

  6. Fault-tolerant Sensor Fusion for Marine Navigation

    DEFF Research Database (Denmark)

    Blanke, Mogens

    2006-01-01

    Reliability of navigation data are critical for steering and manoeuvring control, and in particular so at high speed or in critical phases of a mission. Should faults occur, faulty instruments need be autonomously isolated and faulty information discarded. This paper designs a navigation solution...... events where the fault-tolerant sensor fusion provided uninterrupted navigation data despite temporal instrument defects...

  7. Study on the multi-sensors monitoring and information fusion technology of dangerous cargo container

    Science.gov (United States)

    Xu, Shibo; Zhang, Shuhui; Cao, Wensheng

    2017-10-01

    In this paper, monitoring system of dangerous cargo container based on multi-sensors is presented. In order to improve monitoring accuracy, multi-sensors will be applied inside of dangerous cargo container. Multi-sensors information fusion solution of monitoring dangerous cargo container is put forward, and information pre-processing, the fusion algorithm of homogenous sensors and information fusion based on BP neural network are illustrated, applying multi-sensors in the field of container monitoring has some novelty.

  8. Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks

    CERN Document Server

    Abdelgawad, Ahmed

    2012-01-01

    This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources (e.g., sensors) in order to achieve inferences.  These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative.   Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong variations of pressure, temperature, radiation, and electromagnetic noise; Uses simulation and experimental results to evaluate algorithms presented and includes real test-bed; Includes case study implementing data fusion algorithms on a remote monitoring framework for sand production in oil pipelines.

  9. Multiple sensor monitoring in nickel alloy turning for tool wear assessment via sensor fusion

    OpenAIRE

    Segreto, T.; Simeone, A.; Teti, R.

    2013-01-01

    A multiple sensor monitoring system comprising cutting force, acoustic emission and vibration sensing units was employed for tool state assessment during turning of Inconel 718 nickel alloy. Feature extraction was realised by processing the detected sensor signals in order to reduce the high dimensionality of the sensorial data. The extracted features were fused to realise a sensor fusion methodology based on neural network pattern recognition for decision making on tool wear condition.

  10. Sensor Fusion for Nuclear Proliferation Activity Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Adel Ghanem, Ph D

    2007-03-30

    The objective of Phase 1 of this STTR project is to demonstrate a Proof-of-Concept (PoC) of the Geo-Rad system that integrates a location-aware SmartTag (made by ZonTrak) and a radiation detector (developed by LLNL). It also includes the ability to transmit the collected radiation data and location information to the ZonTrak server (ZonService). The collected data is further transmitted to a central server at LLNL (the Fusion Server) to be processed in conjunction with overhead imagery to generate location estimates of nuclear proliferation and radiation sources.

  11. Multi-Sensor Data Fusion for Future Telematics Application

    Directory of Open Access Journals (Sweden)

    Seong-Baek Kim

    2003-12-01

    Full Text Available In this paper, we present multi-sensor data fusion for telematics application. Successful telematics can be realized through the integration of navigation and spatial information. The well-determined acquisition of vehicle's position plays a vital role in application service. The development of GPS is used to provide the navigation data, but the performance is limited in areas where poor satellite visibility environment exists. Hence, multi-sensor fusion including IMU (Inertial Measurement Unit, GPS (Global Positioning System, and DMI (Distance Measurement Indicator is required to provide the vehicle's position to service provider and driver behind the wheel. The multi-sensor fusion is implemented via algorithm based on Kalman Filtering technique. Navigation accuracy can be enhanced using this filtering approach. For the verification of fusion approach, land vehicle test was performed and the results were discussed. Results showed that the horizontal position errors were suppressed around 1 meter level accuracy under simulated Non-GPS availability environment. Under normal GPS environment, the horizontal position errors were under 40 cm in curve trajectory and 27cm in linear trajectory, which are definitely depending on vehicular dynamics.

  12. Dynamic network based learning systems for sensor information fusion

    Science.gov (United States)

    Verma, Dinesh; Julier, Simon

    2017-05-01

    In order to get the modularity and reconfigurability for sensor information fusion services in modern battle-spaces, dynamic service composition and dynamic topology determination is needed. In the current state-of-the-art, such information fusion services are composed manually and in a programmatic manner. In this paper, we consider an approach towards more automation by assuming that the topology of a solution is provided, and automatically choosing the different types and kinds of algorithms which can be used at each step. This includes the use of contextual information and techniques such as multi-arm bandits for investing the exploration and exploitation tradeoff.

  13. Sensor-fusion-based biometric identity verification

    Energy Technology Data Exchange (ETDEWEB)

    Carlson, J.J.; Bouchard, A.M.; Osbourn, G.C.; Martinez, R.F.; Bartholomew, J.W. [Sandia National Labs., Albuquerque, NM (United States); Jordan, J.B.; Flachs, G.M.; Bao, Z.; Zhu, L. [New Mexico State Univ., Las Cruces, NM (United States). Electronic Vision Research Lab.

    1998-02-01

    Future generation automated human biometric identification and verification will require multiple features/sensors together with internal and external information sources to achieve high performance, accuracy, and reliability in uncontrolled environments. The primary objective of the proposed research is to develop a theoretical and practical basis for identifying and verifying people using standoff biometric features that can be obtained with minimal inconvenience during the verification process. The basic problem involves selecting sensors and discovering features that provide sufficient information to reliably verify a person`s identity under the uncertainties caused by measurement errors and tactics of uncooperative subjects. A system was developed for discovering hand, face, ear, and voice features and fusing them to verify the identity of people. The system obtains its robustness and reliability by fusing many coarse and easily measured features into a near minimal probability of error decision algorithm.

  14. Sensor-fusion-based biometric identity verification

    International Nuclear Information System (INIS)

    Carlson, J.J.; Bouchard, A.M.; Osbourn, G.C.; Martinez, R.F.; Bartholomew, J.W.; Jordan, J.B.; Flachs, G.M.; Bao, Z.; Zhu, L.

    1998-02-01

    Future generation automated human biometric identification and verification will require multiple features/sensors together with internal and external information sources to achieve high performance, accuracy, and reliability in uncontrolled environments. The primary objective of the proposed research is to develop a theoretical and practical basis for identifying and verifying people using standoff biometric features that can be obtained with minimal inconvenience during the verification process. The basic problem involves selecting sensors and discovering features that provide sufficient information to reliably verify a person's identity under the uncertainties caused by measurement errors and tactics of uncooperative subjects. A system was developed for discovering hand, face, ear, and voice features and fusing them to verify the identity of people. The system obtains its robustness and reliability by fusing many coarse and easily measured features into a near minimal probability of error decision algorithm

  15. Secure Data Fusion in Wireless Multimedia Sensor Networks via Compressed Sensing

    Directory of Open Access Journals (Sweden)

    Rui Gao

    2015-01-01

    Full Text Available The paper proposes a novel secure data fusion strategy based on compressed image sensing and watermarking; namely, the algorithm exploits the sparsity in the image encryption. The approach relies on l1-norm regularization, common in compressive sensing, to enhance the detection of sparsity over wireless multimedia sensor networks. The resulting algorithms endow sensor nodes with learning abilities and allow them to learn the sparse structure from the still image data, and also utilize the watermarking approach to achieve authentication mechanism. We provide the total transmission volume and the energy consumption performance analysis of each node, and summarize the peak signal to noise ratio values of the proposed method. We also show how to adaptively select the sampling parameter. Simulation results illustrate the advantage of the proposed strategy for secure data fusion.

  16. Noncontact Sleep Study by Multi-Modal Sensor Fusion

    Directory of Open Access Journals (Sweden)

    Ku-young Chung

    2017-07-01

    Full Text Available Polysomnography (PSG is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

  17. Ozone Monitoring Based on Multi-Sensor Information Fusion Techniques

    Directory of Open Access Journals (Sweden)

    Yang GAO

    2014-07-01

    Full Text Available As a strong oxidizer, ozone has much damage to a variety of things. So when using ozone to execute purification, we need to monitor and control its concentration strictly. The monitoring and control of the ozone concentration can greatly reduce the damage caused by ozone concentration. Thus, multi - sensor information fusion technique is applied, and then fuses the gathered multi-sensor data information. It realizes real time monitoring, forecasting and controlling, which can improve the accuracy of ozone concentration collection, and the careless error is eliminated effectively and the system error is reduced efficiently.

  18. Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments

    Directory of Open Access Journals (Sweden)

    Hongling Wang

    2018-01-01

    Full Text Available The first application of utilizing unique information-fusion SLAM (IF-SLAM methods is developed for mobile robots performing simultaneous localization and mapping (SLAM adapting to search and rescue (SAR environments in this paper. Several fusion approaches, parallel measurements filtering, exploration trajectories fusing, and combination sensors’ measurements and mobile robots’ trajectories, are proposed. The novel integration particle filter (IPF and optimal improved EKF (IEKF algorithms are derived for information-fusion systems to perform SLAM task in SAR scenarios. The information-fusion architecture consists of multirobots and multisensors (MAM; multiple robots mount on-board laser range finder (LRF sensors, localization sonars, gyro odometry, Kinect-sensor, RGB-D camera, and other proprioceptive sensors. This information-fusion SLAM (IF-SLAM is compared with conventional methods, which indicates that fusion trajectory is more consistent with estimated trajectories and real observation trajectories. The simulations and experiments of SLAM process are conducted in both cluttered indoor environment and outdoor collapsed unstructured scenario, and experimental results validate the effectiveness of the proposed information-fusion methods in improving SLAM performances adapting to SAR scenarios.

  19. Adaptive, integrated sensor processing to compensate for drift and uncertainty: a stochastic 'neural' approach.

    Science.gov (United States)

    Tang, T B; Chen, H; Murray, A F

    2004-02-01

    An adaptive stochastic classifier based on a simple, novel neural architecture--the Continuous Restricted Boltzmann Machine (CRBM) is demonstrated. Together with sensors and signal conditioning circuits, the classifier is capable of measuring and classifying (with high accuracy) the H+ ion concentration, in the presence of both random noise and sensor drift. Training on-line, the stochastic classifier is able to overcome significant drift of real incomplete sensor data dynamically. As analogue hardware, this signal-level sensor fusion scheme is therefore suitable for real-time analysis in a miniaturised multisensor microsystem such as a Lab-in-a-Pill (LIAP).

  20. Cooperative aquatic sensing using the telesupervised adaptive ocean sensor fleet

    Science.gov (United States)

    Dolan, John M.; Podnar, Gregg W.; Stancliff, Stephen; Low, Kian Hsiang; Elfes, Alberto; Higinbotham, John; Hosler, Jeffrey; Moisan, Tiffany; Moisan, John

    2009-09-01

    Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth's climate and ecology. Typical ocean sensing is done with satellites or in situ buoys and research ships which are slow to reposition. Cloud cover inhibits study of localized transient phenomena such as Harmful Algal Blooms (HAB). A fleet of extended-deployment surface autonomous vehicles will enable in situ study of characteristics of HAB, coastal pollutants, and related phenomena. We have developed a multiplatform telesupervision architecture that supports adaptive reconfiguration based on environmental sensor inputs. Our system allows the autonomous repositioning of smart sensors for HAB study by networking a fleet of NOAA OASIS (Ocean Atmosphere Sensor Integration System) surface autonomous vehicles. In situ measurements intelligently modify the search for areas of high concentration. Inference Grid and complementary information-theoretic techniques support sensor fusion and analysis. Telesupervision supports sliding autonomy from high-level mission tasking, through vehicle and data monitoring, to teleoperation when direct human interaction is appropriate. This paper reports on experimental results from multi-platform tests conducted in the Chesapeake Bay and in Pittsburgh, Pennsylvania waters using OASIS platforms, autonomous kayaks, and multiple simulated platforms to conduct cooperative sensing of chlorophyll-a and water quality.

  1. Cognitive foundations for model-based sensor fusion

    Science.gov (United States)

    Perlovsky, Leonid I.; Weijers, Bertus; Mutz, Chris W.

    2003-08-01

    Target detection, tracking, and sensor fusion are complicated problems, which usually are performed sequentially. First detecting targets, then tracking, then fusing multiple sensors reduces computations. This procedure however is inapplicable to difficult targets which cannot be reliably detected using individual sensors, on individual scans or frames. In such more complicated cases one has to perform functions of fusing, tracking, and detecting concurrently. This often has led to prohibitive combinatorial complexity and, as a consequence, to sub-optimal performance as compared to the information-theoretic content of all the available data. It is well appreciated that in this task the human mind is by far superior qualitatively to existing mathematical methods of sensor fusion, however, the human mind is limited in the amount of information and speed of computation it can cope with. Therefore, research efforts have been devoted toward incorporating "biological lessons" into smart algorithms, yet success has been limited. Why is this so, and how to overcome existing limitations? The fundamental reasons for current limitations are analyzed and a potentially breakthrough research and development effort is outlined. We utilize the way our mind combines emotions and concepts in the thinking process and present the mathematical approach to accomplishing this in the current technology computers. The presentation will summarize the difficulties encountered by intelligent systems over the last 50 years related to combinatorial complexity, analyze the fundamental limitations of existing algorithms and neural networks, and relate it to the type of logic underlying the computational structure: formal, multivalued, and fuzzy logic. A new concept of dynamic logic will be introduced along with algorithms capable of pulling together all the available information from multiple sources. This new mathematical technique, like our brain, combines conceptual understanding with

  2. CONTEXT-CAPTURE MULTI-VALUED DECISION FUSION WITH FAULT TOLERANT CAPABILITY FOR WIRELESS SENSOR NETWORKS

    OpenAIRE

    Jun Wu; Shigeru Shimamoto

    2011-01-01

    Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty contextcapture. However, bursty context and multi-valued data are important characteristics of WSNs. One on hand, the local decisions from sensors usually have bursty and contextual characteristics. Fusion center must capture the bursty context information from the sensors. On the other hand, in pract...

  3. Discrete Kalman Filter based Sensor Fusion for Robust Accessibility Interfaces

    International Nuclear Information System (INIS)

    Ghersi, I; Miralles, M T; Mariño, M

    2016-01-01

    Human-machine interfaces have evolved, benefiting from the growing access to devices with superior, embedded signal-processing capabilities, as well as through new sensors that allow the estimation of movements and gestures, resulting in increasingly intuitive interfaces. In this context, sensor fusion for the estimation of the spatial orientation of body segments allows to achieve more robust solutions, overcoming specific disadvantages derived from the use of isolated sensors, such as the sensitivity of magnetic-field sensors to external influences, when used in uncontrolled environments. In this work, a method for the combination of image-processing data and angular-velocity registers from a 3D MEMS gyroscope, through a Discrete-time Kalman Filter, is proposed and deployed as an alternate user interface for mobile devices, in which an on-screen pointer is controlled with head movements. Results concerning general performance of the method are presented, as well as a comparative analysis, under a dedicated test application, with results from a previous version of this system, in which the relative-orientation information was acquired directly from MEMS sensors (3D magnetometer-accelerometer). These results show an improved response for this new version of the pointer, both in terms of precision and response time, while keeping many of the benefits that were highlighted for its predecessor, giving place to a complementary method for signal acquisition that can be used as an alternative-input device, as well as for accessibility solutions. (paper)

  4. An alternative sensor fusion method for object orientation using low-cost MEMS inertial sensors

    Science.gov (United States)

    Bouffard, Joshua L.

    This thesis develops an alternative sensor fusion approach for object orientation using low-cost MEMS inertial sensors. The alternative approach focuses on the unique challenges of small UAVs. Such challenges include the vibrational induced noise onto the accelerometer and bias offset errors of the rate gyroscope. To overcome these challenges, a sensor fusion algorithm combines the measured data from the accelerometer and rate gyroscope to achieve a single output free from vibrational noise and bias offset errors. One of the most prevalent sensor fusion algorithms used for orientation estimation is the Extended Kalman filter (EKF). The EKF filter performs the fusion process by first creating the process model using the nonlinear equations of motion and then establishing a measurement model. With the process and measurement models established, the filter operates by propagating the mean and covariance of the states through time. The success of EKF relies on the ability to establish a representative process and measurement model of the system. In most applications, the EKF measurement model utilizes the accelerometer and GPS-derived accelerations to determine an estimate of the orientation. However, if the GPS-derived accelerations are not available then the measurement model becomes less reliable when subjected to harsh vibrational environments. This situation led to the alternative approach, which focuses on the correlation between the rate gyroscope and accelerometer-derived angle. The correlation between the two sensors then determines how much the algorithm will use one sensor over the other. The result is a measurement that does not suffer from the vibrational noise or from bias offset errors.

  5. Infrared sensors and sensor fusion; Proceedings of the Meeting, Orlando, FL, May 19-21, 1987

    International Nuclear Information System (INIS)

    Buser, R.G.; Warren, F.B.

    1987-01-01

    The present conference discusses topics in the fields of IR sensor multifunctional design; image modeling, simulation, and detection; IR sensor configurations and components; thermal sensor arrays; silicide-based IR sensors; and IR focal plane array utilization. Attention is given to the fusion of lidar and FLIR for target segmentation and enhancement, the synergetic integration of thermal and visual images for computer vision, the 'Falcon Eye' FLIR system, multifunctional electrooptics and multiaperture sensors for precision-guided munitions, and AI approaches to data integration. Also discussed are the comparative performance of Ir silicide and Pt silicide photodiodes, high fill-factor silicide monolithic arrays, and the characterization of noise in staring IR focal plane arrays

  6. Sensor Fusion - Sonar and Stereo Vision, Using Occupancy Grids and SIFT

    DEFF Research Database (Denmark)

    Plascencia, Alfredo; Bendtsen, Jan Dimon

    2006-01-01

    The main contribution of this paper is to present a sensor fusion approach to scene environment mapping as part of a SDF (Sensor Data Fusion) architecture. This approach involves combined sonar and stereo vision readings. Sonar readings are interpreted using probability density functions to the o......The main contribution of this paper is to present a sensor fusion approach to scene environment mapping as part of a SDF (Sensor Data Fusion) architecture. This approach involves combined sonar and stereo vision readings. Sonar readings are interpreted using probability density functions...

  7. Relative Vessel Motion Tracking using Sensor Fusion, Aruco Markers, and MRU Sensors

    Directory of Open Access Journals (Sweden)

    Sondre Sanden Tordal

    2017-04-01

    Full Text Available This paper presents a novel approach for estimating the relative motion between two moving offshore vessels. The method is based on a sensor fusion algorithm including a vision system and two motion reference units (MRUs. The vision system makes use of the open-source computer vision library OpenCV and a cube with Aruco markers placed onto each of the cube sides. The Extended Quaternion Kalman Filter (EQKF is used for bad pose rejection for the vision system. The presented sensor fusion algorithm is based on the Indirect Feedforward Kalman Filter for error estimation. The system is self-calibrating in the sense that the Aruco cube can be placed in an arbitrary location on the secondary vessel. Experimental 6-DOF results demonstrate the accuracy and efficiency of the proposed sensor fusion method compared with the internal joint sensors of two Stewart platforms and the industrial robot. The standard deviation error was found to be 31mm or better when the Arcuo cube was placed at three different locations.

  8. Environmental Perception and Sensor Data Fusion for Unmanned Ground Vehicle

    Directory of Open Access Journals (Sweden)

    Yibing Zhao

    2013-01-01

    Full Text Available Unmanned Ground Vehicles (UGVs that can drive autonomously in cross-country environment have received a good deal of attention in recent years. They must have the ability to determine whether the current terrain is traversable or not by using onboard sensors. This paper explores new methods related to environment perception based on computer image processing, pattern recognition, multisensors data fusion, and multidisciplinary theory. Kalman filter is used for low-level fusion of physical level, thus using the D-S evidence theory for high-level data fusion. Probability Test and Gaussian Mixture Model are proposed to obtain the traversable region in the forward-facing camera view for UGV. One feature set including color and texture information is extracted from areas of interest and combined with a classifier approach to resolve two types of terrain (traversable or not. Also, three-dimension data are employed; the feature set contains components such as distance contrast of three-dimension data, edge chain-code curvature of camera image, and covariance matrix based on the principal component method. This paper puts forward one new method that is suitable for distributing basic probability assignment (BPA, based on which D-S theory of evidence is employed to integrate sensors information and recognize the obstacle. The subordination obtained by using the fuzzy interpolation is applied to calculate the basic probability assignment. It is supposed that the subordination is equal to correlation coefficient in the formula. More accurate results of object identification are achieved by using the D-S theory of evidence. Control on motion behavior or autonomous navigation for UGV is based on the method, which is necessary for UGV high speed driving in cross-country environment. The experiment results have demonstrated the viability of the new method.

  9. Neuromorphic Audio-Visual Sensor Fusion on a Sound-Localising Robot

    Directory of Open Access Journals (Sweden)

    Vincent Yue-Sek Chan

    2012-02-01

    Full Text Available This paper presents the first robotic system featuring audio-visual sensor fusion with neuromorphic sensors. We combine a pair of silicon cochleae and a silicon retina on a robotic platform to allow the robot to learn sound localisation through self-motion and visual feedback, using an adaptive ITD-based sound localisation algorithm. After training, the robot can localise sound sources (white or pink noise in a reverberant environment with an RMS error of 4 to 5 degrees in azimuth. In the second part of the paper, we investigate the source binding problem. An experiment is conducted to test the effectiveness of matching an audio event with a corresponding visual event based on their onset time. The results show that this technique can be quite effective, despite its simplicity.

  10. Dynamic resource management for adaptive distributed information fusion in large volume surveillance

    Science.gov (United States)

    Farahbod, Roozbeh; Glässer, Uwe; Wehn, Hans

    2008-03-01

    We propose a highly adaptive and auto-configurable, multi-layer network architecture for distributed information fusion to address large volume surveillance challenges, assuming a multitude of different sensor types on multiple mobile platforms for intelligence, surveillance and reconnaissance. Our focus is on network enabled operations to efficiently manage and improve employment of a set of mobile resources, their information fusion engines and networking capabilities under dynamically changing and essentially unpredictable conditions. A high-level model of the proposed architecture is formally described in abstract functional and operational terms based on the Abstract State Machine formalism. This description of the underlying design concepts provides a concise and precise blueprint for reasoning about key system attributes at an intuitive level of understanding.

  11. Tele-Supervised Adaptive Ocean Sensor Fleet

    Science.gov (United States)

    Lefes, Alberto; Podnar, Gregg W.; Dolan, John M.; Hosler, Jeffrey C.; Ames, Troy J.

    2009-01-01

    The Tele-supervised Adaptive Ocean Sensor Fleet (TAOSF) is a multi-robot science exploration architecture and system that uses a group of robotic boats (the Ocean-Atmosphere Sensor Integration System, or OASIS) to enable in-situ study of ocean surface and subsurface characteristics and the dynamics of such ocean phenomena as coastal pollutants, oil spills, hurricanes, or harmful algal blooms (HABs). The OASIS boats are extended- deployment, autonomous ocean surface vehicles. The TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy. One feature of TAOSF is the adaptive re-planning of the activities of the OASIS vessels based on sensor input ( smart sensing) and sensorial coordination among multiple assets. The architecture also incorporates Web-based communications that permit control of the assets over long distances and the sharing of data with remote experts. Autonomous hazard and assistance detection allows the automatic identification of hazards that require human intervention to ensure the safety and integrity of the robotic vehicles, or of science data that require human interpretation and response. Also, the architecture is designed for science analysis of acquired data in order to perform an initial onboard assessment of the presence of specific science signatures of immediate interest. TAOSF integrates and extends five subsystems developed by the participating institutions: Emergent Space Tech - nol ogies, Wallops Flight Facility, NASA s Goddard Space Flight Center (GSFC), Carnegie Mellon University, and Jet Propulsion Laboratory (JPL). The OASIS Autonomous Surface Vehicle (ASV) system, which includes the vessels as well as the land-based control and communications infrastructure developed for them, controls the hardware of each platform (sensors, actuators, etc.), and also provides a low-level waypoint navigation capability. The Multi-Platform Simulation Environment from GSFC is a surrogate

  12. Energy-efficient Organization of Wireless Sensor Networks with Adaptive Forecasting

    Directory of Open Access Journals (Sweden)

    Dao-Wei Bi

    2008-04-01

    Full Text Available Due to the wide potential applications of wireless sensor networks, this topic has attracted great attention. The strict energy constraints of sensor nodes result in great challenges for energy efficiency. This paper proposes an energy-efficient organization method. The organization of wireless sensor networks is formulated for target tracking. Target localization is achieved by collaborative sensing with multi-sensor fusion. The historical localization results are utilized for adaptive target trajectory forecasting. Combining autoregressive moving average (ARMA model and radial basis function networks (RBFNs, robust target position forecasting is performed. Moreover, an energyefficient organization method is presented to enhance the energy efficiency of wireless sensor networks. The sensor nodes implement sensing tasks are awakened in a distributed manner. When the sensor nodes transfer their observations to achieve data fusion, the routing scheme is obtained by ant colony optimization. Thus, both the operation and communication energy consumption can be minimized. Experimental results verify that the combination of ARMA model and RBFN can estimate the target position efficiently and energy saving is achieved by the proposed organization method in wireless sensor networks.

  13. A dynamic and context-aware semantic mediation service for discovering and fusion of heterogeneous sensor data

    Directory of Open Access Journals (Sweden)

    Mohamed Bakillah

    2013-06-01

    Full Text Available Sensors play an increasingly critical role in capturing and distributing observation of phenomena in our environment. The Semantic Sensor Web enables interoperability to support various applications that use data made available by semantically heterogeneous sensor services. However, several challenges still need to be addressed to achieve this vision. More particularly, mechanisms that can support context-aware semantic mapping that adapts to dynamic metadata of sensors are required. Semantic mapping for Sensor Web is required to support sensor data fusion, sensor data discovery and retrieval, and automatic semantic annotation, to name only a few applications. This paper presents a context-aware ontology-based semantic mediation service for heterogeneous sensor services. The semantic mediation service is context-aware and dynamic because it takes into account the real-time variability of thematic, spatial and temporal features that describe sensor data in different contexts. The semantic mediation service integrates rule-based reasoning to support resolution of semantic heterogeneities. An application scenario is presented showing how the semantic mediation service can improve sensor data interpretation, reuse, and sharing in static and dynamic settings.

  14. Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm

    Directory of Open Access Journals (Sweden)

    Daniel H. De La Iglesia

    2017-10-01

    Full Text Available The use of electric bikes (e-bikes has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.

  15. Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm.

    Science.gov (United States)

    De La Iglesia, Daniel H; Villarrubia, Gabriel; De Paz, Juan F; Bajo, Javier

    2017-10-31

    The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.

  16. A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes

    Directory of Open Access Journals (Sweden)

    Detong Kong

    2012-02-01

    Full Text Available Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

  17. Advances in Multi-Sensor Information Fusion: Theory and Applications 2017

    Directory of Open Access Journals (Sweden)

    Xue-Bo Jin

    2018-04-01

    Full Text Available The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s, and it has been applied in many fields. For example, manufacturing and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this special issue is to report innovative ideas and solutions for multi-sensor information fusion in the emerging applications era, focusing on development, adoption, and applications.

  18. SENSOR FUSION CONTROL SYSTEM FOR COMPUTER INTEGRATED MANUFACTURING

    Directory of Open Access Journals (Sweden)

    C.M. Kumile

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: Manufacturing companies of today face unpredictable, high frequency market changes driven by global competition. To stay competitive, these companies must have the characteristics of cost-effective rapid response to the market needs. As an engineering discipline, mechatronics strives to integrate mechanical, electronic, and computer systems optimally in order to create high precision products and manufacturing processes. This paper presents a methodology of increasing flexibility and reusability of a generic computer integrated manufacturing (CIM cell-control system using simulation and modelling of mechatronic sensory system (MSS concepts. The utilisation of sensors within the CIM cell is highlighted specifically for data acquisition, analysis, and multi-sensor data fusion. Thus the designed reference architecture provides comprehensive insight for the functions and methodologies of a generic shop-floor control system (SFCS, which consequently enables the rapid deployment of a flexible system.

    AFRIKAANSE OPSOMMING: Hedendaagse vervaardigingsondernemings ervaar gereeld onvoorspelbare markveranderinge wat aangedryf word deur wêreldwye mededinging. Om kompeterend te bly moet hierdie ondernemings die eienskappe van kosteeffektiwiteit en snelle-respons op markfluktuasies toon. Megatronika streef daarna om meganiese, elektroniese en rekenaarstelsels optimaal te integreer om hoëpresisieprodukte en produksieprosesse daar te stel. Hierdie artikel suggereer 'n metodologie vir toenemende aanpasbaarheid en herbruikbaarheid van 'n generiese rekenaargeïntegreerde vervaardigingsel-beheersisteem deur die gebruik van simulasie en die modellering van megatroniese sensorsisteemkonsepte. Die aanwending van sensors binne die sel fasiliteer datavaslegging, ontleding en multisensordatafusie. Sodoende verskaf die ontwerpte argitektuur insig in die funksie en metodologie van 'n generiese stukwerkwinkelbeheersisteem wat die vinnige

  19. Adaptive Sensing Based on Profiles for Sensor Systems

    Directory of Open Access Journals (Sweden)

    Yoshiteru Ishida

    2009-10-01

    Full Text Available This paper proposes a profile-based sensing framework for adaptive sensor systems based on models that relate possibly heterogeneous sensor data and profiles generated by the models to detect events. With these concepts, three phases for building the sensor systems are extracted from two examples: a combustion control sensor system for an automobile engine, and a sensor system for home security. The three phases are: modeling, profiling, and managing trade-offs. Designing and building a sensor system involves mapping the signals to a model to achieve a given mission.

  20. Estimating Orientation Using Magnetic and Inertial Sensors and Different Sensor Fusion Approaches: Accuracy Assessment in Manual and Locomotion Tasks

    Directory of Open Access Journals (Sweden)

    Elena Bergamini

    2014-10-01

    Full Text Available Magnetic and inertial measurement units are an emerging technology to obtain 3D orientation of body segments in human movement analysis. In this respect, sensor fusion is used to limit the drift errors resulting from the gyroscope data integration by exploiting accelerometer and magnetic aiding sensors. The present study aims at investigating the effectiveness of sensor fusion methods under different experimental conditions. Manual and locomotion tasks, differing in time duration, measurement volume, presence/absence of static phases, and out-of-plane movements, were performed by six subjects, and recorded by one unit located on the forearm or the lower trunk, respectively. Two sensor fusion methods, representative of the stochastic (Extended Kalman Filter and complementary (Non-linear observer filtering, were selected, and their accuracy was assessed in terms of attitude (pitch and roll angles and heading (yaw angle errors using stereophotogrammetric data as a reference. The sensor fusion approaches provided significantly more accurate results than gyroscope data integration. Accuracy improved mostly for heading and when the movement exhibited stationary phases, evenly distributed 3D rotations, it occurred in a small volume, and its duration was greater than approximately 20 s. These results were independent from the specific sensor fusion method used. Practice guidelines for improving the outcome accuracy are provided.

  1. Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liao

    2013-12-01

    Full Text Available Fault diagnosis (FD and data fusion (DF technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2 sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

  2. Reliability of measured data for pH sensor arrays with fault diagnosis and data fusion based on LabVIEW.

    Science.gov (United States)

    Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

    2013-12-13

    Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

  3. Asynchronous Sensor fuSion for Improved Safety of air Traffic (ASSIST), Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — SSCI proposes to develop, implement and test a collision detection system for unmanned aerial vehicles (UAV), referred to as the Asynchronous Sensor fuSion for...

  4. Distributed data fusion across multiple hard and soft mobile sensor platforms

    Science.gov (United States)

    Sinsley, Gregory

    One of the biggest challenges currently facing the robotics field is sensor data fusion. Unmanned robots carry many sophisticated sensors including visual and infrared cameras, radar, laser range finders, chemical sensors, accelerometers, gyros, and global positioning systems. By effectively fusing the data from these sensors, a robot would be able to form a coherent view of its world that could then be used to facilitate both autonomous and intelligent operation. Another distinct fusion problem is that of fusing data from teammates with data from onboard sensors. If an entire team of vehicles has the same worldview they will be able to cooperate much more effectively. Sharing worldviews is made even more difficult if the teammates have different sensor types. The final fusion challenge the robotics field faces is that of fusing data gathered by robots with data gathered by human teammates (soft sensors). Humans sense the world completely differently from robots, which makes this problem particularly difficult. The advantage of fusing data from humans is that it makes more information available to the entire team, thus helping each agent to make the best possible decisions. This thesis presents a system for fusing data from multiple unmanned aerial vehicles, unmanned ground vehicles, and human observers. The first issue this thesis addresses is that of centralized data fusion. This is a foundational data fusion issue, which has been very well studied. Important issues in centralized fusion include data association, classification, tracking, and robotics problems. Because these problems are so well studied, this thesis does not make any major contributions in this area, but does review it for completeness. The chapter on centralized fusion concludes with an example unmanned aerial vehicle surveillance problem that demonstrates many of the traditional fusion methods. The second problem this thesis addresses is that of distributed data fusion. Distributed data fusion

  5. Evolutionary Adaptive Discovery of Phased Array Sensor Signal Identification

    Energy Technology Data Exchange (ETDEWEB)

    Timothy R. McJunkin; Milos Manic

    2011-05-01

    Tomography, used to create images of the internal properties and features of an object, from phased array ultasonics is improved through many sophisiticated methonds of post processing of data. One approach used to improve tomographic results is to prescribe the collection of more data, from different points of few so that data fusion might have a richer data set to work from. This approach can lead to rapid increase in the data needed to be stored and processed. It also does not necessarily lead to have the needed data. This article describes a novel approach to utilizing the data aquired as a basis for adapting the sensors focusing parameters to locate more precisely the features in the material: specifically, two evolutionary methods of autofocusing on a returned signal are coupled with the derivations of the forumulas for spatially locating the feature are given. Test results of the two novel methods of evolutionary based focusing (EBF) illustrate the improved signal strength and correction of the position of feature using the optimized focal timing parameters, called Focused Delay Identification (FoDI).

  6. A Radiosonde Using a Humidity Sensor Array with a Platinum Resistance Heater and Multi-Sensor Data Fusion

    Science.gov (United States)

    Shi, Yunbo; Luo, Yi; Zhao, Wenjie; Shang, Chunxue; Wang, Yadong; Chen, Yinsheng

    2013-01-01

    This paper describes the design and implementation of a radiosonde which can measure the meteorological temperature, humidity, pressure, and other atmospheric data. The system is composed of a CPU, microwave module, temperature sensor, pressure sensor and humidity sensor array. In order to effectively solve the humidity sensor condensation problem due to the low temperatures in the high altitude environment, a capacitive humidity sensor including four humidity sensors to collect meteorological humidity and a platinum resistance heater was developed using micro-electro-mechanical-system (MEMS) technology. A platinum resistance wire with 99.999% purity and 0.023 mm in diameter was used to obtain the meteorological temperature. A multi-sensor data fusion technique was applied to process the atmospheric data. Static and dynamic experimental results show that the designed humidity sensor with platinum resistance heater can effectively tackle the sensor condensation problem, shorten response times and enhance sensitivity. The humidity sensor array can improve measurement accuracy and obtain a reliable initial meteorological humidity data, while the multi-sensor data fusion technique eliminates the uncertainty in the measurement. The radiosonde can accurately reflect the meteorological changes. PMID:23857263

  7. A radiosonde using a humidity sensor array with a platinum resistance heater and multi-sensor data fusion.

    Science.gov (United States)

    Shi, Yunbo; Luo, Yi; Zhao, Wenjie; Shang, Chunxue; Wang, Yadong; Chen, Yinsheng

    2013-07-12

    This paper describes the design and implementation of a radiosonde which can measure the meteorological temperature, humidity, pressure, and other atmospheric data. The system is composed of a CPU, microwave module, temperature sensor, pressure sensor and humidity sensor array. In order to effectively solve the humidity sensor condensation problem due to the low temperatures in the high altitude environment, a capacitive humidity sensor including four humidity sensors to collect meteorological humidity and a platinum resistance heater was developed using micro-electro-mechanical-system (MEMS) technology. A platinum resistance wire with 99.999% purity and 0.023 mm in diameter was used to obtain the meteorological temperature. A multi-sensor data fusion technique was applied to process the atmospheric data. Static and dynamic experimental results show that the designed humidity sensor with platinum resistance heater can effectively tackle the sensor condensation problem, shorten response times and enhance sensitivity. The humidity sensor array can improve measurement accuracy and obtain a reliable initial meteorological humidity data, while the multi-sensor data fusion technique eliminates the uncertainty in the measurement. The radiosonde can accurately reflect the meteorological changes.

  8. A Radiosonde Using a Humidity Sensor Array with a Platinum Resistance Heater and Multi-Sensor Data Fusion

    Directory of Open Access Journals (Sweden)

    Yadong Wang

    2013-07-01

    Full Text Available This paper describes the design and implementation of a radiosonde which can measure the meteorological temperature, humidity, pressure, and other atmospheric data. The system is composed of a CPU, microwave module, temperature sensor, pressure sensor and humidity sensor array. In order to effectively solve the humidity sensor condensation problem due to the low temperatures in the high altitude environment, a capacitive humidity sensor including four humidity sensors to collect meteorological humidity and a platinum resistance heater was developed using micro-electro-mechanical-system (MEMS technology. A platinum resistance wire with 99.999% purity and 0.023 mm in diameter was used to obtain the meteorological temperature. A multi-sensor data fusion technique was applied to process the atmospheric data. Static and dynamic experimental results show that the designed humidity sensor with platinum resistance heater can effectively tackle the sensor condensation problem, shorten response times and enhance sensitivity. The humidity sensor array can improve measurement accuracy and obtain a reliable initial meteorological humidity data, while the multi-sensor data fusion technique eliminates the uncertainty in the measurement. The radiosonde can accurately reflect the meteorological changes.

  9. Multimodal Medical Image Fusion by Adaptive Manifold Filter

    Directory of Open Access Journals (Sweden)

    Peng Geng

    2015-01-01

    Full Text Available Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.

  10. Step Characterization using Sensor Information Fusion and Machine Learning

    Directory of Open Access Journals (Sweden)

    Ricardo Anacleto

    2015-12-01

    Full Text Available A pedestrian inertial navigation system is typically used to suppress the Global Navigation Satellite System limitation to track persons in indoor or in dense environments. However, low- cost inertial systems provide huge location estimation errors due to sensors and pedestrian dead reckoning inherent characteristics. To suppress some of these errors we propose a system that uses two inertial measurement units spread in person’s body, which measurements are aggregated using learning algorithms that learn the gait behaviors. In this work we present our results on using different machine learning algorithms which are used to characterize the step according to its direction and length. This characterization is then used to adapt the navigation algorithm according to the performed classifications.

  11. Speech Adaptation to Kinematic Recording Sensors: Perceptual and Acoustic Findings

    Science.gov (United States)

    Dromey, Christopher; Hunter, Elise; Nissen, Shawn L.

    2018-01-01

    Purpose: This study used perceptual and acoustic measures to examine the time course of speech adaptation after the attachment of electromagnetic sensor coils to the tongue, lips, and jaw. Method: Twenty native English speakers read aloud stimulus sentences before the attachment of the sensors, immediately after attachment, and again 5, 10, 15,…

  12. Sensor Interoperability and Fusion in Fingerprint Verification: A Case Study using Minutiae-and Ridge-Based Matchers

    NARCIS (Netherlands)

    Alonso-Fernandez, F.; Veldhuis, Raymond N.J.; Bazen, A.M.; Fierrez-Aguilar, J.; Ortega-Garcia, J.

    2006-01-01

    Information fusion in fingerprint recognition has been studied in several papers. However, only a few papers have been focused on sensor interoperability and sensor fusion. In this paper, these two topics are studied using a multisensor database acquired with three different fingerprint sensors.

  13. Biomimetic micromechanical adaptive flow-sensor arrays

    NARCIS (Netherlands)

    Krijnen, Gijsbertus J.M.; Floris, J.; Dijkstra, Marcel; Lammerink, Theodorus S.J.; Wiegerink, Remco J.

    2007-01-01

    We report current developments in biomimetic flow-sensors based on flow sensitive mechano-sensors of crickets. Crickets have one form of acoustic sensing evolved in the form of mechanoreceptive sensory hairs. These filiform hairs are highly perceptive to low-frequency sound with energy sensitivities

  14. Infrared processing and sensor fusion for anti-personnel land-mine detection

    NARCIS (Netherlands)

    Schavemaker, J.G.M.; Cremer, F.; Schutte, K.; Breejen, E. den

    2000-01-01

    In this paper we present the results of infrared processing and sensor fusion obtained within the European research project GEODE (Ground Explosive Ordnance DEtection) that strives for the realization of a vehicle-mounted, multi-sensor anti-personnel land-mine detection system for humanitarian

  15. Performance evaluation of multi-sensor data-fusion systems in ...

    Indian Academy of Sciences (India)

    In this paper, the utilization of multi-sensors of different types, their characteristics, and their data-fusion in launch vehicles to achieve the goal of injecting the satellite into a precise orbit is explained. Performance requirements of sensors and their redundancy management in a typical launch vehicle are also included.

  16. Sensor Fusion Techniques for Phased-Array Eddy Current and Phased-Array Ultrasound Data

    Energy Technology Data Exchange (ETDEWEB)

    Arrowood, Lloyd F. [Y-12 National Security Complex, Oak Ridge, TN (United States)

    2018-03-15

    Sensor (or Data) fusion is the process of integrating multiple data sources to produce more consistent, accurate and comprehensive information than is provided by a single data source. Sensor fusion may also be used to combine multiple signals from a single modality to improve the performance of a particular inspection technique. Industrial nondestructive testing may utilize multiple sensors to acquire inspection data depending upon the object under inspection and the anticipated types of defects that can be identified. Sensor fusion can be performed at various levels of signal abstraction with each having its strengths and weaknesses. A multimodal data fusion strategy first proposed by Heideklang and Shokouhi that combines spatially scattered detection locations to improve detection performance of surface-breaking and near-surface cracks in ferromagnetic metals is shown using a surface inspection example and is then extended for volumetric inspections. Utilizing data acquired from an Olympus Omniscan MX2 from both phased array eddy current and ultrasound probes on test phantoms, single and multilevel fusion techniques are employed to integrate signals from the two modalities. Preliminary results demonstrate how confidence in defect identification and interpretation benefit from sensor fusion techniques. Lastly, techniques for integrating data into radiographic and volumetric imagery from computed tomography are described and results are presented.

  17. Phase 1 report on sensor technology, data fusion and data interpretation for site characterization

    International Nuclear Information System (INIS)

    Beckerman, M.

    1991-10-01

    In this report we discuss sensor technology, data fusion and data interpretation approaches of possible maximal usefulness for subsurface imaging and characterization of land-fill waste sites. Two sensor technologies, terrain conductivity using electromagnetic induction and ground penetrating radar, are described and the literature on the subject is reviewed. We identify the maximum entropy stochastic method as one providing a rigorously justifiable framework for fusing the sensor data, briefly summarize work done by us in this area, and examine some of the outstanding issues with regard to data fusion and interpretation. 25 refs., 17 figs

  18. Distributed fusion estimation for sensor networks with communication constraints

    CERN Document Server

    Zhang, Wen-An; Song, Haiyu; Yu, Li

    2016-01-01

    This book systematically presents energy-efficient robust fusion estimation methods to achieve thorough and comprehensive results in the context of network-based fusion estimation. It summarizes recent findings on fusion estimation with communication constraints; several novel energy-efficient and robust design methods for dealing with energy constraints and network-induced uncertainties are presented, such as delays, packet losses, and asynchronous information... All the results are presented as algorithms, which are convenient for practical applications.

  19. An adaptive secret key-directed cryptographic scheme for secure transmission in wireless sensor networks

    International Nuclear Information System (INIS)

    Muhammad, K.; Jan, Z.; Khan, Z

    2015-01-01

    Wireless Sensor Networks (WSNs) are memory and bandwidth limited networks whose main goals are to maximize the network lifetime and minimize the energy consumption and transmission cost. To achieve these goals, different techniques of compression and clustering have been used. However, security is an open and major issue in WSNs for which different approaches are used, both in centralized and distributed WSNs' environments. This paper presents an adaptive cryptographic scheme for secure transmission of various sensitive parameters, sensed by wireless sensors to the fusion center for further processing in WSNs such as military networks. The proposed method encrypts the sensitive captured data of sensor nodes using various encryption procedures (bitxor operation, bits shuffling, and secret key based encryption) and then sends it to the fusion center. At the fusion center, the received encrypted data is decrypted for taking further necessary actions. The experimental results with complexity analysis, validate the effectiveness and feasibility of the proposed method in terms of security in WSNs. (author)

  20. Residual stress condition monitoring via sensor fusion in turning of Inconel 718

    OpenAIRE

    Simeone, A.; Segreto, T.; Teti, R.

    2013-01-01

    A multiple sensor monitoring system, equipped with cutting force, acoustic emission and vibration sensing units, was employed in association with advanced procedures for signal analysis, sensor fusion and cognitive decision making for residual stress evaluation in turning of Inconel 718 nickel alloy. Two signal processing and feature extraction methodologies, based respectively on sensory data statistical evaluation and Principal Component Analysis, were applied to the sensor signals generate...

  1. Adaptive Sampling in Autonomous Marine Sensor Networks

    National Research Council Canada - National Science Library

    Eickstedt, Donald P

    2006-01-01

    ... oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new...

  2. Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking.

    Science.gov (United States)

    Ligorio, Gabriele; Bergamini, Elena; Pasciuto, Ilaria; Vannozzi, Giuseppe; Cappozzo, Aurelio; Sabatini, Angelo Maria

    2016-01-26

    Information from complementary and redundant sensors are often combined within sensor fusion algorithms to obtain a single accurate observation of the system at hand. However, measurements from each sensor are characterized by uncertainties. When multiple data are fused, it is often unclear how all these uncertainties interact and influence the overall performance of the sensor fusion algorithm. To address this issue, a benchmarking procedure is presented, where simulated and real data are combined in different scenarios in order to quantify how each sensor's uncertainties influence the accuracy of the final result. The proposed procedure was applied to the estimation of the pelvis orientation using a waist-worn magnetic-inertial measurement unit. Ground-truth data were obtained from a stereophotogrammetric system and used to obtain simulated data. Two Kalman-based sensor fusion algorithms were submitted to the proposed benchmarking procedure. For the considered application, gyroscope uncertainties proved to be the main error source in orientation estimation accuracy for both tested algorithms. Moreover, although different performances were obtained using simulated data, these differences became negligible when real data were considered. The outcome of this evaluation may be useful both to improve the design of new sensor fusion methods and to drive the algorithm tuning process.

  3. Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm

    Directory of Open Access Journals (Sweden)

    Yang Bai

    2015-04-01

    Full Text Available As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST retrieved from Thermal Infra-Red (TIR images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial- and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT in order to derive high resolution synthetic data. The synthetic images were evaluated for both testing and simulated satellite images. The average difference (AD and absolute average difference (AAD are smaller than 1.7 K, where the correlation coefficient (CC and root-mean-square error (RMSE are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time.

  4. Rider trunk and bicycle pose estimation with fusion of force/inertial sensors.

    Science.gov (United States)

    Zhang, Yizhai; Chen, Kuo; Yi, Jingang

    2013-09-01

    Estimation of human pose in physical human-machine interactions such as bicycling is challenging because of highly-dimensional human motion and lack of inexpensive, effective motion sensors. In this paper, we present a computational scheme to estimate both the rider trunk pose and the bicycle roll angle using only inertial and force sensors. The estimation scheme is built on a rider-bicycle dynamic model and the fusion of the wearable inertial sensors and the bicycle force sensors. We take advantages of the attractive properties of the robust force measurements and the motion-sensitive inertial measurements. The rider-bicycle dynamic model provides the underlying relationship between the force and the inertial measurements. The extended Kalman filter-based sensor fusion design fully incorporates the dynamic effects of the force measurements. The performance of the estimation scheme is demonstrated through extensive indoor and outdoor riding experiments.

  5. Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis.

    Science.gov (United States)

    Jiang, Wen; Xie, Chunhe; Zhuang, Miaoyan; Shou, Yehang; Tang, Yongchuan

    2016-09-15

    Sensor data fusion technology is widely employed in fault diagnosis. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-number, Z = (A, B), can represent the fuzziness and the reliability of information simultaneously, where the first component A represents a fuzzy restriction on the values of uncertain variables and the second component B is a measure of the reliability of A. In order to model and process the uncertainties in a sensor data fusion system reasonably, in this paper, a novel method combining the Z-number and Dempster-Shafer (D-S) evidence theory is proposed, where the Z-number is used to model the fuzziness and reliability of the sensor data and the D-S evidence theory is used to fuse the uncertain information of Z-numbers. The main advantages of the proposed method are that it provides a more robust measure of reliability to the sensor data, and the complementary information of multi-sensors reduces the uncertainty of the fault recognition, thus enhancing the reliability of fault detection.

  6. Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Wen Jiang

    2016-09-01

    Full Text Available Sensor data fusion technology is widely employed in fault diagnosis. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-number, Z = (A, B, can represent the fuzziness and the reliability of information simultaneously, where the first component A represents a fuzzy restriction on the values of uncertain variables and the second component B is a measure of the reliability of A. In order to model and process the uncertainties in a sensor data fusion system reasonably, in this paper, a novel method combining the Z-number and Dempster–Shafer (D-S evidence theory is proposed, where the Z-number is used to model the fuzziness and reliability of the sensor data and the D-S evidence theory is used to fuse the uncertain information of Z-numbers. The main advantages of the proposed method are that it provides a more robust measure of reliability to the sensor data, and the complementary information of multi-sensors reduces the uncertainty of the fault recognition, thus enhancing the reliability of fault detection.

  7. Distributed Autonomous Control Action Based on Sensor and Mission Fusion

    National Research Council Canada - National Science Library

    Konyk, Jr., Stephen; Swaminathan, Ashish

    2005-01-01

    The research effort focuses on the exploration of sensing and control techniques in the framework of fusion and distributed control pertinent to the problem of deploying autonomous unmanned surface...

  8. Data Fusion Based on Node Trust Evaluation in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhou Jianming

    2014-01-01

    Full Text Available Abnormal behavior detection and trust evaluation mode of traditional sensor node have a single function without considering all the factors, and the trust value algorithm is relatively complicated. To avoid these above disadvantages, a trust evaluation model based on the autonomous behavior of sensor node is proposed in this paper. Each sensor node has the monitoring privilege and obligation. Neighboring sensor nodes can monitor each other. Their direct and indirect trust values can be achieved by using a relatively simple calculation method, the synthesis trust value of which could be got according to the composition rule of D-S evidence theory. Firstly, the cluster head assigns different weighted value for the data from each sensor node, then the weight vector is set according to the synthesis trust value, the data fusion processing is executed, and finally the cluster head sensor node transmits the fused result to the base station. Simulation experiment results demonstrate that the trust evaluation model can rapidly, exactly, and effectively recognize malicious sensor node and avoid malicious sensor node becoming cluster head sensor node. The proposed algorithm can greatly increase the safety and accuracy of data fusion, improve communication efficiency, save energy of sensor node, suit different application fields, and deploy environments.

  9. Dynamic estimation of water hyacinth area using fusion of satellite and GPS sensors

    Science.gov (United States)

    Sun, Ling; Zhu, Zesheng

    2017-08-01

    The interaction of water hyacinth area with growth is known to be strongly influenced by area size, but little is known about the interdependent role that size and time have on dynamic estimation of water hyacinth area. We report on the fusion of satellite and GPS sensor data into area growth model as a function of area and time. We employ a multi-sensor fusion technique that is able to generate uniform data of fitting area growth model with complete control of area and time. Evidence of an overall Goodness of Fit Index of 0.9753 was obtained by using conventional statistic analysis. These findings suggest that the multi-sensor fusion technique readily supports area growth model development with highly resolution. The differential equation is good at describing the spatial spread of water hyacinth. Moreover, it was found that area growth model enjoy an appreciable advantage when it comes to harvesting water hyacinth.

  10. Sensor Fault Diagnosis in Quadrotors Using Nonlinear Adaptive Estimators

    Science.gov (United States)

    2014-10-02

    Sensor Fault Diagnosis in Quadrotors Using Nonlinear Adaptive Estimators Remus C Avram1, Xiaodong Zhang2 and Jacob Campbell3 1,2 Wright State...excessive noise as a result of temperature variations, vibration, etc. This paper presents a sensor fault diagnostic method for quadrotor UAVs. Specifically...in the UAV dy- namics. Thus, the faults in accelerometer and gyroscope can be represented as virtual actuator faults in the quadrotor state equations

  11. Reliability estimates for selected sensors in fusion applications

    International Nuclear Information System (INIS)

    Cadwallader, L.C.

    1996-09-01

    This report presents the results of a study to define several types of sensors in use, the qualitative reliability (failure modes) and quantitative reliability (average failure rates) for these types of process sensors. Temperature, pressure, flow, and level sensors are discussed for water coolant and for cryogenic coolants. The failure rates that have been found are useful for risk assessment and safety analysis. Repair times and calibration intervals are also given when found in the literature. All of these values can also be useful to plant operators and maintenance personnel. Designers may be able to make use of these data when planning systems. The final chapter in this report discusses failure rates for several types of personnel safety sensors, including ionizing radiation monitors, toxic and combustible gas detectors, humidity sensors, and magnetic field sensors. These data could be useful to industrial hygienists and other safety professionals when designing or auditing for personnel safety

  12. Extended Logic Intelligent Processing System for a Sensor Fusion Processor Hardware

    Science.gov (United States)

    Stoica, Adrian; Thomas, Tyson; Li, Wei-Te; Daud, Taher; Fabunmi, James

    2000-01-01

    The paper presents the hardware implementation and initial tests from a low-power, highspeed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) is described, which combines rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor signals in compact low power VLSI. The development of the ELIPS concept is being done to demonstrate the interceptor functionality which particularly underlines the high speed and low power requirements. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Processing speeds of microseconds have been demonstrated using our test hardware.

  13. All Source Adaptive Fusion for Aided Navigation in Non-GPS Environment

    National Research Council Canada - National Science Library

    Klausutis, Timothy J; Wehling, Ric; Lames, Matthew; Aboutalib, Omar; Awalt, Bruce; Fund, Alex; Thai, Bea; Leibs, Jeremy

    2007-01-01

    An innovative approach for navigation in non-GPS environment is presented based on all source adaptive fusion of any available information encompassing passive imaging data, digital elevation terrain...

  14. An Adaptive Lossless Data Compression Scheme for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jonathan Gana Kolo

    2012-01-01

    Full Text Available Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs.

  15. Kalman filter-based EM-optical sensor fusion for needle deflection estimation.

    Science.gov (United States)

    Jiang, Baichuan; Gao, Wenpeng; Kacher, Daniel; Nevo, Erez; Fetics, Barry; Lee, Thomas C; Jayender, Jagadeesan

    2018-04-01

    In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects. In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme. Bending model-based estimations and EM-based direct estimation are used as the measurement vectors in the Kalman filter, thus establishing an online estimation approach. Static tip bending experiments show that the fusion method can reduce the mean error of the tip position estimation from 29.23 mm of the optical sensor-based approach to 3.15 mm of the fusion-based approach and from 39.96 to 6.90 mm, at the MRI isocenter and the MRI entrance, respectively. This work established a novel sensor fusion scheme that incorporates model information, which enables real-time tracking of needle deflection with MRI compatibility, in a free-hand operating setup.

  16. Advanced data visualization and sensor fusion: Conversion of techniques from medical imaging to Earth science

    Science.gov (United States)

    Savage, Richard C.; Chen, Chin-Tu; Pelizzari, Charles; Ramanathan, Veerabhadran

    1993-01-01

    Hughes Aircraft Company and the University of Chicago propose to transfer existing medical imaging registration algorithms to the area of multi-sensor data fusion. The University of Chicago's algorithms have been successfully demonstrated to provide pixel by pixel comparison capability for medical sensors with different characteristics. The research will attempt to fuse GOES (Geostationary Operational Environmental Satellite), AVHRR (Advanced Very High Resolution Radiometer), and SSM/I (Special Sensor Microwave Imager) sensor data which will benefit a wide range of researchers. The algorithms will utilize data visualization and algorithm development tools created by Hughes in its EOSDIS (Earth Observation SystemData/Information System) prototyping. This will maximize the work on the fusion algorithms since support software (e.g. input/output routines) will already exist. The research will produce a portable software library with documentation for use by other researchers.

  17. Proximal soil sensors and data fusion for precision agriculture

    NARCIS (Netherlands)

    Mahmood, H.S.

    2013-01-01

    different remote and proximal soil sensors are available today that can scan entire fields and give detailed information on various physical, chemical, mechanical and biological soil properties. The first objective of this thesis was to evaluate different proximal soil sensors available today and to

  18. Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tengyue Zou

    2017-05-01

    Full Text Available Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k-means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.

  19. Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks.

    Science.gov (United States)

    Zou, Tengyue; Li, Zhenjia; Li, Shuyuan; Lin, Shouying

    2017-05-04

    Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k -means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.

  20. Distributed adaptive diagnosis of sensor faults using structural response data

    Science.gov (United States)

    Dragos, Kosmas; Smarsly, Kay

    2016-10-01

    The reliability and consistency of wireless structural health monitoring (SHM) systems can be compromised by sensor faults, leading to miscalibrations, corrupted data, or even data loss. Several research approaches towards fault diagnosis, referred to as ‘analytical redundancy’, have been proposed that analyze the correlations between different sensor outputs. In wireless SHM, most analytical redundancy approaches require centralized data storage on a server for data analysis, while other approaches exploit the on-board computing capabilities of wireless sensor nodes, analyzing the raw sensor data directly on board. However, using raw sensor data poses an operational constraint due to the limited power resources of wireless sensor nodes. In this paper, a new distributed autonomous approach towards sensor fault diagnosis based on processed structural response data is presented. The inherent correlations among Fourier amplitudes of acceleration response data, at peaks corresponding to the eigenfrequencies of the structure, are used for diagnosis of abnormal sensor outputs at a given structural condition. Representing an entirely data-driven analytical redundancy approach that does not require any a priori knowledge of the monitored structure or of the SHM system, artificial neural networks (ANN) are embedded into the sensor nodes enabling cooperative fault diagnosis in a fully decentralized manner. The distributed analytical redundancy approach is implemented into a wireless SHM system and validated in laboratory experiments, demonstrating the ability of wireless sensor nodes to self-diagnose sensor faults accurately and efficiently with minimal data traffic. Besides enabling distributed autonomous fault diagnosis, the embedded ANNs are able to adapt to the actual condition of the structure, thus ensuring accurate and efficient fault diagnosis even in case of structural changes.

  1. Computer vision and sensor fusion for detecting buried objects

    Energy Technology Data Exchange (ETDEWEB)

    Clark, G.A.; Hernandez, J.E.; Sengupta, S.K.; Sherwood, R.J.; Schaich, P.C.; Buhl, M.R.; Kane, R.J.; DelGrande, N.K.

    1992-10-01

    Given multiple images of the surface of the earth from dual-band infrared sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. Supervised learning pattern classifiers (including neural networks,) are used. We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing information from multiple sensor types. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved problem of detecting buried land mines from an airborne standoff platform.

  2. Extending the Lifetime of Sensor Networks through Adaptive Reclustering

    Directory of Open Access Journals (Sweden)

    Gianluigi Ferrari

    2007-06-01

    Full Text Available We analyze the lifetime of clustered sensor networks with decentralized binary detection under a physical layer quality-of-service (QoS constraint, given by the maximum tolerable probability of decision error at the access point (AP. In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal for the lifetime of a single sensor. We show the benefits, in terms of longer network lifetime, of adaptive reclustering. We also derive an analytical framework for the computation of the network lifetime and the penalty, in terms of time delay and energy consumption, brought by adaptive reclustering. On the other hand, absence of reclustering leads to a shorter network lifetime, and we show the impact of various clustering configurations under different QoS conditions. Our results show that the organization of sensors in a few big clusters is the winning strategy to maximize the network lifetime. Moreover, the observation of the phenomenon should be frequent in order to limit the penalties associated with the reclustering procedure. We also apply the developed framework to analyze the energy consumption associated with the proposed reclustering protocol, obtaining results in good agreement with the performance of realistic wireless sensor networks. Finally, we present simulation results on the lifetime of IEEE 802.15.4 wireless sensor networks, which enrich the proposed analytical framework and show that typical networking performance metrics (such as throughput and delay are influenced by the sensor network lifetime.

  3. Extending the Lifetime of Sensor Networks through Adaptive Reclustering

    Directory of Open Access Journals (Sweden)

    Ferrari Gianluigi

    2007-01-01

    Full Text Available We analyze the lifetime of clustered sensor networks with decentralized binary detection under a physical layer quality-of-service (QoS constraint, given by the maximum tolerable probability of decision error at the access point (AP. In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal for the lifetime of a single sensor. We show the benefits, in terms of longer network lifetime, of adaptive reclustering. We also derive an analytical framework for the computation of the network lifetime and the penalty, in terms of time delay and energy consumption, brought by adaptive reclustering. On the other hand, absence of reclustering leads to a shorter network lifetime, and we show the impact of various clustering configurations under different QoS conditions. Our results show that the organization of sensors in a few big clusters is the winning strategy to maximize the network lifetime. Moreover, the observation of the phenomenon should be frequent in order to limit the penalties associated with the reclustering procedure. We also apply the developed framework to analyze the energy consumption associated with the proposed reclustering protocol, obtaining results in good agreement with the performance of realistic wireless sensor networks. Finally, we present simulation results on the lifetime of IEEE 802.15.4 wireless sensor networks, which enrich the proposed analytical framework and show that typical networking performance metrics (such as throughput and delay are influenced by the sensor network lifetime.

  4. Sensor fusion control system for computer integrated manufacturing

    CSIR Research Space (South Africa)

    Kumile, CM

    2007-08-01

    Full Text Available -floor control using sensors previously missing in manufacturing research. The contribution is in the ease and the elegance that the concept provides finite state/ automata activities as well as the production engineering elements such as planning...

  5. Health-Enabled Smart Sensor Fusion Technology, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — It has been proven that the combination of smart sensors with embedded metadata and wireless technologies present real opportunities for significant improvements in...

  6. A new approach to self-localization for mobile robots using sensor data fusion

    International Nuclear Information System (INIS)

    Moshiri, B.; Asharif, M.; Hoseim Nezhad, R.

    2002-01-01

    This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMB mark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods usually require explicit measurement of actual motion of the robot. Some recent methods use the smart encoder trailer or long range finder sensors such as ultrasonic or laser range finders for automatic calibration. Manual measurement is necessary in the case of the robots that are not equipped with long-range detectors or such smart encoder trailer. Our proposed approach uses an environment map that is created by fusion of proximity data, in order to calibrate the odometry error automatically. In the new approach, the systematic part of the error is adaptively estimated and compensated by an efficient and incremental maximum likelihood algorithm. Actually, environment map data are fused with the odometry and current sensory data in order to acquire the maximum likelihood estimation. The advantages of the proposed approach are demonstrated in some experiments with Khepera robot. It is shown that the amount of pose estimation error is reduced by a percentage of more than 80%

  7. Novel Orthogonal Signal Based Decomposition of Digital Signals: Application to Sensor Fusion

    Directory of Open Access Journals (Sweden)

    Abdul Faheem Mohed

    2010-03-01

    Full Text Available In this research paper, a novel orthogonal decomposition of an arbitrary “digital” signal is proposed. An approach to attack the problem of wireless sensor fusion using digital signal processing techniques is discussed. The merits of the proposed orthogonal decomposition are briefly discussed. Simulation results are presented to illustrate the effectiveness of the proposed method.

  8. Performance of Hall sensor-based devices for magnetic field diagnosis at fusion reactors

    Czech Academy of Sciences Publication Activity Database

    Bolshakova, I.; Ďuran, Ivan; Holyaka, R.; Hristoforou, E.; Marusenkov, A.

    2007-01-01

    Roč. 5, č. 1 (2007), s. 283-288 ISSN 1546-198X R&D Projects: GA AV ČR KJB100430504 Institutional research plan: CEZ:AV0Z20430508 Keywords : Galvanomagnetic * Sensor * Fusion Reactor * Magnetic Diagnostics * Radiation Hardness Subject RIV: BG - Nuclear, Atomic and Molecular Physics, Colliders Impact factor: 1.587, year: 2007

  9. Comparison of belief functions and voting method for fusion of mine detection sensors

    NARCIS (Netherlands)

    Milisavljevic, N.; Broek, S.P. van den; Bloch, I.; Schwering, P.B.W.; Lensen, H.A.; Acheroy, M.

    2001-01-01

    In this paper, two methods for fusion of mine detection sensors are presented, based on belief functions and on voting procedures, respectively. Their application is illustrated and compared on a real multisensor data set collected at the TNO test facilities under the HOM 2000 project. This set

  10. Localization in orchards using Extended Kalman Filter for sensor-fusion - A FroboMind component

    DEFF Research Database (Denmark)

    Christiansen, Martin Peter; Jensen, Kjeld; Ellekilde, Lars-Peter

    Using the detected trees seen in gure 4(b) a localised SLAM map of the surroundings area, can be created an used to determine the localisation of the tractor. This kind of sensor-fusion is used, to keep the amount of prior information about outlay of the orchard to a minimum, so it can be used...

  11. Weighted Measurement Fusion Quantized Filtering with Bandwidth Constraints and Missing Measurements in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jian Ding

    2014-01-01

    Full Text Available This paper is concerned with the estimation problem of a dynamic stochastic variable in a sensor network, where the quantization of scalar measurement, the optimization of the bandwidth scheduling, and the characteristic of transmission channels are considered. For the imperfect channels with missing measurements in sensor networks, two weighted measurement fusion (WMF quantized Kalman filters based on the quantized measurements arriving at the fusion center are presented. One is dependent on the known message of whether a measurement is received. The other is dependent on the probability of missing measurements. They have the reduced computational cost and same accuracy as the corresponding centralized fusion filter. The approximate solution for the optimal bandwidth-scheduling problem is given under a limited bandwidth constraint. Furthermore, the vector measurement case is also discussed. The simulation research shows the effectiveness.

  12. Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm

    OpenAIRE

    De La Iglesia, Daniel H.; Villarubia, Gabriel; De Paz, Juan F.; Bajo, Javier

    2017-01-01

    [EN]The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fus...

  13. Soft sensor design by multivariate fusion of image features and process measurements

    DEFF Research Database (Denmark)

    Lin, Bao; Jørgensen, Sten Bay

    2011-01-01

    is obtained by filtering the original data block augmented with time lagged variables such that improved predictive performance of the quality variable results. Key issues regarding data preprocessing and extraction of suitable image features are discussed with a case study, the on-line estimation of nitrogen......This paper presents a multivariate data fusion procedure for design of dynamic soft sensors where suitably selected image features are combined with traditional process measurements to enhance the performance of data-driven soft sensors. A key issue of fusing multiple sensor data, i.e. to determine...

  14. Visual sensor fusion for active security in robotic industrial environments

    Science.gov (United States)

    Robla, Sandra; Llata, Jose R.; Torre-Ferrero, Carlos; Sarabia, Esther G.; Becerra, Victor; Perez-Oria, Juan

    2014-12-01

    This work presents a method of information fusion involving data captured by both a standard charge-coupled device (CCD) camera and a time-of-flight (ToF) camera to be used in the detection of the proximity between a manipulator robot and a human. Both cameras are assumed to be located above the work area of an industrial robot. The fusion of colour images and time-of-flight information makes it possible to know the 3D localization of objects with respect to a world coordinate system. At the same time, this allows to know their colour information. Considering that ToF information given by the range camera contains innacuracies including distance error, border error, and pixel saturation, some corrections over the ToF information are proposed and developed to improve the results. The proposed fusion method uses the calibration parameters of both cameras to reproject 3D ToF points, expressed in a common coordinate system for both cameras and a robot arm, in 2D colour images. In addition to this, using the 3D information, the motion detection in a robot industrial environment is achieved, and the fusion of information is applied to the foreground objects previously detected. This combination of information results in a matrix that links colour and 3D information, giving the possibility of characterising the object by its colour in addition to its 3D localisation. Further development of these methods will make it possible to identify objects and their position in the real world and to use this information to prevent possible collisions between the robot and such objects.

  15. Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory

    Directory of Open Access Journals (Sweden)

    Jesse S. Jin

    2010-10-01

    Full Text Available Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

  16. Sensor fusion approaches for EMI and GPR-based subsurface threat identification

    Science.gov (United States)

    Torrione, Peter; Morton, Kenneth, Jr.; Besaw, Lance E.

    2011-06-01

    Despite advances in both electromagnetic induction (EMI) and ground penetrating radar (GPR) sensing and related signal processing, neither sensor alone provides a perfect tool for detecting the myriad of possible buried objects that threaten the lives of Soldiers and civilians. However, while neither GPR nor EMI sensing alone can provide optimal detection across all target types, the two approaches are highly complementary. As a result, many landmine systems seek to make use of both sensing modalities simultaneously and fuse the results from both sensors to improve detection performance for targets with widely varying metal content and GPR responses. Despite this, little work has focused on large-scale comparisons of different approaches to sensor fusion and machine learning for combining data from these highly orthogonal phenomenologies. In this work we explore a wide array of pattern recognition techniques for algorithm development and sensor fusion. Results with the ARA Nemesis landmine detection system suggest that nonlinear and non-parametric classification algorithms provide significant performance benefits for single-sensor algorithm development, and that fusion of multiple algorithms can be performed satisfactorily using basic parametric approaches, such as logistic discriminant classification, for the targets under consideration in our data sets.

  17. Kalman Filter Sensor Fusion for Mecanum Wheeled Automated Guided Vehicle Localization

    Directory of Open Access Journals (Sweden)

    Sang Won Yoon

    2015-01-01

    Full Text Available The Mecanum automated guided vehicle (AGV, which can move in any direction by using a special wheel structure with a LIM-wheel and a diagonally positioned roller, holds considerable promise for the field of industrial electronics. A conventional method for Mecanum AGV localization has certain limitations, such as slip phenomena, because there are variations in the surface of the road and ground friction. Therefore, precise localization is a very important issue for the inevitable slip phenomenon situation. So a sensor fusion technique is developed to cope with this drawback by using the Kalman filter. ENCODER and StarGazer were used for sensor fusion. StarGazer is a position sensor for an image recognition device and always generates some errors due to the limitations of the image recognition device. ENCODER has also errors accumulating over time. On the other hand, there are no moving errors. In this study, we developed a Mecanum AGV prototype system and showed by simulation that we can eliminate the disadvantages of each sensor. We obtained the precise localization of the Mecanum AGV in a slip phenomenon situation via sensor fusion using a Kalman filter.

  18. Enhanced Resolution of Microwave Sounder Imagery through Fusion with Infrared Sensor Data

    Directory of Open Access Journals (Sweden)

    Igor Yanovsky

    2017-10-01

    Full Text Available The images acquired by microwave sensors are blurry and have low resolution. On the other hand, the images obtained using infrared/visible sensors are often of higher resolution. In this paper, we develop a data fusion methodology and apply it to enhance the resolution of a microwave image using the data from a collocated infrared/visible sensor. Such an approach takes advantage of the spatial resolution of the infrared instrument and the sensing accuracy of the microwave instrument. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. We tested our method using a precipitation scene captured with the Advanced Microwave Sounding Unit (AMSU-B microwave instrument and the Advanced Very High Resolution Radiometer (AVHRR infrared instrument and compared the results to simultaneous radar observations. We show that the data fusion product is better than the original AMSU-B and AVHRR observations across all statistical indicators.

  19. Summary of sensor evaluation for the Fusion ELectromagnetic Induction eXperiment (FELIX)

    International Nuclear Information System (INIS)

    Knott, M.J.

    1982-08-01

    As part of the First Wall/Blanket/Shield Engineering Test Program, a test bed called FELIX (Fusion ELectromagnetic Induction eXperiment) is now under construction at ANL. Its purpose will be to test, evaluate, and develop computer codes for the prediction of electromagnetically induced phenomenon in a magnetic environment modeling that of a fusion reaction. Crucial to this process is the sensing and recording of the various induced effects. Sensor evaluation for FELIX has reached the point where most sensor types have been evaluated and preliminary decisions are being made as to type and quantity for the initial FELIX experiments. These early experiments, the first, flat plate experiment in particular, will be aimed at testing the sensors as well as the pertinent theories involved. The reason for these evaluations, decisions, and proof tests is the harsh electrical and magnetic environment that FELIX presents

  20. Multi-Sensor Fusion with Interacting Multiple Model Filter for Improved Aircraft Position Accuracy

    Directory of Open Access Journals (Sweden)

    Changho Lee

    2013-03-01

    Full Text Available The International Civil Aviation Organization (ICAO has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS, Automatic Dependent Surveillance-Broadcast (ADS-B, multilateration (MLAT and wide-area multilateration (WAM systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter.

  1. Multi-Source Sensor Fusion for Small Unmanned Aircraft Systems Using Fuzzy Logic

    Science.gov (United States)

    Cook, Brandon; Cohen, Kelly

    2017-01-01

    As the applications for using small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) continue to grow in the coming years, it is imperative that intelligent sensor fusion techniques be explored. In BVLOS scenarios the vehicle position must accurately be tracked over time to ensure no two vehicles collide with one another, no vehicle crashes into surrounding structures, and to identify off-nominal scenarios. Therefore, in this study an intelligent systems approach is used to estimate the position of sUAS given a variety of sensor platforms, including, GPS, radar, and on-board detection hardware. Common research challenges include, asynchronous sensor rates and sensor reliability. In an effort to realize these challenges, techniques such as a Maximum a Posteriori estimation and a Fuzzy Logic based sensor confidence determination are used.

  2. On-the-go throughput prediction in a combine harvester using sensor fusion

    DEFF Research Database (Denmark)

    Hermann, Dan; Bilde, Morten L.; Andersen, Nils Axel

    2017-01-01

    The paper addresses design of a clean grain throughput observer for a combine harvester, i.e. delay free yield sensing. The aim is to predict grain throughput changes using the forward speed and a throughput sensor in the feederhouse. By utilising a grain flow model and sensor fusion an estimate...... of the current grain throughput is obtained, hence the effect from the lag in the momentary yield sensor reading due to material transport delays can be reduced. Statistical change detection is used to detect feederhouse load condition as well as sensor discrepancies using the observer innovation signal....... The system is able to predict changes originating from forward speed and local crop density variations. Also temporary sensor discrepancies are detected and compensated in the grain flow estimate....

  3. Fusion of Smartphone Motion Sensors for Physical Activity Recognition

    NARCIS (Netherlands)

    Shoaib, M.; Bosch, S.; Durmaz, O.; Scholten, Johan; Havinga, Paul J.M.

    2014-01-01

    For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting

  4. Multi-Source Multi-Sensor Information Fusion

    Indian Academy of Sciences (India)

    M. Senthilkumar (Newgen Imaging) 1461 1996 Oct 15 13:05:22

    accuracy. The complete process of MUSSIF can involve study of several allied disciplines: signal/image processing, numerical algorithms, statistical estimation, sensor management, control and optimization, neural networks, fuzzy systems, and data base management. Many principles and techniques from these fields carry ...

  5. Multi-Sensor Data Fusion: An Unscented Least Squares Approach

    Science.gov (United States)

    2011-07-01

    the unscented transformation. 14th International Conference on Information Fusion Chicago, Illinois, USA, July 5-8, 2011 978-0-9824438-3-5 ©2011 ISIF 1...AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES Presented at the 14th International Conference on...Estimation Error 0 0.02 0.04 0.06 0.08 0.1 0 0.5 1 1.5 2 2.5 3 3.5 4 J Noise Variance Trad UT (b) Noise Sensitivity of Cost Figure 1. Simulation I

  6. Noise-exploitation and adaptation in neuromorphic sensors

    Science.gov (United States)

    Hindo, Thamira; Chakrabartty, Shantanu

    2012-04-01

    Even though current micro-nano fabrication technology has reached integration levels where ultra-sensitive sensors can be fabricated, the sensing performance (resolution per joule) of synthetic systems are still orders of magnitude inferior to those observed in neurobiology. For example, the filiform hairs in crickets operate at fundamental limits of noise; auditory sensors in a parasitoid fly can overcome fundamental limitations to precisely localize ultra-faint acoustic signatures. Even though many of these biological marvels have served as inspiration for different types of neuromorphic sensors, the main focus these designs have been to faithfully replicate the biological functionalities, without considering the constructive role of "noise". In man-made sensors device and sensor noise are typically considered as a nuisance, where as in neurobiology "noise" has been shown to be a computational aid that enables biology to sense and operate at fundamental limits of energy efficiency and performance. In this paper, we describe some of the important noise-exploitation and adaptation principles observed in neurobiology and how they can be systematically used for designing neuromorphic sensors. Our focus will be on two types of noise-exploitation principles, namely, (a) stochastic resonance; and (b) noise-shaping, which are unified within our previously reported framework called Σ▵ learning. As a case-study, we describe the application of Σ▵ learning for the design of a miniature acoustic source localizer whose performance matches that of its biological counterpart(Ormia Ochracea).

  7. Keystrokes Inference Attack on Android: A Comparative Evaluation of Sensors and Their Fusion

    Directory of Open Access Journals (Sweden)

    Ahmed Al-Haiqi

    2014-11-01

    Full Text Available Introducing motion sensors into smartphones contributed to a wide range of applications in human-phone interaction, gaming, and many others. However, built-in sensors that detect subtle motion changes (e.g. accelerometers, might also reveal information about taps on touch screens: the main user input mode. Few researchers have already demonstrated the idea of exploiting motion sensors as side-channels into inferring keystrokes. Taken at most as initial explorations, much research is still needed to analyze the practicality of the new threat and examine various aspects of its implementation. One important aspect affecting directly the attack effectiveness is the selection of the right combination of sensors, to supply inference data. Although other aspects also play crucial role (e.g. the features set, we start in this paper by focusing on the comparison of different available sensors, in terms of the inference accuracy. We consider individual sensors shipped on Android phones, and study few options of preprocessing their raw datasets as well as fusing several sensors' readings. Our results indicate an outstanding performance of the gyroscope, and the potential of sensors data fusion. However, it seems that sensors with magnetometer component or the accelerometer alone have less benefit in the context of the adverted attack.

  8. Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution

    NARCIS (Netherlands)

    Pham, T.Q.; Van Vliet, L.J.; Schutte, K.

    2006-01-01

    We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to

  9. Robust site security using smart seismic array technology and multi-sensor data fusion

    Science.gov (United States)

    Hellickson, Dean; Richards, Paul; Reynolds, Zane; Keener, Joshua

    2010-04-01

    Traditional site security systems are susceptible to high individual sensor nuisance alarm rates that reduce the overall system effectiveness. Visual assessment of intrusions can be intensive and manually difficult as cameras are slewed by the system to non intrusion areas or as operators respond to nuisance alarms. Very little system intrusion performance data are available other than discrete sensor alarm indications that provide no real value. This paper discusses the system architecture, integration and display of a multi-sensor data fused system for wide area surveillance, local site intrusion detection and intrusion classification. The incorporation of a novel seismic array of smart sensors using FK Beamforming processing that greatly enhances the overall system detection and classification performance of the system is discussed. Recent test data demonstrates the performance of the seismic array within several different installations and its ability to classify and track moving targets at significant standoff distances with exceptional immunity to background clutter and noise. Multi-sensor data fusion is applied across a suite of complimentary sensors eliminating almost all nuisance alarms while integrating within a geographical information system to feed a visual-fusion display of the area being secured. Real-time sensor detection and intrusion classification data is presented within a visual-fusion display providing greatly enhanced situational awareness, system performance information and real-time assessment of intrusions and situations of interest with limited security operator involvement. This approach scales from a small local perimeter to very large geographical area and can be used across multiple sites controlled at a single command and control station.

  10. LAND COVER CLASSIFICATION OF MULTI-SENSOR IMAGES BY DECISION FUSION USING WEIGHTS OF EVIDENCE MODEL

    Directory of Open Access Journals (Sweden)

    P. Li

    2012-07-01

    Full Text Available This paper proposed a novel method of decision fusion based on weights of evidence model (WOE. The probability rules from classification results from each separate dataset were fused using WOE to produce the posterior probability for each class. The final classification was obtained by maximum probability. The proposed method was evaluated in land cover classification using two examples. The results showed that the proposed method effectively combined multisensor data in land cover classification and obtained higher classification accuracy than the use of single source data. The weights of evidence model provides an effective decision fusion method for improved land cover classification using multi-sensor data.

  11. Sensor data monitoring and decision level fusion scheme for early fire detection

    Science.gov (United States)

    Rizogiannis, Constantinos; Thanos, Konstantinos Georgios; Astyakopoulos, Alkiviadis; Kyriazanos, Dimitris M.; Thomopoulos, Stelios C. A.

    2017-05-01

    The aim of this paper is to present the sensor monitoring and decision level fusion scheme for early fire detection which has been developed in the context of the AF3 Advanced Forest Fire Fighting European FP7 research project, adopted specifically in the OCULUS-Fire control and command system and tested during a firefighting field test in Greece with prescribed real fire, generating early-warning detection alerts and notifications. For this purpose and in order to improve the reliability of the fire detection system, a two-level fusion scheme is developed exploiting a variety of observation solutions from air e.g. UAV infrared cameras, ground e.g. meteorological and atmospheric sensors and ancillary sources e.g. public information channels, citizens smartphone applications and social media. In the first level, a change point detection technique is applied to detect changes in the mean value of each measured parameter by the ground sensors such as temperature, humidity and CO2 and then the Rate-of-Rise of each changed parameter is calculated. In the second level the fire event Basic Probability Assignment (BPA) function is determined for each ground sensor using Fuzzy-logic theory and then the corresponding mass values are combined in a decision level fusion process using Evidential Reasoning theory to estimate the final fire event probability.

  12. A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion.

    Science.gov (United States)

    Tang, Yongchuan; Zhou, Deyun; Xu, Shuai; He, Zichang

    2017-04-22

    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.

  13. Entity Recognition Via Multimodal Sensor Fusion With Smart Phones

    Science.gov (United States)

    2015-03-26

    external to the device and unrelated to users, such as earth - quake detection, has revealed the utility of the cell phone to be a sensor of more than just...the following for each frame: the mean, variance, Frequency -Domain entropy, FFT energy, and the correlation. When selecting features, Sun et al...the mean, variance, Frequency -Domain entropy, and FFT energy. As such, for a system capable of rec- ognizing standing, walking, biking, and running

  14. Phenomenology-Based Inverse Scattering for Sensor Information Fusion

    Science.gov (United States)

    2006-09-15

    integrate signals from sensors with language communication messages only at a high cognitive levels of logical predicates. First, information has to...communication messages have to be pre-processed, the relevant data extracted and formulated as logical statements at a similar level of abstraction. The...closely related to the ability to integrate signals subliminally . For example, during everyday conversations, human eye gaze as well as visual processing

  15. Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning

    Directory of Open Access Journals (Sweden)

    Sungho Kim

    2018-01-01

    Full Text Available This paper presents a novel double weight-based synthetic aperture radar (SAR and infrared (IR sensor fusion method (DW-SIF for automatic ground target recognition (ATR. IR-based ATR can provide accurate recognition because of its high image resolution but it is affected by the weather conditions. On the other hand, SAR-based ATR shows a low recognition rate due to the noisy low resolution but can provide consistent performance regardless of the weather conditions. The fusion of an active sensor (SAR and a passive sensor (IR can lead to upgraded performance. This paper proposes a doubly weighted neural network fusion scheme at the decision level. The first weight ( α can measure the offline sensor confidence per target category based on the classification rate for an evaluation set. The second weight ( β can measure the online sensor reliability based on the score distribution for a test target image. The LeNet architecture-based deep convolution network (14 layers is used as an individual classifier. Doubly weighted sensor scores are fused by two types of fusion schemes, such as the sum-based linear fusion scheme ( α β -sum and neural network-based nonlinear fusion scheme ( α β -NN. The experimental results confirmed the proposed linear fusion method ( α β -sum to have the best performance among the linear fusion schemes available (SAR-CNN, IR-CNN, α -sum, β -sum, α β -sum, and Bayesian fusion. In addition, the proposed nonlinear fusion method ( α β -NN showed superior target recognition performance to linear fusion on the OKTAL-SE-based synthetic database.

  16. Multi-sensor data fusion for improved prediction of apple fruit firmness and soluble solids content

    Science.gov (United States)

    Mendoza, Fernando; Lu, Renfu; Cen, Haiyan

    2011-06-01

    Several nondestructive technologies have been developed for assessing the firmness and soluble solids content (SSC) of apples. Each of these technologies has its merits and limitations in predicting the two quality parameters. With the concept of multi-sensor data fusion, different sensors would work synergistically and complementarily to improve the quality prediction of apples. In this research, four sensing systems (i.e., an acoustic sensor, a bioyield firmness tester, a miniature near-infrared (NIR) spectrometer, and an online hyperspectral scattering system) were evaluated and combined for nondestructive prediction of firmness and SSC of 'Jonagold' (JG), 'Golden Delicious' (GD), and 'Delicious' (RD) apples. A total of 6,535 apples harvested in 2009 and 2010 were used for analysis. Each of the four sensors showed various degrees of ability to predict apple quality. Better predictions of the firmness and, in most cases, of the SSC were obtained using sensors fusion than using individual sensors, as measured by number of latent variables, correlation coefficient, and standard error of prediction (SEP). Results obtained from the two harvest seasons with the multi-sensor fusion approach were quite consistent, confirming the validity and robustness of the proposed approach. The SEPs for firmness measurement of JG, GD and RD using the best combination of two-sensor data were reduced by 13.3, 19.7 and 7.9% for the 2009 data and 16.0, 12.6 and 4.7% for the 2010 data; and using all four-sensor data by 21.8, 25.6 and 13.6% in 2009, and 14.9, 21.9, and 7.9% in 2010, respectively. For SSC prediction, using the two-sensor data (i.e., NIR and scattering) improved predictions for JG, GD and RD apples harvested in 2009, with their SEP values being reduced by 10.4, 6.6 and 6.8%, respectively. This research demonstrated that the fused systems provided more complete complementary information and, thus, were more powerful than individual sensors in prediction of apple quality.

  17. Sensor-Data Fusion for Multi-Person Indoor Location Estimation.

    Science.gov (United States)

    Mohebbi, Parisa; Stroulia, Eleni; Nikolaidis, Ioanis

    2017-10-18

    We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable or unwilling to carry a smartphone, or any other "wearable" device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors) and eponymous wearable sensors (smartphones interacting with Estimote beacons), and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors' coverage of the monitored space and the quality of the location estimates.

  18. Multi-sensor data fusion for land vehicle attitude estimation using a fuzzy expert system

    Directory of Open Access Journals (Sweden)

    Jau-Hsiung Wang

    2006-01-01

    Full Text Available In Inertial Navigation Systems (INS, the attitude estimated from gyro measurements by the Kalman filter is subject to an unbound error growth during the stand-alone mode, especially for land vehicle applications using low-cost sensors. To improve the attitude estimation of a land vehicle, this paper applies a fuzzy expert system to assist in multi-sensor data fusion from MEMS accelerometers, MEMS gyroscopes and a digital compass based on their complementary motion detection characteristics. Field test results have shown that drift-free and smooth attitude estimation can be achieved and will lead to a significant performance improvement for velocity and position estimation.

  19. A Bayes-Maximum Entropy method for multi-sensor data fusion

    Energy Technology Data Exchange (ETDEWEB)

    Beckerman, M.

    1991-01-01

    In this paper we introduce a Bayes-Maximum Entropy formalism for multi-sensor data fusion, and present an application of this methodology to the fusion of ultrasound and visual sensor data as acquired by a mobile robot. In our approach the principle of maximum entropy is applied to the construction of priors and likelihoods from the data. Distances between ultrasound and visual points of interest in a dual representation are used to define Gibbs likelihood distributions. Both one- and two-dimensional likelihoods are presented, and cast into a form which makes explicit their dependence upon the mean. The Bayesian posterior distributions are used to test a null hypothesis, and Maximum Entropy Maps used for navigation are updated using the resulting information from the dual representation. 14 refs., 9 figs.

  20. Evolving RBF neural networks for adaptive soft-sensor design.

    Science.gov (United States)

    Alexandridis, Alex

    2013-12-01

    This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

  1. Sensor fusion IV: Control paradigms and data structures; Proceedings of the Meeting, Boston, MA, Nov. 12-15, 1991

    Science.gov (United States)

    Schenker, Paul S. (Editor)

    1992-01-01

    Various papers on control paradigms and data structures in sensor fusion are presented. The general topics addressed include: decision models and computational methods, sensor modeling and data representation, active sensing strategies, geometric planning and visualization, task-driven sensing, motion analysis, models motivated biology and psychology, decentralized detection and distributed decision, data fusion architectures, robust estimation of shapes and features, application and implementation. Some of the individual subjects considered are: the Firefly experiment on neural networks for distributed sensor data fusion, manifold traversing as a model for learning control of autonomous robots, choice of coordinate systems for multiple sensor fusion, continuous motion using task-directed stereo vision, interactive and cooperative sensing and control for advanced teleoperation, knowledge-based imaging for terrain analysis, physical and digital simulations for IVA robotics.

  2. Multi-sensor radiation detection, imaging, and fusion

    Energy Technology Data Exchange (ETDEWEB)

    Vetter, Kai [Department of Nuclear Engineering, University of California, Berkeley, CA 94720 (United States); Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States)

    2016-01-01

    Glenn Knoll was one of the leaders in the field of radiation detection and measurements and shaped this field through his outstanding scientific and technical contributions, as a teacher, his personality, and his textbook. His Radiation Detection and Measurement book guided me in my studies and is now the textbook in my classes in the Department of Nuclear Engineering at UC Berkeley. In the spirit of Glenn, I will provide an overview of our activities at the Berkeley Applied Nuclear Physics program reflecting some of the breadth of radiation detection technologies and their applications ranging from fundamental studies in physics to biomedical imaging and to nuclear security. I will conclude with a discussion of our Berkeley Radwatch and Resilient Communities activities as a result of the events at the Dai-ichi nuclear power plant in Fukushima, Japan more than 4 years ago. - Highlights: • .Electron-tracking based gamma-ray momentum reconstruction. • .3D volumetric and 3D scene fusion gamma-ray imaging. • .Nuclear Street View integrates and associates nuclear radiation features with specific objects in the environment. • Institute for Resilient Communities combines science, education, and communities to minimize impact of disastrous events.

  3. Sensor data fusion for textured reconstruction and virtual representation of alpine scenes

    Science.gov (United States)

    Häufel, Gisela; Bulatov, Dimitri; Solbrig, Peter

    2017-10-01

    The concept of remote sensing is to provide information about a wide-range area without making physical contact with this area. If, additionally to satellite imagery, images and videos taken by drones provide a more up-to-date data at a higher resolution, or accurate vector data is downloadable from the Internet, one speaks of sensor data fusion. The concept of sensor data fusion is relevant for many applications, such as virtual tourism, automatic navigation, hazard assessment, etc. In this work, we describe sensor data fusion aiming to create a semantic 3D model of an extremely interesting yet challenging dataset: An alpine region in Southern Germany. A particular challenge of this work is that rock faces including overhangs are present in the input airborne laser point cloud. The proposed procedure for identification and reconstruction of overhangs from point clouds comprises four steps: Point cloud preparation, filtering out vegetation, mesh generation and texturing. Further object types are extracted in several interesting subsections of the dataset: Building models with textures from UAV (Unmanned Aerial Vehicle) videos, hills reconstructed as generic surfaces and textured by the orthophoto, individual trees detected by the watershed algorithm, as well as the vector data for roads retrieved from openly available shapefiles and GPS-device tracks. We pursue geo-specific reconstruction by assigning texture and width to roads of several pre-determined types and modeling isolated trees and rocks using commercial software. For visualization and simulation of the area, we have chosen the simulation system Virtual Battlespace 3 (VBS3). It becomes clear that the proposed concept of sensor data fusion allows a coarse reconstruction of a large scene and, at the same time, an accurate and up-to-date representation of its relevant subsections, in which simulation can take place.

  4. Sensor fusion: Spatial reasoning and scene interpretation; Proceedings of the Meeting, Cambridge, MA, Nov. 7-9, 1988

    Science.gov (United States)

    Schenker, Paul S. (Editor)

    1989-01-01

    The present conference discusses topics in the fusion of active and passive sensors, object estimation and verification, three-dimensional representation and knowledge integration, three-dimensional perception from multisensor data, the representation of uncertainty in multisensor fusion, and sensor calibration and registration. Also discussed are the areas of multisensor target detection and classification, multisensor processing architectures, knowledge structures and spatial reasoning, sensory interfaces to telerobotic systems, and navigation with spatial data bases.

  5. Exploiting Social Media Sensor Networks through Novel Data Fusion Techniques

    Energy Technology Data Exchange (ETDEWEB)

    Kouri, Tina [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    Unprecedented amounts of data are continuously being generated by sensors (“hard” data) and by humans (“soft” data), and this data needs to be exploited to its full potential. The first step in exploiting this data is determine how the hard and soft data are related to each other. In this project we fuse hard and soft data, using the attributes of each (e.g., time and space), to gain more information about interesting events. Next, we attempt to use social networking textual data to predict the present (i.e., predict that an interesting event is occurring and details about the event) using data mining, machine learning, natural language processing, and text analysis techniques.

  6. Multi-sensor fusion of Landsat 8 thermal infrared (TIR) and panchromatic (PAN) images.

    Science.gov (United States)

    Jung, Hyung-Sup; Park, Sung-Whan

    2014-12-18

    Data fusion is defined as the combination of data from multiple sensors such that the resulting information is better than would be possible when the sensors are used individually. The multi-sensor fusion of panchromatic (PAN) and thermal infrared (TIR) images is a good example of this data fusion. While a PAN image has higher spatial resolution, a TIR one has lower spatial resolution. In this study, we have proposed an efficient method to fuse Landsat 8 PAN and TIR images using an optimal scaling factor in order to control the trade-off between the spatial details and the thermal information. We have compared the fused images created from different scaling factors and then tested the performance of the proposed method at urban and rural test areas. The test results show that the proposed method merges the spatial resolution of PAN image and the temperature information of TIR image efficiently. The proposed method may be applied to detect lava flows of volcanic activity, radioactive exposure of nuclear power plants, and surface temperature change with respect to land-use change.

  7. Multi-Sensor Fusion of Landsat 8 Thermal Infrared (TIR and Panchromatic (PAN Images

    Directory of Open Access Journals (Sweden)

    Hyung-Sup Jung

    2014-12-01

    Full Text Available Data fusion is defined as the combination of data from multiple sensors such that the resulting information is better than would be possible when the sensors are used individually. The multi-sensor fusion of panchromatic (PAN and thermal infrared (TIR images is a good example of this data fusion. While a PAN image has higher spatial resolution, a TIR one has lower spatial resolution. In this study, we have proposed an efficient method to fuse Landsat 8 PAN and TIR images using an optimal scaling factor in order to control the trade-off between the spatial details and the thermal information. We have compared the fused images created from different scaling factors and then tested the performance of the proposed method at urban and rural test areas. The test results show that the proposed method merges the spatial resolution of PAN image and the temperature information of TIR image efficiently. The proposed method may be applied to detect lava flows of volcanic activity, radioactive exposure of nuclear power plants, and surface temperature change with respect to land-use change.

  8. Sensor-Data Fusion for Multi-Person Indoor Location Estimation

    Directory of Open Access Journals (Sweden)

    Parisa Mohebbi

    2017-10-01

    Full Text Available We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable or unwilling to carry a smartphone, or any other “wearable” device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors and eponymous wearable sensors (smartphones interacting with Estimote beacons, and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors’ coverage of the monitored space and the quality of the location estimates.

  9. Adapting Mobile Beacon-Assisted Localization in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Wei Dong

    2009-04-01

    Full Text Available The ability to automatically locate sensor nodes is essential in many Wireless Sensor Network (WSN applications. To reduce the number of beacons, many mobile-assisted approaches have been proposed. Current mobile-assisted approaches for localization require special hardware or belong to centralized localization algorithms involving some deterministic approaches due to the fact that they explicitly consider the impreciseness of location estimates. In this paper, we first propose a range-free, distributed and probabilistic Mobile Beacon-assisted Localization (MBL approach for static WSNs. Then, we propose another approach based on MBL, called Adapting MBL (A-MBL, to increase the efficiency and accuracy of MBL by adapting the size of sample sets and the parameter of the dynamic model during the estimation process. Evaluation results show that the accuracy of MBL and A-MBL outperform both Mobile and Static sensor network Localization (MSL and Arrival and Departure Overlap (ADO when both of them use only a single mobile beacon for localization in static WSNs.

  10. Adapting mobile beacon-assisted localization in wireless sensor networks.

    Science.gov (United States)

    Teng, Guodong; Zheng, Kougen; Dong, Wei

    2009-01-01

    The ability to automatically locate sensor nodes is essential in many Wireless Sensor Network (WSN) applications. To reduce the number of beacons, many mobile-assisted approaches have been proposed. Current mobile-assisted approaches for localization require special hardware or belong to centralized localization algorithms involving some deterministic approaches due to the fact that they explicitly consider the impreciseness of location estimates. In this paper, we first propose a range-free, distributed and probabilistic Mobile Beacon-assisted Localization (MBL) approach for static WSNs. Then, we propose another approach based on MBL, called Adapting MBL (A-MBL), to increase the efficiency and accuracy of MBL by adapting the size of sample sets and the parameter of the dynamic model during the estimation process. Evaluation results show that the accuracy of MBL and A-MBL outperform both Mobile and Static sensor network Localization (MSL) and Arrival and Departure Overlap (ADO) when both of them use only a single mobile beacon for localization in static WSNs.

  11. An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

    Directory of Open Access Journals (Sweden)

    Bingfei Fan

    2017-05-01

    Full Text Available Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm.

  12. Wrap-Around Out-the-Window Sensor Fusion System

    Science.gov (United States)

    Fox, Jeffrey; Boe, Eric A.; Delgado, Francisco; Secor, James B.; Clark, Michael R.; Ehlinger, Kevin D.; Abernathy, Michael F.

    2009-01-01

    The Advanced Cockpit Evaluation System (ACES) includes communication, computing, and display subsystems, mounted in a van, that synthesize out-the-window views to approximate the views of the outside world as it would be seen from the cockpit of a crewed spacecraft, aircraft, or remote control of a ground vehicle or UAV (unmanned aerial vehicle). The system includes five flat-panel display units arranged approximately in a semicircle around an operator, like cockpit windows. The scene displayed on each panel represents the view through the corresponding cockpit window. Each display unit is driven by a personal computer equipped with a video-capture card that accepts live input from any of a variety of sensors (typically, visible and/or infrared video cameras). Software running in the computers blends the live video images with synthetic images that could be generated, for example, from heads-up-display outputs, waypoints, corridors, or from satellite photographs of the same geographic region. Data from a Global Positioning System receiver and an inertial navigation system aboard the remote vehicle are used by the ACES software to keep the synthetic and live views in registration. If the live image were to fail, the synthetic scenes could still be displayed to maintain situational awareness.

  13. A Remote Sensing Image Fusion Method based on adaptive dictionary learning

    Science.gov (United States)

    He, Tongdi; Che, Zongxi

    2018-01-01

    This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

  14. Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor

    Directory of Open Access Journals (Sweden)

    Chang Liu

    2016-03-01

    Full Text Available Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle navigation tasks in Global Positioning System–denied environments. This article presents a computationally efficient visual–inertial fusion algorithm, by separating orientation fusion from the position fusion process. The algorithm is designed to perform 6 degree-of-freedom state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localisation and mapping algorithm measurement. It also recovers the visual scale for the monocular visual-based simultaneous localisation and mapping. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, whereas the position fusion is based on a 13-state linear Kalman filter. The elimination of the magnetometer sensor avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the accelerometer is factory calibrated. The resulting algorithm shows a significant computational reduction over the conventional extended Kalman filter, with competitive accuracy. Moreover, the separation between orientation and position fusion processes enables the algorithm to be easily implemented onto two individual hardware elements and thus allows the two fusion processes to be executed concurrently.

  15. Adaptive Information Access on Multiple Applications Support Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee

    2014-01-01

    information is challenged by dynamic nature of information elements. These challenges are more prominent in case of wireless sensor network (WSN) applications, as the information that the sensor node collects are mostly dynamic in nature (say, temperature). Therefore, it is likely that there can be a mismatch......Accessing information remotely to dynamically changing information elements cannot be avoided and has become a required functionality for various network services. Most applications require up-to-date information which is reliable and accurate. The information reliability in terms of using correct...... is used for safety and security monitoring purposes. In this paper, we evaluate different access strategies to remote dynamic information and compare between achieving information reliability (mismatch probability) and the associated power consumption. Lastly, based on the models, we propose an adaptive...

  16. Closed loop adaptive optics for microscopy without a wavefront sensor.

    Science.gov (United States)

    Kner, Peter; Winoto, Lukman; Agard, David A; Sedat, John W

    2010-02-24

    A three-dimensional wide-field image of a small fluorescent bead contains more than enough information to accurately calculate the wavefront in the microscope objective back pupil plane using the phase retrieval technique. The phase-retrieved wavefront can then be used to set a deformable mirror to correct the point-spread function (PSF) of the microscope without the use of a wavefront sensor. This technique will be useful for aligning the deformable mirror in a widefield microscope with adaptive optics and could potentially be used to correct aberrations in samples where small fluorescent beads or other point sources are used as reference beacons. Another advantage is the high resolution of the retrieved wavefont as compared with current Shack-Hartmann wavefront sensors. Here we demonstrate effective correction of the PSF in 3 iterations. Starting from a severely aberrated system, we achieve a Strehl ratio of 0.78 and a greater than 10-fold increase in maximum intensity.

  17. Swarm Robot Control for Human Services and Moving Rehabilitation by Sensor Fusion

    Directory of Open Access Journals (Sweden)

    Tresna Dewi

    2014-01-01

    Full Text Available A current trend in robotics is fusing different types of sensors having different characteristics to improve the performance of a robot system and also benefit from the reduced cost of sensors. One type of robot that requires sensor fusion for its application is the service robot. To achieve better performance, several service robots are preferred to work together, and, hence, this paper concentrates on swarm service robots. Swarm service mobile robots operating within a fixed area need to cope with dynamic changes in the environment, and they must also be capable of avoiding dynamic and static obstacles. This study applies sensor fusion and swarm concept for service mobile robots in human services and rehabilitation environment. The swarm robots follow the human moving trajectory to provide support to human moving and perform several tasks required in their living environment. This study applies a reference control and proportional-integral (PI control for the obstacle avoidance function. Various computer simulations are performed to verify the effectiveness of the proposed method.

  18. Camera-laser fusion sensor system and environmental recognition for humanoids in disaster scenarios

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Inho [Institute for Human and Machine Cognition (IHMC), Florida (United States); Oh, Jaesung; Oh, Jun-Ho [Korea Advanced Institute of Science and Technology (KAIST), Daejeon (Korea, Republic of); Kim, Inhyeok [NAVER Green Factory, Seongnam (Korea, Republic of)

    2017-06-15

    This research aims to develop a vision sensor system and a recognition algorithm to enable a humanoid to operate autonomously in a disaster environment. In disaster response scenarios, humanoid robots that perform manipulation and locomotion tasks must identify the objects in the environment from those challenged by the call by the United States’ Defense Advanced Research Projects Agency, e.g., doors, valves, drills, debris, uneven terrains, and stairs, among others. In order for a humanoid to undertake a number of tasks, we con- struct a camera–laser fusion system and develop an environmental recognition algorithm. Laser distance sensor and motor are used to obtain 3D cloud data. We project the 3D cloud data onto a 2D image according to the intrinsic parameters of the camera and the distortion model of the lens. In this manner, our fusion sensor system performs functions such as those performed by the RGB-D sensor gener- ally used in segmentation research. Our recognition algorithm is based on super-pixel segmentation and random sampling. The proposed approach clusters the unorganized cloud data according to geometric characteristics, namely, proximity and co-planarity. To assess the feasibility of our system and algorithm, we utilize the humanoid robot, DRC-HUBO, and the results are demonstrated in the accompanying video.

  19. Camera-laser fusion sensor system and environmental recognition for humanoids in disaster scenarios

    International Nuclear Information System (INIS)

    Lee, Inho; Oh, Jaesung; Oh, Jun-Ho; Kim, Inhyeok

    2017-01-01

    This research aims to develop a vision sensor system and a recognition algorithm to enable a humanoid to operate autonomously in a disaster environment. In disaster response scenarios, humanoid robots that perform manipulation and locomotion tasks must identify the objects in the environment from those challenged by the call by the United States’ Defense Advanced Research Projects Agency, e.g., doors, valves, drills, debris, uneven terrains, and stairs, among others. In order for a humanoid to undertake a number of tasks, we con- struct a camera–laser fusion system and develop an environmental recognition algorithm. Laser distance sensor and motor are used to obtain 3D cloud data. We project the 3D cloud data onto a 2D image according to the intrinsic parameters of the camera and the distortion model of the lens. In this manner, our fusion sensor system performs functions such as those performed by the RGB-D sensor gener- ally used in segmentation research. Our recognition algorithm is based on super-pixel segmentation and random sampling. The proposed approach clusters the unorganized cloud data according to geometric characteristics, namely, proximity and co-planarity. To assess the feasibility of our system and algorithm, we utilize the humanoid robot, DRC-HUBO, and the results are demonstrated in the accompanying video.

  20. A Gaussian Process Data Modelling and Maximum Likelihood Data Fusion Method for Multi-Sensor CMM Measurement of Freeform Surfaces

    Directory of Open Access Journals (Sweden)

    Mingyu Liu

    2016-12-01

    Full Text Available Nowadays, the use of freeform surfaces in various functional applications has become more widespread. Multi-sensor coordinate measuring machines (CMMs are becoming popular and are produced by many CMM manufacturers since their measurement ability can be significantly improved with the help of different kinds of sensors. Moreover, the measurement accuracy after data fusion for multiple sensors can be improved. However, the improvement is affected by many issues in practice, especially when the measurement results have bias and there exists uncertainty regarding the data modelling method. This paper proposes a generic data modelling and data fusion method for the measurement of freeform surfaces using multi-sensor CMMs and attempts to study the factors which affect the fusion result. Based on the data modelling method for the original measurement datasets and the statistical Bayesian inference data fusion method, this paper presents a Gaussian process data modelling and maximum likelihood data fusion method for supporting multi-sensor CMM measurement of freeform surfaces. The datasets from different sensors are firstly modelled with the Gaussian process to obtain the mean surfaces and covariance surfaces, which represent the underlying surfaces and associated measurement uncertainties. Hence, the mean surfaces and the covariance surfaces are fused together with the maximum likelihood principle so as to obtain the statistically best estimated underlying surface and associated measurement uncertainty. With this fusion method, the overall measurement uncertainty after fusion is smaller than each of the single-sensor measurements. The capability of the proposed method is demonstrated through a series of simulations and real measurements of freeform surfaces on a multi-sensor CMM. The accuracy of the Gaussian process data modelling and the influence of the form error and measurement noise are also discussed and demonstrated in a series of experiments

  1. An automatic fall detection framework using data fusion of Doppler radar and motion sensor network.

    Science.gov (United States)

    Liu, Liang; Popescu, Mihail; Skubic, Marjorie; Rantz, Marilyn

    2014-01-01

    This paper describes the ongoing work of detecting falls in independent living senior apartments. We have developed a fall detection system with Doppler radar sensor and implemented ceiling radar in real senior apartments. However, the detection accuracy on real world data is affected by false alarms inherent in the real living environment, such as motions from visitors. To solve this issue, this paper proposes an improved framework by fusing the Doppler radar sensor result with a motion sensor network. As a result, performance is significantly improved after the data fusion by discarding the false alarms generated by visitors. The improvement of this new method is tested on one week of continuous data from an actual elderly person who frequently falls while living in her senior home.

  2. Metal Hall sensors for the new generation fusion reactors of DEMO scale

    Science.gov (United States)

    Bolshakova, I.; Bulavin, M.; Kargin, N.; Kost, Ya.; Kuech, T.; Kulikov, S.; Radishevskiy, M.; Shurygin, F.; Strikhanov, M.; Vasil'evskii, I.; Vasyliev, A.

    2017-11-01

    For the first time, the results of on-line testing of metal Hall sensors based on nano-thickness (50-70) nm gold films, which was conducted under irradiation by high-energy neutrons up to the high fluences of 1 · 1024 n · m-2, are presented. The testing has been carried out in the IBR-2 fast pulsed reactor in the neutron flux with the intensity of 1.5 · 1017 n · m-2 · s-1 at the Joint Institute for Nuclear Research. The energy spectrum of neutron flux was very close to that expected for the ex-vessel sensors locations in the ITER experimental reactor. The magnetic field sensitivity of the gold sensors was stable within the whole fluence range under research. Also, sensitivity values at the start and at the end of irradiation session were equal within the measurement error (fusion reactors of DEMO scale.

  3. IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion

    Directory of Open Access Journals (Sweden)

    Omid Dehzangi

    2017-11-01

    Full Text Available The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF expansion of human gait cycles in order to capture joint 2 dimensional (2D spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level and late (decision score level multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class

  4. IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion.

    Science.gov (United States)

    Dehzangi, Omid; Taherisadr, Mojtaba; ChangalVala, Raghvendar

    2017-11-27

    The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF) expansion of human gait cycles in order to capture joint 2 dimensional (2D) spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN) learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level) and late (decision score level) multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF) method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class identification task

  5. Fusion

    International Nuclear Information System (INIS)

    Naraghi, M.

    1976-01-01

    It is proposed that Iran as a world's potential supplier of fossile fuel should participate in fusion research and gain experience in this new field. Fusion, as an ultimate source of energy in future, and the problems concerned with the fusion reactors are reviewed. Furthermore; plasma heating, magnetic and inertial confinement in a fusion reactor are discussed. A brief description of tokamak, theta pinch and magnetic mirror reactors is also included

  6. Adaptive Media Access Control for Energy Harvesting - Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Fafoutis, Xenofon; Dragoni, Nicola

    2012-01-01

    ODMAC (On-Demand Media Access Control) is a recently proposed MAC protocol designed to support individual duty cycles for Energy Harvesting — Wireless Sensor Networks (EH-WSNs). Individual duty cycles are vital for EH-WSNs, because they allow nodes to adapt their energy consumption to the ever......-changing environmental energy sources. In this paper, we present an improved and extended version of ODMAC and we analyze it by means of an analytical model that can approximate several performance metrics in an arbitrary network topology. The simulations and the analytical experiments show ODMAC's ability to satisfy...

  7. A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data.

    Science.gov (United States)

    Farias, Gonzalo; Fabregas, Ernesto; Peralta, Emmanuel; Vargas, Héctor; Hermosilla, Gabriel; Garcia, Gonzalo; Dormido, Sebastián

    2018-02-25

    Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks.

  8. RGB-D, Laser and Thermal Sensor Fusion for People following in a Mobile Robot

    Directory of Open Access Journals (Sweden)

    Loreto Susperregi

    2013-06-01

    Full Text Available Detecting and tracking people is a key capability for robots that operate in populated environments. In this paper, we used a multiple sensor fusion approach that combines three kinds of sensors in order to detect people using RGB-D vision, lasers and a thermal sensor mounted on a mobile platform. The Kinect sensor offers a rich data set at a significantly low cost, however, there are some limitations to its use in a mobile platform, mainly that the Kinect algorithms for people detection rely on images captured by a static camera. To cope with these limitations, this work is based on the combination of the Kinect and a Hokuyo laser and a thermopile array sensor. A real-time particle filter system merges the information provided by the sensors and calculates the position of the target, using probabilistic leg and thermal patterns, image features and optical flow to this end. Experimental results carried out with a mobile platform in a Science museum have shown that the combination of different sensory cues increases the reliability of the people following system.

  9. A High-Level MultiFunction Radar Simulation for Studying the Performance of MultiSensor Data Fusion Systems

    NARCIS (Netherlands)

    Huizing, A.G.; Bossee, E.

    1998-01-01

    This paper presents the basic requirements for a simulation of the main capabilities of a shipborne MultiFunction Radar (MFR) that can be used in conjunction with other sensor simulations in scenarios for studying Multi Sensor Data Fusion (MSDF) systems. This simulation is being used to support an

  10. Adaptive Multipath Key Reinforcement for Energy Harvesting Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Di Mauro, Alessio; Dragoni, Nicola

    2015-01-01

    Energy Harvesting - Wireless Sensor Networks (EH-WSNs) constitute systems of networked sensing nodes that are capable of extracting energy from the environment and that use the harvested energy to operate in a sustainable state. Sustainability, seen as design goal, has a significant impact...... on the design of the security protocols for such networks, as the nodes have to adapt and optimize their behaviour according to the available energy. Traditional key management schemes do not take energy into account, making them not suitable for EH-WSNs. In this paper we propose a new multipath key...... reinforcement scheme specifically designed for EH-WSNs. The proposed scheme allows each node to take into consideration and adapt to the amount of energy available in the system. In particular, we present two approaches, one static and one fully dynamic, and we discuss some experimental results....

  11. Fusion

    CERN Document Server

    Mahaffey, James A

    2012-01-01

    As energy problems of the world grow, work toward fusion power continues at a greater pace than ever before. The topic of fusion is one that is often met with the most recognition and interest in the nuclear power arena. Written in clear and jargon-free prose, Fusion explores the big bang of creation to the blackout death of worn-out stars. A brief history of fusion research, beginning with the first tentative theories in the early 20th century, is also discussed, as well as the race for fusion power. This brand-new, full-color resource examines the various programs currently being funded or p

  12. Adaptive Multichannel Radiation Sensors for Plant Parameter Monitoring

    Science.gov (United States)

    Mollenhauer, Hannes; Remmler, Paul; Schuhmann, Gudrun; Lausch, Angela; Merbach, Ines; Assing, Martin; Mollenhauer, Olaf; Dietrich, Peter; Bumberger, Jan

    2016-04-01

    Nutrients such as nitrogen are playing a key role in the plant life cycle. They are much needed for chlorophyll production and other plant cell components. Therefore, the crop yield is strongly affected by plant nutrient status. Due to the spatial and temporal variability of soil characteristics or swaying agricultural inputs the plant development varies within a field. Thus, the determination of these fluctuations in the plant development is valuable for a detection of stress conditions and optimization of fertilisation due to its high environmental and economic impact. Plant parameters play crucial roles in plant growth estimation and prediction since they are used as indicators of plant performance. Especially indices derived out of remote sensing techniques provide quantitative information about agricultural crops instantaneously, and above all, non-destructively. Due to the specific absorption of certain plant pigments, a characteristic spectral signature can be seen in the visible and IR part of the electromagnetic spectrum, known as narrow-band peaks. In an analogous manner, the presence and concentration of different nutrients cause a characteristic spectral signature. To this end, an adequate remote sensing monitoring concept is needed, considering heterogeneity and dynamic of the plant population and economical aspects. This work will present the development and field investigations of an inexpensive multichannel radiation sensor to observe the incoming and reflected specific parts or rather distinct wavelengths of the solar light spectrum on the crop and facilitate the determination of different plant indices. Based on the selected sensor wavelengths, the sensing device allows the detection of specific parameters, e.g. plant vitality, chlorophyll content or nitrogen content. Besides the improvement of the sensor characteristic, the simple wavelength adaption, and the price-performance ratio, the achievement of appropriate energy efficiency as well as a

  13. Design and Analysis of a Data Fusion Scheme in Mobile Wireless Sensor Networks Based on Multi-Protocol Mobile Agents.

    Science.gov (United States)

    Wu, Chunxue; Wu, Wenliang; Wan, Caihua; Bekkering, Ernst; Xiong, Naixue

    2017-11-03

    Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and changing network connections complicate event detection and measurement. We present a data fusion scheme for use in mobile wireless sensor networks with high energy efficiency and low network delays, that still produces reliable results. In the first phase, we used a network simulation where mobile agents dynamically select the next hop migration node based on the stability parameter of the link, and perform the data fusion at the migration node. Agents use the fusion results to decide if it should return the fusion results to the processing center or continue to collect more data. In the second phase. The feasibility of data fusion at the node level is confirmed by an experimental design where fused data from color sensors show near-identical results to actual physical temperatures. These results are potentially important for new large-scale sensor network applications.

  14. Design and Analysis of a Data Fusion Scheme in Mobile Wireless Sensor Networks Based on Multi-Protocol Mobile Agents

    Directory of Open Access Journals (Sweden)

    Chunxue Wu

    2017-11-01

    Full Text Available Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and changing network connections complicate event detection and measurement. We present a data fusion scheme for use in mobile wireless sensor networks with high energy efficiency and low network delays, that still produces reliable results. In the first phase, we used a network simulation where mobile agents dynamically select the next hop migration node based on the stability parameter of the link, and perform the data fusion at the migration node. Agents use the fusion results to decide if it should return the fusion results to the processing center or continue to collect more data. In the second phase. The feasibility of data fusion at the node level is confirmed by an experimental design where fused data from color sensors show near-identical results to actual physical temperatures. These results are potentially important for new large-scale sensor network applications.

  15. Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

    Science.gov (United States)

    Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

    2017-12-01

    Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was

  16. Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

    Science.gov (United States)

    Jiao, Jing; Yue, Jianhai; Pei, Di

    2017-10-01

    Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.

  17. Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

    Directory of Open Access Journals (Sweden)

    Zhenghua Chen

    2015-01-01

    Full Text Available Location-based services (LBS have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.

  18. Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization.

    Science.gov (United States)

    Chen, Zhenghua; Zou, Han; Jiang, Hao; Zhu, Qingchang; Soh, Yeng Chai; Xie, Lihua

    2015-01-05

    Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.

  19. Identifying and tracking pedestrians based on sensor fusion and motion stability predictions.

    Science.gov (United States)

    Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Maria; de la Escalera, Arturo

    2010-01-01

    The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.

  20. Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

    Directory of Open Access Journals (Sweden)

    Arturo de la Escalera

    2010-08-01

    Full Text Available The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem and dense disparity maps and u-v disparity (vision subsystem. Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.

  1. Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

    Science.gov (United States)

    Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Mª; de la Escalera, Arturo

    2010-01-01

    The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle. PMID:22163639

  2. Optical Communication System for Remote Monitoring and Adaptive Control of Distributed Ground Sensors Exhibiting Collective Intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Cameron, S.M.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-11-01

    Comprehensive management of the battle-space has created new requirements in information management, communication, and interoperability as they effect surveillance and situational awareness. The objective of this proposal is to expand intelligent controls theory to produce a uniquely powerful implementation of distributed ground-based measurement incorporating both local collective behavior, and interoperative global optimization for sensor fusion and mission oversight. By using a layered hierarchal control architecture to orchestrate adaptive reconfiguration of autonomous robotic agents, we can improve overall robustness and functionality in dynamic tactical environments without information bottlenecks. In this concept, each sensor is equipped with a miniaturized optical reflectance modulator which is interactively monitored as a remote transponder using a covert laser communication protocol from a remote mothership or operative. Robot data-sharing at the ground level can be leveraged with global evaluation criteria, including terrain overlays and remote imaging data. Information sharing and distributed intelli- gence opens up a new class of remote-sensing applications in which small single-function autono- mous observers at the local level can collectively optimize and measure large scale ground-level signals. AS the need for coverage and the number of agents grows to improve spatial resolution, cooperative behavior orchestrated by a global situational awareness umbrella will be an essential ingredient to offset increasing bandwidth requirements within the net. A system of the type described in this proposal will be capable of sensitively detecting, tracking, and mapping spatial distributions of measurement signatures which are non-stationary or obscured by clutter and inter- fering obstacles by virtue of adaptive reconfiguration. This methodology could be used, for example, to field an adaptive ground-penetrating radar for detection of underground structures in

  3. The addition of a sagittal image fusion improves the prostate cancer detection in a sensor-based MRI /ultrasound fusion guided targeted biopsy.

    Science.gov (United States)

    Günzel, Karsten; Cash, Hannes; Buckendahl, John; Königbauer, Maximilian; Asbach, Patrick; Haas, Matthias; Neymeyer, Jörg; Hinz, Stefan; Miller, Kurt; Kempkensteffen, Carsten

    2017-01-13

    To explore the diagnostic benefit of an additional image fusion of the sagittal plane in addition to the standard axial image fusion, using a sensor-based MRI/US fusion platform. During July 2013 and September 2015, 251 patients with at least one suspicious lesion on mpMRI (rated by PI-RADS) were included into the analysis. All patients underwent MRI/US targeted biopsy (TB) in combination with a 10 core systematic prostate biopsy (SB). All biopsies were performed on a sensor-based fusion system. Group A included 162 men who received TB by an axial MRI/US image fusion. Group B comprised 89 men in whom the TB was performed with an additional sagittal image fusion. The median age in group A was 67 years (IQR 61-72) and in group B 68 years (IQR 60-71). The median PSA level in group A was 8.10 ng/ml (IQR 6.05-14) and in group B 8.59 ng/ml (IQR 5.65-12.32). In group A the proportion of patients with a suspicious digital rectal examination (DRE) (14 vs. 29%, p = 0.007) and the proportion of primary biopsies (33 vs 46%, p = 0.046) were significantly lower. The rate of PI-RADS 3 lesions were overrepresented in group A compared to group B (19 vs. 9%; p = 0.044). Classified according to PI-RADS 3, 4 and 5, the detection rates of TB were 42, 48, 75% in group A and 25, 74, 90% in group B. The rate of PCa with a Gleason score ≥7 missed by TB was 33% (18 cases) in group A and 9% (5 cases) in group B; p-value 0.072. An explorative multivariate binary logistic regression analysis revealed that PI-RADS, a suspicious DRE and performing an additional sagittal image fusion were significant predictors for PCa detection in TB. 9 PCa were only detected by TB with sagittal fusion (sTB) and sTB identified 10 additional clinically significant PCa (Gleason ≥7). Performing an additional sagittal image fusion besides the standard axial fusion appears to improve the accuracy of the sensor-based MRI/US fusion platform.

  4. Sensor fusion for structural tilt estimation using an acceleration-based tilt sensor and a gyroscope

    Science.gov (United States)

    Liu, Cheng; Park, Jong-Woong; Spencer, B. F., Jr.; Moon, Do-Soo; Fan, Jiansheng

    2017-10-01

    A tilt sensor can provide useful information regarding the health of structural systems. Most existing tilt sensors are gravity/acceleration based and can provide accurate measurements of static responses. However, for dynamic tilt, acceleration can dramatically affect the measured responses due to crosstalk. Thus, dynamic tilt measurement is still a challenging problem. One option is to integrate the output of a gyroscope sensor, which measures the angular velocity, to obtain the tilt; however, problems arise because the low-frequency sensitivity of the gyroscope is poor. This paper proposes a new approach to dynamic tilt measurements, fusing together information from a MEMS-based gyroscope and an acceleration-based tilt sensor. The gyroscope provides good estimates of the tilt at higher frequencies, whereas the acceleration measurements are used to estimate the tilt at lower frequencies. The Tikhonov regularization approach is employed to fuse these measurements together and overcome the ill-posed nature of the problem. The solution is carried out in the frequency domain and then implemented in the time domain using FIR filters to ensure stability. The proposed method is validated numerically and experimentally to show that it performs well in estimating both the pseudo-static and dynamic tilt measurements.

  5. An adaptive technique for a redundant-sensor navigation system.

    Science.gov (United States)

    Chien, T.-T.

    1972-01-01

    An on-line adaptive technique is developed to provide a self-contained redundant-sensor navigation system with a capability to utilize its full potentiality in reliability and performance. This adaptive system is structured as a multistage stochastic process of detection, identification, and compensation. It is shown that the detection system can be effectively constructed on the basis of a design value, specified by mission requirements, of the unknown parameter in the actual system, and of a degradation mode in the form of a constant bias jump. A suboptimal detection system on the basis of Wald's sequential analysis is developed using the concept of information value and information feedback. The developed system is easily implemented, and demonstrates a performance remarkably close to that of the optimal nonlinear detection system. An invariant transformation is derived to eliminate the effect of nuisance parameters such that the ambiguous identification system can be reduced to a set of disjoint simple hypotheses tests. By application of a technique of decoupled bias estimation in the compensation system the adaptive system can be operated without any complicated reorganization.

  6. Establishment and Application of Coalmine Gas Prediction Model Based on Multi-Sensor Data Fusion Technology

    Directory of Open Access Journals (Sweden)

    Wenyu Lv

    2014-04-01

    Full Text Available Undoubtedly an accident involving gas is one of the greater disasters that can occur in a coalmine, thus being able to predict when an accident involving gas might occur is an essential aspect in loss prevention and the reduction of safety hazards. However, the traditional methods concerning gas safety prediction is hindered by multi-objective and non-continuous problem. The coalmine gas prediction model based on multi-sensor data fusion technology (CGPM-MSDFT was established through analysis of accidents involving gas using artificial neural network to fuse multi- sensor data, using an improved algorithm designed to train the network and using an early stop method to resolve the over-fitting problem, the network test and field application results show that this model can provide a new direction for research into predicting the likelihood of a gas related incident within a coalmine. It will have a broad application prospect in coal mining.

  7. A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion

    Directory of Open Access Journals (Sweden)

    Wen Jiang

    2017-03-01

    Full Text Available Fault diagnosis is an important research direction in modern industry. In this paper, a new fault diagnosis method based on multi-sensor data fusion is proposed, in which the Dempster–Shafer (D–S evidence theory is employed to model the uncertainty. Firstly, Gaussian types of fault models and test models are established by observations of sensors. After the models are determined, the intersection area between test model and fault models is transformed into a set of BPAs (basic probability assignments, and a weighted average combination method is used to combine the obtained BPAs. Finally, through some given decision making rules, diagnostic results can be obtained. The proposed method in this paper is tested by the Iris data set and actual measurement data of the motor rotor, which verifies the effectiveness of the proposed method.

  8. Detecting Pedestrian Flocks by Fusion of Multi-Modal Sensors in Mobile Phones

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun; Wirz, Martin; Roggen, Daniel

    2012-01-01

    Previous work on the recognition of human movement patterns has mainly focused on movements of individuals. This paper addresses the joint identification of the indoor movement of multiple persons forming a cohesive whole - specifically a flock - with clustering approaches operating on features...... derived from multiple sensor modalities of modern smartphones. Automatic detection of flocks has several important applications, including evacuation management and socially aware computing. The novelty of this paper is, firstly, to use data fusion techniques to combine several sensor modalities (Wi......Fi, accelerometer and compass) to improve recognition accuracy over previous unimodal approaches. Secondly, improve the recognition of flocks using hierarchical clustering. We use a dataset comprising 16 subjects forming one to four flocks walking in a building on single and multiple floors. With the best settings...

  9. An Inertial and Optical Sensor Fusion Approach for Six Degree-of-Freedom Pose Estimation

    Science.gov (United States)

    He, Changyu; Kazanzides, Peter; Sen, Hasan Tutkun; Kim, Sungmin; Liu, Yue

    2015-01-01

    Optical tracking provides relatively high accuracy over a large workspace but requires line-of-sight between the camera and the markers, which may be difficult to maintain in actual applications. In contrast, inertial sensing does not require line-of-sight but is subject to drift, which may cause large cumulative errors, especially during the measurement of position. To handle cases where some or all of the markers are occluded, this paper proposes an inertial and optical sensor fusion approach in which the bias of the inertial sensors is estimated when the optical tracker provides full six degree-of-freedom (6-DOF) pose information. As long as the position of at least one marker can be tracked by the optical system, the 3-DOF position can be combined with the orientation estimated from the inertial measurements to recover the full 6-DOF pose information. When all the markers are occluded, the position tracking relies on the inertial sensors that are bias-corrected by the optical tracking system. Experiments are performed with an augmented reality head-mounted display (ARHMD) that integrates an optical tracking system (OTS) and inertial measurement unit (IMU). Experimental results show that under partial occlusion conditions, the root mean square errors (RMSE) of orientation and position are 0.04° and 0.134 mm, and under total occlusion conditions for 1 s, the orientation and position RMSE are 0.022° and 0.22 mm, respectively. Thus, the proposed sensor fusion approach can provide reliable 6-DOF pose under long-term partial occlusion and short-term total occlusion conditions. PMID:26184191

  10. Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

    Directory of Open Access Journals (Sweden)

    Hailun Wang

    2017-01-01

    Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

  11. Sensor fusion: lane marking detection and autonomous intelligent cruise control system

    Science.gov (United States)

    Baret, Marc; Baillarin, S.; Calesse, C.; Martin, Lionel

    1995-12-01

    In the past few years MATRA and RENAULT have developed an Autonomous Intelligent Cruise Control (AICC) system based on a LIDAR sensor. This sensor incorporating a charge coupled device was designed to acquire pulsed laser diode emission reflected by standard car reflectors. The absence of moving mechanical parts, the large field of view, the high measurement rate and the very good accuracy for distance range and angular position of targets make this sensor very interesting. It provides the equipped car with the distance and the relative speed of other vehicles enabling the safety distance to be controlled by acting on the throttle and the automatic gear box. Experiments in various real traffic situations have shown the limitations of this kind of system especially on bends. All AICC sensors are unable to distinguish between a bend and a change of lane. This is easily understood if we consider a road without lane markings. This fact has led MATRA to improve its AICC system by providing the lane marking information. Also in the scope of the EUREKA PROMETHEUS project, MATRA and RENAULT have developed a lane keeping system in order to warn of the drivers lack of vigilance. Thus, MATRA have spread this system to far field lane marking detection and have coupled it with the AICC system. Experiments will be carried out on roads to estimate the gain in performance and comfort due to this fusion.

  12. Fault tolerant multi-sensor fusion based on the information gain

    Science.gov (United States)

    Hage, Joelle Al; El Najjar, Maan E.; Pomorski, Denis

    2017-01-01

    In the last decade, multi-robot systems are used in several applications like for example, the army, the intervention areas presenting danger to human life, the management of natural disasters, the environmental monitoring, exploration and agriculture. The integrity of localization of the robots must be ensured in order to achieve their mission in the best conditions. Robots are equipped with proprioceptive (encoders, gyroscope) and exteroceptive sensors (Kinect). However, these sensors could be affected by various faults types that can be assimilated to erroneous measurements, bias, outliers, drifts,… In absence of a sensor fault diagnosis step, the integrity and the continuity of the localization are affected. In this work, we present a muti-sensors fusion approach with Fault Detection and Exclusion (FDE) based on the information theory. In this context, we are interested by the information gain given by an observation which may be relevant when dealing with the fault tolerance aspect. Moreover, threshold optimization based on the quantity of information given by a decision on the true hypothesis is highlighted.

  13. Signal processing, sensor fusion, and target recognition; Proceedings of the Meeting, Orlando, FL, Apr. 20-22, 1992

    Science.gov (United States)

    Libby, Vibeke; Kadar, Ivan

    Consideration is given to a multiordered mapping technique for target prioritization, a neural network approach to multiple-target-tracking problems, a multisensor fusion algorithm for multitarget multibackground classification, deconvolutiom of multiple images of the same object, neural networks and genetic algorithms for combinatorial optimization of sensor data fusion, classification of atmospheric acoustic signals from fixed-wing aircraft, and an optics approach to sensor fusion for target recognition. Also treated are a zoom lens for automatic target recognition, a hybrid model for the analysis of radar sensors, an innovative test bed for developing and assessing air-to-air noncooperative target identification algorithms, SAR imagery scene segmentation using fractal processing, sonar feature-based bandwidth compression, laboratory experiments for a new sonar system, computational algorithms for discrete transform using fixed-size filter matrices, and pattern recognition for power systems.

  14. A decision support system for fusion of hard and soft sensor information based on probabilistic latent semantic analysis technique

    Science.gov (United States)

    Shirkhodaie, Amir; Elangovan, Vinayak; Alkilani, Amjad; Habibi, Mohammad

    2013-05-01

    This paper presents an ongoing effort towards development of an intelligent Decision-Support System (iDSS) for fusion of information from multiple sources consisting of data from hard (physical sensors) and soft (textural sources. Primarily, this paper defines taxonomy of decision support systems for latent semantic data mining from heterogeneous data sources. A Probabilistic Latent Semantic Analysis (PLSA) approach is proposed for latent semantic concepts search from heterogeneous data sources. An architectural model for generating semantic annotation of multi-modality sensors in a modified Transducer Markup Language (TML) is described. A method for TML messages fusion is discussed for alignment and integration of spatiotemporally correlated and associated physical sensory observations. Lastly, the experimental results which exploit fusion of soft/hard sensor sources with support of iDSS are discussed.

  15. Adaptive Naive Bayes classification for wireless sensor networks

    NARCIS (Netherlands)

    Zwartjes, G.J.

    2017-01-01

    Wireless Sensor Networks are tiny devices equipped with sensors and wireless communication. These devices observe environments and communicatie about these observations. Machine Learning techniques are of interest for Wireless Sensor Network applications since they can reduce the amount of needed

  16. Multiplatform Mission Planning and Operations Simulation Environment for Adaptive Remote Sensors

    Science.gov (United States)

    Smith, G.; Ball, C.; O'Brien, A.; Johnson, J. T.

    2017-12-01

    We report on the design and development of mission simulator libraries to support the emerging field of adaptive remote sensors. We will outline the current state of the art in adaptive sensing, provide analysis of how the current approach to performing observing system simulation experiments (OSSEs) must be changed to enable adaptive sensors for remote sensing, and present an architecture to enable their inclusion in future OSSEs.The growing potential of sensors capable of real-time adaptation of their operational parameters calls for a new class of mission planning and simulation tools. Existing simulation tools used in OSSEs assume a fixed set of sensor parameters in terms of observation geometry, frequencies used, resolution, or observation time, which allows simplifications to be made in the simulation and allows sensor observation errors to be characterized a priori. Adaptive sensors may vary these parameters depending on the details of the scene observed, so that sensor performance is not simple to model without conducting OSSE simulations that include sensor adaptation in response to varying observational environment. Adaptive sensors are of significance to resource-constrained, small satellite platforms because they enable the management of power and data volumes while providing methods for multiple sensors to collaborate.The new class of OSSEs required to utilize adaptive sensors located on multiple platforms must answer the question: If the physical act of sensing has a cost, how does the system determine if the science value of a measurement is worth the cost and how should that cost be shared among the collaborating sensors?Here we propose to answer this question using an architecture structured around three modules: ADAPT, MANAGE and COLLABORATE. The ADAPT module is a set of routines to facilitate modeling of adaptive sensors, the MANAGE module will implement a set of routines to facilitate simulations of sensor resource management when power and data

  17. Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver

    Directory of Open Access Journals (Sweden)

    Chan-Gun Lee

    2016-06-01

    Full Text Available Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE before and after applying our mechanism are significantly reduced from 6.3 × 10−1 to 5.3 × 10−7, respectively.

  18. Trust Model of Wireless Sensor Networks and Its Application in Data Fusion.

    Science.gov (United States)

    Chen, Zhenguo; Tian, Liqin; Lin, Chuang

    2017-03-28

    In order to ensure the reliability and credibility of the data in wireless sensor networks (WSNs), this paper proposes a trust evaluation model and data fusion mechanism based on trust. First of all, it gives the model structure. Then, the calculation rules of trust are given. In the trust evaluation model, comprehensive trust consists of three parts: behavior trust, data trust, and historical trust. Data trust can be calculated by processing the sensor data. Based on the behavior of nodes in sensing and forwarding, the behavior trust is obtained. The initial value of historical trust is set to the maximum and updated with comprehensive trust. Comprehensive trust can be obtained by weighted calculation, and then the model is used to construct the trust list and guide the process of data fusion. Using the trust model, simulation results indicate that energy consumption can be reduced by an average of 15%. The detection rate of abnormal nodes is at least 10% higher than that of the lightweight and dependable trust system (LDTS) model. Therefore, this model has good performance in ensuring the reliability and credibility of the data. Moreover, the energy consumption of transmitting was greatly reduced.

  19. Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach.

    Science.gov (United States)

    Girrbach, Fabian; Hol, Jeroen D; Bellusci, Giovanni; Diehl, Moritz

    2017-05-19

    The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem.

  20. An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis.

    Science.gov (United States)

    Tang, Yongchuan; Zhou, Deyun; Zhuang, Miaoyan; Fang, Xueyi; Xie, Chunhe

    2017-09-18

    As an important tool of information fusion, Dempster-Shafer evidence theory is widely applied in handling the uncertain information in fault diagnosis. However, an incorrect result may be obtained if the combined evidence is highly conflicting, which may leads to failure in locating the fault. To deal with the problem, an improved evidential-Induced Ordered Weighted Averaging (IOWA) sensor data fusion approach is proposed in the frame of Dempster-Shafer evidence theory. In the new method, the IOWA operator is used to determine the weight of different sensor data source, while determining the parameter of the IOWA, both the distance of evidence and the belief entropy are taken into consideration. First, based on the global distance of evidence and the global belief entropy, the α value of IOWA is obtained. Simultaneously, a weight vector is given based on the maximum entropy method model. Then, according to IOWA operator, the evidence are modified before applying the Dempster's combination rule. The proposed method has a better performance in conflict management and fault diagnosis due to the fact that the information volume of each evidence is taken into consideration. A numerical example and a case study in fault diagnosis are presented to show the rationality and efficiency of the proposed method.

  1. Adaptive Genetic Algorithm for Sensor Coarse Signal Processing

    OpenAIRE

    Xuan Huang; Wenhua Zeng

    2014-01-01

    As with the development of computer technology and informatization, network technique, sensor technique and communication technology become three necessary components of information industry. As the core technique of sensor application, signal processing mainly determines the sensor performances. For this reason, study on signal processing mode is very important to sensors and the application of sensor network. In this paper, we introduce a new sensor coarse signal processing mode based on ad...

  2. Multistream sensor fusion-based prognostics model for systems with single failure modes

    International Nuclear Information System (INIS)

    Fang, Xiaolei; Paynabar, Kamran; Gebraeel, Nagi

    2017-01-01

    Advances in sensor technology have facilitated the capability of monitoring the degradation of complex engineering systems through the analysis of multistream degradation signals. However, the varying levels of correlation with physical degradation process for different sensors, high-dimensionality of the degradation signals and cross-correlation among different signal streams pose significant challenges in monitoring and prognostics of such systems. To address the foregoing challenges, we develop a three-step multi-sensor prognostic methodology that utilizes multistream signals to predict residual useful lifetimes of partially degraded systems. We first identify the informative sensors via the penalized (log)-location-scale regression. Then, we fuse the degradation signals of the informative sensors using multivariate functional principal component analysis, which is capable of modeling the cross-correlation of signal streams. Finally, the third step focuses on utilizing the fused signal features for prognostics via adaptive penalized (log)-location-scale regression. We validate our multi-sensor prognostic methodology using simulation study as well as a case study of aircraft turbofan engines available from NASA repository.

  3. AUVs as integrated, adaptive acoustic sensors for ocean exploration

    Science.gov (United States)

    Schmidt, Henrik; Edwards, Joseph R.; Liu, Te-Chih; Montanari, Monica

    2004-05-01

    Autonomous underwater vehicles (AUV) are rapidly being transitioned into operational systems for national defense, offshore exploration, and ocean science. AUVs provide excellent sensor platform control, allowing for, e.g., accurate acoustic mapping of seabeds not easily reached by conventional platforms, such as the deep ocean. However, the full potential of the robotic platforms is far from exhausted by such applications. Thus, for example, most seabed-mapping applications use imaging sonar technology, the data volume of which cannot be transmitted back to the operators in real time due to the severe bandwidth limitation of the acoustic communication. The sampling patterns are therefore in general being preprogramed and the data are being stored for postmission analysis. This procedure is therefore associated with indiscriminate distribution of the sampling throughout the area of interest, irrespective of whether features of interest are present or not. However, today's computing technology allows for a significant amount of signal processing and analysis to be performed on the platforms, where the results may then be used for real-time adaptive sampling to optimally concentrate the sampling in area of interest, and compress the results to a few parameters which may be transmitted back to the operators. Such adaptive sensing concepts combining environmental acoustics, signal processing, and robotics are currently being developed for concurrent detection, localization, and classification of buried objects, with application to littoral mine countermeasures, deep ocean seabed characterization, and marine archeology. [Work supported by ONR and NATO Undersea Research Center.

  4. Reward and Punishment based Cooperative Adaptive Sampling in Wireless Sensor Networks

    NARCIS (Netherlands)

    Masoum, Alireza; Meratnia, Nirvana; Taghikhaki, Zahra; Havinga, Paul J.M.

    2010-01-01

    Energy conservation is one of the main concerns in wireless sensor networks. One of the mechanisms to better manage energy in wireless sensor networks is adaptive sampling, by which instead of using a fixed frequency interval for sensing and data transmission, the wireless sensor network employs a

  5. Fusion

    Science.gov (United States)

    Herman, Robin

    1990-10-01

    The book abounds with fascinating anecdotes about fusion's rocky path: the spurious claim by Argentine dictator Juan Peron in 1951 that his country had built a working fusion reactor, the rush by the United States to drop secrecy and publicize its fusion work as a propaganda offensive after the Russian success with Sputnik; the fortune Penthouse magazine publisher Bob Guccione sank into an unconventional fusion device, the skepticism that met an assertion by two University of Utah chemists in 1989 that they had created "cold fusion" in a bottle. Aimed at a general audience, the book describes the scientific basis of controlled fusion--the fusing of atomic nuclei, under conditions hotter than the sun, to release energy. Using personal recollections of scientists involved, it traces the history of this little-known international race that began during the Cold War in secret laboratories in the United States, Great Britain and the Soviet Union, and evolved into an astonishingly open collaboration between East and West.

  6. Fusion of Haptic and Gesture Sensors for Rehabilitation of Bimanual Coordination and Dexterous Manipulation

    Directory of Open Access Journals (Sweden)

    Ningbo Yu

    2016-03-01

    Full Text Available Disabilities after neural injury, such as stroke, bring tremendous burden to patients, families and society. Besides the conventional constrained-induced training with a paretic arm, bilateral rehabilitation training involves both the ipsilateral and contralateral sides of the neural injury, fitting well with the fact that both arms are needed in common activities of daily living (ADLs, and can promote good functional recovery. In this work, the fusion of a gesture sensor and a haptic sensor with force feedback capabilities has enabled a bilateral rehabilitation training therapy. The Leap Motion gesture sensor detects the motion of the healthy hand, and the omega.7 device can detect and assist the paretic hand, according to the designed cooperative task paradigm, as much as needed, with active force feedback to accomplish the manipulation task. A virtual scenario has been built up, and the motion and force data facilitate instantaneous visual and audio feedback, as well as further analysis of the functional capabilities of the patient. This task-oriented bimanual training paradigm recruits the sensory, motor and cognitive aspects of the patient into one loop, encourages the active involvement of the patients into rehabilitation training, strengthens the cooperation of both the healthy and impaired hands, challenges the dexterous manipulation capability of the paretic hand, suits easy of use at home or centralized institutions and, thus, promises effective potentials for rehabilitation training.

  7. Fusion of Haptic and Gesture Sensors for Rehabilitation of Bimanual Coordination and Dexterous Manipulation

    Science.gov (United States)

    Yu, Ningbo; Xu, Chang; Li, Huanshuai; Wang, Kui; Wang, Liancheng; Liu, Jingtai

    2016-01-01

    Disabilities after neural injury, such as stroke, bring tremendous burden to patients, families and society. Besides the conventional constrained-induced training with a paretic arm, bilateral rehabilitation training involves both the ipsilateral and contralateral sides of the neural injury, fitting well with the fact that both arms are needed in common activities of daily living (ADLs), and can promote good functional recovery. In this work, the fusion of a gesture sensor and a haptic sensor with force feedback capabilities has enabled a bilateral rehabilitation training therapy. The Leap Motion gesture sensor detects the motion of the healthy hand, and the omega.7 device can detect and assist the paretic hand, according to the designed cooperative task paradigm, as much as needed, with active force feedback to accomplish the manipulation task. A virtual scenario has been built up, and the motion and force data facilitate instantaneous visual and audio feedback, as well as further analysis of the functional capabilities of the patient. This task-oriented bimanual training paradigm recruits the sensory, motor and cognitive aspects of the patient into one loop, encourages the active involvement of the patients into rehabilitation training, strengthens the cooperation of both the healthy and impaired hands, challenges the dexterous manipulation capability of the paretic hand, suits easy of use at home or centralized institutions and, thus, promises effective potentials for rehabilitation training. PMID:26999149

  8. Classification of paddy rice through multi-temporal multi-sensor data fusion

    Science.gov (United States)

    Im, Jungho; Park, Seonyoung

    2017-04-01

    Rice is one of important food resources in the world and its consumption continues to increase with increasing world population. Accurate paddy rice mapping and monitoring are crucial for food security and agricultural mitigation because they enable us to forecast rice production. There have been studies for paddy rice classification using optical sensor data. However, optical sensor data has a limitation for data acquisition due to cloud contamination. Active Synthetic Aperture Radar (SAR) data have been used to complement the cloud problems of optical sensor images. Integration of the multispectral and SAR data can produce the more reliable crop classification results than from a single sensor data. In addition, as paddy rice has distinct phenology, many studies used phenology features from multi-temporal data for detecting paddy rice. Thus, this study aims at mapping paddy rice by expanding the spectral and temporal dimensions of data. In this study, we conducted paddy rice classification through fusion of multi-temporal optical sensor (Landsat) and SAR (RADARSAT-1 and ALSO PALSAR) data using two machine learning approaches—random forest (RF) and support vector machines (SVM) over two study sites (Dangjin-si in South Korea and Sutter County, California in the United States). This study examined six scenarios to identify the effect of the expansion of data dimension. Each scenario has a different combination of data sources and seasonal characteristics. We examined variable importance to identify which sensor data collected at which season are important to classify paddy rice. In addition, this study proposed a new index called Paddy rice Mapping Index (PMI) for effective paddy rice classification considering the spectral and temporal characteristics of paddy rice. Scenario 6 that uses optical sensor and SAR multi temporal data showed the highest overall accuracy (site 1: 98.67%; site 2: 93.87%) for paddy rice classification among six scenarios. Both machine

  9. A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements

    Directory of Open Access Journals (Sweden)

    Halim Tannous

    2016-11-01

    Full Text Available Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject’s movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96° compared to the one obtained using inertial sensors (5.04°. The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation.

  10. Proceedings of the workshop on adaptive grid methods for fusion plasmas

    Energy Technology Data Exchange (ETDEWEB)

    Koniges, A.E.; Craddock, G.G.; Schnack, D.D.; Strauss, H.R.

    1995-07-01

    The purpose of the workshop was to assemble workers, both within and outside of the fusion-related computations areas, for discussion regarding the issues of dynamically adaptive gridding. There were three invited talks related to adaptive gridding application experiences in various related fields of computational fluid dynamics (CFD), and nine short talks reporting on the progress of adaptive techniques in the specific areas of scrape-off-layer (SOL) modeling and magnetohydrodynamic (MHD) stability. Adaptive mesh methods have been successful in a number of diverse fields of CFD for over a decade. The method involves dynamic refinement of computed field profiles in a way that disperses uniformly the numerical errors associated with discrete approximations. Because the process optimizes computational effort, adaptive mesh methods can be used to study otherwise the intractable physical problems that involve complex boundary shapes or multiple spatial/temporal scales. Recent results indicate that these adaptive techniques will be required for tokamak fluid-based simulations involving the diverted tokamak SOL modeling and MHD simulations problems related to the highest priority ITER relevant issues.Individual papers are indexed separately on the energy data bases.

  11. Enhancing Sensor Array Intelligence by Bayesian Fusion of Information Multiplicity Generated by Multiple Processors

    Directory of Open Access Journals (Sweden)

    Prabha VERMA

    2014-11-01

    Full Text Available The paper presents a new data processing method for sensor array based pattern recognition problem. The primary motivation is to improve the odor recognition efficiency of electronic nose systems. The method creates a set of virtual experts in which individual expert members are defined by a different combination of a feature extractor and a radial basis function (RBF neural network classifier. In this work the outputs from five different linear feature extraction methods: principal component analysis (PCA, independent component analysis (ICA, singular value decomposition (SVD, linear discriminant analysis (LDA and partial-least-square regression (PLSR, are fed separately as inputs to five different RBF neural networks. The parameters defining each RBF network are optimized separately by training them as independent decision makers. Since a given feature extractor processes raw data with specific perspective about the data structure, and RBF network generates a set of class likelihood values, the set of virtual experts generate alternate sets of class likelihood values. Bayesian product rule for fusion is then applied for combining these class likelihood values into class posterior probabilities. The class declaration is finally done by maximum posterior probability. The method has been validated by analyzing 9 chemical and 7 non-chemical data sets. The enhancement in classification rate up to 33.3 % has been found. The reason for system performance improvement is that the multiple feature extractors generate varied representations of raw data by exploring diversity of hidden attributes, and Bayesian fusion works on the extended information provided by several experts.

  12. Fusion of data from GPS receivers based on a multi-sensor Kalman filter

    Directory of Open Access Journals (Sweden)

    Marcin MĄKA

    2008-01-01

    Full Text Available In the age of continually developing satellite navigation practically every ship is equipped with GPS receivers, providing the coordinates of her position. However, relying solely on the navigational data from one autonomous receiver the navigator may expect that a given position is burdened with significant errors or that the position data will be lost. This results from the shortcoming of GPS systems which are susceptible to disturbances affecting their operation. One method to substantially reduce such risk is a navigational system that makes use of a number of sources for accurate position determination. The obtained data are processed, which involves data integration and filtration in order to further diminish measurement errors. One possible solution is the application of a system based on an algorithm of multi-sensor navigational data fusion using a Kalman filter. After a brief description of the algorithm, this article presents some results of the fusion of data from parallel position measurements, where the data come from two mobile GPS receivers. The said solution is intended to be implemented in a navigational decision support system on board a sea-going vessel.

  13. Adaptive Reliable $H_\\infty $ Static Output Feedback Control Against Markovian Jumping Sensor Failures.

    Science.gov (United States)

    Zhai, Ding; An, Liwei; Ye, Dan; Zhang, Qingling

    2018-03-01

    This paper investigates the adaptive static output feedback (SOF) control problem for continuous-time linear systems with stochastic sensor failures. A multi-Markovian variable is introduced to denote the failure scaling factors for each sensor. Different from the existing results, the failure parameters are stochastically jumping and their bounds of are unknown. An adaptive reliable SOF control method is proposed, where the controller parameters are updated automatically to compensate for the failure effects on systems. A novel cubic absolute Lyapunov function is proposed to design adaptive laws only using measured output with sensor failures, and the convergence of jumping adaptive parameters is ensured by a trajectory initialization approach. The resultant designs can guarantee the asymptotic stability with an adaptive performance of closed-loop systems regardless of sensor failures. Finally, the simulation results on the "Raptor-90" helicopter are given to show the effectiveness of the proposed approaches.

  14. Adaptive electric potential sensors for smart signal acquisition and processing

    International Nuclear Information System (INIS)

    Prance, R J; Beardsmore-Rust, S; Prance, H; Harland, C J; Stiffell, P B

    2007-01-01

    Current applications of the Electric Potential Sensor operate in a strongly (capacitively) coupled limit, with the sensor physically close to or touching the source. This mode of operation screens the sensor effectively from the majority of external noise. To date however the full capability of these sensors operating in a remote mode has not been realised outside of a screened environment (Faraday cage). This paper describes the results of preliminary work in tailoring the response of the sensors to particular signals and so reject background noise, thereby enhancing both the dynamic range and signal to noise ratio significantly

  15. Multi-Sensor Building Fire Alarm System with Information Fusion Technology Based on D-S Evidence Theory

    Directory of Open Access Journals (Sweden)

    Qian Ding

    2014-10-01

    Full Text Available Multi-sensor and information fusion technology based on Dempster-Shafer evidence theory is applied in the system of a building fire alarm to realize early detecting and alarming. By using a multi-sensor to monitor the parameters of the fire process, such as light, smoke, temperature, gas and moisture, the range of fire monitoring in space and time is expanded compared with a single-sensor system. Then, the D-S evidence theory is applied to fuse the information from the multi-sensor with the specific fire model, and the fire alarm is more accurate and timely. The proposed method can avoid the failure of the monitoring data effectively, deal with the conflicting evidence from the multi-sensor robustly and improve the reliability of fire warning significantly.

  16. Fine Particle Sensor Based on Multi-Angle Light Scattering and Data Fusion

    Directory of Open Access Journals (Sweden)

    Wenjia Shao

    2017-05-01

    Full Text Available Meteorological parameters such as relative humidity have a significant impact on the precision of PM2.5 measurement instruments based on light scattering. Instead of adding meteorological sensors or dehumidification devices used widely in commercial PM2.5 measurement instruments, a novel particle sensor based on multi-angle light scattering and data fusion is proposed to eliminate the effect of meteorological factors. Three photodiodes are employed to collect the scattered light flux at three distinct angles. Weather index is defined as the ratio of scattered light fluxes collected at the 40° and 55° angles, which can be used to distinguish the mass median diameter variation caused by different meteorological parameters. Simulations based on Lorenz-Mie theory and field experiments establish the feasibility of this scheme. Experimental results indicate that mass median diameter has less effect on the photodiode at the 55° angle in comparison with photodiodes at the 40° angle and 140° angle. After correction using the weather index, the photodiode at the 40° angle yielded the best results followed by photodiodes at the 55° angle and the 140° angle.

  17. Clay content prediction using on-the-go proximal soil sensor fusion

    DEFF Research Database (Denmark)

    Tabatabai, Salman; Knadel, Maria; Greve, Mogens Humlekrog

    on soil usability, very few studies so far have provided robust and accurate predictions for fields with high clay content variability. An on-the-go multi-sensor platform was used to measure topsoil (25cm) VNIR spectra and temperature as well as electrical conductivity of top 30cm and top 90cm in 5 fields...... least squares regression (PLSR) and support vector machines regression (SVMR) were performed using VNIR spectra, EC and soil temperature as predictors and clay content as the response variable. PLSR and SVMR models were validated using full and 20-segment cross-validation respectively. The results were...... highly accurate with R2 of 0.91 and 0.93, root mean square error (RMSE) of 1.19 and 1.08, and ratio of performance to interquartile range (RPIQ) of 4.6 and 5.1 for PLSR and SVMR respectively. This shows the high potential of on-the-go soil sensor fusion to predict soil clay content and automate...

  18. Robust and reliable banknote authentification and print flaw detection with opto-acoustical sensor fusion methods

    Science.gov (United States)

    Lohweg, Volker; Schaede, Johannes; Türke, Thomas

    2006-02-01

    The authenticity checking and inspection of bank notes is a high labour intensive process where traditionally every note on every sheet is inspected manually. However with the advent of more and more sophisticated security features, both visible and invisible, and the requirement of cost reduction in the printing process, it is clear that automation is required. As more and more print techniques and new security features will be established, total quality security, authenticity and bank note printing must be assured. Therefore, this factor necessitates amplification of a sensorial concept in general. We propose a concept for both authenticity checking and inspection methods for pattern recognition and classification for securities and banknotes, which is based on the concept of sensor fusion and fuzzy interpretation of data measures. In the approach different methods of authenticity analysis and print flaw detection are combined, which can be used for vending or sorting machines, as well as for printing machines. Usually only the existence or appearance of colours and their textures are checked by cameras. Our method combines the visible camera images with IR-spectral sensitive sensors, acoustical and other measurements like temperature and pressure of printing machines.

  19. Sensor Fusion of Cameras and a Laser for City-Scale 3D Reconstruction

    Directory of Open Access Journals (Sweden)

    Yunsu Bok

    2014-11-01

    Full Text Available This paper presents a sensor fusion system of cameras and a 2D laser sensorfor large-scale 3D reconstruction. The proposed system is designed to capture data on afast-moving ground vehicle. The system consists of six cameras and one 2D laser sensor,and they are synchronized by a hardware trigger. Reconstruction of 3D structures is doneby estimating frame-by-frame motion and accumulating vertical laser scans, as in previousworks. However, our approach does not assume near 2D motion, but estimates free motion(including absolute scale in 3D space using both laser data and image features. In orderto avoid the degeneration associated with typical three-point algorithms, we present a newalgorithm that selects 3D points from two frames captured by multiple cameras. The problemof error accumulation is solved by loop closing, not by GPS. The experimental resultsshow that the estimated path is successfully overlaid on the satellite images, such that thereconstruction result is very accurate.

  20. Sensor fusion of cameras and a laser for city-scale 3D reconstruction.

    Science.gov (United States)

    Bok, Yunsu; Choi, Dong-Geol; Kweon, In So

    2014-11-04

    This paper presents a sensor fusion system of cameras and a 2D laser sensorfor large-scale 3D reconstruction. The proposed system is designed to capture data on afast-moving ground vehicle. The system consists of six cameras and one 2D laser sensor,and they are synchronized by a hardware trigger. Reconstruction of 3D structures is doneby estimating frame-by-frame motion and accumulating vertical laser scans, as in previousworks. However, our approach does not assume near 2D motion, but estimates free motion(including absolute scale) in 3D space using both laser data and image features. In orderto avoid the degeneration associated with typical three-point algorithms, we present a newalgorithm that selects 3D points from two frames captured by multiple cameras. The problemof error accumulation is solved by loop closing, not by GPS. The experimental resultsshow that the estimated path is successfully overlaid on the satellite images, such that thereconstruction result is very accurate.

  1. Data dimensionality reduction and data fusion for fast characterization of green coffee samples using hyperspectral sensors.

    Science.gov (United States)

    Calvini, Rosalba; Foca, Giorgia; Ulrici, Alessandro

    2016-10-01

    Hyperspectral sensors represent a powerful tool for chemical mapping of solid-state samples, since they provide spectral information localized in the image domain in very short times and without the need of sample pretreatment. However, due to the large data size of each hyperspectral image, data dimensionality reduction (DR) is necessary in order to develop hyperspectral sensors for real-time monitoring of large sets of samples with different characteristics. In particular, in this work, we focused on DR methods to convert the three-dimensional data array corresponding to each hyperspectral image into a one-dimensional signal (1D-DR), which retains spectral and/or spatial information. In this way, large datasets of hyperspectral images can be converted into matrices of signals, which in turn can be easily processed using suitable multivariate statistical methods. Obviously, different 1D-DR methods highlight different aspects of the hyperspectral image dataset. Therefore, in order to investigate their advantages and disadvantages, in this work, we compared three different 1D-DR methods: average spectrum (AS), single space hyperspectrogram (SSH) and common space hyperspectrogram (CSH). In particular, we have considered 370 NIR-hyperspectral images of a set of green coffee samples, and the three 1D-DR methods were tested for their effectiveness in sensor fault detection, data structure exploration and sample classification according to coffee variety and to coffee processing method. Principal component analysis and partial least squares-discriminant analysis were used to compare the three separate DR methods. Furthermore, low-level and mid-level data fusion was also employed to test the advantages of using AS, SSH and CSH altogether. Graphical Abstract Key steps in hyperspectral data dimenionality reduction.

  2. Real-Time Motion Tracking for Mobile Augmented/Virtual Reality Using Adaptive Visual-Inertial Fusion.

    Science.gov (United States)

    Fang, Wei; Zheng, Lianyu; Deng, Huanjun; Zhang, Hongbo

    2017-05-05

    In mobile augmented/virtual reality (AR/VR), real-time 6-Degree of Freedom (DoF) motion tracking is essential for the registration between virtual scenes and the real world. However, due to the limited computational capacity of mobile terminals today, the latency between consecutive arriving poses would damage the user experience in mobile AR/VR. Thus, a visual-inertial based real-time motion tracking for mobile AR/VR is proposed in this paper. By means of high frequency and passive outputs from the inertial sensor, the real-time performance of arriving poses for mobile AR/VR is achieved. In addition, to alleviate the jitter phenomenon during the visual-inertial fusion, an adaptive filter framework is established to cope with different motion situations automatically, enabling the real-time 6-DoF motion tracking by balancing the jitter and latency. Besides, the robustness of the traditional visual-only based motion tracking is enhanced, giving rise to a better mobile AR/VR performance when motion blur is encountered. Finally, experiments are carried out to demonstrate the proposed method, and the results show that this work is capable of providing a smooth and robust 6-DoF motion tracking for mobile AR/VR in real-time.

  3. A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks.

    Science.gov (United States)

    Zou, Tengyue; Wang, Yuanxia; Wang, Mengyi; Lin, Shouying

    2017-11-06

    Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim's algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms.

  4. A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tengyue Zou

    2017-11-01

    Full Text Available Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim’s algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms.

  5. Embry-Riddle Aeronautical University multispectral sensor and data fusion laboratory: a model for distributed research and education

    Science.gov (United States)

    McMullen, Sonya A. H.; Henderson, Troy; Ison, David

    2017-05-01

    The miniaturization of unmanned systems and spacecraft, as well as computing and sensor technologies, has opened new opportunities in the areas of remote sensing and multi-sensor data fusion for a variety of applications. Remote sensing and data fusion historically have been the purview of large government organizations, such as the Department of Defense (DoD), National Aeronautics and Space Administration (NASA), and National Geospatial-Intelligence Agency (NGA) due to the high cost and complexity of developing, fielding, and operating such systems. However, miniaturized computers with high capacity processing capabilities, small and affordable sensors, and emerging, commercially available platforms such as UAS and CubeSats to carry such sensors, have allowed for a vast range of novel applications. In order to leverage these developments, Embry-Riddle Aeronautical University (ERAU) has developed an advanced sensor and data fusion laboratory to research component capabilities and their employment on a wide-range of autonomous, robotic, and transportation systems. This lab is unique in several ways, for example, it provides a traditional campus laboratory for students and faculty to model and test sensors in a range of scenarios, process multi-sensor data sets (both simulated and experimental), and analyze results. Moreover, such allows for "virtual" modeling, testing, and teaching capability reaching beyond the physical confines of the facility for use among ERAU Worldwide students and faculty located around the globe. Although other institutions such as Georgia Institute of Technology, Lockheed Martin, University of Dayton, and University of Central Florida have optical sensor laboratories, the ERAU virtual concept is the first such lab to expand to multispectral sensors and data fusion, while focusing on the data collection and data products and not on the manufacturing aspect. Further, the initiative is a unique effort among Embry-Riddle faculty to develop multi

  6. arXiv Signal coupling to embedded pitch adapters in silicon sensors

    CERN Document Server

    Artuso, M.; Bezshyiko, I.; Blusk, S.; Bruendler, R.; Bugiel, S.; Dasgupta, R.; Dendek, A.; Dey, B.; Ely, S.; Lionetto, F.; Petruzzo, M.; Polyakov, I.; Rudolph, M.; Schindler, H.; Steinkamp, O.; Stone, S.

    2018-01-01

    We have examined the effects of embedded pitch adapters on signal formation in n-substrate silicon microstrip sensors with data from beam tests and simulation. According to simulation, the presence of the pitch adapter metal layer changes the electric field inside the sensor, resulting in slowed signal formation on the nearby strips and a pick-up effect on the pitch adapter. This can result in an inefficiency to detect particles passing through the pitch adapter region. All these effects have been observed in the beam test data.

  7. Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

    Directory of Open Access Journals (Sweden)

    Chao Li

    2017-12-01

    Full Text Available In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC, the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance.

  8. Adaptive Preheating Duration Control for Low-Power Ambient Air Quality Sensor Networks

    OpenAIRE

    Baek, Yoonchul; Atiq, Mahin K.; Kim, Hyung Seok

    2014-01-01

    Ceramic gas sensors used for measuring ambient air quality have features suitable for practical applications such as healthcare and air quality management, but have a major drawback—large power consumption to preheat the sensor for accurate measurements. In this paper; the adaptive preheating duration control (APC) method is proposed to reduce the power consumption of ambient air quality sensor networks. APC reduces the duration of unnecessary preheating, thereby alleviating power consumption...

  9. Multimode Adaptable Microwave Radar Sensor Based on Leaky-Wave Antennas

    Czech Academy of Sciences Publication Activity Database

    Hudec, P.; Pánek, Petr; Jeník, V.

    2017-01-01

    Roč. 65, č. 9 (2017), s. 3464-3473 ISSN 0018-9480 Institutional support: RVO:67985882 Keywords : adaptable sensor * low-range radar * multimode sensor Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering OBOR OECD: Electrical and electronic engineering Impact factor: 2.897, year: 2016

  10. Multimode Adaptable Microwave Radar Sensor Based on Leaky-Wave Antennas

    Czech Academy of Sciences Publication Activity Database

    Hudec, P.; Pánek, Petr; Jeník, V.

    2017-01-01

    Roč. 65, č. 9 (2017), s. 3464-3473 ISSN 0018-9480 Institutional support: RVO:67985882 Keywords : adaptable sensor * low-range radar * multimode sensor Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 2.897, year: 2016

  11. Block Fusion on Dynamically Adaptive Spacetree Grids for Shallow Water Waves

    KAUST Repository

    Weinzierl, Tobias

    2014-09-01

    © 2014 World Scientific Publishing Company. Spacetrees are a popular formalism to describe dynamically adaptive Cartesian grids. Even though they directly yield a mesh, it is often computationally reasonable to embed regular Cartesian blocks into their leaves. This promotes stencils working on homogeneous data chunks. The choice of a proper block size is sensitive. While large block sizes foster loop parallelism and vectorisation, they restrict the adaptivity\\'s granularity and hence increase the memory footprint and lower the numerical accuracy per byte. In the present paper, we therefore use a multiscale spacetree-block coupling admitting blocks on all spacetree nodes. We propose to find sets of blocks on the finest scale throughout the simulation and to replace them by fused big blocks. Such a replacement strategy can pick up hardware characteristics, i.e. which block size yields the highest throughput, while the dynamic adaptivity of the fine grid mesh is not constrained - applications can work with fine granular blocks. We study the fusion with a state-of-the-art shallow water solver at hands of an Intel Sandy Bridge and a Xeon Phi processor where we anticipate their reaction to selected block optimisation and vectorisation.

  12. Sensor Virtual Adaptable de Concentración de Etanol para Fermentadores Industriales

    Directory of Open Access Journals (Sweden)

    Boris Martínez

    2009-07-01

    Full Text Available Resumen: Los sistemas de control emplean sensores para observar el estado del proceso y tomar decisiones. En ocasiones, se necesita estimar las variables del proceso pues el sensor adecuado no existe, es prohibitivamente costoso o las mediciones son difíciles de realizar. Una solución consiste en inferir las variables no medidas a partir de otras variables mediante sensores virtuales o sensores por software (soft-sensors. En los procesos de fermentación alcohólica, la medición de la concentración del etanol es esencial. Sin embargo, no existen sensores baratos y confiables para medirla en línea ni existe una solución aceptada por todos del modelado de dicha variable. Además, las fermentaciones nunca son iguales pues los microorganismos son muy sensibles a pequeñas desviaciones en las variables involucradas. Por tanto, estos procesos requieren un sistema de estimación adaptable y altamente robusto. En este trabajo se presenta un sensor virtual adaptable para un proceso fermentativo de bioetanol empleando un modelo borroso evolutivo a partir de datos del proceso. Además, el modelo obtenido es compacto y presenta una estructura adecuada para su aplicación futura en estrategias de control, en aras de optimizar la productividad del proceso y disminuir los costos de producción. Palabras clave: bioetanol, procesos fermentativos, sensores virtuales o sensores software, sistemas adaptables, sistemas borrosos

  13. Generic Sensor Data Fusion Services for Web-enabled Environmental Risk Management and Decision-Support Systems

    Science.gov (United States)

    Sabeur, Zoheir; Middleton, Stuart; Veres, Galina; Zlatev, Zlatko; Salvo, Nicola

    2010-05-01

    The advancement of smart sensor technology in the last few years has led to an increase in the deployment of affordable sensors for monitoring the environment around Europe. This is generating large amounts of sensor observation information and inevitably leading to problems about how to manage large volumes of data as well as making sense out the data for decision-making. In addition, the various European Directives (Water Framework Diectives, Bathing Water Directives, Habitat Directives, etc.. ) which regulate human activities in the environment and the INSPIRE Directive on spatial information management regulations have implicitely led the designated European Member States environment agencies and authorities to put in place new sensor monitoring infrastructure and share information about environmental regions under their statutory responsibilities. They will need to work cross border and collectively reach environmental quality standards. They will also need to regularly report to the EC on the quality of the environments of which they are responsible and make such information accessible to the members of the public. In recent years, early pioneering work on the design of service oriented architecture using sensor networks has been achieved. Information web-services infrastructure using existing data catalogues and web-GIS map services can now be enriched with the deployment of new sensor observation and data fusion and modelling services using OGC standards. The deployment of the new services which describe sensor observations and intelligent data-processing using data fusion techniques can now be implemented and provide added value information with spatial-temporal uncertainties to the next generation of decision support service systems. The new decision support service systems have become key to implement across Europe in order to comply with EU environmental regulations and INSPIRE. In this paper, data fusion services using OGC standards with sensor

  14. Multi-rate cubature Kalman filter based data fusion method with residual compensation to adapt to sampling rate discrepancy in attitude measurement system.

    Science.gov (United States)

    Guo, Xiaoting; Sun, Changku; Wang, Peng

    2017-08-01

    This paper investigates the multi-rate inertial and vision data fusion problem in nonlinear attitude measurement systems, where the sampling rate of the inertial sensor is much faster than that of the vision sensor. To fully exploit the high frequency inertial data and obtain favorable fusion results, a multi-rate CKF (Cubature Kalman Filter) algorithm with estimated residual compensation is proposed in order to adapt to the problem of sampling rate discrepancy. During inter-sampling of slow observation data, observation noise can be regarded as infinite. The Kalman gain is unknown and approaches zero. The residual is also unknown. Therefore, the filter estimated state cannot be compensated. To obtain compensation at these moments, state error and residual formulas are modified when compared with the observation data available moments. Self-propagation equation of the state error is established to propagate the quantity from the moments with observation to the moments without observation. Besides, a multiplicative adjustment factor is introduced as Kalman gain, which acts on the residual. Then the filter estimated state can be compensated even when there are no visual observation data. The proposed method is tested and verified in a practical setup. Compared with multi-rate CKF without residual compensation and single-rate CKF, a significant improvement is obtained on attitude measurement by using the proposed multi-rate CKF with inter-sampling residual compensation. The experiment results with superior precision and reliability show the effectiveness of the proposed method.

  15. Statistical Sensor Fusion of a 9-DOF Mems Imu for Indoor Navigation

    Science.gov (United States)

    Chow, J. C. K.

    2017-09-01

    Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8-89.5 % and the dead-reckoned positioning accuracy by 72.9-92.8 %, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user's dynamics.

  16. STATISTICAL SENSOR FUSION OF A 9-DOF MEMS IMU FOR INDOOR NAVIGATION

    Directory of Open Access Journals (Sweden)

    J. C. K. Chow

    2017-09-01

    Full Text Available Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.. This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8–89.5 % and the dead-reckoned positioning accuracy by 72.9–92.8 %, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user’s dynamics.

  17. Adaptive and Reactive Security for Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Stankovic, John A

    2007-01-01

    .... WSNs are also susceptible to malicious, non-random security attacks. For example, a wireless sensor network deployed in remote regions to detect and classify targets could be rendered inoperative by various security attacks...

  18. Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis

    Science.gov (United States)

    Markovic, Marko; Dosen, Strahinja; Popovic, Dejan; Graimann, Bernhard; Farina, Dario

    2015-12-01

    Objective. Myoelectric activity volitionally generated by the user is often used for controlling hand prostheses in order to replicate the synergistic actions of muscles in healthy humans during grasping. Muscle synergies in healthy humans are based on the integration of visual perception, heuristics and proprioception. Here, we demonstrate how sensor fusion that combines artificial vision and proprioceptive information with the high-level processing characteristics of biological systems can be effectively used in transradial prosthesis control. Approach. We developed a novel context- and user-aware prosthesis (CASP) controller integrating computer vision and inertial sensing with myoelectric activity in order to achieve semi-autonomous and reactive control of a prosthetic hand. The presented method semi-automatically provides simultaneous and proportional control of multiple degrees-of-freedom (DOFs), thus decreasing overall physical effort while retaining full user control. The system was compared against the major commercial state-of-the art myoelectric control system in ten able-bodied and one amputee subject. All subjects used transradial prosthesis with an active wrist to grasp objects typically associated with activities of daily living. Main results. The CASP significantly outperformed the myoelectric interface when controlling all of the prosthesis DOF. However, when tested with less complex prosthetic system (smaller number of DOF), the CASP was slower but resulted with reaching motions that contained less compensatory movements. Another important finding is that the CASP system required minimal user adaptation and training. Significance. The CASP constitutes a substantial improvement for the control of multi-DOF prostheses. The application of the CASP will have a significant impact when translated to real-life scenarious, particularly with respect to improving the usability and acceptance of highly complex systems (e.g., full prosthetic arms) by amputees.

  19. Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis.

    Science.gov (United States)

    Markovic, Marko; Dosen, Strahinja; Popovic, Dejan; Graimann, Bernhard; Farina, Dario

    2015-12-01

    Myoelectric activity volitionally generated by the user is often used for controlling hand prostheses in order to replicate the synergistic actions of muscles in healthy humans during grasping. Muscle synergies in healthy humans are based on the integration of visual perception, heuristics and proprioception. Here, we demonstrate how sensor fusion that combines artificial vision and proprioceptive information with the high-level processing characteristics of biological systems can be effectively used in transradial prosthesis control. We developed a novel context- and user-aware prosthesis (CASP) controller integrating computer vision and inertial sensing with myoelectric activity in order to achieve semi-autonomous and reactive control of a prosthetic hand. The presented method semi-automatically provides simultaneous and proportional control of multiple degrees-of-freedom (DOFs), thus decreasing overall physical effort while retaining full user control. The system was compared against the major commercial state-of-the art myoelectric control system in ten able-bodied and one amputee subject. All subjects used transradial prosthesis with an active wrist to grasp objects typically associated with activities of daily living. The CASP significantly outperformed the myoelectric interface when controlling all of the prosthesis DOF. However, when tested with less complex prosthetic system (smaller number of DOF), the CASP was slower but resulted with reaching motions that contained less compensatory movements. Another important finding is that the CASP system required minimal user adaptation and training. The CASP constitutes a substantial improvement for the control of multi-DOF prostheses. The application of the CASP will have a significant impact when translated to real-life scenarious, particularly with respect to improving the usability and acceptance of highly complex systems (e.g., full prosthetic arms) by amputees.

  20. A locomotion intent prediction system based on multi-sensor fusion.

    Science.gov (United States)

    Chen, Baojun; Zheng, Enhao; Wang, Qining

    2014-07-10

    Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers.

  1. Multi-sensors data and information fusion algorithm for indoor localization

    Directory of Open Access Journals (Sweden)

    XIA Jun

    2015-02-01

    Full Text Available The localization algorithm of based on IMU is one of autonomous localization methods while it possesses the disadvantage of drift error and accumulated error,so this paper proposes a multi-sensors including wearable multi-IMUs and IWSN data and information fusion algorithm for indoor localization.On the one hand,almost all indoor localization algorithms based on IMU use only one IMU while this single-IMU-based algorithm can′t judge the posture of person precisely,one appropriate solution is that we can utilize multi-IMUs to cooperate in localization process,besides,we can fuse position information of multi-IMUs by fuzzy voting scheme.On the other hand,in order to overcome the disadvantage of drift error and accumulate error,combining with IWSN in indoor and fusing the position information calculated by IWSN and multi-IMUs via Kalman Filter algorithm.Experiment results show that the proposed indoor localization algorithm possesses good property in judging posture of person and decreasing drift error and accumulate error comparing with traditional IMU-based indoor localization algorithm.

  2. DYNAMIC AND INCREMENTAL EXPLORATION STRATEGY IN FUSION ADAPTIVE RESONANCE THEORY FOR ONLINE REINFORCEMENT LEARNING

    Directory of Open Access Journals (Sweden)

    Budhitama Subagdja

    2016-06-01

    Full Text Available One of the fundamental challenges in reinforcement learning is to setup a proper balance between exploration and exploitation to obtain the maximum cummulative reward in the long run. Most protocols for exploration bound the overall values to a convergent level of performance. If new knowledge is inserted or the environment is suddenly changed, the issue becomes more intricate as the exploration must compromise the pre-existing knowledge. This paper presents a type of multi-channel adaptive resonance theory (ART neural network model called fusion ART which serves as a fuzzy approximator for reinforcement learning with inherent features that can regulate the exploration strategy. This intrinsic regulation is driven by the condition of the knowledge learnt so far by the agent. The model offers a stable but incremental reinforcement learning that can involve prior rules as bootstrap knowledge for guiding the agent to select the right action. Experiments in obstacle avoidance and navigation tasks demonstrate that in the configuration of learning wherein the agent learns from scratch, the inherent exploration model in fusion ART model is comparable to the basic E-greedy policy. On the other hand, the model is demonstrated to deal with prior knowledge and strike a balance between exploration and exploitation.

  3. Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach

    Directory of Open Access Journals (Sweden)

    Mengyun Liu

    2017-12-01

    Full Text Available After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to “see” which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web

  4. Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach.

    Science.gov (United States)

    Liu, Mengyun; Chen, Ruizhi; Li, Deren; Chen, Yujin; Guo, Guangyi; Cao, Zhipeng; Pan, Yuanjin

    2017-12-08

    After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to "see" which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for

  5. Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

    Science.gov (United States)

    Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

    2015-12-01

    NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, MODIS, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. HySDS is a Hybrid-Cloud Science Data System that has been developed and applied under NASA AIST, MEaSUREs, and ACCESS grants. HySDS uses the SciFlow workflow engine to partition analysis workflows into parallel tasks (e.g. segmenting by time or space) that are pushed into a durable job queue. The tasks are "pulled" from the queue by worker Virtual Machines (VM's) and executed in an on-premise Cloud (Eucalyptus or OpenStack) or at Amazon in the public Cloud or govCloud. In this way, years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the transferred data. We are using HySDS to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a MEASURES grant. We will present the architecture of HySDS, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. Our system demonstrates how one can pull A-Train variables (Levels 2 & 3) on-demand into the Amazon Cloud, and cache only those variables that are heavily used, so that any number of compute jobs can be

  6. Extending lifetime of wireless sensor networks using multi-sensor ...

    Indian Academy of Sciences (India)

    Multi-sensor data fusion; optimization techniques; energy aware routing; ant colony optimization; wireless sensor network ... The executions of proposed arnd low-energy adaptive clustering hierarchy algorithms were carried out and the results show that the proposed algorithms could efficiently reduce the use of energy and ...

  7. DEVELOPMENT OF A PEDESTRIAN INDOOR NAVIGATION SYSTEM BASED ON MULTI-SENSOR FUSION AND FUZZY LOGIC ESTIMATION ALGORITHMS

    Directory of Open Access Journals (Sweden)

    Y. C. Lai

    2015-05-01

    Full Text Available This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS. There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system

  8. Development of a Pedestrian Indoor Navigation System Based on Multi-Sensor Fusion and Fuzzy Logic Estimation Algorithms

    Science.gov (United States)

    Lai, Y. C.; Chang, C. C.; Tsai, C. M.; Lin, S. Y.; Huang, S. C.

    2015-05-01

    This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system to extend its

  9. Testbeam Studies on Pick-Up in Sensors with Embedded Pitch Adapters

    CERN Document Server

    Rehnisch, Laura; The ATLAS collaboration

    2017-01-01

    For silicon strip sensors, the tracking information specifications can lead to challenging requirements for wire bonding. A common strategy is to use external pitch adapters to facilitate this step in the production of detector modules. A novel approach previously discussed in [1], is to implement the pitch adapters in the sensor, by embedding a second layer of metal tracks. The use of these embedded pitch adapters (EPAs) decouples the bond pad layout of the sensor from its implant layout by moving the adaption to the sensor production step. This solution, however, can yield the risk of performance losses due to the increase of inter-strip capacitance, or unwanted capacitive coupling between the metal layers (cross-talk) or the silicon bulk and the second metal layer (pick-up). In the prototyping stage of the ATLAS tracker end-cap upgrade, where different bond-pad layouts on sensor and readout chip lead to extremely challenging wire-bonding conditions, sensors with different geometries of EPA implementations ...

  10. A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres.

    Science.gov (United States)

    Zhu, Yungang; Liu, Dayou; Grosu, Radu; Wang, Xinhua; Duan, Hongying; Wang, Guodong

    2017-09-07

    Disease diagnosis can be performed based on fusing the data acquired by multiple medical sensors from patients, and it is a crucial task in sensor-based e-healthcare systems. However, it is a challenging problem that there are few effective diagnosis methods based on sensor data fusion for atrial hypertrophy disease. In this article, we propose a novel multi-sensor data fusion method for atrial hypertrophy diagnosis, namely, characterized support vector hyperspheres (CSVH). Instead of constructing a hyperplane, as a traditional support vector machine does, the proposed method generates "hyperspheres" to collect the discriminative medical information, since a hypersphere is more powerful for data description than a hyperplane. In detail, CSVH constructs two characterized hyperspheres for the classes of patient and healthy subject, respectively. The hypersphere for the patient class is developed in a weighted version so as to take the diversity of patient instances into consideration. The hypersphere for the class of healthy people keeps furthest away from the patient class in order to achieve maximum separation from the patient class. A query is labelled by membership functions defined based on the two hyperspheres. If the query is rejected by the two classes, the angle information of the query to outliers and overlapping-region data is investigated to provide the final decision. The experimental results illustrate that the proposed method achieves the highest diagnosis accuracy among the state-of-the-art methods.

  11. Proceedings of the Fourteenth Annual Adaptive Sensor Array Processing Workshop. Held in Lexington, Massachusetts on 6-7-June 2006. Volume 1 (CD-ROM)

    National Research Council Canada - National Science Library

    Musto, H

    2006-01-01

    ...: 1 CD-ROM; 4 3/4 in.; 78.7 MB. ABSTRACT: The fourteenth annual workshop on Adaptive Sensor Array Processing had sessions on adaptive radar, adaptive sonar, adaptive communications, adaptive detection and estimation, tracking, advanced...

  12. Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective

    Science.gov (United States)

    Nurzaman, Surya G.

    2016-01-01

    Sensor morphology, the morphology of a sensing mechanism which plays a role of shaping the desired response from physical stimuli from surroundings to generate signals usable as sensory information, is one of the key common aspects of sensing processes. This paper presents a structured review of researches on bioinspired sensor morphology implemented in robotic systems, and discusses the fundamental design principles. Based on literature review, we propose two key arguments: first, owing to its synthetic nature, biologically inspired robotics approach is a unique and powerful methodology to understand the role of sensor morphology and how it can evolve and adapt to its task and environment. Second, a consideration of an integrative view of perception by looking into multidisciplinary and overarching mechanisms of sensor morphology adaptation across biology and engineering enables us to extract relevant design principles that are important to extend our understanding of the unfinished concepts in sensing and perception. PMID:27499843

  13. A multi-sensor fusion framework for improving situational awareness in demanding maritime training

    International Nuclear Information System (INIS)

    Sanfilippo, Filippo

    2017-01-01

    Real offshore operational scenarios can involve a considerable amount of risk. Sophisticated training programmes involving specially designed simulator environments constitute a promising approach for improving an individual's perception and assessment of dangerous situations in real applications. One of the world's most advanced providers of simulators for such demanding offshore operations is the Offshore Simulator Centre AS (OSC). However, even though the OSC provides powerful simulation tools, techniques for visualising operational procedures that can be used to further improve Situational awareness (SA), are still lacking. Providing the OSC with an integrated multi-sensor fusion framework is the goal of this work. The proposed framework is designed to improve planning, execution and assessment of demanding maritime operations by adopting newly-designed risk-evaluation tools. Different information from the simulator scene and from the real world can be collected, such as audio, video, bio-metric data from eye-trackers, other sensor data and annotations. This integration is the base for research on novel SA assessment methodologies. This will serve the industry for the purpose of improving operational effectiveness and safety through the use of simulators. In this work, a training methodology based on the concept of briefing/debriefing is adopted based on previous literature. By using this methodology borrowed from similarly demanding applications, the efficiency of the proposed framework is validated in a conceptual case study. In particular, the training procedure, which was previously performed by Statoil and partners, for the world's first sub-sea gas compression plant, in Aasgard, Norway, is considered and reviewed highlighting the potentials of the proposed framework. - Highlights: • A framework for improving SA in demanding maritime training is proposed. • The proposed framework is integrated with the Offshore Simulator Centre AS (OSC

  14. A Sensor Fusion Algorithm for Filtering Pyrometer Measurement Noise in the Czochralski Crystallization Process

    Directory of Open Access Journals (Sweden)

    M. Komperød

    2011-01-01

    Full Text Available The Czochralski (CZ crystallization process is used to produce monocrystalline silicon for solar cell wafers and electronics. Tight temperature control of the molten silicon is most important for achieving high crystal quality. SINTEF Materials and Chemistry operates a CZ process. During one CZ batch, two pyrometers were used for temperature measurement. The silicon pyrometer measures the temperature of the molten silicon. This pyrometer is assumed to be accurate, but has much high-frequency measurement noise. The graphite pyrometer measures the temperature of a graphite material. This pyrometer has little measurement noise. There is quite a good correlation between the two pyrometer measurements. This paper presents a sensor fusion algorithm that merges the two pyrometer signals for producing a temperature estimate with little measurement noise, while having significantly less phase lag than traditional lowpass- filtering of the silicon pyrometer. The algorithm consists of two sub-algorithms: (i A dynamic model is used to estimate the silicon temperature based on the graphite pyrometer, and (ii a lowpass filter and a highpass filter designed as complementary filters. The complementary filters are used to lowpass-filter the silicon pyrometer, highpass-filter the dynamic model output, and merge these filtered signals. Hence, the lowpass filter attenuates noise from the silicon pyrometer, while the graphite pyrometer and the dynamic model estimate those frequency components of the silicon temperature that are lost when lowpass-filtering the silicon pyrometer. The algorithm works well within a limited temperature range. To handle a larger temperature range, more research must be done to understand the process' nonlinear dynamics, and build this into the dynamic model.

  15. Performance evaluation of multi-sensor data-fusion systems in ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    Details of the NGC-PC system along with sensors for a typical launch vehicle are explained and the characteristics of each sensor/the criteria for selecting the same are included. Sensor redundancy, FDI logic and mission salvage mode options are elaborated along with the sensor and mission performance evaluation tests.

  16. Data Strategies to Support Automated Multi-Sensor Data Fusion in a Service Oriented Architecture

    National Research Council Canada - National Science Library

    Rothenhaus, Kurt J

    2008-01-01

    .... This dissertation provides two software architectural patterns and an auto-fusion process that guide the development of a distributed, accountable and scalable SOA framework to support improved...

  17. Adaptive Interferometry Sensor for Detection of Nanoscale Displacements

    Directory of Open Access Journals (Sweden)

    Roman V. ROMASHKO

    2014-11-01

    Full Text Available In this work we present an adaptive holographic interferometry sensing system for measurement of nanoscale displacements of micro- and macro-objects. The system is based on using dynamic hologram continuously recorded in photorefractive CdTe crystal. Theoretical limit for displacement detection which can be provided by the system is 0.1 nm. It is experimentally demonstrated that system is able to detect a displacement from 0.7 nm up to 266 nm in linear regime. We also present theoretical model of adaptive interferometer operation which take into account a displacement speed. Due to its adaptive properties the measurement system can be used for inspection of sub-micro-scale objects with arbitrary shape and surface profile.

  18. Adaptive preheating duration control for low-power ambient air quality sensor networks.

    Science.gov (United States)

    Baek, Yoonchul; Atiq, Mahin K; Kim, Hyung Seok

    2014-03-20

    Ceramic gas sensors used for measuring ambient air quality have features suitable for practical applications such as healthcare and air quality management, but have a major drawback-large power consumption to preheat the sensor for accurate measurements. In this paper; the adaptive preheating duration control (APC) method is proposed to reduce the power consumption of ambient air quality sensor networks. APC reduces the duration of unnecessary preheating, thereby alleviating power consumption. Furthermore, the APC can allow systems to meet user requirements such as accuracy and periodicity factor when detecting the concentration of a target gas. A performance evaluation of the power consumption of gas sensors is conducted with various user requirements and factors that affect the preheating duration of the gas sensor. This shows that the power consumption of the APC is lower than that of continuous power supply methods and constant power supply/cutoff methods.

  19. Adaptive Preheating Duration Control for Low-Power Ambient Air Quality Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yoonchul Baek

    2014-03-01

    Full Text Available Ceramic gas sensors used for measuring ambient air quality have features suitable for practical applications such as healthcare and air quality management, but have a major drawback—large power consumption to preheat the sensor for accurate measurements. In this paper; the adaptive preheating duration control (APC method is proposed to reduce the power consumption of ambient air quality sensor networks. APC reduces the duration of unnecessary preheating, thereby alleviating power consumption. Furthermore, the APC can allow systems to meet user requirements such as accuracy and periodicity factor when detecting the concentration of a target gas. A performance evaluation of the power consumption of gas sensors is conducted with various user requirements and factors that affect the preheating duration of the gas sensor. This shows that the power consumption of the APC is lower than that of continuous power supply methods and constant power supply/cutoff methods.

  20. Secure adaptive topology control for wireless ad-hoc sensor networks.

    Science.gov (United States)

    Hsueh, Ching-Tsung; Li, Yu-Wei; Wen, Chih-Yu; Ouyang, Yen-Chieh

    2010-01-01

    This paper presents a secure decentralized clustering algorithm for wireless ad-hoc sensor networks. The algorithm operates without a centralized controller, operates asynchronously, and does not require that the location of the sensors be known a priori. Based on the cluster-based topology, secure hierarchical communication protocols and dynamic quarantine strategies are introduced to defend against spam attacks, since this type of attacks can exhaust the energy of sensor nodes and will shorten the lifetime of a sensor network drastically. By adjusting the threshold of infected percentage of the cluster coverage, our scheme can dynamically coordinate the proportion of the quarantine region and adaptively achieve the cluster control and the neighborhood control of attacks. Simulation results show that the proposed approach is feasible and cost effective for wireless sensor networks.

  1. Autonomous sensor manager agents (ASMA)

    Science.gov (United States)

    Osadciw, Lisa A.

    2004-04-01

    Autonomous sensor manager agents are presented as an algorithm to perform sensor management within a multisensor fusion network. The design of the hybrid ant system/particle swarm agents is described in detail with some insight into their performance. Although the algorithm is designed for the general sensor management problem, a simulation example involving 2 radar systems is presented. Algorithmic parameters are determined by the size of the region covered by the sensor network, the number of sensors, and the number of parameters to be selected. With straight forward modifications, this algorithm can be adapted for most sensor management problems.

  2. Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

    Science.gov (United States)

    Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

    2013-12-01

    NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the 'A-Train' platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (MERRA), stratify the comparisons using a classification of the 'cloud scenes' from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically 'sharded' by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will

  3. Adaptive Information Access on Multiple Applications Support Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee

    2014-01-01

    is used for safety and security monitoring purposes. In this paper, we evaluate different access strategies to remote dynamic information and compare between achieving information reliability (mismatch probability) and the associated power consumption. Lastly, based on the models, we propose an adaptive...

  4. Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

    Directory of Open Access Journals (Sweden)

    Thomas Moeckel

    2017-01-01

    Full Text Available An accurate estimation of biomass is needed to understand the spatio-temporal changes of forage resources in pasture ecosystems and to support grazing management decisions. A timely evaluation of biomass is challenging, as it requires efficient means such as technical sensing methods to assess numerous data and create continuous maps. In order to calibrate ultrasonic and spectral sensors, a field experiment with heterogeneous pastures continuously stocked by cows at three grazing intensities was conducted. Sensor data fusion by combining ultrasonic sward height (USH with narrow band normalized difference spectral index (NDSI (R2CV = 0.52 or simulated WorldView2 (WV2 (R2CV = 0.48 satellite broad bands increased the prediction accuracy significantly, compared to the exclusive use of USH or spectral measurements. Some combinations were even better than the use of the full hyperspectral information (R2CV = 0.48. Spectral regions related to plant water content were found to be of particular importance (996–1225 nm. Fusion of ultrasonic and spectral sensors is a promising approach to assess biomass even in heterogeneous pastures. However, the suggested technique may have limited usefulness in the second half of the growing season, due to an increasing abundance of senesced material.

  5. A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion

    Directory of Open Access Journals (Sweden)

    Bailing Liu

    2015-04-01

    Full Text Available An improvement method for the pose accuracy of a robot manipulator by using a multiple-sensor combination measuring system (MCMS is presented. It is composed of a visual sensor, an angle sensor and a series robot. The visual sensor is utilized to measure the position of the manipulator in real time, and the angle sensor is rigidly attached to the manipulator to obtain its orientation. Due to the higher accuracy of the multi-sensor, two efficient data fusion approaches, the Kalman filter (KF and multi-sensor optimal information fusion algorithm (MOIFA, are used to fuse the position and orientation of the manipulator. The simulation and experimental results show that the pose accuracy of the robot manipulator is improved dramatically by 38%~78% with the multi-sensor data fusion. Comparing with reported pose accuracy improvement methods, the primary advantage of this method is that it does not require the complex solution of the kinematics parameter equations, increase of the motion constraints and the complicated procedures of the traditional vision-based methods. It makes the robot processing more autonomous and accurate. To improve the reliability and accuracy of the pose measurements of MCMS, the visual sensor repeatability is experimentally studied. An optimal range of 1 x 0.8 x 1 ~ 2 x 0.8 x 1  m in the field of view (FOV is indicated by the experimental results.

  6. Accurate 3D Positioning for a Mobile Platform in Non-Line-of-Sight Scenarios Based on IMU/Magnetometer Sensor Fusion.

    Science.gov (United States)

    Hellmers, Hendrik; Kasmi, Zakaria; Norrdine, Abdelmoumen; Eichhorn, Andreas

    2018-01-04

    In recent years, a variety of real-time applications benefit from services provided by localization systems due to the advent of sensing and communication technologies. Since the Global Navigation Satellite System (GNSS) enables localization only outside buildings, applications for indoor positioning and navigation use alternative technologies. Ultra Wide Band Signals (UWB), Wireless Local Area Network (WLAN), ultrasonic or infrared are common examples. However, these technologies suffer from fading and multipath effects caused by objects and materials in the building. In contrast, magnetic fields are able to pass through obstacles without significant propagation errors, i.e. in Non-Line of Sight Scenarios (NLoS). The aim of this work is to propose a novel indoor positioning system based on artificially generated magnetic fields in combination with Inertial Measurement Units (IMUs). In order to reach a better coverage, multiple coils are used as reference points. A basic algorithm for three-dimensional applications is demonstrated as well as evaluated in this article. The established system is then realized by a sensor fusion principle as well as a kinematic motion model on the basis of a Kalman filter. Furthermore, a pressure sensor is used in combination with an adaptive filtering method to reliably estimate the platform's altitude.

  7. Pliable Cognitive MAC for Heterogeneous Adaptive Cognitive Radio Sensor Networks.

    Science.gov (United States)

    Al-Medhwahi, Mohammed; Hashim, Fazirulhisyam; Ali, Borhanuddin Mohd; Sali, Aduwati

    2016-01-01

    The rapid expansion of wireless monitoring and surveillance applications in several domains reinforces the trend of exploiting emerging technologies such as the cognitive radio. However, these technologies have to adjust their working concepts to consider the common characteristics of conventional wireless sensor networks (WSNs). The cognitive radio sensor network (CRSN), still an immature technology, has to deal with new networks that might have different types of data, traffic patterns, or quality of service (QoS) requirements. In this paper, we design and model a new cognitive radio-based medium access control (MAC) algorithm dealing with the heterogeneous nature of the developed networks in terms of either the traffic pattern or the required QoS for the node applications. The proposed algorithm decreases the consumed power on several fronts, provides satisfactory levels of latency and spectrum utilization with efficient scheduling, and manages the radio resources for various traffic conditions. An intensive performance evaluation is conducted to study the impact of key parameters such as the channel idle time length, node density, and the number of available channels. The performance evaluation of the proposed algorithm shows a better performance than the comparable protocols. Moreover, the results manifest that the proposed algorithm is suitable for real time monitoring applications.

  8. Self-adapted and tunable graphene strain sensors for detecting both subtle and large human motions.

    Science.gov (United States)

    Tao, Lu-Qi; Wang, Dan-Yang; Tian, He; Ju, Zhen-Yi; Liu, Ying; Pang, Yu; Chen, Yuan-Quan; Yang, Yi; Ren, Tian-Ling

    2017-06-22

    Conventional strain sensors rarely have both a high gauge factor and a large strain range simultaneously, so they can only be used in specific situations where only a high sensitivity or a large strain range is required. However, for detecting human motions that include both subtle and large motions, these strain sensors can't meet the diverse demands simultaneously. Here, we come up with laser patterned graphene strain sensors with self-adapted and tunable performance for the first time. A series of strain sensors with either an ultrahigh gauge factor or a preferable strain range can be fabricated simultaneously via one-step laser patterning, and are suitable for detecting all human motions. The strain sensors have a GF of up to 457 with a strain range of 35%, or have a strain range of up to 100% with a GF of 268. Most importantly, the performance of the strain sensors can be easily tuned by adjusting the patterns of the graphene, so that the sensors can meet diverse demands in both subtle and large motion situations. The graphene strain sensors show significant potential in applications such as wearable electronics, health monitoring and intelligent robots. Furthermore, the facile, fast and low-cost fabrication method will make them possible and practical to be used for commercial applications in the future.

  9. Sensor Data Fusion for Body State Estimation in a Bipedal Robot and Its Feedback Control Application for Stable Walking

    OpenAIRE

    Chen, Ching-Pei; Chen, Jing-Yi; Huang, Chun-Kai; Lu, Jau-Ching; Lin, Pei-Chun

    2015-01-01

    We report on a sensor data fusion algorithm via an extended Kalman filter for estimating the spatial motion of a bipedal robot. Through fusing the sensory information from joint encoders, a 6-axis inertial measurement unit and a 2-axis inclinometer, the robot’s body state at a specific fixed position can be yielded. This position is also equal to the CoM when the robot is in the standing posture suggested by the detailed CAD model of the robot. In addition, this body state is further utilized...

  10. Overview of Sensor Fusion Research at RDECOM NVESD & Recent Results on Vehicle Detection Using Multiple Sensor Nodes

    National Research Council Canada - National Science Library

    Perconti, Philip; Hilger, James; Loew, Murry

    2003-01-01

    .... This paper provides an overview of the on going research at NVESD related to fusing a mixture of active and passive sensors for countermine, dismounted AND mounted soldiers, aviation and unattended...

  11. A Decentralized Quality Aware Adaptive Sampling Strategy in Wireless Sensor Networks

    NARCIS (Netherlands)

    Masoum, Alireza; Meratnia, Nirvana; Havinga, Paul J.M.

    Since WSNs suffer from sever resource constraints, in terms of energy, memory and processing, temporal, spatial and spatio-temporal correlation among sensor data can be exploited by adaptive sampling approaches to find out an optimal sampling strategy, which reduces the number of sampling nodes

  12. Fault Diagnosis for Satellite Sensors and Actuators using Nonlinear Geometric Approach and Adaptive Observers

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2018-01-01

    This paper presents a novel scheme for diagnosis of faults affecting sensors that measure the satellite attitude, body angular velocity, flywheel spin rates, and defects in control torques from reaction wheel motors. The proposed methodology uses adaptive observers to provide fault estimates that...

  13. Widefield fluorescence microscopy with sensor-based conjugate adaptive optics using oblique back illumination.

    Science.gov (United States)

    Li, Jiang; Bifano, Thomas G; Mertz, Jerome

    2016-12-01

    We describe a wavefront sensor strategy for the implementation of adaptive optics (AO) in microscope applications involving thick, scattering media. The strategy is based on the exploitation of multiple scattering to provide oblique back illumination of the wavefront-sensor focal plane, enabling a simple and direct measurement of the flux-density tilt angles caused by aberrations at this plane. Advantages of the sensor are that it provides a large measurement field of view (FOV) while requiring no guide star, making it particularly adapted to a type of AO called conjugate AO, which provides a large correction FOV in cases when sample-induced aberrations arise from a single dominant plane (e.g., the sample surface). We apply conjugate AO here to widefield (i.e., nonscanning) fluorescence microscopy for the first time and demonstrate dynamic wavefront correction in a closed-loop implementation.

  14. An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zichuan Xu

    2010-10-01

    Full Text Available A high degree of reliability for critical data transmission is required in body sensor networks (BSNs. However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.

  15. Adaptive Information Access in Multiple Applications Support Wireless Sensor Network

    DEFF Research Database (Denmark)

    Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee

    2012-01-01

    specific WSN considering its resource constraints, neglecting the return-of-investment and usefulness of the system. In this paper, we bring out the WSN scenario which supports multiple applications and study the challenges that would pose in implementation as each specific application has its own specific...... set of requirements. Lastly, the paper suggests a mechanism by which the information access or acquisition can be adapted as per the requirements of the application. The main parameters focused in this paper are mismatch probability [1] and power dissipation with respect to sampling rate....

  16. An Algorithm of Wireless Sensor Monitoring System

    Directory of Open Access Journals (Sweden)

    Li Hongri

    2018-01-01

    Full Text Available In order to realize more intelligent storage monitoring system, the information fusion model of wireless sensor network for storage environment monitoring is studied on the basis of analyzing information fusion technology. By analyzing the structure of storage monitoring system based on wireless sensor network, a two-layer information fusion method is established. The information fusion of homogeneous sensor based on adaptive weighting and the fusion method of heterogeneous sensor based on radial basis function neural network are designed and verified. The experimental results show that the design method can fuse the storage environment information and realize the accurate identification of the environmental state. Therefore, the algorithm can effectively improve the speed of network training, and the classification effect is good. To a certain extent, it can help enterprises to establish a safe and efficient storage system, to enhance the efficiency of enterprise warehousing operations.

  17. Communal Sensor Network for Adaptive Noise Reduction in Aircraft Engine Nacelles

    Science.gov (United States)

    Jones, Kennie H.; Nark, Douglas M.; Jones, Michael G.

    2011-01-01

    Emergent behavior, a subject of much research in biology, sociology, and economics, is a foundational element of Complex Systems Science and is apropos in the design of sensor network systems. To demonstrate engineering for emergent behavior, a novel approach in the design of a sensor/actuator network is presented maintaining optimal noise attenuation as an adaptation to changing acoustic conditions. Rather than use the conventional approach where sensors are managed by a central controller, this new paradigm uses a biomimetic model where sensor/actuators cooperate as a community of autonomous organisms, sharing with neighbors to control impedance based on local information. From the combination of all individual actions, an optimal attenuation emerges for the global system.

  18. A modified VMAT adaptive radiotherapy for nasopharyngeal cancer patients based on CT-CT image fusion

    International Nuclear Information System (INIS)

    Jin, Xiance; Han, Ce; Zhou, Yongqiang; Yi, Jinling; Yan, Huawei; Xie, Congying

    2013-01-01

    To investigate the feasibility and benefits of a modified adaptive radiotherapy (ART) by replanning in the initial CT (iCT) with new contours from a repeat CT (rCT) based on CT-CT image fusion for nasopharyngeal cancer (NPC) patients underwent volumetric modulated arc radiotherapy (VMAT). Nine NPC patients underwent VMAT treatment with a rCT at 23rd fraction were enrolled in this study. Dosimetric differences for replanning VMAT plans in the iCT and in the rCT were compared. Volumetric and dosimetric changes of gross tumor volume (GTV) and organs at risk (OARs) of this modified ART were also investigated. No dosimetric differences between replanning in the iCT and in the rCT were observed. The average volume of GTV decreased from 78.83 ± 38.42 cm 3 in the iCT to 71.44 ± 37.46 cm 3 in the rCT, but with no significant difference (p = 0.42).The average volume of the left and right parotid decreased from 19.91 ± 4.89 cm 3 and 21.58 ± 6.16 cm 3 in the iCT to 11.80 ± 2.79 cm 3 and 13.29 ± 4.17 cm 3 in the rCT (both p < 0.01), respectively. The volume of other OARs did not shrink very much. No significant differences on PTV GTV and PTV CTV coverage were observed for replanning with this modified ART. Compared to the initial plans, the average mean dose of the left and right parotid after re-optimization were decreased by 62.5 cGy (p = 0.05) and 67.3 cGy (p = 0.02), respectively, and the V5 (the volume receiving 5 Gy) of the left and right parotids were decreased by 7.8% (p = 0.01) and 11.2% (p = 0.001), respectively. There was no significant difference on the dose delivered to other OARs. Patients with NPC undergoing VMAT have significant anatomic and dosimetric changes to parotids. Repeat CT as an anatomic changes reference and re-optimization in the iCT based on CT-CT image fusion was accurate enough to identify the volume changes and to ensure safe dose to parotids

  19. Location-Based Self-Adaptive Routing Algorithm for Wireless Sensor Networks in Home Automation

    Directory of Open Access Journals (Sweden)

    Hong SeungHo

    2011-01-01

    Full Text Available The use of wireless sensor networks in home automation (WSNHA is attractive due to their characteristics of self-organization, high sensing fidelity, low cost, and potential for rapid deployment. Although the AODVjr routing algorithm in IEEE 802.15.4/ZigBee and other routing algorithms have been designed for wireless sensor networks, not all are suitable for WSNHA. In this paper, we propose a location-based self-adaptive routing algorithm for WSNHA called WSNHA-LBAR. It confines route discovery flooding to a cylindrical request zone, which reduces the routing overhead and decreases broadcast storm problems in the MAC layer. It also automatically adjusts the size of the request zone using a self-adaptive algorithm based on Bayes' theorem. This makes WSNHA-LBAR more adaptable to the changes of the network state and easier to implement. Simulation results show improved network reliability as well as reduced routing overhead.

  20. An Emergency-Adaptive Routing Scheme for Wireless Sensor Networks for Building Fire Hazard Monitoring

    Directory of Open Access Journals (Sweden)

    Guilin Zheng

    2011-03-01

    Full Text Available Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.

  1. An Emergency-Adaptive Routing Scheme for Wireless Sensor Networks for Building Fire Hazard Monitoring

    Directory of Open Access Journals (Sweden)

    Yuanyuan Zeng

    2010-06-01

    Full Text Available Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.

  2. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

    Science.gov (United States)

    Srbinovski, Bruno; Magno, Michele; Edwards-Murphy, Fiona; Pakrashi, Vikram; Popovici, Emanuel

    2016-01-01

    Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA. PMID:27043559

  3. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

    Directory of Open Access Journals (Sweden)

    Bruno Srbinovski

    2016-03-01

    Full Text Available Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind. Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources and power hungry sensors (ultrasonic wind sensor and gas sensors. The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

  4. Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection

    Directory of Open Access Journals (Sweden)

    Zhiwen Liu

    2015-08-01

    Full Text Available Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM based on an ant colony optimization algorithm (ACO-RVM for gearboxes’ fault detection. RVM is a sparse probability model based on support vector machine (SVM. RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV.

  5. Proceedings of the Thirteenth Annual Adaptive Sensor Array Processing Workshop. Held in Lexington, Massachusetts on 7-8 June 2005 (CD-ROM)

    National Research Council Canada - National Science Library

    Daneu, J; Musto, H

    2006-01-01

    ...: 1 CD-ROM; 4 3/4 in.; 283 MB. ABSTRACT: The thirteenth annual workshop on Adaptive Sensor Array Processing had sessions on adaptive detection and estimation, distributed sensing and tracking, adaptive SAR, and multi-channel adaptive radar...

  6. Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

    Directory of Open Access Journals (Sweden)

    Chengquan Zhou

    2018-02-01

    Full Text Available To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram–Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT, and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper.

  7. Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method.

    Science.gov (United States)

    Deng, Xinyang; Jiang, Wen

    2017-09-12

    Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model.

  8. A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis.

    Science.gov (United States)

    Xiao, Fuyuan

    2017-10-31

    The multi-sensor data fusion technique plays a significant role in fault diagnosis and in a variety of such applications, and the Dempster-Shafer evidence theory is employed to improve the system performance; whereas, it may generate a counter-intuitive result when the pieces of evidence highly conflict with each other. To handle this problem, a novel multi-sensor data fusion approach on the basis of the distance of evidence, belief entropy and fuzzy preference relation analysis is proposed. A function of evidence distance is first leveraged to measure the conflict degree among the pieces of evidence; thus, the support degree can be obtained to represent the reliability of the evidence. Next, the uncertainty of each piece of evidence is measured by means of the belief entropy. Based on the quantitative uncertainty measured above, the fuzzy preference relations are applied to represent the relative credibility preference of the evidence. Afterwards, the support degree of each piece of evidence is adjusted by taking advantage of the relative credibility preference of the evidence that can be utilized to generate an appropriate weight with respect to each piece of evidence. Finally, the modified weights of the evidence are adopted to adjust the bodies of the evidence in the advance of utilizing Dempster's combination rule. A numerical example and a practical application in fault diagnosis are used as illustrations to demonstrate that the proposal is reasonable and efficient in the management of conflict and fault diagnosis.

  9. Evaluation of Matrix Square Root Operations for UKF within a UAV GPS/INS Sensor Fusion Application

    Directory of Open Access Journals (Sweden)

    Matthew Rhudy

    2011-01-01

    Full Text Available Using an Unscented Kalman Filter (UKF as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF, was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV. The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.

  10. A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Fuyuan Xiao

    2017-10-01

    Full Text Available The multi-sensor data fusion technique plays a significant role in fault diagnosis and in a variety of such applications, and the Dempster–Shafer evidence theory is employed to improve the system performance; whereas, it may generate a counter-intuitive result when the pieces of evidence highly conflict with each other. To handle this problem, a novel multi-sensor data fusion approach on the basis of the distance of evidence, belief entropy and fuzzy preference relation analysis is proposed. A function of evidence distance is first leveraged to measure the conflict degree among the pieces of evidence; thus, the support degree can be obtained to represent the reliability of the evidence. Next, the uncertainty of each piece of evidence is measured by means of the belief entropy. Based on the quantitative uncertainty measured above, the fuzzy preference relations are applied to represent the relative credibility preference of the evidence. Afterwards, the support degree of each piece of evidence is adjusted by taking advantage of the relative credibility preference of the evidence that can be utilized to generate an appropriate weight with respect to each piece of evidence. Finally, the modified weights of the evidence are adopted to adjust the bodies of the evidence in the advance of utilizing Dempster’s combination rule. A numerical example and a practical application in fault diagnosis are used as illustrations to demonstrate that the proposal is reasonable and efficient in the management of conflict and fault diagnosis.

  11. Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yong Yang

    2014-11-01

    Full Text Available This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT. The Sum-Modified-Laplacian (SML-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs.

  12. Munc13-4 functions as a Ca2+ sensor for homotypic secretory granule fusion to generate endosomal exocytic vacuoles.

    Science.gov (United States)

    Woo, Sang Su; James, Declan J; Martin, Thomas F J

    2017-03-15

    Munc13-4 is a Ca 2+ -dependent SNARE (soluble N -ethylmaleimide-sensitive factor attachment protein receptor)- and phospholipid-binding protein that localizes to and primes secretory granules (SGs) for Ca 2+ -evoked secretion in various secretory cells. Studies in mast cell-like RBL-2H3 cells provide direct evidence that Munc13-4 with its two Ca 2+ -binding C2 domains functions as a Ca 2+ sensor for SG exocytosis. Unexpectedly, Ca 2+ stimulation also generated large (>2.4 μm in diameter) Munc13-4 + /Rab7 + /Rab11 + endosomal vacuoles. Vacuole generation involved the homotypic fusion of Munc13-4 + /Rab7 + SGs, followed by a merge with Rab11 + endosomes, and depended on Ca 2+ binding to Munc13-4. Munc13-4 promoted the Ca 2+ -stimulated fusion of VAMP8-containing liposomes with liposomes containing exocytic or endosomal Q-SNAREs and directly interacted with late endosomal SNARE complexes. Thus Munc13-4 is a tethering/priming factor and Ca 2+ sensor for both heterotypic SG-plasma membrane and homotypic SG-SG fusion. Total internal reflection fluorescence microscopy imaging revealed that vacuoles were exocytic and mediated secretion of β-hexosaminidase and cytokines accompanied by Munc13-4 diffusion onto the plasma membrane. The results provide new molecular insights into the mechanism of multigranular compound exocytosis commonly observed in various secretory cells. © 2017 Woo et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  13. Adaptive fusion of human visual sensitive features for surveillance video summarization.

    Science.gov (United States)

    Salehin, Md Musfequs; Paul, Manoranjan

    2017-05-01

    Surveillance video cameras capture large amounts of continuous video streams every day. To analyze or investigate any significant events, it is a laborious and boring job to identify these events from the huge video data if it is done manually. Existing approaches sometimes neglect key frames with significant visual contents and/or select some unimportant frames with low/no activity. To solve this problem, in this paper, a video summarization technique is proposed by combining three multimodal human visual sensitive features, such as foreground objects, motion information, and visual saliency. In a video stream, foreground objects are one of the most important pieces of a video as they contain more detailed information and play a major role in important events. Moreover, motion is another stimulus of a video that significantly attracts human visual attention. To obtain this, motion information is calculated in the spatial domain as well as the frequency domain. Spatial motion information can select object motion accurately; however, it is sensitive to illumination changes. On the other hand, frequency motion information is robust to illumination change, although it is easily affected by noise. Therefore, motion information in both the spatial and the frequency domains is employed. Furthermore, the visual attention cue is a sensitive feature to measure the indication of a user's attraction label for determining key frames. As these features individually cannot perform very well, they are combined to obtain better results. For this purpose, an adaptive linear weighted fusion scheme is proposed to combine the features to rank video frames for summarization. Experimental results reveal that the proposed method outperforms the state-of-the-art methods.

  14. Embedded pitch adapters: A high-yield interconnection solution for strip sensors

    Energy Technology Data Exchange (ETDEWEB)

    Ullán, M., E-mail: miguel.ullan@imb-cnm.csic.es [Centro Nacional de Microelectronica (IMB-CNM, CSIC), Campus UAB-Bellaterra, 08193 Barcelona (Spain); Allport, P.P.; Baca, M.; Broughton, J.; Chisholm, A.; Nikolopoulos, K.; Pyatt, S.; Thomas, J.P.; Wilson, J.A. [School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT (United Kingdom); Kierstead, J.; Kuczewski, P.; Lynn, D. [Brookhaven National Laboratory, Physics Department and Instrumentation Division, Upton, NY 11973-5000 (United States); Hommels, L.B.A. [Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE (United Kingdom); Fleta, C.; Fernandez-Tejero, J.; Quirion, D. [Centro Nacional de Microelectronica (IMB-CNM, CSIC), Campus UAB-Bellaterra, 08193 Barcelona (Spain); Bloch, I.; Díez, S.; Gregor, I.M.; Lohwasser, K. [DESY, Notkestrasse 85, 22607 Hamburg (Germany); and others

    2016-09-21

    A proposal to fabricate large area strip sensors with integrated, or embedded, pitch adapters is presented for the End-cap part of the Inner Tracker in the ATLAS experiment. To implement the embedded pitch adapters, a second metal layer is used in the sensor fabrication, for signal routing to the ASICs. Sensors with different embedded pitch adapters have been fabricated in order to optimize the design and technology. Inter-strip capacitance, noise, pick-up, cross-talk, signal efficiency, and fabrication yield have been taken into account in their design and fabrication. Inter-strip capacitance tests taking into account all channel neighbors reveal the important differences between the various designs considered. These tests have been correlated with noise figures obtained in full assembled modules, showing that the tests performed on the bare sensors are a valid tool to estimate the final noise in the full module. The full modules have been subjected to test beam experiments in order to evaluate the incidence of cross-talk, pick-up, and signal loss. The detailed analysis shows no indication of cross-talk or pick-up as no additional hits can be observed in any channel not being hit by the beam above 170 mV threshold, and the signal in those channels is always below 1% of the signal recorded in the channel being hit, above 100 mV threshold. First results on irradiated mini-sensors with embedded pitch adapters do not show any change in the interstrip capacitance measurements with only the first neighbors connected.

  15. Kernel-Based Sensor Fusion With Application to Audio-Visual Voice Activity Detection

    Science.gov (United States)

    Dov, David; Talmon, Ronen; Cohen, Israel

    2016-12-01

    In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences. Recent studies have approached this problem using kernel methods, by relying particularly on a product of kernels constructed separately for each view. From a graph theory point of view, we analyze this fusion approach in a discrete setting. More specifically, based on a statistical model for the connectivity between data points, we propose an algorithm for the selection of the kernel bandwidth, a parameter, which, as we show, has important implications on the robustness of this fusion approach to interferences. Then, we consider the fusion of audio-visual speech signals measured by a single microphone and by a video camera pointed to the face of the speaker. Specifically, we address the task of voice activity detection, i.e., the detection of speech and non-speech segments, in the presence of structured interferences such as keyboard taps and office noise. We propose an algorithm for voice activity detection based on the audio-visual signal. Simulation results show that the proposed algorithm outperforms competing fusion and voice activity detection approaches. In addition, we demonstrate that a proper selection of the kernel bandwidth indeed leads to improved performance.

  16. Testbeam Studies on Pick-Up in Sensors with Embedded Pitch Adapters

    CERN Document Server

    Rehnisch, Laura; The ATLAS collaboration

    2018-01-01

    Embedded pitch adapters are an alternative solution to external pitch adapters widely used to facilitate the wire-bonding step when connecting silicon strip sensors and readout electronics of different pitch. The pad-pitch adaption can be moved into the sensor fabrication step by implementing a second layer of metal tracks, connected by vias to the primary metal layer of sensor strips. Such a solution, however, might bear the risk of performance losses introduced by various phenomena. One of these effects, the undesired capacitive coupling between the silicon bulk and this second metal layer (pick-up) has been investigated in photon testbeam measurements. For a worst-case embedded pitch adapter design, expected to be maximally susceptible to pick-up, a qualitative analysis has visualized the effect as a function of the location on the second metal layer structure. It was further found that the unwanted effect decreases towards expected values for operating thresholds of the binary readout used. Suggestions fo...

  17. Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric

    Science.gov (United States)

    He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Chen, Ying

    2014-05-01

    A multimodal biometric system has been considered a promising technique to overcome the defects of unimodal biometric systems. We have introduced a fusion scheme to gain a better understanding and fusion method for a face-iris-fingerprint multimodal biometric system. In our case, we use particle swarm optimization to train a set of adaptive Gabor filters in order to achieve the proper Gabor basic functions for each modality. For a closer analysis of texture information, two different local Gabor features for each modality are produced by the corresponding Gabor coefficients. Next, all matching scores of the two Gabor features for each modality are projected to a single-scalar score via a trained, supported, vector regression model for a final decision. A large-scale dataset is formed to validate the proposed scheme using the Facial Recognition Technology database-fafb and CASIA-V3-Interval together with FVC2004-DB2a datasets. The experimental results demonstrate that as well as achieving further powerful local Gabor features of multimodalities and obtaining better recognition performance by their fusion strategy, our architecture also outperforms some state-of-the-art individual methods and other fusion approaches for face-iris-fingerprint multimodal biometric systems.

  18. Distributed service-based approach for sensor data fusion in IoT environments.

    Science.gov (United States)

    Rodríguez-Valenzuela, Sandra; Holgado-Terriza, Juan A; Gutiérrez-Guerrero, José M; Muros-Cobos, Jesús L

    2014-10-15

    The Internet of Things (IoT) enables the communication among smart objects promoting the pervasive presence around us of a variety of things or objects that are able to interact and cooperate jointly to reach common goals. IoT objects can obtain data from their context, such as the home, office, industry or body. These data can be combined to obtain new and more complex information applying data fusion processes. However, to apply data fusion algorithms in IoT environments, the full system must deal with distributed nodes, decentralized communication and support scalability and nodes dynamicity, among others restrictions. In this paper, a novel method to manage data acquisition and fusion based on a distributed service composition model is presented, improving the data treatment in IoT pervasive environments.

  19. Data fusion in multi sensor platforms for wide-area perception

    NARCIS (Netherlands)

    Polychronopoulos, A.; Floudas, N.; Amditis, A.; Bank, D.; Broek, S.P. van den

    2006-01-01

    there is a strong belief that the improvement of preventive safety applications and the extension of their operative range will be achieved by the deployment of multiple sensors with wide fields of view (FOV). The paper contributes to the solution of the problem and introduces distributed sensor

  20. Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

    Science.gov (United States)

    Matthews, Bryan L.; Srivastava, Ashok N.

    2010-01-01

    Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

  1. Hardware implementation of adaptive filtering using charge-coupled devices. [For perimeter security sensors

    Energy Technology Data Exchange (ETDEWEB)

    Donohoe, G.W.

    1977-01-01

    Sandia Laboratories' Digital Systems Division/1734, as part of its work on the Base and Installation Security Systems (BISS) program has been making use of adaptive digital filters to improve the signal-to-noise ratio of perimeter sensor signals. In particular, the Widrow-Hoff least-mean-squares algorithm has been used extensively. This non-recursive linear predictor has been successful in extracting aperiodic signals from periodic noise. The adaptive filter generates a predictor signal which is subtracted from the input signal to produce an error signal. The value of this error is fed back to the filter to improve the quality of the next prediction. Implementation of the Widrow adaptive filter using a Charge-Coupled Device tapped analog delay line, analog voltage multipliers and operational amplifiers is described. The resulting filter adapts to signals with frequency components as high as several megahertz.

  2. Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter.

    Science.gov (United States)

    Chowdhury, Amor; Sarjaš, Andrej

    2016-09-15

    The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.

  3. Combination Adaptive Traffic Algorithm and Coordinated Sleeping in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    M. Udin Harun Al Rasyid

    2014-12-01

    Full Text Available Wireless sensor network (WSN uses a battery as its primary power source, so that WSN will be limited to battery power for long operations. The WSN should be able to save the energy consumption in order to operate in a long time.WSN has the potential to be the future of wireless communications solutions. WSN are small but has a variety of functions that can help human life. WSN has the wide variety of sensors and can communicate quickly making it easier for people to obtain information accurately and quickly. In this study, we combine adaptive traffic algorithms and coordinated sleeping as power‐efficient WSN solution. We compared the performance of our proposed ideas combination adaptive traffic and coordinated sleeping algorithm with non‐adaptive scheme. From the simulation results, our proposed idea has good‐quality data transmission and more efficient in energy consumption, but it has higher delay than that of non‐adaptive scheme. Keywords:WSN,adaptive traffic,coordinated sleeping,beacon order,superframe order.

  4. An Adaptive Technique for a Redundant-Sensor Navigation System. Ph.D. Thesis

    Science.gov (United States)

    Chien, T. T.

    1972-01-01

    An on-line adaptive technique is developed to provide a self-contained redundant-sensor navigation system with a capability to utilize its full potentiality in reliability and performance. The gyro navigation system is modeled as a Gauss-Markov process, with degradation modes defined as changes in characteristics specified by parameters associated with the model. The adaptive system is formulated as a multistage stochastic process: (1) a detection system, (2) an identification system and (3) a compensation system. It is shown that the sufficient statistics for the partially observable process in the detection and identification system is the posterior measure of the state of degradation, conditioned on the measurement history.

  5. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiyuan Ma

    2018-03-01

    Full Text Available Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

  6. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Science.gov (United States)

    Luo, Guangchun; Qin, Ke; Wang, Nan; Niu, Weina

    2018-01-01

    Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy. PMID:29494543

  7. Empirical mode decomposition-adaptive least squares method for dynamic calibration of pressure sensors

    Science.gov (United States)

    Yao, Zhenjian; Wang, Zhongyu; Yi-Lin Forrest, Jeffrey; Wang, Qiyue; Lv, Jing

    2017-04-01

    In this paper, an approach combining empirical mode decomposition (EMD) with adaptive least squares (ALS) is proposed to improve the dynamic calibration accuracy of pressure sensors. With EMD, the original output of the sensor can be represented as sums of zero-mean amplitude modulation frequency modulation components. By identifying and excluding those components involved in noises, the noise-free output could be reconstructed with the useful frequency modulation ones. Then the least squares method is iteratively performed to estimate the optimal order and parameters of the mathematical model. The dynamic characteristic parameters of the sensor can be derived from the model in both time and frequency domains. A series of shock tube calibration tests are carried out to validate the performance of this method. Experimental results show that the proposed method works well in reducing the influence of noise and yields an appropriate mathematical model. Furthermore, comparative experiments also demonstrate the superiority of the proposed method over the existing ones.

  8. Adaptive Reliable Routing Based on Cluster Hierarchy for Wireless Multimedia Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chen Min

    2010-01-01

    Full Text Available As a multimedia information acquisition and processing method, wireless multimedia sensor network(WMSN has great application potential in military and civilian areas. Compared with traditional wireless sensor network, the routing design of WMSN should obtain more attention on the quality of transmission. This paper proposes an adaptive reliable routing based on clustering hierarchy named ARCH, which includes energy prediction and power allocation mechanism. To obtain a better performance, the cluster structure is formed based on cellular topology. The introduced prediction mechanism makes the sensor nodes predict the remaining energy of other nodes, which dramatically reduces the overall information needed for energy balancing. ARCH can dynamically balance the energy consumption of nodes based on the predicted results provided by power allocation. The simulation results prove the efficiency of the proposed ARCH routing.

  9. Adaptive Pulsed Laser Line Extraction for Terrain Reconstruction using a Dynamic Vision Sensor

    Directory of Open Access Journals (Sweden)

    Christian eBrandli

    2014-01-01

    Full Text Available Mobile robots need to know the terrain in which they are moving for path planning and obstacle avoidance. This paper proposes the combination of a bio-inspired, redundancy-suppressing dynamic vision sensor with a pulsed line laser to allow fast terrain reconstruction. A stable laser stripe extraction is achieved by exploiting the sensor’s ability to capture the temporal dynamics in a scene. An adaptive temporal filter for the sensor output allows a reliable reconstruction of 3D terrain surfaces. Laser stripe extractions up to pulsing frequencies of 500Hz were achieved using a line laser of 3mW at a distance of 45cm using an event-based algorithm that exploits the sparseness of the sensor output. As a proof of concept, unstructured rapid prototype terrain samples have been successfully reconstructed with an accuracy of 2mm.

  10. Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion

    International Nuclear Information System (INIS)

    Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

    2014-01-01

    Highlights: • To develop a novel instrumental intelligent test methodology for food sensory analysis. • A novel data fusion was used in instrumental intelligent test methodology. • Linear and nonlinear tools were comparatively used for modeling. • The instrumental test methodology can be imitative of human test behavior. - Abstract: Instrumental test of food quality using perception sensors instead of human panel test is attracting massive attention recently. A novel cross-perception multi-sensors data fusion imitating multiple mammal perception was proposed for the instrumental test in this work. First, three mimic sensors of electronic eye, electronic nose and electronic tongue were used in sequence for data acquisition of rice wine samples. Then all data from the three different sensors were preprocessed and merged. Next, three cross-perception variables i.e., color, aroma and taste, were constructed using principal components analysis (PCA) and multiple linear regression (MLR) which were used as the input of models. MLR, back-propagation artificial neural network (BPANN) and support vector machine (SVM) were comparatively used for modeling, and the instrumental test was achieved for the comprehensive quality of samples. Results showed the proposed cross-perception multi-sensors data fusion presented obvious superiority to the traditional data fusion methodologies, also achieved a high correlation coefficient (>90%) with the human panel test results. This work demonstrated that the instrumental test based on the cross-perception multi-sensors data fusion can actually mimic the human test behavior, therefore is of great significance to ensure the quality of products and decrease the loss of the manufacturers

  11. Cell-cycle dependent expression of a translocation-mediated fusion oncogene mediates checkpoint adaptation in rhabdomyosarcoma.

    Directory of Open Access Journals (Sweden)

    Ken Kikuchi

    2014-01-01

    Full Text Available Rhabdomyosarcoma is the most commonly occurring soft-tissue sarcoma in childhood. Most rhabdomyosarcoma falls into one of two biologically distinct subgroups represented by alveolar or embryonal histology. The alveolar subtype harbors a translocation-mediated PAX3:FOXO1A fusion gene and has an extremely poor prognosis. However, tumor cells have heterogeneous expression for the fusion gene. Using a conditional genetic mouse model as well as human tumor cell lines, we show that that Pax3:Foxo1a expression is enriched in G2 and triggers a transcriptional program conducive to checkpoint adaptation under stress conditions such as irradiation in vitro and in vivo. Pax3:Foxo1a also tolerizes tumor cells to clinically-established chemotherapy agents and emerging molecularly-targeted agents. Thus, the surprisingly dynamic regulation of the Pax3:Foxo1a locus is a paradigm that has important implications for the way in which oncogenes are modeled in cancer cells.

  12. New Low Cost Structure for Dual Axis Mount Solar Tracking System Using Adaptive Solar Sensor

    DEFF Research Database (Denmark)

    Argeseanu, Alin; Ritchie, Ewen; Leban, Krisztina Monika

    2010-01-01

    A solar tracking system is designed to optimize the operation of solar energy receivers. The objective of this paper is proposing a new tracking system structure with two axis. The success strategy of this new project focuses on the economical analysis of solar energy. Therefore it is important...... to determine the most cost effective design, to consider the costs of production and maintenance, and operating. The proposed tracking system uses a new solar sensor position with an adaptive feature....

  13. An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks

    Science.gov (United States)

    Abba, Sani; Lee, Jeong-A

    2015-01-01

    We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network. PMID:26295236

  14. An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks.

    Science.gov (United States)

    Abba, Sani; Lee, Jeong-A

    2015-08-18

    We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.

  15. Opponent-Color Fusion of Multi-Sensor Imagery: Visible, IR and SAR

    National Research Council Canada - National Science Library

    Waxman, A

    1998-01-01

    .... Building on the work reported in two of our earlier papers from IRIS Passive Sensors 1996, we show how opponent-color processing and center-surround shunting neural networks can be used to develop...

  16. A New Localization System for Indoor Service Robots in Low Luminance and Slippery Indoor Environment Using Afocal Optical Flow Sensor Based Sensor Fusion.

    Science.gov (United States)

    Yi, Dong-Hoon; Lee, Tae-Jae; Cho, Dong-Il Dan

    2018-01-10

    In this paper, a new localization system utilizing afocal optical flow sensor (AOFS) based sensor fusion for indoor service robots in low luminance and slippery environment is proposed, where conventional localization systems do not perform well. To accurately estimate the moving distance of a robot in a slippery environment, the robot was equipped with an AOFS along with two conventional wheel encoders. To estimate the orientation of the robot, we adopted a forward-viewing mono-camera and a gyroscope. In a very low luminance environment, it is hard to conduct conventional feature extraction and matching for localization. Instead, the interior space structure from an image and robot orientation was assessed. To enhance the appearance of image boundary, rolling guidance filter was applied after the histogram equalization. The proposed system was developed to be operable on a low-cost processor and implemented on a consumer robot. Experiments were conducted in low illumination condition of 0.1 lx and carpeted environment. The robot moved for 20 times in a 1.5 × 2.0 m square trajectory. When only wheel encoders and a gyroscope were used for robot localization, the maximum position error was 10.3 m and the maximum orientation error was 15.4°. Using the proposed system, the maximum position error and orientation error were found as 0.8 m and within 1.0°, respectively.

  17. A New Localization System for Indoor Service Robots in Low Luminance and Slippery Indoor Environment Using Afocal Optical Flow Sensor Based Sensor Fusion

    Directory of Open Access Journals (Sweden)

    Dong-Hoon Yi

    2018-01-01

    Full Text Available In this paper, a new localization system utilizing afocal optical flow sensor (AOFS based sensor fusion for indoor service robots in low luminance and slippery environment is proposed, where conventional localization systems do not perform well. To accurately estimate the moving distance of a robot in a slippery environment, the robot was equipped with an AOFS along with two conventional wheel encoders. To estimate the orientation of the robot, we adopted a forward-viewing mono-camera and a gyroscope. In a very low luminance environment, it is hard to conduct conventional feature extraction and matching for localization. Instead, the interior space structure from an image and robot orientation was assessed. To enhance the appearance of image boundary, rolling guidance filter was applied after the histogram equalization. The proposed system was developed to be operable on a low-cost processor and implemented on a consumer robot. Experiments were conducted in low illumination condition of 0.1 lx and carpeted environment. The robot moved for 20 times in a 1.5 × 2.0 m square trajectory. When only wheel encoders and a gyroscope were used for robot localization, the maximum position error was 10.3 m and the maximum orientation error was 15.4°. Using the proposed system, the maximum position error and orientation error were found as 0.8 m and within 1.0°, respectively.

  18. A Fusion Approach to Feature Extraction by Wavelet Decomposition and Principal Component Analysis in Transient Signal Processing of SAW Odor Sensor Array

    Directory of Open Access Journals (Sweden)

    Prashant SINGH

    2011-03-01

    Full Text Available This paper presents theoretical analysis of a new approach for development of surface acoustic wave (SAW sensor array based odor recognition system. The construction of sensor array employs a single polymer interface for selective sorption of odorant chemicals in vapor phase. The individual sensors are however coated with different thicknesses. The idea of sensor coating thickness variation is for terminating solvation and diffusion kinetics of vapors into polymer up to different stages of equilibration on different sensors. This is expected to generate diversity in information content of the sensors transient. The analysis is based on wavelet decomposition of transient signals. The single sensor transients have been used earlier for generating odor identity signatures based on wavelet approximation coefficients. In the present work, however, we exploit variability in diffusion kinetics due to polymer thicknesses for making odor signatures. This is done by fusion of the wavelet coefficients from different sensors in the array, and then applying the principal component analysis. We find that the present approach substantially enhances the vapor class separability in feature space. The validation is done by generating synthetic sensor array data based on well-established SAW sensor theory.

  19. Sensor data fusion for automated threat recognition in manned-unmanned infantry platoons

    Science.gov (United States)

    Wildt, J.; Varela, M.; Ulmke, M.; Brüggermann, B.

    2017-05-01

    To support a dismounted infantry platoon during deployment we team it with several unmanned aerial and ground vehicles (UAV and UGV, respectively). The unmanned systems integrate seamlessly into the infantry platoon, providing automated reconnaissance during movement while keeping formation as well as conducting close range reconnaissance during halt. The sensor data each unmanned system provides is continuously analyzed in real time by specialized algorithms, detecting humans in live videos of UAV mounted infrared cameras as well as gunshot detection and bearing by acoustic sensors. All recognized threats are fused into a consistent situational picture in real time, available to platoon and squad leaders as well as higher level command and control (C2) systems. This gives friendly forces local information superiority and increased situational awareness without the need to constantly monitor the unmanned systems and sensor data.

  20. Assessing sensor reliability for multisensor data fusion within the transferable belief model.

    Science.gov (United States)

    Elouedi, Zied; Mellouli, Khaled; Smets, Philippe

    2004-02-01

    This paper presents a method for assessing the reliability of a sensor in a classification problem based on the transferable belief model. First, we develop a method for the evaluation of the reliability of a sensor when considered alone. The method is based on finding the discounting factor minimizing the distance between the pignistic probabilities computed from the discounted beliefs and the actual values of data. Next, we develop a method for assessing the reliability of several sensors that are supposed to work jointly and their readings are aggregated. The discounting factors are computed on the basis of minimizing the distance between the pignistic probabilities computed from the combined discounted belief functions and the actual values of data.

  1. Particle Filter-Based Recursive Data Fusion With Sensor Indexing for Large Core Neutron Flux Estimation

    Science.gov (United States)

    Tamboli, Prakash Kumar; Duttagupta, Siddhartha P.; Roy, Kallol

    2017-06-01

    We introduce a sequential importance sampling particle filter (PF)-based multisensor multivariate nonlinear estimator for estimating the in-core neutron flux distribution for pressurized heavy water reactor core. Many critical applications such as reactor protection and control rely upon neutron flux information, and thus their reliability is of utmost importance. The point kinetic model based on neutron transport conveniently explains the dynamics of nuclear reactor. The neutron flux in the large core loosely coupled reactor is sensed by multiple sensors measuring point fluxes located at various locations inside the reactor core. The flux values are coupled to each other through diffusion equation. The coupling facilitates redundancy in the information. It is shown that multiple independent data about the localized flux can be fused together to enhance the estimation accuracy to a great extent. We also propose the sensor anomaly handling feature in multisensor PF to maintain the estimation process even when the sensor is faulty or generates data anomaly.

  2. A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

    Directory of Open Access Journals (Sweden)

    Leandro Vargas-Meléndez

    2016-08-01

    Full Text Available This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured from Inertial Measurement Unit (IMU sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors’ estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

  3. Adaptive Sensing with Reliable Guarantee under White Gaussian Noise Channels of Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jun Long

    2015-01-01

    Full Text Available Quality of sensing is a fundamental research topic in sensor networks. In this paper, we propose an adaptive sensing technique to guarantee the end-to-end reliability while maximizing the lifetime of sensor networks under additive white Gaussian noise channels. First, we conduct theoretical analysis to obtain optimal node number N∗, node placement d∗, and node transmission structure P∗ under minimum total energy consumption and minimum unit data transmission energy consumption. Then, because sensor nodes closer to the sink consume more energy, nodes far from the sink have more residual energy. Based on this observation, we propose an adaptive sensing technique to achieve balanced network energy consumption. It adopts lower reliability requirement and shorter transmission distance for nodes near the sink and adopts higher reliability requirement and farther transmission distance for nodes far from the sink. Theoretical analysis and experimental results show that our design can improve the network lifetime by several times (1–5 times and network utility by 20% and the desired reliability level is also guaranteed.

  4. A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

    Science.gov (United States)

    Gui, Jinsong; Zhou, Kai; Xiong, Naixue

    2016-01-01

    Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude. PMID:27681731

  5. A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms.

    Science.gov (United States)

    Caldas, Rafael; Mundt, Marion; Potthast, Wolfgang; Buarque de Lima Neto, Fernando; Markert, Bernd

    2017-09-01

    The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Camera-based platform and sensor motion tracking for data fusion in a landmine detection system

    NARCIS (Netherlands)

    Mark, W. van der; Heuvel, J.C. van den; Breejen, E. den; Groen, F.C.A.

    2003-01-01

    Vehicles that serve in the role as landmine detection robots could be an important tool for demining former conflict areas. On the LOTUS platform for humanitarian demining, different sensors are used to detect a wide range of landmine types. Reliable and accurate detection depends on correctly

  7. Performance evaluation of multi-sensor data-fusion systems in ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    mission target even under the dispersed flight environment. The navigation software loaded in the onboard computer uses the accelerometer data and rate sensed by the dry tuned gyro. From the sensor data, the processor computes the vehicle instantaneous attitude, position and velocity vectors with respect to an inertial ...

  8. A Navigation System for the Visually Impaired: A Fusion of Vision and Depth Sensor

    Science.gov (United States)

    Kanwal, Nadia; Bostanci, Erkan; Currie, Keith; Clark, Adrian F.

    2015-01-01

    For a number of years, scientists have been trying to develop aids that can make visually impaired people more independent and aware of their surroundings. Computer-based automatic navigation tools are one example of this, motivated by the increasing miniaturization of electronics and the improvement in processing power and sensing capabilities. This paper presents a complete navigation system based on low cost and physically unobtrusive sensors such as a camera and an infrared sensor. The system is based around corners and depth values from Kinect's infrared sensor. Obstacles are found in images from a camera using corner detection, while input from the depth sensor provides the corresponding distance. The combination is both efficient and robust. The system not only identifies hurdles but also suggests a safe path (if available) to the left or right side and tells the user to stop, move left, or move right. The system has been tested in real time by both blindfolded and blind people at different indoor and outdoor locations, demonstrating that it operates adequately. PMID:27057135

  9. Autonomous underwater vehicle motion tracking using a Kalman filter for sensor fusion

    CSIR Research Space (South Africa)

    Holtzhausen, S

    2008-11-01

    Full Text Available it will be shown how a Kalman Filter is used to estimate the position of an autonomous vehicle in a three dimensional space. The Kalman filter is used to estimate movement and position using measurements from multiple sensors...

  10. Autonomous underwater vehicle motion tracking using a Kalman Filter for sensor fusion

    CSIR Research Space (South Africa)

    Holtzhausen, S

    2008-01-01

    Full Text Available it will be shown how a Kalman Filter is used to estimate the position of an autonomous vehicle in a three dimensional space. The Kalman filter is used to estimate movement and position using measurements from multiple sensors...

  11. Real time implementation of adaptive sliding mode observer based speed sensor less vector control of induction motor

    Directory of Open Access Journals (Sweden)

    Negadi Karim

    2010-01-01

    Full Text Available Sensor less induction motor drives are widely used in industry for their reliability and flexibility. However, rotor flux and speed sensors are required for vector control of induction motor. These sensors are sources of trouble, mainly in hostile environments, and their application reduces the drive robustness. The cost of the sensors is not also negligible. All the reasons lead to development of different sensor less methods for rotor flux and mechanical speed estimation in electrical drives. The paper deals with the speed estimators for applications in sensor less induction motor drive with vector control, which are based on application of model adaptive, based sliding mode observer methods. This paper presents the development and DSP implementation of the speed estimators for applications in sensor less drives with induction motor.

  12. Noise modeling and analysis of an IMU-based attitude sensor: improvement of performance by filtering and sensor fusion

    Science.gov (United States)

    K., Nirmal; A. G., Sreejith; Mathew, Joice; Sarpotdar, Mayuresh; Suresh, Ambily; Prakash, Ajin; Safonova, Margarita; Murthy, Jayant

    2016-07-01

    We describe the characterization and removal of noises present in the Inertial Measurement Unit (IMU) MPU- 6050, which was initially used in an attitude sensor, and later used in the development of a pointing system for small balloon-borne astronomical payloads. We found that the performance of the IMU degraded with time because of the accumulation of different errors. Using Allan variance analysis method, we identified the different components of noise present in the IMU, and verified the results by the power spectral density analysis (PSD). We tried to remove the high-frequency noise using smooth filters such as moving average filter and then Savitzky Golay (SG) filter. Even though we managed to filter some high-frequency noise, these filters performance wasn't satisfactory for our application. We found the distribution of the random noise present in IMU using probability density analysis and identified that the noise in our IMU was white Gaussian in nature. Hence, we used a Kalman filter to remove the noise and which gave us good performance real time.

  13. Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Juan Feng

    2016-01-01

    Full Text Available In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy.

  14. Towards adaptive security for convergent wireless sensor networks in beyond 3G environments

    DEFF Research Database (Denmark)

    Mitseva, Anelia; Aivaloglou, Efthimia; Marchitti, Maria-Antonietta

    2010-01-01

    The integration of wireless sensor networks with different network systems gives rise to many research challenges to ensure security, privacy and trust in the overall architecture. The main contribution of this paper is a generic security, privacy and trust framework providing context......-aware adaptability, flexibility and scalability which allows customisation of wireless sensor networks to a diverse set of application spaces. Suitable protocols and mechanisms are identified, which when combined according to the framework form a complete toolbox solution which fits the architecture of Beyond 3G...... environments. Performance evaluation results demonstrate the feasibility and estimate the benefits of the security framework for a variety of scenarios. Copyright (C) 2008 John Wiley & Sons, Ltd....

  15. A Modified Adaptive Stochastic Resonance for Detecting Faint Signal in Sensors

    Directory of Open Access Journals (Sweden)

    Hengwei Li

    2007-02-01

    Full Text Available In this paper, an approach is presented to detect faint signals with strong noises in sensors by stochastic resonance (SR. We adopt the power spectrum as the evaluation tool of SR, which can be obtained by the fast Fourier transform (FFT. Furthermore, we introduce the adaptive filtering scheme to realize signal processing automatically. The key of the scheme is how to adjust the barrier height to satisfy the optimal condition of SR in the presence of any input. For the given input signal, we present an operable procedure to execute the adjustment scheme. An example utilizing one audio sensor to detect the fault information from the power supply is given. Simulation results show that th

  16. Image sensor system with bio-inspired efficient coding and adaptation.

    Science.gov (United States)

    Okuno, Hirotsugu; Yagi, Tetsuya

    2012-08-01

    We designed and implemented an image sensor system equipped with three bio-inspired coding and adaptation strategies: logarithmic transform, local average subtraction, and feedback gain control. The system comprises a field-programmable gate array (FPGA), a resistive network, and active pixel sensors (APS), whose light intensity-voltage characteristics are controllable. The system employs multiple time-varying reset voltage signals for APS in order to realize multiple logarithmic intensity-voltage characteristics, which are controlled so that the entropy of the output image is maximized. The system also employs local average subtraction and gain control in order to obtain images with an appropriate contrast. The local average is calculated by the resistive network instantaneously. The designed system was successfully used to obtain appropriate images of objects that were subjected to large changes in illumination.

  17. QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding

    Directory of Open Access Journals (Sweden)

    Mohammad Abdur Razzaque

    2014-12-01

    Full Text Available Wireless body sensor networks (WBSNs for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS, in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network’s QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.

  18. QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding

    Science.gov (United States)

    Razzaque, Mohammad Abdur; Javadi, Saeideh S.; Coulibaly, Yahaya; Hira, Muta Tah

    2015-01-01

    Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network's QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts. PMID:25551485

  19. Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

    Directory of Open Access Journals (Sweden)

    Shifei Liu

    2015-01-01

    Full Text Available The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS in an innovative extended Kalman filter (EKF design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a significant improvement in the vehicle’s integrated navigation accuracy.

  20. Biologically-inspired robust and adaptive multi-sensor fusion and active control

    Science.gov (United States)

    Khosla, Deepak; Dow, Paul A.; Huber, David J.

    2009-04-01

    In this paper, we describe a method and system for robust and efficient goal-oriented active control of a machine (e.g., robot) based on processing, hierarchical spatial understanding, representation and memory of multimodal sensory inputs. This work assumes that a high-level plan or goal is known a priori or is provided by an operator interface, which translates into an overall perceptual processing strategy for the machine. Its analogy to the human brain is the download of plans and decisions from the pre-frontal cortex into various perceptual working memories as a perceptual plan that then guides the sensory data collection and processing. For example, a goal might be to look for specific colored objects in a scene while also looking for specific sound sources. This paper combines three key ideas and methods into a single closed-loop active control system. (1) Use high-level plan or goal to determine and prioritize spatial locations or waypoints (targets) in multimodal sensory space; (2) collect/store information about these spatial locations at the appropriate hierarchy and representation in a spatial working memory. This includes invariant learning of these spatial representations and how to convert between them; and (3) execute actions based on ordered retrieval of these spatial locations from hierarchical spatial working memory and using the "right" level of representation that can efficiently translate into motor actions. In its most specific form, the active control is described for a vision system (such as a pantilt- zoom camera system mounted on a robotic head and neck unit) which finds and then fixates on high saliency visual objects. We also describe the approach where the goal is to turn towards and sequentially foveate on salient multimodal cues that include both visual and auditory inputs.

  1. Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application

    Directory of Open Access Journals (Sweden)

    Sara Saeedi

    2015-08-01

    Full Text Available Low-cost inertial and motion sensors embedded on smartphones have provided a new platform for dynamic activity pattern inference. In this research, a comparison has been conducted on different sensor data, feature spaces and feature selection methods to increase the efficiency and reduce the computation cost of activity recognition on the smartphones. We evaluated a variety of feature spaces and a number of classification algorithms from the area of Machine Learning, including Naive Bayes, Decision Trees, Artificial Neural Networks and Support Vector Machine classifiers. A smartphone app that performs activity recognition is being developed to collect data and send them to a server for activity recognition. Using extensive experiments, the performance of various feature spaces has been evaluated. The results showed that the Bayesian Network classifier yields recognition accuracy of 96.21% using four features while requiring fewer computations.

  2. Data-Fusion for a Vision-Aided Radiological Detection System: Sensor dependence and Source Tracking

    Science.gov (United States)

    Stadnikia, Kelsey; Martin, Allan; Henderson, Kristofer; Koppal, Sanjeev; Enqvist, Andreas

    2018-01-01

    The University of Florida is taking a multidisciplinary approach to fuse the data between 3D vision sensors and radiological sensors in hopes of creating a system capable of not only detecting the presence of a radiological threat, but also tracking it. The key to developing such a vision-aided radiological detection system, lies in the count rate being inversely dependent on the square of the distance. Presented in this paper are the results of the calibration algorithm used to predict the location of the radiological detectors based on 3D distance from the source to the detector (vision data) and the detectors count rate (radiological data). Also presented are the results of two correlation methods used to explore source tracking.

  3. POSE ESTIMATION OF UNMANNED AERIAL VEHICLES BASED ON A VISION-AIDED MULTI-SENSOR FUSION

    Directory of Open Access Journals (Sweden)

    G. Abdi

    2016-06-01

    Full Text Available GNSS/IMU navigation systems offer low-cost and robust solution to navigate UAVs. Since redundant measurements greatly improve the reliability of navigation systems, extensive researches have been made to enhance the efficiency and robustness of GNSS/IMU by additional sensors. This paper presents a method for integrating reference data, images taken from UAVs, barometric height data and GNSS/IMU data to estimate accurate and reliable pose parameters of UAVs. We provide improved pose estimations by integrating multi-sensor observations in an EKF algorithm with IMU motion model. The implemented methodology has demonstrated to be very efficient and reliable for automatic pose estimation. The calculated position and attitude of the UAV especially when we removed the GNSS from the working cycle clearly indicate the ability of the purposed methodology.

  4. Adaptive Opportunistic Cooperative Control Mechanism Based on Combination Forecasting and Multilevel Sensing Technology of Sensors for Mobile Internet of Things

    Directory of Open Access Journals (Sweden)

    Yong Jin

    2014-01-01

    Full Text Available In mobile Internet of Things, there are many challenges, including sensing technology of sensors, how and when to join cooperative transmission, and how to select the cooperative sensors. To address these problems, we studied the combination forecasting based on the multilevel sensing technology of sensors, building upon which we proposed the adaptive opportunistic cooperative control mechanism based on the threshold values such as activity probability, distance, transmitting power, and number of relay sensors, in consideration of signal to noise ratio and outage probability. More importantly, the relay sensors would do self-test real time in order to judge whether to join the cooperative transmission, for maintaining the optimal cooperative transmission state with high performance. The mathematical analyses results show that the proposed adaptive opportunistic cooperative control approach could perform better in terms of throughput ratio, packet error rate and delay, and energy efficiency, compared with the direct transmission and opportunistic cooperative approaches.

  5. Multi-Sensor Image Fusion for Target Recognition in the Environment of Network Decision Support Systems

    Science.gov (United States)

    2015-12-01

    healing network operates at the data-link layer, which is the second layer of the Open Systems Interconnection ( OSI ) seven-layer model , as shown in...the network layer, the third layer of the OSI seven-layer model , techniques, and protocols. In this way, distributed groups, as shown in Figure 14...55 III. SIMULATION MODELING OF ATR THROUGH A MESH NETWORK OF IMAGING SENSORS

  6. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

    Science.gov (United States)

    Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Coupled sensor/platform control design for low-level chemical detection with position-adaptive micro-UAVs

    Science.gov (United States)

    Goodwin, Thomas; Carr, Ryan; Mitra, Atindra K.; Selmic, Rastko R.

    2009-05-01

    We discuss the development of Position-Adaptive Sensors [1] for purposes for detecting embedded chemical substances in challenging environments. This concept is a generalization of patented Position-Adaptive Radar Concepts developed at AFRL for challenging conditions such as urban environments. For purposes of investigating the detection of chemical substances using multiple MAV (Micro-UAV) platforms, we have designed and implemented an experimental testbed with sample structures such as wooden carts that contain controlled leakage points. Under this general concept, some of the members of a MAV swarm can serve as external position-adaptive "transmitters" by blowing air over the cart and some of the members of a MAV swarm can serve as external position-adaptive "receivers" that are equipped with chemical or biological (chem/bio) sensors that function as "electronic noses". The objective can be defined as improving the particle count of chem/bio concentrations that impinge on a MAV-based position-adaptive sensor that surrounds a chemical repository, such as a cart, via the development of intelligent position-adaptive control algorithms. The overall effect is to improve the detection and false-alarm statistics of the overall system. Within the major sections of this paper, we discuss a number of different aspects of developing our initial MAV-Based Sensor Testbed. This testbed includes blowers to simulate position-adaptive excitations and a MAV from Draganfly Innovations Inc. with stable design modifications to accommodate our chem/bio sensor boom design. We include details with respect to several critical phases of the development effort including development of the wireless sensor network and experimental apparatus, development of the stable sensor boom for the MAV, integration of chem/bio sensors and sensor node onto the MAV and boom, development of position-adaptive control algorithms and initial tests at IDCAST (Institute for the Development and

  8. 3D-information fusion from very high resolution satellite sensors

    Science.gov (United States)

    Krauss, T.; d'Angelo, P.; Kuschk, G.; Tian, J.; Partovi, T.

    2015-04-01

    In this paper we show the pre-processing and potential for environmental applications of very high resolution (VHR) satellite stereo imagery like these from WorldView-2 or Pl'eiades with ground sampling distances (GSD) of half a metre to a metre. To process such data first a dense digital surface model (DSM) has to be generated. Afterwards from this a digital terrain model (DTM) representing the ground and a so called normalized digital elevation model (nDEM) representing off-ground objects are derived. Combining these elevation based data with a spectral classification allows detection and extraction of objects from the satellite scenes. Beside the object extraction also the DSM and DTM can directly be used for simulation and monitoring of environmental issues. Examples are the simulation of floodings, building-volume and people estimation, simulation of noise from roads, wave-propagation for cellphones, wind and light for estimating renewable energy sources, 3D change detection, earthquake preparedness and crisis relief, urban development and sprawl of informal settlements and much more. Also outside of urban areas volume information brings literally a new dimension to earth oberservation tasks like the volume estimations of forests and illegal logging, volume of (illegal) open pit mining activities, estimation of flooding or tsunami risks, dike planning, etc. In this paper we present the preprocessing from the original level-1 satellite data to digital surface models (DSMs), corresponding VHR ortho images and derived digital terrain models (DTMs). From these components we present how a monitoring and decision fusion based 3D change detection can be realized by using different acquisitions. The results are analyzed and assessed to derive quality parameters for the presented method. Finally the usability of 3D information fusion from VHR satellite imagery is discussed and evaluated.

  9. A Least Square-Based Self-Adaptive Localization Method for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Baoguo Yu

    2016-01-01

    Full Text Available In the wireless sensor network (WSN localization methods based on Received Signal Strength Indicator (RSSI, it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.

  10. Shack-Hartmann wavefront-sensor-based adaptive optics system for multiphoton microscopy.

    Science.gov (United States)

    Cha, Jae Won; Ballesta, Jerome; So, Peter T C

    2010-01-01

    The imaging depth of two-photon excitation fluorescence microscopy is partly limited by the inhomogeneity of the refractive index in biological specimens. This inhomogeneity results in a distortion of the wavefront of the excitation light. This wavefront distortion results in image resolution degradation and lower signal level. Using an adaptive optics system consisting of a Shack-Hartmann wavefront sensor and a deformable mirror, wavefront distortion can be measured and corrected. With adaptive optics compensation, we demonstrate that the resolution and signal level can be better preserved at greater imaging depth in a variety of ex-vivo tissue specimens including mouse tongue muscle, heart muscle, and brain. However, for these highly scattering tissues, we find signal degradation due to scattering to be a more dominant factor than aberration.

  11. LPTA: Location Predictive and Time Adaptive Data Gathering Scheme with Mobile Sink for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chuan Zhu

    2014-01-01

    Full Text Available This paper exploits sink mobility to prolong the lifetime of sensor networks while maintaining the data transmission delay relatively low. A location predictive and time adaptive data gathering scheme is proposed. In this paper, we introduce a sink location prediction principle based on loose time synchronization and deduce the time-location formulas of the mobile sink. According to local clocks and the time-location formulas of the mobile sink, nodes in the network are able to calculate the current location of the mobile sink accurately and route data packets timely toward the mobile sink by multihop relay. Considering that data packets generating from different areas may be different greatly, an adaptive dwelling time adjustment method is also proposed to balance energy consumption among nodes in the network. Simulation results show that our data gathering scheme enables data routing with less data transmission time delay and balance energy consumption among nodes.

  12. Robot path Planning Using  SIFT and Sonar Sensor Fusion

    DEFF Research Database (Denmark)

    Plascencia, Alfredo; Raposo, Hector

    2007-01-01

    This paper presents a novel map building approach for path planning purposes, which takes into account the uncertainty inherent in sensor measurements. To this end, Bayesian estimation and Dempster-Shafer evidential theory are used to fuse the sensory information and to update the occupancy...... are used for path planning using the potential field method. Both yield satisfying results...... and evidential grid maps, respectively. The approach is illustrated using actual measurements from a laboratory robot. The sensory information is obtained from a sonar array and the Scale Invariant Feature Transform (SIFT) algorithm. Finally, the resulting two evidential maps based on Bayes and Dempster theories...

  13. An Efficient and Self-Adapting Localization in Static Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Wei Dong

    2009-08-01

    Full Text Available Localization is one of the most important subjects in Wireless Sensor Networks (WSNs. To reduce the number of beacons and adopt probabilistic methods, some particle filter-based mobile beacon-assisted localization approaches have been proposed, such as Mobile Beacon-assisted Localization (MBL, Adapting MBL (A-MBL, and the method proposed by Hang et al. Some new significant problems arise in these approaches, however. The first question is which probability distribution should be selected as the dynamic model in the prediction stage. The second is whether the unknown node adopts neighbors’ observation in the update stage. The third is how to find a self-adapting mechanism to achieve more flexibility in the adapting stage. In this paper, we give the theoretical analysis and experimental evaluations to suggest which probability distribution in the dynamic model should be adopted to improve the efficiency in the prediction stage. We also give the condition for whether the unknown node should use the observations from its neighbors to improve the accuracy. Finally, we propose a Self-Adapting Mobile Beacon-assisted Localization (SA-MBL approach to achieve more flexibility and achieve almost the same performance with A-MBL.

  14. Sensor Fusion and Autonomy as a Powerful Combination for Biological Assessment in the Marine Environment

    Directory of Open Access Journals (Sweden)

    Mark A. Moline

    2016-02-01

    Full Text Available The ocean environment and the physical and biological processes that govern dynamics are complex. Sampling the ocean to better understand these processes is difficult given the temporal and spatial domains and sampling tools available. Biological systems are especially difficult as organisms possess behavior, operate at horizontal scales smaller than traditional shipboard sampling allows, and are often disturbed by the sampling platforms themselves. Sensors that measure biological processes have also generally not kept pace with the development of physical counterparts as their requirements are as complex as the target organisms. Here, we attempt to address this challenge by advocating the need for sensor-platform combinations to integrate and process data in real-time and develop data products that are useful in increasing sampling efficiencies. Too often, the data of interest is only garnered after post-processing after a sampling effort and the opportunity to use that information to guide sampling is lost. Here we demonstrate a new autonomous platform, where data are collected, analyzed, and data products are output in real-time to inform autonomous decision-making. This integrated capability allows for enhanced and informed sampling towards improving our understanding of the marine environment.

  15. Multi-sensor fusion over the World Trade Center disaster site

    Science.gov (United States)

    Rodarmel, Craig; Scott, Lawrence; Simerlink, Deborah A.; Walker, Jeffrey

    2002-09-01

    The immense size and scope of the rescue and clean-up of the World Trade Center site created a need for data that would provide a total overview of the disaster area. To fulfill this need, the New York State Office for Technology (NYSOFT) contracted with EarthData International to collect airborne remote sensing data over Ground Zero with an airborne light detection and ranging (LIDAR) sensor, a high-resolution digital camera, and a thermal camera. The LIDAR data provided a three-dimensional elevation model of the ground surface that was used for volumetric calculations and also in the orthorectification of the digital images. The digital camera provided high-resolution imagery over the site to aide the rescuers in placement of equipment and other assets. In addition, the digital imagery was used to georeference the thermal imagery and also provided the visual background for the thermal data. The thermal camera aided in the location and tracking of underground fires. The combination of data from these three sensors provided the emergency crews with a timely, accurate overview containing a wealth of information of the rapidly changing disaster site. Because of the dynamic nature of the site, the data was acquired on a daily basis, processed, and turned over to NYSOFT within twelve hours of the collection. During processing, the three datasets were combined and georeferenced to allow them to be inserted into the client's geographic information systems.

  16. Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices

    Directory of Open Access Journals (Sweden)

    John J. Guiry

    2014-03-01

    Full Text Available In this paper, the authors investigate the role that smart devices, including smartphones and smartwatches, can play in identifying activities of daily living. A feasibility study involving N = 10 participants was carried out to evaluate the devices’ ability to differentiate between nine everyday activities. The activities examined include walking, running, cycling, standing, sitting, elevator ascents, elevator descents, stair ascents and stair descents. The authors also evaluated the ability of these devices to differentiate indoors from outdoors, with the aim of enhancing contextual awareness. Data from this study was used to train and test five well known machine learning algorithms: C4.5, CART, Naïve Bayes, Multi-Layer Perceptrons and finally Support Vector Machines. Both single and multi-sensor approaches were examined to better understand the role each sensor in the device can play in unobtrusive activity recognition. The authors found overall results to be promising, with some models correctly classifying up to 100% of all instances.

  17. Measuring indoor occupancy in intelligent buildings using the fusion of vision sensors

    International Nuclear Information System (INIS)

    Liu, Dixin; Guan, Xiaohong; Du, Youtian; Zhao, Qianchuan

    2013-01-01

    In intelligent buildings, practical sensing systems designed to gather indoor occupancy information play an indispensable role in improving occupant comfort and energy efficiency. In this paper, we propose a novel method for occupancy measurement based on the video surveillance now widely used in buildings. In our method, we analyze occupant detection both at the entrance and inside the room. A two-stage static detector is presented based on both appearances and shapes to find the human heads in rooms, and motion-based technology is used for occupant detection at the entrance. To model the change of occupancy and combine the detection results from multiple vision sensors located at entrances and inside rooms for more accurate occupancy estimation, we propose a dynamic Bayesian network-based method. The detection results of each vision sensor play the role of evidence nodes of this network, and thus, we can estimate the true occupancy at time t using the evidence prior to (and including) time t. Experimental results demonstrate the effectiveness and efficiency of the proposed method. (paper)

  18. Soft sensor modelling by time difference, recursive partial least squares and adaptive model updating

    International Nuclear Information System (INIS)

    Fu, Y; Xu, O; Yang, W; Zhou, L; Wang, J

    2017-01-01

    To investigate time-variant and nonlinear characteristics in industrial processes, a soft sensor modelling method based on time difference, moving-window recursive partial least square (PLS) and adaptive model updating is proposed. In this method, time difference values of input and output variables are used as training samples to construct the model, which can reduce the effects of the nonlinear characteristic on modelling accuracy and retain the advantages of recursive PLS algorithm. To solve the high updating frequency of the model, a confidence value is introduced, which can be updated adaptively according to the results of the model performance assessment. Once the confidence value is updated, the model can be updated. The proposed method has been used to predict the 4-carboxy-benz-aldehyde (CBA) content in the purified terephthalic acid (PTA) oxidation reaction process. The results show that the proposed soft sensor modelling method can reduce computation effectively, improve prediction accuracy by making use of process information and reflect the process characteristics accurately. (paper)

  19. Adaptive threshold determination for efficient channel sensing in cognitive radio network using mobile sensors

    Science.gov (United States)

    Morshed, M. N.; Khatun, S.; Kamarudin, L. M.; Aljunid, S. A.; Ahmad, R. B.; Zakaria, A.; Fakir, M. M.

    2017-03-01

    Spectrum saturation problem is a major issue in wireless communication systems all over the world. Huge number of users is joining each day to the existing fixed band frequency but the bandwidth is not increasing. These requirements demand for efficient and intelligent use of spectrum. To solve this issue, the Cognitive Radio (CR) is the best choice. Spectrum sensing of a wireless heterogeneous network is a fundamental issue to detect the presence of primary users' signals in CR networks. In order to protect primary users (PUs) from harmful interference, the spectrum sensing scheme is required to perform well even in low signal-to-noise ratio (SNR) environments. Meanwhile, the sensing period is usually required to be short enough so that secondary (unlicensed) users (SUs) can fully utilize the available spectrum. CR networks can be designed to manage the radio spectrum more efficiently by utilizing the spectrum holes in primary user's licensed frequency bands. In this paper, we have proposed an adaptive threshold detection method to detect presence of PU signal using free space path loss (FSPL) model in 2.4 GHz WLAN network. The model is designed for mobile sensors embedded in smartphones. The mobile sensors acts as SU while the existing WLAN network (channels) works as PU. The theoretical results show that the desired threshold range detection of mobile sensors mainly depends on the noise floor level of the location in consideration.

  20. PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras.

    Science.gov (United States)

    Zheng, Lei; Lukac, Rastislav; Wu, Xiaolin; Zhang, David

    2009-04-01

    Single-sensor digital color cameras use a process called color demosiacking to produce full color images from the data captured by a color filter array (CAF). The quality of demosiacked images is degraded due to the sensor noise introduced during the image acquisition process. The conventional solution to combating CFA sensor noise is demosiacking first, followed by a separate denoising processing. This strategy will generate many noise-caused color artifacts in the demosiacking process, which are hard to remove in the denoising process. Few denoising schemes that work directly on the CFA images have been presented because of the difficulties arisen from the red, green and blue interlaced mosaic pattern, yet a well-designed "denoising first and demosiacking later" scheme can have advantages such as less noise-caused color artifacts and cost-effective implementation. This paper presents a principle component analysis (PCA)-based spatially-adaptive denoising algorithm, which works directly on the CFA data using a supporting window to analyze the local image statistics. By exploiting the spatial and spectral correlations existing in the CFA image, the proposed method can effectively suppress noise while preserving color edges and details. Experiments using both simulated and real CFA images indicate that the proposed scheme outperforms many existing approaches, including those sophisticated demosiacking and denoising schemes, in terms of both objective measurement and visual evaluation.

  1. Rapid and highly integrated FPGA-based Shack-Hartmann wavefront sensor for adaptive optics system

    Science.gov (United States)

    Chen, Yi-Pin; Chang, Chia-Yuan; Chen, Shean-Jen

    2018-02-01

    In this study, a field programmable gate array (FPGA)-based Shack-Hartmann wavefront sensor (SHWS) programmed on LabVIEW can be highly integrated into customized applications such as adaptive optics system (AOS) for performing real-time wavefront measurement. Further, a Camera Link frame grabber embedded with FPGA is adopted to enhance the sensor speed reacting to variation considering its advantage of the highest data transmission bandwidth. Instead of waiting for a frame image to be captured by the FPGA, the Shack-Hartmann algorithm are implemented in parallel processing blocks design and let the image data transmission synchronize with the wavefront reconstruction. On the other hand, we design a mechanism to control the deformable mirror in the same FPGA and verify the Shack-Hartmann sensor speed by controlling the frequency of the deformable mirror dynamic surface deformation. Currently, this FPGAbead SHWS design can achieve a 266 Hz cyclic speed limited by the camera frame rate as well as leaves 40% logic slices for additionally flexible design.

  2. An Adaptive Time-Spread Multiple-Access Policy for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Konstantinos Oikonomou

    2007-05-01

    Full Text Available Sensor networks require a simple and efficient medium access control policy achieving high system throughput with no or limited control overhead in order to increase the network lifetime by minimizing the energy consumed during transmission attempts. Time-spread multiple-access (TSMA policies that have been proposed for ad hoc network environments, can also be employed in sensor networks, since no control overhead is introduced. However, they do not take advantage of any cross-layer information in order to exploit the idiosyncrasies of the particular sensor network environment such as the presence of typically static nodes and a common destination for the forwarded data. An adaptive probabilistic TSMA-based policy, that is proposed and analyzed in this paper, exploits these idiosyncrasies and achieves higher system throughput than the existing TSMA-based policies without any need for extra control overhead. As it is analytically shown in this paper, the proposed policy always outperforms the existing TSMA-based policies, if certain parameter values are properly set; the analysis also provides for these proper values. It is also shown that the proposed policy is characterized by a certain convergence period and that high system throughput is achieved for long convergence periods. The claims and expectations of the provided analysis are supported by simulation results presented in this paper.

  3. Energy Efficient Fuzzy Adaptive Verification Node Selection-Based Path Determination in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Muhammad Akram

    2017-10-01

    Full Text Available Wireless sensor networks are supplied with limited energy resources and are usually installed in unattended and unfriendly environments. These networks are also highly exposed to security attacks aimed at draining the energy of the network to render it unresponsive. Adversaries launch counterfeit report injection attacks and false vote injection attacks through compromised sensor nodes. Several filtering solutions have been suggested for detecting and filtering false reports during the multi-hop forwarding process. However, almost all such schemes presuppose a conventional underlying protocol for data routing that do not consider the attack status or energy dissipation on the route. Each design provides approximately the equivalent resilience in terms of protection against compromised node. However, the energy consumption characteristics of each design differ. We propose a fuzzy adaptive path selection to save energy and avoid the emergence of favored paths. Fresh authentication keys are generated periodically, and these are shared with the filtering nodes to restrict compromised intermediate filtering nodes from the verification process. The scheme helps delay the emergence of hotspot problems near the base station and exhibits improved energy conserving behavior in wireless sensor networks. The proposed scheme provides an extended network lifetime and better false data filtering capacity.

  4. Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors

    Directory of Open Access Journals (Sweden)

    Yanran Li

    2017-03-01

    Full Text Available Quantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement units (IMUs and surface electromyography (EMG sensors. With wearable sensors worn on the tested upper limbs, subjects were asked to perform eleven straightforward, specifically designed canonical upper-limb functional tasks. A series of machine learning algorithms were applied to the recorded motion data to produce evaluation indicators, which is able to reflect the level of upper-limb motor function abnormality. Sixteen healthy subjects and eighteen stroke subjects with substantial hemiparesis were recruited in the experiment. The combined IMU and EMG data yielded superior performance over the IMU data alone and the EMG data alone, in terms of decreased normal data variation rate (NDVR and improved determination coefficient (DC from a regression analysis between the derived indicator and routine clinical assessment score. Three common unsupervised learning algorithms achieved comparable performance with NDVR around 10% and strong DC around 0.85. By contrast, the use of a supervised algorithm was able to dramatically decrease the NDVR to 6.55%. With the proposed framework, all the produced indicators demonstrated high agreement with the routine clinical assessment scale, indicating their capability of assessing upper-limb motor functions. This study offers a feasible solution to motor function assessment in an objective and quantitative manner, especially suitable for home and community use.

  5. A novel fusion method of improved adaptive LTP and two-directional two-dimensional PCA for face feature extraction

    Science.gov (United States)

    Luo, Yuan; Wang, Bo-yu; Zhang, Yi; Zhao, Li-ming

    2018-03-01

    In this paper, under different illuminations and random noises, focusing on the local texture feature's defects of a face image that cannot be completely described because the threshold of local ternary pattern (LTP) cannot be calculated adaptively, a local three-value model of improved adaptive local ternary pattern (IALTP) is proposed. Firstly, the difference function between the center pixel and the neighborhood pixel weight is established to obtain the statistical characteristics of the central pixel and the neighborhood pixel. Secondly, the adaptively gradient descent iterative function is established to calculate the difference coefficient which is defined to be the threshold of the IALTP operator. Finally, the mean and standard deviation of the pixel weight of the local region are used as the coding mode of IALTP. In order to reflect the overall properties of the face and reduce the dimension of features, the two-directional two-dimensional PCA ((2D)2PCA) is adopted. The IALTP is used to extract local texture features of eyes and mouth area. After combining the global features and local features, the fusion features (IALTP+) are obtained. The experimental results on the Extended Yale B and AR standard face databases indicate that under different illuminations and random noises, the algorithm proposed in this paper is more robust than others, and the feature's dimension is smaller. The shortest running time reaches 0.329 6 s, and the highest recognition rate reaches 97.39%.

  6. Fusion and Sense Making of Heterogeneous Sensor Network and Other Sources

    Science.gov (United States)

    2017-03-16

    attentions. A supervised learning -based image classification system often demands a large number of labeled training images . However, a large number...self-taught learning , domain adaptation, semi- supervised learning and etc. By taking the web images as normal training DISTRIBUTION A. Approved for...27/2016 Abstract: To train a scene classifier with good generalization capability, a large number of human labeled training images are often

  7. Reovirus FAST Proteins Drive Pore Formation and Syncytiogenesis Using a Novel Helix-Loop-Helix Fusion-Inducing Lipid Packing Sensor

    Science.gov (United States)

    Sarker, Muzaddid; de Antueno, Roberto; Langelaan, David N.; Parmar, Hiren B.; Shin, Kyungsoo; Rainey, Jan K.; Duncan, Roy

    2015-01-01

    Pore formation is the most energy-demanding step during virus-induced membrane fusion, where high curvature of the fusion pore rim increases the spacing between lipid headgroups, exposing the hydrophobic interior of the membrane to water. How protein fusogens breach this thermodynamic barrier to pore formation is unclear. We identified a novel fusion-inducing lipid packing sensor (FLiPS) in the cytosolic endodomain of the baboon reovirus p15 fusion-associated small transmembrane (FAST) protein that is essential for pore formation during cell-cell fusion and syncytiogenesis. NMR spectroscopy and mutational studies indicate the dependence of this FLiPS on a hydrophobic helix-loop-helix structure. Biochemical and biophysical assays reveal the p15 FLiPS preferentially partitions into membranes with high positive curvature, and this partitioning is impeded by bis-ANS, a small molecule that inserts into hydrophobic defects in membranes. Most notably, the p15 FLiPS can be functionally replaced by heterologous amphipathic lipid packing sensors (ALPS) but not by other membrane-interactive amphipathic helices. Furthermore, a previously unrecognized amphipathic helix in the cytosolic domain of the reptilian reovirus p14 FAST protein can functionally replace the p15 FLiPS, and is itself replaceable by a heterologous ALPS motif. Anchored near the cytoplasmic leaflet by the FAST protein transmembrane domain, the FLiPS is perfectly positioned to insert into hydrophobic defects that begin to appear in the highly curved rim of nascent fusion pores, thereby lowering the energy barrier to stable pore formation. PMID:26061049

  8. Reovirus FAST Proteins Drive Pore Formation and Syncytiogenesis Using a Novel Helix-Loop-Helix Fusion-Inducing Lipid Packing Sensor.

    Directory of Open Access Journals (Sweden)

    Jolene Read

    2015-06-01

    Full Text Available Pore formation is the most energy-demanding step during virus-induced membrane fusion, where high curvature of the fusion pore rim increases the spacing between lipid headgroups, exposing the hydrophobic interior of the membrane to water. How protein fusogens breach this thermodynamic barrier to pore formation is unclear. We identified a novel fusion-inducing lipid packing sensor (FLiPS in the cytosolic endodomain of the baboon reovirus p15 fusion-associated small transmembrane (FAST protein that is essential for pore formation during cell-cell fusion and syncytiogenesis. NMR spectroscopy and mutational studies indicate the dependence of this FLiPS on a hydrophobic helix-loop-helix structure. Biochemical and biophysical assays reveal the p15 FLiPS preferentially partitions into membranes with high positive curvature, and this partitioning is impeded by bis-ANS, a small molecule that inserts into hydrophobic defects in membranes. Most notably, the p15 FLiPS can be functionally replaced by heterologous amphipathic lipid packing sensors (ALPS but not by other membrane-interactive amphipathic helices. Furthermore, a previously unrecognized amphipathic helix in the cytosolic domain of the reptilian reovirus p14 FAST protein can functionally replace the p15 FLiPS, and is itself replaceable by a heterologous ALPS motif. Anchored near the cytoplasmic leaflet by the FAST protein transmembrane domain, the FLiPS is perfectly positioned to insert into hydrophobic defects that begin to appear in the highly curved rim of nascent fusion pores, thereby lowering the energy barrier to stable pore formation.

  9. Design and Implementation of a Smart LED Lighting System Using a Self Adaptive Weighted Data Fusion Algorithm

    Directory of Open Access Journals (Sweden)

    Wen-Tsai Sung

    2013-12-01

    Full Text Available This work aims to develop a smart LED lighting system, which is remotely controlled by Android apps via handheld devices, e.g., smartphones, tablets, and so forth. The status of energy use is reflected by readings displayed on a handheld device, and it is treated as a criterion in the lighting mode design of a system. A multimeter, a wireless light dimmer, an IR learning remote module, etc. are connected to a server by means of RS 232/485 and a human computer interface on a touch screen. The wireless data communication is designed to operate in compliance with the ZigBee standard, and signal processing on sensed data is made through a self adaptive weighted data fusion algorithm. A low variation in data fusion together with a high stability is experimentally demonstrated in this work. The wireless light dimmer as well as the IR learning remote module can be instructed directly by command given on the human computer interface, and the reading on a multimeter can be displayed thereon via the server. This proposed smart LED lighting system can be remotely controlled and self learning mode can be enabled by a single handheld device via WiFi transmission. Hence, this proposal is validated as an approach to power monitoring for home appliances, and is demonstrated as a digital home network in consideration of energy efficiency.

  10. Multivariable Regression and Adaptive Neurofuzzy Inference System Predictions of Ash Fusion Temperatures Using Ash Chemical Composition of US Coals

    Directory of Open Access Journals (Sweden)

    Shahab Karimi

    2014-01-01

    Full Text Available In this study, the effects of ratios of dolomite, base/acid, silica, SiO2/Al2O3, and Fe2O3/CaO, base and acid oxides, and 11 oxides (SiO2, Al2O3, CaO, MgO, MnO, Na2O, K2O, Fe2O3, TiO2, P2O5, and SO3 on ash fusion temperatures for 1040 US coal samples from 12 states were evaluated using regression and adaptive neurofuzzy inference system (ANFIS methods. Different combinations of independent variables were examined to predict ash fusion temperatures in the multivariable procedure. The combination of the “11 oxides + (Base/Acid + Silica ratio” was the best predictor. Correlation coefficients (R2 of 0.891, 0.917, and 0.94 were achieved using nonlinear equations for the prediction of initial deformation temperature (IDT, softening temperature (ST, and fluid temperature (FT, respectively. The mentioned “best predictor” was used as input to the ANFIS system as well, and the correlation coefficients (R2 of the prediction were enhanced to 0.97, 0.98, and 0.99 for IDT, ST, and FT, respectively. The prediction precision that was achieved in this work exceeded that reported in previously published works.

  11. Graph-based sensor fusion for classification of transient acoustic signals.

    Science.gov (United States)

    Srinivas, Umamahesh; Nasrabadi, Nasser M; Monga, Vishal

    2015-03-01

    Advances in acoustic sensing have enabled the simultaneous acquisition of multiple measurements of the same physical event via co-located acoustic sensors. We exploit the inherent correlation among such multiple measurements for acoustic signal classification, to identify the launch/impact of munition (i.e., rockets, mortars). Specifically, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between the cepstral features extracted from these different measurements. Additionally, we employ symbolic dynamic filtering-based features, which offer improvements over the traditional cepstral features in terms of robustness to signal distortions. Experiments on real acoustic data sets show that our proposed algorithm outperforms conventional classifiers as well as the recently proposed joint sparsity models for multisensor acoustic classification. Additionally our proposed algorithm is less sensitive to insufficiency in training samples compared to competing approaches.

  12. Sensor Data Fusion for Body State Estimation in a Bipedal Robot and Its Feedback Control Application for Stable Walking

    Directory of Open Access Journals (Sweden)

    Ching-Pei Chen

    2015-02-01

    Full Text Available We report on a sensor data fusion algorithm via an extended Kalman filter for estimating the spatial motion of a bipedal robot. Through fusing the sensory information from joint encoders, a 6-axis inertial measurement unit and a 2-axis inclinometer, the robot’s body state at a specific fixed position can be yielded. This position is also equal to the CoM when the robot is in the standing posture suggested by the detailed CAD model of the robot. In addition, this body state is further utilized to provide sensory information for feedback control on a bipedal robot with walking gait. The overall control strategy includes the proposed body state estimator as well as the damping controller, which regulates the body position state of the robot in real-time based on instant and historical position tracking errors. Moreover, a posture corrector for reducing unwanted torque during motion is addressed. The body state estimator and the feedback control structure are implemented in a child-size bipedal robot and the performance is experimentally evaluated.

  13. A Ubiquitous and Low-Cost Solution for Movement Monitoring and Accident Detection Based on Sensor Fusion

    Directory of Open Access Journals (Sweden)

    Filipe Felisberto

    2014-05-01

    Full Text Available The low average birth rate in developed countries and the increase in life expectancy have lead society to face for the first time an ageing situation. This situation associated with the World’s economic crisis (which started in 2008 forces the need of equating better and more efficient ways of providing more quality of life for the elderly. In this context, the solution presented in this work proposes to tackle the problem of monitoring the elderly in a way that is not restrictive for the life of the monitored, avoiding the need for premature nursing home admissions. To this end, the system uses the fusion of sensory data provided by a network of wireless sensors placed on the periphery of the user. Our approach was also designed with a low-cost deployment in mind, so that the target group may be as wide as possible. Regarding the detection of long-term problems, the tests conducted showed that the precision of the system in identifying and discerning body postures and body movements allows for a valid monitorization and rehabilitation of the user. Moreover, concerning the detection of accidents, while the proposed solution presented a near 100% precision at detecting normal falls, the detection of more complex falls (i.e., hampered falls will require further study.

  14. Adaptive Fusion of Information for Seeing into Ordos Basin, China: A China-Germany-US Joint Venture.

    Science.gov (United States)

    Yeh, T. C. J.; Yin, L.; Sauter, M.; Hu, R.; Ptak, T.; Hou, G. C.

    2014-12-01

    Adaptive fusion of information for seeing into geological basins is the theme of this joint venture. The objective of this venture is to initiate possible collaborations between scientists from China, Germany, and US to develop innovative technologies, which can be utilized to characterize geological and hydrological structures and processes as well as other natural resources in regional scale geological basins of hundreds of thousands of kilometers (i.e., the Ordos Basin, China). This adaptive fusion of information aims to assimilate active (manmade) and passive (natural) hydrologic and geophysical tomography surveys to enhance our ability of seeing into hydrogeological basins at the resolutions of our interests. The active hydrogeophysical tomography refers to recently developed hydraulic tomgoraphic surveys by Chinese and German scientists, as well as well-established geophysical tomography surveys (such as electrical resistivity tomography, cross-borehole radars, electrical magnetic surveys). These active hydrogeophysical tomgoraphic surveys have been proven to be useful high-resolution surveys for geological media of tens and hundreds of meters wide and deep. For basin-scale (i.e., tens and hundreds of kilometers) problems, their applicabilities are however rather limited. The passive hydrogeophysical tomography refers to unexplored technologies that exploit natural stimuli as energy sources for tomographic surveys, which include direct lightning strikes, groundwater level fluctuations due to earthquakes, river stage fluctuations, precipitation storms, barometric pressure variations, and long term climate changes. These natural stimuli are spatially varying, recurrent, and powerful, influencing geological media over great distances and depths (e.g., tens and hundreds of kilometers). Monitoring hydrological and geophysical responses of geological media to these stimuli at different locations is tantamount to collecting data of naturally occurring tomographic

  15. A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images

    Directory of Open Access Journals (Sweden)

    Jie Xue

    2017-12-01

    Full Text Available Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and temporal resolutions, a number of image fusion algorithms, such as spatial and temporal adaptive reflectance fusion model (STARFM and enhanced STARFM (ESTARFM, have been recently developed. To capitalize on information available in a fusion process, we propose a Bayesian data fusion approach that incorporates the temporal correlation information in the image time series and casts the fusion problem as an estimation problem in which the fused image is obtained by the Maximum A Posterior (MAP estimator. The proposed approach provides a formal framework for the fusion of remotely sensed images with a rigorous statistical basis; it imposes no requirements on the number of input image pairs; and it is suitable for heterogeneous landscapes. The approach is empirically tested with both simulated and real-life acquired Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS images. Experimental results demonstrate that the proposed method outperforms STARFM and ESTARFM, especially for heterogeneous landscapes. It produces surface reflectances highly correlated with those of the reference Landsat images. It gives spatio-temporal fusion of remotely sensed images a solid theoretical and empirical foundation that may be extended to solve more complicated image fusion problems.

  16. Adaptive Sensor Optimization and Cognitive Image Processing Using Autonomous Optical Neuroprocessors; TOPICAL

    International Nuclear Information System (INIS)

    CAMERON, STEWART M.

    2001-01-01

    Measurement and signal intelligence demands has created new requirements for information management and interoperability as they affect surveillance and situational awareness. Integration of on-board autonomous learning and adaptive control structures within a remote sensing platform architecture would substantially improve the utility of intelligence collection by facilitating real-time optimization of measurement parameters for variable field conditions. A problem faced by conventional digital implementations of intelligent systems is the conflict between a distributed parallel structure on a sequential serial interface functionally degrading bandwidth and response time. In contrast, optically designed networks exhibit the massive parallelism and interconnect density needed to perform complex cognitive functions within a dynamic asynchronous environment. Recently, all-optical self-organizing neural networks exhibiting emergent collective behavior which mimic perception, recognition, association, and contemplative learning have been realized using photorefractive holography in combination with sensory systems for feature maps, threshold decomposition, image enhancement, and nonlinear matched filters. Such hybrid information processors depart from the classical computational paradigm based on analytic rules-based algorithms and instead utilize unsupervised generalization and perceptron-like exploratory or improvisational behaviors to evolve toward optimized solutions. These systems are robust to instrumental systematics or corrupting noise and can enrich knowledge structures by allowing competition between multiple hypotheses. This property enables them to rapidly adapt or self-compensate for dynamic or imprecise conditions which would be unstable using conventional linear control models. By incorporating an intelligent optical neuroprocessor in the back plane of an imaging sensor, a broad class of high-level cognitive image analysis problems including geometric

  17. An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring.

    Science.gov (United States)

    Shu, Tongxin; Xia, Min; Chen, Jiahong; Silva, Clarence de

    2017-11-05

    Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy.

  18. An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring

    Directory of Open Access Journals (Sweden)

    Tongxin Shu

    2017-11-01

    Full Text Available Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA, while achieving around the same Normalized Mean Error (NME, DDASA is superior in saving 5.31% more battery energy.

  19. Sensor fusion of electron paramagnetic resonance and magnetorelaxometry data for quantitative magnetic nanoparticle imaging

    International Nuclear Information System (INIS)

    Coene, A; Leliaert, J; Crevecoeur, G; Dupré, L

    2017-01-01

    Magnetorelaxometry (MRX) imaging and electron paramagnetic resonance (EPR) are two non-invasive techniques capable of recovering the magnetic nanoparticle (MNP) distribution. Both techniques solve an ill-posed inverse problem in order to find the spatial MNP distribution. A lot of research has been done on increasing the stability of these inverse problems with the main objective to improve the quality of MNP imaging. In this paper a proof of concept is presented in which the sensor data of both techniques is fused into EPR–MRX, with the intention to stabilize the inverse problem. First, both techniques are compared by reconstructing several phantoms with different sizes for various noise levels and calculating stability, sensitivity and reconstruction quality parameters for these cases. This study reveals that both techniques are sensitive to different information from the MNP distributions and generate complementary measurement data. As such, their merging might stabilize the inverse problem. In a next step we investigated how both techniques need to be combined to reduce their respective drawbacks, such as a high number of required measurements and reduced stability, and to improve MNP reconstructions. We were able to stabilize both techniques, increase reconstruction quality by an average of 5% and reduce measurement times by 88%. These improvements could make EPR–MRX a valuable and accurate technique in a clinical environment. (paper)

  20. Real-time localization of mobile device by filtering method for sensor fusion

    Science.gov (United States)

    Fuse, Takashi; Nagara, Keita

    2017-06-01

    Most of the applications with mobile devices require self-localization of the devices. GPS cannot be used in indoor environment, the positions of mobile devices are estimated autonomously by using IMU. Since the self-localization is based on IMU of low accuracy, and then the self-localization in indoor environment is still challenging. The selflocalization method using images have been developed, and the accuracy of the method is increasing. This paper develops the self-localization method without GPS in indoor environment by integrating sensors, such as IMU and cameras, on mobile devices simultaneously. The proposed method consists of observations, forecasting and filtering. The position and velocity of the mobile device are defined as a state vector. In the self-localization, observations correspond to observation data from IMU and camera (observation vector), forecasting to mobile device moving model (system model) and filtering to tracking method by inertial surveying and coplanarity condition and inverse depth model (observation model). Positions of a mobile device being tracked are estimated by system model (forecasting step), which are assumed as linearly moving model. Then estimated positions are optimized referring to the new observation data based on likelihood (filtering step). The optimization at filtering step corresponds to estimation of the maximum a posterior probability. Particle filter are utilized for the calculation through forecasting and filtering steps. The proposed method is applied to data acquired by mobile devices in indoor environment. Through the experiments, the high performance of the method is confirmed.

  1. Robust Rapid Change-Point Detection in Multi-Sensor Data Fusion and Behavior Research

    Science.gov (United States)

    2011-02-25

    I(gk, fk) +O(1) = b′∑K k=1 I(gk, fk) +O(1) and Var (1)(Mk) = O(b ′), as b → ∞, see Siegmund [26, p. 171]. Hence, E(1) ( max 1≤k≤m Mk ) = b′∑K k=1 I...where the last inequality follows from the fact that( E(1) ∣∣Mk − b′∑K k=1 I(gk, fk) ∣∣)2 ≤ E(1)(Mk − a∑K k=1 I(gk, fk) )2 = Var (1)(Mk) + ( E(1)Mk...D. L. and Johnstone, I. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81, 425–455. [5] Durrett, R. (1996). Probability

  2. An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

    Science.gov (United States)

    Liu, Yahui; Fan, Xiaoqian; Lv, Chen; Wu, Jian; Li, Liang; Ding, Dawei

    2018-02-01

    Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise.

  3. Adaptive Security in ODMAC for Multihop Energy Harvesting Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Di Mauro, Alessio; Fafoutis, Xenofon; Dragoni, Nicola

    2015-01-01

    Energy Harvesting Wireless Sensor Networks (EH-WSNs) represent an interesting new paradigm where individual nodes forming a network are powered by energy sources scavenged from the surrounding environment. This technique provides numerous advantages, but also new design challenges. Securing...... the communications under energy constraints represents one of these key challenges. The amount of energy available is theoretically infinite in the long run but highly variable over short periods of time, and managing it is a crucial aspect. In this paper we present an adaptive approach for security in multihop EH......-WSNs which allows different nodes to dynamically choose the most appropriate energy-affecting parameters such as encryption algorithm and key size, providing in this way energy savings. In order to provide evidence of the approach's feasibility in a real-world network, we have designed and implemented...

  4. Cofactors As Metabolic Sensors Driving Cell Adaptation in Physiology and Disease

    Directory of Open Access Journals (Sweden)

    Nabil Rabhi

    2017-11-01

    Full Text Available Chromatin architectures and epigenetic fingerprint regulation are fundamental for genetically determined biological processes. Chemical modifications of the chromatin template sensitize the genome to intracellular metabolism changes to set up diverse functional adaptive states. Accumulated evidence suggests that the action of epigenetic modifiers is sensitive to changes in dietary components and cellular metabolism intermediates, linking nutrition and energy metabolism to gene expression plasticity. Histone posttranslational modifications create a code that acts as a metabolic sensor, translating changes in metabolism into stable gene expression patterns. These observations support the notion that epigenetic reprograming-linked energy input is connected to the etiology of metabolic diseases and cancer. In the present review, we introduce the role of epigenetic cofactors and their relation with nutrient intake and we question the links between epigenetic regulation and the development of metabolic diseases.

  5. An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications

    Science.gov (United States)

    Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

    2014-01-01

    Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications. PMID:24521942

  6. Dynamic properties of magneto-sensitive elastomer cantilevers as adaptive sensor elements

    Science.gov (United States)

    Becker, T. I.; Raikher, Yu L.; Stolbov, O. V.; Böhm, V.; Zimmermann, K.

    2017-09-01

    In engineering applications, one of the actual scientific challenges is a development of sensor systems with complex adaptive behaviour and operating sensitivity. Smart materials like magneto-sensitive elastomers (MSEs) offer great potential for designing such intelligent devices, because they possess unique magnetic-field dependent properties. This paper deals with the investigation of the free vibrational behaviour displayed by cantilever beams made of MSEs containing magnetically soft particles in the presence of a uniform magnetic field. The presented setup is considered as a prototype of a sensitive unit for transforming mechanical stimuli of the base into vibrations of the MSE element, which could be converted in a facile way into electromagnetic signals for measuring and analysing. It is shown experimentally that for the MSE beams of several different lengths and compositions, the first eigenfrequency depends strongly on the strength of the applied magnetic field. The developed theoretical model extends the conventional vibrational dynamics of thin rods to allow for the ponderomotive torque induced by magneto-mechanical interactions experienced by an MSE beam. The model has two adjustable parameters that characterise the geometric configuration of the cantilever beam and its magnetic material properties. It is found that the values of these parameters are ‘universally’ valid, i.e., they provide good agreement between the theoretical eigenfrequency dependencies and the experimental results for all used MSE beams. The evidence presented ensures a good basis for further investigations of the sensitivity and amplitude-frequency characteristics of MSE vibration sensor elements.

  7. Adaptive Data Gathering in Mobile Sensor Networks Using Speedy Mobile Elements

    Science.gov (United States)

    Lai, Yongxuan; Xie, Jinshan; Lin, Ziyu; Wang, Tian; Liao, Minghong

    2015-01-01

    Data gathering is a key operator for applications in wireless sensor networks; yet it is also a challenging problem in mobile sensor networks when considering that all nodes are mobile and the communications among them are opportunistic. This paper proposes an efficient data gathering scheme called ADG that adopts speedy mobile elements as the mobile data collector and takes advantage of the movement patterns of the network. ADG first extracts the network meta-data at initial epochs, and calculates a set of proxy nodes based on the meta-data. Data gathering is then mapped into the Proxy node Time Slot Allocation (PTSA) problem that schedules the time slots and orders, according to which the data collector could gather the maximal amount of data within a limited period. Finally, the collector follows the schedule and picks up the sensed data from the proxy nodes through one hop of message transmissions. ADG learns the period when nodes are relatively stationary, so that the collector is able to pick up the data from them during the limited data gathering period. Moreover, proxy nodes and data gathering points could also be timely updated so that the collector could adapt to the change of node movements. Extensive experimental results show that the proposed scheme outperforms other data gathering schemes on the cost of message transmissions and the data gathering rate, especially under the constraint of limited data gathering period. PMID:26389903

  8. RheoStim: Development of an Adaptive Multi-Sensor to Prevent Venous Stasis

    Directory of Open Access Journals (Sweden)

    Sören Weyer

    2016-03-01

    Full Text Available Chronic venous insufficiency of the lower limbs is often underestimated and, in the absence of therapy, results in increasingly severe complications, including therapy-resistant tissue defects. Therefore, early diagnosis and adequate therapy is of particular importance. External counter pulsation (ECP therapy is a method used to assist the venous system. The main principle of ECP is to squeeze the inner leg vessels by muscle contractions, which are evoked by functional electrical stimulation. A new adaptive trigger method is proposed, which improves and supplements the current therapeutic options by means of pulse synchronous electro-stimulation of the muscle pump. For this purpose, blood flow is determined by multi-sensor plethysmography. The hardware design and signal processing of this novel multi-sensor plethysmography device are introduced. The merged signal is used to determine the phase of the cardiac cycle, to ensure stimulation of the muscle pump during the filling phase of the heart. The pulse detection of the system is validated against a gold standard and provides a sensitivity of 98% and a false-negative rate of 2% after physical exertion. Furthermore, flow enhancement of the system has been validated by duplex ultrasonography. The results show a highly increased blood flow in the popliteal vein at the knee.

  9. Vibration suppression in cutting tools using collocated piezoelectric sensors/actuators with an adaptive control algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Radecki, Peter P [Los Alamos National Laboratory; Farinholt, Kevin M [Los Alamos National Laboratory; Park, Gyuhae [Los Alamos National Laboratory; Bement, Matthew T [Los Alamos National Laboratory

    2008-01-01

    The machining process is very important in many engineering applications. In high precision machining, surface finish is strongly correlated with vibrations and the dynamic interactions between the part and the cutting tool. Parameters affecting these vibrations and dynamic interactions, such as spindle speed, cut depth, feed rate, and the part's material properties can vary in real-time, resulting in unexpected or undesirable effects on the surface finish of the machining product. The focus of this research is the development of an improved machining process through the use of active vibration damping. The tool holder employs a high bandwidth piezoelectric actuator with an adaptive positive position feedback control algorithm for vibration and chatter suppression. In addition, instead of using external sensors, the proposed approach investigates the use of a collocated piezoelectric sensor for measuring the dynamic responses from machining processes. The performance of this method is evaluated by comparing the surface finishes obtained with active vibration control versus baseline uncontrolled cuts. Considerable improvement in surface finish (up to 50%) was observed for applications in modern day machining.

  10. Adaptive Multi-Sensor Perception for Driving Automation in Outdoor Contexts

    Directory of Open Access Journals (Sweden)

    Annalisa Milella

    2014-08-01

    Full Text Available In this research, adaptive perception for driving automation is discussed so as to enable a vehicle to automatically detect driveable areas and obstacles in the scene. It is especially designed for outdoor contexts where conventional perception systems that rely on a priori knowledge of the terrain's geometric properties, appearance properties, or both, is prone to fail, due to the variability in the terrain properties and environmental conditions. In contrast, the proposed framework uses a self-learning approach to build a model of the ground class that is continuously adjusted online to reflect the latest ground appearance. The system also features high flexibility, as it can work using a single sensor modality or a multi-sensor combination. In the context of this research, different embodiments have been demonstrated using range data coming from either a radar or a stereo camera, and adopting self-supervised strategies where monocular vision is automatically trained by radar or stereo vision. A comprehensive set of experimental results, obtained with different ground vehicles operating in the field, are presented to validate and assess the performance of the system.

  11. Wavefront sensorless adaptive optics versus sensor-based adaptive optics for in vivo fluorescence retinal imaging (Conference Presentation)

    Science.gov (United States)

    Wahl, Daniel J.; Zhang, Pengfei; Jian, Yifan; Bonora, Stefano; Sarunic, Marinko V.; Zawadzki, Robert J.

    2017-02-01

    Adaptive optics (AO) is essential for achieving diffraction limited resolution in large numerical aperture (NA) in-vivo retinal imaging in small animals. Cellular-resolution in-vivo imaging of fluorescently labeled cells is highly desirable for studying pathophysiology in animal models of retina diseases in pre-clinical vision research. Currently, wavefront sensor-based (WFS-based) AO is widely used for retinal imaging and has demonstrated great success. However, the performance can be limited by several factors including common path errors, wavefront reconstruction errors and an ill-defined reference plane on the retina. Wavefront sensorless (WFS-less) AO has the advantage of avoiding these issues at the cost of algorithmic execution time. We have investigated WFS-less AO on a fluorescence scanning laser ophthalmoscopy (fSLO) system that was originally designed for WFS-based AO. The WFS-based AO uses a Shack-Hartmann WFS and a continuous surface deformable mirror in a closed-loop control system to measure and correct for aberrations induced by the mouse eye. The WFS-less AO performs an open-loop modal optimization with an image quality metric. After WFS-less AO aberration correction, the WFS was used as a control of the closed-loop WFS-less AO operation. We can easily switch between WFS-based and WFS-less control of the deformable mirror multiple times within an imaging session for the same mouse. This allows for a direct comparison between these two types of AO correction for fSLO. Our results demonstrate volumetric AO-fSLO imaging of mouse retinal cells labeled with GFP. Most significantly, we have analyzed and compared the aberration correction results for WFS-based and WFS-less AO imaging.

  12. Multistatic Surveillance and Reconnaissance: Sensor, Signals and Data Fusion (Surveillance et Reconnaissance Multistatiques : Fusion des capteurs, des signaux et des donnees)

    Science.gov (United States)

    2009-04-01

    capteurs , des signaux et des données) Research and Technology Organisation (NATO) BP 25, F-92201 Neuilly-sur-Seine Cedex, France RTO-EN-SET-133...Multistatiques : Fusion des capteurs , des signaux et des données) The material in this publication was assembled to support a Lecture Series under the...Surveillance et Reconnaissance Multistatiques : Fusion des capteurs , des signaux et des données (RTO-EN-SET-133) Synthèse Les systèmes radar

  13. Model-Data Fusion and Adaptive Sensing for Large Scale Systems: Applications to Atmospheric Release Incidents

    Science.gov (United States)

    Madankan, Reza

    All across the world, toxic material clouds are emitted from sources, such as industrial plants, vehicular traffic, and volcanic eruptions can contain chemical, biological or radiological material. With the growing fear of natural, accidental or deliberate release of toxic agents, there is tremendous interest in precise source characterization and generating accurate hazard maps of toxic material dispersion for appropriate disaster management. In this dissertation, an end-to-end framework has been developed for probabilistic source characterization and forecasting of atmospheric release incidents. The proposed methodology consists of three major components which are combined together to perform the task of source characterization and forecasting. These components include Uncertainty Quantification, Optimal Information Collection, and Data Assimilation. Precise approximation of prior statistics is crucial to ensure performance of the source characterization process. In this work, an efficient quadrature based method has been utilized for quantification of uncertainty in plume dispersion models that are subject to uncertain source parameters. In addition, a fast and accurate approach is utilized for the approximation of probabilistic hazard maps, based on combination of polynomial chaos theory and the method of quadrature points. Besides precise quantification of uncertainty, having useful measurement data is also highly important to warranty accurate source parameter estimation. The performance of source characterization is highly affected by applied sensor orientation for data observation. Hence, a general framework has been developed for the optimal allocation of data observation sensors, to improve performance of the source characterization process. The key goal of this framework is to optimally locate a set of mobile sensors such that measurement of textit{better} data is guaranteed. This is achieved by maximizing the mutual information between model predictions

  14. Adaptive redundant speech transmission over wireless multimedia sensor networks based on estimation of perceived speech quality.

    Science.gov (United States)

    Kang, Jin Ah; Kim, Hong Kook

    2011-01-01

    An adaptive redundant speech transmission (ARST) approach to improve the perceived speech quality (PSQ) of speech streaming applications over wireless multimedia sensor networks (WMSNs) is proposed in this paper. The proposed approach estimates the PSQ as well as the packet loss rate (PLR) from the received speech data. Subsequently, it decides whether the transmission of redundant speech data (RSD) is required in order to assist a speech decoder to reconstruct lost speech signals for high PLRs. According to the decision, the proposed ARST approach controls the RSD transmission, then it optimizes the bitrate of speech coding to encode the current speech data (CSD) and RSD bitstream in order to maintain the speech quality under packet loss conditions. The effectiveness of the proposed ARST approach is then demonstrated using the adaptive multirate-narrowband (AMR-NB) speech codec and ITU-T Recommendation P.563 as a scalable speech codec and the PSQ estimation, respectively. It is shown from the experiments that a speech streaming application employing the proposed ARST approach significantly improves speech quality under packet loss conditions in WMSNs.

  15. An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks.

    Science.gov (United States)

    Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Zhang, Xuekun

    2015-12-03

    Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks.

  16. An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chunyang Lei

    2015-12-01

    Full Text Available Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT, Machine-to-Machine (M2M communications, Vehicular-to-Vehicular (V2V communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks.

  17. An Adaptive Connectivity-based Centroid Algorithm for Node Positioning in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Aries Pratiarso

    2015-06-01

    Full Text Available In wireless sensor network applications, the position of nodes is randomly distributed following the contour of the observation area. A simple solution without any measurement tools is provided by range-free method. However, this method yields the coarse estimating position of the nodes. In this paper, we propose Adaptive Connectivity-based (ACC algorithm. This algorithm is a combination of Centroid as range-free based algorithm, and hop-based connectivity algorithm. Nodes have a possibility to estimate their own position based on the connectivity level between them and their reference nodes. Each node divides its communication range into several regions where each of them has a certain weight depends on the received signal strength. The weighted value is used to obtain the estimated position of nodes. Simulation result shows that the proposed algorithm has up to 3 meter error of estimated position on 100x100 square meter observation area, and up to 3 hop counts for 80 meters' communication range. The proposed algorithm performs an average error positioning up to 10 meters better than Weighted Centroid algorithm. Keywords: adaptive, connectivity, centroid, range-free.

  18. An adaptive jitter mechanism for reactive route discovery in sensor networks.

    Science.gov (United States)

    Cordero, Juan Antonio; Yi, Jiazi; Clausen, Thomas

    2014-08-08

    This paper analyses the impact of jitter when applied to route discovery in reactive (on-demand) routing protocols. In multi-hop non-synchronized wireless networks, jitter--a small, random variation in the timing of message emission--is commonly employed, as a means to avoid collisions of simultaneous transmissions by adjacent routers over the same channel. In a reactive routing protocol for sensor and ad hoc networks, jitter is recommended during the route discovery process, specifically, during the network-wide flooding of route request messages, in order to avoid collisions. Commonly, a simple uniform jitter is recommended. Alas, this is not without drawbacks: when applying uniform jitter to the route discovery process, an effect called delay inversion is observed. This paper, first, studies and quantifies this delay inversion effect. Second, this paper proposes an adaptive jitter mechanism, designed to alleviate the delay inversion effect and thereby to reduce the route discovery overhead and (ultimately) allow the routing protocol to find more optimal paths, as compared to uniform jitter. This paper presents both analytical and simulation studies, showing that the proposed adaptive jitter can effectively decrease the cost of route discovery and increase the path quality.

  19. The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors

    Directory of Open Access Journals (Sweden)

    Robin van der Schalie

    2018-01-01

    Full Text Available Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. The arrival of L-band missions has led to an increased focus on the integration of L-band-based soil moisture retrievals in climate records, emphasizing the need to improve our understanding based on its added value within a multi-sensor framework. The three evaluated approaches were developed on 10-year passive microwave data (2003–2013 from two different satellite sensors, i.e., SMOS (2010–2013 and AMSR-E (2003–2011, and are based on a neural network (NN, regressions (REG, and the Land Parameter Retrieval Model (LPRM. The ability of the different approaches to best match AMSR-E and SMOS in their overlapping period was tested using an inter-comparison exercise between the SMOS and AMSR-E datasets, while the skill of the individual soil moisture products, based on anomalies, was evaluated using two verification techniques; first, a data assimilation technique that links precipitation information to the quality of soil moisture (expressed as the Rvalue, and secondly the triple collocation analysis (TCA. ASCAT soil moisture was included in the skill evaluation, representing the active microwave-based counterpart of soil moisture retrievals. Besides a semi-global analysis, explicit focus was placed on two regions that have strong land–atmosphere coupling, the Sahel (SA and the central Great Plains (CGP of North America. The NN approach gives the highest correlation coefficient between SMOS and AMSR-E, closely followed by LPRM and REG, while the absolute error is approximately the same for all three approaches. The Rvalue and TCA show the strength of using different satellite sources and the impact of different merging approaches on the skill to correctly capture soil moisture anomalies. The

  20. Uncertainty representation, quantification and evaluation for data and information fusion

    CSIR Research Space (South Africa)

    De Villiers, Johan P

    2015-07-01

    Full Text Available Mathematical and uncertainty modelling is an important component of data fusion (the fusion of unprocessed sensor data) and information fusion (the fusion of processed or interpreted data). If uncertainties in the modelling process...

  1. Quantitative characterization of pulverized coal and biomass–coal blends in pneumatic conveying pipelines using electrostatic sensor arrays and data fusion techniques

    International Nuclear Information System (INIS)

    Qian, Xiangchen; Wang, Chao; Yan, Yong; Shao, Jiaqing; Wang, Lijuan; Zhou, Hao

    2012-01-01

    Quantitative data about the dynamic behaviour of pulverized coal and biomass–coal blends in fuel injection pipelines allow power plant operators to detect variations in fuel supply and oscillations in the flow at an early stage, enable them to balance fuel distribution between fuel feeding pipes and ultimately to achieve higher combustion efficiency and lower greenhouse gas emissions. Electrostatic sensor arrays and data fusion algorithms are combined to provide a non-intrusive solution to the measurement of fuel particle velocity, relative solid concentration and flow stability under pneumatic conveying conditions. Electrostatic sensor arrays with circular and arc-shaped electrodes are integrated in the same sensing head to measure ‘averaged’ and ‘localized’ characteristics of pulverized fuel flow. Data fusion techniques are applied to optimize and integrate the results from the sensor arrays. Experimental tests were conducted on the horizontal section of a 150 mm bore pneumatic conveyor circulating pulverized coal and sawdust under various flow conditions. Test results suggest that pure coal particles travel faster and carry more electrostatic charge than biomass–coal blends. As more biomass particles are added to the flow, the overall velocity of the flow reduces, the electrostatic charge level on particles decreases and the flow becomes less stable compared to the pure coal flow. (paper)

  2. Quantitative characterization of pulverized coal and biomass-coal blends in pneumatic conveying pipelines using electrostatic sensor arrays and data fusion techniques

    Science.gov (United States)

    Qian, Xiangchen; Yan, Yong; Shao, Jiaqing; Wang, Lijuan; Zhou, Hao; Wang, Chao

    2012-08-01

    Quantitative data about the dynamic behaviour of pulverized coal and biomass-coal blends in fuel injection pipelines allow power plant operators to detect variations in fuel supply and oscillations in the flow at an early stage, enable them to balance fuel distribution between fuel feeding pipes and ultimately to achieve higher combustion efficiency and lower greenhouse gas emissions. Electrostatic sensor arrays and data fusion algorithms are combined to provide a non-intrusive solution to the measurement of fuel particle velocity, relative solid concentration and flow stability under pneumatic conveying conditions. Electrostatic sensor arrays with circular and arc-shaped electrodes are integrated in the same sensing head to measure ‘averaged’ and ‘localized’ characteristics of pulverized fuel flow. Data fusion techniques are applied to optimize and integrate the results from the sensor arrays. Experimental tests were conducted on the horizontal section of a 150 mm bore pneumatic conveyor circulating pulverized coal and sawdust under various flow conditions. Test results suggest that pure coal particles travel faster and carry more electrostatic charge than biomass-coal blends. As more biomass particles are added to the flow, the overall velocity of the flow reduces, the electrostatic charge level on particles decreases and the flow becomes less stable compared to the pure coal flow.

  3. Sensoring Fusion Data from the Optic and Acoustic Emissions of Electric Arcs in the GMAW-S Process for Welding Quality Assessment

    Directory of Open Access Journals (Sweden)

    Eber Huanca Cayo

    2012-05-01

    Full Text Available The present study shows the relationship between welding quality and optical-acoustic emissions from electric arcs, during welding runs, in the GMAW-S process. Bead on plate welding tests was carried out with pre-set parameters chosen from manufacturing standards. During the welding runs interferences were induced on the welding path using paint, grease or gas faults. In each welding run arc voltage, welding current, infrared and acoustic emission values were acquired and parameters such as arc power, acoustic peaks rate and infrared radiation rate computed. Data fusion algorithms were developed by assessing known welding quality parameters from arc emissions. These algorithms have showed better responses when they are based on more than just one sensor. Finally, it was concluded that there is a close relation between arc emissions and quality in welding and it can be measured from arc emissions sensing and data fusion algorithms.

  4. Sensoring Fusion Data from the Optic and Acoustic Emissions of Electric Arcs in the GMAW-S Process for Welding Quality Assessment

    Science.gov (United States)

    Alfaro, Sadek Crisóstomo Absi; Cayo, Eber Huanca

    2012-01-01

    The present study shows the relationship between welding quality and optical-acoustic emissions from electric arcs, during welding runs, in the GMAW-S process. Bead on plate welding tests was carried out with pre-set parameters chosen from manufacturing standards. During the welding runs interferences were induced on the welding path using paint, grease or gas faults. In each welding run arc voltage, welding current, infrared and acoustic emission values were acquired and parameters such as arc power, acoustic peaks rate and infrared radiation rate computed. Data fusion algorithms were developed by assessing known welding quality parameters from arc emissions. These algorithms have showed better responses when they are based on more than just one sensor. Finally, it was concluded that there is a close relation between arc emissions and quality in welding and it can be measured from arc emissions sensing and data fusion algorithms. PMID:22969330

  5. Sensors

    CERN Document Server

    Pigorsch, Enrico

    1997-01-01

    This is the 5th edition of the Metra Martech Directory "EUROPEAN CENTRES OF EXPERTISE - SENSORS." The entries represent a survey of European sensors development. The new edition contains 425 detailed profiles of companies and research institutions in 22 countries. This is reflected in the diversity of sensors development programmes described, from sensors for physical parameters to biosensors and intelligent sensor systems. We do not claim that all European organisations developing sensors are included, but this is a good cross section from an invited list of participants. If you see gaps or omissions, or would like your organisation to be included, please send details. The data base invites the formation of effective joint ventures by identifying and providing access to specific areas in which organisations offer collaboration. This issue is recognised to be of great importance and most entrants include details of collaboration offered and sought. We hope the directory on Sensors will help you to find the ri...

  6. Sensors

    Energy Technology Data Exchange (ETDEWEB)

    Jensen, H. [PBI-Dansensor A/S (Denmark); Toft Soerensen, O. [Risoe National Lab., Materials Research Dept. (Denmark)

    1999-10-01

    A new type of ceramic oxygen sensors based on semiconducting oxides was developed in this project. The advantage of these sensors compared to standard ZrO{sub 2} sensors is that they do not require a reference gas and that they can be produced in small sizes. The sensor design and the techniques developed for production of these sensors are judged suitable by the participating industry for a niche production of a new generation of oxygen sensors. Materials research on new oxygen ion conducting conductors both for applications in oxygen sensors and in fuel was also performed in this project and finally a new process was developed for fabrication of ceramic tubes by dip-coating. (EHS)

  7. Service-oriented architecture of adaptive, intelligent data acquisition and processing systems for long-pulse fusion experiments

    International Nuclear Information System (INIS)

    Gonzalez, J.; Ruiz, M.; Barrera, E.; Lopez, J.M.; Arcas, G. de; Vega, J.

    2010-01-01

    The data acquisition systems used in long-pulse fusion experiments need to implement data reduction and pattern recognition algorithms in real time. In order to accomplish these operations, it is essential to employ software tools that allow for hot swap capabilities throughout the temporal evolution of the experiments. This is very important because processing needs are not equal during different phases of the experiment. The intelligent test and measurement system (ITMS) developed by UPM and CIEMAT is an example of a technology for implementing scalable data acquisition and processing systems based on PXI and CompactPCI hardware. In the ITMS platform, a set of software tools allows the user to define the processing algorithms associated with the different experimental phases using state machines driven by software events. These state machines are specified using the State Chart XML (SCXML) language. The software tools are developed using JAVA, JINI, an SCXML engine and several LabVIEW applications. Within this schema, it is possible to execute data acquisition and processing applications in an adaptive way. The power of SCXML semantics and the ability to work with XML user-defined data types allow for very easy programming of the ITMS platform. With this approach, the ITMS platform is a suitable solution for implementing scalable data acquisition and processing systems based on a service-oriented model with the ability to easily implement remote participation applications.

  8. Sensor Data Fusion and Integration of the Human Element. (la Fusion de donnees de senseur et l’integration du facteur humain)

    Science.gov (United States)

    1999-02-01

    information generated. Dr George E A Reid Chairman, Technical Planning Committee La fusion de donnees de senseur et l’integration du facteur humain...RTO MP-12) Synthese Les Operateurs des futurs systemes militaires sur un champ de bataille en environnement numerise devront faire face ä des...seulement d’appretier les capacites du Systeme, mais aussi d’avoir confiance dans les informations qu’il genere. Dr George E A Reid President du

  9. Adaptive Multi-Node Multiple Input and Multiple Output (MIMO) Transmission for Mobile Wireless Multimedia Sensor Networks

    Science.gov (United States)

    Cho, Sunghyun; Choi, Ji-Woong; You, Cheolwoo

    2013-01-01

    Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase. PMID:24152920

  10. An interprojection sensor fusion approach to estimate blocked projection signal in synchronized moving grid-based CBCT system

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Hong; Kong, Vic [Department of Radiation Oncology, Georgia Regents University, Augusta, Georgia 30912 (United States); Ren, Lei; Giles, William; Zhang, You [Department of Radiation Oncology, Duke University, Durham, North Carolina 27710 (United States); Jin, Jian-Yue, E-mail: jjin@gru.edu [Department of Radiation Oncology, Georgia Regents University, Augusta, Georgia 30912 and Department of Radiology, Georgia Regents University, Augusta, Georgia 30912 (United States)

    2016-01-15

    Purpose: A preobject grid can reduce and correct scatter in cone beam computed tomography (CBCT). However, half of the signal in each projection is blocked by the grid. A synchronized moving grid (SMOG) has been proposed to acquire two complimentary projections at each gantry position and merge them into one complete projection. That approach, however, suffers from increased scanning time and the technical difficulty of accurately merging the two projections per gantry angle. Herein, the authors present a new SMOG approach which acquires a single projection per gantry angle, with complimentary grid patterns for any two adjacent projections, and use an interprojection sensor fusion (IPSF) technique to estimate the blocked signal in each projection. The method may have the additional benefit of reduced imaging dose due to the grid blocking half of the incident radiation. Methods: The IPSF considers multiple paired observations from two adjacent gantry angles as approximations of the blocked signal and uses a weighted least square regression of these observations to finally determine the blocked signal. The method was first tested with a simulated SMOG on a head phantom. The signal to noise ratio (SNR), which represents the difference of the recovered CBCT image to the original image without the SMOG, was used to evaluate the ability of the IPSF in recovering the missing signal. The IPSF approach was then tested using a Catphan phantom on a prototype SMOG assembly installed in a bench top CBCT system. Results: In the simulated SMOG experiment, the SNRs were increased from 15.1 and 12.7 dB to 35.6 and 28.9 dB comparing with a conventional interpolation method (inpainting method) for a projection and the reconstructed 3D image, respectively, suggesting that IPSF successfully recovered most of blocked signal. In the prototype SMOG experiment, the authors have successfully reconstructed a CBCT image using the IPSF-SMOG approach. The detailed geometric features in the

  11. An interprojection sensor fusion approach to estimate blocked projection signal in synchronized moving grid-based CBCT system

    International Nuclear Information System (INIS)

    Zhang, Hong; Kong, Vic; Ren, Lei; Giles, William; Zhang, You; Jin, Jian-Yue

    2016-01-01

    Purpose: A preobject grid can reduce and correct scatter in cone beam computed tomography (CBCT). However, half of the signal in each projection is blocked by the grid. A synchronized moving grid (SMOG) has been proposed to acquire two complimentary projections at each gantry position and merge them into one complete projection. That approach, however, suffers from increased scanning time and the technical difficulty of accurately merging the two projections per gantry angle. Herein, the authors present a new SMOG approach which acquires a single projection per gantry angle, with complimentary grid patterns for any two adjacent projections, and use an interprojection sensor fusion (IPSF) technique to estimate the blocked signal in each projection. The method may have the additional benefit of reduced imaging dose due to the grid blocking half of the incident radiation. Methods: The IPSF considers multiple paired observations from two adjacent gantry angles as approximations of the blocked signal and uses a weighted least square regression of these observations to finally determine the blocked signal. The method was first tested with a simulated SMOG on a head phantom. The signal to noise ratio (SNR), which represents the difference of the recovered CBCT image to the original image without the SMOG, was used to evaluate the ability of the IPSF in recovering the missing signal. The IPSF approach was then tested using a Catphan phantom on a prototype SMOG assembly installed in a bench top CBCT system. Results: In the simulated SMOG experiment, the SNRs were increased from 15.1 and 12.7 dB to 35.6 and 28.9 dB comparing with a conventional interpolation method (inpainting method) for a projection and the reconstructed 3D image, respectively, suggesting that IPSF successfully recovered most of blocked signal. In the prototype SMOG experiment, the authors have successfully reconstructed a CBCT image using the IPSF-SMOG approach. The detailed geometric features in the

  12. Improved laser-based triangulation sensor with enhanced range and resolution through adaptive optics-based active beam control.

    Science.gov (United States)

    Reza, Syed Azer; Khwaja, Tariq Shamim; Mazhar, Mohsin Ali; Niazi, Haris Khan; Nawab, Rahma

    2017-07-20

    Various existing target ranging techniques are limited in terms of the dynamic range of operation and measurement resolution. These limitations arise as a result of a particular measurement methodology, the finite processing capability of the hardware components deployed within the sensor module, and the medium through which the target is viewed. Generally, improving the sensor range adversely affects its resolution and vice versa. Often, a distance sensor is designed for an optimal range/resolution setting depending on its intended application. Optical triangulation is broadly classified as a spatial-signal-processing-based ranging technique and measures target distance from the location of the reflected spot on a position sensitive detector (PSD). In most triangulation sensors that use lasers as a light source, beam divergence-which severely affects sensor measurement range-is often ignored in calculations. In this paper, we first discuss in detail the limitations to ranging imposed by beam divergence, which, in effect, sets the sensor dynamic range. Next, we show how the resolution of laser-based triangulation sensors is limited by the interpixel pitch of a finite-sized PSD. In this paper, through the use of tunable focus lenses (TFLs), we propose a novel design of a triangulation-based optical rangefinder that improves both the sensor resolution and its dynamic range through adaptive electronic control of beam propagation parameters. We present the theory and operation of the proposed sensor and clearly demonstrate a range and resolution improvement with the use of TFLs. Experimental results in support of our claims are shown to be in strong agreement with theory.

  13. Optimal Fusion of Sensors

    DEFF Research Database (Denmark)

    Larsen, Thomas Dall

    This thesis deals with the problem of fusing and managing data concerning the state or identity of a given object. Focus is put on the challenges occurring within the field of mobile robot navigation. The main problem here will often be to keep track of the position and orientation of the robot w...

  14. The Telesupervised Adaptive Ocean Sensor Fleet (TAOSF) Architecture: Coordination of Multiple Oceanic Robot Boats

    Science.gov (United States)

    Elfes, Alberto; Podnar, Gregg W.; Dolan, John M.; Stancliff, Stephen; Lin, Ellie; Hosler, Jeffrey C.; Ames, Troy J.; Higinbotham, John; Moisan, John R.; Moisan, Tiffany A.; hide

    2008-01-01

    Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth s climate and ecology. Ocean sensing is typically done with satellites, buoys, and crewed research ships. The limitations of these systems include the fact that satellites are often blocked by cloud cover, and buoys and ships have spatial coverage limitations. This paper describes a multi-robot science exploration software architecture and system called the Telesupervised Adaptive Ocean Sensor Fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the OASIS platforms, to enable in-situ study of phenomena in the ocean/atmosphere interface, as well as on the ocean surface and sub-surface. The OASIS platforms are extended deployment autonomous ocean surface vehicles, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a sliding autonomy control architecture, where the operating mode of the vessels ranges from autonomous control to teleoperated human control. TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We discuss the overall TAOSF architecture, describe field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.

  15. Cooperative and Adaptive Network Coding for Gradient Based Routing in Wireless Sensor Networks with Multiple Sinks

    Directory of Open Access Journals (Sweden)

    M. E. Migabo

    2017-01-01

    Full Text Available Despite its low computational cost, the Gradient Based Routing (GBR broadcast of interest messages in Wireless Sensor Networks (WSNs causes significant packets duplications and unnecessary packets transmissions. This results in energy wastage, traffic load imbalance, high network traffic, and low throughput. Thanks to the emergence of fast and powerful processors, the development of efficient network coding strategies is expected to enable efficient packets aggregations and reduce packets retransmissions. For multiple sinks WSNs, the challenge consists of efficiently selecting a suitable network coding scheme. This article proposes a Cooperative and Adaptive Network Coding for GBR (CoAdNC-GBR technique which considers the network density as dynamically defined by the average number of neighbouring nodes, to efficiently aggregate interest messages. The aggregation is performed by means of linear combinations of random coefficients of a finite Galois Field of variable size GF(2S at each node and the decoding is performed by means of Gaussian elimination. The obtained results reveal that, by exploiting the cooperation of the multiple sinks, the CoAdNC-GBR not only improves the transmission reliability of links and lowers the number of transmissions and the propagation latency, but also enhances the energy efficiency of the network when compared to the GBR-network coding (GBR-NC techniques.

  16. A Knowledge-Based Step Length Estimation Method Based on Fuzzy Logic and Multi-Sensor Fusion Algorithms for a Pedestrian Dead Reckoning System

    Directory of Open Access Journals (Sweden)

    Ying-Chih Lai

    2016-05-01

    Full Text Available The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE method for a Pedestrian Dead Reckoning (PDR system with features including a wide range of step lengths, a self-contained system, and real-time computing, based on the multi-sensor fusion and Fuzzy Logic (FL algorithms. The wide-range SLE developed in this study was achieved by using a knowledge-based method to model the walking patterns of the user. The input variables of the FL are step strength and frequency, and the output is the estimated step length. Moreover, a waist-mounted sensor module has been developed using low-cost inertial sensors. Since low-cost sensors suffer from various errors, a calibration procedure has been utilized to improve accuracy. The proposed PDR scheme in this study demonstrates its ability to be implemented on waist-mounted devices in real time and is suitable for the indoor and outdoor environments considered in this study without the need for map information or any pre-installed infrastructure. The experiment results show that the maximum distance error was within 1.2% of 116.51 m in an indoor environment and was 1.78% of 385.2 m in an outdoor environment.

  17. Adaptive Sensor Tuning for Seismic Event Detection in Environment with Electromagnetic Noise

    Science.gov (United States)

    Ziegler, Abra E.

    The goal of this research is to detect possible microseismic events at a carbon sequestration site. Data recorded on a continuous downhole microseismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project, were evaluated using machine learning and reinforcement learning techniques to determine their effectiveness at seismic event detection on a dataset with electromagnetic noise. The data were recorded from a passive vertical monitoring array consisting of 16 levels of 3-component 15 Hz geophones installed in the field and continuously recording since January 2014. Electromagnetic and other noise recorded on the array has significantly impacted the utility of the data and it was necessary to characterize and filter the noise in order to attempt event detection. Traditional detection methods using short-term average/long-term average (STA/LTA) algorithms were evaluated and determined to be ineffective because of changing noise levels. To improve the performance of event detection and automatically and dynamically detect seismic events using effective data processing parameters, an adaptive sensor tuning (AST) algorithm developed by Sandia National Laboratories was utilized. AST exploits neuro-dynamic programming (reinforcement learning) trained with historic event data to automatically self-tune and determine optimal detection parameter settings. The key metric that guides the AST algorithm is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The effects that changes in neighborhood configuration have on signal detection were explored, as it was determined that neighborhood-based detections significantly reduce the number of both missed and false detections in ground-truthed data. The performance of the AST algorithm was

  18. A Self-Adaptive Dynamic Recognition Model for Fatigue Driving Based on Multi-Source Information and Two Levels of Fusion

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-09-01

    Full Text Available To improve the effectiveness and robustness of fatigue driving recognition, a self-adaptive dynamic recognition model is proposed that incorporates information from multiple sources and involves two sequential levels of fusion, constructed at the feature level and the decision level. Compared with existing models, the proposed model introduces a dynamic basic probability assignment (BPA to the decision-level fusion such that the weight of each feature source can change dynamically with the real-time fatigue feature measurements. Further, the proposed model can combine the fatigue state at the previous time step in the decision-level fusion to improve the robustness of the fatigue driving recognition. An improved correction strategy of the BPA is also proposed to accommodate the decision conflict caused by external disturbances. Results from field experiments demonstrate that the effectiveness and robustness of the proposed model are better than those of models based on a single fatigue feature and/or single-source information fusion, especially when the most effective fatigue features are used in the proposed model.

  19. Complimentary Advanced Fusion Exploration

    National Research Council Canada - National Science Library

    Alford, Mark G; Jones, Eric C; Bubalo, Adnan; Neumann, Melissa; Greer, Michael J

    2005-01-01

    .... The focus areas were in the following regimes: multi-tensor homographic computer vision image fusion, out-of-sequence measurement and track data handling, Nash bargaining approaches to sensor management, pursuit-evasion game theoretic modeling...

  20. Evolution of Heat Sensors Drove Shifts in Thermosensation between Xenopus Species Adapted to Different Thermal Niches.

    Science.gov (United States)

    Saito, Shigeru; Ohkita, Masashi; Saito, Claire T; Takahashi, Kenji; Tominaga, Makoto; Ohta, Toshio

    2016-05-20

    Temperature is one of the most critical environmental factors affecting survival, and thus species that inhabit different thermal niches have evolved thermal sensitivities suitable for their respective habitats. During the process of shifting thermal niches, various types of genes expressed in diverse tissues, including those of the peripheral to central nervous systems, are potentially involved in the evolutionary changes in thermosensation. To elucidate the molecular mechanisms behind the evolution of thermosensation, thermal responses were compared between two species of clawed frogs (Xenopus laevis and Xenopus tropicalis) adapted to different thermal environments. X. laevis was much more sensitive to heat stimulation than X. tropicalis at the behavioral and neural levels. The activity and sensitivity of the heat-sensing TRPA1 channel were higher in X. laevis compared with those of X. tropicalis The thermal responses of another heat-sensing channel, TRPV1, also differed between the two Xenopus species. The species differences in Xenopus TRPV1 heat responses were largely determined by three amino acid substitutions located in the first three ankyrin repeat domains, known to be involved in the regulation of rat TRPV1 activity. In addition, Xenopus TRPV1 exhibited drastic species differences in sensitivity to capsaicin, contained in chili peppers, between the two Xenopus species. Another single amino acid substitution within Xenopus TRPV1 is responsible for this species difference, which likely alters the neural and behavioral responses to capsaicin. These combined subtle amino acid substitutions in peripheral thermal sensors potentially serve as a driving force for the evolution of thermal and chemical sensation. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  1. A Nonlinear Adaptive Approach to Isolation of Sensor Faults and Component Faults, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Impact Technologies, LLC in collaboration with Wright State University and Pratt & Whitney, propose to develop innovative methods to differentiate sensor failure...

  2. Improving the Forecast Accuracy of an Ocean Observation and Prediction System by Adaptive Control of the Sensor Network

    Science.gov (United States)

    Talukder, A.; Panangadan, A. V.; Blumberg, A. F.; Herrington, T.; Georgas, N.

    2008-12-01

    The New York Harbor Observation and Prediction System (NYHOPS) is a real-time, estuarine and coastal ocean observing and modeling system for the New York Harbor and surrounding waters. Real-time measurements from in-situ mobile and stationary sensors in the NYHOPS networks are assimilated into marine forecasts in order to reduce the discrepancy with ground truth. The forecasts are obtained from the ECOMSED hydrodynamic model, a shallow water derivative of the Princeton Ocean Model. Currently, all sensors in the NYHOPS system are operated in a fixed mode with uniform sampling rates. This technology infusion effort demonstrates the use of Model Predictive Control (MPC) to autonomously adapt the operation of both mobile and stationary sensors in response to changing events that are -automatically detected from the ECOMSED forecasts. The controller focuses sensing resources on those regions that are expected to be impacted by the detected events. The MPC approach involves formulating the problem of calculating the optimal sensor parameters as a constrained multi-objective optimization problem. We have developed an objective function that takes into account the spatiotemporal relationship of the in-situ sensor locations and the locations of events detected by the model. Experiments in simulation were carried out using data collected during a freshwater flooding event. The location of the resulting freshwater plume was calculated from the corresponding model forecasts and was used by the MPC controller to derive control parameters for the sensing assets. The operational parameters that are controlled include the sampling rates of stationary sensors, paths of unmanned underwater vehicles (UUVs), and data transfer routes between sensors and the central modeling computer. The simulation experiments show that MPC-based sensor control reduces the RMS error in the forecast by a factor of 380% as compared to uniform sampling. The paths of multiple UUVs were simultaneously

  3. A Novel Energy-Efficient Multi-Sensor Fusion Wake-Up Control Strategy Based on a Biomimetic Infectious-Immune Mechanism for Target Tracking.

    Science.gov (United States)

    Zhou, Jie; Liang, Yan; Shen, Qiang; Feng, Xiaoxue; Pan, Quan

    2018-04-18

    A biomimetic distributed infection-immunity model (BDIIM), inspired by the immune mechanism of an infected organism, is proposed in order to achieve a high-efficiency wake-up control strategy based on multi-sensor fusion for target tracking. The resultant BDIIM consists of six sub-processes reflecting the infection-immunity mechanism: occurrence probabilities of direct-infection (DI) and cross-infection (CI), immunity/immune-deficiency of DI and CI, pathogen amount of DI and CI, immune cell production, immune memory, and pathogen accumulation under immunity state. Furthermore, a corresponding relationship between the BDIIM and sensor wake-up control is established to form the collaborative wake-up method. Finally, joint surveillance and target tracking are formulated in the simulation, in which we show that the energy cost and position tracking error are reduced to 50.8% and 78.9%, respectively. Effectiveness of the proposed BDIIM algorithm is shown, and this model is expected to have a significant role in guiding the performance improvement of multi-sensor networks.

  4. Modeling and simulation of adaptive multimodal optical sensors for target tracking in the visible to near infrared

    Science.gov (United States)

    Presnar, Michael D.

    This work investigates an integrated aerial remote sensor design approach to address moving target detection and tracking problems within highly cluttered, dynamic ground-based scenes. Sophisticated simulation methodologies and scene phenomenology validations have resulted in advancements in artificial multimodal truth video synthesis. Complex modeling of novel micro-opto-electro-mechanical systems (MOEMS) devices, optical systems, and detector arrays has resulted in a proof of concept for a state-of-the-art imaging spectropolarimeter sensor model that does not suffer from typical multimodal image registration problems. Test methodology developed for this work provides the ability to quantify performance of a target tracking application with varying ground scenery, flight characteristics, or sensor specifications. The culmination of this research is an end-to-end simulated demonstration of multimodal aerial remote sensing and target tracking. Deeply hidden target recognition is shown to be enhanced through the fusing of panchromatic, hyperspectral, and polarimetric image modalities. The Digital Imaging and Remote Sensing Image Generation model was leveraged to synthesize truth spectropolarimetric sensor-reaching radiance image cubes comprised of coregistered Stokes vector bands in the visible to near-infrared. An intricate synthetic urban scene containing numerous moving vehicular targets was imaged from a virtual sensor aboard an aerial platform encircling a stare point. An adaptive sensor model was designed with a superpixel array of MOEMS devices fabricated atop a division of focal plane detector. Degree of linear polarization (DoLP) imagery is acquired by combining three adjacent micropolarizer outputs within each 2x2 superpixel whose respective transmissions vary with wavelength, relative angle of polarization, and wire-grid spacing. A novel micromirror within each superpixel adaptively relays light between a panchromatic imaging channel and a hyperspectral

  5. Reactive Power based Model Reference Neural Learning Adaptive System for Speed Estimation in Sensor-less Induction Motor Drives

    Directory of Open Access Journals (Sweden)

    K Sedhuraman

    2012-12-01

    Full Text Available In this paper, a novel reactive power based model reference neural learning adaptive system (RP-MRNLAS is proposed. The model reference adaptive system (MRAS based speed estimation is one of the most popular methods used for sensor-less controlled induction motor drives. In conventional MRAS, the error adaptation is done using a Proportional-integral-(PI. The non-linear mapping capability of a neural network (NN and the powerful learning algorithms have increased the applications of NN in power electronics and drives. Thus, a neural learning algorithm is used for the adaptation mechanism in MRAS and is often referred to as a model reference neural learning adaptive system (MRNLAS. In MRNLAS, the error between the reference and neural learning adaptive models is back propagated to adjust the weights of the neural network for rotor speed estimation. The two different methods of MRNLAS are flux based (RF-MRNLAS and reactive power based (RP-MRNLAS. The reactive power- based methods are simple and free from integral equations as compared to flux based methods. The advantage of the reactive power based method and the NN learning algorithms are exploited in this work to yield a RPMRNLAS. The performance of the proposed RP-MRNLAS is analyzed extensively. The proposed RP-MRNLAS is compared in terms of accuracy and integrator drift problems with popular rotor flux-based MRNLAS for the same system and validated through Matlab/Simulink. The superiority of the RP- MRNLAS technique is demonstrated

  6. A Framework for a Distributed and Adaptive Query Processing Engine for Wireless Sensor Networks

    NARCIS (Netherlands)

    Chatterjea, Supriyo; Honda, S; Iwaoka, H; van Hoesel, L.F.W.; Havinga, Paul J.M.

    Wireless sensor networks (WSNs) are formed of tiny, highly energy-constrained sensor nodes that are equipped with wireless transceivers and can be used primarily in environmental monitoring applications. The nodes communicate with one another by autonomously creating ad-hoc multihop networks which

  7. Reactive and adaptive monitoring to secure aggregation in wireless sensor networks

    NARCIS (Netherlands)

    Labraoui, Nabila; Gueroui, Mourad; Aliouat, Makhlouf; Petit, Jonathan

    Data aggregation is considered as one of the fundamental distributed data processing procedures for saving the energy and minimizing the medium access layer contention in wireless sensor networks. However, sensor networks are likely to be deployed in an untrusted environment, which make them

  8. Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors

    NARCIS (Netherlands)

    Garcia de Marina Peinado, Hector; Espinosa, Felipe; Santos, Carlos

    Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way,

  9. Changes, adaptations, and applications of a bio inspired machine vision sensor - biomed 2013.

    Science.gov (United States)

    Streeter, Robert W; Barrett, Steven F; Wright, Cameron H G; Dean, Brian K

    2013-01-01

    The Musca domestica (common housefly) biomimetic sensor project is an ongoing endeavor at the University of Wyoming. The project has developed a sensor platform based on the rudimentary function of a fly’s compound eye, including the inherent hyperacuity (high sensitivity to motion) present in the insect’s vision. The design portion of the project is nearing completion, and application driven characterization can now begin. NASA is funding research into utilizing the sensor for efficient, fast, and inexpensive target tracking applications. This stage of the project is very preliminary, but is advancing and offers unique challenges and opportunities. his paper provides background information on the sensor design being considered, discusses the advances in the most current sensor platform, and offers an application currently being investigated.

  10. Accurate positioning of pedestrains in mixed indoor/outdoor settings : A particle filter approach to sensor and map fusion

    DEFF Research Database (Denmark)

    Toftkjær, Thomas

    using a dierent procedure. Fourthly, the thesis proposes the use of cyber foraging and cloud computing to handle the heavy computational burden of particle lters on ressource constrained devices such as smartphones. Implementing a positioning framework as a cloud service introduces a new service type...... into the cloud computing domain, thus allowing state-of-the-art positioning techniques, such as the ones presented in this thesis, to be used by application developers in applications without the concern of implementation details. Finally, to allow smartphones to compensate for the additional energy consumption...... sensor types: The motion sensor, the absolute positioning sensor, and the relative sensor. ProPosition and Proloc is tested in real-world scenarios through two experiment series. Thirdly, this thesis proposes the Pervasive Positioning Location Model. This location model combines lightweight model...

  11. Adaptive Aggregation Routing to Reduce Delay for Multi-Layer Wireless Sensor Networks.

    Science.gov (United States)

    Li, Xujing; Liu, Anfeng; Xie, Mande; Xiong, Neal N; Zeng, Zhiwen; Cai, Zhiping

    2018-04-16

    The quality of service (QoS) regarding delay, lifetime and reliability is the key to the application of wireless sensor networks (WSNs). Data aggregation is a method to effectively reduce the data transmission volume and improve the lifetime of a network. In the previous study, a common strategy required that data wait in the queue. When the length of the queue is greater than or equal to the predetermined aggregation threshold ( N t ) or the waiting time is equal to the aggregation timer ( T t ), data are forwarded at the expense of an increase in the delay. The primary contributions of the proposed Adaptive Aggregation Routing (AAR) scheme are the following: (a) the senders select the forwarding node dynamically according to the length of the data queue, which effectively reduces the delay. In the AAR scheme, the senders send data to the nodes with a long data queue. The advantages are that first, the nodes with a long data queue need a small amount of data to perform aggregation; therefore, the transmitted data can be fully utilized to make these nodes aggregate. Second, this scheme balances the aggregating and data sending load; thus, the lifetime increases. (b) An improved AAR scheme is proposed to improve the QoS. The aggregation deadline ( T t ) and the aggregation threshold ( N t ) are dynamically changed in the network. In WSNs, nodes far from the sink have residual energy because these nodes transmit less data than the other nodes. In the improved AAR scheme, the nodes far from the sink have a small value of T t and N t to reduce delay, and the nodes near the sink are set to a large value of T t and N t to reduce energy consumption. Thus, the end to end delay is reduced, a longer lifetime is achieved, and the residual energy is fully used. Simulation results demonstrate that compared with the previous scheme, the performance of the AAR scheme is improved. This scheme reduces the delay by 14.91%, improves the lifetime by 30.91%, and increases energy

  12. An LPV Adaptive Observer for Updating a Map Applied to an MAF Sensor in a Diesel Engine

    Directory of Open Access Journals (Sweden)

    Zhiyuan Liu

    2015-10-01

    Full Text Available In this paper, a new method for mass air flow (MAF sensor error compensation and an online updating error map (or lookup table due to installation and aging in a diesel engine is developed. Since the MAF sensor error is dependent on the engine operating point, the error model is represented as a two-dimensional (2D map with two inputs, fuel mass injection quantity and engine speed. Meanwhile, the 2D map representing the MAF sensor error is described as a piecewise bilinear interpolation model, which can be written as a dot product between the regression vector and parameter vector using a membership function. With the combination of the 2D map regression model and the diesel engine air path system, an LPV adaptive observer with low computational load is designed to estimate states and parameters jointly. The convergence of the proposed algorithm is proven under the conditions of persistent excitation and given inequalities. The observer is validated against the simulation data from engine software enDYNA provided by Tesis. The results demonstrate that the operating point-dependent error of the MAF sensor can be approximated acceptably by the 2D map from the proposed method.

  13. An application of neural network for Structural Health Monitoring of an adaptive wing with an array of FBG sensors

    International Nuclear Information System (INIS)

    Mieloszyk, Magdalena; Skarbek, Lukasz; Ostachowicz, Wieslaw; Krawczuk, Marek

    2011-01-01

    This paper presents an application of neural networks to determinate the level of activation of shape memory alloy actuators of an adaptive wing. In this concept the shape of the wing can be controlled and altered thanks to the wing design and the use of integrated shape memory alloy actuators. The wing is assumed as assembled from a number of wing sections that relative positions can be controlled independently by thermal activation of shape memory actuators. The investigated wing is employed with an array of Fibre Bragg Grating sensors. The Fibre Bragg Grating sensors with combination of a neural network have been used to Structural Health Monitoring of the wing condition. The FBG sensors are a great tool to control the condition of composite structures due to their immunity to electromagnetic fields as well as their small size and weight. They can be mounted onto the surface or embedded into the wing composite material without any significant influence on the wing strength. The paper concentrates on analysis of the determination of the twisting moment produced by an activated shape memory alloy actuator. This has been analysed both numerically using the finite element method by a commercial code ABAQUS (registered) and experimentally using Fibre Bragg Grating sensor measurements. The results of the analysis have been then used by a neural network to determine twisting moments produced by each shape memory alloy actuator.

  14. An LPV Adaptive Observer for Updating a Map Applied to an MAF Sensor in a Diesel Engine

    Science.gov (United States)

    Liu, Zhiyuan; Wang, Changhui

    2015-01-01

    In this paper, a new method for mass air flow (MAF) sensor error compensation and an online updating error map (or lookup table) due to installation and aging in a diesel engine is developed. Since the MAF sensor error is dependent on the engine operating point, the error model is represented as a two-dimensional (2D) map with two inputs, fuel mass injection quantity and engine speed. Meanwhile, the 2D map representing the MAF sensor error is described as a piecewise bilinear interpolation model, which can be written as a dot product between the regression vector and parameter vector using a membership function. With the combination of the 2D map regression model and the diesel engine air path system, an LPV adaptive observer with low computational load is designed to estimate states and parameters jointly. The convergence of the proposed algorithm is proven under the conditions of persistent excitation and given inequalities. The observer is validated against the simulation data from engine software enDYNA provided by Tesis. The results demonstrate that the operating point-dependent error of the MAF sensor can be approximated acceptably by the 2D map from the proposed method. PMID:26512675

  15. An LPV Adaptive Observer for Updating a Map Applied to an MAF Sensor in a Diesel Engine.

    Science.gov (United States)

    Liu, Zhiyuan; Wang, Changhui

    2015-10-23

    In this paper, a new method for mass air flow (MAF) sensor error compensation and an online updating error map (or lookup table) due to installation and aging in a diesel engine is developed. Since the MAF sensor error is dependent on the engine operating point, the error model is represented as a two-dimensional (2D) map with two inputs, fuel mass injection quantity and engine speed. Meanwhile, the 2D map representing the MAF sensor error is described as a piecewise bilinear interpolation model, which can be written as a dot product between the regression vector and parameter vector using a membership function. With the combination of the 2D map regression model and the diesel engine air path system, an LPV adaptive observer with low computational load is designed to estimate states and parameters jointly. The convergence of the proposed algorithm is proven under the conditions of persistent excitation and given inequalities. The observer is validated against the simulation data from engine software enDYNA provided by Tesis. The results demonstrate that the operating point-dependent error of the MAF sensor can be approximated acceptably by the 2D map from the proposed method.

  16. Experimental Comparison Between Mahoney and Complementary Sensor Fusion Algorithm for Attitude Determination by Raw Sensor Data of Xsens Imu on Buoy

    Science.gov (United States)

    Jouybari, A.; Ardalan, A. A.; Rezvani, M.-H.

    2017-09-01

    The accurate measurement of platform orientation plays a critical role in a range of applications including marine, aerospace, robotics, navigation, human motion analysis, and machine interaction. We used Mahoney filter, Complementary filter and Xsens Kalman filter for achieving Euler angle of a dynamic platform by integration of gyroscope, accelerometer, and magnetometer measurements. The field test has been performed in Kish Island using an IMU sensor (Xsens MTi-G-700) that installed onboard a buoy so as to provide raw data of gyroscopes, accelerometers, magnetometer measurements about 25 minutes. These raw data were used to calculate the Euler angles by Mahoney filter and Complementary filter, while the Euler angles collected by XSense IMU sensor become the reference of the Euler angle estimations. We then compared Euler angles which calculated by Mahoney Filter and Complementary Filter with reference to the Euler angles recorded by the XSense IMU sensor. The standard deviations of the differences between the Mahoney Filter, Complementary Filter Euler angles and XSense IMU sensor Euler angles were about 0.5644, 0.3872, 0.4990 degrees and 0.6349, 0.2621, 2.3778 degrees for roll, pitch, and heading, respectively, so the numerical result assert that Mahoney filter is precise for roll and heading angles determination and Complementary filter is precise only for pitch determination, it should be noted that heading angle determination by Complementary filter has more error than Mahoney filter.

  17. A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring

    Directory of Open Access Journals (Sweden)

    Radek Martinek

    2017-04-01

    Full Text Available This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS, and the Normalized Least Mean Square (NLMS Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs, filtered from abdominal maternal phonocardiograms (mPCGs by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV.

  18. A Long-Distance RF-Powered Sensor Node with Adaptive Power Management for IoT Applications.

    Science.gov (United States)

    Pizzotti, Matteo; Perilli, Luca; Del Prete, Massimo; Fabbri, Davide; Canegallo, Roberto; Dini, Michele; Masotti, Diego; Costanzo, Alessandra; Franchi Scarselli, Eleonora; Romani, Aldo

    2017-07-28

    We present a self-sustained battery-less multi-sensor platform with RF harvesting capability down to -17 dBm and implementing a standard DASH7 wireless communication interface. The node operates at distances up to 17 m from a 2 W UHF carrier. RF power transfer allows operation when common energy scavenging sources (e.g., sun, heat, etc.) are not available, while the DASH7 communication protocol makes it fully compatible with a standard IoT infrastructure. An optimized energy-harvesting module has been designed, including a rectifying antenna (rectenna) and an integrated nano-power DC/DC converter performing maximum-power-point-tracking (MPPT). A nonlinear/electromagnetic co-design procedure is adopted to design the rectenna, which is optimized to operate at ultra-low power levels. An ultra-low power microcontroller controls on-board sensors and wireless protocol, to adapt the power consumption to the available detected power by changing wake-up policies. As a result, adaptive behavior can be observed in the designed platform, to the extent that the transmission data rate is dynamically determined by RF power. Among the novel features of the system, we highlight the use of nano-power energy harvesting, the implementation of specific hardware/software wake-up policies, optimized algorithms for best sampling rate implementation, and adaptive behavior by the node based on the power received.

  19. A Long-Distance RF-Powered Sensor Node with Adaptive Power Management for IoT Applications

    Science.gov (United States)

    del Prete, Massimo; Fabbri, Davide; Canegallo, Roberto; Dini, Michele; Costanzo, Alessandra

    2017-01-01

    We present a self-sustained battery-less multi-sensor platform with RF harvesting capability down to −17 dBm and implementing a standard DASH7 wireless communication interface. The node operates at distances up to 17 m from a 2 W UHF carrier. RF power transfer allows operation when common energy scavenging sources (e.g., sun, heat, etc.) are not available, while the DASH7 communication protocol makes it fully compatible with a standard IoT infrastructure. An optimized energy-harvesting module has been designed, including a rectifying antenna (rectenna) and an integrated nano-power DC/DC converter performing maximum-power-point-tracking (MPPT). A nonlinear/electromagnetic co-design procedure is adopted to design the rectenna, which is optimized to operate at ultra-low power levels. An ultra-low power microcontroller controls on-board sensors and wireless protocol, to adapt the power consumption to the available detected power by changing wake-up policies. As a result, adaptive behavior can be observed in the designed platform, to the extent that the transmission data rate is dynamically determined by RF power. Among the novel features of the system, we highlight the use of nano-power energy harvesting, the implementation of specific hardware/software wake-up policies, optimized algorithms for best sampling rate implementation, and adaptive behavior by the node based on the power received. PMID:28788084

  20. A Long-Distance RF-Powered Sensor Node with Adaptive Power Management for IoT Applications

    Directory of Open Access Journals (Sweden)

    Matteo Pizzotti

    2017-07-01

    Full Text Available We present a self-sustained battery-less multi-sensor platform with RF harvesting capability down to −17 dBm and implementing a standard DASH7 wireless communication interface. The node operates at distances up to 17 m from a 2 W UHF carrier. RF power transfer allows operation when common energy scavenging sources (e.g., sun, heat, etc. are not available, while the DASH7 communication protocol makes it fully compatible with a standard IoT infrastructure. An optimized energy-harvesting module has been designed, including a rectifying antenna (rectenna and an integrated nano-power DC/DC converter performing maximum-power-point-tracking (MPPT. A nonlinear/electromagnetic co-design procedure is adopted to design the rectenna, which is optimized to operate at ultra-low power levels. An ultra-low power microcontroller controls on-board sensors and wireless protocol, to adapt the power consumption to the available detected power by changing wake-up policies. As a result, adaptive behavior can be observed in the designed platform, to the extent that the transmission data rate is dynamically determined by RF power. Among the novel features of the system, we highlight the use of nano-power energy harvesting, the implementation of specific hardware/software wake-up policies, optimized algorithms for best sampling rate implementation, and adaptive behavior by the node based on the power received.

  1. Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions.

    Science.gov (United States)

    Sharmin, Moushumi; Raij, Andrew; Epstien, David; Nahum-Shani, Inbal; Beck, J Gayle; Vhaduri, Sudip; Preston, Kenzie; Kumar, Santosh

    2015-09-01

    We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.

  2. Adaptive Wireless Ad-hoc Sensor Networks for Long-term and Event-oriented Environmental Monitoring

    Science.gov (United States)

    Bumberger, Jan; Mollenhauer, Hannes; Remmler, Paul; Chirila, Andrei Marian; Mollenhauer, Olaf; Hutschenreuther, Tino; Toepfer, Hannes; Dietrich, Peter

    2016-04-01

    Ecosystems are often characterized by their high heterogeneity, complexity and dynamic. Hence, single point measurements are often not sufficient for their complete representation. The application of wireless sensor networks in terrestrial and aquatic environmental systems offer significant benefits as a better consideration to the local test conditions, due to the simple adjustment of the sensor distribution, the sensor types and the sample rate. Another advantage of wireless ad-hoc sensor networks is their self-organizing behavior, resulting in a major reduction in installation and operation costs and time. In addition, individual point measurements with a sensor are significantly improved by measuring at several points continuously. In this work a concept and realization for Long-term ecosystem research is given in the field monitoring of micrometeorology and soil parameters for the interaction of biotic and abiotic processes. This long term analyses are part of the Global Change Experimental Facility (GCEF), a large field-based experimental platform to assess the effects of climate change on ecosystem functions and processes under different land-use scenarios. Regarding to the adaptive behavior of the network, also a mobile version was developed to overcome the lack of information of temporally and spatially fixed measurements for the detection and recording of highly dynamic or time limited processes. First results of different field campaigns are given to present the potentials and limitations of this application in environmental science, especially for the monitoring of the interaction of biotic and abiotic processes, soil-atmosphere interaction and the validation of remote sensing data.

  3. Simple method for adaptive filtering of motion artifacts in E-textile wearable ECG sensors.

    Science.gov (United States)

    Alkhidir, Tamador; Sluzek, Andrzej; Yapici, Murat Kaya

    2015-08-01

    In this paper, we have developed a simple method for adaptive out-filtering of the motion artifact from the electrocardiogram (ECG) obtained by using conductive textile electrodes. The textile electrodes were placed on the left and the right wrist to measure ECG through lead-1 configuration. The motion artifact was induced by simple hand movements. The reference signal for adaptive filtering was obtained by placing additional electrodes at one hand to capture the motion of the hand. The adaptive filtering was compared to independent component analysis (ICA) algorithm. The signal-to-noise ratio (SNR) for the adaptive filtering approach was higher than independent component analysis in most cases.

  4. Highly Adaptive Primary Mirror Having Embedded Actuators, Sensors, and Neural Control, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — Xinetics has demonstrated the technology required to fabricate a self-compensating highly adaptive silicon carbide primary mirror system having embedded actuators,...

  5. Adaptive management of energy consumption, reliability and delay of wireless sensor node: Application to IEEE 802.15.4 wireless sensor node.

    Science.gov (United States)

    Kone, Cheick Tidjane; Mathias, Jean-Denis; De Sousa, Gil

    2017-01-01

    Designing a Wireless Sensor Network (WSN) to achieve a high Quality of Service (QoS) (network performance and durability) is a challenging problem. We address it by focusing on the performance of the 802.15.4 communication protocol because the IEEE 802.15.4 Standard is actually considered as one of the reference technologies in WSNs. In this paper, we propose to control the sustainable use of resources (i.e., energy consumption, reliability and timely packet transmission) of a wireless sensor node equipped with photovoltaic cells by an adaptive tuning not only of the MAC (Medium Access Control) parameters but also of the sampling frequency of the node. To do this, we use one of the existing control approaches, namely the viability theory, which aims to preserve the functions and the controls of a dynamic system in a set of desirable states. So, an analytical model, describing the evolution over time of nodal resources, is derived and used by a viability algorithm for the adaptive tuning of the IEEE 802.15.4 MAC protocol. The simulation analysis shows that our solution allows ensuring indefinitely, in the absence of hardware failure, the operations (lifetime duration, reliability and timely packet transmission) of an 802.15.4 WSN and one can temporarily increase the sampling frequency of the node beyond the regular sampling one. This latter brings advantages for agricultural and environmental applications such as precision agriculture, flood or fire prevention. Main results show that our current approach enable to send more information when critical events occur without the node runs out of energy. Finally, we argue that our approach is generic and can be applied to other types of WSN.

  6. A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles.

    Science.gov (United States)

    Meng, Xiaoli; Wang, Heng; Liu, Bingbing

    2017-09-18

    Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization.

  7. 3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion

    Directory of Open Access Journals (Sweden)

    Qingxu Dou

    2016-11-01

    Full Text Available We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR, Passive Magnetic Fields (PMF, Magnetic Gradiometer (MG, Low Frequency Electromagnetic Fields (LFEM and Vibro-Acoustics (VA. As part of the MCS algorithm, a novel formulation of the extended Kalman Filter (EKF is proposed for marching existing utility tracks from a scan cross-section (scs to the next one; novel rules for initializing utilities based on hypothesized detections on the first scs and for associating predicted utility tracks with hypothesized detections in the following scss are introduced. Algorithms are proposed for generating virtual scan lines based on given hypothesized detections when different sensors do not share common scan lines, or when only the coordinates of the hypothesized detections are provided without any information of the actual survey scan lines. The performance of the proposed system is evaluated with both synthetic data and real data. The experimental results in this work demonstrate that the proposed MCS algorithm can locate multiple buried utility segments simultaneously, including both straight and curved utilities, and can separate intersecting segments. By using the probabilities of a hypothesized detection being a pipe or a cable together with its 3D coordinates, the MCS algorithm is able to discriminate a pipe and a cable close to each other. The MCS algorithm can be used for both post- and on-site processing. When it is used on site, the detected tracks on the current scs can help to determine the location and direction of the next scan line. The proposed “multi-utility multi-sensor” system has no limit to the number of buried utilities or the number of sensors, and the more sensor data used, the more buried utility segments can be detected with more accurate location

  8. Technical Description of Radar and Optical Sensors Contributing to Joint UK-Australian Satellite Tracking, Data-fusion and Cueing Experiment

    Science.gov (United States)

    Eastment, J.; Ladd, D.; Donnelly, P.; Ash, A.; Harwood, N.; Ritchie, I.; Smith, C.; Bennett, J.; Rutten, M.; Gordon, N.

    2014-09-01

    DSTL, DSTO, EOS and STFC have recently participated in a campaign of co-ordinated observations with both radar and optical sensors in order to demonstrate and to refine methodologies for orbit determination, data fusion and cross-sensor cueing. The experimental programme is described in detail in the companion paper by Harwood et al. At the STFC Chilbolton Observatory in Southern England, an S-band radar on a 25 m diameter fully-steerable dish antenna was used to measure object range and radar cross-section. At the EOS Space Systems facility on Mount Stromlo, near Canberra, Australia, an optical system comprising a 2 m alt / az observatory, with Coude path laser tracking at 400W power, was used to acquire, lock and laser track the cued objects, providing accurate orbit determinations for each. DSTO, located at Edinburgh, Australia, operated an optical system consisting of a small commercial telescope and mount, measuring the direction to the objects. Observation times were limited to the evening solar terminator period. Data from these systems was processed independently, using DSTL-developed and DSTO / EOS-developed algorithms, to perform orbit determination and to cross-cue: (i) the radar, based on the optical measurements; (ii) the optical system, based on the radar measurements; and (iii) the radar, using its own prior observations (self-cueing). In some cases, TLEs were used to initialise the orbit determination process; in other cases, the cues were derived entirely from sensor data. In all 3 scenarios, positive results were obtained for a variety of satellites in low earth orbits, demonstrating the feasibility of the different cue generation techniques. The purpose of this paper is to describe the technical characteristics of the radar and optical systems used, the modes of operation employed to acquire the observations, and details of the parameters measured and the data formats.

  9. Adaptive Decentralized Control of Mobile Underwater Sensor Networks and Robots for Modeling Underwater Phenomena

    Directory of Open Access Journals (Sweden)

    Carrick Detweiler

    2014-05-01

    Full Text Available Understanding the dynamics of bodies of water and their impact on the global environment requires sensing information over the full volume of water. In this article, we develop a gradient-based decentralized controller that dynamically adjusts the depth of a network of underwater sensors to optimize sensing for computing maximally detailed volumetric models. We prove that the controller converges to a local minimum and show how the controller can be extended to work with hybrid robot and sensor network systems. We implement the controller on an underwater sensor network with depth adjustment capabilities. Through simulations and in-situ experiments, we verify the functionality and performance of the system and algorithm.

  10. A Self-Adaptive Wireless Sensor Network Coverage Method for Intrusion Tolerance Based on Trust Value

    Directory of Open Access Journals (Sweden)

    Zuo Chen

    2015-01-01

    Full Text Available The sensor is quite easily attacked or invaded during the process of the node coverage optimization. It is a great challenge to make sure that the wireless sensor network could still carry out a secure communication and reliable coverage under the condition of being attacked. Therefore, this paper proposes a network coverage method for intrusion tolerance based on trust value of nodes by combining the trust value model with the reliable coverage optimization. It first estimates trust value of nodes through which to regulate the perception radius and decision-making radius. Furthermore, this algorithm also combines the classical methods of wireless network coverage, such as GSO and PSO, to realize the networks coverage of invasive tolerant sensor. After comparing with the conventional single cover mechanism, it can improve the security and coverage rate of network under the condition of invasion. The simulation results verify the effectiveness of the algorithm.

  11. Development of CMOS Pixel Sensors fully adapted to the ILD Vertex Detector Requirements

    CERN Document Server

    Winter, Marc; Besson, Auguste; Claus, Gilles; Dorokhov, Andrei; Goffe, Mathieu; Hu-Guo, Christine; Morel, Frederic; Valin, Isabelle; Voutsinas, Georgios; Zhang, Liang

    2012-01-01

    CMOS Pixel Sensors are making steady progress towards the specifications of the ILD vertex detector. Recent developments are summarised, which show that these devices are close to comply with all major requirements, in particular the read-out speed needed to cope with the beam related background. This achievement is grounded on the double- sided ladder concept, which allows combining signals generated by a single particle in two different sensors, one devoted to spatial resolution and the other to time stamp, both assembled on the same mechanical support. The status of the development is overviewed as well as the plans to finalise it using an advanced CMOS process.

  12. A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

    Directory of Open Access Journals (Sweden)

    Huan Zhou

    2017-09-01

    Full Text Available Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model-unscented Kalman filter (IMM-UKF algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variable, after the interaction, by considering the external link of IMM-UKF, which constitutes the cubature-principle-assisted IMM method (CPIMM for solving the non-Gaussian problem, and leads to an adaptive matrix to balance the contribution of the state. The algorithm provides filtering solutions by considering the internal link of IMM-UKF, which is called a new adaptive UKF algorithm (NAUKF to address sensor faults. The proposed CPIMM-NAUKF is evaluated in a numerical simulation and two practical experiments including one navigation experiment and one maneuvering target tracking experiment. The simulation and experiment results show that the proposed CPIMM-NAUKF has greater filtering precision and faster convergence than the existing IMM-UKF. The proposed algorithm achieves a very good tracking performance, and will be effective and applicable in the field of maneuvering target tracking.

  13. Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems

    Energy Technology Data Exchange (ETDEWEB)

    Jassar, S.; Zhao, L. [Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON (Canada); Liao, Z. [Department of Architectural Science, Ryerson University (Canada)

    2009-08-15

    The heating systems are conventionally controlled by open-loop control systems because of the absence of practical methods for estimating average air temperature in the built environment. An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed. This modeling technique has the advantage of expert knowledge of fuzzy inference systems (FISs) and learning capability of artificial neural networks (ANNs). A hybrid learning algorithm, which combines the least-square method and the back-propagation algorithm, is used to identify the parameters of the network. This paper describes an adaptive network based inferential sensor that can be used to design closed-loop control for space heating systems. The research aims to improve the overall performance of heating systems, in terms of energy efficiency and thermal comfort. The average air temperature results estimated by using the developed model are strongly in agreement with the experimental results. (author)

  14. Rasiowa completion versus Keisler saturation: Towards a pragmatics of infinite fusion

    Energy Technology Data Exchange (ETDEWEB)

    Tomasik, J.A. [Univ. of Clermont (France)

    1996-12-31

    The goal of this survey note is to make a step towards a semiotical approach to the control of Sensor Data Fusion systems. We try to adapt infinitistic methods of Rasiowa and Keisler as prototypes of a many-one pragmatics for the control of SDF in order to provide a faithful system - semantical or/and syntactical - adequate for simultaneous interpretation of signals received through several sensors. Intuitively, it is clear that the amount of the information captured during the fusion process depends strongly on the fusion itself. Nevertheless we can find (c.f. works of Kokar`s group) the following implicit heuristic in semiotical investigations on Sensory Data Fusion Z`s accepted.

  15. Adaptive life simulator: A novel approach to modeling the cardiovascular system

    Energy Technology Data Exchange (ETDEWEB)

    Kangas, L.J.; Keller, P.E.; Hashem, S. [and others

    1995-06-01

    In this paper, an adaptive life simulator (ALS) is introduced. The ALS models a subset of the dynamics of the cardiovascular behavior of an individual by using a recurrent artificial neural network. These models are developed for use in applications that require simulations of cardiovascular systems, such as medical mannequins, and in medical diagnostic systems. This approach is unique in that each cardiovascular model is developed from physiological measurements of an individual. Any differences between the modeled variables and the actual variables of an individual can subsequently be used for diagnosis. This approach also exploits sensor fusion applied to biomedical sensors. Sensor fusion optimizes the utilization of the sensors. The advantage of sensor fusion has been demonstrated in applications including control and diagnostics of mechanical and chemical processes.

  16. Alert: An Adaptive Low-Latency Event-Driven MAC Protocol for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Vinod Namboodiri

    2011-01-01

    Full Text Available Collection of rare but delay-critical messages from a group of sensor nodes is a key process in many wireless sensor network applications. This is particularly important for security-related applications like intrusion detection and fire alarm systems. An event sensed by multiple sensor nodes in the network can trigger many messages to be sent simultaneously. We present Alert, a MAC protocol for collecting event-triggered urgent messages from a group of sensor nodes with minimum latency and without requiring any cooperation or prescheduling among the senders or between senders and receiver during protocol execution. Alert is designed to handle multiple simultaneous messages from different nodes efficiently and reliably, minimizing the overall delay to collect all messages along with the delay to get the first message. Moreover, the ability of the network to handle a large number of simultaneous messages does not come at the cost of excessive delays when only a few messages need to be handled. We analyze Alert and evaluate its feasibility and performance with an implementation on commodity hardware. We further compare Alert with existing approaches through simulations and show the performance improvement possible through Alert.

  17. RAHIM: Robust Adaptive Approach Based on Hierarchical Monitoring Providing Trust Aggregation for Wireless Sensor Networks

    NARCIS (Netherlands)

    Labraoui, Nabila; Gueroui, Mourad; Aliouat, Makhlouf; Petit, Jonathan

    2011-01-01

    In-network data aggregation has a great impact on the energy consumption in large-scale wireless sensor networks. However, the resource constraints and vulnerable deployment environments challenge the application of this technique in terms of security and efficiency. A compromised node may forge

  18. Simulation studies of hydrodynamic aspects of magneto-inertial fusion and high order adaptive algorithms for Maxwell equations

    Science.gov (United States)

    Wu, Lingling

    Three-dimensional simulations of the formation and implosion of plasma liners for the Plasma Jet Induced Magneto Inertial Fusion (PJMIF) have been performed using multiscale simulation technique based on the FronTier code. In the PJMIF concept, a plasma liner, formed by merging of a large number of radial, highly supersonic plasma jets, implodes on the target in the form of two compact plasma toroids, and compresses it to conditions of the nuclear fusion ignition. The propagation of a single jet with Mach number 60 from the plasma gun to the merging point was studied using the FronTier code. The simulation result was used as input to the 3D jet merger problem. The merger of 144, 125, and 625 jets and the formation and heating of plasma liner by compression waves have been studied and compared with recent theoretical predictions. The main result of the study is the prediction of the average Mach number reduction and the description of the liner structure and properties. We have also compared the effect of different merging radii. Spherically symmetric simulations of the implosion of plasma liners and compression of plasma targets have also been performed using the method of front tracking. The cases of single deuterium and xenon liners and double layer deuterium - xenon liners compressing various deuterium-tritium targets have been investigated, optimized for maximum fusion energy gains, and compared with theoretical predictions and scaling laws of [P. Parks, On the efficacy of imploding plasma liners for magnetized fusion target compression, Phys. Plasmas 15, 062506 (2008)]. In agreement with the theory, the fusion gain was significantly below unity for deuterium - tritium targets compressed by Mach 60 deuterium liners. In the most optimal setup for a given chamber size that contained a target with the initial radius of 20 cm compressed by 10 cm thick, Mach 60 xenon liner, the target ignition and fusion energy gain of 10 was achieved. Simulations also showed that

  19. Monitoring of Gene Expression in Bacteria during Infections Using an Adaptable Set of Bioluminescent, Fluorescent and Colorigenic Fusion Vectors

    OpenAIRE

    Uliczka, Frank; Pisano, Fabio; Kochut, Annika; Opitz, Wiebke; Herbst, Katharina; Stolz, Tatjana; Dersch, Petra

    2011-01-01

    A family of versatile promoter-probe plasmids for gene expression analysis was developed based on a modular expression plasmid system (pZ). The vectors contain different replicons with exchangeable antibiotic cassettes to allow compatibility and expression analysis on a low-, midi- and high-copy number basis. Suicide vector variants also permit chromosomal integration of the reporter fusion and stable vector derivatives can be used for in vivo or in situ expression studies under non-selective...

  20. Wild life passer species recognition from a technical passage through data fusion of a wireless sensor network

    Science.gov (United States)

    Gazis, A.; Katsiri, E.

    2017-09-01

    This paper presents a Wireless Sensor Network (WSN) system which was created as a project about protecting wildlife using sensor networks following the assistance of the department of Electrical and Computer Engineering of the Democritus University of Thrace. An automated process was implemented, regarding the recognition of a passenger (ie human, wolf, bear, etc.) traversing a box-shaped underground passage, such as the ones located along main highways fusing Width, Height and Weight values. These were measured using low-cost distance (beam) and weight (S-type load) micro-sensors and stored in a central repository. Moreover, the information provided by the WSN was analyzed, via a variety of methods including a neural pattern recognition network as well as clustering algorithms, which were able to recognize the kind of passenger, with certainty scores over 90%. The main concern, regarding the future, is the evaluation of these passages in respect to their effectiveness, i.e. whether they are frequently utilized by animals. This information was further analysed by appropriate information systems, in order to provide insights about the effectiveness of such mitigation structures.