WorldWideScience

Sample records for adaptive sensor fusion

  1. Adaptive sensor fusion

    Kadar, Ivan

    1995-07-01

    A perceptual reasoning system adaptively extracting, associating, and fusing information from multiple sources, at various levels of abstraction, is considered as the building block for the next generation of surveillance systems. A system architecture is presented which makes use of both centralized and distributed predetection fusion combined with intelligent monitor and control coupling both on-platform and off-board track and decision level fusion results. The goal of this system is to create a `gestalt fused sensor system' whose information product is greater than the sum of the information products from the individual sensors and has performance superior to either individual or a sub-group of combined sensors. The application of this architectural concept to the law enforcement arena (e.g. drug interdiction) utilizing multiple spatially and temporally diverse surveillance platforms and/or information sources, is used to illustrate the benefits of the adaptive perceptual reasoning system concept.

  2. Adaptive sensor fusion using genetic algorithms

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

    1994-08-01

    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.

  3. Adaptive sensor fusion using genetic algorithms

    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

  4. Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Network

    Priya Mohite

    2015-01-01

    The data fusion process has led to an evolution for emerging Wireless Sensor Networks (WSNs) and examines the impact of various factors on energy consumption. Significantly there has always been a constant effort to enhance network efficiency without decreasing the quality of information. Based on Adaptive Fusion Steiner Tree (AFST), this paper proposes a heuristic algorithm called Modified Adaptive Fusion Steiner Tree (M-AFST) for energy efficient routing which not only does adaptively adjus...

  5. Adaptive Decision Fusion with a Guidance Sensor in Wireless Sensor Networks

    Zhaohua Yu; Qiang Ling; Yi Yu

    2015-01-01

    In wireless sensor networks, the fusion center collects the dates from the sensor nodes and makes the optimal decision fusion, while the optimal decision fusion rules need the performance parameters of each sensor node. However, sensors, particularly low-cost and low-precision sensors, are usually displaced in harsh environment and their performance parameters can be easily affected by the environment and hardly be known in advance. In order to resolve this issue, we take a heterogeneous wire...

  6. Adaptive information filter for the fusion of data from the object-detecting sensors of an autonomous vehicle

    Becker, J.C. [Technical Univ. Braunschweig (Germany). Inst. of Control Engineering

    2000-07-01

    This paper describes an adaptive information filter for the fusion of sensor data of an autonomous vehicle. The vehicle sensor system for object detection consists of a stereo vision sensor, four laserscanners and a radar sensor and provides a high redundancy in the observed area in front of the vehicle. The derivation of the information filter as well as its application to sensor data fusion is presented. Maneuver of observed targets are detected and the filter parameter are adapted accordingly. The information filter fusion is compared to the Kalman filter based measurement fusion. (orig.)

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

    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

  8. Multi-rate sensor fusion-based adaptive discrete finite-time synergetic control for flexible-joint mechanical systems

    This paper proposes an adaptive discrete finite-time synergetic control (ADFTSC) scheme based on a multi-rate sensor fusion estimator for flexible-joint mechanical systems in the presence of unmeasured states and dynamic uncertainties. Multi-rate sensors are employed to observe the system states which cannot be directly obtained by encoders due to the existence of joint flexibilities. By using an extended Kalman filter (EKF), the finite-time synergetic controller is designed based on a sensor fusion estimator which estimates states and parameters of the mechanical system with multi-rate measurements. The proposed controller can guarantee the finite-time convergence of tracking errors by the theoretical derivation. Simulation and experimental studies are included to validate the effectiveness of the proposed approach. (general)

  9. Sensor Data Fusion

    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...... to the occupied and empty regions. Scale Invariant Feature Transform (SIFT) feature descriptors are interpreted using gaussian probabilistic error models. The use of occupancy grids is proposed for representing the sensor readings. The Bayesian estimation approach is applied to update the sonar array......  and the SIFT descriptors' uncertainty grids. The sensor fusion yields a significant reduction in the uncertainty of the occupancy grid compared to the individual sensor readings....

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

    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 tr

  11. Sensor fusion for social robotics

    Duffy, Brian R.; Garcia, C.; Rooney, Colm, (Thesis); O'Hare, G.M.P.

    2000-01-01

    This paper advocates the application of sensor fusion for the visualisation of social robotic behaviour. Experiments with the Virtual Reality Workbench integrate the key elements of Virtual Reality and robotics in a coherent and systematic manner. The deliberative focusing of attention and sensor fusion between vision systems and sonar sensors is implemented on autonomous mobile robots functioning in standard office environments

  12. Distributed multi-sensor fusion

    Scheffel, Peter; Fish, Robert; Knobler, Ron; Plummer, Thomas

    2008-03-01

    McQ has developed a broad based capability to fuse information in a geographic area from multiple sensors to build a better understanding of the situation. The paper will discuss the fusion architecture implemented by McQ to use many sensors and share their information. This multi sensor fusion architecture includes data sharing and analysis at the individual sensor, at communications nodes that connect many sensors together, at the system server/user interface, and across multi source information available through networked services. McQ will present a data fusion architecture that integrates a "Feature Information Base" (FIB) with McQ's well known Common Data Interchange Format (CDIF) data structure. The distributed multi sensor fusion provides enhanced situation awareness for the user.

  13. Sensor fusion for mobile robot navigation

    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

  14. Data Fusion and Sensors Model

    2000-01-01

    In this paper, we take the model of Laser range finder based on synchronized scanner as example, show how to use data fusion method in the process of sensor model designing to get more robust output. Also we provide our idea on the relation of sensor model, data fusion and system structure, and in the paper, there is a solution that transform the parameter space to get linear model for Kalman filter.

  15. Sensor Fusion for Augmented Reality

    Gustafsson, Fredrik; Schön, Thomas; Hol, Jeroen

    2009-01-01

    The problem of estimating the position and orientation (pose) of a camera is approached by fusing measurements from inertial sensors (accelerometers and rate gyroscopes) and a camera. The sensor fusion approach described in this contribution is based on nonlinear filtering using the measurements from these complementary sensors. This way, accurate and robust pose estimates are available for the primary purpose of augmented reality applications, but with the secondary effect of reducing comput...

  16. Multiple Sensor Fusion and Motion Control of Snake Robot Based on Soft-Computing

    Choi, Woo-Kyung; Kim, Seong-Joo; Jeon, Hong-Tae

    2007-01-01

    The goal of this paper in a snake robot and sensor fusion is that the snake robot which imitates a real snake's an activity and being adapted to topography and has multiple sensors operates well with considering environment around it. To avoid overloads of a processor and process a huge data of multiple sensors in distribute methods, fusion module of sensor is constructed in a module. In a low level of sensor processes, we worked sensor fusion

  17. Optimal Fusion of Sensors

    Larsen, Thomas Dall

    within some global frame of reference using a wide variety of sensors providing odometric, inertial and absolute data concerning the robot and its surroundings. Kalman filters have for a long time been widely used to solve this problem. However, when measurements are delayed or the mobile robot...

  18. Analytical performance evaluation for autonomous sensor fusion

    Chang, K. C.

    2008-04-01

    A distributed data fusion system consists of a network of sensors, each capable of local processing and fusion of sensor data. There has been a great deal of work in developing distributed fusion algorithms applicable to a network centric architecture. Currently there are at least a few approaches including naive fusion, cross-correlation fusion, information graph fusion, maximum a posteriori (MAP) fusion, channel filter fusion, and covariance intersection fusion. However, in general, in a distributed system such as the ad hoc sensor networks, the communication architecture is not fixed. Each node has knowledge of only its local connectivity but not the global network topology. In those cases, the distributed fusion algorithm based on information graph type of approach may not scale due to its requirements to carry long pedigree information for decorrelation. In this paper, we focus on scalable fusion algorithms and conduct analytical performance evaluation to compare their performance. The goal is to understand the performance of those algorithms under different operating conditions. Specifically, we evaluate the performance of channel filter fusion, Chernoff fusion, Shannon Fusion, and Battachayya fusion algorithms. We also compare their results to NaÃve fusion and "optimal" centralized fusion algorithms under a specific communication pattern.

  19. Exploiting Real-Time FPGA Based Adaptive Systems Technology for Real-Time Sensor Fusion in Next Generation Automotive Safety Systems

    Chappell, Steve; Preston, Dan; Olmstead, Dave; Flint, Bob; Sullivan, Chris

    2011-01-01

    We present a system for the boresighting of sensors using inertial measurement devices as the basis for developing a range of dynamic real-time sensor fusion applications. The proof of concept utilizes a COTS FPGA platform for sensor fusion and real-time correction of a misaligned video sensor. We exploit a custom-designed 32-bit soft processor core and C-based design & synthesis for rapid, platform-neutral development. Kalman filter and sensor fusion techniques established in advanced aviation systems are applied to automotive vehicles with results exceeding typical industry requirements for sensor alignment. Results of the static and the dynamic tests demonstrate that using inexpensive accelerometers mounted on (or during assembly of) a sensor and an Inertial Measurement Unit (IMU) fixed to a vehicle can be used to compute the misalignment of the sensor to the IMU and thus vehicle. In some cases the model predications and test results exceeded the requirements by an order of magnitude with a 3-sigma or ...

  20. Sensor fusion for intelligent alarm analysis

    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. Sensor Fusion for Autonomous Mobile Robot Navigation

    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 constantl....... 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.......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...

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

    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.

  3. Optimal decision fusion given sensor rules

    Yunmin ZHU; Xiaorong LI

    2005-01-01

    When all the rules of sensor decision are known,the optimal distributed decision fusion,which relies only on the joint conditional probability densities,can be derived for very general decision systems.They include those systems with interdependent sensor observations and any network structure.It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman-Pearson criterion.Local decision rules of a sensor with communication from other sensors that are optimal for the sensor itself are also presented,which take the form of a generalized likelihood ratio test.Numerical examples are given to reveal some interesting phenomena that communication between sensors can improve performance of a senor decision,but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.

  4. Enhanced chemical weapon warning via sensor fusion

    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.

  5. Time/Computationally Optimal Network Architecture: Wireless Sensor Fusion

    Devi, Gadi Gayathri; Kumari, Priya; Jyoshna, Eslavath; Deepika; Murthy, Garimella Rama

    2013-01-01

    In this research paper, the problems dealing with sensor network architecture, sensor fusion are addressed. Time/Computationally optimal network architectures are investigated. Some novel ideas on sensor fusion are proposed.

  6. New method for sensor data fusion in machine vision

    Wang, Yuan-Fang

    1991-09-01

    In this paper, we propose a new scheme for sensor data fusion in machine vision. The proposed scheme uses Kalman filter as the sensor data integration tool and hierarchical B- spline surface as the recording data structure. Kalman filter is used to obtain statistically optimal estimations of the imaged surface structure based on external sensor measurements. Hierarchical B-spline surface maintains high-order surface derivative continuity, may be adaptively refined, possesses desirable local control property, and is storage efficient. Hence, it is used to record the reconstructed surface structure.

  7. Maneuvering Vehicle Tracking Based on Multi-sensor Fusion

    CHENYing; HANChong-Zhao

    2005-01-01

    Maneuvering targets tracking is a fundamental task in intelligent vehicle research. This paper focuses on the problem of fusion between radar and image sensors in targets tracking. In order to improve positioning accuracy and narrow down the image working area, a novel method that integrates radar filter with image intensity is proposed to establish an adaptive vision window.A weighted Hausdorff distance is introduced to define the functional relationship between image and model projection, and a modified simulated annealing algorithm is used to find optimum orientation parameter. Furthermore, the global state is estimated, which refers to the distributed data fusion algorithm. Experiment results show that our method is accurate.

  8. Fusion of Noisy Multi-sensor Imagery

    Anima Mishra

    2008-01-01

    Full Text Available Interest in fusing multiple sensor data for both military and civil applications has beengrowing. Some of the important applications integrate image information from multiple sensorsto aid in navigation guidance, object detection and recognition, medical diagnosis, datacompression, etc. While, human beings may visually inspect various images and integrateinformation, it is of interest to develop algorithms that can fuse various input imagery to producea composite image. Fusion of images from various sensor modalities is expected to produce anoutput that captures all the relevant information in the input. The standard multi-resolution-based edge fusion scheme has been reviewed in this paper. A theoretical framework is given forthis edge fusion method by showing how edge fusion can be framed as information maximisation.However, the presence of noise complicates the situation. The framework developed is used toshow that for noisy images, all edges no longer correspond to information. In this paper, varioustechniques have been presented for fusion of noisy multi-sensor images.  These techniques aredeveloped for a single resolution as well as using multi-resolution decomposition. Some of thetechniques are based on modifying edge maps by filtering images, while others depend onalternate definition of information maps. Both these approaches can also be combined.Experiments show that the proposed algorithms work well for various kinds of noisy multi-sensor images.

  9. Sensor Fusion for Electromagnetic Stress Measurement and Material Characterisation

    Wilson, John; Tian, Gui; Morozov, Maxim; Qubaa, Abd

    2010-01-01

    Sensor fusion for electromagnetic NDE at different stages and levels has been discussed and three case studies for fusion at sensor and feature levels have been investigated. Instead of applying innovative mathematical techniques to utilise multiple sensors to improve the fidelity of defect and material characterisation, physics based sensor fusion is investigated. It has been shown that the three types of sensing system fusion, feature selection and integration and information combination fo...

  10. Sensor Data Fusion in Automotive Applications

    Lytrivis, Panagiotis; Thomaidis, George; Amditis, Angelos

    2009-01-01

    This chapter has summarized the state-of-the-art in sensor data fusion for automotive applications, showing that this is a relatively new discipline in the automotive research area, compared to signal processing, image processing or radar processing. Thus, there is a

  11. Data fusion and sensor management for nuclear power plant safety

    The paper describes the implementation of the data-sensor fusion and sensor management technology for accident management through simulated severe accident (SA) scenarios subjected to study. The organization of the present paper is as follows. As the data-sensor fusion and sensor management is an emerging technology which is not widely known, in Sec. 2, the definition and goals of data-sensor fusion and sensor management technology is described. In Sec. 3 fits, with reference to Kalman filtering as an information filter, statistical data-sensor fusion technology is described. This is followed by deterministic data-sensor fusion technology using gross plant state variables and neural networks (NN) and the implementation for severe accident management in NPPs. In Sec. 4, the sensor management technology is described. Finally, the performance of the data-sensor fusion technology for NPP safety is discussed. 12 refs, 6 figs

  12. Sensor fusion for precision agriculture

    Information-based management of crop production systems known as precision agriculture relies on different sensor technologies aimed at characterization of spatial heterogeneity of a cropping environment. Remote and proximal sensing systems have been deployed to obtain high-resolution data pertainin...

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

    2015-01-01

    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)...

  14. City Data Fusion: Sensor Data Fusion in the Internet of Things

    Wang, Meisong; Perera, Charith; Jayaraman, Prem Prakash; Zhang, Miranda; Strazdins, Peter; Ranjan, Rajiv

    2015-01-01

    Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework....

  15. Application of Multi-Sensors Information Fusion for Self-protection System of Robot

    Qiuhong Gao

    2013-01-01

    Full Text Available This paper developed a kind of robot self-protection system using the multi-sensors information fusion technology. This system used five groups of photoelectric sensor and ultrasonic sensor which were installed in different direction of the robot. In this study, signals were gathered by using the complement of ranging of photoelectric sensor and ultrasonic sensor. Then the signals were sent to MCU to achieve multi-sensors information fusion.Core fusion technology was the adaptive weighted fusion estimation algorithm, which can make measurement data more accurate. With such technology, an accurate robot self-protection command was made as to avoid obstacles, to judge narrow highland and to prevent dropping. The experiment results validated its good self-protection function.

  16. Sensor Fusion for Augmented Reality

    Hol, Jeroen; Schön, Thomas; Gustafsson, Fredrik; Slycke, Per

    2007-01-01

    In Augmented Reality (AR), the position and orientation of the camera have to be estimated with high accuracy and low latency. This nonlinear estimation problem is studied in the present paper. The proposed solution makes use of measurements from inertial sensors and computer vision. These measurements are fused using a Kalman filtering framework, incorporating a rather detailed model for the dynamics of the camera. Experiments show that the resulting filter provides good estimates of the cam...

  17. Sensor fusion for improved indoor navigation

    Emilsson, Erika; Rydell, Joakim

    2012-09-01

    A reliable indoor positioning system providing high accuracy has the potential to increase the safety of first responders and military personnel significantly. To enable navigation in a broad range of environments and obtain more accurate and robust positioning results, we propose a multi-sensor fusion approach. We describe and evaluate a positioning system, based on sensor fusion between a foot-mounted inertial measurement unit (IMU) and a camera-based system for simultaneous localization and mapping (SLAM). The complete system provides accurate navigation in many relevant environments without depending on preinstalled infrastructure. The camera-based system uses both inertial measurements and visual data, thereby enabling navigation also in environments and scenarios where one of the sensors provides unreliable data during a few seconds. When sufficient light is available, the camera-based system generally provides good performance. The foot-mounted system provides accurate positioning when distinct steps can be detected, e.g., during walking and running, even in dark or smoke-filled environments. By combining the two systems, the integrated positioning system can be expected to enable accurate navigation in almost all kinds of environments and scenarios. In this paper we present results from initial tests, which show that the proposed sensor fusion improves the navigation solution considerably in scenarios where either the foot-mounted or camera-based system is unable to navigate on its own.

  18. Noncoherent fusion detection in wireless sensor networks

    Yang, Fucheng

    2013-01-01

    The main motivation of this thesis is to design low-complexity high efficiency noncoherent fusion rules for the parallel triple-layer wireless sensor networks (WSNs) based on frequency-hopping Mary frequency shift keying (FH/MFSK) techniques, which are hence referred to as the FH/MFSK WSNs. The FH/MFSKWSNs may be employed to monitor single or multiple source events (SEs)with each SE having multiple states. In the FH/MFSKWSNs, local decisions made by local sensor nodes (LSNs) are transmitted t...

  19. Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

    Schenker, Paul S.

    1992-11-01

    Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

  20. Energy Efficient Distributed Data Fusion In Multihop Wireless Sensor Networks

    Huang, Yi

    2010-01-01

    This thesis addresses a transmission energy problem for wireless sensor networks. There are two types of wireless sensor networks. One is single-hop sensor network where data from each sensor is directly transmitted to a fusion center, and the other is multihop sensor network where data is relayed through adjacent sensors. In the absence of a moving agent for data collection, multihop sensor network is typically much more energy efficient than single-hop sensor network since the former avoids...

  1. Sensor Activation and Radius Adaptation (SARA) in Heterogeneous Sensor Networks

    Bartolini, Novella; la Porta, Thomas; Petrioli, Chiara; Silvestri, Simone

    2010-01-01

    In this paper we address the problem of prolonging the lifetime of wireless sensor networks (WSNs) deployed to monitor an area of interest. In this scenario, a helpful approach is to reduce coverage redundancy and therefore the energy expenditure due to coverage. We introduce the first algorithm which reduces coverage redundancy by means of Sensor Activation and sensing Radius Adaptation (SARA)in a general applicative scenario with two classes of devices: sensors that can adapt their sensing range (adjustable sensors) and sensors that cannot (fixed sensors). In particular, SARA activates only a subset of all the available sensors and reduces the sensing range of the adjustable sensors that have been activated. In doing so, SARA also takes possible heterogeneous coverage capabilities of sensors belonging to the same class into account. It specifically addresses device heterogeneity by modeling the coverage problem in the Laguerre geometry through Voronoi-Laguerre diagrams. SARA executes quickly and is guarante...

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

    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.

  3. Desensitized Optimal Filtering and Sensor Fusion Tool Kit Project

    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...

  4. Study of data fusion algorithms applied to unattended ground sensor network

    Pannetier, B.; Moras, J.; Dezert, Jean; Sella, G.

    2014-06-01

    In this paper, data obtained from wireless unattended ground sensor network are used for tracking multiple ground targets (vehicles, pedestrians and animals) moving on and off the road network. The goal of the study is to evaluate several data fusion algorithms to select the best approach to establish the tactical situational awareness. The ground sensor network is composed of heterogeneous sensors (optronic, radar, seismic, acoustic, magnetic sensors) and data fusion nodes. The fusion nodes are small hardware platforms placed on the surveillance area that communicate together. In order to satisfy operational needs and the limited communication bandwidth between the nodes, we study several data fusion algorithms to track and classify targets in real time. A multiple targets tracking (MTT) algorithm is integrated in each data fusion node taking into account embedded constraint. The choice of the MTT algorithm is motivated by the limit of the chosen technology. In the fusion nodes, the distributed MTT algorithm exploits the road network information in order to constrain the multiple dynamic models. Then, a variable structure interacting multiple model (VS-IMM) is adapted with the road network topology. This algorithm is well-known in centralized architecture, but it implies a modification of other data fusion algorithms to preserve the performances of the tracking under constraints. Based on such VS-IMM MTT algorithm, we adapt classical data fusion techniques to make it working in three architectures: centralized, distributed and hierarchical. The sensors measurements are considered asynchronous, but the fusion steps are synchronized on all sensors. Performances of data fusion algorithms are evaluated using simulated data and also validated on real data. The scenarios under analysis contain multiple targets with close and crossing trajectories involving data association uncertainties.

  5. Fusion of multiple sensor types in computer vision systems

    Mayo, Donald R.

    2007-01-01

    This research provides analysis of several approaches to the fusion of multiple dissimilar sensors to supplement simple color vision detection and recognition. Non-visible sensor systems can enhance computer vision systems. Our research investigates using thermal infrared (IR) sensors in combination with color data for object detection and recognition. We analyze several types of high-level and low-level sensor fusion to compare error rates with raw color and raw IR error rates in detecti...

  6. Multi Level Fusion of Competitive Sensors for Automotive Environment Perception

    Haberjahn, Mathias; Kozempel, Karsten

    2013-01-01

    A reference sensor system, consisting of a multilayer laser scanner and a stereo camera system, is used for detecting vehicle surroundings. Via a novel multi level multi sensor fusion framework the heterogeneous sensor information can be fused on three succeeding processing levels (low, mid and high level). Here the low level fusion achieved the highest accuracy in the description of the object hypotheses. Detection and processing faults can be ecognized and reduced by competing sensor in...

  7. Fluorescent sensors based on bacterial fusion proteins

    Fluorescence proteins are widely used as markers for biomedical and technological purposes. Therefore, the aim of this project was to create a fluorescent sensor, based in the green and cyan fluorescent protein, using bacterial S-layers proteins as scaffold for the fluorescent tag. We report the cloning, expression and purification of three S-layer fluorescent proteins: SgsE-EGFP, SgsE-ECFP and SgsE-13aa-ECFP, this last containing a 13-amino acid rigid linker. The pH dependence of the fluorescence intensity of the S-layer fusion proteins, monitored by fluorescence spectroscopy, showed that the ECFP tag was more stable than EGFP. Furthermore, the fluorescent fusion proteins were reassembled on silica particles modified with cationic and anionic polyelectrolytes. Zeta potential measurements confirmed the particle coatings and indicated their colloidal stability. Flow cytometry and fluorescence microscopy showed that the fluorescence of the fusion proteins was pH dependent and sensitive to the underlying polyelectrolyte coating. This might suggest that the fluorescent tag is not completely exposed to the bulk media as an independent moiety. Finally, it was found out that viscosity enhanced the fluorescence intensity of the three fluorescent S-layer proteins. (paper)

  8. Fluorescent sensors based on bacterial fusion proteins

    Prats Mateu, Batirtze; Kainz, Birgit; Pum, Dietmar; Sleytr, Uwe B.; Toca-Herrera, José L.

    2014-06-01

    Fluorescence proteins are widely used as markers for biomedical and technological purposes. Therefore, the aim of this project was to create a fluorescent sensor, based in the green and cyan fluorescent protein, using bacterial S-layers proteins as scaffold for the fluorescent tag. We report the cloning, expression and purification of three S-layer fluorescent proteins: SgsE-EGFP, SgsE-ECFP and SgsE-13aa-ECFP, this last containing a 13-amino acid rigid linker. The pH dependence of the fluorescence intensity of the S-layer fusion proteins, monitored by fluorescence spectroscopy, showed that the ECFP tag was more stable than EGFP. Furthermore, the fluorescent fusion proteins were reassembled on silica particles modified with cationic and anionic polyelectrolytes. Zeta potential measurements confirmed the particle coatings and indicated their colloidal stability. Flow cytometry and fluorescence microscopy showed that the fluorescence of the fusion proteins was pH dependent and sensitive to the underlying polyelectrolyte coating. This might suggest that the fluorescent tag is not completely exposed to the bulk media as an independent moiety. Finally, it was found out that viscosity enhanced the fluorescence intensity of the three fluorescent S-layer proteins.

  9. Uncertain information fusion with robust adaptive neural networks-fuzzy reasoning

    Zhang Yinan; Sun Qingwei; Quan He; Jin Yonggao; Quan Taifan

    2006-01-01

    In practical multi-sensor information fusion systems,there exists uncertainty about the network structure,active state of sensors,and information itself (including fuzziness,randomness,incompleteness as well as roughness,etc). Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.

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

    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

  11. Data fusion and sensor management for nuclear power plant safety

    The paper describes the implementation of the data-sensor fusion and sensor management technology for accident management through simulated severe accident (SA) scenarios subjected to study. By means of accident management the appropriate prompt actions to be taken to avoid nuclear accident (SA) scenarios subjected to study. By means of accident management the appropriate prompt actions to be taken to avoid nuclear accidents are meant, while such accidents are deemed to somehow be imminent during plant operation. The organisation of the present paper is as follows. As the data-sensor fusion and sensor management is an emerging technology which is not widely known, in Sec. 2, the definition and goals of data-sensor fusion and sensor management technology is described. In Sec. 3 first, with reference to Kalman filtering as an information filter, statistical data-sensor fusion technology is described. This is followed by the examples of deterministic data-sensor fusion technology using gross plant state variables and neural networks (NN) and the implementation for severe accident management in NPPs. In Sec. 4, the sensor management technology is described. Finally, the performance of the data-sensor fusion technology for NPP safety is discussed. (orig./WL)

  12. PERSON AUTHENTICATION USING MULTIPLE SENSOR DATA FUSION

    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.

  13. Sensor fusion for intelligent process control.

    Connors, John J. (PPG Industries, Inc., Harmar Township, PA); Hill, Kevin (PPG Industries, Inc., Harmar Township, PA); Hanekamp, David (PPG Industries, Inc., Harmar Township, PA); Haley, William F. (PPG Industries, Inc., Wichita Falls, TX); Gallagher, Robert J.; Gowin, Craig (PPG Industries, Inc., Batavia, IL); Farrar, Arthur R. (PPG Industries, Inc., Wichita Falls, TX); Sheaffer, Donald A.; DeYoung, Mark A. (PPG Industries, Inc., Mt. Zion, IL); Bertram, Lee A.; Dodge, Craig (PPG Industries, Inc., Mt. Zion, IL); Binion, Bruce (PPG Industries, Inc., Mt. Zion, IL); Walsh, Peter M.; Houf, William G.; Desam, Padmabhushana R. (University of Utah, Salt Lake City, UT); Tiwary, Rajiv (PPG Industries, Inc., Harmar Township, PA); Stokes, Michael R. (PPG Industries, Inc.); Miller, Alan J. (PPG Industries, Inc., Mt. Zion, IL); Michael, Richard W. (PPG Industries, Inc., Lincoln, AL); Mayer, Raymond M. (PPG Industries, Inc., Harmar Township, PA); Jiao, Yu (PPG Industries, Inc., Harmar Township, PA); Smith, Philip J. (University of Utah, Salt Lake City, UT); Arbab, Mehran (PPG Industries, Inc., Harmar Township, PA); Hillaire, Robert G.

    2004-08-01

    An integrated system for the fusion of product and process sensors and controls for production of flat glass was envisioned, having as its objective the maximization of throughput and product quality subject to emission limits, furnace refractory wear, and other constraints. Although the project was prematurely terminated, stopping the work short of its goal, the tasks that were completed show the value of the approach and objectives. Though the demonstration was to have been done on a flat glass production line, the approach is applicable to control of production in the other sectors of the glass industry. Furthermore, the system architecture is also applicable in other industries utilizing processes in which product uniformity is determined by ability to control feed composition, mixing, heating and cooling, chemical reactions, and physical processes such as distillation, crystallization, drying, etc. The first phase of the project, with Visteon Automotive Systems as industrial partner, was focused on simulation and control of the glass annealing lehr. That work produced the analysis and computer code that provide the foundation for model-based control of annealing lehrs during steady state operation and through color and thickness changes. In the second phase of the work, with PPG Industries as the industrial partner, the emphasis was on control of temperature and combustion stoichiometry in the melting furnace, to provide a wider operating window, improve product yield, and increase energy efficiency. A program of experiments with the furnace, CFD modeling and simulation, flow measurements, and sensor fusion was undertaken to provide the experimental and theoretical basis for an integrated, model-based control system utilizing the new infrastructure installed at the demonstration site for the purpose. In spite of the fact that the project was terminated during the first year of the second phase of the work, the results of these first steps toward implementation

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

    Marwah Almasri; Khaled Elleithy; Abrar Alajlan

    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...

  15. Local adaptation and the evolution of chromosome fusions.

    Guerrero, Rafael F; Kirkpatrick, Mark

    2014-10-01

    We use forward and coalescent models of population genetics to study chromosome fusions that reduce the recombination between two locally adapted loci. Under a continent-island model, a fusion spreads and reaches a polymorphic equilibrium when it causes recombination between locally adapted alleles to be less than their selective advantage. In contrast, fusions in a two-deme model always spread; whether it reaches a polymorphic equilibrium or becomes fixed depends on the relative recombination rates of fused homozygotes and heterozygotes. Neutral divergence around fusion polymorphisms is markedly increased, showing peaks at the point of fusion and at the locally adapted loci. Local adaptation could explain the evolution of many of chromosome fusions, which are some of the most common chromosome rearrangements in nature. PMID:24964074

  16. Adaptive sensor array algorithm for structural health monitoring of helmet

    Zou, Xiaotian; Tian, Ye; Wu, Nan; Sun, Kai; Wang, Xingwei

    2011-04-01

    The adaptive neural network is a standard technique used in nonlinear system estimation and learning applications for dynamic models. In this paper, we introduced an adaptive sensor fusion algorithm for a helmet structure health monitoring system. The helmet structure health monitoring system is used to study the effects of ballistic/blast events on the helmet and human skull. Installed inside the helmet system, there is an optical fiber pressure sensors array. After implementing the adaptive estimation algorithm into helmet system, a dynamic model for the sensor array has been developed. The dynamic response characteristics of the sensor network are estimated from the pressure data by applying an adaptive control algorithm using artificial neural network. With the estimated parameters and position data from the dynamic model, the pressure distribution of the whole helmet can be calculated following the Bazier Surface interpolation method. The distribution pattern inside the helmet will be very helpful for improving helmet design to provide better protection to soldiers from head injuries.

  17. Multivariate Sensitivity Analysis of Time-of-Flight Sensor Fusion

    Schwarz, Sebastian; Sjöström, Mårten; Olsson, Roger

    2014-09-01

    Obtaining three-dimensional scenery data is an essential task in computer vision, with diverse applications in various areas such as manufacturing and quality control, security and surveillance, or user interaction and entertainment. Dedicated Time-of-Flight sensors can provide detailed scenery depth in real-time and overcome short-comings of traditional stereo analysis. Nonetheless, they do not provide texture information and have limited spatial resolution. Therefore such sensors are typically combined with high resolution video sensors. Time-of-Flight Sensor Fusion is a highly active field of research. Over the recent years, there have been multiple proposals addressing important topics such as texture-guided depth upsampling and depth data denoising. In this article we take a step back and look at the underlying principles of ToF sensor fusion. We derive the ToF sensor fusion error model and evaluate its sensitivity to inaccuracies in camera calibration and depth measurements. In accordance with our findings, we propose certain courses of action to ensure high quality fusion results. With this multivariate sensitivity analysis of the ToF sensor fusion model, we provide an important guideline for designing, calibrating and running a sophisticated Time-of-Flight sensor fusion capture systems.

  18. Development of a modular Kalman Filter based sensor fusion algorithm for air vehicles

    López Milla, Javier

    2013-01-01

    [ANGLÈS] Kalman Filter is nowadays one of the most used tool to perform sensor fusion in navigation environments, i.e. combine information from several different sensors to obtain the optimal navigation solution. However, there is no single algorithm for Kalman Filter and each of them must be adapted to the concrete problem. This implies that there are no two Kalman Filters that are exactly the same, complicating an objective comparison between them, mainly if implemented by different people....

  19. Desensitized Optimal Filtering and Sensor Fusion Tool Kit Project

    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...

  20. Real-Time Blackboards For Sensor Fusions

    Johnson, Donald H.; Shaw, Scott W.; Reynolds, Steven; Himayat, Nageen

    1989-09-01

    Multi-sensor fusion, at the most basic level, can be cast into a concise, elegant model. Reality demands, however, that this model be modified and augmented. These modifications often result in software systems that are confusing in function and difficult to debug. This problem can be ameliorated by adopting an object-oriented, data-flow programming style. For real-time applications, this approach simplifies data communications and storage management. The concept of object-oriented, data-flow programming is conveniently embodied in the black-board style of software architecture. Blackboard systems allow diverse programs access to a central data base. When the blackboard is described as an object, it can be distributed over multiple processors for real-time applications. Choosing the appropriate parallel architecture is the subject of ongoing research. A prototype blackboard has been constructed to fuse optical image regions and Doppler radar events. The system maintains tracks of simulated targets in real time. The results of this simulation have been used to direct further research on real-time blackboard systems.

  1. A framework for context-aware sensor fusion

    Martí Muñoz, Enrique David

    2015-01-01

    Sensor fusion is a mature but very active research field, included in the more general discipline of information fusion. It studies how to combine data coming from different sensors, in such way that the resulting information is better in some sense –more complete, accurate or stable– than any of the original sources used individually. Context is defined as everything that constraints or affects the process of solving a problem, without being part of the problem or the solution itself. Over ...

  2. ADAPTIVE FUSION ALGORITHMS BASED ON WEIGHTED LEAST SQUARE METHOD

    SONG Kaichen; NIE Xili

    2006-01-01

    Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion,are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.

  3. Hierarchical adaptation scheme for multiagent data fusion and resource management in situation analysis

    Benaskeur, Abder R.; Roy, Jean

    2001-08-01

    Sensor Management (SM) has to do with how to best manage, coordinate and organize the use of sensing resources in a manner that synergistically improves the process of data fusion. Based on the contextual information, SM develops options for collecting further information, allocates and directs the sensors towards the achievement of the mission goals and/or tunes the parameters for the realtime improvement of the effectiveness of the sensing process. Conscious of the important role that SM has to play in modern data fusion systems, we are currently studying advanced SM Concepts that would help increase the survivability of the current Halifax and Iroquois Class ships, as well as their possible future upgrades. For this purpose, a hierarchical scheme has been proposed for data fusion and resource management adaptation, based on the control theory and within the process refinement paradigm of the JDL data fusion model, and taking into account the multi-agent model put forward by the SASS Group for the situation analysis process. The novelty of this work lies in the unified framework that has been defined for tackling the adaptation of both the fusion process and the sensor/weapon management.

  4. Multi-sensor image fusion and its applications

    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,

  5. Overcoming adverse weather conditions with a common optical path, multiple sensors, and intelligent image fusion system

    Ng, Joseph; Piacentino, Michael; Caldwell, Brian

    2008-04-01

    Mission success is highly dependent on the ability to accomplish Surveillance, Situation Awareness, Target Detection and Classification, but is challenging under adverse weather conditions. This paper introduces an engineering prototype to address the image collection challenges using a Common Optical Path, Multiple Sensors and an Intelligent Image Fusion System, and provides illustrations and sample fusion images. Panavision's advanced wide spectrum optical design has permitted a suite of imagers to perform observations through a common optical path with a common field of view, thereby aligning images and facilitating optimized downstream image processing. The adaptable design also supports continuous zoom or Galilean lenses for multiple field of views. The Multiple Sensors include: (1) High-definition imaging sensors that are small, have low power consumption and a wide dynamic range; (2) EMCCD sensors that transition from daylight to starlight, even under poor weather conditions, with sensitivity down to 0.00025 Lux; and (3) SWIR sensors that, with the advancement in InGaAs, are able to generate ultra-high sensitivity images from 1-1.7μm reflective light and can achieve imaging through haze and some types of camouflage. The intelligent fusion of multiple sensors provides high-resolution color information with previously impossible sensitivity and contrast. With the integration of Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs), real-time Image Processing and Fusion Algorithms can facilitate mission success in a small, low power package.

  6. Data fusion for adaptive control in manufacturing: Impact on engineering information models

    Bray, O.H.

    1997-01-01

    Data fusion is the integration and analysis of data from multiple sensors to develop a more accurate understanding of a situation and determine how to respond to it. Although data fusion can be applied in many situations, this paper focuses on its application to manufacturing and how it changes some of the more traditional, less adaptive information models that support the design and manufacturing functions. The paper consists of four parts: Section 1 defines data fusion and explains its impact on manufacturing. Section 2 describes an information system architecture and explains the natural language-based information modeling methodology used by this research project. Section 3 identifies the major design and manufacturing functions, reviews the information models required to support them, and then shows how these models must be extended to support data fusion. Section 4 discusses the future directions of this work. This report is one of three produced by an FY93 LDRD project, Information Integration for Data Fusion. The project confirmed: (1) that the natural language-based information modeling methodology could be used effectively in data fusion areas, and (2) that commonalities could be found that would allow synergy across various data fusion areas, such as defense, manufacturing, and health care. The project found five common objects that are the basis for all of the data fusion areas examined: targets, behaviors, environments, signatures, and sensors. Many of these objects and the specific facts related to them were common across several models and could easily be reused. In some cases, even the terminology remained the same. This commonality is important with the growing use of multisensor data fusion. Data fusion is much more difficult if each type of sensor uses its own objects and models rather than building on a common set. Information model integration at the conceptual level is much easier than at the implementation level.

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

    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,

  8. Inertial sensor and ultra-wideband sensor fusion : Precision positioning of robot platform

    Frisk, Mikael; Nilsson, Albin

    2014-01-01

    In order to determine the orientation and position of an object it is common to measure linear and angular motion with an inertial sensor. To further improve the positioning an ultra-wideband sensor can be used simultaneously and integrated in the final solution with sensor fusion. This study has evaluated an ultra- wideband sensor, and also integrated it with a pre- existing solution for positioning using inertial sensors, in order to determine if the solution is viable for positioning of an...

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

    Shahina Begum; Shaibal Barua; Mobyen Uddin Ahmed

    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...

  10. Inertial and optical sensor fusion to compensate for partial occlusions in surgical tracking systems

    He, Changyu; Liu, Yue

    2015-08-01

    To solve the occlusion problem in optical tracking system (OTS) for surgical navigation, this paper proposes a sensor fusion approach and an adaptive display method to handle cases where partial or total occlusion occurs. In the sensor fusion approach, the full 6D pose information provided by the optical tracker is used to estimate the bias of the inertial sensors when all of the markers are visible. When partial occlusion occurs, the optical system can track the position of at least one marker which can be combined with the orientation estimated from the inertial measurements to recover the full 6D pose information. When all the markers are invisible, the position tracking will be realized based on outputs of the Inertial Measurement Unit (IMU) which may generate increasing drifting error. To alert the user when the drifting error is great enough to influence the navigation, the images adaptive to the drifting error are displayed in the field of the user's view. The experiments are performed with an augmented reality HMD which displays the AR images and the hybrid tracking system (HTS) which consists of an OTS and an IMU. Experimental result shows that with proposed sensor fusion approach the 6D pose of the head with respect to the reference frame can be estimated even under partial occlusion conditions. With the help of the proposed adaptive display method, the users can recover the scene of markers when the error is considered to be relatively high.

  11. Projective Method for Generic Sensor Fusion Problem

    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

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

    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

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

    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.

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

    Bahrepour, M.; Meratnia, N.; Havinga, P.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 ev

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

    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.

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

    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.

  17. Data Fusion in Distributed Multi-sensor System

    GUO Hang; YU Min

    2004-01-01

    This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors' data processing. First, a residual χ2-test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real-time data. A pseudolite (PL) simulation example is given.

  18. A Motion Tracking and Sensor Fusion Module for Medical Simulation.

    Shen, Yunhe; Wu, Fan; Tseng, Kuo-Shih; Ye, Ding; Raymond, John; Konety, Badrinath; Sweet, Robert

    2016-01-01

    Here we introduce a motion tracking or navigation module for medical simulation systems. Our main contribution is a sensor fusion method for proximity or distance sensors integrated with inertial measurement unit (IMU). Since IMU rotation tracking has been widely studied, we focus on the position or trajectory tracking of the instrument moving freely within a given boundary. In our experiments, we have found that this module reliably tracks instrument motion. PMID:27046606

  19. Sensor Fusion and Model Verification for a Mobile Robot

    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...

  20. Multiple image sensor data fusion through artificial neural networks

    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...

  1. Fault-tolerant Sensor Fusion for Marine Navigation

    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...

  2. RBUKF Sensor Data Fusion for Localization of Unmanned Mobile Platform

    Longmei Zhao

    2013-10-01

    Full Text Available Due to the limited localization precision of single sensor, a sensor data fusion is introduced based on Rao-Blackwellization Unscented Kalman Filter (RBUKF that fuses the sensor data of a GPS receiver, one gyro and one compass. RBUKF algorithm is compared with that of Extended Kalman Filter (EKF and Unscented Kalman Filter (UKF in this study. The experimental results show that the RBUKF algorithm can more effectively improve tracking accuracy and reduce computational complexity than the other algorithms and has practical significance.

  3. Fusion of intraoperative force sensoring, surface reconstruction and biomechanical modeling

    Röhl, S.; Bodenstedt, S.; Küderle, C.; Suwelack, S.; Kenngott, H.; Müller-Stich, B. P.; Dillmann, R.; Speidel, S.

    2012-02-01

    Minimally invasive surgery is medically complex and can heavily benefit from computer assistance. One way to help the surgeon is to integrate preoperative planning data into the surgical workflow. This information can be represented as a customized preoperative model of the surgical site. To use it intraoperatively, it has to be updated during the intervention due to the constantly changing environment. Hence, intraoperative sensor data has to be acquired and registered with the preoperative model. Haptic information which could complement the visual sensor data is still not established. In addition, biomechanical modeling of the surgical site can help in reflecting the changes which cannot be captured by intraoperative sensors. We present a setting where a force sensor is integrated into a laparoscopic instrument. In a test scenario using a silicone liver phantom, we register the measured forces with a reconstructed surface model from stereo endoscopic images and a finite element model. The endoscope, the instrument and the liver phantom are tracked with a Polaris optical tracking system. By fusing this information, we can transfer the deformation onto the finite element model. The purpose of this setting is to demonstrate the principles needed and the methods developed for intraoperative sensor data fusion. One emphasis lies on the calibration of the force sensor with the instrument and first experiments with soft tissue. We also present our solution and first results concerning the integration of the force sensor as well as accuracy to the fusion of force measurements, surface reconstruction and biomechanical modeling.

  4. Sensor fusion-based map building for mobile robot exploration

    To carry out exploration tasks in unknown or partially unknown environments, a mobile robot needs to acquire and maintain models of its environment. In doing so, several sensors of same nature and/or heterogeneous sensor configurations may be used by the robot to achieve reliable performances. However, this in turn poses the problem of sensor fusion-based map building: How to interpret, combine and integrate sensory information in order to build a proper representation of the environment. Specifically, the goal of this thesis is to probe integration algorithms for Occupancy Grid (OG) based map building using odometry, ultrasonic rangefinders, and stereo vision. Three different uncertainty calculi are presented here which are used for sensor fusion-based map building purposes. They are based on probability theory, Dempster-Shafer theory of evidence, and fuzzy set theory. Besides, two different sensor models are depicted which are used to translate sensing data into range information. Experimental examples of OGs built from real data recorded by two robots in office-like environment are presented. They show the feasibility of the proposed approach for building both sonar and visual based OGs. A comparison among the presented uncertainty calculi is performed in a sonar-based framework. Finally, the fusion of both sonar and visual information based of the fuzzy set theory is depicted. (author)

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

    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.

  6. Integration of multiple sensor fusion in controller design.

    Abdelrahman, Mohamed; Kandasamy, Parameshwaran

    2003-04-01

    The main focus of this research is to reduce the risk of a catastrophic response of a feedback control system when some of the feedback data from the system sensors are not reliable, while maintaining a reasonable performance of the control system. In this paper a methodology for integrating multiple sensor fusion into the controller design is presented. The multiple sensor fusion algorithm produces, in addition to the estimate of the measurand, a parameter that measures the confidence in the estimated value. This confidence is integrated as a parameter into the controller to produce fast system response when the confidence in the estimate is high, and a slow response when the confidence in the estimate is low. Conditions for the stability of the system with the developed controller are discussed. This methodology is demonstrated on a cupola furnace model. The simulations illustrate the advantages of the new methodology. PMID:12708539

  7. Multi-sensor data fusion framework for CNC machining monitoring

    Duro, João A.; Padget, Julian A.; Bowen, Chris R.; Kim, H. Alicia; Nassehi, Aydin

    2016-01-01

    Reliable machining monitoring systems are essential for lowering production time and manufacturing costs. Existing expensive monitoring systems focus on prevention/detection of tool malfunctions and provide information for process optimisation by force measurement. An alternative and cost-effective approach is monitoring acoustic emissions (AEs) from machining operations by acting as a robust proxy. The limitations of AEs include high sensitivity to sensor position and cutting parameters. In this paper, a novel multi-sensor data fusion framework is proposed to enable identification of the best sensor locations for monitoring cutting operations, identifying sensors that provide the best signal, and derivation of signals with an enhanced periodic component. Our experimental results reveal that by utilising the framework, and using only three sensors, signal interpretation improves substantially and the monitoring system reliability is enhanced for a wide range of machining parameters. The framework provides a route to overcoming the major limitations of AE based monitoring.

  8. Sensor Fusion for Nuclear Proliferation Activity Monitoring

    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.

  9. Comparative analysis of different adaptive filters for tracking lower segments of a human body using inertial motion sensors

    For all segments and tests, a modified Kalman filter and a quasi-static sensor fusion algorithm were equally accurate (precision and accuracy ∼2–3°) compared to normalized least mean squares filtering, recursive least-squares filtering and standard Kalman filtering. The aims were to: (1) compare adaptive filtering techniques used for sensor fusion and (2) evaluate the precision and accuracy for a chosen adaptive filter. Motion sensors (based on inertial measurement units) are limited by accumulative integration errors arising from sensor bias. This drift can partly be handled with adaptive filtering techniques. To advance the measurement technique in this area, a new modified Kalman filter is developed. Differences in accuracy were observed during different tests especially drift in the internal/external rotation angle. This drift can be minimized if the sensors include magnetometers. (paper)

  10. Multi-sensor image fusion using discrete wavelet frame transform

    Zhenhua Li(李振华); Zhongliang Jing(敬忠良); Shaoyuan Sun(孙韶媛)

    2004-01-01

    An algorithm is presented for multi-sensor image fusion using discrete wavelet frame transform (DWFT).The source images to be fused are firstly decomposed by DWFT. The fusion process is the combining of the source coefficients. Before the image fusion process, image segmentation is performed on each source image in order to obtain the region representation of each source image. For each source image, the salience of each region in its region representation is calculated. By overlapping all these region representations of all the source images, we produce a shared region representation to label all the input images. The fusion process is guided by these region representations. Region match measure of the source images is calculated for each region in the shared region representation. When fusing the similar regions, weighted averaging mode is performed; otherwise selection mode is performed. Experimental results using real data show that the proposed algorithm outperforms the traditional pyramid transform based or discrete wavelet transform (DWT) based algorithms in multi-sensor image fusion.

  11. Sensor-fusion-based biometric identity verification

    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.

  12. Sensor-fusion-based biometric identity verification

    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

  13. Radiofrequency Positioning System Aided With Sensor Fusion

    Vicens Oviedo, Miguel Ángel

    2015-01-01

    Sistema de posicionamiento por radio frecuencia que utiliza sensores inerciales. Con la fusión de estos dos sistemas mejoramos la navegación. During the last few years, different navigation systems with varying degrees of accuracy have appeared, such as: LORAN (Long Range Navigation), Decca, GNSS (GLObal NAvigation Satellite System), the latter being the most widely used nowadays. GNSS, which includes GPS (Global Positioning System) and GLONASS (Global Navigation Satellite System) , consis...

  14. Multisensor fusion using the sensor algorithm research expert system

    Bullock, Michael E.; Miltonberger, Thomas W.; Reinholdsten, Paul A.; Wilson, Kathleen

    1991-08-01

    A method for object recognition using a multisensor model-based approach has been developed. The sensor algorithm research expert system (SARES) is a sun-based workstation for model-based object recognition algorithm development. SARES is a means to perform research into multiple levels of geometric and scattering models, image and signal feature extraction, hypothesis management, and matching strategies. SARES multisensor fusion allows for multiple geometric representations and decompositions, and sensor location transformations, as well as feature prediction, matching, and evidence accrual. It is shown that the fusion algorithm can exploit the synergistic information contained in IR and synthetic aperture radar (SAR) imagery yielding increased object recognition accuracy and confidence over single sensor exploitation alone. The fusion algorithm has the added benefit of reducing the number of computations by virtue of simplified object model combinatorics. That is, the additional sensor information eliminates a large number of the incorrect object hypotheses early in the algorithm. This provides a focus of attention to those object hypotheses which are closest to the correct hypothesis.

  15. Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

    Wenyu Zhang; Zhenjiang Zhang

    2015-01-01

    Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any ty...

  16. Adaptive Fusion of Stochastic Information for Imaging Fractured Vadose Zones

    Daniels, J.; Yeh, J.; Illman, W.; Harri, S.; Kruger, A.; Parashar, M.

    2004-12-01

    A stochastic information fusion methodology is developed to assimilate electrical resistivity tomography, high-frequency ground penetrating radar, mid-range-frequency radar, pneumatic/gas tracer tomography, and hydraulic/tracer tomography to image fractures, characterize hydrogeophysical properties, and monitor natural processes in the vadose zone. The information technology research will develop: 1) mechanisms and algorithms for fusion of large data volumes ; 2) parallel adaptive computational engines supporting parallel adaptive algorithms and multi-physics/multi-model computations; 3) adaptive runtime mechanisms for proactive and reactive runtime adaptation and optimization of geophysical and hydrological models of the subsurface; and 4) technologies and infrastructure for remote (pervasive) and collaborative access to computational capabilities for monitoring subsurface processes through interactive visualization tools. The combination of the stochastic fusion approach and information technology can lead to a new level of capability for both hydrologists and geophysicists enabling them to "see" into the earth at greater depths and resolutions than is possible today. Furthermore, the new computing strategies will make high resolution and large-scale hydrological and geophysical modeling feasible for the private sector, scientists, and engineers who are unable to access supercomputers, i.e., an effective paradigm for technology transfer.

  17. Tele-Supervised Adaptive Ocean Sensor Fleet

    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

  18. Diagnostics and data fusion of robotic sensors

    Robotic systems for remediation of hazardous waste sites must be highly reliable to avoid equipment failures and subsequent possible exposure of personnel to hazardous environments. Safe, efficient cleanup operations also require accurate, complete knowledge of the task space. This paper presents progress made on a 18 month program to meet these needs. To enhance robot reliability, a conceptual design of a monitoring and diagnostic system is being developed to predict the onset of mechanical failure modes, provide maximum lead time to make operational changes or repairs, and minimize the occurrence of on-site breakdowns. To ensure safe operation, a comprehensive software package is being developed that will fuse data from multiple surface mapping sensors and poses so as to reduce the error effects in individual data points and provide accurate 3-D maps of a work space

  19. Agent Based Sensor and Data Fusion in Forest Fire Observer

    &#;erić, Ljiljana; Stipani&#;ev, Darko; &#;tula, Maja

    2009-01-01

    Inspirited by the formal theory of perception and technology of sensor network we have introduced the idea of observer network as a reliable framework for data and information fusion. Our ideas have been successfully tested in the case of forest fire observer network. Observer network was implemented using multi-agent technology. A special multi agent shell was designed for this purpose having software system desirable features like modularity and flexibility. The system was implemented in nu...

  20. Multimodal image fusion in sensor networks using independent component analysis

    Cvejic, N; Bull, DR; Canagarajah, CN

    2007-01-01

    We present a novel image fusion algorithm based on ICA that has an improved performance over sensor networks. It employs segmentation to determine the most important regions in the input images and consequently fuses the ICA coefficients from the given regions. Sparse coding of the coefficients in ICA domain is used to minimize noise transferred from input images into the fused output. Experimental results confirm that the proposed method outperforms other state-of- the-art methods in the sen...

  1. Bayesian Fusion Algorithm for Inferring Trust in Wireless Sensor Networks

    Mohammad Momani; Subhash Challa; Rami Alhmouz

    2010-01-01

    This paper introduces a new Bayesian fusion algorithm to combine more than one trust component (data trust and communication trust) to infer the overall trust between nodes. This research work proposes that one trust component is not enough when deciding on whether or not to trust a specific node in a wireless sensor network. This paper discusses and analyses the results from the communication trust component (binary) and the data trust component (continuous) and proves that either component ...

  2. Sensor fusion by pseudo information measure: a mobile robot application.

    Asharif, Mohammad Reza; Moshiri, Behzad; HoseinNezhad, Reza

    2002-07-01

    In any autonomous mobile robot, one of the most important issues to be designed and implemented is environment perception. In this paper, a new approach is formulated in order to perform sensory data integration for generation of an occupancy grid map of the environment. This method is an extended version of the Bayesian fusion method for independent sources of information. The performance of the proposed method of fusion and its sensitivity are discussed. Map building simulation for a cylindrical robot with eight ultrasonic sensors and mapping implementation for a Khepera robot have been separately tried in simulation and experimental works. A new neural structure is introduced for conversion of proximity data that are given by Khepera IR sensors to occupancy probabilities. Path planning experiments have also been applied to the resulting maps. For each map, two factors are considered and calculated: the fitness and the augmented occupancy of the map with respect to the ideal map. The length and the least distance to obstacles were the other two factors that were calculated for the routes that are resulted by path planning experiments. Experimental and simulation results show that by using the new fusion formulas, more informative maps of the environment are obtained. By these maps more appropriate routes could be achieved. Actually, there is a tradeoff between the length of the resulting routes and their safety and by choosing the proper fusion function, this tradeoff is suitably tuned for different map building applications. PMID:12160343

  3. Sensor fusion in identified visual interneurons.

    Parsons, Matthew M; Krapp, Holger G; Laughlin, Simon B

    2010-04-13

    Animal locomotion often depends upon stabilization reflexes that use sensory feedback to maintain trajectories and orientation. Such stabilizing reflexes are critically important for the blowfly, whose aerodynamic instability permits outstanding maneuverability but increases the demands placed on flight control. Flies use several sensory systems to drive reflex responses, and recent studies have provided access to the circuitry responsible for combining and employing these sensory inputs. We report that lobula plate VS neurons combine inputs from two optical sensors, the ocelli and the compound eyes. Both systems deliver essential information on in-flight rotations, but our neuronal recordings reveal that the ocelli encode this information in three axes, whereas the compound eyes encode in nine. The difference in dimensionality is reconciled by tuning each VS neuron to the ocellar axis closest to its compound eye axis. We suggest that this simple projection combines the speed of the ocelli with the accuracy of the compound eyes without compromising either. Our findings also support the suggestion that the coordinates of sensory information processing are aligned with axes controlling the natural modes of the fly's flight to improve the efficiency with which sensory signals are transformed into appropriate motor commands. PMID:20303270

  4. Inertial Sensor Error Reduction through Calibration and Sensor Fusion.

    Lambrecht, Stefan; Nogueira, Samuel L; Bortole, Magdo; Siqueira, Adriano A G; Terra, Marco H; Rocon, Eduardo; Pons, José L

    2016-01-01

    This paper presents the comparison between cooperative and local Kalman Filters (KF) for estimating the absolute segment angle, under two calibration conditions. A simplified calibration, that can be replicated in most laboratories; and a complex calibration, similar to that applied by commercial vendors. The cooperative filters use information from either all inertial sensors attached to the body, Matricial KF; or use information from the inertial sensors and the potentiometers of an exoskeleton, Markovian KF. A one minute walking trial of a subject walking with a 6-DoF exoskeleton was used to assess the absolute segment angle of the trunk, thigh, shank, and foot. The results indicate that regardless of the segment and filter applied, the more complex calibration always results in a significantly better performance compared to the simplified calibration. The interaction between filter and calibration suggests that when the quality of the calibration is unknown the Markovian KF is recommended. Applying the complex calibration, the Matricial and Markovian KF perform similarly, with average RMSE below 1.22 degrees. Cooperative KFs perform better or at least equally good as Local KF, we therefore recommend to use cooperative KFs instead of local KFs for control or analysis of walking. PMID:26901198

  5. Multimodal Medical Image Fusion by Adaptive Manifold Filter

    Peng Geng; Shuaiqi Liu; Shanna Zhuang

    2015-01-01

    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,...

  6. Compressible Data Fusion Based on Minimum Energy Consumption in Wireless Sensor Network

    Xu Chang

    2014-01-01

    Data fusion problem is one of the research hotspots in wireless sensor network. Aiming at the shortage of excessive energy consumption in existing fusion method, a data fusion program is proposed based on minimum energy consumption according to the theory of compressive sensing. This paper first analyzes the impact of different fusion modes on data collection performance, considers the impact of routing and mixed Compressive Sensing (CS) fusion on energy optimization, models the data fusion p...

  7. An RBF neural network approach towards precision motion system with selective sensor fusion

    Yang, R; Er, PV; Wang, Z.; Tan, KK

    2016-01-01

    A radial basis function (RBF) neural network approach with a fusion of multiple signal candidates in precision motion control is studied in this paper. Sensor weightages are assigned to sensor measurements according to the selector attributes and approximated using RBF neural network in multi-sensor fusion. A specific application towards precision motion control of a linear motor system using a magnetic encoder and a soft position sensor in conjunction with an analog velocity sensor is demons...

  8. Dempster Shafer Sensor Fusion for Autonomously Driving Vehicles : Association Free Tracking of Dynamic Objects

    Högger, Andreas

    2016-01-01

    Autonomous driving vehicles introduce challenging research areas combining differ-ent disciplines. One challenge is the detection of obstacles with different sensors and the combination of information to generate a comprehensive representation of the environment, which can be used for path planning and decision making.The sensor fusion is demonstrated using two Velodyne multi beam laser scanners, but it is possible to extend the proposed sensor fusion framework for different sensor types. Sensor...

  9. Analysis of tracking and identification characteristics of diverse systems and data sources for sensor fusion

    Wilson, Dean A.

    2001-01-01

    In the Command and Control mission, new technologies such as 'sensor fusion' are designed to help reduce operator workload and increase situational awareness. This thesis explored the tracking characteristics of diverse sensors and sources of data and their contributions to a fused tactical picture. The fundamental building blocks of any sensor fusion algorithm are the tracking algorithm associated with each of the sensors on the sensor platform. In support of this study the MATLAB program 'f...

  10. Cognitive foundations for model-based sensor fusion

    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

  11. Virtual Sensors and Data Fusion in a Multi-Level Context Computing Architecture

    Pietropaoli, Bastien; Dominici, Michele; Weis, Frédéric

    2013-01-01

    Computing context is a major subject of interest in smart homes. In this paper, we present how we adapted a general purpose multi-level architecture for the computation of contextual data to a prototype of smart home. After a quick explanation of why we use different methods at different levels of abstraction, we focus more on the low-level data fusion. To do this, we present the basics of belief functions theory and how we apply this theory to sensors to obtain stable abstractions. By doing ...

  12. System Dynamics and Adaptive Control for MEMS Gyroscope Sensor

    Juntao Fei; Hongfei Ding

    2010-01-01

    This paper presents an adaptive control approach for Micro-Electro-Mechanical Systems (MEMS) z-axis gyroscope sensor. The dynamical model of MEMS gyroscope sensor is derived and adaptive state tracking control for MEMS gyroscope is developed. The proposed adaptive control approaches can estimate the angular velocity and the damping and stiffness coefficients including the coupling terms due to the fabrication imperfection. The stability of the closed-loop systems is established with the propo...

  13. Research of Multi-sensor Images Based on Color Fusion Methods

    Chunlong Yao; Wei Pan; Lan Shen; Xu Li

    2013-01-01

    With the development of image sensor technology, multi-sensor image fusion technology emerged and was widely used in the field of military surveillance, medical diagnosis, remote sensing, intelligent robot and so on. However, the current image fusion technology mainly focuses on the research of gray images, the color image fusion is rarely. Because color image contains more information compared with gray image, the research on color image fusion technology is becoming more and more urgent. In...

  14. Data Fusion Using Robust Estimator for Uncertain Noisy Systems Over Sensor Networks

    BijanMoaveni; FatemehEbrahimi

    2013-01-01

    This paper, verifies the problem of designing robust state estimator for multiple sensor networks with uncertain model and noisy measurements. Multi sensor data fusion by measurement fusion and state vector fusion structure using the Kalman filter were introduced. The standard Kalman filter requires an accurate system model. In order to get accurate information in modelling signals and sensors, the information form of robust Kalman filter by using the Krein space approach is proposed. Also, a...

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

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

    2016-04-01

    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.

  16. Adaptive Sensing Based on Profiles for Sensor Systems

    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.

  17. AN INFORMATION FUSION METHOD FOR SENSOR DATA RECTIFICATION

    Zhang Zhen; Xu Lizhong; Harry Hua Li; Shi Aiye; Han Hua; Wang Huibin

    2012-01-01

    In the applications of water regime monitoring,incompleteness,and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information.Based on the spatial and temporal correlation of water regime monitoring information,this paper addresses this issue and proposes an information fusion method to implement data rectification.An improved Back Propagation (BP) neural network is used to perform data fusion on the hardware platform of a stantion unit,which takes Field-Programmable Gate Array (FPGA) as the core component.In order to verify the effectiveness,five measurements including water level,discharge and velocity are selected from three different points in a water regime monitoring station.The simulation results show that this method can recitify random errors as well as gross errors significantly.

  18. Adaptive Genetic Algorithm for Sensor Coarse Signal Processing

    Xuan Huang

    2014-03-01

    Full Text Available 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 adaptive genetic algorithm. This algorithm selects crossover, mutation probability adaptively and compensates multiple operators commutatively to optimize the search process, so that we can obtain the global optimum solution. Based on the proposed algorithm, using auto-correlative characteristic parameter extraction method, it achieves smaller test error in sensor coarse signal processing mode of processing interference signal. We evaluate the proposed approach on a set of data. The experimental results show that, the proposed approach is able to improve the performance in different experimental setting

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

    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

  20. Intelligent Sensor Validation And Fusion For Vehicle Guidance Using Probabilistic And Fuzzy Methods

    Agogino, Alice; Goebel, Kai; Alag, Sanam

    1997-01-01

    This study reports on a method to accomplish sensor validation and fusion in Intelligent Transportation Systems (ITS). The method is based on probabilistic and fuzzy techniques that express a confidence in the sensor data and take into account environmental factors and the state of the system. Sensor data fusion uses the confidence assigned to each sensor reading and integrates them into one reading. Noise and failure are filtered from the data and lead to a safety improvement in ITS.

  1. ELIPS: Toward a Sensor Fusion Processor on a Chip

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

    1998-01-01

    The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI. The first demonstration of the ELIPS concept targets interceptor functionality; other applications, mainly in robotics and autonomous systems are considered for the future. The main assumption behind ELIPS is that fuzzy, rule-based and neural forms of computation can serve as the main primitives of an "intelligent" processor. Thus, in the same way classic processors are designed to optimize the hardware implementation of a set of fundamental operations, ELIPS is developed as an efficient implementation of computational intelligence primitives, and relies on a set of fuzzy set, fuzzy inference and neural modules, built in programmable analog hardware. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Following software demonstrations on several interceptor data, three important ELIPS building blocks (a fuzzy set preprocessor, a rule-based fuzzy system and a neural network) have been fabricated in analog VLSI hardware and demonstrated microsecond-processing times.

  2. Evolutionary Adaptive Discovery of Phased Array Sensor Signal Identification

    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).

  3. Dynamic data-driven sensor network adaptation for border control

    Bein, Doina; Madan, Bharat B.; Phoha, Shashi; Rajtmajer, Sarah; Rish, Anna

    2013-06-01

    Given a specific scenario for the border control problem, we propose a dynamic data-driven adaptation of the associated sensor network via embedded software agents which make sensor network control, adaptation and collaboration decisions based on the contextual information value of competing data provided by different multi-modal sensors. We further propose the use of influence diagrams to guide data-driven decision making in selecting the appropriate action or course of actions which maximize a given utility function by designing a sensor embedded software agent that uses an influence diagram to make decisions about whether to engage or not engage higher level sensors for accurately detecting human presence in the region. The overarching goal of the sensor system is to increase the probability of target detection and classification and reduce the rate of false alarms. The proposed decision support software agent is validated experimentally on a laboratory testbed for multiple border control scenarios.

  4. On adapting data assimilation framework to data fusion of multi-scale precipitation observations

    Lin, X.; Zhang, S. Q.; Ebtehaj, M.

    2012-12-01

    Data assimilation (DA) based on estimation theories has been a powerful tool to extract information from a wide range of data sources to produce optimal estimates of physical parameters. In operational NWP applications, the available information essentially consists of observations, the physical laws governing the dynamical and physical processes of the atmosphere, as well as the associated uncertainties of these information sources. The DA framework can be adapted to the multi-sensor multi-scale data fusion problem, in which the primary goal is not to define the initial condition of a forecast as in operational DA systems, but to produce an estimate of the physical field of interest as accurate as possible. In this work we explore the DA framework in an application of combining multi-sensor multi-scale precipitation data to produce an integrated observation-based precipitation analysis. Similar to a DA system, an objective function is optimized with minimization of analysis error variance, provided that the error characteristics for each data source are estimated and described by its error covariance. Wavelet-transform is employed to obtain scale-decomposed and sparse representation of signal distribution. We present a prototype data fusion system including the estimation/fusion algorithm based on data assimilation methodologies, the estimation of uncertainty variance database for satellite retrievals, and the data registration for different platforms. A set of case studies such as hurricanes and mesoscale convective complexes are used to evaluate the general paradigm of the data fusion system and to investigate the impact from individual data source, different spatial resolutions and observation error distributions.

  5. Sensor fusion III: 3-D perception and recognition; Proceedings of the Meeting, Boston, MA, Nov. 5-8, 1990

    Schenker, Paul S. (Editor)

    1991-01-01

    The volume on data fusion from multiple sources discusses fusing multiple views, temporal analysis and 3D motion interpretation, sensor fusion and eye-to-hand coordination, and integration in human shape perception. Attention is given to surface reconstruction, statistical methods in sensor fusion, fusing sensor data with environmental knowledge, computational models for sensor fusion, and evaluation and selection of sensor fusion techniques. Topics addressed include the structure of a scene from two and three projections, optical flow techniques for moving target detection, tactical sensor-based exploration in a robotic environment, and the fusion of human and machine skills for remote robotic operations. Also discussed are K-nearest-neighbor concepts for sensor fusion, surface reconstruction with discontinuities, a sensor-knowledge-command fusion paradigm for man-machine systems, coordinating sensing and local navigation, and terrain map matching using multisensing techniques for applications to autonomous vehicle navigation.

  6. Application of multi-sensor information fusion technology on fault diagnosis of hydraulic system

    The structural layers and methods of multi-sensor information fusion technology are analysed, and its application in fault diagnosis of hydraulic system is discussed. Aiming at hydraulic system, a model of hydraulic fault diagnosis system based on multi-sensor information fusion technology is presented. Choosing and implementing the method of information fusion reasonably, the model can fuse and calculate various fault characteristic parameters in hydraulic system effectively and provide more valuable result for fault diagnosis of hydraulic system.

  7. Bathtub-Shaped Failure Rate of Sensors for Distributed Detection and Fusion

    Junhai Luo; Tao Li

    2014-01-01

    We study distributed detection and fusion in sensor networks with bathtub-shaped failure (BSF) rate of the sensors which may or not send data to the Fusion Center (FC). The reliability of semiconductor devices is usually represented by the failure rate curve (called the “bathtub curve”), which can be divided into the three following regions: initial failure period, random failure period, and wear-out failure period. Considering the possibility of the failed sensors which still work but in a ...

  8. Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device

    Xiang He; Aloi, Daniel N.; Jia Li

    2015-01-01

    Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph struct...

  9. Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods

    Otazu Porter, Xavier; González-Audicana, María; Fors Aldrich, Octavi; Núñez de Murga, Jorge, 1955-

    2005-01-01

    Usual image fusion methods inject features from a high spatial resolution panchromatic sensor into every low spatial resolution multispectral band trying to preserve spectral signatures and improve spatial resolution to that of the panchromatic sensor. The objective is to obtain the image that would be observed by a sensor with the same spectral response (i.e., spectral sensitivity and quantum efficiency) as the multispectral sensors and the spatial resolution of the panchromatic sensor. But ...

  10. Chromatic adaptation performance of different RGB sensors

    Susstrunk, Sabine E.; Holm, Jack M.; Finlayson, Graham D.

    2000-12-01

    Chromatic adaptation transforms are used in imaging system to map image appearance to colorimetry under different illumination sources. In this paper, the performance of different chromatic adaptation transforms (CAT) is compared with the performance of transforms based on RGB primaries that have been investigated in relation to standard color spaces for digital still camera characterization and image interchange. The chromatic adaptation transforms studied are von Kries, Bradford, Sharp, and CMCCAT2000. The RGB primaries investigated are ROMM, ITU-R BT.709, and 'prime wavelength' RGB. The chromatic adaptation model used is a von Kries model that linearly scales post-adaptation cone response with illuminant dependent coefficients. The transforms were evaluated using 16 sets of corresponding color dat. The actual and predicted tristimulus values were converted to CIELAB, and three different error prediction metrics, (Delta) ELab, (Delta) ECIE94, and (Delta) ECMC(1:1) were applied to the results. One-tail Student-t tests for matched pairs were calculated to compare if the variations in errors are statistically significant. For the given corresponding color data sets, the traditional chromatic adaptation transforms, Sharp CAT and CMCCAT2000, performed best. However, some transforms based on RGB primaries also exhibit good chromatic adaptation behavior, leading to the conclusion that white-point independent RGB spaces for image encoding can be defined. This conclusion holds only if the linear von Kries model is considered adequate to predict chromatic adaptation behavior.

  11. Multimodal Medical Image Fusion by Adaptive Manifold Filter.

    Geng, Peng; Liu, Shuaiqi; Zhuang, Shanna

    2015-01-01

    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. PMID:26664494

  12. Multimodal Medical Image Fusion by Adaptive Manifold Filter

    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.

  13. Adaptive computational resource allocation for sensor networks

    WANG Dian-hong; FEI E; YAN Yu-jie

    2008-01-01

    To efficiently utilize the limited computational resource in real-time sensor networks, this paper focu-ses on the challenge of computational resource allocation in sensor networks and provides a solution with the method of economies. It designs a mieroeconomic system in which the applications distribute their computational resource consumption across sensor networks by virtue of mobile agent. Further, it proposes the market-based computational resource allocation policy named MCRA which satisfies the uniform consumption of computational energy in network and the optimal division of the single computational capacity for multiple tasks. The simula-tion in the scenario of target tracing demonstrates that MCRA realizes an efficient allocation of computational re-sources according to the priority of tasks, achieves the superior allocation performance and equilibrium perform-ance compared to traditional allocation policies, and ultimately prolongs the system lifetime.

  14. SENSOR FUSION CONTROL SYSTEM FOR COMPUTER INTEGRATED MANUFACTURING

    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

  15. Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks

    Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng

    2016-01-01

    Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002

  16. Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks.

    Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng

    2016-01-01

    Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback-Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002

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

    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.

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

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

    2013-01-01

    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. PMID:24351636

  19. Adaptive inferential sensors based on evolving fuzzy models

    Angelov, Plamen; Kordon, Arthur

    2010-01-01

    A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible in...

  20. Adaptive Energy-Aware Gathering Strategy for Wireless Sensor Networks

    E M Saad; Awadalla, M. H.; R. R. Darwish

    2009-01-01

    Energy hole problem is considered one of the most severe threats in wireless sensor networks. In this paper the idea of exploiting sink mobility for the purpose of culling the energy hole problem in hierarchical large-scale wireless sensor networks based on bees algorithm is presented. In the proposed scheme, a mobile sink equipped with a powerful transceiver and battery, traverses the entire field, and periodically gathers data from network cluster heads. The mobile sink follows an adaptive ...

  1. Context-Aware Personal Navigation Using Embedded Sensor Fusion in Smartphones

    Sara Saeedi

    2014-03-01

    Full Text Available Context-awareness is an interesting topic in mobile navigation scenarios where the context of the application is highly dynamic. Using context-aware computing, navigation services consider the situation of user, not only in the design process, but in real time while the device is in use. The basic idea is that mobile navigation services can provide different services based on different contexts—where contexts are related to the user’s activity and the device placement. Context-aware systems are concerned with the following challenges which are addressed in this paper: context acquisition, context understanding, and context-aware application adaptation. The proposed approach in this paper is using low-cost sensors in a multi-level fusion scheme to improve the accuracy and robustness of context-aware navigation system. The experimental results demonstrate the capabilities of the context-aware Personal Navigation Systems (PNS for outdoor personal navigation using a smartphone.

  2. Context-aware personal navigation using embedded sensor fusion in smartphones.

    Saeedi, Sara; Moussa, Adel; El-Sheimy, Naser

    2014-01-01

    Context-awareness is an interesting topic in mobile navigation scenarios where the context of the application is highly dynamic. Using context-aware computing, navigation services consider the situation of user, not only in the design process, but in real time while the device is in use. The basic idea is that mobile navigation services can provide different services based on different contexts-where contexts are related to the user's activity and the device placement. Context-aware systems are concerned with the following challenges which are addressed in this paper: context acquisition, context understanding, and context-aware application adaptation. The proposed approach in this paper is using low-cost sensors in a multi-level fusion scheme to improve the accuracy and robustness of context-aware navigation system. The experimental results demonstrate the capabilities of the context-aware Personal Navigation Systems (PNS) for outdoor personal navigation using a smartphone. PMID:24670715

  3. Neural network sensor fusion: Creation of a virtual sensor for cloud-base height estimation

    Pasika, Hugh Joseph Christopher

    2000-10-01

    Sensor fusion has become a significant area of signal processing research that draws on a variety of tools. Its goals are many, however in this thesis, the creation of a virtual sensor is paramount. In particular, neural networks are used to simulate the output of a LIDAR (LASER. RADAR) that measures cloud-base height. Eye-safe LIDAR is more accurate than the standard tool that would be used for such measurement; the ceilometer. The desire is to make cloud-base height information available at a network of ground-based meteorological stations without actually installing LIDAR sensors. To accomplish this, fifty-seven sensors ranging from multispectral satellite information to standard atmospheric measurements such as temperature and humidity, are fused in what can only be termed as a very complex, nonlinear environment. The result is an accurate prediction of cloud-base height. Thus, a virtual sensor is created. A total of four different learning algorithms were studied; two global and two local. In each case, the very best state-of-the-art learning algorithms have been selected. Local methods investigated are the regularized radial basis function network, and the support vector machine. Global methods include the standard backpropagation with momentum trained multilayer perceptron (used as a benchmark) and the multilayer perceptron trained via the Kalman filter algorithm. While accuracy is the primary concern, computational considerations potentially limit the application of several of the above techniques. Thus, in all cases care was taken to minimize computational cost. For example in the case of the support vector machine, a method of partitioning the problem in order to reduce memory requirements and make the optimization over a large data set feasible was employed and in the Kalman algorithm case, node-decoupling was used to dramatically reduce the number of operations required. Overall, the methods produced somewhat equivalent mean squared errors indicating

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

    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.

  5. Adaptive and mobile ground sensor array.

    Holzrichter, Michael Warren; O' Rourke, William T.; Zenner, Jennifer; Maish, Alexander B.

    2003-12-01

    The goal of this LDRD was to demonstrate the use of robotic vehicles for deploying and autonomously reconfiguring seismic and acoustic sensor arrays with high (centimeter) accuracy to obtain enhancement of our capability to locate and characterize remote targets. The capability to accurately place sensors and then retrieve and reconfigure them allows sensors to be placed in phased arrays in an initial monitoring configuration and then to be reconfigured in an array tuned to the specific frequencies and directions of the selected target. This report reviews the findings and accomplishments achieved during this three-year project. This project successfully demonstrated autonomous deployment and retrieval of a payload package with an accuracy of a few centimeters using differential global positioning system (GPS) signals. It developed an autonomous, multisensor, temporally aligned, radio-frequency communication and signal processing capability, and an array optimization algorithm, which was implemented on a digital signal processor (DSP). Additionally, the project converted the existing single-threaded, monolithic robotic vehicle control code into a multi-threaded, modular control architecture that enhances the reuse of control code in future projects.

  6. Development of a landmine detection system with nuclear sensors and data fusion

    The problem of landmines in Egypt and the detection techniques currently used for humanitarian demining are described and discussed. Most of these techniques depend on metal detectors. This makes the demining of vast areas of contaminated lands difficult, dangerous, slow and very costly processes. Although some of these techniques are very effective to locate metal or metal like anomalies, but they suffer from high false alarm rates because they are not capable to identify these anomalies as mines. However, in the last four decades, techniques based on using neutrons of different energies have proved themselves as powerful tools for elemental analysis and are therefore capable to identify explosive materials and to confirm the presence or not of a landmine. Results of the activities running through the IAEA TC project, EGY1024 for developing and adapting the two main promising nuclear techniques based on measuring the density variation of hydrogen by measuring thermal neutrons backscattered from buried object are given and discussed. In addition, the use of two nuclear sensors with other two sensors based on EMI and GPR in an integrated system with data fusion and how to integrate these sensors onto a land vehicle are described and discussed. (author)

  7. Emergent Adaptive Noise Reduction from Communal Cooperation of Sensor Grid

    Jones, Kennie H.; Jones, Michael G.; Nark, Douglas M.; Lodding, Kenneth N.

    2010-01-01

    In the last decade, the realization of small, inexpensive, and powerful devices with sensors, computers, and wireless communication has promised the development of massive sized sensor networks with dense deployments over large areas capable of high fidelity situational assessments. However, most management models have been based on centralized control and research has concentrated on methods for passing data from sensor devices to the central controller. Most implementations have been small but, as it is not scalable, this methodology is insufficient for massive deployments. Here, a specific application of a large sensor network for adaptive noise reduction demonstrates a new paradigm where communities of sensor/computer devices assess local conditions and make local decisions from which emerges a global behaviour. This approach obviates many of the problems of centralized control as it is not prone to single point of failure and is more scalable, efficient, robust, and fault tolerant

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

    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...... three key properties of EH-WSNs: adaptability of energy consumption, distributed energy-aware load balancing and support for different application-specific requirements....

  9. Asynchronous Sensor fuSion for Improved Safety of air Traffic (ASSIST) Project

    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...

  10. Dynamic reweighting of three modalities for sensor fusion.

    Hwang, Sungjae; Agada, Peter; Kiemel, Tim; Jeka, John J

    2014-01-01

    We simultaneously perturbed visual, vestibular and proprioceptive modalities to understand how sensory feedback is re-weighted so that overall feedback remains suited to stabilizing upright stance. Ten healthy young subjects received an 80 Hz vibratory stimulus to their bilateral Achilles tendons (stimulus turns on-off at 0.28 Hz), a ± 1 mA binaural monopolar galvanic vestibular stimulus at 0.36 Hz, and a visual stimulus at 0.2 Hz during standing. The visual stimulus was presented at different amplitudes (0.2, 0.8 deg rotation about ankle axis) to measure: the change in gain (weighting) to vision, an intramodal effect; and a change in gain to vibration and galvanic vestibular stimulation, both intermodal effects. The results showed a clear intramodal visual effect, indicating a de-emphasis on vision when the amplitude of visual stimulus increased. At the same time, an intermodal visual-proprioceptive reweighting effect was observed with the addition of vibration, which is thought to change proprioceptive inputs at the ankles, forcing the nervous system to rely more on vision and vestibular modalities. Similar intermodal effects for visual-vestibular reweighting were observed, suggesting that vestibular information is not a "fixed" reference, but is dynamically adjusted in the sensor fusion process. This is the first time, to our knowledge, that the interplay between the three primary modalities for postural control has been clearly delineated, illustrating a central process that fuses these modalities for accurate estimates of self-motion. PMID:24498252

  11. Dynamic reweighting of three modalities for sensor fusion.

    Sungjae Hwang

    Full Text Available We simultaneously perturbed visual, vestibular and proprioceptive modalities to understand how sensory feedback is re-weighted so that overall feedback remains suited to stabilizing upright stance. Ten healthy young subjects received an 80 Hz vibratory stimulus to their bilateral Achilles tendons (stimulus turns on-off at 0.28 Hz, a ± 1 mA binaural monopolar galvanic vestibular stimulus at 0.36 Hz, and a visual stimulus at 0.2 Hz during standing. The visual stimulus was presented at different amplitudes (0.2, 0.8 deg rotation about ankle axis to measure: the change in gain (weighting to vision, an intramodal effect; and a change in gain to vibration and galvanic vestibular stimulation, both intermodal effects. The results showed a clear intramodal visual effect, indicating a de-emphasis on vision when the amplitude of visual stimulus increased. At the same time, an intermodal visual-proprioceptive reweighting effect was observed with the addition of vibration, which is thought to change proprioceptive inputs at the ankles, forcing the nervous system to rely more on vision and vestibular modalities. Similar intermodal effects for visual-vestibular reweighting were observed, suggesting that vestibular information is not a "fixed" reference, but is dynamically adjusted in the sensor fusion process. This is the first time, to our knowledge, that the interplay between the three primary modalities for postural control has been clearly delineated, illustrating a central process that fuses these modalities for accurate estimates of self-motion.

  12. Bayesian Fusion Algorithm for Inferring Trust in Wireless Sensor Networks

    Mohammad Momani

    2010-07-01

    Full Text Available This paper introduces a new Bayesian fusion algorithm to combine more than one trust component (data trust and communication trust to infer the overall trust between nodes. This research work proposes that one trust component is not enough when deciding on whether or not to trust a specific node in a wireless sensor network. This paper discusses and analyses the results from the communication trust component (binary and the data trust component (continuous and proves that either component by itself, can mislead the network and eventually cause a total breakdown of the network. As a result of this, new algorithms are needed to combine more than one trust component to infer the overall trust. The proposed algorithm is simple and generic as it allows trust components to be added and deleted easily. Simulation results demonstrate that a node is highly trustworthy provided that both trust components simultaneously confirm its trustworthiness and conversely, a node is highly untrustworthy if its untrustworthiness is asserted by both components.

  13. Weight-Based Clustering Decision Fusion Algorithm for Distributed Target Detection in Wireless Sensor Networks

    Haiping Huang; Lei Chen; Xiao Cao; Ruchuan Wang; Qianyi Wang

    2013-01-01

    We use a great deal of wireless sensor nodes to detect target signal that is more accurate than the traditional single radar detection method. Each local sensor detects the target signal in the region of interests and collects relevant data, and then it sends the respective data to the data fusion center (DFC) for aggregation processing and judgment making whether the target signal exists or not. However, the current judgment fusion rules such as Counting Rule (CR) and Clustering-Counting Rul...

  14. A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

    ZiQi Hao; ZhenJiang Zhang; Han-Chieh Chao

    2015-01-01

    As limited energy is one of the tough challenges in wireless sensor networks (WSN), energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use k-means algorithm to form the nodes into clusters, which can signifi...

  15. Energy-Efficient Target Tracking in Wireless Sensor Networks: A Quantized Measurement Fusion Framework

    Yan Zhou; Dongli Wang; Tingrui Pei; Yonghong Lan

    2014-01-01

    Optimizing the design of tracking system under energy and bandwidth constraints in wireless sensor networks (WSN) is of paramount importance. In this paper, the problem of collaborative target tracking in WSNs is considered in a framework of quantized measurement fusion. First, the measurement in each local sensor is quantized by probabilistic quantization scheme and transmitted to a fusion center (FC). Then, the quantized messages are fused and sequential importance resampling (SIR) particle...

  16. Sensor Web Dynamic Measurement Techniques and Adaptive Observing Strategies

    Talabac, Stephen J.

    2004-01-01

    Sensor Web observing systems may have the potential to significantly improve our ability to monitor, understand, and predict the evolution of rapidly evolving, transient, or variable environmental features and events. This improvement will come about by integrating novel data collection techniques, new or improved instruments, emerging communications technologies and protocols, sensor mark-up languages, and interoperable planning and scheduling systems. In contrast to today's observing systems, "event-driven" sensor webs will synthesize real- or near-real time measurements and information from other platforms and then react by reconfiguring the platforms and instruments to invoke new measurement modes and adaptive observation strategies. Similarly, "model-driven" sensor webs will utilize environmental prediction models to initiate targeted sensor measurements or to use a new observing strategy. The sensor web concept contrasts with today's data collection techniques and observing system operations concepts where independent measurements are made by remote sensing and in situ platforms that do not share, and therefore cannot act upon, potentially useful complementary sensor measurement data and platform state information. This presentation describes NASA's view of event-driven and model-driven Sensor Webs and highlights several research and development activities at the Goddard Space Flight Center.

  17. Principles of data-fusion in multi-sensor systems for non-destructive testing

    Chioclea, Shmuel; Dickstein, Phineas

    2000-05-01

    In recent years, there has been progress in the application of measurement and control systems that engage multi-sensor arrays. Several algorithms and techniques have been developed for the integration of the information obtained from the sensors. The fusion of the data may be complicated due to the fact that each sensor has its own performance characteristics, and because different sensors may detect different physical phenomena. As a result, data fusion turns out to be a multidisciplinary field, which applies principles adopted from other fields such as signal processing, artificial intelligence, statistics, and The Theory of Information. The data fusion machine tries to imitate the human brain, in combining data from numerous sensors and making optimal inferences about the environment. The present paper provides a critical review of data fusion algorithms and techniques and a trenchant summary of the experience gained to date from the several preliminary NDT studies which have been applying multi-sensor data fusion systems. Consequently, this paper provides a list of rules and criteria to be followed in future applications of data fusion to nondestructive testing.

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

    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.

  19. Statistical sensor fusion of ECG data using automotive-grade sensors

    Koenig, A.; Rehg, T.; Rasshofer, R.

    2015-11-01

    Driver states such as fatigue, stress, aggression, distraction or even medical emergencies continue to be yield to severe mistakes in driving and promote accidents. A pathway towards improving driver state assessment can be found in psycho-physiological measures to directly quantify the driver's state from physiological recordings. Although heart rate is a well-established physiological variable that reflects cognitive stress, obtaining heart rate contactless and reliably is a challenging task in an automotive environment. Our aim was to investigate, how sensory fusion of two automotive grade sensors would influence the accuracy of automatic classification of cognitive stress levels. We induced cognitive stress in subjects and estimated levels from their heart rate signals, acquired from automotive ready ECG sensors. Using signal quality indices and Kalman filters, we were able to decrease Root Mean Squared Error (RMSE) of heart rate recordings by 10 beats per minute. We then trained a neural network to classify the cognitive workload state of subjects from heart rate and compared classification performance for ground truth, the individual sensors and the fused heart rate signal. We obtained an increase of 5 % higher correct classification by fusing signals as compared to individual sensors, staying only 4 % below the maximally possible classification accuracy from ground truth. These results are a first step towards real world applications of psycho-physiological measurements in vehicle settings. Future implementations of driver state modeling will be able to draw from a larger pool of data sources, such as additional physiological values or vehicle related data, which can be expected to drive classification to significantly higher values.

  20. Multi-sensor Data Fusion for Improved Prediction of Apple Fruit Firmness and Soluble Solids Content

    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 these quality parameters. With the concept of multi-sensor data fusion, different sensors would work ...

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

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

    2012-01-01

    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 (WiFi...

  2. A comparison of decision-level sensor-fusion methods for anti-personnel landmine detection.

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

    2001-01-01

    We present the sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining. The syste

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

    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 demin

  4. A technique for sensors fusion with limited number of common measures

    Quaranta, Carlo; Balzarotti, Giorgio

    2015-06-01

    An algorithm is proposed here that overcomes the problem of the not exhaustive number of common measures from sensors of different kind. In the presence of a suite of heterogeneous sensors, the data fusion process has to deal with the problem of managing different information, generally not directly comparable. The analysis of the mathematical model is carried out considering a data fusion system between radar and Infrared Search and Track (IRST) where the measurement of the range is achieved by radar only. Simulation results demonstrate the effectiveness of the algorithm as regards the fusion process, tracking and correctness of association among tracks from different sensors. A comparison with a known approach from the literature about the fusion equation is also performed.

  5. Thermal characterization of FBG sensors for nuclear fusion reactor relevant applications

    The FBG (Fiber Bragg Grating) sensors are having potential applications in Fusion reactor applications where huge electric and magnetic field environment is present. This is due to the sensor being immune to electric and magnetic fields. The utilization of optical sensors for the tokamak research has got special attention towards structural monitoring, strain sensing applications and temperature monitoring as per the advantages. The monitoring and preventive maintenance of such structures can be more effectively carried out with FBG sensors compared to conventional sensors like thermocouples, RTD etc. We present the theoretical and experimental investigations carried out on the thermal response of FBGs and LPGs for the measurement of temperatures upto 250 degrees centigrade. The results reveal good sensitivity and resolution in the measurement of temperatures. The present work reports the development and temperature characterization of the FBG sensors demand is highlighted for applications in the Fusion reactor machines. (author)

  6. Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

    Rand, Robert S.; Khuon, Timothy; Truslow, Eric

    2016-07-01

    A proposed framework using spectral and spatial information is introduced for neural net multisensor data fusion. This consists of a set of independent-sensor neural nets, one for each sensor (type of data), coupled to a fusion net. The neural net of each sensor is trained from a representative data set of the particular sensor to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. During the initial processing, three-dimensional (3-D) point cloud data (PCD) are segmented using a multidimensional mean-shift algorithm into clustered objects. Concurrently, multiband spectral imagery data (multispectral or hyperspectral) are spectrally segmented by the stochastic expectation-maximization into a cluster map containing (spectral-based) pixel classes. For the proposed sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The success of the approach in utilizing sensor synergism for an enhanced classification is demonstrated for the specific case of classifying hyperspectral imagery and PCD extracted from LIDAR, obtained from an airborne data collection over the campus of University of Southern Mississippi, Gulfport, Mississippi.

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

    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

  8. An Adaptive Amplifier System for Wireless Sensor Network Applications

    Carlos Marqués; Eduardo Romero; Mónica Lovay; Gabriela Peretti

    2012-01-01

    This paper presents an adaptive amplifier that is part of a sensor node in a wireless sensor network. The system presents a target gain that has to be maintained without direct human intervention despite the presence of faults. In addition, its bandwidth must be as large as possible. The system is composed of a software-based built-in self-test scheme implemented in the node that checks all the available gains in the amplifiers, a reconfigurable amplifier, and a genetic algorithm (GA) for rec...

  9. Distributed fusion estimation for sensor networks with communication constraints

    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.

  10. Image Fusion Method based on Single Coherent Composition from Multiple Imagining Sensors

    Tohid Sedghi

    2014-01-01

    Development of new imaging sensors arises the need for image processing techniques that can effectively fuse images from different sensors into a single coherent composition for interpretation. In order to make use of inherent redundancy and extended coverage of multiple sensors, we propose a multi-scale approach for pixel level image fusion. The ultimate goal is to reduce human/machine error in detection and recognition of objects. Results show that proposed methods has lots of superiority o...

  11. Noise-exploitation and adaptation in neuromorphic sensors

    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).

  12. Sensor Fusion Based Fault-Tolerant Attitude Estimation Solutions for Small Unmanned Aerial Vehicles

    Gross, Jason Nicholas

    Navigation-grade inertial sensors are often too expensive and too heavy for use in most Small Unmanned Aerial Vehicle (SUAV) systems. Low-cost Micro-Electrical-Mechanical-Systems (MEMS) inertial sensors provide an attractive alternative, but currently do not provide an adequate navigation solution alone due to the presence of sensor bias. Toward addressing this problem, this research focuses on the development and experimental evaluation of sensor fusion algorithms to combine partially redundant information from low-cost sensor to achieve accurate SUAV attitude estimation. To conduct this research, several sets of SUAVs flight data that include measurements from a low-cost MEMS based Inertial Measurement Unit, a Global Positioning System receiver, and a set of low-grade tri-axial magnetometers are used to evaluate a variety of algorithms. In order to provide a baseline for performance evaluation, attitude measurements obtained directly with a high-quality mechanical vertical gyroscope are used as an independent attitude 'truth'. In addition, as a part of this project, a custom SUAV avionics system was developed to provide a platform for fault-tolerant flight control research. The overall goal of this research is to provide high-accuracy attitude estimation during nominal sensor performance conditions and in the event of sensors failures, while using only low-cost components. To achieve this goal, this study is carried out in three phases. The specific aim of the first phase is to obtain high-accuracy under nominal sensor conditions. During this phase, two different nonlinear Kalman filtering methods are applied to various sensor fusion formulations and evaluated with respect to estimation accuracy over diverse sets of flight data. Next, during the second phase, sensor fusion based calibration techniques are explored to further enhance estimation accuracy. Finally, the third phase of the study considers the design of a sensor fusion attitude estimation architecture

  13. Sensor management for identity fusion on a mobile robot

    Larsen, Thomas Dall; Andersen, Nils Axel; Ravn, Ole

    1998-01-01

    This paper addresses the problem of identity fusion, i.e. the problem of selecting one of several identity hypotheses concerning an observed object. Two problems are considered. Firstly the problem of preserving the information in the representation and fusion of measurements relating to identity...

  14. Toward Mobile Sensor Fusion Platform for Context-Aware Services

    Sashima, Akio; Ikeda, Takeshi; Kurumatani, Koichi

    2010-01-01

    In current implementation of the healthcare service based on mobile sensing architecture, sensor discovery and communication protocols are predefined. How it can communicate with environmental sensors in an ad-hoc manner is an important issue. Users do not stay in their homes. They visit various places, such as commercial facilities. How can users access environmental sensors in such public spaces? A mechanism to discover and communicate with the environmental sensors is necessary to realize ...

  15. A Multifunctional Joint Angle Sensor with Measurement Adaptability

    Wei Quan

    2013-11-01

    Full Text Available The paper presents a multifunctional joint sensor with measurement adaptability for biological engineering applications, such as gait analysis, gesture recognition, etc. The adaptability is embodied in both static and dynamic environment measurements, both of body pose and in motion capture. Its multifunctional capabilities lay in its ability of simultaneous measurement of multiple degrees of freedom (MDOF with a single sensor to reduce system complexity. The basic working mode enables 2DOF spatial angle measurement over big ranges and stands out for its applications on different joints of different individuals without recalibration. The optional advanced working mode enables an additional DOF measurement for various applications. By employing corrugated tube as the main body, the sensor is also characterized as flexible and wearable with less restraints. MDOF variations are converted to linear displacements of the sensing elements. The simple reconstruction algorithm and small outputs volume are capable of providing real-time angles and long-term monitoring. The performance assessment of the built prototype is promising enough to indicate the feasibility of the sensor.

  16. Adaptive data filtering of inertial sensors with variable bandwidth.

    Alam, Mushfiqul; Rohac, Jan

    2015-01-01

    MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing. PMID:25648711

  17. Adaptive region-based multimodal image fusion using ICA bases

    Cvejic, N; Canagarajah, CN; Bull, DR

    2006-01-01

    In this paper, we present a novel multimodal image fusion algorithm in ICA domain. It uses segmentation to determine the most important regions in the input images and consequently fuses the ICA coefficients from given regions using the Piella fusion metric to maximise the quality of the fused image. The proposed method exhibits significantly higher performance than the basic ICA algorithm and improvement over other state-of-the-art algorithms In this paper, we present a novel multimodal i...

  18. Information Fusion of Online Oil Monitoring System Using Multiple Sensors

    高慧良; 周新聪; 程海明; 赵春华; 严新平

    2004-01-01

    Machine lubrication contains abundant information on the equipment operation.Nowadays, most measuring methods are based on offline sampling or on online measuring with a single sensor.An online oil monitoring system with multiple sensors was designed.The measurement data was processed with a fuzzy intelligence system.Information from integrated sensors in an oil online monitoring system was evaluated using fuzzy logic.The analyses show that the multiple sensors evaluation results are more reliable than online monitoring systems with single sensors.

  19. Research of Multi-sensor Images Based on Color Fusion Methods

    Chunlong Yao

    2013-11-01

    Full Text Available With the development of image sensor technology, multi-sensor image fusion technology emerged and was widely used in the field of military surveillance, medical diagnosis, remote sensing, intelligent robot and so on. However, the current image fusion technology mainly focuses on the research of gray images, the color image fusion is rarely. Because color image contains more information compared with gray image, the research on color image fusion technology is becoming more and more urgent. In this paper, the realization of several typical color image fusion algorithms were discussed, the principle and their respective advantages and disadvantages were analyzed. Secondly, according to the different characteristics of visible image and infrared image, this paper proposes a color image fusion algorithm based on Curve let transform, this algorithm will combine visible image, infrared image with its negative respectively fusion, color mapping rules are in couple with the human visual characteristics. Experiments show that color fusion images obtained are richer in color, they contains more details and recognize easily.

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

    Gabriele Ligorio

    2016-01-01

    Full Text Available 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.

  1. Adaptive neuro-fuzzy fusion of sensor data

    Petković, Dalibor

    2014-11-01

    A framework is proposed, which consolidates the benefits of a fuzzy rationale and a neural system. The framework joins together Kalman separating and delicate processing guideline i.e. ANFIS to structure an effective information combination strategy for the target following framework. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Fuzzy versatile combination calculation is a compelling device to make the genuine quality of the leftover covariance steady with its hypothetical worth. ANFIS indicates great taking in and forecast proficiencies, which makes it a productive device to manage experienced vulnerabilities in any framework. A neural system is presented, which can concentrate the measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab/Simulink and the exhibitions are explored.

  2. Research on the Strategy of Underwater United Detection Fusion and Communication Using Multi-sensor

    Zhenhua Xu; Jianguo Huang; Hai Huang; Qunfei Zhang

    2011-01-01

    In order to solve the distributed detection fusion problem of underwater target detection,when the signal to noise ratio (SNR) of the acoustic channel is low,a new strategy for united detection fusion and communication using multiple sensors was proposed.The performance of detection fusion was studied and compared based on the Neyman-Pearson principle when the binary phase shift keying (BPSK) and on-off keying (OOK) modes were used by the local sensors.The comparative simulation and analysis between the optimal likelihood ratio test and the proposed strategy was completed,and both the theoretical analysis and simulation indicate that using the proposed new strategy could improve the detection performance effectively.In theory,the proposed strategy of united detection fusion and communication is of great significance to the establishment of an underwater target detection system.

  3. Performance evaluation of multi-sensor data-fusion systems in launch vehicles

    B N Suresh; K Sivan

    2004-04-01

    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. The role of an integrated system level-test bed for evaluating multi-sensors and mission performance in a typical launch vehicle mission is described. Some of the typical simulation results to evaluate the effect of the sensors on the overall system are highlighted.

  4. Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth

    Mushfiqul Alam

    2015-02-01

    Full Text Available MEMS (micro-electro-mechanical system-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU, which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor’s behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer’s data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing.

  5. Reliability estimates for selected sensors in fusion applications

    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.

  6. Reliability estimates for selected sensors in fusion applications

    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

  7. Maneuvering Vehicle Tracking Based on Multi-sensor Fusion%基于多传感融合的路面机动目标跟踪

    陈莹; 韩崇昭

    2005-01-01

    Maneuvering targets tracking is a fundamental task in intelligent vehicle research. This paper focuses on the problem of fusion between radar and image sensors in targets tracking. In order to improve positioning accuracy and narrow down the image working area, a novel method that integrates radar filter with image intensity is proposed to establish an adaptive vision window.A weighted Hausdorff distance is introduced to define the functional relationship between image and model projection, and a modified simulated annealing algorithm is used to find optimum orientation parameter. Furthermore, the global state is estimated, which refers to the distributed data fusion algorithm. Experiment results show that our method is accurate.

  8. Sensor fusion to enable next generation low cost Night Vision systems

    Schweiger, R.; Franz, S.; Löhlein, O.; Ritter, W.; Källhammer, J.-E.; Franks, J.; Krekels, T.

    2010-04-01

    The next generation of automotive Night Vision Enhancement systems offers automatic pedestrian recognition with a performance beyond current Night Vision systems at a lower cost. This will allow high market penetration, covering the luxury as well as compact car segments. Improved performance can be achieved by fusing a Far Infrared (FIR) sensor with a Near Infrared (NIR) sensor. However, fusing with today's FIR systems will be too costly to get a high market penetration. The main cost drivers of the FIR system are its resolution and its sensitivity. Sensor cost is largely determined by sensor die size. Fewer and smaller pixels will reduce die size but also resolution and sensitivity. Sensitivity limits are mainly determined by inclement weather performance. Sensitivity requirements should be matched to the possibilities of low cost FIR optics, especially implications of molding of highly complex optical surfaces. As a FIR sensor specified for fusion can have lower resolution as well as lower sensitivity, fusing FIR and NIR can solve performance and cost problems. To allow compensation of FIR-sensor degradation on the pedestrian detection capabilities, a fusion approach called MultiSensorBoosting is presented that produces a classifier holding highly discriminative sub-pixel features from both sensors at once. The algorithm is applied on data with different resolution and on data obtained from cameras with varying optics to incorporate various sensor sensitivities. As it is not feasible to record representative data with all different sensor configurations, transformation routines on existing high resolution data recorded with high sensitivity cameras are investigated in order to determine the effects of lower resolution and lower sensitivity to the overall detection performance. This paper also gives an overview of the first results showing that a reduction of FIR sensor resolution can be compensated using fusion techniques and a reduction of sensitivity can be

  9. Autonomous navigation vehicle system based on robot vision and multi-sensor fusion

    Wu, Lihong; Chen, Yingsong; Cui, Zhouping

    2011-12-01

    The architecture of autonomous navigation vehicle based on robot vision and multi-sensor fusion technology is expatiated in this paper. In order to acquire more intelligence and robustness, accurate real-time collection and processing of information are realized by using this technology. The method to achieve robot vision and multi-sensor fusion is discussed in detail. The results simulated in several operating modes show that this intelligent vehicle has better effects in barrier identification and avoidance and path planning. And this can provide higher reliability during vehicle running.

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

    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.

  11. Sensor Fusion of Gaussian Mixtures for Ballistic Target Tracking in the Re-Entry Phase.

    Lu, Kelin; Zhou, Rui

    2016-01-01

    A sensor fusion methodology for the Gaussian mixtures model is proposed for ballistic target tracking with unknown ballistic coefficients. To improve the estimation accuracy, a track-to-track fusion architecture is proposed to fuse tracks provided by the local interacting multiple model filters. During the fusion process, the duplicate information is removed by considering the first order redundant information between the local tracks. With extensive simulations, we show that the proposed algorithm improves the tracking accuracy in ballistic target tracking in the re-entry phase applications. PMID:27537883

  12. Data fusion on a distributed heterogeneous sensor network

    Lamborn, Peter; Williams, Pamela J.

    2006-04-01

    Alarm-based sensor systems are being explored as a tool to expand perimeter security for facilities and force protection. However, the collection of increased sensor data has resulted in an insufficient solution that includes faulty data points. Data analysis is needed to reduce nuisance and false alarms, which will improve officials' decision making and confidence levels in the system's alarms. Moreover, operational costs can be allayed and losses mitigated if authorities are alerted only when a real threat is detected. In the current system, heuristics such as persistence of alarm and type of sensor that detected an event are used to guide officials' responses. We hypothesize that fusing data from heterogeneous sensors in the sensor field can provide more complete situational awareness than looking at individual sensor data. We propose a two stage approach to reduce false alarms. First, we use self organizing maps to cluster sensors based on global positioning coordinates and then train classifiers on the within cluster data to obtain a local view of the event. Next, we train a classifier on the local results to compute a global solution. We investigate the use of machine learning techniques, such as k-nearest neighbor, neural networks, and support vector machines to improve alarm accuracy. On simulated sensor data, the proposed approach identifies false alarms with greater accuracy than a weighted voting algorithm.

  13. Data fusion on a distributed heterogeneous sensor network.

    Lamborn, Peter; Williams, Pamela J.

    2006-02-01

    Alarm-based sensor systems are being explored as a tool to expand perimeter security for facilities and force protection. However, the collection of increased sensor data has resulted in an insufficient solution that includes faulty data points. Data analysis is needed to reduce nuisance and false alarms, which will improve officials decision making and confidence levels in the system's alarms. Moreover, operational costs can be allayed and losses mitigated if authorities are alerted only when a real threat is detected. In the current system, heuristics such as persistence of alarm and type of sensor that detected an event are used to guide officials responses. We hypothesize that fusing data from heterogeneous sensors in the sensor field can provide more complete situational awareness than looking at individual sensor data. We propose a two stage approach to reduce false alarms. First, we use self organizing maps to cluster sensors based on global positioning coordinates and then train classifiers on the within cluster data to obtain a local view of the event. Next, we train a classifier on the local results to compute a global solution. We investigate the use of machine learning techniques, such as k-nearest neighbor, neural networks, and support vector machines to improve alarm accuracy. On simulated sensor data, the proposed approach identifies false alarms with greater accuracy than a weighted voting algorithm.

  14. Scalable sensor management for automated fusion and tactical reconnaissance

    Walls, Thomas J.; Wilson, Michael L.; Partridge, Darin C.; Haws, Jonathan R.; Jensen, Mark D.; Johnson, Troy R.; Petersen, Brad D.; Sullivan, Stephanie W.

    2013-05-01

    The capabilities of tactical intelligence, surveillance, and reconnaissance (ISR) payloads are expanding from single sensor imagers to integrated systems-of-systems architectures. Increasingly, these systems-of-systems include multiple sensing modalities that can act as force multipliers for the intelligence analyst. Currently, the separate sensing modalities operate largely independent of one another, providing a selection of operating modes but not an integrated intelligence product. We describe here a Sensor Management System (SMS) designed to provide a small, compact processing unit capable of managing multiple collaborative sensor systems on-board an aircraft. Its purpose is to increase sensor cooperation and collaboration to achieve intelligent data collection and exploitation. The SMS architecture is designed to be largely sensor and data agnostic and provide flexible networked access for both data providers and data consumers. It supports pre-planned and ad-hoc missions, with provisions for on-demand tasking and updates from users connected via data links. Management of sensors and user agents takes place over standard network protocols such that any number and combination of sensors and user agents, either on the local network or connected via data link, can register with the SMS at any time during the mission. The SMS provides control over sensor data collection to handle logging and routing of data products to subscribing user agents. It also supports the addition of algorithmic data processing agents for feature/target extraction and provides for subsequent cueing from one sensor to another. The SMS architecture was designed to scale from a small UAV carrying a limited number of payloads to an aircraft carrying a large number of payloads. The SMS system is STANAG 4575 compliant as a removable memory module (RMM) and can act as a vehicle specific module (VSM) to provide STANAG 4586 compliance (level-3 interoperability) to a non-compliant sensor system

  15. STUDY ON THE COAL-ROCK INTER-FACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE

    Ren Fang; Yang Zhaojian; Xiong Shibo

    2003-01-01

    The coal-rock interface recognition method based on multi-sensor data fusion technique is put forward because of the localization of single type sensor recognition method. The measuring theory based on multi-sensor data fusion technique is analyzed, and hereby the test platform of recognition system is manufactured. The advantage of data fusion with the fuzzy neural network (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carried out. The experiments show that in various conditions the method can always acquire a much higher recognition rate than normal ones.

  16. Adaptive wavefront sensor based on the Talbot phenomenon.

    Podanchuk, Dmytro V; Goloborodko, Andrey A; Kotov, Myhailo M; Kovalenko, Andrey V; Kurashov, Vitalij N; Dan'ko, Volodymyr P

    2016-04-20

    A new adaptive method of wavefront sensing is proposed and demonstrated. The method is based on the Talbot self-imaging effect, which is observed in an illuminating light beam with strong second-order aberration. Compensation of defocus and astigmatism is achieved with an appropriate choice of size of the rectangular unit cell of the diffraction grating, which is performed iteratively. A liquid-crystal spatial light modulator is used for this purpose. Self-imaging of rectangular grating in the astigmatic light beam is demonstrated experimentally. High-order aberrations are detected with respect to the compensated second-order aberration. The comparative results of wavefront sensing with a Shack-Hartmann sensor and the proposed sensor are adduced. PMID:27140122

  17. Novel adaptive laser scanning sensor for reverse engineering measurement

    Zhao Ji; Ma Zi; Lin Na; Zhu Quanmin

    2007-01-01

    In this paper, a series of new techniques are used to optimize typical laser scanning sensor. The integrated prototype is compared with traditional approach to demonstrate the much improved performance. In the research and development, camera calibration is achieved by extracting characteristic points of the laser plane, so that the calibration efficiency is improved significantly. With feedback control of its intensity, the laser is automatically adjusted for different material. A modified algorithm is presented to improve the accuracy of laser stripe extraction. The fusion of data extracted from left and right camera is completed with re-sampling technique. The scanner is integrated with a robot arm and some other machinery for on-line measurement and inspection, which provides a flexible measurement tool for reverse engineering.

  18. Multi-Sensor Data Fusion Using Bayesian Programming : an Automotive Application

    Coué, Christophe; Fraichard, Thierry; Bessiere, Pierre; Mazer, Emmanuel

    2002-01-01

    A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This...

  19. Using Bayesian Programming for Multi-Sensor Data Fusion in Automotive Applications

    Coué, Christophe; Fraichard, Thierry; Bessiere, Pierre; Mazer, Emmanuel

    2002-01-01

    A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing sytem providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This ...

  20. Multi sensor fusion of camera and 3D laser range finder for object recognition

    Klimentjew, Denis; Hendrich, Norman; Zhang, jianwei

    2010-01-01

    This paper proposes multi sensor fusion based on an effective calibration method for a perception system designed for mobile robots and intended for later object recognition. The perception system consists of a camera and a three-dimensional laser range finder. The three-dimensional laser range finder is based on a two-dimensional laser scanner and a pan-tilt unit as a moving platform. The calibration permits the coalescence of the two most important sensors for three-dim...

  1. Multi sensor fusion framework for indoor-outdoor localization of limited resource mobile robots

    Pedro Albertos; Ángel Valera; Ángel Soriano; Marina Vallés; Leonardo Marín

    2013-01-01

    This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU) on an event based schedule, using fewer resources (execution time and bandwidth) but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estim...

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

    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%

  3. Adaptive Framework for Data Distribution in Wireless Sensor Networks

    Mukherjee, Subhabrata; Mukherjee, Amitava

    2012-01-01

    In recent years, the wireless sensor network (WSN) is playing a key role in sensing, collecting and disseminating information in various applications. An important feature associated with WSN is to develop an efficient data distribution and routing scheme to ensure better quality of service (QoS) that reduces the power consumption and the end-to-end data delivery time. In this work, we propose an adaptive framework to transmit data packets from a source to the sink in WSN across multiples paths with strategically distributed data packets so as to minimize the power consumption as well as the end-to-end data delivery time.

  4. Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device.

    He, Xiang; Aloi, Daniel N; Li, Jia

    2015-01-01

    Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design. PMID:26694387

  5. Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device

    Xiang He

    2015-12-01

    Full Text Available Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer, wireless signal strength indicators (WiFi, Bluetooth, Zigbee, and visual sensors (LiDAR, camera. People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.

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

    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....

  7. Health-Enabled Smart Sensor Fusion Technology Project

    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...

  8. Multi-resolution, multi-sensor image fusion: general fusion framework

    Palubinskas, Gintautas; Reinartz, Peter

    2011-01-01

    Multi-resolution image fusion also known as pansharpening aims to include spatial information from a high resolution image, e.g. panchromatic or Synthetic Aperture Radar (SAR) image, into a low resolution image, e.g. multi-spectral or hyper-spectral image, while preserving spectral properties of a low resolution image. A signal processing view at this problem allowed us to perform a systematic classification of most known multi-resolution image fusion approaches and resul...

  9. Hybrid Navigation using Sensor Fusion in Hand-Held Devices

    Calero Scanlan, David

    2013-01-01

    This thesis analyses the performance that can be obtained in navigation applications by using the camera and sensors embedded in a mobile phone (and GPS when available). The project includes the development of image processing algorithms to extract useful observations for navigation. Navigation is based on the determination of the trajectory, ie, time, position, velocity and altitude. For a good navigation experience a period of calibration and characterization of embedded mobile sensors such...

  10. Feedback Control and Sensor Fusion of Vision and Force

    Olsson, Tomas

    2004-01-01

    This thesis deals with feedback control using two different sensor types, force sensors and cameras. In many tasks robotics compliance is required in order to avoid damage to the workpiece. Force and vision are the most useful sensing capabilities for a robot system operating in an unknown or uncalibrated environment. An overview of vision based estimation, control and vision/force control is given. Two different control algorithms based on a hybrid force/vision structure are presented, using...

  11. Multi-sensor data fusion in sensor-based control: application to multi-camera visual servoing

    Kermorgant, Olivier; Chaumette, F.

    2011-01-01

    A low-level sensor fusion scheme is presented for the positioning of a multi-sensor robot. This non-hierarchical framework can be used for robot arms or other velocity- controlled robots, and is part of the task function approach. A stability analysis is presented for the general case, then several control laws illustrate the versatility of the framework. This approach is applied to the multi-camera eye-in-hand/eye- to-hand configuration in visual servoing. Experimental results point out the ...

  12. Bathtub-Shaped Failure Rate of Sensors for Distributed Detection and Fusion

    Junhai Luo

    2014-01-01

    Log-likelihood Ratio Test (ELRT rule is derived. Finally, the ROC curve for this model is presented. The simulation results show that the ELRT rule improves the robust performance of the system, compared with the traditional fusion rule without considering sensor failures.

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

    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...

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

    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 cont

  15. Detection of anti-personnel land-mines using sensor-fusion techniques

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

    1999-01-01

    In this paper we present the sensor-fusion results based on the measurements obtained within the European research project GEODE (Ground Explosive Ordnance DEtection system) that strives for the realisation of a vehicle-mounted, multisensor, anti-personnel land-mine detection system for humanitarian

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

    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

  17. Fusion: ultra-high-speed and IR image sensors

    Etoh, T. Goji; Dao, V. T. S.; Nguyen, Quang A.; Kimata, M.

    2015-08-01

    Most targets of ultra-high-speed video cameras operating at more than 1 Mfps, such as combustion, crack propagation, collision, plasma, spark discharge, an air bag at a car accident and a tire under a sudden brake, generate sudden heat. Researchers in these fields require tools to measure the high-speed motion and heat simultaneously. Ultra-high frame rate imaging is achieved by an in-situ storage image sensor. Each pixel of the sensor is equipped with multiple memory elements to record a series of image signals simultaneously at all pixels. Image signals stored in each pixel are read out after an image capturing operation. In 2002, we developed an in-situ storage image sensor operating at 1 Mfps 1). However, the fill factor of the sensor was only 15% due to a light shield covering the wide in-situ storage area. Therefore, in 2011, we developed a backside illuminated (BSI) in-situ storage image sensor to increase the sensitivity with 100% fill factor and a very high quantum efficiency 2). The sensor also achieved a much higher frame rate,16.7 Mfps, thanks to the wiring on the front side with more freedom 3). The BSI structure has another advantage that it has less difficulties in attaching an additional layer on the backside, such as scintillators. This paper proposes development of an ultra-high-speed IR image sensor in combination of advanced nano-technologies for IR imaging and the in-situ storage technology for ultra-highspeed imaging with discussion on issues in the integration.

  18. Botnet Detection Architecture Based on Heterogeneous Multi-sensor Information Fusion

    HaiLong Wang

    2011-12-01

    Full Text Available As technology has been developed rapidly, botnet threats to the global cyber community are also increasing. And the botnet detection has recently become a major research topic in the field of network security. Most of the current detection approaches work only on the evidence from single information source, which can not hold all the traces of botnet and hardly achieve high accuracy. In this paper, a novel botnet detection architecture based on heterogeneous multi-sensor information fusion is proposed. The architecture is designed to carry out information integration in the three fusion levels of data, feature, and decision. As the core component, a feature extraction module is also elaborately designed. And an extended algorithm of the Dempster-Shafer (D-S theory is proved and adopted in decision fusion. Furthermore, a representative case is provided to illustrate that the detection architecture can effectively fuse the complicated information from various sensors, thus to achieve better detection effect.

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

    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.

  20. Adaptive Multichannel Radiation Sensors for Plant Parameter Monitoring

    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

  1. Sensor Data Fusion using Unscented Kalman Filter for VOR-based Vision Tracking System for Mobile Robots

    Ahmad, Omar; Bona, Basilio; Anjum, Muhammad Latif

    2014-01-01

    This paper presents sensor data fusion using Unscented Kalman Filter (UKF) to implement high performance vestibulo-ocular reflex (VOR) based vision tracking system for mobile robots. Information from various sensors is required to be integrated using an efficient sensor fusion algorithm to achieve a continuous and robust vision tracking system. We use data from low cost accelerometer, gyroscope, and encoders to calculate robot motion information. The Unscented Kalman Filter is used as an effi...

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

    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.

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

    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.

  4. Naval target classification by fusion of IR and EO sensors

    Giompapa, S.; Croci, R.; Di Stefano, R.; Farina, A.; Gini, F.; Graziano, A.; Lapierre, F.

    2007-10-01

    This paper describes the classification function of naval targets performed by an infrared camera (IR) and an electro-optical camera (EO) that operate in a more complex multisensor system for the surveillance of a coastal region. The following naval targets are considered: high speed dinghy, motor boat, fishing boat, oil tanker. Target classification is automatically performed by exploiting the knowledge of the sensor confusion matrix (CM). The CM is analytically computed as a function of the sensor noise features, the sensor resolution, and the dimension of the involved image database. For both the sensors, a database of images is generated exploiting a three-dimensional (3D) Computer Aided Design (CAD) of the target, for the four types of ship mentioned above. For the EO camera, the image generation is simply obtained by the projection of the 3D CAD on the camera focal plane. For the IR images simulation, firstly the surface temperatures are computed using an Open-source Software for Modelling and Simulation of Infrared Signatures (OSMOSIS) that efficiently integrates the dependence of the emissivity upon the surface temperature, the wavelength, and the elevation angle. The software is applicable to realistic ship geometries. Secondly, these temperatures and the environment features are used to predict realistic IR images. The local decisions on the class are made using the elements of the confusion matrix of each sensor and they are fused according to a maximum likelihood (ML) rule. The global performance of the classification process is measured in terms of the global confusion matrix of the integrated system. This analytical approach can effectively reduce the computational load of a Monte Carlo simulation, when the sensors described here are introduced in a more complex multisensor system for the maritime surveillance.

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

    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

  6. Sensor fusion in head pose tracking for augmented reality

    Persa, S.F.

    2006-01-01

    The focus of this thesis is on studying diverse techniques, methods and sensors for position and orientation determination with application to augmented reality applications. In Chapter 2 we reviewed a variety of existing techniques and systems for position determination. From a practical point of v

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

    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.

  8. A novel method of range measuring for a mobile robot based on multi-sensor information fusion

    The traditional measuring range for a mobile robot is based on a sonar sensor. Because of different working environments, it is very difficult to obtain high precision by using just one single method of range measurement. So, a hybrid sonar sensor and laser scanner method is put forward to overcome these shortcomings. A novel fusion model is proposed based on basic theory and a method of information fusion. An optimal measurement result has been obtained with information fusion from different sensors. After large numbers of experiments and performance analysis, a conclusion can be drawn that the laser scanner and sonar sensor method with multi-sensor information fusion have a higher precision than the single method of sonar. It can also be the same with different environments

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

    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

  10. Multi-sensor data fusion for measurement of complex freeform surfaces

    Ren, M. J.; Liu, M. Y.; Cheung, C. F.; Yin, Y. H.

    2016-01-01

    Along with the rapid development of the science and technology in fields such as space optics, multi-scale enriched freeform surfaces are widely used to enhance the performance of the optical systems in both functionality and size reduction. Multi-sensor technology is considered as one of the promising methods to measure and characterize these surfaces at multiple scales. This paper presents a multi-sensor data fusion based measurement method to purposely extract the geometric information of the components with different scales which is used to establish a holistic geometry of the surface via data fusion. To address the key problems of multi-sensor data fusion, an intrinsic feature pattern based surface registration method is developed to transform the measured datasets to a common coordinate frame. Gaussian zero-order regression filter is then used to separate each measured data in different scales, and the datasets are fused based on an edge intensity data fusion algorithm within the same wavelength. The fused data at different scales is then merged to form a new surface with holistic multiscale information. Experimental study is presented to verify the effectiveness of the proposed method.

  11. A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

    ZiQi Hao

    2015-01-01

    Full Text Available As limited energy is one of the tough challenges in wireless sensor networks (WSN, energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use k-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

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

    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.

  13. Workshop on adaptive grid methods for fusion plasmas

    Wiley, J.C. [Univ. of Texas, Austin, TX (United States)

    1995-07-01

    The author describes a general `hp` finite element method with adaptive grids. The code was based on the work of Oden, et al. The term `hp` refers to the method of spatial refinement (h), in conjunction with the order of polynomials used as a part of the finite element discretization (p). This finite element code seems to handle well the different mesh grid sizes occuring between abuted grids with different resolutions.

  14. Step Characterization using Sensor Information Fusion and Machine Learning

    Ricardo Anacleto; Lino Figueiredo; Ana Almeida; Paulo Novais; António Meireles

    2015-01-01

    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 algorithm...

  15. Recent Results And Challenges In Development Of Metallic Hall Sensors For Fusion Reactors

    Ďuran, Ivan; Sentkerestiová, J.; Kohout, Michal; Mušálek, Radek; Viererbl, L.; Kovařík, Karel

    Vol. 1612. MELVILLE: American Institute of Physics, 2014 - (Gorini, G.; Orsitto, F.; Sozzi, C.; Tardocchi, M.), s. 31-34. (AIP Conference Proceedings. 1612). ISBN 978-0-7354-1248-4. ISSN 0094-243X. [International Conference on Fusion Reactor Diagnostics. Villa Monastero,Varenna (IT), 09.09.2013-13.09.2013] R&D Projects: GA MŠk(CZ) LM2011021 Institutional support: RVO:61389021 ; RVO:68378271 Keywords : Hall sensors * fusion * magnetic diagnostic * radiation hardness Subject RIV: BL - Plasma and Gas Discharge Physics; BL - Plasma and Gas Discharge Physics (FZU-D) http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.4894020

  16. An Adaptive Amplifier System for Wireless Sensor Network Applications

    Mónica Lovay

    2012-01-01

    Full Text Available This paper presents an adaptive amplifier that is part of a sensor node in a wireless sensor network. The system presents a target gain that has to be maintained without direct human intervention despite the presence of faults. In addition, its bandwidth must be as large as possible. The system is composed of a software-based built-in self-test scheme implemented in the node that checks all the available gains in the amplifiers, a reconfigurable amplifier, and a genetic algorithm (GA for reconfiguring the node resources that runs on a host computer. We adopt a PSoC device from Cypress for the node implementation. The performance evaluation of the scheme presented is made by adopting four different types of fault models in the amplifier gains. The fault simulation results show that GA finds the target gain with low error, maintains the bandwidth above the minimum tolerable bandwidth, and presents a runtime lower than exhaustive search method.

  17. Adaptive Congestion Control Protocol (ACCP for Wireless Sensor Networks

    James DzisiGadze

    2013-10-01

    Full Text Available In Wireless Sensor Networks (WSN when an event is detected there is an increase in data traffic that mightlead to packets being transmitted through the network close to the packet handling capacity of the WSN.The WSN experiences a decrease in network performance due to packet loss, long delays, and reduction inthroughput. In this paper we developed an adaptive congestion control algorithm that monitors networkutilization and adjust traffic levels and/or increases network resources to improve throughput and conserveenergy. The traffic congestion control protocol DelStatic is developed by introducing backpressuremechanism into NOAH. We analyzed various routing protocols and established that DSR has a higherresource congestion control capability. The proposed protocol, ACCP uses a sink switching algorithm totrigger DelStatic or DSR feedback to a congested node based on its Node Rank. From the simulationresults, ACCP protocol does not only improve throughput but also conserves energy which is critical tosensor application survivability on the field. Our Adaptive Congestion control achieved reliability, highthroughput and energy efficiency.

  18. Multisource Adaptive Data Distribution and Routing in Wireless Sensor Networks

    Mukherjee, Subhabrata; Naskar, Mrinal K; Mukherjee, Amitava

    2012-01-01

    The wireless sensor network is a collection of energy-constrained nodes. Their objective is to sense, collect and process information for some ad-hoc purpose. Typically the nodes are deployed in geographically inaccessible regions. Thus the most challenging task is to design a network with minimal power consumption. As the nodes have to collect and process data very fast, minimizing data delivery time is another objective. In addition to this, when multiple sources transmit data simultaneously, the network load gradually increases and it may lead to congestion. In this paper we have proposed an adaptive framework in which multiple sources transmit data simultaneously with minimal end-to-end data delivery time and minimal energy consumption besides ensuring that congestion remains at an optimum low so that minimal number of data packets are dropped. This paper presents an adaptive framework to achieve the above-mentioned objectives. This framework has been used over Mac 802.11 and extensive simulations have be...

  19. An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing

    Zhang, Qiong; Maldague, Xavier

    2016-01-01

    A novel nonsubsampled contourlet transform (NSCT) based image fusion approach, implementing an adaptive-Gaussian (AG) fuzzy membership method, compressed sensing (CS) technique, total variation (TV) based gradient descent reconstruction algorithm, is proposed for the fusion computation of infrared and visible images. Compared with wavelet, contourlet, or any other multi-resolution analysis method, NSCT has many evident advantages, such as multi-scale, multi-direction, and translation invariance. As is known, a fuzzy set is characterized by its membership function (MF), while the commonly known Gaussian fuzzy membership degree can be introduced to establish an adaptive control of the fusion processing. The compressed sensing technique can sparsely sample the image information in a certain sampling rate, and the sparse signal can be recovered by solving a convex problem employing gradient descent based iterative algorithm(s). In the proposed fusion process, the pre-enhanced infrared image and the visible image are decomposed into low-frequency subbands and high-frequency subbands, respectively, via the NSCT method as a first step. The low-frequency coefficients are fused using the adaptive regional average energy rule; the highest-frequency coefficients are fused using the maximum absolute selection rule; the other high-frequency coefficients are sparsely sampled, fused using the adaptive-Gaussian regional standard deviation rule, and then recovered by employing the total variation based gradient descent recovery algorithm. Experimental results and human visual perception illustrate the effectiveness and advantages of the proposed fusion approach. The efficiency and robustness are also analyzed and discussed through different evaluation methods, such as the standard deviation, Shannon entropy, root-mean-square error, mutual information and edge-based similarity index.

  20. Knowledge-based imaging-sensor fusion system

    Westrom, George

    1989-01-01

    An imaging system which applies knowledge-based technology to supervise and control both sensor hardware and computation in the imaging system is described. It includes the development of an imaging system breadboard which brings together into one system work that we and others have pursued for LaRC for several years. The goal is to combine Digital Signal Processing (DSP) with Knowledge-Based Processing and also include Neural Net processing. The system is considered a smart camera. Imagine that there is a microgravity experiment on-board Space Station Freedom with a high frame rate, high resolution camera. All the data cannot possibly be acquired from a laboratory on Earth. In fact, only a small fraction of the data will be received. Again, imagine being responsible for some experiments on Mars with the Mars Rover: the data rate is a few kilobits per second for data from several sensors and instruments. Would it not be preferable to have a smart system which would have some human knowledge and yet follow some instructions and attempt to make the best use of the limited bandwidth for transmission. The system concept, current status of the breadboard system and some recent experiments at the Mars-like Amboy Lava Fields in California are discussed.

  1. Unique sensor fusion system for coordinate-measuring machine tasks

    Nashman, Marilyn; Yoshimi, Billibon; Hong, Tsai Hong; Rippey, William G.; Herman, Martin

    1997-09-01

    This paper describes a real-time hierarchical system that fuses data from vision and touch sensors to improve the performance of a coordinate measuring machine (CMM) used for dimensional inspection tasks. The system consists of sensory processing, world modeling, and task decomposition modules. It uses the strengths of each sensor -- the precision of the CMM scales and the analog touch probe and the global information provided by the low resolution camera -- to improve the speed and flexibility of the inspection task. In the experiment described, the vision module performs all computations in image coordinate space. The part's boundaries are extracted during an initialization process and then the probe's position is continuously updated as it scans and measures the part surface. The system fuses the estimated probe velocity and distance to the part boundary in image coordinates with the estimated velocity and probe position provided by the CMM controller. The fused information provides feedback to the monitor controller as it guides the touch probe to scan the part. We also discuss integrating information from the vision system and the probe to autonomously collect data for 2-D to 3-D calibration, and work to register computer aided design (CAD) models with images of parts in the workplace.

  2. Sensor Fusion and Calibration of Inertial Sensors, Vision, Ultra-Wideband and GPS

    Hol, Jeroen D.

    2011-01-01

    The usage of inertial sensors has traditionally been confined primarily to the aviation and marine industry due to their associated cost and bulkiness. During the last decade, however, inertial sensors have undergone a rather dramatic reduction in both size and cost with the introduction of MEMS technology. As a result of this trend, inertial sensors have become commonplace for many applications and can even be found in many consumer products, for instance smart phones, cameras and game conso...

  3. Initial Realization of a Sensor Fusion Based Onboard Maritime Integrated PNT Unit

    Ralf Ziebold

    2013-03-01

    Full Text Available This paper introduces the basic concept of the Position Navigation and Timing (PNT Module as future part of a ship side Integrated Navigation System (INS. Core of the PNT Module is a sensor fusion based processing system (PNT Unit. The paper will focus on important aspects and first results of the initial practical realization of such a PNT Unit, including a realization of a Consistent Common Reference System (CCRS, GNSS/IMU tightly coupled positioning results as well as contingency performance of the inertial sensors.

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

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

    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 approa...

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

    Shrabani Bhattacharya; R Appavu Raj

    2004-04-01

    We have adopted the state-vector fusion technique for fusing multiple sensors track data to provide complete and precise trajectory information about the flight vehicle under test, for the purpose of flight safety monitoring and decisionmaking at Test Range. The present paper brings out the performance of the algorithm for different process noise and measurement noise using simulated as well as real track data.

  6. Context-Awareness at the Service of Sensor Fusion Systems: Inverting the Usual Scheme

    Martí, Enrique David; García, Jesús; José M. Molina

    2011-01-01

    Many works on context-aware systems make use of location, navigation or tracking services offered by an underlying sensor fusion module, as part of the relevant contextual information. The obtained knowledge is typically consumed only by the high level layers of the system, in spite that context itself represents a valuable source of information from which every part of the implemented system could take benefit. This paper closes the loop, analyzing how can context knowledge be applied to imp...

  7. Hardening approach to use CMOS image sensors for fusion by inertial confinement diagnostics

    Paillet, Philippe; Goiffon, Vincent; Chabane, Aziouz; Girard, Sylvain; Rousseau, Adrien; Darbon, Stéphane; Duhamel, Olivier; Raine, Mélanie; Cervantes, Paola; Gaillardin, Marc; Bourgade, Jean-Luc; Magnan, Pierre; Glebov, Vladimir Yu; Pien, Gregory

    2013-01-01

    A hardening method is proposed to enable the use of CMOS image sensors for Fusion by Inertial Confinement Diagnostics. The mitigation technique improves their radiation tolerance using a reset mode implemented in the device. The results obtained evidence a reduction of more than 70% in the number of transient white pixels induced in the pixel array by the mixed neutron and γ-ray pulsed radiation environment.

  8. Consistent Map Building Based on Sensor Fusion for Indoor Service Robot

    Luo, Ren C.; Lai, Chun C.

    2010-01-01

    This chapter presents a consistent map construction in a unitary SLAM (simultaneously localization and mapping) process through the sensor fusion approach and optimal alignment technologies. The system will autonomously provide the environment geometrical structure for intelligent robot service in a building. In order to build the consistent map, a CI (Covariance Intersection) rule fuses the uncertainty from wheel encoder and ICP (Iterative Closest Point) result as a robust initial value of t...

  9. Hardening approach to use CMOS image sensors for fusion by inertial confinement diagnostics

    A hardening method is proposed to enable the use of CMOS image sensors for Fusion by Inertial Confinement Diagnostics. The mitigation technique improves their radiation tolerance using a reset mode implemented in the device. The results obtained evidence a reduction of more than 70% in the number of transient white pixels induced in the pixel array by the mixed neutron and γ-ray pulsed radiation environment. (authors)

  10. Bayesian nonlinear filtering using quadrature and cubature rules applied to sensor data fusion for positioning

    Fernandez Prades, Carles; Vilà Valls, Jordi

    2010-01-01

    This paper shows the applicability of recently-developed Gaussian nonlinear filters to sensor data fusion for positioning purposes. After providing a brief review of Bayesian nonlinear filtering, we specially address square-root, derivative-free algorithms based on the Gaussian assumption and approximation rules for numerical integration, namely the Gauss-Hermite quadrature rule and the cubature rule. Then, we propose a motion model based on the observations taken by an Inertial Measurement U...

  11. Resistive sensor and electromagnetic actuator for feedback stabilization of liquid metal walls in fusion reactors

    Mirhoseini, S H M

    2016-01-01

    Liquid metal walls in fusion reactors will be subject to instabilities, turbulence, induced currents, error fields and temperature gradients that will make them locally bulge, thus entering in contact with the plasma, or deplete, hence exposing the underlying solid substrate. To prevent this, research has begun to actively stabilize static or flowing liquid metal layers by locally applying forces in feedback with thickness measurements. Here we present resistive sensors of liquid metal thickness and demonstrate jxB actuators, to locally control it.

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

    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.

  13. Image-Based Multi-Sensor Data Representation and Fusion Via 2D Non-Linear Convolution

    Aaron R. Rababaah

    2012-04-01

    Full Text Available Sensor data fusion is the process of combining data collected from multi sensors of homogeneous or heterogeneous modalities to perform inferences that may not be possible using a single sensor. This process encompasses several stages to arrive at a sound reliable decision making end result. These stages include: senor-signal preprocessing, sub-object refinement, object refinement, situation refinement, threat refinement and process refinement. Every stage draws from different domains to achieve its requirements and goals. Popular methods for sensor data fusion include: ad-hock and heuristic-based, classical hypothesis-based, Bayesian inference, fuzzy inference, neural networks, etc. in this work, we introduce a new data fusion model that contributes to the area of multi-senor/source data fusion. The new fusion model relies on image processing theory to map stimuli from sensors onto an energy map and uses non-linear convolution to combine the energy responses on the map onto a single fused response map. This response map is then fed into a process of transformations to extract an inference that estimates the output state response as a normalized amplitude level. This new data fusion model is helpful to identify sever events in the monitored environment. An efficiency comparison with similar fuzzy-logic fusion model revealed that our proposed model is superior in time complexity as validated theoretically and experimentally.

  14. Adaptive polarization image fusion based on regional energy dynamic weighted average

    ZHAO Yong-qiang; PAN Quan; ZHANG Hong-cai

    2005-01-01

    According to the principle of polarization imaging and the relation between Stokes parameters and the degree of linear polarization, there are much redundant and complementary information in polarized images. Since man-made objects and natural objects can be easily distinguished in images of degree of linear polarization and images of Stokes parameters contain rich detailed information of the scene, the clutters in the images can be removed efficiently while the detailed information can be maintained by combining these images. An algorithm of adaptive polarization image fusion based on regional energy dynamic weighted average is proposed in this paper to combine these images. Through an experiment and simulations,most clutters are removed by this algorithm. The fusion method is used for different light conditions in simulation, and the influence of lighting conditions on the fusion results is analyzed.

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

    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.

  16. Model-Based Adaptive Iterative Hard Thresholding Compressive Sensing in Sensor Network for Volcanic Earthquake Detection

    Guojin Liu; Qian Zhang; Yuyuan Yang; Zhenzhi Yin; Bin Zhu

    2015-01-01

    Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for volcanic eruption detection, where the volcano-seismic signals were collected and processed by sensor nodes. However, it is faced with the limitation of energy resources and the transmission bottleneck of sensors in WSN. In this paper, a Model-Based Adaptive Iterative Hard Thresholding (MAIHT) compressive sensing scheme is developed, where a large number of inexpensive sensors are used to collect ...

  17. Covariance Intersection Fusion Kalman Estimators for Multi-Sensor System with Colored Measurement Noises

    Wen-Juan Qi

    2013-07-01

    Full Text Available For multi-sensor system with colored measurement noises, using the observation transformation, the system can be converted into an equivalent system with correlated measurement noises. Based on this method, using the classical Kalman filtering, this study proposed a Covariance Intersection (CI fusion Kalman estimator, which can handle the fused filtering, prediction and smoothing problems. The advantage of the proposed method is that it can avoid the computation of the cross-covariances among the local filtering errors and can reduce the computational burden significantly, as well as the CI fusion algorithm can be used in the uncertain system with unknown cross-covariances. Based on classical Kalman filtering theory, the centralized fusion and three weighted fusion (weighted by matrices, scalars and diagonal estimators are also presented respectively. Their accuracy comparisons are given. The geometric interpretations based on covariance ellipses are also given. The experiment results show that the accuracy of the CI fuser is higher than that of the each local smoothers and is lower that that of the centralized fusion Kalman smoother or the optimal fuser weighted by matrix. The MSE curves show that the accuracy of the CI fuser is close to the optimal fuser weighted by matrix in most instances, which means that our proposed method has higher accuracy and good performance.

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

    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.

  19. Two-level Robust Measurement Fusion Kalman Filter for Clustering Sensor Networks

    ZHANG Peng; QI Wen-Juan; DENG Zi-Li

    2014-01-01

    This paper investigates the distributed fusion Kalman filtering over clustering sensor networks. The sensor network is partitioned as clusters by the nearest neighbor rule and each cluster consists of sensing nodes and cluster-head. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, two-level robust measurement fusion Kalman filter is presented for the clustering sensor network systems with uncertain noise variances. It can significantly reduce the communication load and save energy when the number of sensors is very large. A Lyapunov equation approach for the robustness analysis is presented, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented, and the robust accuracy relations among the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the two-level weighted measurement fuser is equal to that of the global centralized robust fuser and is higher than those of each local robust filter and each local weighted measurement fuser. A simulation example shows the correctness and effectiveness of the proposed results.

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

    Lin, Bao; Jørgensen, Sten Bay

    2011-01-01

    multivariate analysis technique from RGB pictures. The color information is also transformed to hue, saturation and intensity components. Both sets of image features are combined with traditional process measurements to obtain an inferential model by partial least squares (PLS) regression. A dynamic PLS model......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...... the weight of each regressor, is achieved through multivariate regression. The framework is described and illustrated with applications to cement kiln systems that are characterized by off-line quality measurements and on-line analyzers with limited reliability. Image features are extracted with a...

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

    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.

  2. Approach towards sensor placement, selection and fusion for real-time condition monitoring of precision machines

    Er, Poi Voon; Teo, Chek Sing; Tan, Kok Kiong

    2016-02-01

    Moving mechanical parts in a machine will inevitably generate vibration profiles reflecting its operating conditions. Vibration profile analysis is a useful tool for real-time condition monitoring to avoid loss of performance and unwanted machine downtime. In this paper, we propose and validate an approach for sensor placement, selection and fusion for continuous machine condition monitoring. The main idea is to use a minimal series of sensors mounted at key locations of a machine to measure and infer the actual vibration spectrum at a critical point where it is not suitable to mount a sensor. The locations for sensors' mountings which are subsequently used for vibration inference are identified based on sensitivity calibration at these locations moderated with normalized Fisher Information (NFI) associated with the measurement quality of the sensor at that location. Each of the identified sensor placement location is associated with one or more sensitive frequencies for which it ranks top in terms of the moderated sensitivities calibrated. A set of Radial Basis Function (RBF), each of them associated with a range of sensitive frequencies, is used to infer the vibration at the critical point for that frequency. The overall vibration spectrum of the critical point is then fused from these components. A comprehensive set of experimental results for validation of the proposed approach is provided in the paper.

  3. A Phase-Shifting Zernike Wavefront Sensor for the Palomar P3K Adaptive Optics System

    Wallace, J. Kent; Crawford, Sam; Loya, Frank; Moore, James

    2012-01-01

    A phase-shifting Zernike wavefront sensor has distinct advantages over other types of wavefront sensors. Chief among them are: 1) improved sensitivity to low-order aberrations and 2) efficient use of photons (hence reduced sensitivity to photon noise). We are in the process of deploying a phase-shifting Zernike wavefront sensor to be used with the realtime adaptive optics system for Palomar. Here we present the current state of the Zernike wavefront sensor to be integrated into the high-order adaptive optics system at Mount Palomar's Hale Telescope.

  4. Fusion of Force-Torque Sensors, Inertial Measurements Units and Proprioception for a Humanoid Kinematics-Dynamics Observation

    Benallegue, Mehdi; Mifsud, Alexis; Lamiraux, Florent

    2015-01-01

    We present a scheme where the measurements obtained through inertial measurement units (IMU), contact-force sensors and proprioception (joint encoders) are merged in order to observe humanoid unactuated floating-base dynamics. The sensor data fusion is implemented using an Extended Kalman Filter. The prediction part is constituted by viscoelastic contacts assumption and a model expressing at the origin the full body dynamics. The correction is achieved using embedded IMU and force sensor. Sim...

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

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

    2015-01-01

    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. PMID:25569750

  6. Context Aware Multisensor Image Fusion for Military Sensor Networks using Multi Agent System

    Sutagundar, Ashok V; 10.5121/ijasuc.2011.2113

    2011-01-01

    This paper proposes a Context Aware Agent based Military Sensor Network (CAMSN) to form an improved infrastructure for multi-sensor image fusion. It considers contexts driven by a node and sink. The contexts such as general and critical object detection are node driven where as sensing time (such as day or night) is sink driven. The agencies used in the scheme are categorized as node and sink agency. Each agency employs a set of static and mobile agents to perform dedicated tasks. Node agency performs context sensing and context interpretation based on the sensed image and sensing time. Node agency comprises of node manager agent, context agent and node blackboard (NBB). Context agent gathers the context from the target and updates the NBB, Node manager agent interprets the context and passes the context information to sink node by using flooding mechanism. Sink agency mainly comprises of sink manager agent, fusing agent, and sink black board. A context at the sensor node triggers the fusion process at the si...

  7. Coordinated Workload Scheduling in Hierarchical Sensor Networks for Data Fusion Applications

    Xiao-Lin Li; Jian-Nong Cao

    2008-01-01

    To minimize the execution time of a sensing task over a multi-hop hierarchical sensor network, we present acoordinated scheduling method following the divisible load scheduling paradigm. The proposed scheduling strategy builds on eliminating transmission collisions and idle gaps between two successive data transmissions. We consider a sensor network consisting of several clusters. In a cluster, after related raw data measured by source nodes are collected at the fusion node,in-network data aggregation is further considered. The scheduling strategies consist of two phases: intra-cluster scheduling and inter-cluster scheduling. Intra-cluster scheduling deals with assigning different fractions of a sensing workload among source nodes in each cluster; inter-cluster scheduling involves the distribution of fused data among all fusion nodes. Closed-form solutions to the problem of task scheduling are derived. Finally, numerical examples are presented to demonstrate the impacts of different system parameters such as the number of sensor nodes, measurement, communication, and processing speed, on the finish time and energy consumption.

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

    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.

  9. Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

    Shifei Liu; Mohamed Maher Atia; Yanbin Gao; Aboelmagd Noureldin

    2015-01-01

    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...

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

    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.

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

    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. PMID:22163639

  12. Convolutional neural network based sensor fusion for forward looking ground penetrating radar

    Sakaguchi, Rayn; Crosskey, Miles; Chen, David; Walenz, Brett; Morton, Kenneth

    2016-05-01

    Forward looking ground penetrating radar (FLGPR) is an alternative buried threat sensing technology designed to offer additional standoff compared to downward looking GPR systems. Due to additional flexibility in antenna configurations, FLGPR systems can accommodate multiple sensor modalities on the same platform that can provide complimentary information. The different sensor modalities present challenges in both developing informative feature extraction methods, and fusing sensor information in order to obtain the best discrimination performance. This work uses convolutional neural networks in order to jointly learn features across two sensor modalities and fuse the information in order to distinguish between target and non-target regions. This joint optimization is possible by modifying the traditional image-based convolutional neural network configuration to extract data from multiple sources. The filters generated by this process create a learned feature extraction method that is optimized to provide the best discrimination performance when fused. This paper presents the results of applying convolutional neural networks and compares these results to the use of fusion performed with a linear classifier. This paper also compares performance between convolutional neural networks architectures to show the benefit of fusing the sensor information in different ways.

  13. Mean-shift tracking algorithm based on adaptive fusion of multi-feature

    Yang, Kai; Xiao, Yanghui; Wang, Ende; Feng, Junhui

    2015-10-01

    The classic mean-shift tracking algorithm has achieved success in the field of computer vision because of its speediness and efficiency. However, classic mean-shift tracking algorithm would fail to track in some complicated conditions such as some parts of the target are occluded, little color difference between the target and background exists, or sudden change of illumination and so on. In order to solve the problems, an improved algorithm is proposed based on the mean-shift tracking algorithm and adaptive fusion of features. Color, edges and corners of the target are used to describe the target in the feature space, and a method for measuring the discrimination of various features is presented to make feature selection adaptive. Then the improved mean-shift tracking algorithm is introduced based on the fusion of various features. For the purpose of solving the problem that mean-shift tracking algorithm with the single color feature is vulnerable to sudden change of illumination, we eliminate the effects by the fusion of affine illumination model and color feature space which ensures the correctness and stability of target tracking in that condition. Using a group of videos to test the proposed algorithm, the results show that the tracking correctness and stability of this algorithm are better than the mean-shift tracking algorithm with single feature space. Furthermore the proposed algorithm is more robust than the classic algorithm in the conditions of occlusion, target similar with background or illumination change.

  14. Prospects Of Steady State Magnetic Diagnostic Of Fusion Reactors Based On Metallic Hall Sensors

    Ďuran, Ivan; Sentkerestiová, J.; Kovařík, Karel; Viererbl, L.

    Vol. 1442. MELVILLE: American Institute of Physics, 2012 - (Kallne, J.; Ryutov, D.; Gorini, G.; Sozzi, C.; Tardocchi, M.), s. 317-324. (AIP Conference Proceedings. 1442). ISBN 978-0-7354-1038-1. ISSN 0094-243X. [International Workshop on Fusion Neutrons and Subcritical Nuclear Fission (FUNFI). Varenna (IT), 12.09.2011-15.09.2011] R&D Projects: GA MPO 2A-1TP1/101; GA MŠk(CZ) LM2011021 Institutional research plan: CEZ:AV0Z20430508 Keywords : Hall sensors * fusion * magnetic diagnostic * radiation hardness Subject RIV: BL - Plasma and Gas Discharge Physics http://proceedings.aip.org/resource/2/apcpcs/1442/1/317_1

  15. Fusion

    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

  16. Adaptive SVM fusion for robust multi-biometrics verification with missing data

    Zhai, Xiuna; Zhao, Yan; Wang, Jingyan; Li, Yongping

    2013-03-01

    Conventional multimodal biometrics systems usually do not account for missing data (missing modalities or incomplete score lists) that is commonly encountered in real applications. The presence of missing data in multimodal biometric systems can be inconvenient to the client, as the system will reject the submitted biometric data and request for another trial. In such cases, robust multimodal biometric verification is needed. In this paper, we present the criteria, fusion method and performance metrics of a robust multimodal biometrics verification system that verifies the client's identity at any condition of data missing. A novel adaptive SVM classification method is proposed for missing dimensional values, which can handle the missing data in multimodal biometrics. We show that robust multibiometrics imposes additional requirements on multimodal fusion when compared to conventional multibiometrics. We also argue that the usual performance metrics of false accept and false reject rates are insufficient yardsticks for robust verification and propose new metrics against which we benchmark our system.

  17. Status of Development of High Temperature Radiation Hardened Hall Sensors for Energy Producing Fusion Devices

    Kovařík, Karel; Ďuran, Ivan; Sentkerestiová, J.; Oszwaldowski, M.; Viererbl, L.; Boshakova, I.; Holyaka, R.; Erashok, V.

    Vol. 2. Prague : MATFYZPRESS, 2012 - (Šafránková, J.; Pavlů, J.), s. 216-221 ISBN 978-80-7378-225-2. - (WDS. 2). [Annual Conference of Doctoral Students - WDS 2012 /21./. Prague (CZ), 29.05.2012-01.06.2012] R&D Projects: GA ČR GAP205/10/2055; GA MŠk 7G10072; GA MŠk(CZ) LG11018; GA MŠk LA08048 Institutional support: RVO:61389021 Keywords : Hall sensor * fusion * tokamak Subject RIV: BL - Plasma and Gas Discharge Physics http://www.mff.cuni.cz/veda/konference/wds/proc/proc-contents.php?year=2012#ppm

  18. Sensor Fusion for Accurate Ego-Motion Estimation in a Moving Platform

    Chuho Yi; Jungwon Cho

    2015-01-01

    With the coming of “Internet of things” (IoT) technology, many studies have sought to apply IoT to mobile platforms, such as smartphones, robots, and moving vehicles. An estimation of ego-motion in a moving platform is an essential and important method to build a map and to understand the surrounding environment. In this paper, we describe an ego-motion estimation method using a vision sensor that is widely used in IoT systems. Then, we propose a new fusion method to improve the accuracy of m...

  19. A Self-Adaptive Particle Swarm Optimization Based Multiple Source Localization Algorithm in Binary Sensor Networks

    Long Cheng; Yan Wang; Shuai Li

    2015-01-01

    With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. In this paper, a self-adaptive particle swarm optimization based multiple source localization method is proposed. A detection model based on Neyman-Pearson criterion is introduced. Then the maximum likelihood estimator is employed to establish the objective funct...

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

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

    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...

  1. Estimation of tool wear during CNC milling using neural network-based sensor fusion

    Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

    2007-01-01

    Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

  2. Integrated multi-sensor fusion for mapping and localization in outdoor environments for mobile robots

    Emter, Thomas; Petereit, Janko

    2014-05-01

    An integrated multi-sensor fusion framework for localization and mapping for autonomous navigation in unstructured outdoor environments based on extended Kalman filters (EKF) is presented. The sensors for localization include an inertial measurement unit, a GPS, a fiber optic gyroscope, and wheel odometry. Additionally a 3D LIDAR is used for simultaneous localization and mapping (SLAM). A 3D map is built while concurrently a localization in a so far established 2D map is estimated with the current scan of the LIDAR. Despite of longer run-time of the SLAM algorithm compared to the EKF update, a high update rate is still guaranteed by sophisticatedly joining and synchronizing two parallel localization estimators.

  3. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection.

    Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

    2016-01-01

    Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

  4. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    Sungho Kim

    2016-07-01

    Full Text Available Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR images or infrared (IR images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter and an asymmetric morphological closing filter (AMCF, post-filter into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic

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

    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

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

    Changyu He

    2015-07-01

    Full Text Available 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.

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

    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.

  8. Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform

    Wu, Zhi-guo; Wang, Ming-jia; Han, Guang-liang

    2011-08-01

    Being an efficient method of information fusion, image fusion has been used in many fields such as machine vision, medical diagnosis, military applications and remote sensing. In this paper, Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing, including segmentation, target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First, the two original images are decomposed by wavelet transform. Then, based on the PCNN, a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength, so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So, the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment, the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range, which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore, by this algorithm, the threshold adjusting constant is estimated by appointed iteration number. Furthermore, In order to sufficient reflect order of the firing time, the threshold adjusting constant αΘ is estimated by appointed iteration number. So after the iteration achieved, each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules, the experiments upon Multi-focus image are done. Moreover

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

    Lee, Chan-Gun; Dao, Nhu-Ngoc; Jang, Seonmin; Kim, Deokhwan; Kim, Yonghun; Cho, Sungrae

    2016-01-01

    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. PMID:27294941

  10. Optimal fusion rule for distributed detection in clustered wireless sensor networks

    Aldalahmeh, Sami A.; Ghogho, Mounir; McLernon, Des; Nurellari, Edmond

    2016-01-01

    We consider distributed detection in a clustered wireless sensor network (WSN) deployed randomly in a large field for the purpose of intrusion detection. The WSN is modeled by a homogeneous Poisson point process. The sensor nodes (SNs) compute local decisions about the intruder's presence and send them to the cluster heads (CHs). A stochastic geometry framework is employed to derive the optimal cluster-based fusion rule (OCR), which is a weighted average of the local decision sum of each cluster. Interestingly, this structure reduces the effect of false alarm on the detection performance. Moreover, a generalized likelihood ratio test (GLRT) for cluster-based fusion (GCR) is developed to handle the case of unknown intruder's parameters. Simulation results show that the OCR performance is close to the Chair-Varshney rule. In fact, the latter benchmark can be reached by forming more clusters in the network without increasing the SN deployment intensity. Simulation results also show that the GCR performs very closely to the OCR when the number of clusters is large enough. The performance is further improved when the SN deployment intensity is increased.

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

    Lee, Chan-Gun; Dao, Nhu-Ngoc; Jang, Seonmin; Kim, Deokhwan; Kim, Yonghun; Cho, Sungrae

    2016-01-01

    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. PMID:27294941

  12. A NOVEL ALGORITHM OF MULTI-SENSOR IMAGE FUSION BASED ON WAVELET PACKET TRANSFORM

    2006-01-01

    In order to enhance the image information from multi-sensor and to improve the abilities of theinformation analysis and the feature extraction, this letter proposed a new fusion approach in pixel level bymeans of the Wavelet Packet Transform (WPT). The WPT is able to decompose an image into low frequencyband and high frequency band in higher scale. It offers a more precise method for image analysis than Wave-let Transform (WT). Firstly, the proposed approach employs HIS (Hue, Intensity, Saturation) transform toobtain the intensity component of CBERS (China-Brazil Earth Resource Satellite) multi-spectral image. ThenWPT transform is employed to decompose the intensity component and SPOT (Systeme Pour I'Observationde la Therre ) image into low frequency band and high frequency band in three levels. Next, two high fre-quency coefficients and low frequency coefficients of the images are combined by linear weighting strategies.Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approachcan fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM(Histogram Matched)-based fusion algorithm and WT-based fusion approach.

  13. Particle filter based visual tracking with multi-cue adaptive fusion

    Anping Li; Zhongliang Jing; Shiqiang Hu

    2005-01-01

    @@ To improve the robustness of visual tracking in complex environments such as: cluttered backgrounds, partial occlusions, similar distraction and pose variations, a novel tracking method based on adaptive fusion and particle filter is proposed in this paper. In this method, the image color and shape cues are adaptively fused to represent the target observation; fuzzy logic is applied to dynamically adjust each cue weight according to its associated reliability in the past frame; particle filter is adopted to deal with non-linear and non-Gaussian problems in visual tracking. The method is demonstrated to be robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion for a test image sequence.

  14. A Perspective on information fusion problems, Extended Abstract, Journal of Distributed Sensor Neworks

    Rao, Nageswara S [ORNL

    2009-04-01

    Information fusion problems have a rich history spanning four centuries and several disciplines as diverse as political economy, reliability engineering, target tracking, bioinformatics, forecasting, distributed detection, robotics, cyber security, nuclear engineering, distributed sensor networks, and others. Over the past decade, the area of information fusion has been established as a discipline by itself with several contributions to its foundations as well as applications. In a basic formulation of the information fusion problem, each component is characterized by a probability distribution. The goal is to estimate a fusion rule for combining the outputs of components to achieve a specified objective such as better performance or functionality compared to the components. If the sensor error distributions are known, several fusion rule estimation problems have been formulated and solved using deterministic methods. In the area of pattern recognition a weighted majority fuser was shown to be optimal in combining outputs from pattern recognizers under statistical independence conditions. A simpler version of this problem corresponds to the Condorcet Jury theorem proposed in 1786. This result was rediscovered since then in other disciplines including by von Neumann in 1959 in building reliable computing devices. The distributed detection problem, studied extensively in the target tracking area, can be viewed as a generalization of the above two problems. In these works, the underlying distributions are assumed to be known, which is quite reasonable in the areas these methods are applied. In a different formulation, we consider estimating the fuser based on empirical data when no information is available about the underlying distributions of components. Using the empirical estimation methods, this problem is shown to be solvable in principle, and the fuser performance may be sharpened based on the specific formulation. The isolation fusers perform at least as good

  15. Multi-sensor multi-resolution image fusion for improved vegetation and urban area classification

    Kumar, U.; Milesi, C.; Nemani, R. R.; Basu, S.

    2015-06-01

    In this paper, we perform multi-sensor multi-resolution data fusion of Landsat-5 TM bands (at 30 m spatial resolution) and multispectral bands of World View-2 (WV-2 at 2 m spatial resolution) through linear spectral unmixing model. The advantages of fusing Landsat and WV-2 data are two fold: first, spatial resolution of the Landsat bands increases to WV-2 resolution. Second, integration of data from two sensors allows two additional SWIR bands from Landsat data to the fused product which have advantages such as improved atmospheric transparency and material identification, for example, urban features, construction materials, moisture contents of soil and vegetation, etc. In 150 separate experiments, WV-2 data were clustered in to 5, 10, 15, 20 and 25 spectral classes and data fusion were performed with 3x3, 5x5, 7x7, 9x9 and 11x11 kernel sizes for each Landsat band. The optimal fused bands were selected based on Pearson product-moment correlation coefficient, RMSE (root mean square error) and ERGAS index and were subsequently used for vegetation, urban area and dark objects (deep water, shadows) classification using Random Forest classifier for a test site near Golden Gate Bridge, San Francisco, California, USA. Accuracy assessment of the classified images through error matrix before and after fusion showed that the overall accuracy and Kappa for fused data classification (93.74%, 0.91) was much higher than Landsat data classification (72.71%, 0.70) and WV-2 data classification (74.99%, 0.71). This approach increased the spatial resolution of Landsat data to WV-2 spatial resolution while retaining the original Landsat spectral bands with significant improvement in classification.

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

    Wei Dong; Kougen Zheng; Guodong Teng

    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,...

  17. A Security Adaptation Reference Monitor for Wireless Sensor Network

    El-Maliki, Tewfiq; Seigneur, Jean-Marc

    2012-01-01

    Security in Wireless Sensor Network has become a hot research topic due to their wide deployment and the increasing new runtime attacks they are facing. We observe that traditional security protocols address conventional security problems and cannot deal with dynamic attacks such as sinkhole dynamic behavior. Moreover, they use resources, and limit the efficient use of sensor resources and inevitably the overall network efficiency is not guaranteed. Therefore, the requirements of new security...

  18. Base isolation technique for tokamak type fusion reactor using adaptive control

    In this paper relating to the isolation device of heavy structure such as nuclear fusion reactor, a control rule for reducing the response acceleration and relative displacement simultaneously was formulated, and the aseismic performance was improved by employing the adaptive control method of changing the damping factors of the system adaptively every moment. The control rule was studied by computer simulation, and the aseismic effect was evaluated in an experiment employing a scale model. As a results, the following conclusions were obtained. (1) By employing the control rule presented in this paper, both absolute acceleration and relative displacement can be reduced simultaneously without making the system unstable. (2) By introducing this control rule in a scale model assuming the Tokamak type fusion reactor, the response acceleration can be suppressed down to 78 % and also the relative displacement to 79 % as compared with the conventional aseismic method. (3) The sensitivities of absolute acceleration and relative displacement with respect to the control gain are not equal. However, by employing the relative weighting factor between the absolute acceleration and relative displacement, it is possible to increase the control capability for any kind of objective structures and appliances. (author)

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

    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.

  20. Pose estimation of surgical instrument using sensor data fusion with optical tracker and IMU based on Kalman filter

    Oh Hyunmin

    2015-01-01

    Full Text Available Tracking system is essential for Image Guided Surgery(IGS. The Optical Tracking Sensor(OTS has been widely used as tracking system for IGS due to its high accuracy and easy usage. However, OTS has a limit that tracking fails when occlusion of marker occurs. In this paper, sensor fusion with OTS and Inertial Measurement Unit(IMU is proposed to solve this problem. The proposed algorithm improves the accuracy of tracking system by eliminating scattering error of the sensor and supplements the disadvantages of OTS and IMU through sensor fusion based on Kalman filter. Also, coordinate axis calibration method that improves the accuracy is introduced. The performed experiment verifies the effectualness of the proposed algorithm.

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

    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.

  2. Motion-sensor fusion-based gesture recognition and its VLSI architecture design for mobile devices

    Zhu, Wenping; Liu, Leibo; Yin, Shouyi; Hu, Siqi; Tang, Eugene Y.; Wei, Shaojun

    2014-05-01

    With the rapid proliferation of smartphones and tablets, various embedded sensors are incorporated into these platforms to enable multimodal human-computer interfaces. Gesture recognition, as an intuitive interaction approach, has been extensively explored in the mobile computing community. However, most gesture recognition implementations by now are all user-dependent and only rely on accelerometer. In order to achieve competitive accuracy, users are required to hold the devices in predefined manner during the operation. In this paper, a high-accuracy human gesture recognition system is proposed based on multiple motion sensor fusion. Furthermore, to reduce the energy overhead resulted from frequent sensor sampling and data processing, a high energy-efficient VLSI architecture implemented on a Xilinx Virtex-5 FPGA board is also proposed. Compared with the pure software implementation, approximately 45 times speed-up is achieved while operating at 20 MHz. The experiments show that the average accuracy for 10 gestures achieves 93.98% for user-independent case and 96.14% for user-dependent case when subjects hold the device randomly during completing the specified gestures. Although a few percent lower than the conventional best result, it still provides competitive accuracy acceptable for practical usage. Most importantly, the proposed system allows users to hold the device randomly during operating the predefined gestures, which substantially enhances the user experience.

  3. Multi Sensor Fusion Framework for Indoor-Outdoor Localization of Limited Resource Mobile Robots

    Pedro Albertos

    2013-10-01

    Full Text Available This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU on an event based schedule, using fewer resources (execution time and bandwidth but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estimation error covariance exceeds a predefined limit. The proposed experimental platforms are based on the LEGO Mindstorm NXT, and consist of a differential wheel mobile robot navigating indoors with a zenithal camera as global sensor, and an Ackermann steering mobile robot navigating outdoors with a SBG Systems GPS accessed through an IGEP board that also serves as datalogger. The IMU in both robots is built using the NXT motor encoders along with one gyroscope, one compass and two accelerometers from Hitecnic, placed according to a particle based dynamic model of the robots. The tests performed reflect the correct performance and low execution time of the proposed framework. The robustness and stability is observed during a long walk test in both indoors and outdoors environments.

  4. Multi sensor fusion framework for indoor-outdoor localization of limited resource mobile robots.

    Marín, Leonardo; Vallés, Marina; Soriano, Ángel; Valera, Ángel; Albertos, Pedro

    2013-01-01

    This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU) on an event based schedule, using fewer resources (execution time and bandwidth) but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estimation error covariance exceeds a predefined limit. The proposed experimental platforms are based on the LEGO Mindstorm NXT, and consist of a differential wheel mobile robot navigating indoors with a zenithal camera as global sensor, and an Ackermann steering mobile robot navigating outdoors with a SBG Systems GPS accessed through an IGEP board that also serves as datalogger. The IMU in both robots is built using the NXT motor encoders along with one gyroscope, one compass and two accelerometers from Hitecnic, placed according to a particle based dynamic model of the robots. The tests performed reflect the correct performance and low execution time of the proposed framework. The robustness and stability is observed during a long walk test in both indoors and outdoors environments. PMID:24152933

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

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

    2012-01-01

    Nowadays, due to wide applicability of Wireless Sensor Network (WSN) added by the low cost sensor devices, its popularity among the researchers and industrialists are very much visible. A substantial amount of works can be seen in the literature on WSN which are mainly focused on application...... 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...

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

    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...... of information between the one which user uses and the one which is available at the source at the same instant of time. This leads to unreliable information being used by the application user for processing. Information accessing with minimal mismatch is desired by the WSN applications especially when WSN...

  7. An adaptive distributed data aggregation based on RCPC for wireless sensor networks

    Hua, Guogang; Chen, Chang Wen

    2006-05-01

    One of the most important design issues in wireless sensor networks is energy efficiency. Data aggregation has significant impact on the energy efficiency of the wireless sensor networks. With massive deployment of sensor nodes and limited energy supply, data aggregation has been considered as an essential paradigm for data collection in sensor networks. Recently, distributed source coding has been demonstrated to possess several advantages in data aggregation for wireless sensor networks. Distributed source coding is able to encode sensor data with lower bit rate without direct communication among sensor nodes. To ensure reliable and high throughput transmission with the aggregated data, we proposed in this research a progressive transmission and decoding of Rate-Compatible Punctured Convolutional (RCPC) coded data aggregation with distributed source coding. Our proposed 1/2 RSC codes with Viterbi algorithm for distributed source coding are able to guarantee that, even without any correlation between the data, the decoder can always decode the data correctly without wasting energy. The proposed approach achieves two aspects in adaptive data aggregation for wireless sensor networks. First, the RCPC coding facilitates adaptive compression corresponding to the correlation of the sensor data. When the data correlation is high, higher compression ration can be achieved. Otherwise, lower compression ratio will be achieved. Second, the data aggregation is adaptively accumulated. There is no waste of energy in the transmission; even there is no correlation among the data, the energy consumed is at the same level as raw data collection. Experimental results have shown that the proposed distributed data aggregation based on RCPC is able to achieve high throughput and low energy consumption data collection for wireless sensor networks

  8. An on-line adaptive estimation method for water holdup measurement in oil–water two-phase flow with a conductance/capacitance sensor

    Wu, Hao; Tan, Chao; Dong, Feng

    2016-07-01

    Oil–water two-phase flow widely exists in industrial processes such as petroleum engineering and chemical engineering. Accurate and real-time measurement of water holdup is an important problem requiring urgent solutions. In this work, a conductance and capacitance combination sensor (CCCS) system with four conductance rings and two concave capacitance plates was validated for its measurement performance of in situ water holdup through dynamic experiments. An online adaptive weight Kalman estimation (OAWKE) fusion algorithm for the CCCS system is proposed to fuse the conductance measurement and capacitance measurement. The algorithm has fast and dynamic response for the water holdup measurement of oil–water two-phase flow and has improved measurement precision by the adaptive data fusion method. The OAWKE fusion algorithm also has the ability to deal with the abrupt change of water holdup during the measurement process. Therefore, in the low water conductivity condition (tap water), the CCCS system with the OAWKE fusion algorithm can dynamically estimate the real-time full range water holdup of oil–water two-phase flow, which has prospects in the petroleum industry.

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

    Iida, Fumiya; Nurzaman, Surya G

    2016-08-01

    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

  10. Multiple sensor detection of process phenomena in laser powder bed fusion

    Lane, Brandon; Whitenton, Eric; Moylan, Shawn

    2016-05-01

    Laser powder bed fusion (LPBF) is an additive manufacturing (AM) process in which a high power laser melts metal powder layers into complex, three-dimensional shapes. LPBF parts are known to exhibit relatively high residual stresses, anisotropic microstructure, and a variety of defects. To mitigate these issues, in-situ measurements of the melt-pool phenomena may illustrate relationships between part quality and process signatures. However, phenomena such as spatter, plume formation, laser modulation, and melt-pool oscillations may require data acquisition rates exceeding 10 kHz. This hinders use of relatively data-intensive, streaming imaging sensors in a real-time monitoring and feedback control system. Single-point sensors such as photodiodes provide the temporal bandwidth to capture process signatures, while providing little spatial information. This paper presents results from experiments conducted on a commercial LPBF machine which incorporated synchronized, in-situ acquisition of a thermal camera, high-speed visible camera, photodiode, and laser modulation signal during fabrication of a nickel alloy 625 AM part with an overhang geometry. Data from the thermal camera provides temperature information, the visible camera provides observation of spatter, and the photodiode signal provides high temporal bandwidth relative brightness stemming from the melt pool region. In addition, joint-time frequency analysis (JTFA) was performed on the photodiode signal. JTFA results indicate what digital filtering and signal processing are required to highlight particular signatures. Image fusion of the synchronized data obtained over multiple build layers allows visual comparison between the photodiode signal and relating phenomena observed in the imaging detectors.

  11. A unique approach to the development of adaptive sensor systems for future spacecraft

    Schappell, R. T.; Tietz, J. C.; Sivertson, W. E.; Wilson, R. G.

    1979-01-01

    In the Shuttle era, it should be possible to develop adaptive remote sensor systems serving more directly specific researcher and user needs and at the same time alleviating the data management problem via intelligent sensor capabilities. The present paper provides a summary of such an approach, wherein specific capabilities have been developed for future global monitoring applications. A detailed description of FILE-I (Feature Identification and Location Experiment) is included along with a summary of future experiments currently under development.

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

    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. The application of machine learning in multi sensor data fusion for activity recognition in mobile device space

    Marhoubi, Asmaa H.; Saravi, Sara; Edirisinghe, Eran A.

    2015-05-01

    The present generation of mobile handheld devices comes equipped with a large number of sensors. The key sensors include the Ambient Light Sensor, Proximity Sensor, Gyroscope, Compass and the Accelerometer. Many mobile applications are driven based on the readings obtained from either one or two of these sensors. However the presence of multiple-sensors will enable the determination of more detailed activities that are carried out by the user of a mobile device, thus enabling smarter mobile applications to be developed that responds more appropriately to user behavior and device usage. In the proposed research we use recent advances in machine learning to fuse together the data obtained from all key sensors of a mobile device. We investigate the possible use of single and ensemble classifier based approaches to identify a mobile device's behavior in the space it is present. Feature selection algorithms are used to remove non-discriminant features that often lead to poor classifier performance. As the sensor readings are noisy and include a significant proportion of missing values and outliers, we use machine learning based approaches to clean the raw data obtained from the sensors, before use. Based on selected practical case studies, we demonstrate the ability to accurately recognize device behavior based on multi-sensor data fusion.

  14. Secure Tracking in Sensor Networks using Adaptive Extended Kalman Filter

    Fard, Ali P

    2012-01-01

    Location information of sensor nodes has become an essential part of many applications in Wireless Sensor Networks (WSN). The importance of location estimation and object tracking has made them the target of many security attacks. Various methods have tried to provide location information with high accuracy, while lots of them have neglected the fact that WSNs may be deployed in hostile environments. In this paper, we address the problem of securely tracking a Mobile Node (MN) which has been noticed very little previously. A novel secure tracking algorithm is proposed based on Extended Kalman Filter (EKF) that is capable of tracking a Mobile Node (MN) with high resolution in the presence of compromised or colluding malicious beacon nodes. It filters out and identifies the malicious beacon data in the process of tracking. The proposed method considerably outperforms the previously proposed secure algorithms in terms of either detection rate or MSE. The experimental data based on different settings for the netw...

  15. Nanoparticle-dispersed metamaterial sensors for adaptive coded aperture imaging applications

    Nehmetallah, Georges; Banerjee, Partha; Aylo, Rola; Rogers, Stanley

    2011-09-01

    We propose tunable single-layer and multi-layer (periodic and with defect) structures comprising nanoparticle dispersed metamaterials in suitable hosts, including adaptive coded aperture constructs, for possible Adaptive Coded Aperture Imaging (ACAI) applications such as in microbolometry, pressure/temperature sensors, and directed energy transfer, over a wide frequency range, from visible to terahertz. These structures are easy to fabricate, are low-cost and tunable, and offer enhanced functionality, such as perfect absorption (in the case of bolometry) and low cross-talk (for sensors). Properties of the nanoparticle dispersed metamaterial are determined using effective medium theory.

  16. Adaptation of the ASTEC code system to accident scenarios in fusion installations

    Highlights: ► IRSN has a first version of ASTEC able to model an accident in ITER. ► Models are developed to make possible water/air ingress simulations in the vessel. ► Some thermal-hydraulic calculations in agreement with MELCOR are discussed. -- Abstract: ASTEC is a code system aiming to compute severe accident scenarios and their consequences in nuclear fission Pressurized Water Reactors (PWRs). Its capabilities have been recently extended to address the main accident sequences which may occur in the fusion installations, in particular in ITER. The purpose of this paper is to present a synthesis of the work that has been performed on ASTEC as part of its adaptation to fusion ITER facility, in particular concerning the development of some specific models (dust behavior, jet impaction and wall oxidation), the state of validation of the code and some first calculations for accident transients considered in the basis design. Comparisons with the MELCOR code, selected by ITER for their own safety analysis are provided and show a good agreement between both codes

  17. Adaptive Home System Using Wireless Sensor Network And Multi Agent System

    Jayarani Kamble

    2014-03-01

    Full Text Available Smart Home is an emerging technology growing continuously which includes number of new technologies which helps to improve human’s quality of living. This paper proposes an adaptive home system for optimum utilization of power, through Artificial Intelligence and Wireless Sensor network. Artificial Intelligence is a technology for developing adaptive system that can perceive the enviornmrnt, learn from the environment and can make decision using Rule based system.Zigbee is a wireless sensor network used to efficiently deliver solution for an energy management and efficiency for adaptive home. An algorithm used in adaptive home system is based on software agent approach that reduce the energy consumption at home by considering the user’s occupancy, temperature and user’s preferences as input to the system.

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

    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.

  19. A Sensor Fusion Method for Tracking Vertical Velocity and Height Based on Inertial and Barometric Altimeter Measurements

    Angelo Maria Sabatini

    2014-07-01

    Full Text Available A sensor fusion method was developed for vertical channel stabilization by fusing inertial measurements from an Inertial Measurement Unit (IMU and pressure altitude measurements from a barometric altimeter integrated in the same device (baro-IMU. An Extended Kalman Filter (EKF estimated the quaternion from the sensor frame to the navigation frame; the sensed specific force was rotated into the navigation frame and compensated for gravity, yielding the vertical linear acceleration; finally, a complementary filter driven by the vertical linear acceleration and the measured pressure altitude produced estimates of height and vertical velocity. A method was also developed to condition the measured pressure altitude using a whitening filter, which helped to remove the short-term correlation due to environment-dependent pressure changes from raw pressure altitude. The sensor fusion method was implemented to work on-line using data from a wireless baro-IMU and tested for the capability of tracking low-frequency small-amplitude vertical human-like motions that can be critical for stand-alone inertial sensor measurements. Validation tests were performed in different experimental conditions, namely no motion, free-fall motion, forced circular motion and squatting. Accurate on-line tracking of height and vertical velocity was achieved, giving confidence to the use of the sensor fusion method for tracking typical vertical human motions: velocity Root Mean Square Error (RMSE was in the range 0.04–0.24 m/s; height RMSE was in the range 5–68 cm, with statistically significant performance gains when the whitening filter was used by the sensor fusion method to track relatively high-frequency vertical motions.

  20. Adaptive sensor placement for target tracking in the presence of uncertainties

    Punithakumar, Kumaradevan; Kirubarajan, Thiagalingam; Hernandez, Marcel L.

    2004-01-01

    Recently a general framework for sensor resource management, which has been shown to allow efficient and effective utilization of a multisensor system was introduced in5. The basis of this technique is to use the Posterior Cramer-Rao Lower Bound (PCRLB) to quantify and control the optimal achievable accuracy of target state estimation. In the current paper we extend this framework by addressing the issues of imperfect sensor placement and uncertain sensor movement (e.g., sensor drift). In contrast the previous work considered only the case where the sensor location is known exactly. The crucial consideration is then how these two forms of uncertainty affect the sensor management strategy. If unaccounted for, these uncertainties will render the output of the resource manager useless. We adjust the PCRLB to account for sensor location uncertainty, and we also allow for measurement origin uncertainty (missed target originated detections and false alarms). The work is motivated by the problem of tracking a submarine by adaptively deploying sonobuoys from a helicopter. Simulation results are presented to show the advantages of accounting for sensor location uncertainty within this focal domain of anti-submarine warfare. The same technique can be used for tracking using unattended ground sensors (UGS) or unmanned aerial vehicles (UAV).

  1. A QoS-Driven Self-Adaptive Architecture For Wireless Sensor Networks

    Jemal, Ahmed; Ben Halima, Riadh

    2013-01-01

    6 pages International audience Recently, Wireless Sensor Networks (WSN) have become increasingly used to perform distributed sensing and convey useful information. These kinds of environments are complex, heterogeneous and often affected by unpredictable behavior and poor management. This fostered considerable research on designs and techniques that enhance these systems with an adaptation behavior. In this paper, we focus on the self-adaptation branch of the research and give an overvi...

  2. A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

    Loureiro, Antonio A. F.; Carlos M. S. Figueiredo; Eduardo F. Nakamura

    2009-01-01

    Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts ...

  3. Laboratory Testing the Layer Oriented Wavefront Sensor for the Multiconjugate Adaptive optics Demonstrator

    Arcidiacono, Carmelo; Lombini, Matteo; Diolaiti, Emiliano; Farinato, Jacopo; Ragazzoni, Roberto

    2009-01-01

    The Multiconjugate Adaptive optics Demonstrator (MAD) for ESO-Very Large Telescopes (VLT) will demonstrate on sky the MultiConjugate Adaptive Optics (MCAO) technique. In this paper the laboratory tests relative to the first preliminary acceptance in Europe of the Layer Oriented (LO) Wavefront Sensor (WFS) for MAD will be described: the capabilities of the LO approach have been checked and the ability of the WFS to measure phase screens positioned at different altitudes has been experimented. ...

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

    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.

  5. Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking

    Filiz Bunyak

    2007-08-01

    Full Text Available This paper makes new contributions in motion detection, object segmentation and trajectory estimation to create a successful object tracking system. A new efficient motion detection algorithm referred to as the flux tensor is used to detect moving objects in infrared video without requiring background modeling or contour extraction. The flux tensor-based motion detector when applied to infrared video is more accurate than thresholding ”hot-spots”, and is insensitive to shadows as well as illumination changes in the visible channel. In real world monitoring tasks fusing scene information from multiple sensors and sources is a useful core mechanism to deal with complex scenes, lighting conditions and environmental variables. The object segmentation algorithm uses level set-based geodesic active contour evolution that incorporates the fusion of visible color and infrared edge informations in a novel manner. Touching or overlapping objects are further refined during the segmentation process using an appropriate shapebased model. Multiple object tracking using correspondence graphs is extended to handle groups of objects and occlusion events by Kalman filter-based cluster trajectory analysis and watershed segmentation. The proposed object tracking algorithm was successfully tested on several difficult outdoor multispectral videos from stationary sensors and is not confounded by shadows or illumination variations.

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

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

    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 meteorologica...

  7. Adaptive feature annotation for large video sensor networks

    Cai, Yang; Bunn, Andrew; Liang, Peter; Yang, Bing

    2013-10-01

    We present an adaptive feature extraction and annotation algorithm for articulating traffic events from surveillance cameras. We use approximate median filter for moving object detection, motion energy image and convex hull for lane detection, and adaptive proportion models for vehicle classification. It is found that our approach outperforms three-dimensional modeling and scale-independent feature transformation algorithms in terms of robustness. The multiresolution-based video codec algorithm enables a quality-of-service-aware video streaming according to the data traffic. Furthermore, our empirical data shows that it is feasible to use the metadata to facilitate the real-time communication between an infrastructure and a vehicle for safer and more efficient traffic control.

  8. Optimizing Power and Buffer Congestion on Wireless Sensor Nodes Using CAP (Coordinated Adaptive Power Management Technique

    Gauri Joshi

    2011-05-01

    Full Text Available Limited hardware capabilities and very limited battery power supply are the two main constraints thatarise because of small size and low cost of the wireless sensor nodes. Power optimization is highlydesired at all the levels in order to have a long lived Wireless Sensor Network (WSN. Prolonging the lifespan of the network is the prime focus in highly energy constrained wireless sensor networks. Sufficientnumber of active nodes can only ensure proper coverage of the sensing field and connectivity of thenetwork. If large number of wireless sensor nodes get their batteries depleted over a short time span thenit is not possible to maintain the network. In order to have long lived network it is mandatory to havelong lived sensor nodes and hence power optimization at node level becomes equally important as poweroptimization at network level. In this paper need for a dynamically adaptive sensor node is signified inorder to optimize power at individual nodes along with the reduction in data loss due to buffercongestion.We have analyzed a sensor node with fixed service rate (processing rate and transmission rate and asensor node with variable service rates for its power consumption and data loss in small sized buffersunder varying traffic (workload conditions. For variable processing rate Dynamic Voltage FrequencyScaling (DVFS technique is considered and for variable transmission rate Dynamic Modulation Scaling(DMS technique is considered. Comparing the results of a dynamically adaptive sensor node with thatof a fixed service rate sensor node shows improvement in the lifetime of node as well as reduction in thedata loss due to buffer congestion. Further we have tried to coordinate the service rates of computationunit and communication unit on a sensor node which give rise to Coordinated Adaptive Power (CAPmanagement. The main objective of CAP Management is to save the power during normal periods andreduce the data loss due to buffer congestion (overflow

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

    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. PMID:27257964

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

    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. PMID:27257964

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

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

    2013-01-01

    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 diagn...

  12. Towards adaptive security for convergent wireless sensor networks in beyond 3G environments

    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 adapt...

  13. Sensors in Unmanned Robotic Vehicle

    B. Rohini

    2008-05-01

    Full Text Available Unmanned tracked vehicles are developed for deployment in dangerous zones that are notsafe for human existence. These vehicles are to be fitted with various sensors for safe manoeuvre.Wide range of sensors for vehicle control, vision, and navigation are employed. The main purposeof the sensors is to infer the intended parameter precisely for further utilisation. Software isinseparable part of the sensors and plays major role in scaling, noise reduction, and fusion.Sensor fusion is normally adapted to enhance the decision-making. Vehicle location  andorientation can be sensed through global positioning system, accelerometer, gyroscope, andcompass. The unmanned vehicle can be navigated with the help of CCD camera, radar, lidar,ultrasonic sensor, piezoelectric sensor, microphone, etc.  Proximity sensors like capacitive andRF proximity detectors can detect obstacles in close vicinity.  This paper presents an overviewof sensors normally deployed in unmanned tracked vehicles.

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

    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. Wavefront detection method of a single-sensor based adaptive optics system.

    Wang, Chongchong; Hu, Lifa; Xu, Huanyu; Wang, Yukun; Li, Dayu; Wang, Shaoxin; Mu, Quanquan; Yang, Chengliang; Cao, Zhaoliang; Lu, Xinghai; Xuan, Li

    2015-08-10

    In adaptive optics system (AOS) for optical telescopes, the reported wavefront sensing strategy consists of two parts: a specific sensor for tip-tilt (TT) detection and another wavefront sensor for other distortions detection. Thus, a part of incident light has to be used for TT detection, which decreases the light energy used by wavefront sensor and eventually reduces the precision of wavefront correction. In this paper, a single Shack-Hartmann wavefront sensor based wavefront measurement method is presented for both large amplitude TT and other distortions' measurement. Experiments were performed for testing the presented wavefront method and validating the wavefront detection and correction ability of the single-sensor based AOS. With adaptive correction, the root-mean-square of residual TT was less than 0.2 λ, and a clear image was obtained in the lab. Equipped on a 1.23-meter optical telescope, the binary stars with angle distance of 0.6″ were clearly resolved using the AOS. This wavefront measurement method removes the separate TT sensor, which not only simplifies the AOS but also saves light energy for subsequent wavefront sensing and imaging, and eventually improves the detection and imaging capability of the AOS. PMID:26367988

  16. An emergency-adaptive routing scheme for wireless sensor networks for building fire hazard monitoring.

    Zeng, Yuanyuan; Sreenan, Cormac J; Sitanayah, Lanny; Xiong, Naixue; Park, Jong Hyuk; Zheng, Guilin

    2011-01-01

    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. PMID:22163774

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

    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.

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

    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.

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

    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.

  20. Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter.

    Atrsaei, Arash; Salarieh, Hassan; Alasty, Aria

    2016-09-01

    Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized. PMID:27428461

  1. SA-MAC:Self-Stabilizing Adaptive MAC Protocol for Wireless Sensor Networks

    波澄; 韩君泽; 李向阳; 王昱; 肖波

    2014-01-01

    A common method of prolonging the lifetime of wireless sensor networks is to use low power duty cycling protocol. Existing protocols consist of two categories: sender-initiated and receiver-initiated. In this paper, we present SA-MAC, a self-stabilizing adaptive MAC protocol for wireless sensor networks. SA-MAC dynamically adjusts the transmission time-slot, waking up time-slot, and packet detection pattern according to current network working condition, such as packet length and wake-up patterns of neighboring nodes. In the long run, every sensor node will find its own transmission phase so that the network will enter a stable stage when the network load and qualities are static. We conduct extensive experiments to evaluate the energy consumption, packet reception rate of SA-MAC in real sensor networking systems. Our results indicate that SA-MAC outperforms other existing protocols.

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

    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

    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

  4. Novel Paradigm for Constructing Masses in Dempster-Shafer Evidence Theory for Wireless Sensor Network’s Multisource Data Fusion

    Zhenjiang Zhang; Tonghuan Liu; Wenyu Zhang

    2014-01-01

    Dempster-Shafer evidence theory (DSET) is a flexible and popular paradigm for multisource data fusion in wireless sensor networks (WSNs). This paper presents a novel and easy implementing method computing masses from the hundreds of pieces of data collected by a WSN. The transfer model is based on the Mahalanobis distance (MD), which is an effective method to measure the similarity between an object and a sample. Compared to the existing methods, the proposed method concerns the statistical f...

  5. Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion

    2014-01-01

    For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the ...

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

    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.

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

    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

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

    Wilson, Brian; Manipon, Gerald; Hua, Hook; Fetzer, Eric

    2014-05-01

    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 (ECMWF), 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 a hybrid Cloud (private eucalyptus & public Amazon). Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. 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 present the architecture of SciReduce, describe the

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

    Hong SeungHo; Li XiaoHui; Fang KangLing

    2011-01-01

    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 disco...

  10. SLEACH: Secure Low- Energy Adaptive Clustering Hierarchy Protocol for Wireless Sensor Networks

    WANG Xiao-yun; YANG Li-zhen; CHEN Ke-fei

    2005-01-01

    LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol is a basic clustering-based routing protocol of sensor networks. In this paper, we present the design of SLEACH, a secure extension for the LEACH protocol. We divide SLEACH into four phases and fit inexpensive cryptographic operations to each part of the protocol functionality to create an efficient, practical protocol. Then we give security analyses of SLEACH. Our security analyses show that our scheme is robust against any external attacker or compromised nodes in the sensor network

  11. Adaptive Square-Shaped Trajectory-Based Service Location Protocol in Wireless Sensor Networks

    Hwa-Jung Lim

    2010-04-01

    Full Text Available In this paper we propose an adaptive square-shaped trajectory (ASST-based service location method to ensure load scalability in wireless sensor networks. This first establishes a square-shaped trajectory over the nodes that surround a target point computed by the hash function and any user can access it, using the hash. Both the width and the size of the trajectory are dynamically adjustable, depending on the number of queries made to the service information on the trajectory. The number of sensor nodes on the trajectory varies in proportion to the changing trajectory shape, allowing high loads to be distributed around the hot spot area.

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

    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

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

    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

  14. Online Adaptive Decision Fusion Framework Based on Entropic Projections onto Convex Sets with Application to Wildfire Detection in Video

    Gunay, Osman; Kose, Kivanc; Cetin, A Enis

    2011-01-01

    In this paper, an Entropy functional based online Adaptive Decision Fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms each of which yielding its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights which are updated online according to an active fusion method based on performing entropic projections onto convex sets describing sub-algorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video based wildfire detection system is developed to evaluate the performance of the algorithm in handling the problems where data arrives sequentially. In this case, the oracle is the security guard of the forest lookout tower verifying the decision of the combined algorithm. Simulation results are...

  15. Autonomous sensor manager agents (ASMA)

    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.

  16. 国外手爪中多传感器数据融合技术的研究概况%An overseas survey of multi-sensor integration and data fusion techniques of robotics gripper

    童利标; 徐科军; 梅涛

    2001-01-01

    近年来,多传感器数据融合技术已引起世界范围 内的普遍关注,成为一个新兴的技术方向,并已成功地应用在军事系统、交通系统和智能机 器人等研究领域。手爪是空间智能机器人的关键部件之一,它集成了视觉、触觉和力觉等多 种传感器。为了能进行灵活和自适应的操作,机器人手爪必须要采用多传感器数据融合技术 。该文介绍了机器人手爪中多传感器集成和数据融合技术的研究概况,对我国机器人的研究 发展具有一定的借鉴意义。%The multi-sensor data fusion technique, which has been applied to the research fields sucessfully such as military systems, traffic systems, intelligent robots, etc, has become a great attention all over the world and has been a new research direction in rec ent years. The gripper is one of the key components of space intelligent robot w hich integrates many sensors such as vision sensors, tactile sensors,force senso rs, etc. In order to complete the flexible and adaptive operations, the grip per must adopt sensor data fusion techniques. In this paper, a survey of multi- sensor integration and data fusion techniques is made,which may be a reference t o the development of robot in China.

  17. Medical case-based retrieval: integrating query MeSH terms for query-adaptive multi-modal fusion

    Seco de Herrera, Alba G.; Foncubierta-Rodríguez, Antonio; Müller, Henning

    2015-03-01

    Advances in medical knowledge give clinicians more objective information for a diagnosis. Therefore, there is an increasing need for bibliographic search engines that can provide services helping to facilitate faster information search. The ImageCLEFmed benchmark proposes a medical case-based retrieval task. This task aims at retrieving articles from the biomedical literature that are relevant for differential diagnosis of query cases including a textual description and several images. In the context of this campaign many approaches have been investigated showing that the fusion of visual and text information can improve the precision of the retrieval. However, fusion does not always lead to better results. In this paper, a new query-adaptive fusion criterion to decide when to use multi-modal (text and visual) or only text approaches is presented. The proposed method integrates text information contained in MeSH (Medical Subject Headings) terms extracted and visual features of the images to find synonym relations between them. Given a text query, the query-adaptive fusion criterion decides when it is suitable to also use visual information for the retrieval. Results show that this approach can decide if a text or multi{modal approach should be used with 77.15% of accuracy.

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

    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.

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

    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.

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

    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 cm3 in the iCT to 71.44 ± 37.46 cm3 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 cm3 and 21.58 ± 6.16 cm3 in the iCT to 11.80 ± 2.79 cm3 and 13.29 ± 4.17 cm3 in the rCT (both p < 0.01), respectively. The volume of other OARs did not shrink very much. No significant differences on PTVGTV and PTVCTV 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

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

    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....

  2. ADAPTIVITY OF A COLORING ALGORITHM TO UNRELIABLE COMMUNICATIONS FOR DATA GATHERING IN WIRELESS SENSOR NETWORKS

    Ichrak Amdouni; Pascale Minet; Cedric Adjih

    2013-01-01

    Wireless sensor networks (WSNs) are prone to node/link failures, message losses, and dynamic node joins and departures. For instance, in data gathering applications that constitute a common type of applications in WSNs, links between nodes and their parent in the data gathering tree may be broken. Protocols supporting such applications should adapt their behaviour to guarantee reliable wireless communications while keeping a low overhead. In particular, this paper focuses on the optimization ...

  3. Traffic-Adaptive and Link-Quality-Aware Communication in Wireless Sensor Networks

    Hurni, Philipp

    2013-01-01

    This paper is a summary of the main contribu- tions of the PhD thesis published in [1]. The main research contributions of the thesis are driven by the research question how to design simple, yet efficient and robust run-time adaptive resource allocation schemes within the commu- nication stack of Wireless Sensor Network (WSN) nodes. The thesis addresses several problem domains with con- tributions on different layers of the WSN communication stack. The main contributions can be summarized...

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

    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.

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

    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.

  6. Sensor selection and chemo-sensory optimization: toward an adaptable chemo-sensory system

    Alexander eVergara

    2012-01-01

    Full Text Available Over the past two decades, despite the tremendous research effort performed on chemical sensors and machine olfaction to develop micro-sensory systems that will accomplish the growing existent needs in personal health (implantable sensors, environment monitoring (widely distributed sensor networks, and security/threat detection (chemo/bio warfare agents, simple, low-cost molecular sensing platforms capable of long-term autonomous operation remain beyond the current state-of-the-art of chemical sensing. A fundamental issue within this context is that most of the chemical sensors depend on interactions between the targeted species and the surfaces functionalized with receptors that bind the target species selectively, and that these binding events are coupled with transduction processes that begin to change when they are exposed to the messy world of real samples. With the advent of fundamental breakthroughs at the intersection of materials science, micro/nano-technology, and signal processing, hybrid chemo-sensory systems have incorporated tunable, optimizable operating parameters, through which changes in the response characteristics can be modeled and compensated as the environmental conditions or application needs change.The objective of this article, in this context, is to bring together the key advances at the device, data processing, and system levels that enable chemo-sensory systems to adapt in response to their environments. Accordingly, in this review we will feature the research effort made by selected experts on chemical sensing and information theory, whose work has been devoted to develop strategies that provide tunability and adaptability to single sensor devices or sensory array systems. Particularly, we consider sensor-array selection, modulation of internal sensing parameters, and active sensing. The article ends with some conclusions drawn from the results presented and a visionary look toward the future in terms of how the

  7. Sensor selection and chemo-sensory optimization: toward an adaptable chemo-sensory system.

    Vergara, Alexander; Llobet, Eduard

    2011-01-01

    Over the past two decades, despite the tremendous research on chemical sensors and machine olfaction to develop micro-sensory systems that will accomplish the growing existent needs in personal health (implantable sensors), environment monitoring (widely distributed sensor networks), and security/threat detection (chemo/bio warfare agents), simple, low-cost molecular sensing platforms capable of long-term autonomous operation remain beyond the current state-of-the-art of chemical sensing. A fundamental issue within this context is that most of the chemical sensors depend on interactions between the targeted species and the surfaces functionalized with receptors that bind the target species selectively, and that these binding events are coupled with transduction processes that begin to change when they are exposed to the messy world of real samples. With the advent of fundamental breakthroughs at the intersection of materials science, micro- and nano-technology, and signal processing, hybrid chemo-sensory systems have incorporated tunable, optimizable operating parameters, through which changes in the response characteristics can be modeled and compensated as the environmental conditions or application needs change. The objective of this article, in this context, is to bring together the key advances at the device, data processing, and system levels that enable chemo-sensory systems to "adapt" in response to their environments. Accordingly, in this review we will feature the research effort made by selected experts on chemical sensing and information theory, whose work has been devoted to develop strategies that provide tunability and adaptability to single sensor devices or sensory array systems. Particularly, we consider sensor-array selection, modulation of internal sensing parameters, and active sensing. The article ends with some conclusions drawn from the results presented and a visionary look toward the future in terms of how the field may evolve. PMID

  8. Sensor Fusion of Monocular Cameras and Laser Rangefinders for Line-Based Simultaneous Localization and Mapping (SLAM Tasks in Autonomous Mobile Robots

    Xinzheng Zhang

    2012-01-01

    Full Text Available This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM in dynamic environments. The designed approach consists of two features: (i the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM that incorporates two individual Extended Kalman Filter (EKF based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.

  9. Sensor fusion of monocular cameras and laser rangefinders for line-based Simultaneous Localization and Mapping (SLAM) tasks in autonomous mobile robots.

    Zhang, Xinzheng; Rad, Ahmad B; Wong, Yiu-Kwong

    2012-01-01

    This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method. PMID:22368478

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

    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.

  11. POPULATION MEAN ESTIMATE FOR ADAPTIVE MODULATION UNDER LARGE PHASE ERROR IN SINGLE BEAMFORMING SENSOR ARRAY

    G. Vaikundam

    2015-04-01

    Full Text Available Beamforming is a signal processing technique to focus the transmitted energy so that maximum energy is radiated in the intended destination and communication range is enhanced. Data rate improvement in Transmit beamforming can be achieved with adaptive modulation. Though modulation adaptation is possible under zero-mean phase error, it is difficult to adapt it under non-zero mean Gaussian distributed phase error conditions. Phase errors occur due to channel estimation inaccuracies, delay in estimation, sensor drift, quantized feedback etc resulting in increased outage probability and Bit error rate. Preprocessing of beamforming weights adjusted by Sample Mean Estimate (SME solves the problem of adaptive modulation. However, under large phase error variation, the SME method fails. Hence, in this paper, Population Mean Estimate (PME approach is proposed to resolve these drawbacks for a Rayleigh flat fading channel with White Gaussian Noise. To correct the population mean error if any, Least Mean Square correction algorithm is proposed and is tested up to 80% error in PME and the corrected error fall within 10% error. Simulation results for a distributed beamforming sensor array indicate that the proposed method performs better than the SME based existing methods under worst-case phase error distribution.

  12. Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks.

    Zhao, Yubin; Li, Xiaofan; Zhang, Sha; Meng, Tianhui; Zhang, Yiwen

    2016-01-01

    In practical localization system design, researchers need to consider several aspects to make the positioning efficiently and effectively, e.g., the available auxiliary information, sensing devices, equipment deployment and the environment. Then, these practical concerns turn out to be the technical problems, e.g., the sequential position state propagation, the target-anchor geometry effect, the Non-line-of-sight (NLOS) identification and the related prior information. It is necessary to construct an efficient framework that can exploit multiple available information and guide the system design. In this paper, we propose a scalable method to analyze system performance based on the Cramér-Rao lower bound (CRLB), which can fuse all of the information adaptively. Firstly, we use an abstract function to represent all of the wireless localization system model. Then, the unknown vector of the CRLB consists of two parts: the first part is the estimated vector, and the second part is the auxiliary vector, which helps improve the estimation accuracy. Accordingly, the Fisher information matrix is divided into two parts: the state matrix and the auxiliary matrix. Unlike the theoretical analysis, our CRLB can be a practical fundamental limit to denote the system that fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and the NLOS identification and mitigation methods. Thus, the theoretical results are approaching the real case more. In addition, our method is more adaptable than other CRLBs when considering more unknown important factors. We use the proposed method to analyze the wireless sensor network-based indoor localization system. The influence of the hybrid LOS/NLOS channels, the building layout information and the relative height differences between the target and anchors are analyzed. It is demonstrated that our method exploits all of the available information for

  13. ATPM: An Energy Efficient MAC Protocol with Adaptive Transmit Power Scheme for Wireless Sensor Networks

    Qian Hu

    2011-04-01

    Full Text Available The MAC protocol for wireless sensor networks is different from traditional wireless MACs such as IEEE 802.11. Energy conservation is one of the most important goals, while per-node fairness and latency are less important. This paper proposes an energy efficient MAC protocol with adaptive transmit power scheme based on SMAC/AL named ATPM (Adaptive Transmit Power MAC. In SMAC/AL, all the nodes transmit data with a fixed power level, no matter how close the involved nodes are. The proposed ATPM can calculate the distance between the sender and the receiver by measuring the received power, and then adaptively decide the appropriate transmit power level according to the propagation model and distance. Simulations have been done to evaluate the performance of the proposed new protocol, by which we can find out that ATPM can really reduce energy consumption compared with SMAC/AL.

  14. Research of an Adaptive Aggregation Routing Algorithm in Wireless Sensor Networks

    Xiangli Wang

    2012-07-01

    Full Text Available At present, most aggregation routing algorithms for WSNs assume that aggregation expends are so little as to be neglected. But with the growing demand for the collection of multimedia data, the aggregation expends are much larger and can’t be neglect. In view of this problem, this paper proposes an adaptive aggregation routing algorithm with the minimal energy consumption (RMEAAT. Firstly, it constructs an -balancing spanning tree on the basis of SPT and MST as the initial communication path. And then it defines the aggregation benefit with consideration of aggregation expend and transmission expend, and data are adaptively judged whether to aggregate at every node of -balancing spanning tree according to aggregation benefit in the process of transmission. Correspondingly, the initial transmission path is gradually improved in terms of the aggregation judgment. The simulation results show that RMEAAT algorithm has better performance in WSNs with different aggregation expends and fusion degrees.

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

    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

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

    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.

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

    Liu, Bailing; Zhang, Fumin; Qu, Xinghua

    2015-01-01

    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 × 0.8 × 1 ∼ 2 × 0.8 × 1 m in the field of view (FOV) is indicated by the experimental results. PMID:25850067

  18. Delineation of Management Zones in Precision Agriculture by Integration of Proximal Sensing with Multivariate Geostatistics. Examples of Sensor Data Fusion

    Annamaria Castrignanò

    2015-07-01

    Full Text Available Fundamental to the philosophy of Precision Agriculture (PA is the concept of matching inputs to needs. Recent research in PA has focused on use of Management Zones (MZ that are field areas characterised by homogeneous attributes in landscape and soil conditions. Proximal sensing (such as Electromagnetic Induction (EMI, Ground Penetrating Radar (GPR and X-ray fluorescence can complement direct sampling and a multisensory platform can enable us to map soil features unambiguously. Several methods of multi-sensor data analysis have been developed to determine the location of subfield areas. Modern geostatistical techniques, treating variables as continua in a joint attribute and geographic space, offer the potential to analyse such data effectively. The objective of the paper is to show the potential of multivariate geostatistics to create MZ in the perspective of PA by integrating field data from different types of sensors, describing two study cases. In particular, in the first case study, cokriging and factorial cokriging were employed to produce thematic maps of soil trace elements and to delineate homogenous zones, respectively. In the second case, a multivariate geostatistical data-fusion technique (multi collocated cokriging was applied to different geophysical sensor data (GPR and EMI, for stationary estimation of soil water content and for delineating within-field zone with different wetting degree. The results have shown that linking sensors of different type improves the overall assessment of soil and sensor data fusion could be effectively applied to delineate MZs in Precision Agriculture. However, techniques of data integration are urgently required as a result of the proliferation of data from different sources.

  19. Robust Sequential Covariance Intersection Fusion Kalman Filtering over Multi-agent Sensor Networks with Measurement Delays and Uncertain Noise Variances

    QI Wen-Juan; ZHANG Peng; DENG Zi-Li

    2014-01-01

    This paper deals with the problem of designing robust sequential covariance intersection (SCI) fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances. The sensor network is partitioned into clusters by the nearest neighbor rule. Using the minimax robust estimation principle, based on the worst-case conservative sensor network system with conservative upper bounds of noise variances, and applying the unbiased linear minimum variance (ULMV) optimal estimation rule, we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources, and guarantee that the actual filtering error variances have a less-conservative upper-bound. A Lyapunov equation method for robustness analysis is proposed, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter. A simulation example for a tracking system verifies the robustness and robust accuracy relations.

  20. Estimation of attitudes from a low-cost miniaturized inertial platform using Kalman Filter-based sensor fusion algorithm

    N Shantha Kumar; T Jann

    2004-04-01

    Due to costs, size and mass, commercially available inertial navigation systems are not suitable for small, autonomous flying vehicles like ALEX and other UAVs. In contrast, by using modern MEMS (or of similar class) sensors, hardware costs, size and mass can be reduced substantially. However, low-cost sensors often suffer from inaccuracy and are influenced greatly by temperature variation. In this work, such inaccuracies and dependence on temperature variations have been studied, modelled and compensated in order to reach an adequate quality of measurements for the estimation of attitudes. This has been done applying a Kalman Filter-based sensor fusion algorithm that combines sensor models, error parameters and estimation scheme. Attitude estimation from low-cost sensors is first realized in a Matlab/Simulink platform and then implemented on hardware by programming the micro controller and validated. The accuracies of the estimated roll and pitch attitudes are well within the stipulated accuracy level of ±5° for the ALEX. However, the estimation of heading, which is mainly derived from the magnetometer readings, seems to be influenced greatly by the variation in local magnetic field.

  1. Applications of state estimation in multi-sensor information fusion for the monitoring of open pit mine slope deformation

    FU Hua; LIU Yin-ping; XIAO Jian

    2008-01-01

    The traditional open pit mine slope deformation monitoring system can not use the monitoring information coming from many monitoring points at the same time,can only using the monitoring data coming from a key monitoring point, and that is to say it can only handle one-dimensional time series. Given this shortage in the monitoring,the multi-sensor information fusion in the state estimation techniques would be introduced to the slope deformation monitoring system, and by the dynamic characteristics of deformation slope, the open pit slope would be regarded as a dynamic goal, the condition monitoring of which would be regarded as a dynamic target tracking. Distributed Information fusion technology with feedback was used to process the monitoring data and on this basis Klman filtering algorithms was introduced, and the simulation examples was used to prove its effectivenes.

  2. Enhancing chemical identification efficiency by SAW sensor transients through a data enrichment and information fusion strategy—a simulation study

    The paper proposes a new approach for improving the odor recognition efficiency of a surface acoustic wave (SAW) transient sensor system based on a single polymer coating. The vapor identity information is hidden in transient response shapes through dependences on specific vapor solvation and diffusion parameters in the polymer coating. The variations in the vapor exposure and purge durations and the sensor operating frequency have been used to create diversity in transient shapes via termination of the vapor–polymer equilibration process up to different stages. The transient signals were analyzed by the discrete wavelet transform using Daubechies-4 mother wavelet basis. The wavelet approximation coefficients were then processed by principal component analysis for creating feature space. The set of principal components define the vapor identity information. In an attempt to enhance vapor class separability we analyze two types of information fusion methods. In one, the sensor operation frequency is fixed and the sensing and purge durations are varied, and in the second, the sensing and purge durations are fixed and the sensor operating frequency is varied. The fusion is achieved by concatenation of discrete wavelet coefficients corresponding to various transients prior to the principal component analysis. The simulation experiments with polyisobutylene SAW sensor coating for operation frequencies over [55–160] MHz and sensing durations over [5–60] s were analyzed. The target vapors are seven volatile organics: chloroform, chlorobenzene, o-dichlorobenzene, n-heptane, toluene, n-hexane and n-octane whose concentrations were varied over [10–100] ppm. The simulation data were generated using a SAW sensor transient response model that incorporates the viscoelastic effects due to polymer coating and an additive noise source in the output. The analysis reveals that: (i) in single transient analysis the class separability increases with sensing duration for a given

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

    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.

  4. Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster-Shafer method

    Hou, Dibo; He, Huimei; Huang, Pingjie; Zhang, Guangxin; Loaiciga, Hugo

    2013-05-01

    This study presents a method for detecting contamination events of sources of drinking water based on the Dempster-Shafer (D-S) evidence theory. The detection method has the purpose of protecting water supply systems against accidental and intentional contamination events. This purpose is achieved by first predicting future water-quality parameters using an autoregressive (AR) model. The AR model predicts future water-quality parameters using recent measurements of these parameters made with automated (on-line) water-quality sensors. Next, a probabilistic method assigns probabilities to the time series of residuals formed by comparing predicted water-quality parameters with threshold values. Finally, the D-S fusion method searches for anomalous probabilities of the residuals and uses the result of that search to determine whether the current water quality is normal (that is, free of pollution) or contaminated. The D-S fusion method is extended and improved in this paper by weighted averaging of water-contamination evidence and by the analysis of the persistence of anomalous probabilities of water-quality parameters. The extended D-S fusion method makes determinations that have a high probability of being correct concerning whether or not a source of drinking water has been contaminated. This paper's method for detecting water-contamination events was tested with water-quality time series from automated (on-line) water quality sensors. In addition, a small-scale, experimental, water-pipe network was tested to detect water-contamination events. The two tests demonstrated that the extended D-S fusion method achieves a low false alarm rate and high probabilities of detecting water contamination events.

  5. Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster–Shafer method

    This study presents a method for detecting contamination events of sources of drinking water based on the Dempster–Shafer (D-S) evidence theory. The detection method has the purpose of protecting water supply systems against accidental and intentional contamination events. This purpose is achieved by first predicting future water-quality parameters using an autoregressive (AR) model. The AR model predicts future water-quality parameters using recent measurements of these parameters made with automated (on-line) water-quality sensors. Next, a probabilistic method assigns probabilities to the time series of residuals formed by comparing predicted water-quality parameters with threshold values. Finally, the D-S fusion method searches for anomalous probabilities of the residuals and uses the result of that search to determine whether the current water quality is normal (that is, free of pollution) or contaminated. The D-S fusion method is extended and improved in this paper by weighted averaging of water-contamination evidence and by the analysis of the persistence of anomalous probabilities of water-quality parameters. The extended D-S fusion method makes determinations that have a high probability of being correct concerning whether or not a source of drinking water has been contaminated. This paper's method for detecting water-contamination events was tested with water-quality time series from automated (on-line) water quality sensors. In addition, a small-scale, experimental, water-pipe network was tested to detect water-contamination events. The two tests demonstrated that the extended D-S fusion method achieves a low false alarm rate and high probabilities of detecting water contamination events. (paper)

  6. Evaluation of the Segmentation by Multispectral Fusion Approach with Adaptive Operators : Application to Medical Images

    Lamiche Chaabane

    2011-09-01

    Full Text Available With the development of acquisition image techniques, more and more image data from different sources of image become available. Multi-modality image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single modality. In medical imaging based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents the evaluation of the segmentation of MR images using the multispectral fusion approach in the possibility theory context . Some results are presented and discussed.

  7. Large-Scale, Parallel, Multi-Sensor Data Fusion in the Cloud

    Wilson, B. D.; Manipon, G.; Hua, H.

    2012-12-01

    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 periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time "matchups" between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, assemble merged datasets, and compute fused products for further scientific and statistical analysis. To efficiently assemble such decade-scale datasets in a timely manner, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. "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, in which simple tuples (keys & values) are passed between the map and reduce functions, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Thus, SciReduce uses the native datatypes (geolocated grids, swaths, and points) that geo-scientists are familiar with. We are deploying within Sci

  8. QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding

    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.

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

    Kikuchi, Ken; Hettmer, Simone; Aslam, M Imran; Michalek, Joel E; Laub, Wolfram; Wilky, Breelyn A; Loeb, David M; Rubin, Brian P; Wagers, Amy J; Keller, Charles

    2014-01-01

    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. PMID:24453992

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

    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.

  11. Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors

    Carlos Santos

    2012-07-01

    Full Text Available 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, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS based on the Unscented Kalman Filter (UKF using the Fast Optimal Attitude Matrix (FOAM algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.

  12. Adaptive Opportunistic Cooperative Control Mechanism Based on Combination Forecasting and Multilevel Sensing Technology of Sensors for Mobile Internet of Things

    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.

  13. Code division controlled-MAC in wireless sensor network by adaptive binary signature design

    Wei, Lili; Batalama, Stella N.; Pados, Dimitris A.; Suter, Bruce

    2007-04-01

    We consider the problem of signature waveform design for code division medium-access-control (MAC) of wireless sensor networks (WSN). In contract to conventional randomly chosen orthogonal codes, an adaptive signature design strategy is developed under the maximum pre-detection SINR (signal to interference plus noise ratio) criterion. The proposed algorithm utilizes slowest descent cords of the optimization surface to move toward the optimum solution and exhibits, upon eigenvector decomposition, linear computational complexity with respect to signature length. Numerical and simulation studies demonstrate the performance of the proposed method and offer comparisons with conventional signature code sets.

  14. Routing Optimization for Wireless Sensor Network Based on Cloud Adaptive Particle Swarm Optimization Algorithm

    Xu Bao

    2013-01-01

    One of the most important targets of routing algorithm for Wireless Sensor Network (WSN) is to prolong the network lifetime. Aimed at the features of WSN, a new routing optimization approach based on cloud adaptive particle swarm optimization algorithm is put forward in this paper. All paths appear at the same time in one round are fused in one particle, and the coding rule of particle is set down. The particle itself is defined as its position, the number of replaceable relay nodes in paths ...

  15. Adaptive Security in ODMAC for Multihop Energy Harvesting Wireless Sensor Networks

    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...

  16. Adaptive Square-Shaped Trajectory-Based Service Location Protocol in Wireless Sensor Networks

    Hwa-Jung Lim; Joa-Hyoung Lee; Heon-Guil Lee

    2010-01-01

    In this paper we propose an adaptive square-shaped trajectory (ASST)-based service location method to ensure load scalability in wireless sensor networks. This first establishes a square-shaped trajectory over the nodes that surround a target point computed by the hash function and any user can access it, using the hash. Both the width and the size of the trajectory are dynamically adjustable, depending on the number of queries made to the service information on the trajectory. The number of ...

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

    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.

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

    Liu, Zhiwen; Guo, Wei; Tang, Zhangchun; Chen, Yongqiang

    2015-01-01

    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). PMID:26334280

  19. A hybrid adaptive routing algorithm for event-driven wireless sensor networks.

    Figueiredo, Carlos M S; Nakamura, Eduardo F; Loureiro, Antonio A F

    2009-01-01

    Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

  20. Experience with wavefront sensor and deformable mirror interfaces for wide-field adaptive optics systems

    Basden, A G; Bharmal, N A; Bitenc, U; Brangier, M; Buey, T; Butterley, T; Cano, D; Chemla, F; Clark, P; Cohen, M; Conan, J -M; de Cos, F J; Dickson, C; Dipper, N A; Dunlop, C N; Feautrier, P; Fusco, T; Gach, J L; Gendron, E; Geng, D; Goodsell, S J; Gratadour, D; Greenaway, A H; Guesalaga, A; Guzman, C D; Henry, D; Holck, D; Hubert, Z; Huet, J M; Kellerer, A; Kulcsar, C; Laporte, P; Roux, B Le; Looker, N; Longmore, A J; Marteaud, M; Martin, O; Meimon, S; Morel, C; Morris, T J; Myers, R M; Osborn, J; Perret, D; Petit, C; Raynaud, H; Reeves, A P; Rousset, G; Lasheras, F Sanchez; Rodriguez, M Sanchez; Santos, J D; Sevin, A; Sivo, G; Stadler, E; Stobie, B; Talbot, G; Todd, S; Vidal, F; Younger, E J

    2016-01-01

    Recent advances in adaptive optics (AO) have led to the implementation of wide field-of-view AO systems. A number of wide-field AO systems are also planned for the forthcoming Extremely Large Telescopes. Such systems have multiple wavefront sensors of different types, and usually multiple deformable mirrors (DMs). Here, we report on our experience integrating cameras and DMs with the real-time control systems of two wide-field AO systems. These are CANARY, which has been operating on-sky since 2010, and DRAGON, which is a laboratory adaptive optics real-time demonstrator instrument. We detail the issues and difficulties that arose, along with the solutions we developed. We also provide recommendations for consideration when developing future wide-field AO systems.

  1. LPTA: Location Predictive and Time Adaptive Data Gathering Scheme with Mobile Sink for Wireless Sensor Networks

    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.

  2. Energy-Aware Adaptive Cooperative FEC Protocol in MIMO Channel for Wireless Sensor Networks

    Yong Jin

    2013-01-01

    Full Text Available We propose an adaptive cooperative forward error correction (ACFEC based on energy efficiency combining Reed-Solomon (RS coder algorithm and multiple input multiple output (MIMO channel technology with monitoring signal-to-noise ratio (SNR in wireless sensor networks. First, we propose a new Markov chain model for FEC based on RS codes and derive the expressions for QoS on the basis of this model, which comprise four metrics: throughput, packet error rate, delay, and energy efficiency. Then, we apply RS codes with the MIMO channel technology to the cross-layer design. Numerical and simulation results show that the joint design of MIMO and adaptive cooperative FEC based on RS codes can achieve considerable spectral efficiency gain, real-time performance, reliability, and energy utility.

  3. Sensor Fusion of Monocular Cameras and Laser Rangefinders for Line-Based Simultaneous Localization and Mapping (SLAM) Tasks in Autonomous Mobile Robots

    Xinzheng Zhang; Rad, Ahmad B.; Yiu-Kwong Wong

    2012-01-01

    This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-senso...

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

    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.

  5. An Efficient and Self-Adapting Localization in Static Wireless Sensor Networks

    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.

  6. A smartphone localization algorithm using RSSI and inertial sensor measurement fusion

    Li, William Wei-Liang; Iltis, Ronald A.; Win, Moe Z

    2013-01-01

    Indoor navigation using the existing wireless infrastructure and mobile devices is a very active research area. The major challenge is to leverage the extensive smartphone sensor suite to achieve location tracking with high accuracy. In this paper, we develop a navigation algorithm which fuses the WiFi received signal strength indicator (RSSI) and smartphone inertial sensor measurements. A sequential Monte Carlo filter is developed for inertial sensor based tracking, and a radiolocation algor...

  7. SIGNED GRAPH APPROACH IN ADAPTIVE TRANSMISSION POWER TO ENHANCE THE LIFETIME OF WIRELESS SENSOR NETWORKS

    A. Babu Karuppiah

    2014-01-01

    Full Text Available A Wireless Sensor Network (WSN comprises a collection of sensor nodes networked for applications like surveillance, battlefield, monitoring of habitat. Nodes in a WSN are usually highly energy-constrained and expected to operate for long periods from limited on-board energy reserves. When a node transmits data to a destination node the data is overheard by the nodes that are in the coverage range of the transmitting node or the forwarding node. Due to this, the individual nodes might waste their energy in sensing data that are not destined to it and as a result the drain in the energy of the node is more resulting in much reduced network life time. As power is a limiting factor in a WSN, the major challenge in deploying a WSN is to enhance the network life time. So, it becomes inevitable to devise an efficient method of conserving the power. In this study, a novel algorithm, Signed Graph based Adaptive Transmission Power (SGATP is developed to avoid redundancy in sensing the data thereby enhancing the life time of the network. The concept of adapting the transmission power based on the distance of the next neighbor is proposed while a node communicates with the Cluster Head during Intrusion Detection. The simulation results show that the average network life time is greatly improvised by 96.8% when the proposed method is adopted.

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

    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.

  9. Application of fibre Bragg grating sensors for structural health monitoring of an adaptive wing

    This paper presents the concept of application of fibre Bragg grating (FBG) sensors for structural health monitoring (SHM) of an adaptive wing. In this concept, the shape of the wing is controlled and altered due to the wing design and the use of integrated shape memory alloy (SMA) actuators. FBG sensors are great tools for controlling 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 skin without any significant influence on the wing strength. In the first part of the paper a determination of the twisting moments produced by activation of the SMA actuators is presented. As a first step, a numerical analysis using a finite element method (FEM) commercial code ABAQUS® is presented. Then a comparison between strain values measured by FBG sensors and determined numerically is used for determination of the real value of the activation moment of every SMA actuator. Two types of damage scenarios are analysed and discussed in the paper. The first scenario is reduction of the twisting moment values produced by one of the SMA actuators. The second scenario is outer skin damage. In both damage scenarios, a neural network is used for damage detection and localization

  10. An Adaptive Time-Spread Multiple-Access Policy for Wireless Sensor Networks

    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.

  11. An Adaptive Sampling System for Sensor Nodes in Body Area Networks.

    Rieger, R; Taylor, J

    2014-04-23

    The importance of body sensor networks to monitor patients over a prolonged period of time has increased with an advance in home healthcare applications. Sensor nodes need to operate with very low-power consumption and under the constraint of limited memory capacity. Therefore, it is wasteful to digitize the sensor signal at a constant sample rate, given that the frequency contents of the signals vary with time. Adaptive sampling is established as a practical method to reduce the sample data volume. In this paper a low-power analog system is proposed, which adjusts the converter clock rate to perform a peak-picking algorithm on the second derivative of the input signal. The presented implementation does not require an analog-to-digital converter or a digital processor in the sample selection process. The criteria for selecting a suitable detection threshold are discussed, so that the maximum sampling error can be limited. A circuit level implementation is presented. Measured results exhibit a significant reduction in the average sample frequency and data rate of over 50% and 38% respectively. PMID:24760918

  12. Distributed Service-Based Approach for Sensor Data Fusion in IoT Environments

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

    2014-01-01

    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. PMID:25320907

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

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

    2014-01-01

    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. PMID:25320907

  14. Distributed Service-Based Approach for Sensor Data Fusion in IoT Environments

    Sandra Rodríguez-Valenzuela

    2014-10-01

    Full Text Available 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.

  15. Image fusion

    Pavel, M.

    1993-01-01

    The topics covered include the following: a system overview of the basic components of a system designed to improve the ability of a pilot to fly through low-visibility conditions such as fog; the role of visual sciences; fusion issues; sensor characterization; sources of information; image processing; and image fusion.

  16. Adaptive Sensor Optimization and Cognitive Image Processing Using Autonomous Optical Neuroprocessors; TOPICAL

    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. A Self-Adaptive Dynamic Recognition Model for Fatigue Driving Based on Multi-Source Information and Two Levels of Fusion

    Wei Sun; Xiaorui Zhang; Srinivas Peeta; Xiaozheng He; Yongfu Li; Senlai Zhu

    2015-01-01

    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 ...

  18. Distributed Service-Based Approach for Sensor Data Fusion in IoT Environments

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

    2014-01-01

    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 mu...

  19. Improved GSO optimized ESN soft-sensor model of flotation process based on multisource heterogeneous information fusion.

    Wang, Jie-sheng; Han, Shuang; Shen, Na-na

    2014-01-01

    For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:24982935

  20. Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion

    Jie-sheng Wang

    2014-01-01

    Full Text Available For predicting the key technology indicators (concentrate grade and tailings recovery rate of flotation process, an echo state network (ESN based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO algorithm is proposed. Firstly, the color feature (saturation and brightness and texture features (angular second moment, sum entropy, inertia moment, etc. based on grey-level co-occurrence matrix (GLCM are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process.

  1. Multiple objective optimization for active sensor management

    Page, Scott F.; Dolia, Alexander N.; Harris, Chris J.; White, Neil M.

    2005-03-01

    The performance of a multi-sensor data fusion system is inherently constrained by the configuration of the given sensor suite. Intelligent or adaptive control of sensor resources has been shown to offer improved fusion performance in many applications. Common approaches to sensor management select sensor observation tasks that are optimal in terms of a measure of information. However, optimising for information alone is inherently sub-optimal as it does not take account of any other system requirements such as stealth or sensor power conservation. We discuss the issues relating to developing a suite of performance metrics for optimising multi-sensor systems and propose some candidate metrics. In addition it may not always be necessary to maximize information gain, in some cases small increases in information gain may take place at the cost of large sensor resource requirements. Additionally, the problems of sensor tasking and placement are usually treated separately, leading to a lack of coherency between sensor management frameworks. We propose a novel approach based on a high level decentralized information-theoretic sensor management architecture that unifies the processes of sensor tasking and sensor placement into a single framework. Sensors are controlled using a minimax multiple objective optimisation approach in order to address probability of target detection, sensor power consumption, and sensor survivability whilst maintaining a target estimation covariance threshold. We demonstrate the potential of the approach through simulation of a multi-sensor, target tracking scenario and compare the results with a single objective information based approach.

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

    Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng, E-mail: qschen@ujs.edu.cn

    2014-09-02

    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.

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

    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

  4. Biologically-inspired robust and adaptive multi-sensor fusion and active control

    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.

  5. Preliminary tests of a possible outdoor light adaptation solution for a fly inspired visual sensor: a biomimetic solution - biomed 2011.

    Dean, Brian K; Wright, Cameron H G; Barrett, Steven F

    2011-01-01

    Two previous papers, presented at RMBS in 2009 and 2010, introduced a fly inspired vision sensor that could adapt to indoor light conditions by mimicking the light adaptation process of the commonhousefly, Muscadomestica. A new system has been designed that should allow the sensor to adapt to outdoor light conditions which will enable the sensor’s use inapplications such as: unmanned aerial vehicle (UAV) obstacle avoidance, UAV landing support, target tracking, wheelchair guidance, large structure monitoring, and many other outdoor applications. A sensor of this type is especially suited for these applications due to features of hyperacuity (or an ability to achieve movement resolution beyond the theoretical limit), extreme sensitivity to motion, and (through software simulation) image edge extraction, motion detection, and orientation and location of a line.Many of these qualities are beyond the ability of traditional computervision sensors such as charge coupled device (CCD) arrays.To achieve outdoor light adaptation, a variety of design obstacles have to be overcome such as infrared interference, dynamic range expansion, and light saturation. The newly designed system overcomes the latter two design obstacles by mimicking the fly’s solution of logarithmic compression followed by removal of the average background light intensity. This paper presents the new design and the preliminary tests that were conducted to determine its effectiveness. PMID:21525612

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

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

    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...

  7. Accurate fusion of robot, camera and wireless sensors for surveillance applications

    Gilbert, A.; Illingworth, J; Bowden, R.; Capitan, J; Merino, L.

    2009-01-01

    Often within the field of tracking people within only fixed cameras are used. This can mean that when the the illumination of the image changes or object occlusion occurs, the tracking can fail. We propose an approach that uses three simultaneous separate sensors. The fixed surveillance cameras track objects of interest cross camera through incrementally learning relationships between regions on the image. Cameras and laser rangefinder sensors onboard robots also provide an estimate of the pe...

  8. Multi-Sensor Fusion of Infrared and Electro-Optic Signals for High Resolution Night Images

    Victor Lawrence; Xiaopeng Huang; Hong Man; Ravi Netravali

    2012-01-01

    Electro-optic (EO) image sensors exhibit the properties of high resolution and low noise level at daytime, but they do not work in dark environments. Infrared (IR) image sensors exhibit poor resolution and cannot separate objects with similar temperature. Therefore, we propose a novel framework of IR image enhancement based on the information (e.g., edge) from EO images, which improves the resolution of IR images and helps us distinguish objects at night. Our framework superimposing/blending ...

  9. Camera-based platform and sensor motion tracking for data fusion in a landmine detection system

    van der Mark, Wannes; van den Heuvel, Johan C.; den Breejen, Eric; Groen, Frans C. A.

    2003-09-01

    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 combining the observations from the different sensors on the moving platform. Currently a method based on odometry is used to merge the readings from the sensors. In this paper a vision based approach is presented which can estimate the relative sensor pose and position together with the vehicle motion. To estimate the relative position and orientation of sensors, techniques from camera calibration are used. The platform motion is estimated from tracked features on the ground. A new approach is presented which can reduce the influence of tracking errors or other outliers on the accuracy of the ego-motion estimate. Overall, the new vision based approach for sensor localization leads to better estimates then the current odometry based method.

  10. Adapting computational optimization concepts from aeronautics to nuclear fusion reactor design

    Baelmans M.; Reiter D.; Dekeyser W.

    2012-01-01

    Even on the most powerful supercomputers available today, computational nuclear fusion reactor divertor design is extremely CPU demanding, not least due to the large number of design variables and the hybrid micro-macro character of the flows. Therefore, automated design methods based on optimization can greatly assist current reactor design studies. Over the past decades, “adjoint methods” for shape optimization have proven their virtue in the field of aerodynamics. Applications include drag...

  11. 无线传感器网络数据融合研究综述%Wireless Sensor Network Data Fusion Research Summary

    魏秀蓉

    2015-01-01

    In this paper, the development of data fusion in recent years is summarized, and the network structure and algorithm of wireless sensor network data fusion module is studied, and the advantages and disadvantages are summarized. Finally, based on the research of data fusion in wireless sensor networks, the existing problems in the ifeld are pointed out in this paper.%针对近些年来数据融合的发展状况进行了综述,对现在的无线传感器网络数据融合模块的网络结构及算法进行分类研究,概述其思想并指出优缺点。文章在对无线传感器网络数据融合研究的基础上,指出该领域内存在的问题。

  12. Experience with wavefront sensor and deformable mirror interfaces for wide-field adaptive optics systems

    Basden, A. G.; Atkinson, D.; Bharmal, N. A.; Bitenc, U.; Brangier, M.; Buey, T.; Butterley, T.; Cano, D.; Chemla, F.; Clark, P.; Cohen, M.; Conan, J.-M.; de Cos, F. J.; Dickson, C.; Dipper, N. A.; Dunlop, C. N.; Feautrier, P.; Fusco, T.; Gach, J. L.; Gendron, E.; Geng, D.; Goodsell, S. J.; Gratadour, D.; Greenaway, A. H.; Guesalaga, A.; Guzman, C. D.; Henry, D.; Holck, D.; Hubert, Z.; Huet, J. M.; Kellerer, A.; Kulcsar, C.; Laporte, P.; Le Roux, B.; Looker, N.; Longmore, A. J.; Marteaud, M.; Martin, O.; Meimon, S.; Morel, C.; Morris, T. J.; Myers, R. M.; Osborn, J.; Perret, D.; Petit, C.; Raynaud, H.; Reeves, A. P.; Rousset, G.; Sanchez Lasheras, F.; Sanchez Rodriguez, M.; Santos, J. D.; Sevin, A.; Sivo, G.; Stadler, E.; Stobie, B.; Talbot, G.; Todd, S.; Vidal, F.; Younger, E. J.

    2016-06-01

    Recent advances in adaptive optics (AO) have led to the implementation of wide field-of-view AO systems. A number of wide-field AO systems are also planned for the forthcoming Extremely Large Telescopes. Such systems have multiple wavefront sensors of different types, and usually multiple deformable mirrors (DMs). Here, we report on our experience integrating cameras and DMs with the real-time control systems of two wide-field AO systems. These are CANARY, which has been operating on-sky since 2010, and DRAGON, which is a laboratory AO real-time demonstrator instrument. We detail the issues and difficulties that arose, along with the solutions we developed. We also provide recommendations for consideration when developing future wide-field AO systems.

  13. Multimode delta-E effect magnetic field sensors with adapted electrodes

    Zabel, Sebastian; Reermann, Jens; Fichtner, Simon; Kirchhof, Christine; Quandt, Eckhard; Wagner, Bernhard; Schmidt, Gerhard; Faupel, Franz

    2016-05-01

    We present an analytical and experimental study on low-noise piezoelectric thin film resonators that utilize the delta-E effect of a magnetostrictive layer to measure magnetic fields at low frequencies. Calculations from a physical model of the electromechanical resonator enable electrode designs to efficiently operate in the first and second transversal bending modes. As predicted by our calculations, the adapted electrode design improves the sensitivity by a factor of 6 and reduces the dynamic range of the sensor output by 16 dB, which significantly eases the requirements on readout electronics. Magnetic measurements show a bandwidth of 100 Hz at a noise level of about 100 pTHz-0.5.

  14. Load-adaptive practical multi-channel communications in wireless sensor networks.

    Islam, Md Shariful; Alam, Muhammad Mahbub; Hong, Choong Seon; Lee, Sungwon

    2010-01-01

    In recent years, a significant number of sensor node prototypes have been designed that provide communications in multiple channels. This multi-channel feature can be effectively exploited to increase the overall capacity and performance of wireless sensor networks (WSNs). In this paper, we present a multi-channel communications system for WSNs that is referred to as load-adaptive practical multi-channel communications (LPMC). LPMC estimates the active load of a channel at the sink since it has a more comprehensive view of the network behavior, and dynamically adds or removes channels based on the estimated load. LPMC updates the routing path to balance the loads of the channels. The nodes in a path use the same channel; therefore, they do not need to switch channels to receive or forward packets. LPMC has been evaluated through extensive simulations, and the results demonstrate that it can effectively increase the delivery ratio, network throughput, and channel utilization, and that it can decrease the end-to-end delay and energy consumption. PMID:22163685

  15. Load-Adaptive Practical Multi-Channel Communications in Wireless Sensor Networks

    Choong Seon Hong

    2010-09-01

    Full Text Available In recent years, a significant number of sensor node prototypes have been designed that provide communications in multiple channels. This multi-channel feature can be effectively exploited to increase the overall capacity and performance of wireless sensor networks (WSNs. In this paper, we present a multi-channel communications system for WSNs that is referred to as load-adaptive practical multi-channel communications (LPMC. LPMC estimates the active load of a channel at the sink since it has a more comprehensive view of the network behavior, and dynamically adds or removes channels based on the estimated load. LPMC updates the routing path to balance the loads of the channels. The nodes in a path use the same channel; therefore, they do not need to switch channels to receive or forward packets. LPMC has been evaluated through extensive simulations, and the results demonstrate that it can effectively increase the delivery ratio, network throughput, and channel utilization, and that it can decrease the end-to-end delay and energy consumption.

  16. Adaptive Multi-sensor Perception for Driving Automation in Outdoor Contexts

    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.

  17. RheoStim: Development of an Adaptive Multi-Sensor to Prevent Venous Stasis.

    Weyer, Sören; Weishaupt, Fabio; Kleeberg, Christian; Leonhardt, Steffen; Teichmann, Daniel

    2016-01-01

    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. PMID:27023544

  18. RheoStim: Development of an Adaptive Multi-Sensor to Prevent Venous Stasis

    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.

  19. Design and analysis of self-adapted task scheduling strategies in wireless sensor networks.

    Guo, Wenzhong; Xiong, Naixue; Chao, Han-Chieh; Hussain, Sajid; Chen, Guolong

    2011-01-01

    In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm's ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms. PMID:22163971

  20. Self Adaptive Trust Model for Secure Geographic Routing in Wireless Sensor Networks

    P. Raghu Vamsi

    2015-02-01

    Full Text Available The presence of malicious nodes in the ad hoc and sensor networks poses serious security attacks during routing which affects the network performance. To address such attacks, numerous researchers have proposed defense techniques using a human behavior pattern called trust. Among existing solutions, direct observations based trust models have gained significant attention in the research community. In this paper, the authors propose a Self Adaptive Trust Model (SATM of secure geographic routing in wireless sensor networks (WSNs. Unlike conventional weight based trust models, SATM intelligently assigns the weights associated with the network activities. These weights are applied to compute the final trust value. SATM considers direct observations to restrict the reputation based attacks. Due to the flexible and intelligent weight computation, SATM dynamically detects the malicious nodes and direct the traffic towards trustworthy nodes. SATM has been incorporated into Greedy Perimeter Stateless Routing (GPSR protocol. Simulation results using the network simulator NS-2 have shown that GPSR with SATM is robust against detecting malicious nodes.

  1. Cost analysis of hybrid adaptive routing protocol for heterogeneous wireless sensor network

    NONITA SHARMA; AJAY K SHARMA

    2016-03-01

    This study aims to explore the impact of heterogeneity on a hybrid algorithm called Multi Adaptive Filter Algorithm by constructing series of experiments. Here, the simulations were made between ‘Total Energy Spent’ and ‘Number of Sources’ considering temporal correlation. The results were drawn from the trace information generated using ‘Monte Carlo’ simulation methods. After keen analysis, the results show that different levels of heterogeneity are best suited for correlated event detections. Moreover, based on the conclusions drawn,it can be safely inferred that n-level heterogeneity reduces the total energy spent close to 60%. Further, cost analysis recommends that adding progressive nodes preserves the cost factor in the bracket of 230–280$/Joule. Thenovel approach can immensely help the future solution providers to overcome the battery limitations of wireless sensor networks. This study provides insights into designing heterogeneous wireless sensor networks and aims atproviding the cost-benefit analysis that can be used in selecting the critical parameters of the network.

  2. Design and Analysis of Self-Adapted Task Scheduling Strategies in Wireless Sensor Networks

    Sajid Hussain

    2011-06-01

    Full Text Available In a wireless sensor network (WSN, the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and  scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO algorithm for the dynamic alliance (DPSO-DA with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm’s ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms.

  3. Adaptive Data Gathering in Mobile Sensor Networks Using Speedy Mobile Elements

    Yongxuan Lai

    2015-09-01

    Full Text Available 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.

  4. An efficient and adaptive mutual authentication framework for heterogeneous wireless sensor network-based applications.

    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

  5. Observability considerations for multi-sensor and product fusion: Bias, information content, and validation (Invited)

    Reid, J. S.; Zhang, J.; Hyer, E. J.; Campbell, J. R.; Christopher, S. A.; Ferrare, R. A.; Leptoukh, G. G.; Stackhouse, P. W.

    2009-12-01

    With the successful development of many aerosol products from the NASA A-train as well as new operational geostationary and polar orbiting sensors, the scientific community now has a host of new parameters to use in their analyses. The variety and quality of products has reached a point where the community has moved from basic observation-based science to sophisticated multi-component research that addresses the complex atmospheric environment. In order for these satellite data contribute to the science their uncertainty levels must move from semi-quantitative to quantitative. Initial attempts to quantify uncertainties have led to some recent debate in the community as to the efficacy of aerosol products from current and future NASA satellite sensors. In an effort to understand the state of satellite product fidelity, the Naval Research Laboratory and a newly reformed Global Energy and Water Cycle Experiment (GEWEX) aerosol panel have both initiated assessments of the nature of aerosol remote sensing uncertainty and bias. In this talk we go over areas of specific concern based on the authors’ experiences with the data, emphasizing the multi-sensor problem. We first enumerate potential biases, including retrieval, sampling/contextual, and cognitive bias. We show examples of how these biases can subsequently lead to the pitfalls of correlated/compensating errors, tautology, and confounding. The nature of bias is closely related to the information content of the sensor signal and its subsequent application to the derived aerosol quantity of interest (e.g., optical depth, flux, index of refraction, etc.). Consequently, purpose-specific validation methods must be employed, especially when generating multi-sensor products. Indeed, cloud and lower boundary condition biases in particular complicate the more typical methods of regressional bias elimination and histogram matching. We close with a discussion of sequestration of uncertainty in multi-sensor applications of

  6. A Stable Switch Method Based on Fusion in Uncalibrated Visual Servoing

    2007-01-01

    Stable switch control between multiple cameras for uncalibrated visual servoing was studied. Switch images based on fusion were presented to get the continuous dynamic image Jacobian matrix among robots and distributed visual sensors. The designed fusion algorithm is suitable to have dynamically adjustable fusion weights,and the fusion structure was analyzed. Simulations and experiments without any knowledge of mobile robots and uncalibrated visual sensors show that the method has higher adaptability than the traditional instant switch control method. The method can enhance the system stability at the switching process.

  7. Multivariable Regression and Adaptive Neurofuzzy Inference System Predictions of Ash Fusion Temperatures Using Ash Chemical Composition of US Coals

    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.

  8. Design and Implementation of a Smart LED Lighting System Using a Self Adaptive Weighted Data Fusion Algorithm

    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.

  9. Adaptive Fusion of Information for Seeing into Ordos Basin, China: A China-Germany-US Joint Venture.

    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

  10. An adaptive optics system for solid-state laser systems used in inertial confinement fusion

    Using adaptive optics the authors have obtained nearly diffraction-limited 5 kJ, 3 nsec output pulses at 1.053 microm from the Beamlet demonstration system for the National Ignition Facility (NIF). The peak Strehl ratio was improved from 0.009 to 0.50, as estimated from measured wavefront errors. They have also measured the relaxation of the thermally induced aberrations in the main beam line over a period of 4.5 hours. Peak-to-valley aberrations range from 6.8 waves at 1.053 microm within 30 minutes after a full system shot to 3.9 waves after 4.5 hours. The adaptive optics system must have enough range to correct accumulated thermal aberrations from several shots in addition to the immediate shot-induced error. Accumulated wavefront errors in the beam line will affect both the design of the adaptive optics system for NIF and the performance of that system

  11. An Adaptive Strategy for an Optimized Collision-Free Slot Assignment in Multichannel Wireless Sensor Networks

    Pascale Minet

    2013-07-01

    Full Text Available Convergecast is the transmission paradigm used by data gathering applications in wireless sensor networks (WSNs. For efficiency reasons, a collision-free slotted medium access is typically used: time slots are assigned to non-conflicting transmitters. Furthermore, in any slot, only the transmitters and the corresponding receivers are awake, the other nodes sleeping in order to save energy. Since a multichannel network increases the throughput available to the application and reduces interference, multichannel slot assignment is an emerging research domain in WSNs. First, we focus on a multichannel time slot assignment that minimizes the data gathering delays. We compute the optimal time needed for a raw data convergecast in various multichannel topologies. Then, we focus on how to adapt such an assignment to dynamic demands of transmissions (e.g., alarms, temporary additional application needs and retransmissions. We formalize the problem using linear programming, and we propose an incremental technique that operates on an optimized primary schedule to provide bonus slots to meet new transmission needs. We propose AMSA, an Adaptive Multichannel Slot Assignment algorithm, which takes advantage of bandwidth spatial reuse, and we evaluate its performances in terms of the number of slots required, slot reuse, throughput and the number of radio state switches.

  12. ADAPTIVITY OF A COLORING ALGORITHM TO UNRELIABLE COMMUNICATIONS FOR DATA GATHERING IN WIRELESS SENSOR NETWORKS

    Ichrak Amdouni

    2013-01-01

    Full Text Available Wireless sensor networks (WSNs are prone to node/link failures, message losses, and dynamic node joins and departures. For instance, in data gathering applications that constitute a common type of applications in WSNs, links between nodes and their parent in the data gathering tree may be broken. Protocols supporting such applications should adapt their behaviour to guarantee reliable wireless communications while keeping a low overhead. In particular, this paper focuses on the optimization of a known coloring algorithm called SERENA (‘SchEdule RoutEr Node Activity’. SERENA assigns colors to nodes such that no two interfering nodes share the same color. Each color is mapped to a time slot during which nodes having the associated color can transmit data. To ensure collision free communications, SERENA should be aware about the set of interfering nodes. However, in case of topology changes, this set may vary. Consequently, SERENA should adapt to this. Our solutions proactively select one or more parent backups and guarantee that the coloring remains valid if a parent is replaced by its backup. Simulation results show that reliability is obtained at the price of a small increase in the number of colors used to color the network.

  13. An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks.

    Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Zhang, Xuekun

    2015-01-01

    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. PMID:26633421

  14. An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks

    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.

  15. Laboratory testing the layer oriented wavefront sensor for the multiconjugate adaptive optics demonstrator

    Arcidiacono, Carmelo; Lombini, Matteo; Diolaiti, Emiliano; Farinato, Jacopo; Ragazzoni, Roberto

    2006-06-01

    The Multiconjugate Adaptive optics Demonstrator (MAD) for ESO-Very Large Telescopes (VLT) will demonstrate on sky the MultiConjugate Adaptive Optics (MCAO) technique. In this paper the laboratory tests relative to the first preliminary acceptance in Europe of the Layer Oriented (LO) Wavefront Sensor (WFS) for MAD will be described: the capabilities of the LO approach have been checked and the ability of the WFS to measure phase screens positioned at different altitudes has been experimented. The LO WFS was opto-mechanically integrated and aligned in INAF - Astrophysical Observatory of Arcetri before the delivering to ESO (Garching) to be installed on the final optical bench. The LO WFS looks for up to 8 reference stars on a 2arcmin Field of View and up to 8 pyramids can be positioned where the focal spot images of the reference stars form, splitting the light in four beams. Then two objectives conjugated at different altitudes simultaneously produce a quadruple pupil image of each reference star. An optical bench setup and transparent plastic screens have been used to simulate telescope and static atmospheric layers at different altitudes and a set of optical fibers as (white) light source. The plastic screens set has been characterized using an inteferometer and the wave-front measurements compared to the LO WFS ones have shown correlation up to ~95%.

  16. Noise modeling and analysis of an IMU-based attitude sensor: improvement of performance by filtering and sensor fusion

    Nirmal, K; Mathew, Joice; Sarpotdar, Mayuresh; Suresh, Ambily; Prakash, Ajin; Safonova, Margarita; Murthy, Jayant

    2016-01-01

    We describe the characterization and removal of noise present in the Inertial Measurement Unit (IMU) MPU-6050. This IMU was initially used in an attitude sensor (AS) developed in-house, and subsequently implemented in a pointing and stabilization platform developed for small balloon-borne astronomical payloads. We found that the performance of the IMU degrades with time due to the accumulation of different errors. Using the Allan variance analysis method, we identified the different components of noise present in the IMU and verified the results using a power spectral density analysis (PSD). We tried to remove the high-frequency noise using smoothing filters, such as moving average filter and Savitzky-Golay filter. Although we did manage to filter some of the high-frequency noise, the performance of these filters was not satisfactory for our application. We found the distribution of the random noise present in the IMU using a probability density analysis, and identified the noise to be white Gaussian in natur...

  17. A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation.

    Vargas-Meléndez, Leandro; Boada, Beatriz L; Boada, María Jesús L; Gauchía, Antonio; Díaz, Vicente

    2016-01-01

    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. PMID:27589763

  18. Relative pose estimation of satellites using PMD-/CCD-sensor data fusion

    Tzschichholz, Tristan; Boge, Toralf; Schilling, Klaus

    2015-04-01

    Rendezvous & Docking to passive objects, as of relevance for space debris removal, raises new challenges with respect to relative navigation. Whenever the position and orientation (pose) of an object is required in terrestrial and in space applications, sensor systems such as laser scanners and stereo vision systems are often employed. This paper presents an approach to pose estimation using a 3D time-of-flight camera for ranging information in combination with a high resolution grayscale camera. We have designed a pose estimation method that fuses the data streams of the two sensors in order to benefit from each sensors' advantages. A rigorous test campaign on a Real-Time Hardware-In-The-Loop Rendezvous and Docking Simulator - the European Proximity Operations Simulator (EPOS) - was performed in order to evaluate the performance of the resulting algorithm. The proposed pose estimation method does not exceed an average distance error of 3 cm while being capable of providing pose estimates at up to 60 FPS on recent hardware. Thus, when regarding proximity operations, an attractive sensor system is used to characterize the dynamics of the target object for safe approach results.

  19. Camera-based platform and sensor motion tracking for data fusion in a landmine detection system

    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 combi

  20. Fusion of Redundant Aided-inertial Sensors with Decentralised Kalman Filter for Autonomous Underwater Vehicle Navigation

    Vaibhav Awale

    2015-11-01

    Full Text Available Most submarines carry more than one set of inertial navigation system (INS for redundancy and reliability. Apart from INS systems, the submarine carries other sensors that provide different navigation information. A major challenge is to combine these sensors and INS estimates in an optimal and robust manner for navigation. This issue has been addressed by Farrell1. The same approach is used in this paper to combine different sensor measurements along with INS system. However, since more than one INS system is available onboard, it would be better to use multiple INS systems at the same time to obtain a better estimate of states and to provide autonomy in the event of failure of one INS system. This would require us to combine the estimates obtained from local filters (one set of INS system integrated with external sensors, in some optimal way to provide a global estimate. Individual sensor and IMU measurements cannot be accessed in this scenario. Also, autonomous operation requires no sharing of information among local filters. Hence a decentralised Kalman filter approach is considered for combining the estimates of local filters to give a global estimate. This estimate would not be optimal, however. A better optimal estimate can be obtained by accessing individual measurements and augmenting the state vector in Kalman filter, but in that case, corruption of one INS system will lead to failure of the whole filter. Hence to ensure satisfactory performance of the filter even in the event of failure of some INS system, a decentralised Kalman filtering approach is considered.

  1. Image-based ATR utilizing adaptive clutter filter detection, LLRT classification, and Volterra fusion with application to side-looking sonar

    Aridgides, Tom; Fernández, Manuel

    2010-04-01

    An improved automatic target recognition (ATR) processing string has been developed. The overall processing string consists of pre-processing, subimage adaptive clutter filtering, detection, feature extraction, optimal subset feature selection, feature orthogonalization and classification processing blocks. The objects that are classified by three distinct ATR strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. These three ATR processing strings were individually developed and tuned by researchers from different companies. The utility of the overall processing strings and their fusion was demonstrated with an extensive side-looking sonar dataset. In this paper we describe a new processing improvement: six additional classification features are extracted, using primarily target shadow information and a feature extraction window whose length is now made variable as a function of range. This new ATR processing improvement resulted in a 3:1 reduction in false alarms. Two advanced fusion algorithms are subsequently applied: First, a nonlinear Volterra expansion (2nd order) feature-LLRT fusion algorithm is employed. Second, a repeated application of a subset Volterra feature selection / feature orthogonalization / LLRT fusion block is utilized. It is shown that cascaded Volterra feature- LLRT fusion of the ATR processing strings outperforms baseline "summing" and single-stage Volterra feature-LLRT fusion algorithms, yielding significant improvements over the best single ATR processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate.

  2. Adaptation of Decoy Fusion Strategy for Existing Multi-Stage Search Workflows

    Ivanov, Mark V.; Levitsky, Lev I.; Gorshkov, Mikhail V.

    2016-06-01

    A number of proteomic database search engines implement multi-stage strategies aiming at increasing the sensitivity of proteome analysis. These approaches often employ a subset of the original database for the secondary stage of analysis. However, if target-decoy approach (TDA) is used for false discovery rate (FDR) estimation, the multi-stage strategies may violate the underlying assumption of TDA that false matches are distributed uniformly across the target and decoy databases. This violation occurs if the numbers of target and decoy proteins selected for the second search are not equal. Here, we propose a method of decoy database generation based on the previously reported decoy fusion strategy. This method allows unbiased TDA-based FDR estimation in multi-stage searches and can be easily integrated into existing workflows utilizing popular search engines and post-search algorithms.

  3. Pose Estimation of Unmanned Aerial Vehicles Based on a Vision-Aided Multi-Sensor Fusion

    Abdi, G.; Samadzadegan, F.; Kurz, F.

    2016-06-01

    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. Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application

    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.

  5. Edge preserved enhancement of medical images using adaptive fusion-based denoising by shearlet transform and total variation algorithm

    Gupta, Deep; Anand, Radhey Shyam; Tyagi, Barjeev

    2013-10-01

    Edge preserved enhancement is of great interest in medical images. Noise present in medical images affects the quality, contrast resolution, and most importantly, texture information and can make post-processing difficult also. An enhancement approach using an adaptive fusion algorithm is proposed which utilizes the features of shearlet transform (ST) and total variation (TV) approach. In the proposed method, three different denoised images processed with TV method, shearlet denoising, and edge information recovered from the remnant of the TV method and processed with the ST are fused adaptively. The result of enhanced images processed with the proposed method helps to improve the visibility and detectability of medical images. For the proposed method, different weights are evaluated from the different variance maps of individual denoised image and the edge extracted information from the remnant of the TV approach. The performance of the proposed method is evaluated by conducting various experiments on both the standard images and different medical images such as computed tomography, magnetic resonance, and ultrasound. Experiments show that the proposed method provides an improvement not only in noise reduction but also in the preservation of more edges and image details as compared to the others.

  6. An ACOA-AFSA Fusion Routing Algorithm for Underwater Wireless Sensor Network

    Ming Xu; Yingjie Xiao; Chaojian Shi; Xinqiang Chen; Huafeng Wu

    2012-01-01

    Due to intrinsic properties of aqueous environments, routing protocols for underwater wireless sensor network (UWSN) have to cope with many challenges such as long propagation delay, bad robustness, and high energy consumption. Basic ant colony optimization algorithm (ACOA) is an intelligent heuristic algorithm which has good robustness, distributed computing and combines with other algorithms easily. But its disadvantage is that it may converge at local solution, not global solution. Artific...

  7. Sensor messaging protocol handler and adapter interfaces implementation in microcontroller development case

    Niemi, J

    2016-01-01

    Nowadays technologies such as the wireless connectivity, embedded devices and MEMS provide a possibility to build tiny compact devices which have sensing, computing and networking capabilities. The sensors connected to the embedded devices give new possibilities and environments for collecting sensor data and passing it to the networks or other devices. Wireless sensor node technology, as the name suggest, is purposely designed to collect sensor data from the sensor network cluster(s) or a si...

  8. 3D-information fusion from very high resolution satellite sensors

    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. Adapting computational optimization concepts from aeronautics to nuclear fusion reactor design

    Baelmans M.

    2012-10-01

    Full Text Available Even on the most powerful supercomputers available today, computational nuclear fusion reactor divertor design is extremely CPU demanding, not least due to the large number of design variables and the hybrid micro-macro character of the flows. Therefore, automated design methods based on optimization can greatly assist current reactor design studies. Over the past decades, “adjoint methods” for shape optimization have proven their virtue in the field of aerodynamics. Applications include drag reduction for wing and wing-body configurations. Here we demonstrate that also for divertor design, these optimization methods have a large potential. Specifically, we apply the continuous adjoint method to the optimization of the divertor geometry in a 2D poloidal cross section of an axisymmetric tokamak device (as, e.g., JET and ITER, using a simplified model for the plasma edge. The design objective is to spread the target material heat load as much as possible by controlling the shape of the divertor, while maintaining the full helium ash removal capabilities of the vacuum pumping system.

  10. Multi-Sensor Fusion of Infrared and Electro-Optic Signals for High Resolution Night Images

    Victor Lawrence

    2012-07-01

    Full Text Available Electro-optic (EO image sensors exhibit the properties of high resolution and low noise level at daytime, but they do not work in dark environments. Infrared (IR image sensors exhibit poor resolution and cannot separate objects with similar temperature. Therefore, we propose a novel framework of IR image enhancement based on the information (e.g., edge from EO images, which improves the resolution of IR images and helps us distinguish objects at night. Our framework superimposing/blending the edges of the EO image onto the corresponding transformed IR image improves their resolution. In this framework, we adopt the theoretical point spread function (PSF proposed by Hardie et al. for the IR image, which has the modulation transfer function (MTF of a uniform detector array and the incoherent optical transfer function (OTF of diffraction-limited optics. In addition, we design an inverse filter for the proposed PSF and use it for the IR image transformation. The framework requires four main steps: (1 inverse filter-based IR image transformation; (2 EO image edge detection; (3 registration; and (4 blending/superimposing of the obtained image pair. Simulation results show both blended and superimposed IR images, and demonstrate that blended IR images have better quality over the superimposed images. Additionally, based on the same steps, simulation result shows a blended IR image of better quality when only the original IR image is available.

  11. Sensor Fusion and Autonomy as a Powerful Combination for Biological Assessment in the Marine Environment

    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.

  12. Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices

    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.

  13. Measuring indoor occupancy in intelligent buildings using the fusion of vision sensors

    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)

  14. Wireless displacement sensing system for bridges using multi-sensor fusion

    Accurate displacement sensing or estimation is an important task for reliably assessing the condition of civil infrastructure such as bridges and buildings, because the structural displacement describes the behavior of a structure and indicates structural safety according to the design limit. However, it is difficult to directly measure the displacement of a bridge structure due to the inaccessibility of a reference point especially when bridges are built over a highway, a river or the sea. As an alternative, an indirect displacement estimation using two different types of measurements such as strain and acceleration (i.e., multimetric data) has been developed. While the approach has been seen as promising, the combination of the traditional sensing system based on wired sensors and the multimetric data-based algorithm is inappropriate or impractical in real-world applications of the approach. This paper proposes a new displacement sensing system by incorporating wireless sensor technology with the multimetric data-based algorithm, which can address the difficulties and issues found in the traditional sensing system to realize a practical means of measuring displacement in full-scale bridges. The proposed wireless displacement sensing system enables (a) time-synchronized acceleration and strain measurement, (b) high-precision strain sensing and (c) improved applicability due to the wireless communication as well as the previous two features. The effectiveness of the proposed system is experimentally verified in laboratory and full-scale experiments. (paper)

  15. 无线传感网络中的目标分类融合%Classification Fusion in Wireless Sensor Networks

    刘春婷; 霍宏; 方涛; 李德仁; 沈晓

    2006-01-01

    In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.

  16. A novel adaptive, real-time algorithm to detect gait events from wearable sensors.

    Chia Bejarano, Noelia; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Monticone, Marco; Ferrante, Simona

    2015-05-01

    A real-time, adaptive algorithm based on two inertial and magnetic sensors placed on the shanks was developed for gait-event detection. For each leg, the algorithm detected the Initial Contact (IC), as the minimum of the flexion/extension angle, and the End Contact (EC) and the Mid-Swing (MS), as minimum and maximum of the angular velocity, respectively. The algorithm consisted of calibration, real-time detection, and step-by-step update. Data collected from 22 healthy subjects (21 to 85 years) walking at three self-selected speeds were used to validate the algorithm against the GaitRite system. Comparable levels of accuracy and significantly lower detection delays were achieved with respect to other published methods. The algorithm robustness was tested on ten healthy subjects performing sudden speed changes and on ten stroke subjects (43 to 89 years). For healthy subjects, F1-scores of 1 and mean detection delays lower than 14 ms were obtained. For stroke subjects, F1-scores of 0.998 and 0.944 were obtained for IC and EC, respectively, with mean detection delays always below 31 ms. The algorithm accurately detected gait events in real time from a heterogeneous dataset of gait patterns and paves the way for the design of closed-loop controllers for customized gait trainings and/or assistive devices. PMID:25069118

  17. Routing Optimization for Wireless Sensor Network Based on Cloud Adaptive Particle Swarm Optimization Algorithm

    Xu Bao

    2013-11-01

    Full Text Available One of the most important targets of routing algorithm for Wireless Sensor Network (WSN is to prolong the network lifetime. Aimed at the features of WSN, a new routing optimization approach based on cloud adaptive particle swarm optimization algorithm is put forward in this paper. All paths appear at the same time in one round are fused in one particle, and the coding rule of particle is set down. The particle itself is defined as its position, the number of replaceable relay nodes in paths is defined as the velocity of particle. Cloud algorithm is used to optimize the inertia weight of particle. Optimize rules are laid out, and both residual energy of nodes and variance of all paths’ length are considered in objective function. Simulations find out the best value of balance factor in objective function, also prove that this approach can control the energy consumption of network, enhance the viability of nodes, and prolong the lifetime of network.  

  18. Adaptive ant-based routing in wireless sensor networks using Energy Delay metrics

    Yao-feng WEN; Yu-quan CHEN; Min PAN

    2008-01-01

    To find the optimal routing is always an important topic in wireless sensor networks (WSNs). Considering a WSN where the nodes have limited energy, we propose a novel Energy*Delay model based on ant algorithms ("E&D ANTS" for short)to minimize the time delay in transferring a fixed number of data packets in an energy-constrained manner in one round. Our goal is not only to maximize the lifetime of the network but also to provide real-time data transmission services. However, because of the tradeoff of energy and delay in wireless network systems, the reinforcement learning (RL) algorithm is introduced to train the model. In this survey, the paradigm of E&D ANTS is explicated and compared to other ant-based routing algorithms like AntNet and AntChain about the issues of routing information, routing overhead and adaptation. Simulation results show that our method performs about seven times better than AntNet and also outperforms AntChain by more than 150% in terms of energy cost and delay per round.

  19. The Telesupervised Adaptive Ocean Sensor Fleet (TAOSF) Architecture: Coordination of Multiple Oceanic Robot Boats

    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.; Kulczycki, Eric A.

    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.

  20. An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications

    Pardeep Kumar

    2014-02-01

    Full Text Available 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.

  1. An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services.

    Chen, Shih-Yeh; Lai, Chin-Feng; Hwang, Ren-Hung; Lai, Ying-Hsun; Wang, Ming-Shi

    2015-12-01

    As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services. PMID:26490152

  2. Adaptive Weighted Measurement Fusion Unscented Kalman Filter for Multisensor System%多传感器加权观测融合自适应UKF滤波器

    郝钢; 叶秀芬

    2011-01-01

    For the multiaensor nonlinear systems which have the same measurement function, an adaptive unscented Kalman filter is presented based on the Sage-Husa estimator. This algorithm can estimate the measurement noise variances R(J) of the subsystems by the correlated functions matrix of these educed sequences, and its convergence is also proved. The algorithm avoids the disadvantage of classic Sage-Husa estimator when the Q and R are all unknown. To take full advantage of the information of multisensor systems and improve the filtering accuracy, the adaptive weighted measurement fusion unscented Kalman filter is obtained by using the weighted least squares ( WLS) method. A simulation example for a nonlinear system with 3 sensors shows its effectiveness.%对于带有相同观测方程和未知噪声统计的非线性多传感器系统,提出了一种基于Sage-Husa估计的自适应UKF滤波算法.该算法利用导出的平稳随机序列的相关函数估计系统观测噪声方差统计R(j),并证明了其收敛性.进而利用Sage-Husa估计算法得到自适应UKF滤波算法.该方法避免了传统Sage和Husa的自适应滤波算法不能处理Q和R均未知的系统的局限性.为了将多传感器信息加以充分利用,提高滤波精度,本文利用加权最小二乘法(WLS),实现了多传感器加权观测融合自适应UKF滤波器.一个带3传感器非线性系统的仿真例子说明了该算法的有效性.

  3. Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

    Lei Si; Zhongbin Wang; Xinhua Liu; Chao Tan; Jing Xu; Kehong Zheng

    2015-01-01

    In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the e...

  4. Reovirus FAST Proteins Drive Pore Formation and Syncytiogenesis Using a Novel Helix-Loop-Helix Fusion-Inducing Lipid Packing Sensor.

    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.

  5. A Ubiquitous and Low-Cost Solution for Movement Monitoring and Accident Detection Based on Sensor Fusion

    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.

  6. Novel Paradigm for Constructing Masses in Dempster-Shafer Evidence Theory for Wireless Sensor Network’s Multisource Data Fusion

    Zhenjiang Zhang

    2014-04-01

    Full Text Available Dempster-Shafer evidence theory (DSET is a flexible and popular paradigm for multisource data fusion in wireless sensor networks (WSNs. This paper presents a novel and easy implementing method computing masses from the hundreds of pieces of data collected by a WSN. The transfer model is based on the Mahalanobis distance (MD, which is an effective method to measure the similarity between an object and a sample. Compared to the existing methods, the proposed method concerns the statistical features of the observed data and it is good at transferring multi-dimensional data to belief assignment correctly and effectively. The main processes of the proposed method, which include the calculation of the intersection classes of the power set and the algorithm mapping MDs to masses, are described in detail. Experimental results in transformer fault diagnosis show that the proposed method has a high accuracy in constructing masses from multidimensional data for DSET. Additionally, the results also prove that higher dimensional data brings higher accuracy in transferring data to mass.

  7. Novel paradigm for constructing masses in Dempster-Shafer evidence theory for wireless sensor network's multisource data fusion.

    Zhang, Zhenjiang; Liu, Tonghuan; Zhang, Wenyu

    2014-01-01

    Dempster-Shafer evidence theory (DSET) is a flexible and popular paradigm for multisource data fusion in wireless sensor networks (WSNs). This paper presents a novel and easy implementing method computing masses from the hundreds of pieces of data collected by a WSN. The transfer model is based on the Mahalanobis distance (MD), which is an effective method to measure the similarity between an object and a sample. Compared to the existing methods, the proposed method concerns the statistical features of the observed data and it is good at transferring multi-dimensional data to belief assignment correctly and effectively. The main processes of the proposed method, which include the calculation of the intersection classes of the power set and the algorithm mapping MDs to masses, are described in detail. Experimental results in transformer fault diagnosis show that the proposed method has a high accuracy in constructing masses from multidimensional data for DSET. Additionally, the results also prove that higher dimensional data brings higher accuracy in transferring data to mass. PMID:24759109

  8. Wavefront response matrix for closed-loop adaptive optics system based on non-modulation pyramid wavefront sensor

    Wang, Jianxin; Bai, Fuzhong; Ning, Yu; Li, Fei; Jiang, Wenhan

    2012-06-01

    Pyramid wavefront sensor (PWFS) is a kind of wavefront sensor with high spatial resolution and high energy utilization. In this paper an adaptive optics system with PWFS as wavefront sensor and liquid-crystal spatial light modulator (LC-SLM) as wavefront corrector is built in the laboratory. The wavefront response matrix is a key element in the close-loop operation. It can be obtained by measuring the real response to given aberrations, which is easily contaminated by noise and influenced by the inherent aberration in the optical system. A kind of analytic solution of response matrix is proposed, with which numerical simulation and experiment are also implemented to verify the performance of closed-loop correction of static aberration based on linear reconstruction theory. Results show that this AO system with the proposed matrix can work steadily in closed-loop operation.

  9. Real-time atmospheric imaging and processing with hybrid adaptive optics and hardware accelerated lucky-region fusion (LRF) algorithm

    Liu, Jony Jiang; Carhart, Gary W.; Beresnev, Leonid A.; Aubailly, Mathieu; Jackson, Christopher R.; Ejzak, Garrett; Kiamilev, Fouad E.

    2014-09-01

    Atmospheric turbulences can significantly deteriorate the performance of long-range conventional imaging systems and create difficulties for target identification and recognition. Our in-house developed adaptive optics (AO) system, which contains high-performance deformable mirrors (DMs) and the fast stochastic parallel gradient decent (SPGD) control mechanism, allows effective compensation of such turbulence-induced wavefront aberrations and result in significant improvement on the image quality. In addition, we developed advanced digital synthetic imaging and processing technique, "lucky-region" fusion (LRF), to mitigate the image degradation over large field-of-view (FOV). The LRF algorithm extracts sharp regions from each image obtained from a series of short exposure frames and fuses them into a final improved image. We further implemented such algorithm into a VIRTEX-7 field programmable gate array (FPGA) and achieved real-time video processing. Experiments were performed by combining both AO and hardware implemented LRF processing technique over a near-horizontal 2.3km atmospheric propagation path. Our approach can also generate a universal real-time imaging and processing system with a general camera link input, a user controller interface, and a DVI video output.

  10. Service-oriented architecture of adaptive, intelligent data acquisition and processing systems for long-pulse fusion experiments

    Gonzalez, J. [Grupo de Investigacion en Instrumentacion y Acustica Aplicada. Universidad Politecnica de Madrid, Crta. Valencia Km-7 Madrid 28031 (Spain); Ruiz, M., E-mail: mariano.ruiz@upm.e [Grupo de Investigacion en Instrumentacion y Acustica Aplicada. Universidad Politecnica de Madrid, Crta. Valencia Km-7 Madrid 28031 (Spain); Barrera, E.; Lopez, J.M.; Arcas, G. de [Grupo de Investigacion en Instrumentacion y Acustica Aplicada. Universidad Politecnica de Madrid, Crta. Valencia Km-7 Madrid 28031 (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain)

    2010-07-15

    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.

  11. Service-oriented architecture of adaptive, intelligent data acquisition and processing systems for long-pulse fusion experiments

    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.

  12. Services oriented architecture for adaptive and intelligent data acquisition and processing systems in long pulse fusion experiments

    Data acquisition systems used in long pulse fusion experiments require to implement data reduction and pattern recognition algorithms in real time. In order to accomplish these operations is essential to dispose software tools that allow hot swap capabilities throughout the temporal evolution of the experiments. This is very important because the processing needs are not equal in the different experiment's phases. The intelligent test and measurement system (ITMS) developed by UPM and CIEMAT is an example of technology for implementing scalable data acquisition and processing systems based in PXI and compact PCI hardware. In the ITMS platform a set of software tools allows the user to define the processing associated with the different experiment's phases using state machines driven by software events. These state machines are specified using State Chart XML (SCXML) language. The software tools are developed using: JAVA, JINI, a SCXML engine and several LabVIEW applications. With this schema it is possible to execute data acquisition and processing applications in an adaptive way. The powerful of SCXML semantics and the possibility of to work with XML user defined data types allow a very easy programming of ITMS platform. With this approach ITMS platform is a suitable solution for implementing scalable data acquisition and processing systems, based in a services oriented model, with ease possibility for implement remote participation applications. (authors)

  13. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

    The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults.

  14. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

    Salahshoor, Karim [Department of Instrumentation and Automation, Petroleum University of Technology, Tehran (Iran, Islamic Republic of); Kordestani, Mojtaba; Khoshro, Majid S. [Department of Control Engineering, Islamic Azad University South Tehran branch (Iran, Islamic Republic of)

    2010-12-15

    The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults. (author)

  15. An adaptive wing for a small-aircraft application with a configuration of fibre Bragg grating sensors

    In this paper a concept of an adaptive wing for small-aircraft applications with an array of fibre Bragg grating (FBG) sensors has been presented and discussed. 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 concept has been tested numerically by the use of the finite element method. For numerical calculations the commercial finite element package ABAQUS® has been employed. A finite element model of the wing has been prepared in order to estimate the values of the wing twisting angles and distributions of the twist for various activation scenarios. Based on the results of numerical analysis the locations and numbers of the FBG sensors have also been determined. The results of numerical calculations obtained by the authors confirmed the usefulness of the assumed wing control strategy. Based on them and the concept developed of the adaptive wing, a wing demonstration stand has been designed and built. The stand has been used to verify experimentally the performance of the adaptive wing and the usefulness of the FBG sensors for evaluation of the wing condition

  16. Evolution of Heat Sensors Drove Shifts in Thermosensation between Xenopus Species Adapted to Different Thermal Niches.

    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. PMID:27022021

  17. Development and Application of Non-Linear Image Enhancement and Multi-Sensor Fusion Techniques for Hazy and Dark Imaging

    Rahman, Zia-ur

    2005-01-01

    The purpose of this research was to develop enhancement and multi-sensor fusion algorithms and techniques to make it safer for the pilot to fly in what would normally be considered Instrument Flight Rules (IFR) conditions, where pilot visibility is severely restricted due to fog, haze or other weather phenomenon. We proposed to use the non-linear Multiscale Retinex (MSR) as the basic driver for developing an integrated enhancement and fusion engine. When we started this research, the MSR was being applied primarily to grayscale imagery such as medical images, or to three-band color imagery, such as that produced in consumer photography: it was not, however, being applied to other imagery such as that produced by infrared image sources. However, we felt that it was possible by using the MSR algorithm in conjunction with multiple imaging modalities such as long-wave infrared (LWIR), short-wave infrared (SWIR), and visible spectrum (VIS), we could substantially improve over the then state-of-the-art enhancement algorithms, especially in poor visibility conditions. We proposed the following tasks: 1) Investigate the effects of applying the MSR to LWIR and SWIR images. This consisted of optimizing the algorithm in terms of surround scales, and weights for these spectral bands; 2) Fusing the LWIR and SWIR images with the VIS images using the MSR framework to determine the best possible representation of the desired features; 3) Evaluating different mixes of LWIR, SWIR and VIS bands for maximum fog and haze reduction, and low light level compensation; 4) Modifying the existing algorithms to work with video sequences. Over the course of the 3 year research period, we were able to accomplish these tasks and report on them at various internal presentations at NASA Langley Research Center, and in presentations and publications elsewhere. A description of the work performed under the tasks is provided in Section 2. The complete list of relevant publications during the research

  18. Regional Mapping of Coupled Fluxes of Carbon and Water Using Multi-Sensor Fusion Techniques

    Schull, M. A.; Anderson, M. C.; Semmens, K. A.; Yang, Y.; Gao, F.; Hain, C.; Houborg, R.

    2014-12-01

    In an ever-changing climate there is an increasing need to measure the fluxes of water, energy and carbon for decision makers to implement policies that will help mitigate the effects of climate change. In an effort to improve drought monitoring, water resource management and agriculture assessment capabilities, a multi-scale and multi-sensor framework for routine mapping of land-surface fluxes of water and energy at field to regional scales has been established. The framework uses the ALEXI (Atmosphere Land Exchange Inverse)/DisALEXI (Disaggregated ALEXI) suite of land-surface models forced by remotely sensed data from Landsat, MODIS (MODerate resolution Imaging Spectroradiometer), and GOES (Geostationary Operational Environmental Satellite). Land-surface temperature (LST) can be an effective substitute for in-situ surface moisture observations and a valuable metric for constraining land-surface fluxes at sub-field scales. The adopted multi-scale thermal-based land surface modeling framework facilitates regional to local downscaling of water and energy fluxes by using a combination of shortwave reflective and thermal infrared (TIR) imagery from GOES (4-10 km; hourly), MODIS (1 km; daily), and Landsat (30-100 m; bi-weekly). In this research the ALEXI/DisALEXI modeling suite is modified to incorporate carbon fluxes using a stomatal resistance module, which replaces the Priestley-Taylor latent heat approximation. In the module, canopy level nominal light-use-efficiency (βn) is the parameter that modulates the flux of water and carbon in and out of the canopy. Leaf chlorophyll (Chl) is a key parameter for quantifying variability in photosynthetic efficiency to facilitate the spatial distribution of coupled carbon and water retrievals. Spatial distribution of Chl are retrieved from Landsat (30 m) using a surface reflectance dataset as input to the REGularized canopy reFLECtance (REGFLEC) tool. The modified ALEXI/DisALEXI suite is applied to regions of rain fed and

  19. Sensors

    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)

  20. Sensors

    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...